diff --git "a/1012.jsonl" "b/1012.jsonl" new file mode 100644--- /dev/null +++ "b/1012.jsonl" @@ -0,0 +1,447 @@ +{"seq_id": "587223335", "text": "import glob\n\nfrom torchvision.transforms import transforms\n\nfrom inpainting.load import VideoDataset\nfrom inpainting.visualize import show_image\n\ntime = 8\nbatch_size = 8\nepochs = 100\nlearning_rate = 1e-3\nsize = (256, 256)\n\ntransform = transforms.Compose([\n transforms.Resize(size),\n transforms.ToTensor()\n])\n\nvideo_dataset = VideoDataset(list(glob.glob('../data/raw/video/DAVIS/JPEGImages/480p/*')), sequence_length=time,\n transform=transform)\n\nframes, frame_dir = video_dataset[0]\n\nshow_image(frames[0:5])\n", "sub_path": "tests/video_loader_test.py", "file_name": "video_loader_test.py", "file_ext": "py", "file_size_in_byte": 541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torchvision.transforms.transforms.Compose", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 14, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.Resize", "line_number": 15, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 15, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.ToTensor", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 16, "usage_type": "name"}, {"api_name": "inpainting.load.VideoDataset", "line_number": 19, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 19, "usage_type": "call"}, {"api_name": "inpainting.visualize.show_image", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "277621948", "text": "# A few lines of code to get a simple velocity uncertainty estimate for a real station \n# Prototype for what's going into the larger velocity difference estimator. \n\nimport numpy as np \nimport datetime as dt\nimport lssq_model_errors\nimport gps_input_pipeline\nimport offsets\nimport gps_ts_functions\nimport gps_seasonal_removals\n\ndef read_real_gps_data(station, starttime, endtime):\n\t[newData, offset_obj, eq_obj] = gps_input_pipeline.get_station_data(station, 'pbo', 'NA');\n\tnewData=offsets.remove_offsets(newData, offset_obj);\n\tnewData=gps_ts_functions.remove_outliers(newData, 10); # 10 mm outlier\n\tnewData=offsets.remove_offsets(newData, eq_obj);\n\tnewData=gps_seasonal_removals.make_detrended_ts(newData, 1, 'lssq');\n\tnewData=gps_ts_functions.impose_time_limits(newData, starttime, endtime);\n\tx = gps_ts_functions.get_float_times(newData.dtarray);\n\ty = newData.dE;\n\tsig= newData.Se;\n\treturn [x, y, sig];\n\ndef get_slope_uncertainty(station, starttime, endtime):\n\t[x, y, sig] = read_real_gps_data(station, starttime, endtime);\n\tparams, covm = lssq_model_errors.AVR(x, y, sig);\n\tslope = params[0];\n\tsigma = np.sqrt(covm[0][0]);\n\treturn slope, sigma;\n\nif __name__==\"__main__\":\n\tstation = \"P157\";\n\tstarttime1=dt.datetime.strptime(\"20140317\",\"%Y%m%d\");\n\tendtime1=dt.datetime.strptime(\"20161207\",\"%Y%m%d\");\t\n\tstarttime2=dt.datetime.strptime(\"20161210\",\"%Y%m%d\");\n\tendtime2=dt.datetime.strptime(\"20181210\",\"%Y%m%d\");\n\n\tslope1, sigma1 = get_slope_uncertainty(station, starttime1, endtime1);\n\tslope2, sigma2 = get_slope_uncertainty(station, starttime2, endtime2);\n\tdeltaV = abs(slope1-slope2);\n\toverall_sigma = sigma1+sigma2;\n\tprint(\"Station \"+station);\n\tprint(\"Interval 1: %f +- %f mm/yr\" % (slope1, sigma1) );\n\tprint(\"Interval 2: %f +- %f mm/yr\" % (slope2, sigma2) );\n\tprint(\"Delta V : %f +- %f\" % (deltaV, overall_sigma) );\n", "sub_path": "specific_experiments/lssq_param_errors/deltav_unc_real_station.py", "file_name": "deltav_unc_real_station.py", "file_ext": "py", "file_size_in_byte": 1820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "gps_input_pipeline.get_station_data", "line_number": 13, "usage_type": "call"}, {"api_name": "offsets.remove_offsets", "line_number": 14, "usage_type": "call"}, {"api_name": "gps_ts_functions.remove_outliers", "line_number": 15, "usage_type": "call"}, {"api_name": "offsets.remove_offsets", "line_number": 16, "usage_type": "call"}, {"api_name": "gps_seasonal_removals.make_detrended_ts", "line_number": 17, "usage_type": "call"}, {"api_name": "gps_ts_functions.impose_time_limits", "line_number": 18, "usage_type": "call"}, {"api_name": "gps_ts_functions.get_float_times", "line_number": 19, "usage_type": "call"}, {"api_name": "lssq_model_errors.AVR", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "250031542", "text": "from django.shortcuts import render\nfrom Sales.models import Car\n\ndef home(request):\n cars = Car.objects.filter(is_classic=True)\n\n makes = sorted(set([car.make for car in cars]))\n\n final_cars = []\n\n for car in cars:\n photos = car.photos.replace(\"'\",'').replace('[','').replace(']','').split(', ')\n if len(photos) >= 1 and photos[0] != '':\n main_photo = photos[0]\n else:\n main_photo = 'https://i.kym-cdn.com/entries/icons/facebook/000/021/572/maxresdefault.jpg'\n \n final_cars.append({\n 'id':car.id,\n 'make':car.make,\n 'model':car.model,\n 'year':car.year,\n 'price':car.price,\n 'mileage':car.mileage,\n 'body_style':car.body_style,\n 'fuel_type':car.fuel_type,\n 'description':car.description,\n 'main_photo':main_photo,\n 'photos':photos,\n })\n\n context = {\n 'title':'Classic',\n 'cars':final_cars,\n 'makes':makes,\n 'header':'https://images7.alphacoders.com/301/301642.jpg'\n }\n\n return render(request, 'Sales/index.html', context)", "sub_path": "Classic/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "Sales.models.Car.objects.filter", "line_number": 5, "usage_type": "call"}, {"api_name": "Sales.models.Car.objects", "line_number": 5, "usage_type": "attribute"}, {"api_name": "Sales.models.Car", "line_number": 5, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "332948328", "text": "import redis\r\nimport json\r\nimport time\r\n\r\n#建立redis连接，将decode_responses设置为True则是将结果由bytes解码为string\r\nredis_con = redis.StrictRedis(decode_responses=True)\r\n\r\n\r\nclass RedisUtils:\r\n\r\n def __init__(self):\r\n self.con = redis_con\r\n self.task_list = 'image_recognize_task'\r\n\r\n def push_task(self, value):\r\n \"\"\"下发图片识别任务到任务队列\"\"\"\r\n self.con.lpush(self.task_list, json.dumps(value))\r\n\r\n def get_task(self, max_num=1):\r\n \"\"\"\r\n 获取任务，可一次获取多个任务\r\n :param max_num: 最大任务数，默认是1\r\n :return: 任务列表\r\n \"\"\"\r\n # self.con.brpop-->('image_recognize_task', '{\"path\": \"/home/ubuntu/MyFiles/flask_web/static/images/ \\\r\n # image_06687_1556105676710988.jpg\", \"key\": \"image_06687_1556105676710988.jpg\"}')\r\n task = self.con.brpop(self.task_list)[1] # 从redis队列获取任务，该方法会一直阻塞直到队列中有任务\r\n tasks = []\r\n # print(task)\r\n tasks.append(json.loads(task))\r\n for i in range(max_num - 1):\r\n task = self.con.lpop(self.task_list)\r\n if task:\r\n tasks.append(json.loads(task))\r\n else:\r\n break\r\n return tasks\r\n\r\n def save_result(self, result):\r\n \"\"\"识别结果写入redis\"\"\"\r\n # result = json.dumps(result)\r\n key = result['key']\r\n value = result['result']\r\n #print(value)\r\n self.con.set(key, value, ex=60) # 识别结果在redis中保留1分钟\r\n\r\n def get_result(self, key):\r\n \"\"\"根据key（每次上传的图片都对应一个key），在redis中获取对应图片的识别结果，如果超过10s还没拿到结果则认为识别失败\"\"\"\r\n i = 1\r\n while i < 200: # 若20*0.5s都未读到结果，return None\r\n result = self.con.get(key)\r\n if result:\r\n return eval(result) # 将字符串转化为list\r\n time.sleep(0.5)\r\n i += 1\r\n return None\r\n\r\n\r\nif __name__ == '__main__':\r\n pass\r\n", "sub_path": "AI_service_deployment/code/flask_web/utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "redis.StrictRedis", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "435166234", "text": "def handleTree(tree):\n id = tree['id']\n if not tree.get('sub_items'):\n return\n tmp = [{\n 'id': item['id'],\n 'name': item['name']\n } for item in tree['sub_items']]\n r[id] = tmp\n for item in tree['sub_items']:\n handleTree(item)\n\n# create Coupon\n\n\nfrom coupon.models import Coupon, CouponInfo, CouponCard\nfrom company.models import Company\nfrom sitez.models import SiteUser\nfrom store.models import Product\nimport datetime, random\n\n\nsiteuser = SiteUser.objects.get(pk=354)\ncompany = Company.objects.get(pk=500)\nproduct = Product.objects.get(pk=24155)\n\nc1 = Coupon.objects.create(name='test3')\nc1.company = company\nc1.sufficient_condiction = 3\nc1.reduction = 1\nc1.get_limit = ['need_credit']\nc1.relate_all_products = True\nCouponInfo.objects.create(coupon=c1, product=product)\nc1.save()\n\ncoupon = c1\nnow = datetime.datetime.now() - datetime.timedelta(days=5)\ndatetime_iso = now.isoformat().replace('T', ' ')\ndata = {\n 'name': coupon.name,\n 'coupon': coupon,\n 'company_id': company.id,\n 'card_id': ''.join(random.sample('01234567890123456789', 16)),\n 'siteuser_id': siteuser.id,\n 'start_date': datetime_iso,\n 'end_date': datetime.date.today() + datetime.timedelta(days=5),\n 'type': 'binded',\n}\ncc4 = CouponCard.objects.create(**data)\n", "sub_path": "backup/snippets/snippets/script.py", "file_name": "script.py", "file_ext": "py", "file_size_in_byte": 1305, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sitez.models.SiteUser.objects.get", "line_number": 23, "usage_type": "call"}, {"api_name": "sitez.models.SiteUser.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sitez.models.SiteUser", "line_number": 23, "usage_type": "name"}, {"api_name": "company.models", "line_number": 24, "usage_type": "name"}, {"api_name": "company.models.Company.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "company.models.Company.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "company.models.Company", "line_number": 24, "usage_type": "name"}, {"api_name": "store.models.Product.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "store.models.Product.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "store.models.Product", "line_number": 25, "usage_type": "name"}, {"api_name": "coupon.models.Coupon.objects.create", "line_number": 27, "usage_type": "call"}, {"api_name": "coupon.models.Coupon.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "coupon.models.Coupon", "line_number": 27, "usage_type": "name"}, {"api_name": "company.models", "line_number": 28, "usage_type": "name"}, {"api_name": "coupon.models.CouponInfo.objects.create", "line_number": 33, "usage_type": "call"}, {"api_name": "coupon.models.CouponInfo.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "coupon.models.CouponInfo", "line_number": 33, "usage_type": "name"}, {"api_name": "coupon.models", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "coupon.models.name", "line_number": 40, "usage_type": "attribute"}, {"api_name": "coupon.models", "line_number": 40, "usage_type": "name"}, {"api_name": "coupon.models", "line_number": 41, "usage_type": "name"}, {"api_name": "company.models.id", "line_number": 42, "usage_type": "attribute"}, {"api_name": "company.models", "line_number": 42, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 46, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 46, "usage_type": "call"}, {"api_name": "coupon.models.CouponCard.objects.create", "line_number": 49, "usage_type": "call"}, {"api_name": "coupon.models.CouponCard.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "coupon.models.CouponCard", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "409732967", "text": "#Author: Emma Carli \nfrom PIL import Image\npathlist=list(r\"C:\\Users\\New\\OneDrive - University of Glasgow\\Work\\Internship LAM\\Figures\") \nsavelist=list(r\"C:\\Users\\New\\OneDrive - University of Glasgow\\Work\\Internship LAM\\Figures\\Polar\\Full_Comparison\")\n\nf = open(r\"C:\\Users\\New\\OneDrive - University of Glasgow\\Work\\Internship LAM\\STARS.txt\") #Open the list of stars considered in this project\nstar = f.readline() #Read the first line of the text file\nwhile star: #Keep reading one line at a time till you get to the end of the list.\n starlist=list(star) #Turn the star's name into a list\n del starlist[9] #Remove the \"return\" character\n \n \n #Load images\n linkcomparison = pathlist + list('\\Polar\\C5C16Comparison') + list('\\\\') + starlist + list('_c5c16.png')\n linkcomparison= ''.join(linkcomparison)\n comparison=Image.open(linkcomparison)\n linkacfc5= pathlist + list('\\Polar\\C5') + list('\\\\') + starlist + list('_ACF.png')\n linkacfc5= ''.join(linkacfc5)\n acfc5=Image.open(linkacfc5)\n linkEMc5= pathlist + list('\\Polar\\C5') + list('\\\\') + starlist + list('_EyeMatching.png')\n linkEMc5= ''.join(linkEMc5)\n eyematchingc5=Image.open(linkEMc5)\n linkLTTc5 = pathlist + list('\\Polar\\C5') + list('\\\\') + starlist + list('_LTT.png')\n linkLTTc5= ''.join(linkLTTc5)\n LTTc5=Image.open(linkLTTc5)\n linkacfc16= pathlist + list('\\Polar\\C16') + list('\\\\') + starlist + list('_ACF.png')\n linkacfc16= ''.join(linkacfc16)\n acfc16=Image.open(linkacfc16)\n linkEMc16= pathlist + list('\\Polar\\C16') + list('\\\\') + starlist + list('_EyeMatching.png')\n linkEMc16= ''.join(linkEMc16)\n eyematchingc16=Image.open(linkEMc16)\n linkLTTc16= pathlist + list('\\Polar\\C16') + list('\\\\') + starlist + list('_LTT.png')\n linkLTTc16= ''.join(linkLTTc16)\n LTTc16=Image.open(linkLTTc16)\n \n #Create a new image\n finalfig=Image.new('RGB',(1080,1600),color=(1000,1000,1000))\n \n #Paste images in in right place\n finalfig.paste(comparison)\n finalfig.paste(acfc5,box=(20,1010))\n finalfig.paste(acfc16,box=(600,1010))\n finalfig.paste(LTTc5,box=(20,721))\n finalfig.paste(LTTc16,box=(600,721))\n finalfig.paste(eyematchingc5,box=(20,1300))\n finalfig.paste(eyematchingc16,box=(600,1300))\n \n #Save the figure\n figpathlist= savelist + list('\\\\') + starlist + list('_FullComparison.png') \n figpath= ''.join(figpathlist)\n finalfig.save(figpath)\n\n star = f.readline() #Proceed to reading next line of text file \nf.close() #Close the file (the list of stars)", "sub_path": "Auxiliary_Codes/Stitching images together.py", "file_name": "Stitching images together.py", "file_ext": "py", "file_size_in_byte": 2530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 19, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 28, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 34, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 34, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "393756416", "text": "#!/usr/bin/env python3\n# Depth Map Visualization Test for use in the Camera Calibrator\n#\n# Software License Agreement (BSD License)\n#\n# Copyright (c) 2017, Qinematiq GmbH\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above\n# copyright notice, this list of conditions and the following\n# disclaimer in the documentation and/or other materials provided\n# with the distribution.\n# * Neither the name of the Willow Garage nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\nimport cv2\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nimport colorsys\n\nfrom typing import List, Tuple\n\nFILENAME = \"mat0038.yml\"\n\nCOLORS = [\n (255, 0, 0, 255),\n (255, 136, 0, 255),\n (255, 255, 104, 255),\n (47, 255, 0, 255),\n (47, 145, 0, 255),\n (0, 255, 248, 255),\n (0, 165, 255, 255),\n (0, 80, 255, 255),\n (116, 0, 255, 255),\n (185, 50, 255, 255),\n (185, 106, 255, 255),\n (255, 187, 254, 255),\n]\n\n\ndef show_image(image: np.ndarray) -> None:\n plt.imshow(image, cmap='gray', interpolation='bicubic')\n plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis\n plt.show()\n\n\ndef load_real_image(file: str) -> np.ndarray:\n fs = cv2.FileStorage(file, 0)\n image = fs.getNode(\"realImage\").mat()\n return image\n\n\ndef load_depth_map(file: str) -> np.ndarray:\n fs = cv2.FileStorage(file, 0)\n image = fs.getNode(\"depthMap\").mat()\n return image\n\n\ndef interpolate_colors(color_list: List[Tuple[int, int, int, int]]) -> List[Tuple[int, int, int, int]]:\n colors_hsv = [colorsys.rgb_to_hsv(color[0], color[1], color[2]) for color in color_list]\n interpolated_colors = []\n for i in range(0, len(colors_hsv)-1):\n interpolated_colors.append(colors_hsv[i])\n interpolated_color = interpolate_color(colors_hsv[i], colors_hsv[i+1])\n interpolated_colors.append(interpolated_color)\n interpolated_colors.append(colors_hsv[-1])\n\n colors_rgb = [colorsys.hsv_to_rgb(*color) for color in interpolated_colors]\n colors_rgba = [(int(color[0]), int(color[1]), int(color[2]), 255) for color in colors_rgb]\n return colors_rgba\n\n\ndef interpolate_color(color1: Tuple[float, float, int], color2: Tuple[float, float, int]) -> Tuple[float, float, int]:\n h = interpolate_component(color1[0], color2[0])\n s = interpolate_component(color1[1], color2[1])\n v = int(interpolate_component(float(color1[2]), float(color2[2])))\n return h, s, v\n\n\ndef interpolate_component(component1: float, component2: float) -> float:\n difference = component2 - component1\n interpolated_component = component1 + difference\n return interpolated_component\n\n\ndef color_depth_map(depth_map: np.ndarray) -> np.ndarray:\n round_starting_depth = 0\n max_depth = depth_map.max()\n round_depth = max_depth / len(COLORS)\n\n colored = np.zeros((depth_map.shape[0], depth_map.shape[1], 4), np.uint8)\n\n for i in range(len(COLORS)):\n round_end_depth = round_starting_depth + round_depth\n mask = cv2.inRange(depth_map, round_starting_depth, round_end_depth)\n colored[mask > 0] = COLORS[i]\n round_starting_depth = round_end_depth\n\n return colored\n\n\ndef mix_images(real_image: np.ndarray, colored_depth_map: np.ndarray) -> np.ndarray:\n if colored_depth_map.shape != real_image.shape:\n colored_depth_map = cv2.resize(colored_depth_map, (real_image.shape[1], real_image.shape[0]))\n mixed = cv2.addWeighted(real_image, 1.0, colored_depth_map, 0.5, 0.0)\n return mixed\n\n\nif __name__ == \"__main__\":\n real_img = load_real_image(FILENAME)\n depth = load_depth_map(FILENAME)\n COLORS = interpolate_colors(COLORS)\n colored_depth = color_depth_map(depth)\n mixed_image = mix_images(real_img, colored_depth)\n show_image(mixed_image)\n", "sub_path": "visualizer_test.py", "file_name": "visualizer_test.py", "file_ext": "py", "file_size_in_byte": 4949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.ndarray", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "cv2.FileStorage", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.FileStorage", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 74, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 80, "usage_type": "name"}, {"api_name": "colorsys.rgb_to_hsv", "line_number": 81, "usage_type": "call"}, {"api_name": "colorsys.hsv_to_rgb", "line_number": 89, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 94, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 112, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 123, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 125, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "202786807", "text": "#!/usr/bin/env python3\n\nimport requests\nimport json\nimport os\n\nos.chdir(\"/data/feedback/\")\nfor file in os.listdir():\n with open(file, \"r\") as f:\n feedback = {}\n colume = [\"title\", \"name\", \"date\", \"feedback\"]\n n = 0\n for line in f:\n feedback[colume[n]] = line.rstrip(\"\\n\")\n n += 1\n #json_feedback = json.dumps(feedback)\n #print(json_feedback)\n response = requests.post(\"http://35.188.2.103/feedback/\", json=feedback)\n print(response.status_code)\n\n", "sub_path": "Interacting_with_webserver/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 527, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.chdir", "line_number": 7, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "503884221", "text": "import sklearn as sk\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom nltk.corpus import stopwords\nimport string\nimport nltk\nnltk.download('wordnet')\nnltk.download('stopwords')\nimport tensorflow\nfrom sklearn.model_selection import train_test_split\nfrom collections import Counter\nfrom pandas import DataFrame\nfrom matplotlib import pyplot\nimport re\nfrom nltk.stem import WordNetLemmatizer \nfrom keras.preprocessing.text import Tokenizer\nfrom sklearn.preprocessing import StandardScaler\n\n\n\ndef process_news(news):\n _news = news.replace('b\\\"', \"\")\n _news = _news.replace('b\\'', \"\")\n _news = _news.lower()\n _news = re.sub(\"[^a-zA-Z]\", \" \",_news)\n _news = re.sub('[\\s]+', ' ', _news)\n \n tokens = _news.split(\" \")\n if \"\" in tokens:\n tokens.remove(\"\")\n \n lemmatizer = WordNetLemmatizer() \n tokens = [lemmatizer.lemmatize(w) for w in tokens]\n #remove punctuation from each token\n table = str.maketrans('', '', string.punctuation)\n tokens = [w.translate(table) for w in tokens]\n \n # remove remaining tokens that are not alphabetic\n tokens = [word for word in tokens if word.isalpha()]\n # filter out stop words\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n # filter out short tokens\n tokens = [word for word in tokens if len(word) > 1]\n \n _news = ' '.join(tokens) \n \n return _news\n\n\ndef read_data():\n\n data = pd.read_csv(\"Combined_News_DJIA.csv\")\n \n dfs = []\n data[\"News\"] = \"\"\n for i in range(1,25):\n col = \"Top\"+str(i)\n data[\"News\"] = data[\"News\"] +\" \"+ data[col]\n data = data.dropna()\n data['PreProcessedNews'] = data['News'].map(process_news)\n \n data = data[['Date', 'News', 'PreProcessedNews', 'Label']]\n \n stock_prices = \"upload_DJIA_table.csv\"\n stock_data = pd.read_csv(stock_prices)\n \n print(data.head(2))\n print(stock_data.head(2))\n \n merged_dataframe = pd.merge(data, stock_data, how='inner', on = 'Date')\n\n \n \n \n Xy_train = merged_dataframe[:int(len(data)*0.6)]\n Xy_valid = merged_dataframe[int(len(data)*0.6):int(len(data)*0.8)]\n Xy_test = merged_dataframe[int(len(data)*0.8):]\n \n return merged_dataframe, Xy_train, Xy_valid, Xy_test\n\n\n\ndef create_timeShiftSet(X_train,X_test,y_train,y_test,shift=1,del_first=False, scaleX=False, scaleY=False):\n \"\"\"Function to build a training and testing set composed of previous time steps\n It returns new X_train and X_test matrixes.\"\"\"\n \n x_gather = pd.concat((X_train,X_test))\n y_gather = pd.concat((y_train,y_test))\n \n # Create the shifted columns\n if type(shift)==int: # if a number of shifts is given\n newX = pd.concat([x_gather.shift(k) for k in range(1+del_first.real,1+del_first.real+shift)],axis=1)\n newX.columns = range(shift)\n newX = newX.interpolate(method=\"linear\").iloc[1+shift:] # Shift creates NaN...\n \n newX_train = newX.loc[X_train.index[1+shift:],:].values\n newX_test = newX.loc[X_test.index,:].values\n newy_train = y_gather.loc[X_train.index[1+shift:]].values\n newy_test = y_gather.loc[X_test.index].values\n \n elif type(shift) in [list,tuple]: # if a list of shifts to respect is given\n newX = pd.concat([x_gather.shift(k) for k in shift],axis=1)\n newX.columns = shift\n newX = newX.interpolate(method=\"linear\").iloc[max(shift):del_first.real:] # Shift creates NaN...\n \n newX_train = newX.loc[X_train.index[max(shift):],:].values\n newX_test = newX.loc[X_test.index,:].values\n newy_train = y_gather.loc[X_train.index[max(shift):]].values\n newy_test = y_gather.loc[X_test.index].values\n \n if scaleX:\n xScaler = StandardScaler().fit(newX_train)\n newX_train = xScaler.transform(newX_train)\n newX_test = xScaler.transform(newX_test)\n if scaleY:\n yScaler = StandardScaler().fit(newy_train.reshape(-1,1))\n newy_train = yScaler.transform(newy_train.reshape(-1,1)).ravel()\n newy_test = yScaler.transform(newy_test.reshape(-1,1)).ravel()\n \n return newX_train, newX_test, newy_train, newy_test", "sub_path": "Time Series/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 4212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "nltk.download", "line_number": 9, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 10, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 27, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 28, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 34, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 43, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 43, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "7406518", "text": "import numpy as np\nimport pyprelude.FPToolBox as fp\nimport operator as op\nfrom functools import reduce\nfrom scipy.integrate import quad\nfrom collections import OrderedDict\nfrom scipy.integrate import quad, dblquad\n\n# get max(0,x)\ndef posi(x):\n if x > 0:\n return x\n\n return 0\n\n# get unique representation of an edge\ndef e_repr(e):\n return tuple(sorted(e))\n\n# transform ordereddict to numpy array\ndef od2arr(od):\n return np.array(od2arrh(od))\n\n# help function of od2arr \ndef od2arrh(od):\n # the basic case\n if 'OrderedDict' not in str(type(od)):\n return od\n\n # recursive call\n return list(map(od2arrh, list(od.values())))\n\n# return ordered set for B_curl(a,b)\ndef B_curl(n, k):\n tmp = B_curl_h(n, k)\n return list(map(tuple, tmp))\n\ndef B_curl_h(n, k):\n if n == 1:\n return [[k]]\n\n if k == 0:\n return fp.lmap(lambda xs: addhd(0, xs), B_curl_h(n - 1, 0))\n \n # for each possible value, get one\n res = []\n for i in range(k + 1):\n res += fp.lmap(lambda xs: addhd(i, xs), B_curl_h(n - 1, k - i))\n\n return res\n\n# the number of elements in B_curl\ndef B_curl_num(n, k):\n if n == 1:\n return 1\n\n if k == 0:\n return 1\n \n res = 0\n for i in range(k + 1):\n res += B_curl_num(n - 1, k - i)\n\n return res\n\n# return ordered set for A_curl(a,b)\ndef A_curl(n, k):\n res = []\n for k0 in range(k + 1):\n res += B_curl(n, k0)\n\n return res\n\n# return ordered set for A_curl(a,b)\ndef A_curl_num(n, k):\n res = 0\n for k0 in range(k + 1):\n res += B_curl_num(n, k0)\n\n return res\n\n# function to fast add head to a list\ndef addhd(hd, xs):\n xs.insert(0, hd)\n return xs\n\n\n# value similarity check: values are same with same keys\ndef similarMaps(map1, map2, epsilon):\n errMsg = 'all correct'\n errMap = {}\n for key in map1.keys():\n if key in map2.keys():\n if abs(map1[key] - map2[key]) > epsilon:\n errMsg = 'something wrong'\n errMap[key] = (map1[key], map2[key])\n\n return (errMsg, errMap)\n\n\n# choose function\ndef choose(n, r):\n r = min(r, n-r)\n numer = reduce(op.mul, range(n, n-r, -1), 1)\n denom = reduce(op.mul, range(1, r+1), 1)\n return numer//denom\n\n# multichoose function\ndef mChoose(n, alpha):\n x = 1\n for a in alpha:\n x *= choose(n, a)\n n -= a\n if n < 0:\n return 'error: |alpha| > n'\n return x\n\n# the eta function\ndef eta(a, b):\n def eta1(x):\n# if a is None:\n# a = - float('inf')\n#\n# if b is None:\n# b = float('inf')\n\n if x >= a and x <= b:\n return 1\n else:\n return 0\n return eta1\n\n# the zeta function\ndef zeta(a, b):\n def zeta1(x):\n if x < a:\n return a\n elif x > b:\n return b\n else:\n return x\n return zeta1\n\n# delta functor\ndef delta(a, b, f):\n def delta1(x):\n if x < a:\n return f(a)\n elif x > b:\n return f(b)\n else:\n return f(x)\n return delta1\n\n# 1-dim distribution verify\ndef pdfVeri(func, lb, ub):\n errMsg = ''\n # check if integration over lb ub equal to 1\n total0 = quad(func, lb, ub)\n if contain(total0, 1) is False:\n errMsg = 'not a distribution in lb to ub:\\t' + str(total0)\n return errMsg\n\n # check if intergration over all equal to 1\n # total1 = quad(func, -np.inf, np.inf)\n delta = ub - lb\n total1 = quad(func, lb - delta, ub)\n if contain(total1, 1) is False:\n errMsg = 'not a distribution in whole space:\\t' + str(total1)\n return errMsg\n\n # check if to large difference\n diff = abs(total1[0] - total0[0])\n err = min(total0[1], total1[1])\n if diff > err:\n errMsg = ('may exist nonzero value outside lb to ub:\\t' \n + str(total0) + '\\t' + str(total1))\n return errMsg\n\n return 'all correct, diff:\\t' + str(diff)\n\n\n# check if value is in the range\ndef contain(vTuple, val):\n res, err = vTuple\n if val < res - err or val > res + err: \n return False\n else:\n return True\n\n# make edge matrix\ndef edge_mat(g, f_diag, f_other, *args):\n res = OrderedDict([])\n for e in g.edges():\n tmpRow = OrderedDict([])\n for f in g.edges():\n if e_repr(e) == e_repr(f):\n tmpRow[f] = f_diag(g, e, f, *args)\n else:\n tmpRow[f] = f_other(g, e, f, *args)\n res[e] = tmpRow\n\n return od2arr(res)\n\ndef edge_vec(g, e, f_diag, f_other, *args):\n res = OrderedDict([])\n for f in g.edges():\n if e_repr(e) == e_repr(f):\n res[f] = f_diag(g, f, *args)\n else:\n res[f] = f_other(g, f, *args)\n\n return od2arr(res)\n\n# test if integration is to 1\ndef is_prob_dist(func, dim=2):\n if dim ==2:\n res, err = dblquad(func, 0, 1, lambda p: 0, lambda p: 1)\n else:\n res, err = quad(func, 0, 1)\n\n if 1 < res - err or 1 > res + err:\n raise Exception('error: function provided does not integrate to 1')\n\n return True\n", "sub_path": "src/commonFuncs.py", "file_name": "commonFuncs.py", "file_ext": "py", "file_size_in_byte": 5088, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "pyprelude.FPToolBox.lmap", "line_number": 43, "usage_type": "call"}, {"api_name": "pyprelude.FPToolBox", "line_number": 43, "usage_type": "name"}, {"api_name": "pyprelude.FPToolBox.lmap", "line_number": 48, "usage_type": "call"}, {"api_name": "pyprelude.FPToolBox", "line_number": 48, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 104, "usage_type": "call"}, {"api_name": "operator.mul", "line_number": 104, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 105, "usage_type": "call"}, {"api_name": "operator.mul", "line_number": 105, "usage_type": "attribute"}, {"api_name": "scipy.integrate.quad", "line_number": 159, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 167, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 193, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 195, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 206, "usage_type": "call"}, {"api_name": "scipy.integrate.dblquad", "line_number": 218, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "557781974", "text": "from django.shortcuts import render, HttpResponseRedirect\nfrom .models import Todo_app\nfrom django.utils import timezone\nfrom django.views.decorators.csrf import csrf_exempt\n\n\ndef home(request):\n todotext = Todo_app.objects.all().order_by(\"addDate\")\n context = {\n 'todo': todotext\n }\n return render(request, 'home.html', context)\n\n\n@csrf_exempt\ndef addtodo(request):\n current_date = timezone.now()\n content = request.POST['content']\n create_obj = Todo_app.objects.create(addDate=current_date, text=content)\n todo_lenght = Todo_app.objects.all().count()\n return HttpResponseRedirect('/')\n\n\n@csrf_exempt\ndef delete(request, iditem):\n Todo_app.objects.get(id=iditem).delete()\n return HttpResponseRedirect('/')\n", "sub_path": "ToDo/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 747, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "models.Todo_app.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Todo_app.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.Todo_app", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Todo_app.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Todo_app.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Todo_app", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Todo_app.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Todo_app.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Todo_app", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 21, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Todo_app.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Todo_app.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Todo_app", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "53940797", "text": "import setuptools\n\nwith open(\"README.md\", \"r\", encoding='utf8') as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"swaglyrics\",\n version=\"0.2.2\",\n author=\"Aadi Bajpai\",\n author_email=\"aadibajpai@gmail.com\",\n description=\"Fetch the currently playing song from Spotify and display lyrics on cmd or in a browser tab.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/aadibajpai/SwagLyrics-For-Spotify\",\n entry_points={'console_scripts': ['swaglyrics=swaglyrics.__main__:main']},\n packages=setuptools.find_packages(),\n license='MIT',\n include_package_data=True,\n install_requires=['flask', 'requests', 'beautifulsoup4', 'pywin32; platform_system==\"Windows\"'],\n keywords='spotify lyrics python genius',\n classifiers=(\n \"Programming Language :: Python :: 3\",\n \"Framework :: Flask\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: Microsoft :: Windows :: Windows 10\",\n \"Operating System :: POSIX :: Linux\",\n \"Intended Audience :: End Users/Desktop\",\n ),\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "69587759", "text": "import random\nfrom ipaddress import IPv6Address\nimport socket\nimport struct\nfrom os import listdir\nfrom os.path import isfile, join\n\nimport Constants\n\n''' This method checks is byte is representation of an offset\nbyte. Returns true if its offset byte and false otherwise '''\n\n\ndef is_offset_byte(byte):\n offset_bits = (1 << 7) + (1 << 6)\n return byte & offset_bits > 0\n\n\n''' This method returns 2 byte random number '''\n\n\ndef random_int():\n lower_bound = 0\n upper_bound = (1 << 16) - 1\n return random.randint(lower_bound, upper_bound)\n\n\n''' This method takes dns packet data and offset where it starts construction\n of string. It constructs string considering compression rules and returns\n first byte offset of Question Type '''\n\n\ndef get_compressed_text(dns_packet_data, offset):\n q_name = \"\"\n\n while True:\n octet = dns_packet_data[offset]\n # check octet if its offset byte\n if is_offset_byte(octet):\n # return string and point offset one byte right\n new_off = int.from_bytes(dns_packet_data[offset: offset + 2],\n byteorder=Constants.INTERNET_ENDIANNESS)\n # remove preceding 11 bits and get offset\n new_off &= 0x3fff\n recursive_string = recursive_construction(dns_packet_data, new_off)\n if len(q_name) > 0 and not q_name.endswith(\".\"):\n q_name += \".\"\n return q_name + recursive_string, offset + 2\n\n if octet == 0:\n return q_name, offset + 1\n\n for i in range(octet):\n q_name += chr(dns_packet_data[offset + i + 1])\n # increase offset to point next length octet\n offset += octet + 1\n\n if dns_packet_data[offset] != 0 and not q_name.endswith(\".\"):\n q_name += \".\"\n\n return None, 0\n\n\n''' This method takes dns packet data and offset from headers first byte.\n Finds string, which starts from this offset and keeps it, or at some step\n expands recursively if this string contains some more offset strings. '''\n\n\ndef recursive_construction(dns_packet_data, offset):\n q_name = \"\"\n\n while offset < len(dns_packet_data):\n octet = dns_packet_data[offset]\n # check if it contains offset string\n if is_offset_byte(octet):\n # get offset string and concatenate\n octet = int.from_bytes(dns_packet_data[offset: offset + 2],\n byteorder=Constants.INTERNET_ENDIANNESS)\n octet &= 0x3fff\n if len(q_name) > 0 and not q_name.endswith(\".\"):\n q_name += \".\"\n q_name += recursive_construction(dns_packet_data, octet)\n break\n # check if string ended\n if octet == 0:\n break\n\n # concatenate q_name to octet length string\n for i in range(octet):\n q_name += chr(dns_packet_data[offset + i + 1])\n\n # update offset value to point right after the end\n # of octet length string\n offset += octet + 1\n if dns_packet_data[offset] != 0 and not q_name.endswith(\".\"):\n q_name += \".\"\n\n return q_name\n\n\n''' This method takes dns packet data, answer type and offset where response\n data starts, and base on answer type, returns response data '''\n\n\ndef get_response_data(dns_packet_data, a_type, rd_length, offset):\n\n # check if type is MX\n if a_type == Constants.MX:\n # take preference\n preference = int.from_bytes(dns_packet_data[offset: offset + 2],\n byteorder=Constants.INTERNET_ENDIANNESS)\n # skip 2 byte integer\n offset += 2\n exchange, offset = get_compressed_text(dns_packet_data, offset)\n\n response_data = str(preference) + \" \" + exchange\n return response_data, offset\n\n # check if type is A\n if a_type == Constants.A:\n decimal_ip = int.from_bytes(dns_packet_data[offset: offset + 4],\n byteorder=Constants.INTERNET_ENDIANNESS)\n\n response_data = socket.inet_ntoa(struct.pack('!L', decimal_ip))\n # skip ip addres bytes\n offset += 4\n\n return response_data, offset\n\n # check if type is TXT\n if a_type == Constants.TXT:\n text = \"\"\n\n text_length = dns_packet_data[offset]\n offset += 1\n\n # read text\n for i in range(text_length):\n text += chr(dns_packet_data[offset + i])\n # update offset value\n offset += text_length\n\n return text, offset\n\n # check if type is NS or cname\n if a_type == Constants.NS or a_type == Constants.CNAME:\n name_server, offset = get_compressed_text(dns_packet_data, offset)\n return name_server, offset\n\n # check if type is AAAA\n if a_type == Constants.AAAA:\n decimal_ipv6 = int.from_bytes(dns_packet_data[offset: offset + 16],\n byteorder=Constants.INTERNET_ENDIANNESS)\n ipv6 = str(IPv6Address(decimal_ipv6))\n offset += 16\n return ipv6, offset\n\n # check if type is SOA\n if a_type == Constants.SOA:\n response_data, offset = get_soa_response(dns_packet_data, offset)\n return response_data, offset\n\n # if type is unknown\n response_data = dns_packet_data[offset: offset + rd_length]\n offset += rd_length\n return response_data, offset\n\n\n''' This method takes dns packet data and offset which points to\n the first byte of response data of soa type field. It extraces\n soa fields and returns them as string. '''\n\n\ndef get_soa_response(dns_packet_data, offset):\n primary_name, offset = get_compressed_text(dns_packet_data, offset)\n r_name, offset = get_compressed_text(dns_packet_data, offset)\n\n serial_number = int.from_bytes(dns_packet_data[offset: offset + 4],\n byteorder=Constants.INTERNET_ENDIANNESS)\n offset += 4\n\n refresh_number = int.from_bytes(dns_packet_data[offset: offset + 4],\n byteorder=Constants.INTERNET_ENDIANNESS)\n offset += 4\n\n retry = int.from_bytes(dns_packet_data[offset: offset + 4],\n byteorder=Constants.INTERNET_ENDIANNESS)\n offset += 4\n\n expire = int.from_bytes(dns_packet_data[offset: offset + 4],\n byteorder=Constants.INTERNET_ENDIANNESS)\n offset += 4\n\n minimum = int.from_bytes(dns_packet_data[offset: offset + 4],\n byteorder=Constants.INTERNET_ENDIANNESS)\n offset += 4\n\n response_data = primary_name + \" \" + r_name + \" \" + str(serial_number)\n response_data += \" \" + str(refresh_number) + \" \" + str(retry) + \" \" + str(expire)\n response_data += \" \" + str(minimum)\n\n return response_data, offset\n\n''' This method takes int representation of a type\n and returns its string value '''\n\n\ndef type_to_string(type_number):\n if type_number == Constants.A:\n return \"A\"\n\n if type_number == Constants.AAAA:\n return \"AAAA\"\n\n if type_number == Constants.NS:\n return \"NS\"\n\n if type_number == Constants.MX:\n return \"MX\"\n\n if type_number == Constants.SOA:\n return \"SOA\"\n\n if type_number == Constants.TXT:\n return \"TXT\"\n\n if type_number == Constants.CNAME:\n return \"CNAME\"\n\n if type_number == Constants.ANY:\n return \"ANY\"\n\n return \"TYPE257\"\n\n\n''' This method takes string type and returns its corresponding\n two byte integer. '''\n\n\ndef type_to_short(type_string):\n\n if type_string == \"A\":\n return Constants.A\n\n if type_string == \"AAAA\":\n return Constants.AAAA\n\n if type_string == \"NS\":\n return Constants.NS\n\n if type_string == \"MX\":\n return Constants.MX\n\n if type_string == \"SOA\":\n return Constants.SOA\n\n if type_string == \"TXT\":\n return Constants.TXT\n\n if type_string == \"CNAME\":\n return Constants.CNAME\n\n return Constants.ANY\n\n\n''' This method takes int representation of a class\n and returns its string value '''\n\n\ndef class_to_string(class_number):\n if class_number == 0x0001:\n return \"IN\"\n\n return \"UNKNOWN\"\n\n\n''' This method checks whether zone file exists\n in path directory '''\n\n\ndef zone_file_exists(zone, path):\n all_files = [f for f in listdir(path)\n if isfile(join(path, f))]\n\n for file in all_files:\n if file == zone:\n return True\n return False\n", "sub_path": "DNS Server/Tools.py", "file_name": "Tools.py", "file_ext": "py", "file_size_in_byte": 8423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "random.randint", "line_number": 25, "usage_type": "call"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 42, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 78, "usage_type": "attribute"}, {"api_name": "Constants.MX", "line_number": 108, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 111, "usage_type": "attribute"}, {"api_name": "Constants.A", "line_number": 120, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 122, "usage_type": "attribute"}, {"api_name": "socket.inet_ntoa", "line_number": 124, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 124, "usage_type": "call"}, {"api_name": "Constants.TXT", "line_number": 131, "usage_type": "attribute"}, {"api_name": "Constants.NS", "line_number": 146, "usage_type": "attribute"}, {"api_name": "Constants.CNAME", "line_number": 146, "usage_type": "attribute"}, {"api_name": "Constants.AAAA", "line_number": 151, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 153, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv6Address", "line_number": 154, "usage_type": "call"}, {"api_name": "Constants.SOA", "line_number": 159, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 179, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 183, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 187, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 191, "usage_type": "attribute"}, {"api_name": "Constants.INTERNET_ENDIANNESS", "line_number": 195, "usage_type": "attribute"}, {"api_name": "Constants.A", "line_number": 209, "usage_type": "attribute"}, {"api_name": "Constants.AAAA", "line_number": 212, "usage_type": "attribute"}, {"api_name": "Constants.NS", "line_number": 215, "usage_type": "attribute"}, {"api_name": "Constants.MX", "line_number": 218, "usage_type": "attribute"}, {"api_name": "Constants.SOA", "line_number": 221, "usage_type": "attribute"}, {"api_name": "Constants.TXT", "line_number": 224, "usage_type": "attribute"}, {"api_name": "Constants.CNAME", "line_number": 227, "usage_type": "attribute"}, {"api_name": "Constants.ANY", "line_number": 230, "usage_type": "attribute"}, {"api_name": "Constants.A", "line_number": 243, "usage_type": "attribute"}, {"api_name": "Constants.AAAA", "line_number": 246, "usage_type": "attribute"}, {"api_name": "Constants.NS", "line_number": 249, "usage_type": "attribute"}, {"api_name": "Constants.MX", "line_number": 252, "usage_type": "attribute"}, {"api_name": "Constants.SOA", "line_number": 255, "usage_type": "attribute"}, {"api_name": "Constants.TXT", "line_number": 258, "usage_type": "attribute"}, {"api_name": "Constants.CNAME", "line_number": 261, "usage_type": "attribute"}, {"api_name": "Constants.ANY", "line_number": 263, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 282, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 283, "usage_type": "call"}]} +{"seq_id": "451228254", "text": "import csv\nimport os\nimport django\nimport sys\n\nos.chdir(\".\")\nprint(\"Current dir=\", end=\"\"), print(os.getcwd())\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nprint(\"BASE_DIR=\", end=\"\"), print(BASE_DIR)\n\nsys.path.append(BASE_DIR)\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"django_database.settings\") # 1. 여기서 프로젝트명.settings입력\ndjango.setup()\n\n# 위의 과정까지가 python manage.py shell을 키는 것과 비슷한 효과\n\nfrom blog.models import * # 2. App이름.models\n\nCSV_PATH = './model.csv' # 3. csv 파일 경로\n\nwith open(CSV_PATH, newline='') as csvfile: # 4. newline =''\n data_reader = csv.DictReader(csvfile)\n\n for row in data_reader:\n print(row)\n Medicine.objects.create( # 5. class명.objects.create\n id=row['id'],\n name=row['name'],\n ingredient=row['ingredient'],\n effect=row['effect'],\n dosage=row['dosage'],\n )", "sub_path": "django_database/csv_db.py", "file_name": "csv_db.py", "file_ext": "py", "file_size_in_byte": 967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.chdir", "line_number": 6, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ.setdefault", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 15, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "473569870", "text": "import pandas as pd\nimport numpy as np\n\nfrom .data_validation import validate_sort\nfrom .utils import add_suffix\n\nfrom .config import config\n\n\ncols_to_normalize = [k for k, v in config.columns.items() if v.get(\"normalize\", False)]\nrenamed_cols = {col: add_suffix(col, \"normalize\") for col in cols_to_normalize}\n\n\ndef _normalize_array(array):\n return (array - array.min()) / (array.max() - array.min())\n\n\ndef _normalize_cols(df):\n norm_cols = (\n df[cols_to_normalize].apply(_normalize_array).rename(columns=renamed_cols)\n )\n\n df = pd.concat((df, norm_cols), axis=1)\n\n return df\n\n\ndef _validate_col_norm(df):\n for col in cols_to_normalize:\n norm_col = add_suffix(col, \"normalize\")\n \n inverse_norm = df[norm_col] * (df[col].max() - df[col].min()) + df[col].min()\n\n status = np.isclose(inverse_norm, df[col]).all()\n\n assert status, f\"{col} inverse normalization does not align.\"\n\n\ndef _validate_normalization(df):\n df.groupby(\"subject\").apply(_validate_col_norm)\n print(\"Inverse normalizations align.\")\n\n\ndef _validate_col_equivalence(df1, df2):\n assert (df1[cols_to_normalize] == df2[cols_to_normalize]).all().all()\n\n\ndef normalize(df):\n print(\"Normalization...\")\n norm_df = df.groupby(\"subject\").apply(_normalize_cols)\n\n _validate_normalization(norm_df)\n _validate_col_equivalence(norm_df, df)\n validate_sort(norm_df)\n\n return norm_df\n", "sub_path": "glucose_forecast/data_normalize.py", "file_name": "data_normalize.py", "file_ext": "py", "file_size_in_byte": 1420, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "config.config.columns.items", "line_number": 10, "usage_type": "call"}, {"api_name": "config.config.columns", "line_number": 10, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 10, "usage_type": "name"}, {"api_name": "utils.add_suffix", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.add_suffix", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 34, "usage_type": "call"}, {"api_name": "data_validation.validate_sort", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "115273651", "text": "\nfrom sdl2 import *\nfrom sdl2.sdlttf import *\nimport sdl2.ext as ext\n\nfrom graphics import Graphics\n\n\ndef main():\n ext.init()\n graphics = Graphics(640, 480)\n\n TTF_Init()\n font = TTF_OpenFont(b\"rebel.ttf\", 16)\n if(not font):\n print(TTF_GetError())\n color = SDL_Color(180, 189, 180)\n\n\n s = \"Un Deux Trois Quatre\"\n text = b\"Un Deux Trois Quatre\"\n text2 = bytes(s, \"utf-8\")\n\n surface = TTF_RenderText_Solid(font, text2, color)\n\n x = 50\n y = 30\n\n srcRect = SDL_Rect(0, 0, surface.contents.w, surface.contents.h)\n dstRect = SDL_Rect(x, y, surface.contents.w, surface.contents.h)\n\n texture = SDL_CreateTextureFromSurface(graphics.renderer, surface)\n graphics.blit_surface(texture, srcRect, dstRect)\n\n\n while(not SDL_QuitRequested()):\n SDL_Delay(250)\n graphics.flip()\n\n\n\n TTF_CloseFont(font)\n\n TTF_Quit()\n ext.quit()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "exemples/font.py", "file_name": "font.py", "file_ext": "py", "file_size_in_byte": 930, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sdl2.ext.init", "line_number": 10, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 10, "usage_type": "name"}, {"api_name": "graphics.Graphics", "line_number": 11, "usage_type": "call"}, {"api_name": "graphics.renderer", "line_number": 32, "usage_type": "attribute"}, {"api_name": "graphics.blit_surface", "line_number": 33, "usage_type": "call"}, {"api_name": "graphics.flip", "line_number": 38, "usage_type": "call"}, {"api_name": "sdl2.ext.quit", "line_number": 45, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "420204413", "text": "from typing import Optional\n\nfrom requests import Response\nfrom schemathesis.models import Case\n\n\ndef no_422(response: Response, case: Case) -> Optional[bool]:\n assert (\n response.status_code != 422\n ), \"422 code should not be used. Instead, 400 should be returned in response to invalid request payloads. https://cloud.ibm.com/docs/api-handbook?topic=api-handbook-status-codes#client-errors-4xx\"\n return None\n\n\ndef no_content_204(response: Response, case: Case) -> Optional[bool]:\n if response.status_code == 204:\n assert (\n not response.content\n ), \"204 response must not include a response body. https://cloud.ibm.com/docs/api-handbook?topic=api-handbook-status-codes#success-2xx\"\n else:\n # skips the test when it's not relevant\n return True\n return None\n", "sub_path": "src/ibm_service_validator/handbook_rules/general_rules/status_code_rules.py", "file_name": "status_code_rules.py", "file_ext": "py", "file_size_in_byte": 822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.Response", "line_number": 7, "usage_type": "name"}, {"api_name": "schemathesis.models.Case", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 7, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 14, "usage_type": "name"}, {"api_name": "schemathesis.models.Case", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "590383748", "text": "# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT license.\n\n\"\"\"\ntf2onnx.rewriter.gruBlock_rewriter - gruBlock support\n\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport logging\nfrom tf2onnx import utils\nfrom tf2onnx.rewriter.gru_rewriter import GRUUnitRewriter\nfrom tf2onnx.rewriter.rnn_utils import RNNUnitType, get_weights_from_const_node, \\\n is_tensor_array_read_op, is_tensor_array_scatter_op\n\n\nlogging.basicConfig(level=logging.INFO)\nlog = logging.getLogger(\"tf2onnx.rewriter.grublock_rewriter\")\n\n# pylint: disable=invalid-name,unused-argument,missing-docstring\n\nclass GRUBlockUnitRewriter(GRUUnitRewriter):\n def __init__(self, g):\n super(GRUBlockUnitRewriter, self).__init__(g)\n self.switch_checkers = {\n # True means we need parse its initial value in later logic.\n # in tensorflow, switch is a good op that we can use to trace other ops that needed\n \"state\": (self._state_switch_check, self._connect_gru_state_to_graph, True),\n \"output\": (self._output_switch_check, self._connect_gru_output_to_graph, False),\n }\n\n def run(self):\n return super(GRUBlockUnitRewriter, self).run_with_unit_type(RNNUnitType.GRUBlockCell)\n\n def get_rnn_scope_name(self, match):\n # take the cell output and go up 3 levels to find the scope:\n # name of h is like root/while/gru_cell/mul_2\n # root is the dynamic rnn's scope name.\n # root/while/gru_cell is cell's scope name\n h_node = match.get_op(\"GRUBlockCell\").inputs[0]\n parts = h_node.name.split('/')\n rnn_scope_name = '/'.join(parts[0:-2])\n return rnn_scope_name\n\n def get_cell_scope_name(self, match):\n cell_node = match.get_op(\"GRUBlockCell\")\n return cell_node.name[:cell_node.name.rfind(\"/\")]\n\n def get_weight_and_bias(self, match):\n\n node = match.get_op(\"GRUBlockCell\")\n # from tf, it can be known that, the inputs index and meaning of input data is:\n # 0-input, 1-state, 2-gate_kernel, 3-hidden_kernel, 4-gate_bias, 5-hidden_bias\n gate_kernel = get_weights_from_const_node(node.inputs[2].inputs[0])\n gate_bias = get_weights_from_const_node(node.inputs[4].inputs[0])\n hidden_kernel = get_weights_from_const_node(node.inputs[3].inputs[0])\n hidden_bias = get_weights_from_const_node(node.inputs[5].inputs[0])\n if not all([gate_kernel, gate_bias, hidden_kernel, hidden_bias]):\n log.debug(\"rnn weights check failed, skip\")\n return None\n log.debug(\"find needed weights\")\n res = {\"gate_kernel\": gate_kernel,\n \"gate_bias\": gate_bias,\n \"hidden_kernel\": hidden_kernel,\n \"hidden_bias\": hidden_bias}\n return res\n\n def find_inputs(self, rnn_scope_name, rnn_props, match, input_blacklist=None):\n cell_node = match.get_op(\"GRUBlockCell\")\n read_node = cell_node.inputs[0]\n utils.make_sure(is_tensor_array_read_op(read_node), \"ta read op check fail\")\n enter_node = read_node.inputs[2]\n utils.make_sure(enter_node.type == \"Enter\", \"enter check fail\")\n scatter_node = enter_node.inputs[0]\n utils.make_sure(is_tensor_array_scatter_op(scatter_node), \"ta scatter check fail\")\n\n node = scatter_node.inputs[2]\n node_id = scatter_node.input[2]\n # dynamic_rnn may insert transpose op if input data format is [B, T, D]\n if node.type == \"Transpose\" and node.name.startswith(rnn_scope_name):\n node_id = node.input[0]\n node = node.inputs[0]\n\n utils.make_sure(not node.name.startswith(rnn_scope_name), \"rnn input should not has rnn scope name\")\n rnn_props.input_node = node\n rnn_props.input_id = node_id\n\n @staticmethod\n def _state_switch_check(enter_target_node_input_id, identity_consumers, match):\n node = match.get_op(\"GRUBlockCell\")\n if node == identity_consumers[0]:\n log.debug(\"find state initializer value at %s\", enter_target_node_input_id)\n return enter_target_node_input_id\n return None\n\n @staticmethod\n def get_rnn_activation(match):\n return \"Tanh\"\n\n def create_rnn_node(self, rnn_props):\n # specify if the RNN is forward, reverse, or bidirectional.\n # Must be one of forward (default), reverse, or bidirectional.\n # Here we won't mark bidirectional/reverse, we will have another rewriter running after this one,\n # which will based on patterns to combine a forward GRU and a backward GRU into a bidirectional one.\n direction = \"forward\"\n num_direction = 1\n # todo: input_forget\n attr = {\"direction\": direction, \"hidden_size\": rnn_props.hidden_size,\n \"activations\": [\"sigmoid\", rnn_props.activation]}\n inputs = rnn_props.onnx_input_ids\n gru_inputs = [\n inputs[\"X\"], inputs[\"W\"], inputs[\"R\"], inputs[\"B\"],\n inputs[\"sequence_lens\"], inputs[\"initial_state\"]]\n\n x_shape = self.g.get_shape(gru_inputs[0])\n x_dtype = self.g.get_dtype(gru_inputs[0])\n x_seq_length = x_shape[0]\n x_batch_size = x_shape[1]\n gru_node = self.g.make_node(\"GRU\", gru_inputs, attr=attr, output_count=2,\n shapes=[[x_seq_length, num_direction, x_batch_size, rnn_props.hidden_size],\n [num_direction, x_batch_size, rnn_props.hidden_size]],\n dtypes=[x_dtype, x_dtype])\n return gru_node\n", "sub_path": "tf2onnx/rewriter/grublock_rewriter.py", "file_name": "grublock_rewriter.py", "file_ext": "py", "file_size_in_byte": 5632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "tf2onnx.rewriter.gru_rewriter.GRUUnitRewriter", "line_number": 24, "usage_type": "name"}, {"api_name": "tf2onnx.rewriter.rnn_utils.RNNUnitType.GRUBlockCell", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tf2onnx.rewriter.rnn_utils.RNNUnitType", "line_number": 35, "usage_type": "name"}, {"api_name": "tf2onnx.rewriter.rnn_utils.get_weights_from_const_node", "line_number": 56, "usage_type": "call"}, {"api_name": "tf2onnx.rewriter.rnn_utils.get_weights_from_const_node", "line_number": 57, "usage_type": "call"}, {"api_name": "tf2onnx.rewriter.rnn_utils.get_weights_from_const_node", "line_number": 58, "usage_type": "call"}, {"api_name": "tf2onnx.rewriter.rnn_utils.get_weights_from_const_node", "line_number": 59, "usage_type": "call"}, {"api_name": "tf2onnx.utils.make_sure", "line_number": 73, "usage_type": "call"}, {"api_name": "tf2onnx.utils", "line_number": 73, "usage_type": "name"}, {"api_name": "tf2onnx.rewriter.rnn_utils.is_tensor_array_read_op", "line_number": 73, "usage_type": "call"}, {"api_name": "tf2onnx.utils.make_sure", "line_number": 75, "usage_type": "call"}, {"api_name": "tf2onnx.utils", "line_number": 75, "usage_type": "name"}, {"api_name": "tf2onnx.utils.make_sure", "line_number": 77, "usage_type": "call"}, {"api_name": "tf2onnx.utils", "line_number": 77, "usage_type": "name"}, {"api_name": "tf2onnx.rewriter.rnn_utils.is_tensor_array_scatter_op", "line_number": 77, "usage_type": "call"}, {"api_name": "tf2onnx.utils.make_sure", "line_number": 86, "usage_type": "call"}, {"api_name": "tf2onnx.utils", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "319317669", "text": "import json\nimport pandas as pd\n\nall = []\nfor i in range(1, 21):\n with open(\"page\" + str(i) + \".txt\") as file:\n content = file.read()\n data = json.loads(content)\n values = data[\"value\"]\n for v in values:\n name = v[\"displayName\"]\n mail = v[\"mail\"]\n other = v[\"userPrincipalName\"]\n all.append([name, mail, other])\ndf = pd.DataFrame(all, columns = ['Name', 'Mail', 'Other'])\ndf.to_csv(\"output.csv\", encoding='utf8')\n", "sub_path": "parse.py", "file_name": "parse.py", "file_ext": "py", "file_size_in_byte": 460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "json.loads", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "84724948", "text": "import modules\n\nimport numpy as np\nimport tensorflow as tf\nimport tensorflowjs\n\nimport json\nimport os\nfrom collections import OrderedDict\n\n\nclass gan(object):\n default_hyperparams = OrderedDict([\n ('tensorboard_dir', 'tensorboard'),\n ('saved_models_dir', 'saved_models'),\n\n ('image_shape', '[64, 64, 3]'),\n\n ('n_epochs', 10),\n ('batch_size', 16),\n\n ('disc_steps_per_gen_step', 1),\n ('disc_learning_rate', 1e-4),\n ('gen_learning_rate', 1e-4),\n ('generator_n_resnet_blocks', 1),\n\n ('lambda_1', 1), # generator's discriminator loss\n ('lambda_2', 10), # generator's cyclic loss\n ('lambda_3', 0), # generator's identity loss\n\n ('tensorboard_summary_period', 100),\n ('tfjs_saving_period', 1000),\n ('tf_config_proto', '{\"log_device_placement\": false}'),\n ('argparse_args', {}),\n ])\n\n\n def __init__(self, **kwargs):\n # hyperparams\n self.__dict__.update(self.default_hyperparams)\n self.__dict__.update(kwargs)\n # deserialize some hyperparams\n for k in ('image_shape', 'tf_config_proto'):\n self.__dict__[k] = json.loads(self.__dict__[k])\n assert isinstance(self.image_shape, list) and len(self.image_shape)==3\n self.tf_config = tf.ConfigProto(**self.tf_config_proto)\n\n # instantiate optimizers\n self.disc_optimizer = tf.train.AdamOptimizer(self.disc_learning_rate)\n self.gen_optimizer = tf.train.AdamOptimizer(self.gen_learning_rate)\n\n # instantiate models\n self.discriminator_A = modules.discriminator(input_shape=self.image_shape)\n self.discriminator_B = modules.discriminator(input_shape=self.image_shape)\n self.generator_A2B = modules.generator(input_shape=self.image_shape,\n n_resnet_blocks=self.generator_n_resnet_blocks)\n self.generator_B2A = modules.generator(input_shape=self.image_shape,\n n_resnet_blocks=self.generator_n_resnet_blocks)\n for model in (self.discriminator_A, self.discriminator_B, self.generator_A2B, self.generator_B2A):\n # 'sgd' and 'mse' are not actually used, just placeholders for compiling\n model.compile('sgd', 'mse')\n \n\n def discriminator_loss(self, real_A, real_B):\n MSE = lambda a,b: tf.reduce_mean((a-b)**2)\n # model outputs\n fake_A = self.generator_B2A(real_B)\n fake_B = self.generator_A2B(real_A)\n fooled_A = self.discriminator_A(fake_A)\n fooled_B = self.discriminator_B(fake_B)\n not_fooled_A = self.discriminator_A(real_A)\n not_fooled_B = self.discriminator_B(real_B)\n\n # log some images\n tf.summary.image(\"A\", real_A, max_outputs=1)\n tf.summary.image(\"A2B(A)\", fake_B, max_outputs=1)\n tf.summary.image(\"B\", real_B, max_outputs=1)\n tf.summary.image(\"B2A(B)\", fake_A, max_outputs=1)\n\n # calculate losses\n fooled_A = MSE(fooled_A, tf.zeros_like(fooled_A))\n fooled_B = MSE(fooled_B, tf.zeros_like(fooled_B))\n not_fooled_A = MSE(not_fooled_A, tf.ones_like(not_fooled_A))\n not_fooled_B = MSE(not_fooled_B, tf.ones_like(not_fooled_B))\n\n # aggregate loss with standard sum\n return fooled_A + fooled_B + not_fooled_A + not_fooled_B\n\n\n def generator_loss(self, real_A, real_B):\n MSE = lambda a,b: tf.reduce_mean((a-b)**2)\n MAE = lambda a,b: tf.reduce_mean(tf.abs(a-b))\n \n fake_A = self.generator_B2A(real_B)\n fake_B = self.generator_A2B(real_A)\n\n # discriminator loss\n fooled_A = self.discriminator_A(fake_A)\n fooled_B = self.discriminator_B(fake_B)\n disc_loss = MSE(fooled_A, tf.ones_like(fooled_A))\n disc_loss += MSE(fooled_A, tf.ones_like(fooled_B))\n\n # cyclic loss\n cycle_A = self.generator_B2A(fake_B)\n cycle_B = self.generator_A2B(fake_A)\n cyclic_loss = MAE(cycle_A, real_A) + MAE(cycle_B, real_B)\n\n # (optional) identity loss\n if self.lambda_3:\n iden_A = self.generator_B2A(real_A)\n iden_B = self.generator_A2B(real_B)\n iden_loss = MAE(iden_A, real_A) + MAE(iden_B, real_B)\n\n # aggregate loss with weighted sum\n loss = self.lambda_1 * disc_loss\n loss += self.lambda_2 * cyclic_loss\n if self.lambda_3:\n loss += self.lambda_3 * iden_loss\n \n return tf.reduce_mean(loss)\n\n\n def fit(self, fnames, classes):\n try: tf.gfile.DeleteRecursively(self.tensorboard_dir)\n except: pass\n tboard_writer = tf.summary.FileWriter(self.tensorboard_dir)\n\n dataset = self.get_dataset(fnames, classes).make_initializable_iterator()\n As, Bs = dataset.get_next()\n\n gen_step = self.fit_generator_step(As, Bs)\n disc_step = self.fit_discriminator_step(As, Bs)\n\n with tf.Session(config=self.tf_config) as sess:\n sess.run([dataset.initializer, tf.global_variables_initializer()])\n # tboard_writer.add_graph(sess.graph)\n\n for epoch in range(self.n_epochs):\n for step in range(len(fnames) // self.batch_size):\n do_gen_step = step%(self.disc_steps_per_gen_step + 1) == 0\n train_step = gen_step if do_gen_step else disc_step\n \n if self.tensorboard_summary_period and (step % self.tensorboard_summary_period) == 0:\n summary, _ = sess.run([tf.summary.merge_all(), train_step])\n tboard_writer.add_summary(summary, step)\n else:\n sess.run([train_step])\n\n if self.tfjs_saving_period and (step % self.tfjs_saving_period) == 0:\n self.save()\n \n self.save()\n \n return self\n \n\n def fit_generator_step(self, As, Bs):\n g_loss = self.generator_loss(As, Bs)\n A2B_vars = self.generator_A2B.trainable_variables\n B2A_vars = self.generator_B2A.trainable_variables\n tf.summary.scalar('generator_loss', g_loss)\n return self.gen_optimizer.minimize(g_loss, var_list=A2B_vars+B2A_vars)\n \n\n def fit_discriminator_step(self, As, Bs):\n d_loss = self.discriminator_loss(As, Bs)\n A_vars = self.discriminator_A.trainable_variables\n B_vars = self.discriminator_B.trainable_variables\n tf.summary.scalar('discriminator_loss', d_loss)\n return self.disc_optimizer.minimize(d_loss, var_list=A_vars+B_vars)\n\n\n def get_dataset(self, fnames, classes):\n ''' Gets dataset.\n Args:\n fnames: a list of image filenames\n classes: a list of classes. `fnames[classes == 1]` are assigned class \"A\"\n \n Returns:\n A tensorflow dataset\n '''\n fnames = np.array(fnames)\n classes = np.array(classes)\n assert len(classes) == len(fnames)\n\n\n def load_image(file):\n image = tf.image.decode_jpeg(tf.read_file(file), channels=3)\n image = tf.expand_dims(image, axis=0)\n image = tf.image.resize_nearest_neighbor(image, self.image_shape[:2])\n image = tf.divide(tf.cast(image[0], 'float32'), 255.)\n return image\n\n As = (tf.data.Dataset.from_tensor_slices(fnames[classes == 1])\n .shuffle(2000)\n .map(load_image))\n Bs = (tf.data.Dataset.from_tensor_slices(fnames[classes != 1])\n .shuffle(2000)\n .map(load_image))\n\n return (tf.data.Dataset.zip((As, Bs))\n .repeat()\n .batch(self.batch_size)\n .prefetch(2000))\n\n\n def save(self):\n to_path = lambda *names: os.path.join(self.saved_models_dir, *names)\n try: tf.gfile.DeleteRecursively(self.saved_models_dir)\n except: pass\n for folder in (self.saved_models_dir, to_path('A2B'), to_path('B2A')):\n tf.gfile.MkDir(folder)\n\n # save argparse args (for the sake of provenance)\n with tf.gfile.GFile(to_path('argparse_args.json'), 'w') as f:\n f.write(json.dumps(self.argparse_args, indent=4))\n\n # save to keras and tfjs\n generators = {'A2B': self.generator_A2B, 'B2A': self.generator_B2A}\n for name, model in generators.items():\n tensorflowjs.converters.save_keras_model(model, to_path(name, 'tfjs'))\n\n", "sub_path": "cyclegan/gan.py", "file_name": "gan.py", "file_ext": "py", "file_size_in_byte": 8404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.OrderedDict", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 50, "usage_type": "attribute"}, {"api_name": "modules.discriminator", "line_number": 53, "usage_type": "call"}, {"api_name": "modules.discriminator", "line_number": 54, "usage_type": "call"}, {"api_name": "modules.generator", "line_number": 55, "usage_type": "call"}, {"api_name": "modules.generator", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.summary.image", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros_like", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.zeros_like", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.ones_like", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.ones_like", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.abs", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.ones_like", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.ones_like", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.gfile.DeleteRecursively", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 126, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 188, "usage_type": "attribute"}, {"api_name": "tensorflow.read_file", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.image.resize_nearest_neighbor", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 190, "usage_type": "attribute"}, {"api_name": "tensorflow.divide", "line_number": 191, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 191, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 194, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 197, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 197, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.zip", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 201, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.DeleteRecursively", "line_number": 209, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 209, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.MkDir", "line_number": 212, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 212, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 215, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 215, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 216, "usage_type": "call"}, {"api_name": "tensorflowjs.converters.save_keras_model", "line_number": 221, "usage_type": "call"}, {"api_name": "tensorflowjs.converters", "line_number": 221, "usage_type": "attribute"}]} +{"seq_id": "506576020", "text": "import re\n\nfrom pygments.lexer import RegexLexer, include, bygroups, using, \\\n this\nfrom pygments.token import Text, Comment, Operator, Keyword, Name, String, \\\n Number, Punctuation, Literal\n\n\nclass RysObjectiveCLexer(RegexLexer):\n \"\"\"Custom Objective-C source code with additional compiler directives\n\n - Added @-directives:\n - @autorelease\n - @required\n - @optional\n\n - Changed keyword values from Name.Built to Keyword.\n Constant and added a few:\n - YES\n - NO\n - Nil\n\n - Added variable modifiers as Keyword.Type:\n - __block\n - __weak\n - __strong\n\n - Moved plain Keywords to Keyword.Type:\n - const\n - static\n\n - \"Fixed\" method call highlighting:\n - Changed from Name.Label to Name.Function\n - Added support for parameterless method calls (a bit hackish)\n\n - Added NS.* as Name.Builtin\n\n - Add import and define as special preprocessor directives\n\n - Add dedicated pattern for class extensions\n\n - Add protocol support\n - First method in formal protocol wasn't being highlighted\n - Add @protocol() directive as keyword\n - I don't think protocols were supported beyond forward-declarations\n\n - Add array and dictionary literals (no numbers though)\n\n - Fix function declaration highlighting\n - This broke something: functions implemented after main() are not\n highlighted properly.\n\n - Add fixed width integer types from inttype.h\n\n - Add integer literals\n\n - Add Boolean literals\n\n - Add @() literal boxing syntax\n\n - Add arc4random_uniform() and arc4random() as Name.Builtin\n\n - Fix top-level NSLog() highlighting\n - Was highlighting last letter of function calls in standalone snippets\n\n - Add @property attribute keywords\n \"\"\"\n\n name = 'Objective-C'\n aliases = ['objective-c', 'objectivec', 'obj-c', 'objc']\n # XXX: objc has .h files too :-/ ... and .mm. Who wrote this?\n filenames = ['*.m']\n mimetypes = ['text/x-objective-c']\n\n #: optional Comment or Whitespace\n _ws = r'(?:\\s|//.*?\\n|/[*].*?[*]/)+'\n\n tokens = {\n 'whitespace': [\n # preprocessor directives: without whitespace\n ('^#if\\s+0', Comment.Preproc, 'if0'),\n ('^#', Comment.Preproc, 'macro'),\n # or with whitespace\n ('^' + _ws + r'#if\\s+0', Comment.Preproc, 'if0'),\n # Next line doesn't allow comments before import statements...\n # ('^' + _ws + '#', Comment.Preproc, 'macro'),\n (r'\\n', Text),\n (r'\\s+', Text),\n (r'\\\\\\n', Text), # line continuation\n (r'//(\\n|(.|\\n)*?[^\\\\]\\n)', Comment.Single),\n (r'/(\\\\\\n)?[*](.|\\n)*?[*](\\\\\\n)?/', Comment.Multiline),\n ],\n 'statements': [\n # Parameterless methods (a bit hackish)\n (r'(\\[)([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s+)'\n r'([a-zA-Z$_][a-zA-Z0-9$_]*)(\\])',\n bygroups(Punctuation, Name, Text, Name.Function, Punctuation)),\n # alloc/init (really hackish)\n (r'(init)(\\])', bygroups(Name.Function, Punctuation)),\n (r'(L|@)?\"', String, 'string'),\n (r\"(L|@)?'(\\\\.|\\\\[0-7]{1,3}|\\\\x[a-fA-F0-9]{1,2}|[^\\\\\\'\\n])'\",\n String.Char),\n (r'(L|@)?\\[', Punctuation), # Literal array\n (r'(L|@)?\\{', Punctuation), # Literal dictionary\n (r'@\\(', Punctuation), # Literal boxed expression\n (r'@\\d+', Number), # Literal integer\n (r'@YES|@NO', Keyword.Constant), # Literal Boolean\n (r'(\\d+\\.\\d*|\\.\\d+|\\d+)[eE][+-]?\\d+[lL]?', Number.Float),\n (r'(\\d+\\.\\d*|\\.\\d+|\\d+[fF])[fF]?', Number.Float),\n (r'0x[0-9a-fA-F]+[Ll]?', Number.Hex),\n # Protocol as a data type\n (r'<[a-zA-Z$_][a-zA-Z0-9$_]*>', Name.Label),\n (r'0[0-7]+[Ll]?', Number.Oct),\n (r'\\d+[Ll]?', Number.Integer),\n (r'[~!%^&*+=|?:<>/-]', Operator),\n (r'[()\\[\\],.]', Punctuation),\n (r'(auto|break|case|continue|default|do|else|enum|extern|'\n r'for|goto|if|register|restricted|return|sizeof|struct|'\n r'switch|typedef|union|volatile|virtual|while|in|@selector|'\n r'@private|@protected|@public|@encode|'\n r'@synchronized|@try|@throw|@catch|@finally|@end|@property|'\n r'@synthesize|@dynamic|@autoreleasepool|@required|@optional|'\n r'@protocol|@blc|copy|nonatomic|readonly|readwrite|strong|'\n r'weak|getter|setter)\\b', Keyword),\n # Custom typedefs used in my tutorials (shouldn't really be\n # here...)\n (r'SpeedFunction', Keyword.Type),\n (r'(int|long|float|short|double|char|unsigned|signed|'\n r'void|id|BOOL|IBOutlet|IBAction|SEL|Class|__block|'\n r'__weak|unichar|int8_t|uint8_t|int16_t|uint16_t|'\n r'int32_t|uint32_t|int64_t|uint64_t|int_least8_t|'\n r'uint_least8_t|int_least16_t|uint_least16_t|'\n r'int_least32_t|uint_least32_t|int_least64_t|'\n r'uint_least64_t|intmax_t|uintmax_t|intptr_t|uintptr_t|'\n r'size_t|__strong|const|static)\\b', Keyword.Type),\n (r'(_{0,2}inline|naked|restrict|thread|typename)\\b',\n Keyword.Reserved),\n (r'__(asm|int8|based|except|int16|stdcall|cdecl|fastcall|int32|'\n r'declspec|finally|int64|try|leave)\\b', Keyword.Reserved),\n (r'(TRUE|FALSE|nil|NULL|Nil|YES|NO|self)\\b', Keyword.Constant),\n # Custom built-in functions\n (r'(arc4random_uniform|arc4random)\\b', Name.Builtin),\n # Highlight method calls that take parameters\n ('[a-zA-Z$_][a-zA-Z0-9$_]*:(?!:)', Name.Function),\n # NSArray, NSString, etc.\n (r'NS[a-zA-Z0-9$_]*', Name.Builtin),\n # Everything else\n ('[a-zA-Z$_][a-zA-Z0-9$_]*', Name),\n ],\n 'root': [\n include('whitespace'),\n # functions\n (r'((?:[a-zA-Z0-9_*\\s])+?(?:\\s|[*]))' # return arguments\n r'([a-zA-Z$_][a-zA-Z0-9$_]*)' # method name\n r'(\\s*\\([^;]*?\\))' # signature\n r'(' + _ws + r')({)',\n bygroups(using(this), Name.Function,\n using(this), Text, Punctuation),\n 'function'),\n # methods\n (r'^([-+])(\\s*)' # method marker\n r'(\\(.*?\\))?(\\s*)' # return type\n r'([a-zA-Z$_][a-zA-Z0-9$_]*:?)', # begin of method name\n bygroups(Keyword, Text, using(this),\n Text, Name.Function),\n 'method'),\n\n # built-in function call (hackish)\n (r'(NS[a-zA-Z0-9$_]*)' # NSLog\n r'(\\s*\\(.*\\)\\s*)(;)', # (...);\n bygroups(Name.Builtin, using(this), Operator)),\n\n\n # function call (not really used?)\n (r'([a-zA-Z$_][a-zA-Z0-9$_]*)' # getRandomInteger\n r'(\\s*\\(.*\\)\\s*)(;)', # (...);\n bygroups(Name, using(this), Operator)),\n\n\n # custom function declarations (hackish)\n (r'([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*)(\\*?)' # int\n r'([a-zA-Z$_][a-zA-Z0-9$_]*)' # getRandomInteger\n r'(\\s*\\(.*\\)\\s*)(;)', # (...);\n bygroups(using(this),\n Text, Operator, Name.Function, using(this), Operator)),\n\n\n\n\n (r'([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*)' # static\n r'([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*)(\\*?)' # int\n r'([a-zA-Z$_][a-zA-Z0-9$_]*)' # getRandomInteger\n r'(\\s*\\(.*\\)\\s*)(;)', # (...);\n bygroups(using(this), Text, using(this),\n Text, Operator, Name.Function, using(this), Operator)),\n\n # function declarations\n (r'((?:[a-zA-Z0-9_*\\s])+?(?:\\s|[*]))' # return arguments\n r'([a-zA-Z$_][a-zA-Z0-9$_]*)' # method name\n r'(\\s*\\([^;]*?\\))' # signature\n r'(' + _ws + r')(;)',\n bygroups(using(this), Name.Function,\n using(this), Text, Punctuation)),\n (r'(@interface|@implementation|@protocol)(\\s+)',\n bygroups(Keyword, Text),\n 'classname'),\n (r'(@class)(\\s+)', bygroups(Keyword, Text),\n 'forward_classname'),\n (r'(\\s*)(@end)(\\s*)', bygroups(Text, Keyword, Text)),\n ('', Text, 'statement'),\n ],\n 'classname': [\n # adopting formal protocols (only for inherited classes?)\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*:\\s*)([a-zA-Z$_][a-zA-Z0-9$_]*)'\n '(\\s*)(<[a-zA-Z$_][a-zA-Z0-9$_]*>)',\n bygroups(Name.Class, Text, Name.Class, Text, Name.Label), '#pop'),\n # interface definition that inherits\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*:\\s*)([a-zA-Z$_][a-zA-Z0-9$_]*)?',\n bygroups(Name.Class, Text, Name.Class), '#pop'),\n # interface definition for a category\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*)(\\([a-zA-Z$_][a-zA-Z0-9$_]*\\))',\n bygroups(Name.Class, Text, Name.Label), '#pop'),\n # interface definition for an extension\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*)(\\(\\))',\n bygroups(Name.Class, Text, Name.Label), '#pop'),\n # formal protocol declaration\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*)(<[a-zA-Z$_][a-zA-Z0-9$_]*>)',\n bygroups(Name.Class, Text, Name.Label), '#pop'),\n # simple interface / implementation\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)', Name.Class, '#pop'),\n ],\n 'forward_classname': [\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*,\\s*)',\n bygroups(Name.Class, Text), 'forward_classname'),\n ('([a-zA-Z$_][a-zA-Z0-9$_]*)(\\s*;?)',\n bygroups(Name.Class, Text), '#pop')\n ],\n 'statement': [\n include('whitespace'),\n include('statements'),\n ('[{}]', Punctuation),\n (';', Punctuation, '#pop'),\n ],\n 'function': [\n include('whitespace'),\n include('statements'),\n (';', Punctuation),\n ('{', Punctuation, '#push'),\n ('}', Punctuation, '#pop'),\n ],\n 'method': [\n include('whitespace'),\n (r'(\\(.*?\\))([a-zA-Z$_][a-zA-Z0-9$_]*)', bygroups(using(this),\n Name.Variable)),\n (r'[a-zA-Z$_][a-zA-Z0-9$_]*:', Name.Function),\n (';', Punctuation, '#pop'),\n ('{', Punctuation, 'function'),\n ('', Text, '#pop'),\n ],\n 'string': [\n (r'\"', String, '#pop'),\n (r'\\\\([\\\\abfnrtv\"\\']|x[a-fA-F0-9]{2,4}|[0-7]{1,3})',\n String.Escape),\n (r'[^\\\\\"\\n]+', String), # all other characters\n (r'\\\\\\n', String), # line continuation\n (r'\\\\', String), # stray backslash\n ],\n 'macro': [\n (r'(import|define)(\\s+)(.*)',\n bygroups(Comment.Preproc, Text, Literal)),\n (r'[^/\\n]+', Comment.Preproc),\n (r'/[*](.|\\n)*?[*]/', Comment.Multiline),\n (r'//.*?\\n', Comment.Single, '#pop'),\n (r'/', Comment.Preproc),\n (r'(?<=\\\\)\\n', Comment.Preproc),\n (r'\\n', Comment.Preproc, '#pop'),\n ],\n 'if0': [\n (r'^\\s*#if.*?(? (dur - 1): \n player.set_position(videos[currentVideo].start)\n\n if key == 27:\n\n quit = True\n\n elif key == 45:\n\n player.action(17)\n player_log.info(\"Volume = \", player.volume())\n\n elif key == 61:\n\n player.action(18)\n player_log.info(\"Volume = \", player.volume())\n\n elif key == 115:\n\n player.quit()\n\n player = None\n\n while player is None:\n\n currentVideo = currentVideo + 1\n\n if currentVideo == len(videos):\n currentVideo = 0\n\n player = playVideo(videos[currentVideo], player_log)\n\n sleep(0.1)\n\nplayer.quit()\n\ncurses.nocbreak()\ncurses.echo()\ncurses.endwin()\n", "sub_path": "tests/pifire.v1.py", "file_name": "pifire.v1.py", "file_ext": "py", "file_size_in_byte": 2653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}, {"api_name": "omxplayer.player.OMXPlayer", "line_number": 33, "usage_type": "call"}, {"api_name": "curses.initscr", "line_number": 58, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 60, "usage_type": "call"}, {"api_name": "curses.cbreak", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 64, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 66, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "curses.nocbreak", "line_number": 118, "usage_type": "call"}, {"api_name": "curses.echo", "line_number": 119, "usage_type": "call"}, {"api_name": "curses.endwin", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "141372917", "text": "from django.contrib import admin\nfrom .AcceptMappingInline import AcceptMappingInline\nfrom django import forms\nfrom uriredirect.models import *\nfrom django.db.models import Q\n\nclass ProfileForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(ProfileForm, self).__init__(*args, **kwargs)\n self.fields['profiles'].queryset = Profile.objects.exclude(token=self.instance.token).exclude(profilesTransitive__token=self.instance.token)\n \nclass ProfileAdmin(admin.ModelAdmin):\n list_display = ('token', 'label','uri')\n form=ProfileForm\n\n def save_related(self, request, form, formsets, change):\n #import pdb; pdb.set_trace()\n super(ProfileAdmin, self).save_related( request, form, formsets, change) \n form.instance.profilesTransitive.remove(*form.instance.profilesTransitive.all())\n for p in form.instance.profiles.all():\n self.add_recursive(form.instance,form.instance.profilesTransitive,p)\n \n def add_recursive(self,instance,qset,profile):\n if profile.id == instance.id :\n raise Exception(\"Profile inheritance loop detected\")\n elif not qset.filter(id=profile.id).exists():\n qset.add(profile)\n for rp in profile.profiles.all():\n self.add_recursive(instance,qset,rp)\n \n ", "sub_path": "uriredirect/admin/ProfileAdmin.py", "file_name": "ProfileAdmin.py", "file_ext": "py", "file_size_in_byte": 1338, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "91982811", "text": "\"\"\"\n此类包含所有代理IP爬取的方法\n\n目前已写代理网站：\n西拉免费代理网站 --> get_xila_proxy_ip()\n\n\"\"\"\n# -*- coding:utf-8 -*-\nimport requests\nimport pymongo\nimport threading\nimport random\nimport time\nimport logging\nfrom lxml import etree\n\n__author__ = 'Evan'\nlogging.basicConfig(level=logging.INFO,\n format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')\nlogger = logging.getLogger(__name__)\n\n\nclass ProxySpider(object):\n\n def __init__(self, config):\n self.config = config # 全局配置文件\n self.all_proxy_ip_table, self.valid_proxy_ip_table = self.config_mongodb() # 初始化Mongodb数据库\n self.thread_pool = threading.Semaphore(value=self.config['THREAD_POOL_MAX']) # 初始化线程池\n\n @staticmethod\n def config_mongodb(host='localhost', port=27017):\n \"\"\"\n 初始化Mongodb数据库\n :param host: 主机名\n :param port: 端口号\n :return: 返回两个集合句柄（所有代理IP集合，有效代理IP集合）\n \"\"\"\n client = pymongo.MongoClient(host=host, port=port)\n database = client['proxy_info']\n all_proxy_ip_table = database['all_proxy_ip']\n valid_proxy_ip_table = database['valid_proxy_ip']\n return all_proxy_ip_table, valid_proxy_ip_table\n\n @staticmethod\n def random_user_agent():\n \"\"\"\n 返回一个随机请求头\n :return:\n \"\"\"\n ua_list = [\n # Chrome UA\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)'\n ' Chrome/73.0.3683.75 Safari/537.36',\n # IE UA\n 'Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko',\n # Microsoft Edge UA\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)'\n ' Chrome/64.0.3282.140 Safari/537.36 Edge/18.17763'\n ]\n return random.choice(ua_list)\n\n def get_xila_proxy_ip(self, page):\n \"\"\"\n URL - http://www.xiladaili.com/https/\n 爬取西拉代理网站的IP地址、端口号、协议类型、代理IP响应速度、代理IP得分保存到MongoDB数据库（all_proxy_ip集合）\n :param page: 爬取页数\n :return:\n \"\"\"\n with self.thread_pool:\n try:\n resp = requests.get(self.config['PROXY_URL'].format(page),\n headers={'User-Agent': self.random_user_agent()})\n # 如果请求失败，再试一次\n if resp.status_code != 200:\n time.sleep(1)\n resp = requests.get(self.config['PROXY_URL'].format(page),\n headers={'User-Agent': self.random_user_agent()})\n\n if resp.status_code == 200:\n html = etree.HTML(resp.text)\n # 获取所有代理IP和端口号\n ip_list = html.xpath('/html/body/div[1]/div[3]/div[2]/table/tbody/tr/td[1]/text()')\n # 获取所有代理协议\n protocol_list = html.xpath('/html/body/div[1]/div[3]/div[2]/table/tbody/tr/td[2]/text()')\n # 获取所有代理IP响应速度\n speed_list = html.xpath('/html/body/div[1]/div[3]/div[2]/table/tbody/tr/td[5]/text()')\n # 获取所有代理IP得分\n score_list = html.xpath('/html/body/div[1]/div[3]/div[2]/table/tbody/tr/td[8]/text()')\n for ip, protocol, speed, score in zip(ip_list, protocol_list, speed_list, score_list):\n # 过滤掉响应速度大于3或者代理得分小于10000的IP\n if float(speed) > 3.0 or int(score) < 10000:\n continue\n data = {\n \"ip\": ip.split(':')[0],\n \"port\": ip.split(':')[1],\n \"protocol\": protocol,\n \"speed\": speed,\n \"score\": score\n }\n # 数据去重，保存到all_proxy_ip集合\n self.all_proxy_ip_table.update_one(data, {\"$set\": data}, upsert=True)\n logger.debug('Page: {} --> The request is successful'.format(page))\n else:\n logger.debug('Page: {} --> Failed, [Request error], status code: {}'.format(page, resp.status_code))\n except Exception as ex:\n logger.debug('Page: {} --> Failed, [Exception error], error msg: {}'.format(page, ex))\n", "sub_path": "spiders/proxy_spider/profile/proxy_spider.py", "file_name": "proxy_spider.py", "file_ext": "py", "file_size_in_byte": 4700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "threading.Semaphore", "line_number": 28, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 38, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 71, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 76, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 80, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "146544701", "text": "\"\"\"Token utility module.\"\"\"\nimport logging\n\nimport requests\n\nfrom orm.common.client.keystone.mock_keystone.keystoneclient import exceptions\nfrom orm.common.client.keystone.mock_keystone.keystoneclient.v2_0 import client as v2_client\nfrom orm.common.client.keystone.mock_keystone.keystoneclient.v3 import client as v3_client\nfrom orm.common.orm_common.utils import dictator\n\n_verify = False\n\nOK_CODE = 200\n_KEYSTONES = {}\nlogger = logging.getLogger(__name__)\n\n\nclass KeystoneNotFoundError(Exception):\n \"\"\"Indicates that the Keystone EP of a certain LCP was not found.\"\"\"\n\n pass\n\n\nclass TokenConf(object):\n \"\"\"The Token Validator configuration class.\"\"\"\n\n def __init__(self, mech_id, password, rms_url, tenant_name, version):\n \"\"\"Initialize the Token Validator configuration.\n\n :param mech_id: Username for Keystone\n :param password: Password for Keystone\n :param rms_url: The entire RMS URL, e.g. 'http://1.3.3.7:8080'\n :param tenant_name: The ORM tenant name\n :param version: Keystone version to use (a string: '3' or '2.0')\n \"\"\"\n self.mech_id = mech_id\n self.password = password\n self.rms_url = rms_url\n self.tenant_name = tenant_name\n self.version = version\n\n\nclass TokenUser(object):\n \"\"\"Class with details about the token user.\"\"\"\n\n def __init__(self, token):\n \"\"\"Initialize the Token User.\n\n :param token: The token object (returned by tokens.validate)\n \"\"\"\n self.token = token.token\n self.user = token.user\n self.tenant = getattr(token, 'tenant', None)\n self.domain = getattr(token, 'domain', None)\n\n\ndef get_token_user(token, conf, lcp_id=None, keystone_ep=None):\n \"\"\"Get a token user.\n\n :param token: The token to validate\n :param conf: A TokenConf object\n :param lcp_id: The ID of the LCP associated with the Keystone instance\n with which the token was created. Ignored if keystone_ep is not None\n :param keystone_ep: The Keystone endpoint, in case we already have it\n :return: False if one of the tokens received (or more) is invalid,\n True otherwise.\n \"\"\"\n # Not using logger.error/exception because in some cases, these flows\n # can be completely valid\n if keystone_ep is None:\n if lcp_id is None:\n message = 'Received None for both keystone_ep and lcp_id!'\n logger.debug(message)\n raise ValueError(message)\n keystone_ep = _find_keystone_ep(conf.rms_url, lcp_id)\n if keystone_ep is None:\n message = 'Keystone EP of LCP %s not found in RMS' % (lcp_id,)\n logger.debug(message)\n logger.critical(\n 'CRITICAL|CON{}KEYSTONE002|X-Auth-Region: {} is not '\n 'reachable (not found in RMS)'.format(\n dictator.get('service_name', 'ORM'), lcp_id))\n raise KeystoneNotFoundError(message)\n\n if conf.version == '3':\n client = v3_client\n elif conf.version == '2.0':\n client = v2_client\n else:\n message = 'Invalid Keystone version: %s' % (conf.version,)\n logger.debug(message)\n raise ValueError(message)\n\n keystone = _get_keystone_client(client, conf, keystone_ep, lcp_id)\n\n try:\n token_info = keystone.tokens.validate(token)\n logger.debug('User token found in Keystone')\n return TokenUser(token_info)\n # Other exceptions raised by validate() are critical errors,\n # so instead of returning False, we'll just let them propagate\n except exceptions.NotFound:\n logger.debug('User token not found in Keystone! Make sure that it is '\n 'correct and that it has not expired yet')\n return None\n\n\ndef _find_keystone_ep(rms_url, lcp_name):\n \"\"\"Get the Keystone EP from RMS.\n\n :param rms_url: RMS server URL\n :param lcp_name: The LCP name\n :return: Keystone EP (string), None if it was not found\n \"\"\"\n if not rms_url:\n message = 'Invalid RMS URL: %s' % (rms_url,)\n logger.debug(message)\n raise ValueError(message)\n\n logger.debug(\n 'Looking for Keystone EP of LCP {} using RMS URL {}'.format(\n lcp_name, rms_url))\n\n response = requests.get('%s/v2/orm/regions?regionname=%s' % (\n rms_url, lcp_name, ), verify=_verify)\n if response.status_code != OK_CODE:\n # The LCP was not found in RMS\n logger.debug('Received bad response code from RMS: {}'.format(\n response.status_code))\n return None\n\n lcp = response.json()\n try:\n for endpoint in lcp['regions'][0]['endpoints']:\n if endpoint['type'] == 'identity':\n return endpoint['publicURL']\n except KeyError:\n logger.debug('Response from RMS came in an unsupported format. '\n 'Make sure that you are using RMS 3.5')\n return None\n\n # Keystone EP not found in the response\n logger.debug('No identity endpoint was found in the response from RMS')\n return None\n\n\ndef _does_user_have_role(keystone, version, user, role, location):\n \"\"\"Check whether a user has a role.\n\n :param keystone: The Keystone client to use\n :param version: Keystone version\n :param user: A dict that represents the user in question\n :param role: The role to check whether the user has\n :param location: Keystone role location\n :return: True if the user has the requested role, False otherwise.\n :raise: client.exceptions.NotFound when the requested role does not exist,\n ValueError when the version is 2.0 but the location is not 'tenant'\n \"\"\"\n location = dict(location)\n if version == '3':\n role = keystone.roles.find(name=role)\n try:\n return keystone.roles.check(role, user=user['user']['id'],\n **location)\n except exceptions.NotFound:\n return False\n except KeyError:\n # Shouldn't be raised when using Keystone's v3/v2.0 API, but let's\n # play on the safe side\n logger.debug('The user parameter came in a wrong format!')\n return False\n elif version == '2.0':\n # v2.0 supports tenants only\n if location.keys()[0] != 'tenant':\n raise ValueError(\n 'Using Keystone v2.0, expected \"tenant\", received: \"%s\"' % (\n location.keys()[0],))\n\n tenant = keystone.tenants.find(name=location['tenant'])\n # v2.0 does not enable us to check for a specific role (unlike v3)\n role_list = keystone.roles.roles_for_user(user.user['id'],\n tenant=tenant)\n return any([user_role.name == role for user_role in role_list])\n\n\ndef _get_keystone_client(client, conf, keystone_ep, lcp_id):\n \"\"\"Get the Keystone client.\n\n :param client: keystoneclient package to use\n :param conf: Token conf\n :param keystone_ep: The Keystone endpoint that RMS returned\n :param lcp_id: The region ID\n\n :return: The instance of Keystone client to use\n \"\"\"\n global _KEYSTONES\n try:\n if keystone_ep not in _KEYSTONES:\n # Instantiate the Keystone client according to the configuration\n _KEYSTONES[keystone_ep] = client.Client(\n username=conf.mech_id,\n password=conf.password,\n tenant_name=conf.tenant_name,\n auth_url=keystone_ep + '/v' + conf.version)\n\n return _KEYSTONES[keystone_ep]\n except Exception:\n logger.critical(\n 'CRITICAL|CON{}KEYSTONE001|Cannot reach Keystone EP: {} of '\n 'region {}. Please contact Keystone team.'.format(\n dictator.get('service_name', 'ORM'), keystone_ep, lcp_id))\n raise\n\n\ndef is_token_valid(token_to_validate, lcp_id, conf, required_role=None,\n role_location=None):\n \"\"\"Validate a token.\n\n :param token_to_validate: The token to validate\n :param lcp_id: The ID of the LCP associated with the Keystone instance\n with which the token was created\n :param conf: A TokenConf object\n :param required_role: The required role for privileged actions,\n e.g. 'admin' (optional).\n :param role_location: The Keystone role location (a dict whose single\n key is either 'domain' or 'tenant', whose value is the location name)\n :return: False if one of the tokens received (or more) is invalid,\n True otherwise.\n :raise: KeystoneNotFoundError when the Keystone EP for the required LCP\n was not found in RMS output,\n client.exceptions.AuthorizationFailure when the connection with the\n Keystone EP could not be established,\n client.exceptions.EndpointNotFound when _our_ authentication\n (as an admin) with Keystone failed,\n ValueError when an invalid Keystone version was specified,\n ValueError when a role or a tenant was not found,\n ValueError when a role is required but role_location is None.\n \"\"\"\n keystone_ep = _find_keystone_ep(conf.rms_url, lcp_id)\n if keystone_ep is None:\n raise KeystoneNotFoundError('Keystone EP of LCP %s not found in RMS' %\n (lcp_id,))\n\n if conf.version == '3':\n client = v3_client\n elif conf.version == '2.0':\n client = v2_client\n else:\n raise ValueError('Invalid Keystone version: %s' % (conf.version,))\n\n keystone = _get_keystone_client(client, conf, keystone_ep, lcp_id)\n\n try:\n user = keystone.tokens.validate(token_to_validate)\n logger.debug('User token found in Keystone')\n # Other exceptions raised by validate() are critical errors,\n # so instead of returning False, we'll just let them propagate\n except exceptions.NotFound:\n logger.debug('User token not found in Keystone! Make sure that it is'\n 'correct and that it has not expired yet')\n return False\n\n if required_role is not None:\n if role_location is None:\n raise ValueError(\n 'A role is required but no role location was specified!')\n\n try:\n logger.debug('Checking role...')\n return _does_user_have_role(keystone, conf.version, user,\n required_role, role_location)\n except exceptions.NotFound:\n raise ValueError('Role %s or tenant %s not found!' % (\n required_role, role_location,))\n else:\n # We know that the token is valid and there's no need to enforce a\n # policy on this operation, so we can let the user pass\n logger.debug('No role to check, authentication finished successfully')\n return True\n", "sub_path": "orm/common/client/keystone/keystone_utils/tokens.py", "file_name": "tokens.py", "file_ext": "py", "file_size_in_byte": 10666, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "orm.common.orm_common.utils.dictator.get", "line_number": 82, "usage_type": "call"}, {"api_name": "orm.common.orm_common.utils.dictator", "line_number": 82, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.v3.client", "line_number": 86, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.v2_0.client", "line_number": 88, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions.NotFound", "line_number": 102, "usage_type": "attribute"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions", "line_number": 102, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 124, "usage_type": "call"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions.NotFound", "line_number": 165, "usage_type": "attribute"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions", "line_number": 165, "usage_type": "name"}, {"api_name": "orm.common.orm_common.utils.dictator.get", "line_number": 211, "usage_type": "call"}, {"api_name": "orm.common.orm_common.utils.dictator", "line_number": 211, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.v3.client", "line_number": 245, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.v2_0.client", "line_number": 247, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions.NotFound", "line_number": 258, "usage_type": "attribute"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions", "line_number": 258, "usage_type": "name"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions.NotFound", "line_number": 272, "usage_type": "attribute"}, {"api_name": "orm.common.client.keystone.mock_keystone.keystoneclient.exceptions", "line_number": 272, "usage_type": "name"}]} +{"seq_id": "594155568", "text": "# -*- coding: utf-8 -*-\n#\n# text_poplate.py\n#\n# Copyright 2016 Басманов Илья \n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n\nimport re\nfrom flask import url_for\n\n\ndef text_populate(data):\n \"\"\" Пост обработка текст, обработка символов @ ~ # \"\"\"\n from app import models\n from modules.page_editor.models import Page\n for num in re.findall (r'@(\\d+)', data):\n azs = models.Azs.query.filter(models.Azs.num == num).first()\n if azs:\n data = data.replace('@'+num, 'АЗС№ {2}'.format(url_for('azs_map.index', azs=azs.id), azs.addr, num ))\n for num in re.findall (r'#(\\d+)', data):\n call = models.Call.query.filter(models.Call.id == num).first()\n if call:\n data = data.replace('#'+num, 'заявка № {2}'.format(url_for('requisition.call', id=call.id), call.call, num ))\n for num in re.findall (r'~(\\d+)', data):\n page = Page.query.filter(Page.id == num).first()\n if page:\n data = data.replace('~'+num, ' {1} '.format(url_for('page_editor.page', id=page.id), page.title))\n return data\n", "sub_path": "core/text_populate.py", "file_name": "text_populate.py", "file_ext": "py", "file_size_in_byte": 1901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "re.findall", "line_number": 30, "usage_type": "call"}, {"api_name": "app.models.Azs.query.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "app.models.Azs", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 34, "usage_type": "call"}, {"api_name": "app.models.Call.query.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "app.models.Call", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 37, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 38, "usage_type": "call"}, {"api_name": "modules.page_editor.models.Page.query.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "modules.page_editor.models.Page.query", "line_number": 39, "usage_type": "attribute"}, {"api_name": "modules.page_editor.models.Page", "line_number": 39, "usage_type": "name"}, {"api_name": "modules.page_editor.models.Page.id", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "457040535", "text": "import speech_recognition as sr\n\n\nmyName = \"Josh Guo\"\nmyDob = \"10/22/2000\"\nmyMf = \"male\"\nmyPhone = \"1234560986\"\nmyAllergy = \"bees stings rash cold death\"\nmySmoke = \"no\"\nmyDrink = \"yes 3 times a week\"\nmyMeds = \"bendryl epipen\"\nmyFamhist = \"allergic to peanut butter\"\nmyCondition = \"allergies to lots of things\"\nmySymptom = \"runny nose, etc\"\n\ntest = sr.AudioFile('TestQ.wav')\nr = sr.Recognizer()\n\nmyName = \"\"\nmyDob = \"\"\n\n\nwith test as source:\n audio = r.record(source)\n a = r.recognize_google(audio)\n list = a.split()\n\n #myName\n myNameBegin = list.index(\"name\")\n myNameEnd = list.index(\"when\")\n for i in list[myNameBegin+1:myNameEnd]:\n myName = myName + i +\" \"\n\n #myDob\n myDobBegin = list.index(\"birthday\")\n #myDobEnd = list.index(\"when\")\n for i in list[myDobBegin + 1:]:\n myDob = myDob + i + \" \"\n\n #myMf\n\n #myPhone\n\n #myAllergy\n\n #mySmoke\n\n #myDrink\n\n #myMeds\n\n #myFamHist\n\n #myCondition\n\n #mySymptom\n\n for i in list:\n print(i)\n", "sub_path": "TestSpeechtoText.py", "file_name": "TestSpeechtoText.py", "file_ext": "py", "file_size_in_byte": 1010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "speech_recognition.AudioFile", "line_number": 16, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "314212858", "text": "# python3\n\nfrom collections import namedtuple\n\nAnswer = namedtuple('answer_type', 'i j len')\n\n\ndef solve_naive(s, t):\n\tans = Answer(0, 0, 0)\n\n\tfor l in reversed(range(min(len(s), len(t)) + 1)):\n\t\tanswers = []\n\t\tfor i in range(len(s)-l+1):\n\t\t\tfor j in range(len(t)-l+1):\n\t\t\t\tif (s[i:i+l] == t[j:j+l]):\n\t\t\t\t\tanswers.append(Answer(i, j, l))\n\t\tif answers:\n\t\t\treturn answers\n\treturn [ans]\n", "sub_path": "c2/week3_hash_tables/5_longest_common_substring/common_substring_naive_all.py", "file_name": "common_substring_naive_all.py", "file_ext": "py", "file_size_in_byte": 384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.namedtuple", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "329803706", "text": "from collections import Counter\nfrom math import sqrt\ninput1 = int(input(\"Type in the first number: \"))\ninput2 = int(input(\"Type in the second number: \"))\n\nfactors1 = []\nfor i in range(0, input1):\n for i in range(2, input1):\n temp = int(input1 / i)\n if temp * i == input1:\n input1 = temp\n factors1.append(i)\nfor i0 in range(0, len(factors1)):\n for i1 in range(2, factors1[i0]):\n temp = int(factors1[i0] / i1)\n if temp * i1 == factors1[i0]:\n factors1.remove(factors1[i0])\n factors1.append(i1)\n factors1.append(temp)\n factors1.sort()\n print(factors1)\nfactors2 = []\nfor i in range(0, input2):\n for i in range(2, input2):\n temp = int(input2 / i)\n if temp * i == input2:\n input2 = temp\n factors2.append(i)\nfor i0 in range(0, len(factors2)):\n for i1 in range(2, factors2[i0]):\n temp = int(factors2[i0] / i1)\n if temp * i1 == factors2[i0]:\n factors2.remove(factors2[i0])\n factors2.append(i1)\n factors2.append(temp)\n factors2.sort()\n print(factors2)\ndef exponents ( factors ):\n answer = []\n x = 0\n trueORfalse = 0\n for i in range(0, len(factors) - 1):\n if trueORfalse == 0:\n n = factors[i]\n trueORfalse = 1\n x += 1\n continue\n else:\n if n == factors[i]:\n x += 1\n else:\n trueORfalse = 0\n answer.append(n)\n answer.append(x)\n x = 0\n return(answer)\nanswer1 = []\nanswer2 = []\nanswer1 = exponents(factors1)\nanswer2 = exponents(factors2)\ncommonNums = list(set(answer1).intersection(answer2))\nuncommonNums = list((Counter(answer1) - Counter(answer2)).elements())\nprint(commonNums)\nprint(uncommonNums)\n# def checkIfWholeNum( num ):\n# if num == int(num):\n# return num\n# else:\n# n = num\n# while True:\n# n *= 2\n# if n == int(n):\n# break\n# return n\n# if input1 > input2:\n# n = input1 - input2\n# i = 1\n# n = input1 / n\n# else:\n# n = input2 - input1\n# i = 0\n# n = input2 / n\n# n = checkIfWholeNum(n)\n# if i == 1:\n# answer = input2 * n\n# else:\n# answer = input1 * n\n# # print(n)\n# print(\"The LCM of {0} and {1} is {2}.\".format(input1, input2, int(answer)))\n", "sub_path": "getLCM.py", "file_name": "getLCM.py", "file_ext": "py", "file_size_in_byte": 2436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.Counter", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "347618761", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\n\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution(object):\n def reorderList(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: None Do not return anything, modify head in-place instead.\n \"\"\"\n if not head:\n return\n\n node_list = []\n\n node = head\n while node:\n node_list.append(node)\n node = node.next\n\n length = len(node_list)\n\n node_list = node_list[-(length / 2):][::-1]\n\n node = head\n\n while node and node_list:\n tmp = node.next\n\n node.next = node_list.pop(0)\n\n node.next.next = tmp\n\n node = tmp\n\n if node:\n node.next = None\n\n return\n\n\nif __name__ == '__main__':\n from utils import list_to_node\n\n solution = Solution()\n\n head = list_to_node([1, 2, 3, 4, 5])\n\n solution.reorderList(head)\n print(head)\n", "sub_path": "leetcode/143.py", "file_name": "143.py", "file_ext": "py", "file_size_in_byte": 1009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "utils.list_to_node", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "276622435", "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 ]\n\n operations = [\n migrations.CreateModel(\n name='Catalog',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=64, verbose_name='\\u7c7b\\u578b')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='ToDo',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=64, verbose_name='\\u540d\\u79f0')),\n ('start_date', models.DateTimeField(verbose_name='\\u5f00\\u59cb\\u65f6\\u95f4')),\n ('end_date', models.DateTimeField(verbose_name='\\u7ed3\\u675f\\u65f6\\u95f4')),\n ('is_accomplish', models.BooleanField(default=False, verbose_name='\\u5b8c\\u6210')),\n ('create_date', models.DateTimeField(auto_now_add=True, verbose_name='\\u521b\\u5efa\\u65f6\\u95f4')),\n ('modify_date', models.DateTimeField(auto_now=True, verbose_name='\\u4fee\\u6539\\u65f6\\u95f4')),\n ('catalog', models.ForeignKey(verbose_name='\\u7c7b\\u578b', to='todo.Catalog')),\n ],\n options={\n 'ordering': ['name'],\n 'verbose_name': 'ToDo',\n 'verbose_name_plural': 'ToDo',\n },\n bases=(models.Model,),\n ),\n ]\n", "sub_path": "todo/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1697, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "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.DateTimeField", "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": "django.db.models.BooleanField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "569245873", "text": "from os import getenv\nimport json\nimport requests\nimport threading\nfrom time import time\nfrom datetime import datetime\nfrom tqdm.auto import tqdm as tqdm_auto\n\nBASE_URL = \"https://api.notion.com/v1\"\n\ndef char_bar(value, filled=\"▓\", empty=\"░\"):\n \"\"\"\n Parameters\n ----------\n value: int, required. Completion amount between 0 and 1.\n filled: str, required. The character for a completed progress.\n [default: \"▓\"].\n empty: str, required. The character for incomplete progress.\n [default: \"░\"]\n \"\"\"\n f = int(value*10)\n e = 10 - f\n return (f * filled) + (e * empty)\n\nclass tqdm_notion(tqdm_auto):\n \"\"\"\n Standard `tqdm.auto.tqdm` but also sends updates to a Notion Database.\n \"\"\"\n\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Parameters\n ----------\n secret: str, required. Notion Integration secret\n [default: ${TQDM_NOTION_SECRET}].\n database_id: str, required. Notion database id.\n [default: ${TQDM_NOTION_DATABASE_ID}].\n unique_property: str, required. The unique property for this database.\n [default: \"Name\"]\n page_title: str, required. Title of the database page.\n [default: {datetime.now().strftime(\"%d/%m, %H:%M\")}]\n progress_property: str, required. Database property name.\n [default: \"Progress\"]\n update_interval_secs: int, required. Minimum time between calls to the API in seconds.\n [default: 1]\n See `tqdm.auto.tqdm.__init__` for other parameters.\n \"\"\"\n if not kwargs.get('disable'):\n kwargs = kwargs.copy()\n\n # Get all the kwargs for later use\n self.secret = kwargs.pop('secret', getenv(\"TQDM_NOTION_SECRET\"))\n self.database_id = kwargs.pop('database_id', getenv(\"TQDM_NOTION_DATABASE_ID\"))\n self.unique_property = kwargs.pop('unique_property', \"Name\")\n self.page_title = kwargs.pop('page_title', datetime.now().strftime(\"%d-%m, %H:%M\"))\n self.progress_property = kwargs.pop('progress_property', \"Progress\")\n self.complete_char = kwargs.pop('complete_char', \"▓\")\n self.incomplete_char = kwargs.pop('incomplete_char', \"░\")\n self.start_time_property = kwargs.pop('start_time_property', \"Start\")\n self.update_interval_secs = kwargs.pop('update_interval_secs', 1)\n\n self.last_update_time = time()\n self.loading = True\n\n # Now we've popped the kwargs we can call super\n super(tqdm_notion, self).__init__(*args, **kwargs)\n\n self.headers = {\n \"Authorization\": f\"Bearer {self.secret}\", \n \"Content-Type\": \"application/json\",\n \"Notion-Version\": \"2021-08-16\"\n }\n \n page = requests.post(f\"{BASE_URL}/pages\", headers=self.headers, data=json.dumps({\n \"parent\": {\n \"database_id\": self.database_id\n },\n \"properties\": {\n self.unique_property: {\n \"title\": [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": self.page_title\n }\n }\n ]\n },\n self.progress_property: {\n \"rich_text\": [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": f\"{self.bar} {self.percent_complete}%\"\n }\n }\n ]\n }\n }\n }))\n self.page_id = page.json()[\"id\"]\n self.loading = False\n\n super(tqdm_notion, self).__init__(*args, **kwargs)\n \n @property\n def percent_complete(self):\n return int((self.n/self.total)*100)\n\n @property\n def bar(self):\n return char_bar(self.n/self.total, filled=self.complete_char, empty=self.incomplete_char)\n\n @property\n def can_update(self):\n interval_elapsed = (\n self.last_update_time is None\n or (time() - self.last_update_time) > self.update_interval_secs\n )\n return not self.loading and interval_elapsed\n\n def update_page(self, force = False):\n \"\"\"\n This method should be called in its own thread to prevent blocking on the request.\n \n Force is used to force update when progress reaches 100%.\n \"\"\"\n if not self.can_update and not force:\n return\n\n bar = self.bar\n\n self.loading = True\n page = requests.patch(f\"{BASE_URL}/pages/{self.page_id}\", headers=self.headers, data=json.dumps({\n \"properties\": {\n self.progress_property: {\n \"rich_text\": [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": f\"{bar} {self.percent_complete}%\"\n }\n }\n ]\n }\n }\n }))\n self.last_update_time = time()\n self.loading = False\n \n def display(self, **kwargs):\n super(tqdm_notion, self).display(**kwargs)\n\n # Start a thread to do the updating so we aren't waiting around for Notion.\n t = threading.Thread(\n name=\"update_page\", target=self.update_page\n )\n t.setDaemon(True)\n t.start()\n\n def clear(self, *args, **kwargs):\n super(tqdm_notion, self).clear(*args, **kwargs)\n if not self.disable:\n # self.dio.write(\"\")\n pass\n\n def close(self):\n self.update_page(force=True)\n super(tqdm_notion, self).close()", "sub_path": "notionIntegration.py", "file_name": "notionIntegration.py", "file_ext": "py", "file_size_in_byte": 5971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tqdm.auto.tqdm", "line_number": 25, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 53, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 75, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 119, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 135, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 135, "usage_type": "call"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "530596780", "text": "import os\n\nimport utils\nimport yaml\n\nfrom core.issues import (\n issue_types,\n issue_utils,\n)\n\n\nclass TestIssuesUtils(utils.BaseTestCase):\n\n def test_get_issues(self):\n issues = {}\n with open(os.path.join(self.plugin_tmp_dir, 'issues.yaml'), 'w') as fd:\n fd.write(yaml.dump(issues))\n\n ret = issue_utils._get_issues()\n self.assertEquals(ret, issues)\n\n def test_add_issue(self):\n issue_utils.add_issue(issue_types.MemoryWarning(\"test\"))\n ret = issue_utils._get_issues()\n self.assertEquals(ret,\n {issue_utils.MASTER_YAML_ISSUES_FOUND_KEY:\n [{'type': 'MemoryWarning',\n 'desc': 'test',\n 'origin': 'testplugin.01part'}]})\n", "sub_path": "tests/unit/test_issues_utils.py", "file_name": "test_issues_utils.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "utils.BaseTestCase", "line_number": 12, "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": "yaml.dump", "line_number": 17, "usage_type": "call"}, {"api_name": "core.issues.issue_utils._get_issues", "line_number": 19, "usage_type": "call"}, {"api_name": "core.issues.issue_utils", "line_number": 19, "usage_type": "name"}, {"api_name": "core.issues.issue_utils.add_issue", "line_number": 23, "usage_type": "call"}, {"api_name": "core.issues.issue_utils", "line_number": 23, "usage_type": "name"}, {"api_name": "core.issues.issue_types.MemoryWarning", "line_number": 23, "usage_type": "call"}, {"api_name": "core.issues.issue_types", "line_number": 23, "usage_type": "name"}, {"api_name": "core.issues.issue_utils._get_issues", "line_number": 24, "usage_type": "call"}, {"api_name": "core.issues.issue_utils", "line_number": 24, "usage_type": "name"}, {"api_name": "core.issues.issue_utils.MASTER_YAML_ISSUES_FOUND_KEY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "core.issues.issue_utils", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "198778274", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /tmp/pip-install-jkXn_D/django/django/contrib/gis/maps/google/zoom.py\n# Compiled at: 2018-07-11 18:15:30\nfrom django.contrib.gis.geos import GEOSGeometry, LinearRing, Polygon, Point\nfrom django.contrib.gis.maps.google.gmap import GoogleMapException\nfrom django.utils.six.moves import xrange\nfrom math import pi, sin, log, exp, atan\nDTOR = pi / 180.0\nRTOD = 180.0 / pi\n\nclass GoogleZoom(object):\n \"\"\"\n GoogleZoom is a utility for performing operations related to the zoom\n levels on Google Maps.\n\n This class is inspired by the OpenStreetMap Mapnik tile generation routine\n `generate_tiles.py`, and the article \"How Big Is the World\" (Hack #16) in\n \"Google Maps Hacks\" by Rich Gibson and Schuyler Erle.\n\n `generate_tiles.py` may be found at:\n http://trac.openstreetmap.org/browser/applications/rendering/mapnik/generate_tiles.py\n\n \"Google Maps Hacks\" may be found at http://safari.oreilly.com/0596101619\n \"\"\"\n\n def __init__(self, num_zoom=19, tilesize=256):\n \"\"\"Initializes the Google Zoom object.\"\"\"\n self._tilesize = tilesize\n self._nzoom = num_zoom\n self._degpp = []\n self._radpp = []\n self._npix = []\n z = tilesize\n for i in xrange(num_zoom):\n self._degpp.append(z / 360.0)\n self._radpp.append(z / (2 * pi))\n self._npix.append(z / 2)\n z *= 2\n\n def __len__(self):\n \"\"\"Returns the number of zoom levels.\"\"\"\n return self._nzoom\n\n def get_lon_lat(self, lonlat):\n \"\"\"Unpacks longitude, latitude from GEOS Points and 2-tuples.\"\"\"\n if isinstance(lonlat, Point):\n lon, lat = lonlat.coords\n else:\n lon, lat = lonlat\n return (\n lon, lat)\n\n def lonlat_to_pixel(self, lonlat, zoom):\n \"\"\"Converts a longitude, latitude coordinate pair for the given zoom level.\"\"\"\n lon, lat = self.get_lon_lat(lonlat)\n npix = self._npix[zoom]\n px_x = round(npix + lon * self._degpp[zoom])\n fac = min(max(sin(DTOR * lat), -0.9999), 0.9999)\n px_y = round(npix + 0.5 * log((1 + fac) / (1 - fac)) * (-1.0 * self._radpp[zoom]))\n return (\n px_x, px_y)\n\n def pixel_to_lonlat(self, px, zoom):\n \"\"\"Converts a pixel to a longitude, latitude pair at the given zoom level.\"\"\"\n if len(px) != 2:\n raise TypeError('Pixel should be a sequence of two elements.')\n npix = self._npix[zoom]\n lon = (px[0] - npix) / self._degpp[zoom]\n lat = RTOD * (2 * atan(exp((px[1] - npix) / (-1.0 * self._radpp[zoom]))) - 0.5 * pi)\n return (\n lon, lat)\n\n def tile(self, lonlat, zoom):\n \"\"\"\n Returns a Polygon corresponding to the region represented by a fictional\n Google Tile for the given longitude/latitude pair and zoom level. This\n tile is used to determine the size of a tile at the given point.\n \"\"\"\n delta = self._tilesize / 2\n px = self.lonlat_to_pixel(lonlat, zoom)\n ll = self.pixel_to_lonlat((px[0] - delta, px[1] - delta), zoom)\n ur = self.pixel_to_lonlat((px[0] + delta, px[1] + delta), zoom)\n return Polygon(LinearRing(ll, (ll[0], ur[1]), ur, (ur[0], ll[1]), ll), srid=4326)\n\n def get_zoom(self, geom):\n \"\"\"Returns the optimal Zoom level for the given geometry.\"\"\"\n if not isinstance(geom, GEOSGeometry) or geom.srid != 4326:\n raise TypeError('get_zoom() expects a GEOS Geometry with an SRID of 4326.')\n env = geom.envelope\n env_w, env_h = self.get_width_height(env.extent)\n center = env.centroid\n for z in xrange(self._nzoom):\n tile_w, tile_h = self.get_width_height(self.tile(center, z).extent)\n if env_w > tile_w or env_h > tile_h:\n if z == 0:\n raise GoogleMapException('Geometry width and height should not exceed that of the Earth.')\n return z - 1\n\n return self._nzoom - 1\n\n def get_width_height(self, extent):\n \"\"\"\n Returns the width and height for the given extent.\n \"\"\"\n ll = Point(extent[:2])\n ul = Point(extent[0], extent[3])\n ur = Point(extent[2:])\n height = ll.distance(ul)\n width = ul.distance(ur)\n return (width, height)", "sub_path": "pycfiles/ka_lite_static-0.17.5-py2-none-any/zoom.py", "file_name": "zoom.py", "file_ext": "py", "file_size_in_byte": 4458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "math.pi", "line_number": 11, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 12, "usage_type": "name"}, {"api_name": "django.utils.six.moves.xrange", "line_number": 37, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 39, "usage_type": "name"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 49, "usage_type": "argument"}, {"api_name": "math.sin", "line_number": 61, "usage_type": "call"}, {"api_name": "math.log", "line_number": 62, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 72, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 72, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 72, "usage_type": "name"}, {"api_name": "django.contrib.gis.geos.Polygon", "line_number": 86, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.LinearRing", "line_number": 86, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.GEOSGeometry", "line_number": 90, "usage_type": "argument"}, {"api_name": "django.utils.six.moves.xrange", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.gis.maps.google.gmap.GoogleMapException", "line_number": 99, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 109, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "193236580", "text": "from PyQt5 import QtGui, QtWidgets\n\n\nclass QCustomQWidget (QtWidgets.QWidget):\n def __init__ (self, parent = None):\n super(QCustomQWidget, self).__init__(parent)\n self.textQVBoxLayout = QtWidgets.QVBoxLayout()\n self.textUpQLabel = QtWidgets.QLabel()\n self.textDownQLabel = QtWidgets.QLabel()\n self.textPath = QtWidgets.QLabel()\n self.textQVBoxLayout.addWidget(self.textUpQLabel)\n self.textQVBoxLayout.addWidget(self.textDownQLabel)\n self.textQVBoxLayout.addWidget(self.textPath)\n self.allQHBoxLayout = QtWidgets.QHBoxLayout()\n self.iconQLabel = QtWidgets.QLabel()\n self.allQHBoxLayout.addWidget(self.iconQLabel, 0)\n self.allQHBoxLayout.addLayout(self.textQVBoxLayout, 1)\n self.setLayout(self.allQHBoxLayout)\n # setStyleSheet\n self.textUpQLabel.setStyleSheet('''\n color: rgb(0, 0, 255);\n ''')\n self.textDownQLabel.setStyleSheet('''\n color: rgb(255, 0, 0);\n ''')\n\n def setTextUp (self, text):\n self.textUpQLabel.setText(text)\n\n def setTextDown (self, text):\n self.textDownQLabel.setText(text)\n\n def setIcon (self, imagePath):\n self.iconQLabel.setPixmap(QtGui.QPixmap(imagePath))\n\n def setTextPath (self, text):\n self.textPath.setText(text)", "sub_path": "venv/QCustomWidgetList.py", "file_name": "QCustomWidgetList.py", "file_ext": "py", "file_size_in_byte": 1338, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 4, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 4, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 7, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 7, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 8, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 9, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 10, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "598334183", "text": "from typing import Tuple, List\nfrom string import whitespace\nfrom subprocess import getstatusoutput\n\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Random.random import getrandbits, \\\n randint\n\nfrom django.utils.safestring import SafeText\n\nfrom apps.grade.poll.models import Poll\nfrom apps.grade.ticket_create.exceptions import InvalidPollException, TicketUsed\nfrom apps.grade.ticket_create.models import PublicKey, \\\n PrivateKey, \\\n UsedTicketStamp\nfrom functools import cmp_to_key\n\nRAND_BITS = 512\n\n\ndef flatten(x):\n result = []\n for el in x:\n if isinstance(el, list):\n if hasattr(el, \"__iter__\") and not isinstance(el, str):\n result.extend(flatten(el))\n else:\n result.append(el)\n else:\n result.append(el)\n\n return result\n\n\ndef gcd(a, b):\n if b > a:\n a, b = b, a\n while a:\n a, b = b % a, a\n return b\n\n\ndef gcwd(u, v):\n u1 = 1\n u2 = 0\n u3 = u\n v1 = 0\n v2 = 1\n v3 = v\n while v3 != 0:\n q = u3 / v3\n t1 = u1 - q * v1\n t2 = u2 - q * v2\n t3 = u3 - q * v3\n u1 = v1\n u2 = v2\n u3 = v3\n v1 = t1\n v2 = t2\n v3 = t3\n return u1, u2, u3\n\n\ndef expMod(a, b, q):\n p = 1\n\n while b > 0:\n if b & 1:\n p = (p * a) % q\n a = (a * a) % q\n b /= 2\n return p\n\n\ndef revMod(a, m):\n x, y, d = gcwd(a, m)\n\n if d != 1:\n return -1\n\n x %= m\n if x < 0:\n x += m\n return x\n\n\ndef cmp(x, y):\n if x < y:\n return -1\n if x == y:\n return 0\n return 1\n\n\ndef poll_cmp(poll1, poll2):\n if poll1.group:\n if poll2.group:\n c = cmp(poll1.group.course.name, poll2.group.course.name)\n if c == 0:\n c = cmp(poll1.group.type, poll2.group.type)\n if c == 0:\n if poll1.studies_type:\n if poll2.studies_type:\n c = cmp(poll1.studies_type, poll2.studies_type)\n if c == 0:\n return cmp(poll1.title, poll2.title)\n else:\n return c\n else:\n return 1\n else:\n if poll2.studies_type:\n return -1\n else:\n return cmp(poll1.title, poll2.title)\n else:\n return c\n else:\n return c\n else:\n return 1\n else:\n if poll2.group:\n return -1\n else:\n if poll1.studies_type:\n if poll2.studies_type:\n c = cmp(poll1.studies_type, poll2.studies_type)\n if c == 0:\n return cmp(poll1.title, poll2.title)\n else:\n return c\n else:\n return 1\n else:\n if poll2.studies_type:\n return -1\n else:\n return cmp(poll1.title, poll2.title)\n\n\ndef generate_rsa_key() -> Tuple[str, str]:\n \"\"\"\n Generates RSA key - that is, a pair (public key, private key)\n both exported in PEM format\n \"\"\"\n\n # wersja bezpieczniejsza\n #key_length = 1024\n #RSAkey = RSA.generate(key_length)\n\n # wersja szybsza\n # do poprawki: tworzenie i usuwanie pliku test_rsa...\n getstatusoutput('ssh-keygen -b 1024 -t \"rsa\" -f test_rsa -N \"\" -q')\n RSAkey = RSA.importKey(open('test_rsa').read())\n getstatusoutput('rm test_rsa*')\n\n def key_to_str(bin_key):\n return bin_key.decode(encoding='ascii', errors='strict')\n\n # Converting the resulting keys to strings should be a safe operation\n # as we explicitly specify the PEM format, which is a textual encoding\n # see https://www.dlitz.net/software/pycrypto/api/current/Crypto.PublicKey.RSA._RSAobj-class.html#exportKey\n privateKey = key_to_str(RSAkey.exportKey('PEM'))\n publicKey = key_to_str(RSAkey.publickey().exportKey('PEM'))\n return (publicKey, privateKey)\n\n\nPollKeys = List[Tuple[Poll, str]]\n\n\ndef save_public_keys(polls_public_keys: PollKeys):\n for (poll, key) in polls_public_keys:\n print(poll)\n pkey = PublicKey(poll=poll,\n public_key=key)\n pkey.save()\n\n\ndef save_private_keys(polls_private_keys: PollKeys):\n for (poll, key) in polls_private_keys:\n pkey = PrivateKey(poll=poll,\n private_key=key)\n pkey.save()\n\n\ndef generate_keys_for_polls(semester=None):\n from apps.enrollment.courses.models.semester import Semester\n if not semester:\n semester = Semester.get_current_semester()\n poll_list = Poll.get_polls_without_keys(semester)\n pub_list = []\n priv_list = []\n i = 1\n for el in poll_list:\n (pub, priv) = generate_rsa_key()\n pub_list.append(pub)\n priv_list.append(priv)\n i = i + 1\n save_public_keys(list(zip(poll_list, pub_list)))\n save_private_keys(list(zip(poll_list, priv_list)))\n print(i - 1)\n return\n\n\ndef group_polls_by_course(poll_list):\n if poll_list == []:\n return []\n\n poll_list.sort(key=cmp_to_key(poll_cmp))\n\n res = []\n act_polls = []\n act_group = poll_list[0].group\n\n for poll in poll_list:\n if not act_group:\n if not poll.group:\n act_polls.append(poll)\n else:\n act_group = poll.group\n res.append(act_polls)\n act_polls = [poll]\n else:\n if poll.group:\n if act_group.course == poll.group.course:\n act_polls.append(poll)\n else:\n act_group = poll.group\n res.append(act_polls)\n act_polls = [poll]\n else:\n act_group = poll.group\n res.append(act_polls)\n act_polls = [poll]\n\n res.append(act_polls)\n\n return res\n\n\ndef connect_groups(groupped_polls, form):\n connected_groups = []\n for polls in groupped_polls:\n if not polls[0].group:\n label = 'join_common'\n else:\n label = 'join_' + str(polls[0].group.course.pk)\n\n if len(polls) == 1:\n connected_groups.append(polls)\n elif form.cleaned_data[label]:\n connected_groups.append(polls)\n else:\n for poll in polls:\n connected_groups.append([poll])\n\n return connected_groups\n\n\ndef generate_keys(poll_list):\n keys = []\n\n for poll in poll_list:\n key = RSA.importKey(PublicKey.objects.get(poll=poll).public_key)\n keys.append((str(key.n), str(key.e)))\n\n return keys\n\n\ndef check_poll_visiblity(user, poll):\n \"\"\"Checks, whether user is a student entitled to the poll.\n\n Raises:\n InvalidPollException: If the user in question is not entitled to the\n poll.\n Student.DoesNotExist: If the user in question is not a student.\n \"\"\"\n if not poll.is_student_entitled_to_poll(user.student):\n raise InvalidPollException\n\n\ndef check_ticket_not_signed(user, poll):\n \"\"\"Checks, if the user is a student with a yet unused ticket for the poll.\n\n Raises:\n TicketUsed: If the user has already used the ticket for the poll.\n Student.DoesNotExist: If the user in question is not a student.\n \"\"\"\n u = UsedTicketStamp.objects.filter(student=user.student, poll=poll)\n if u:\n raise TicketUsed\n\n\ndef mark_poll_used(user, poll):\n \"\"\"Saves the user's stamp for the poll.\n\n Raises:\n Student.DoesNotExist: If the user in question is not a student.\n \"\"\"\n u = UsedTicketStamp(student=user.student,\n poll=poll)\n u.save()\n\n\ndef ticket_check_and_mark(user, poll, ticket):\n check_poll_visiblity(user, poll)\n check_ticket_not_signed(user, poll)\n mark_poll_used(user, poll)\n\n\ndef ticket_check_and_sign(user, poll, ticket):\n check_poll_visiblity(user, poll)\n check_ticket_not_signed(user, poll)\n key = PrivateKey.objects.get(poll=poll)\n signed = key.sign_ticket(ticket)\n mark_poll_used(user, poll)\n\n\ndef ticket_check_and_sign_without_mark(user, poll, ticket):\n check_poll_visiblity(user, poll)\n check_ticket_not_signed(user, poll)\n key = PrivateKey.objects.get(poll=poll)\n signed = key.sign_ticket(ticket)\n return signed\n\n\ndef secure_signer_without_save(user, g, t):\n try:\n return ticket_check_and_sign_without_mark(user, g, t),\n except InvalidPollException:\n return \"Nie jesteś przypisany do tej ankiety\",\n except TicketUsed:\n return \"Bilet już pobrano\",\n\n\ndef secure_mark(user, g, t):\n try:\n return ticket_check_and_mark(user, g, t),\n except InvalidPollException:\n return \"Nie jesteś przypisany do tej ankiety\",\n except TicketUsed:\n return \"Bilet już pobrano\",\n\n\ndef secure_signer(user, g, t):\n try:\n return ticket_check_and_sign(user, g, t),\n except InvalidPollException:\n return \"Nie jesteś przypisany do tej ankiety\",\n except TicketUsed:\n return \"Bilet już pobrano\",\n\n\ndef unblind(poll, st):\n st = st[0]\n if st == \"Nie jesteś przypisany do tej ankiety\":\n return st\n elif st == \"Bilet już pobrano\":\n return st\n else:\n st = st[0]\n key = RSA.importKey(PublicKey.objects.get(poll=poll).public_key)\n return (str(st), str(key.n), str(key.e))\n\n\ndef get_valid_tickets(ticket_list):\n err = []\n val = []\n for group, ticket, st in ticket_list:\n if st == \"Nie jesteś przypisany do tej ankiety\" or \\\n st == \"Bilet już pobrano\":\n err.append((str(group), st))\n else:\n val.append((group, ticket, st))\n return err, val\n\n\ndef to_plaintext(vtl):\n res = \"\"\n for p, t, st in vtl:\n res += '[' + p.title + ']'\n if not p.group:\n res += 'Ankieta ogólna '\n else:\n res += str(p.group.course.name) + \" \"\n res += str(p.group.get_type_display()) + \": \"\n res += str(p.group.get_teacher_full_name()) + \" \"\n if p.studies_type:\n res += 'typ studiów: ' + str(p.studies_type) + \" \"\n\n res += 'id: ' + str(p.pk) + ' '\n res += str(t) + \" \"\n res += str(st) + \" \"\n res += \"---------------------------------- \"\n return SafeText(str(res))\n\n\n# FIXME explanation of ticket parsing code: str(int())\n# The list is split into chunks, some of which are empty, and some of which\n# contain the tickets we want (e.g. ['123123', '', '', 'somecrap', '321321'])\n# The list is iterated until doing int(list[i]) succeeds; at this point\n# it's assumed we've found the key. However, we actually want to return\n# the tickets as strings, not ints.\n# This entire function should be rewritten from scratch\ndef from_plaintext(tickets_plaintext):\n pre_tickets = tickets_plaintext.split('----------------------------------')\n pre_tickets = [[x] for x in pre_tickets]\n for sign in whitespace:\n pre_tickets = [flatten(\n [x.split(sign) for x in ls]) for ls in pre_tickets]\n\n convert = False\n ids_tickets_signed = []\n for poll_info in pre_tickets:\n i = 0\n while i < len(poll_info):\n if convert:\n j = i\n id = -1\n t = -1\n st = -1\n while True:\n try:\n id = int(poll_info[j])\n break\n except BaseException:\n j += 1\n\n j += 1\n while True:\n try:\n t = str(int(poll_info[j]))\n break\n except BaseException:\n j += 1\n\n j += 1\n while True:\n try:\n st = int(poll_info[j])\n break\n except BaseException:\n j += 1\n\n i = j + 1\n convert = False\n ids_tickets_signed.append((id, (t, st)))\n elif poll_info[i].startswith('id:'):\n convert = True\n i += 1\n\n return ids_tickets_signed\n\n\ndef generate_ticket(poll_list):\n # TODO: Docelowo ma być po stronie przeglądarki\n m = getrandbits(RAND_BITS)\n blinded = []\n\n for poll in poll_list:\n key = RSA.importKey(PublicKey.objects.get(poll=poll).public_key)\n n = key.n\n e = key.e\n k = randint(2, n)\n while gcd(n, k) != 1:\n k = randint(1, n)\n\n a = (m % n)\n b = expMod(k, e, n)\n t = (a * b) % n\n\n blinded.append((poll, t, (m, k)))\n return blinded\n", "sub_path": "zapisy/apps/grade/ticket_create/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 12966, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "subprocess.getstatusoutput", "line_number": 154, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA.importKey", "line_number": 155, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 155, "usage_type": "name"}, {"api_name": "subprocess.getstatusoutput", "line_number": 156, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 169, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 169, "usage_type": "name"}, {"api_name": "apps.grade.poll.models.Poll", "line_number": 169, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.PublicKey", "line_number": 175, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey", "line_number": 182, "usage_type": "call"}, {"api_name": "apps.enrollment.courses.models.semester.Semester.get_current_semester", "line_number": 190, "usage_type": "call"}, {"api_name": "apps.enrollment.courses.models.semester.Semester", "line_number": 190, "usage_type": "name"}, {"api_name": "apps.grade.poll.models.Poll.get_polls_without_keys", "line_number": 191, "usage_type": "call"}, {"api_name": "apps.grade.poll.models.Poll", "line_number": 191, "usage_type": "name"}, {"api_name": "functools.cmp_to_key", "line_number": 210, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA.importKey", "line_number": 265, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 265, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.PublicKey.objects.get", "line_number": 265, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PublicKey.objects", "line_number": 265, "usage_type": "attribute"}, {"api_name": "apps.grade.ticket_create.models.PublicKey", "line_number": 265, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.InvalidPollException", "line_number": 280, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.UsedTicketStamp.objects.filter", "line_number": 290, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.UsedTicketStamp.objects", "line_number": 290, "usage_type": "attribute"}, {"api_name": "apps.grade.ticket_create.models.UsedTicketStamp", "line_number": 290, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.TicketUsed", "line_number": 292, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.UsedTicketStamp", "line_number": 301, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey.objects.get", "line_number": 315, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey.objects", "line_number": 315, "usage_type": "attribute"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey", "line_number": 315, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey.objects.get", "line_number": 323, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey.objects", "line_number": 323, "usage_type": "attribute"}, {"api_name": "apps.grade.ticket_create.models.PrivateKey", "line_number": 323, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.InvalidPollException", "line_number": 331, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.TicketUsed", "line_number": 333, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.InvalidPollException", "line_number": 340, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.TicketUsed", "line_number": 342, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.InvalidPollException", "line_number": 349, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.exceptions.TicketUsed", "line_number": 351, "usage_type": "name"}, {"api_name": "Crypto.PublicKey.RSA.importKey", "line_number": 363, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 363, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.PublicKey.objects.get", "line_number": 363, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PublicKey.objects", "line_number": 363, "usage_type": "attribute"}, {"api_name": "apps.grade.ticket_create.models.PublicKey", "line_number": 363, "usage_type": "name"}, {"api_name": "django.utils.safestring.SafeText", "line_number": 396, "usage_type": "call"}, {"api_name": "string.whitespace", "line_number": 409, "usage_type": "name"}, {"api_name": "Crypto.Random.random.getrandbits", "line_number": 458, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA.importKey", "line_number": 462, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 462, "usage_type": "name"}, {"api_name": "apps.grade.ticket_create.models.PublicKey.objects.get", "line_number": 462, "usage_type": "call"}, {"api_name": "apps.grade.ticket_create.models.PublicKey.objects", "line_number": 462, "usage_type": "attribute"}, {"api_name": "apps.grade.ticket_create.models.PublicKey", "line_number": 462, "usage_type": "name"}, {"api_name": "Crypto.Random.random.randint", "line_number": 465, "usage_type": "call"}, {"api_name": "Crypto.Random.random.randint", "line_number": 467, "usage_type": "call"}]} +{"seq_id": "372491159", "text": "import torch as th\nfrom torch.autograd import Variable as V\nimport gc\n\nimport benchmarks.lstm_variants as lstm_variants\n\nif __name__ == '__main__':\n from benchmark_common import benchmark_init\n from common import AttrDict, Bench, tag\nelse:\n from .benchmark_common import benchmark_init\n from .common import AttrDict, Bench, tag\n\n\nlstms = [\n 'SlowLSTM',\n 'LSTM',\n 'GalLSTM',\n 'MoonLSTM',\n 'SemeniutaLSTM',\n 'LayerNormLSTM',\n 'LayerNormGalLSTM',\n 'LayerNormMoonLSTM',\n 'LayerNormSemeniutaLSTM',\n]\n\nBATCH = 10\nSEQ_LEN = 100\nDROPOUT = 0.5\n\n\ndef run_lstm_variant(variant='SlowLSTM', cuda=False, size=128, jit=False):\n assert variant in lstms\n p = AttrDict({'cuda': cuda, 'lstm_kind': variant, 'size': size})\n\n name = '{}_size{}{}{}'.format(variant, size, tag(cuda=cuda), tag(jit=jit))\n\n def C(x):\n if p.cuda:\n x = x.cuda()\n return x\n\n lstm = getattr(lstm_variants, p.lstm_kind)\n x = V(C(th.rand(1, BATCH, p.size)))\n hiddens = (V(C(th.rand(1, BATCH, p.size))), V(C(th.rand(1, BATCH, p.size))))\n th.manual_seed(1234)\n cus = C(lstm(p.size, p.size, dropout=DROPOUT, jit=jit))\n if hasattr(cus, 'mask'):\n cus.mask = C(cus.mask)\n\n iter_timer = Bench(name=name, cuda=cuda, warmup_iters=3)\n\n # Super slow on CPU\n iters = 20 if cuda else 6\n for _ in range(iters):\n gc.collect()\n with iter_timer:\n out, h = x, hiddens\n for i in range(SEQ_LEN):\n out, h = cus(out, h)\n\n return iter_timer\n", "sub_path": "legacy/rnns/benchmarks/lstm_variants_test.py", "file_name": "lstm_variants_test.py", "file_ext": "py", "file_size_in_byte": 1537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "common.AttrDict", "line_number": 34, "usage_type": "call"}, {"api_name": "common.tag", "line_number": 36, "usage_type": "call"}, {"api_name": "benchmarks.lstm_variants", "line_number": 43, "usage_type": "argument"}, {"api_name": "torch.autograd.Variable", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 46, "usage_type": "call"}, {"api_name": "common.Bench", "line_number": 51, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "540548706", "text": "import json\nimport requests\nimport re\nimport discord\nimport datetime\n#store api url\nvocaapi = \"https://vocadb.net/api/\"\n\ndef voca(vocamsg):\n #if error occurs return no value error. User mistyped request or requests\n # request does not exist.\n try:\n #if request does not contain only artist\n if vocamsg[0] != \"a\":\n #if request only contains song, request top song matching request\n if vocamsg[0] == \"s\":\n r = requests.get(vocaapi + \"songs?query=\" + vocamsg[1] + \"&maxResults=1&sort=RatingScore&nameMatchMode=Auto&fields=Artists,ThumbUrl,PVs,Tags&Lang=Default,English\").json()['items']\n if not r: raise Exception(\"NoValue\")\n else: r = r[0]\n #else user requesting specific song and artist.\n # request song and artist separately\n else:\n #request all songs matching user request\n rs = requests.get(vocaapi + \"songs?query=\" + vocamsg[0] + \"&sort=RatingScore&nameMatchMode=Auto&fields=Artists,ThumbUrl,PVs,Tags&Lang=Default,English\").json()['items']\n #if rs is empty return NoValue exception\n if not rs:\n raise Exception(\"NoValue\")\n #request all artists matching user request\n ra = requests.get(vocaapi + \"artists?query=\" + vocamsg[1] + \"&nameMatchMode=Auto&lang=English&sort=FollowerCount\").json()['items']\n\n output = []\n #for each song in rs and each artist in song, if artist matches\n # user requested artist, add songid, artistid and songrating\n # a list and stop loop\n for a in ra:\n for s in rs:\n for sa in s['artists']:\n try:\n if a['id'] == sa['artist']['id']:\n output = s['id']\n break\n #if artist does not have an id, go to next result.\n except KeyError: continue\n #select top song w/ artist from rs\n try:\n for a in rs:\n if output == a['id']:\n r = a\n if not r:\n r = rs[0]\n #if no song w/ artist could be found, return first song in rs\n except: r = rs[0]\n tags = []\n pvs = []\n #put top 5 tags into tags list\n for t in range(5):\n try:\n tags.append(sorted(r['tags'],key=lambda tag: tag['count'],reverse=True)[t]['tag']['name'])\n #if song has less than 5 tags, break loop at final tag\n except IndexError: break\n #put all PV links into pv list\n for p in r['pvs']:\n if p['pvType'] == \"Original\":\n pvs.append(p['url'])\n tags.sort(reverse=True)\n #structure and return embed response.\n embed = discord.Embed(title=r['name'] + ' - ' + r['artistString'],url=\"https://vocadb.net/S/\"+str(r['id']))\n embed.set_thumbnail(url=r['thumbUrl'])\n embed.add_field(name=\"Type\",value = r['songType'])\n embed.add_field(name=\"Duration\",value=str(datetime.timedelta(seconds=int(r['lengthSeconds']))))\n if tags:embed.add_field(name=\"Tags\",value=\", \".join(tags))\n if pvs:embed.add_field(name=\"PV\",value=\"\\n\".join(pvs))\n\n #else if message contains only an artist request artist with most\n # followers\n elif vocamsg[0] == \"a\":\n r = requests.get(vocaapi + \"artists?query=\" + vocamsg[1] + \"&fields=Tags,MainPicture&maxResults=1&nameMatchMode=Auto&lang=English&sort=FollowerCount\").json()['items'][0]\n #put top 5 tags into tags list\n tags = []\n for t in range(5):\n try:\n tags.append(sorted(r['tags'],key=lambda tag: tag['count'],reverse=True)[t]['tag']['name'])\n #if aritst has less than 5 tags, break loop at final tag.\n except IndexError: break\n #structure and return embed response\n embed = discord.Embed(title=r['name'] + \", \" + r['defaultName'],url=\"https://vocadb.net/Ar/\"+str(r['id']))\n embed.set_thumbnail(url=r['mainPicture']['urlTinyThumb'])\n embed.add_field(name=\"Type\",value = r['artistType'])\n embed.add_field(name=\"Tags\",value=\", \".join(tags))\n return embed\n except IndexError: return Exception(\"IndexError\")\n except: return Exception(\"NoValue\")\n", "sub_path": "voca.py", "file_name": "voca.py", "file_ext": "py", "file_size_in_byte": 4678, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 70, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 77, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "406578085", "text": "# --------------\nimport pandas as pd\r\nimport os\r\nimport numpy as np\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\n\r\n# path_train : location of test file\r\n# Code starts here\r\ndf=pd.read_csv(path_train)\r\ndf.head()\r\nprint(df.columns)\r\n\r\ndef label_race(row):\r\n if row['food'] == \"T\":\r\n return 'food'\r\n elif row['recharge'] == \"T\":\r\n return 'recharge'\r\n elif row['support'] == \"T\":\r\n return 'support'\r\n elif row['reminders'] == \"T\":\r\n return 'reminders'\r\n elif row['travel'] == \"T\":\r\n return 'travel' \r\n elif row['nearby'] == \"T\":\r\n return 'nearby'\r\n elif row['movies'] == \"T\":\r\n return 'movies'\r\n elif row['casual'] == \"T\":\r\n return 'casual'\r\n elif row['other'] == \"T\":\r\n return 'other' \r\n\r\ndf['category']=df.apply(label_race,axis=1)\r\n\r\ndf.drop(['food', 'recharge', 'support', 'reminders', 'travel',\r\n 'nearby', 'movies', 'casual', 'other'],axis=1, inplace=True)\r\n\r\nprint(df.head())\n\n\n# --------------\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn import preprocessing\r\n# Sampling only 1000 samples of each category\r\ndf = df.groupby('category').apply(lambda x: x.sample(n=1000, random_state=0))\r\n\r\n# Code starts here\r\nall_text= df['message'].str.lower()\r\nprint(all_text)\r\ntfid_v=TfidfVectorizer(stop_words=\"english\")\r\ntfid_v_model=tfid_v.fit(all_text)\r\nX=tfid_v_model.transform(all_text)\r\nle=preprocessing.LabelEncoder()\r\nle_m=le.fit(df['category'])\r\ny=le_m.transform(df['category'])\n\n\n# --------------\nfrom sklearn.metrics import accuracy_score, classification_report\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.naive_bayes import MultinomialNB\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.svm import LinearSVC\r\n\r\n# Code starts here\r\nX_train,X_val,y_train ,y_val=train_test_split(X,y,test_size=0.3, random_state=42)\r\nlog_reg=LogisticRegression(random_state=0)\r\nlog_reg_model=log_reg.fit(X_train,y_train)\r\ny_pred=log_reg_model.predict(X_val)\r\n\r\nlog_accuracy =accuracy_score(y_val, y_pred)\r\nprint(log_accuracy)\r\nnb=MultinomialNB()\r\n\r\nnb_model=nb.fit(X_train,y_train)\r\ny_nb_pred=nb_model.predict(X_val)\r\n\r\nnb_accuracy =accuracy_score(y_val, y_nb_pred)\r\nprint(nb_accuracy)\r\n\r\nlsvm=LinearSVC(random_state=0)\r\n\r\nlsvm_model=lsvm.fit(X_train,y_train)\r\ny_lsvm_pred=lsvm_model.predict(X_val)\r\n\r\nlsvm_accuracy =accuracy_score(y_val, y_lsvm_pred)\r\nprint(lsvm_accuracy)\r\n\r\n\n\n\n# --------------\n# path_test : Location of test data\r\n\r\n#Loading the dataframe\r\ndf_test = pd.read_csv(path_test)\r\n\r\n#Creating the new column category\r\ndf_test[\"category\"] = df_test.apply (lambda row: label_race (row),axis=1)\r\n\r\n#Dropping the other columns\r\ndrop= [\"food\", \"recharge\", \"support\", \"reminders\", \"nearby\", \"movies\", \"casual\", \"other\", \"travel\"]\r\ndf_test= df_test.drop(drop,1)\r\nprint(df_test.shape)\r\n#df_test = df_test.groupby('category').apply(lambda x: x.sample(n=1000, random_state=0))\r\n# Code starts here\r\nall_text=df_test['message'].str.lower()\r\nX_test=tfid_v_model.transform(all_text)\r\n#X_test=X_test[:9000]\r\ny_test=le_m.transform(df_test['category'])\r\n#y_test=y_test[:9000]\r\ny_pred1=log_reg_model.predict(X_test)\r\nprint(len(y_pred1))\r\nprint(len(y_test))\r\nlog_accuracy_2 =accuracy_score(y_test, y_pred1)\r\n\r\ny_pred2=nb_model.predict(X_test)\r\nnb_accuracy_2 =accuracy_score(y_test, y_pred2)\r\n\r\nprint(log_accuracy_2)\r\nprint(nb_accuracy_2)\r\n\r\ny_pred3=lsvm_model.predict(X_test)\r\nlsvm_accuracy_2 =accuracy_score(y_test, y_pred3)\r\n\r\n\r\n\n\n\n# --------------\nfrom nltk.corpus import stopwords\r\nfrom nltk.stem.wordnet import WordNetLemmatizer\r\nimport string\r\nimport gensim\r\nfrom gensim.models.lsimodel import LsiModel\r\nfrom gensim import corpora\r\nfrom pprint import pprint\r\n# import nltk\r\n# nltk.download('wordnet')\r\n\r\n# Creating a stopwords list\r\nstop = set(stopwords.words('english'))\r\nexclude = set(string.punctuation)\r\nlemma = WordNetLemmatizer()\r\n# Function to lemmatize and remove the stopwords\r\ndef clean(doc):\r\n stop_free = \" \".join([i for i in doc.lower().split() if i not in stop])\r\n punc_free = \"\".join(ch for ch in stop_free if ch not in exclude)\r\n normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\r\n return normalized\r\n\r\n# Creating a list of documents from the complaints column\r\nlist_of_docs = df[\"message\"].tolist()\r\n\r\n# Implementing the function for all the complaints of list_of_docs\r\ndoc_clean = [clean(doc).split() for doc in list_of_docs]\r\n\r\n# Code starts here\r\ndictionary=corpora.Dictionary(doc_clean)\r\ndoc_term_matrix=[dictionary.doc2bow(text) for text in doc_clean]\r\nlsimodel=LsiModel(corpus=doc_term_matrix, num_topics=5, id2word=dictionary)\r\n\r\npprint(lsimodel.print_topics())\r\n\n\n\n# --------------\nfrom gensim.models import LdaModel\r\nfrom gensim.models import CoherenceModel\r\n\r\n# doc_term_matrix - Word matrix created in the last task\r\n# dictionary - Dictionary created in the last task\r\n\r\n# Function to calculate coherence values\r\ndef compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\r\n \"\"\"\r\n Compute c_v coherence for various number of topics\r\n\r\n Parameters:\r\n ----------\r\n dictionary : Gensim dictionary\r\n corpus : Gensim corpus\r\n texts : List of input texts\r\n limit : Max num of topics\r\n\r\n Returns:\r\n -------\r\n topic_list : No. of topics chosen\r\n coherence_values : Coherence values corresponding to the LDA model with respective number of topics\r\n \"\"\"\r\n coherence_values = []\r\n topic_list = []\r\n for num_topics in range(start, limit, step):\r\n model = gensim.models.ldamodel.LdaModel(corpus, random_state = 0, num_topics=num_topics, id2word = dictionary, iterations=10)\r\n topic_list.append(num_topics)\r\n coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\r\n coherence_values.append(coherencemodel.get_coherence())\r\n\r\n return topic_list, coherence_values\r\n\r\n\r\n# Code starts here\r\ntopic_list , coherence_value_list=compute_coherence_values(dictionary=dictionary, corpus=doc_term_matrix, texts=doc_clean, start=1, limit=41, step=5)\r\n\r\nprint(topic_list)\r\nprint(coherence_value_list)\r\nfor m, cv in zip(topic_list, coherence_value_list):\r\n print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))\r\nopt_topic=36\r\nlda_model=LdaModel(corpus=doc_term_matrix, num_topics=opt_topic, id2word = dictionary, iterations=10 , passes=30, random_state=0)\r\n\r\npprint(lda_model.print_topics(5))\r\n\n\n\n", "sub_path": "code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 6443, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "warnings.filterwarnings", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 55, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 83, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 88, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 126, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 144, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 144, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 145, "usage_type": "attribute"}, {"api_name": "nltk.stem.wordnet.WordNetLemmatizer", "line_number": 146, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 161, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 161, "usage_type": "name"}, {"api_name": "gensim.models.lsimodel.LsiModel", "line_number": 163, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 165, "usage_type": "call"}, {"api_name": "gensim.models.ldamodel.LdaModel", "line_number": 196, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 196, "usage_type": "attribute"}, {"api_name": "gensim.models.CoherenceModel", "line_number": 198, "usage_type": "call"}, {"api_name": "gensim.models.LdaModel", "line_number": 212, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "36216447", "text": "import torch.optim as optim\nimport torch\nimport random\nimport numpy as np\nimport torch.nn.functional as F\n\nfrom collections import namedtuple, deque\n\nfrom navigation.networks import QNetwork\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n\nclass Agent:\n \"\"\"Interacts with and learns from the environment.\"\"\"\n\n def __init__(\n self,\n state_size,\n action_size,\n seed=None,\n double_dqn=False,\n network=QNetwork,\n network_kwargs=None,\n buffer_size=int(1e5),\n batch_size=64,\n gamma=0.99,\n tau=1e-3,\n lr=5e-4,\n update_every=4,\n ):\n \"\"\"Initialize an Agent object.\n \n Params\n ======\n state_size (int): dimension of each state\n action_size (int): dimension of each action\n seed (int): random seed\n double_dqn (bool): if True, use a double DQN implementation; otherwise, use standard DQN.\n In standard DQN, the same network both chooses the action that will be taken in state t+1 and evaluates the\n expected reward for that action; in double DQN, the \"local\" network chooses the action, while the \"target\"\n network evaluates it.\n network: (nn.Module): Network class that accepts states as inputs and outputs Q-values for each action\n buffer_size: (int): Replay buffer size\n batch_size: (int): Minibatch size\n gamma: (float): Discount factor\n tau: (float): Soft update rate for target network\n lr: (float): Learning rate\n update_every: (int): How often to update the network\n \"\"\"\n self.state_size = state_size\n self.action_size = action_size\n if seed:\n self.seed = random.seed(seed)\n self.double_dqn = double_dqn\n self.buffer_size = buffer_size\n self.batch_size = batch_size\n self.gamma = gamma\n self.tau = tau\n self.lr = lr\n self.update_every = update_every\n\n # Q-Network\n network_kwargs = network_kwargs if network_kwargs else {}\n self.network_class = network\n if seed:\n self.qnetwork_local = network(state_size=state_size, action_size=action_size, seed=seed, **network_kwargs).to(device)\n self.qnetwork_target = network(state_size=state_size, action_size=action_size, seed=seed, **network_kwargs).to(device)\n else:\n self.qnetwork_local = network(state_size=state_size, action_size=action_size, **network_kwargs).to(device)\n self.qnetwork_target = network(state_size=state_size, action_size=action_size, **network_kwargs).to(device)\n\n self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=self.lr)\n\n # Replay memory\n self.memory = ReplayBuffer(action_size, self.buffer_size, self.batch_size, seed)\n # Initialize time step (for updating every UPDATE_EVERY steps)\n self.t_step = 0\n\n def step(self, state, action, reward, next_state, done):\n # Save experience in replay memory\n self.memory.add(state, action, reward, next_state, done)\n\n # Learn every UPDATE_EVERY time steps.\n self.t_step = (self.t_step + 1) % self.update_every\n if self.t_step == 0:\n # If enough samples are available in memory, get random subset and learn\n if len(self.memory) > self.batch_size:\n experiences = self.memory.sample()\n self.learn(experiences, self.gamma)\n\n def act(self, state, eps=0.0):\n \"\"\"Returns actions for given state as per current policy.\n \n Params\n ======\n state (array_like): current state\n eps (float): epsilon, for epsilon-greedy action selection\n \"\"\"\n state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n self.qnetwork_local.eval()\n with torch.no_grad():\n action_values = self.qnetwork_local(state)\n self.qnetwork_local.train()\n\n # Epsilon-greedy action selection\n if random.random() > eps:\n return np.argmax(action_values.cpu().data.numpy())\n else:\n return random.choice(np.arange(self.action_size))\n\n def learn(self, experiences, gamma):\n \"\"\"Update value parameters using given batch of experience tuples.\n\n Params\n ======\n experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples \n gamma (float): discount factor\n \"\"\"\n states, actions, rewards, next_states, dones = experiences\n\n # compute target\n if self.double_dqn:\n next_actions = self.qnetwork_local(next_states).argmax(dim=1, keepdim=True)\n next_q_value = self.qnetwork_target(next_states).gather(1, next_actions)\n else:\n next_q_value, _ = self.qnetwork_target(next_states).max(dim=1, keepdim=True)\n y_i = rewards + next_q_value * gamma\n\n # compute current\n current_q_value = self.qnetwork_local(states).gather(1, actions)\n\n # calculate loss\n loss = F.mse_loss(current_q_value, y_i)\n\n # update local params\n self.optimizer.zero_grad()\n loss.backward()\n for param in self.qnetwork_local.parameters():\n # for stability\n param.grad.clamp(-1, 1)\n self.optimizer.step()\n\n # ------------------- update target network ------------------- #\n self.soft_update(self.qnetwork_local, self.qnetwork_target, self.tau)\n\n def soft_update(self, local_model, target_model, tau):\n \"\"\"Soft update model parameters.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n tau (float): interpolation parameter \n \"\"\"\n for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)\n\n\nclass ReplayBuffer:\n \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n\n def __init__(self, action_size, buffer_size, batch_size, seed):\n \"\"\"Initialize a ReplayBuffer object.\n\n Params\n ======\n action_size (int): dimension of each action\n buffer_size (int): maximum size of buffer\n batch_size (int): size of each training batch\n seed (int): random seed\n \"\"\"\n self.action_size = action_size\n self.memory = deque(maxlen=buffer_size)\n self.batch_size = batch_size\n self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n self.seed = random.seed(seed)\n\n def add(self, state, action, reward, next_state, done):\n \"\"\"Add a new experience to memory.\"\"\"\n e = self.experience(state, action, reward, next_state, done)\n self.memory.append(e)\n\n def sample(self):\n \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n experiences = random.sample(self.memory, k=self.batch_size)\n\n states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)\n rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n\n return (states, actions, rewards, next_states, dones)\n\n def __len__(self):\n \"\"\"Return the current size of internal memory.\"\"\"\n return len(self.memory)\n", "sub_path": "navigation/agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 7986, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.device", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 11, "usage_type": "attribute"}, {"api_name": "navigation.networks.QNetwork", "line_number": 23, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 102, "usage_type": "call"}, {"api_name": "random.random", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 108, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"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": "collections.deque", "line_number": 175, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 177, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 178, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 193, "usage_type": "attribute"}]} +{"seq_id": "355008819", "text": "from django.db import models\nfrom app.charts.models import Chart\nfrom app.entities.models import Entity\nfrom app.userProfiles.models import UserProfile\n\n\nclass EntityHistory(models.Model):\n # stores the key word of what action occurred to the related entity during the step\n action = models.CharField(\n max_length=200\n )\n\n created = models.DateTimeField(\n auto_now_add=True\n )\n\n updated = models.DateTimeField(\n auto_now=True\n )\n\n # Used to track official histories of an entity to show on the Entity History timeline.\n # Changes made by GroupAdd / GroupEdit are pending false, changes made in a StepChart\n # default to true. Once a step / project is completed, all the relevant histories are\n # change to pending false, becoming official histories\n pending = models.BooleanField(\n default=False\n )\n\n changed_legal_form = models.CharField(\n max_length=50,\n default='',\n blank=True,\n null=True\n )\n\n entity = models.ForeignKey(\n to=Entity,\n on_delete=models.CASCADE,\n related_name='entity_histories',\n blank=True,\n null=True\n )\n\n chart = models.ForeignKey(\n to=Chart,\n on_delete=models.CASCADE,\n related_name='chart_histories',\n blank=True,\n null=True\n )\n\n creator = models.ForeignKey(\n to=UserProfile,\n on_delete=models.SET_NULL,\n related_name='entity_actions',\n blank=True,\n null=True\n )\n\n creating_action = models.ForeignKey(\n to='EntityHistory',\n on_delete=models.CASCADE,\n related_name='affected_entities',\n blank=True,\n null=True\n )\n\n def __str__(self):\n return f'Entity Log #{self.id} for {self.entity.name}'\n", "sub_path": "backend/app/entityHistories/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1789, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "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": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "app.entities.models.Entity", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 38, "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": "app.charts.models.Chart", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "app.userProfiles.models.UserProfile", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "229637850", "text": "# -*- coding:utf-8 -*-\n__author__ = \"Yang Wei Min\"\nfrom Public_Base_Method_Client import Client_Login\nimport Public_Base_Method_Client\nimport requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)#忽略警告\n\n\ndef Get_Car_Inventory():\n \"\"\"\n 作用：获取车辆商品库存信息\n :return:\n \"\"\"\n\n # url = \"https://test.waiqin365.com:443/app/esss/carsale/client/v2/huoQuCheLiangKuCun.action\"\n # url = \"https://\" + client_host +\"/app/esss/carsale/client/v2/huoQuCheLiangKuCun.action\"\n url = \"http://\" + client_host +\"/app/esss/carsale/client/v2/huoQuCheLiangKuCun.action\"\n\n headers = {\n # \"Cookie\": \"login_tenant=8100630123350000887; WQSESSIONID=1385E288642629FBC497434C6AA65AD5; sourceType=CLIENT; x-token=eyJhbGciOiJIUzI1NiJ9.eyJqdGkiOiJqd3QiLCJpYXQiOjE0OTY5OTI5MDAsInN1YiI6IntcImxvZ2luVHlwZVwiOlwiU09VUkNFX1RZUEVfQ0xJRU5UXCIsXCJyZWFsUmVmcmVzaFRva2VuRXhwaXJlTWludXRlXCI6NDMyMDAsXCJyZWFsVG9rZW5FeHBpcmVNaW51dGVcIjoxNDQwMCxcInJlZnJlc2hUb2tlbkV4cGlyZUhvdXJzXCI6NzIwLFwidGVuYW50SWRcIjo4MTAwNjMwMTIzMzUwMDAwODg3LFwidG9rZW5FeHBpcmVIb3Vyc1wiOjI0MCxcInVzZXJJZFwiOjU2MTUzMTc1OTg2Mzg1NTc0NTN9IiwiaXNzIjoiNTYxNTMxNzU5ODYzODU1NzQ1M184MTAwNjMwMTIzMzUwMDAwODg3X0NMSUVOVCIsImV4cCI6MTQ5Nzg1NjkwMH0.Rv3g5nUyXQzkseSX6oztyW_h-ZMTDXJR1qTkqHnaJPc\"\n\n #将参数化的cookie和token进行拼接\n \"Cookie\": \"login_tenant=8100630123350000887; \"+ cookie +\";\"+ token +\"; sourceType=CLIENT; \"\n\n }\n\n request = requests.post(url,headers=headers,verify=False)\n print (request.status_code)\n print (request.content)\n\n\n\ndef TiHuoOrHuiKuShenQingParam():\n \"\"\"\n 作用：提货申请页面初始化、回库申请页面初始化、获取是否有未审核的提货和回库申请接口\n :return:\n \"\"\"\n # url = \"https://\" + client_host + \"/app/esss/carsale/client/v2/huoQuTiHuoOrHuiKuShenQingParam.action\"\n url = \"http://\" + client_host + \"/app/esss/carsale/client/v2/huoQuTiHuoOrHuiKuShenQingParam.action\"\n\n headers = {\n \"Cookie\":\"login_tenant=8100630123350000887;\" + cookie + \";\" + token + \"; sourceType=CLIENT;\"\n }\n\n request = requests.post(url,headers=headers,verify=False)\n print (request.status_code)\n print (request.content)\n\n\n\ndef Get_TiHuo_Record():\n \"\"\"\n 作用：获取提货申请记录接口\n :return:\n \"\"\"\n # url = \"https://\" + client_host + \"/app/esss/carsale/client/v2/huoQuTiHuoShenQingRecords.action\"\n url = \"http://\" + client_host + \"/app/esss/carsale/client/v2/huoQuTiHuoShenQingRecords.action\"\n\n headers = {\n \"Cookie\":\"login_tenant=8100630123350000887;\" + cookie + \";\" + token + \"; sourceType=CLIENT;\"\n }\n\n data = {\n \"conditions.carNo\":\"A888888\",\n \"conditions.confirmStatus\":\"\",\n \"conditions.curPage\":1,\n \"conditions.endDate\":\"\",\n \"conditions.is_enable\":\"\",\n \"conditions.startDate\":\"\",\n \"conditions.submitId\":\"5615317598638557453\"\n }\n\n request = requests.post(url,headers=headers,data=data,verify=False)\n\n print (request.status_code)\n print (request.content)\n\n\n\n\nif __name__ == \"__main__\":\n cookie,token,client_host = Client_Login()\n Get_Car_Inventory()\n TiHuoOrHuiKuShenQingParam()\n Get_TiHuo_Record()\n\n", "sub_path": "Interface_Waiqin/Client_Method_Esss_CarSales.py", "file_name": "Client_Method_Esss_CarSales.py", "file_ext": "py", "file_size_in_byte": 3292, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.exceptions.InsecureRequestWarning", "line_number": 7, "usage_type": "argument"}, {"api_name": "requests.packages", "line_number": 7, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 74, "usage_type": "call"}, {"api_name": "Public_Base_Method_Client.Client_Login", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "74594354", "text": "from machine import Pin\nimport machine, dht\nimport utime, urequests\nfrom urlencode import urlencode\nimport xtools\nimport config\n\nxtools.connect_wifi_led()\n\nled = 2\nd11=dht.DHT11(machine.Pin(15))\npIn4 = Pin(4, Pin.IN, Pin.PULL_UP)\n\npOutled = Pin(led, Pin.OUT)\nAPIKEY = config.KEY\nWEBHOOK_URL=\"https://maker.ifttt.com/trigger/ButtonClick/with/key/\"\nWEBHOOK_URL+=APIKEY + \"?\" \n\nbutton = 1 #Pin(4, Pin.IN, Pin.PULL_UP)\nled = Pin(2, Pin.OUT)\nprint(\"請按下按鍵開關來送出Email…\")\ncnt=0\nwhile True:\n \n d11.measure() # start to measure\n button = pIn4.value()\n #print(button)\n pOutled.value(button)\n if button == 0: # 值 0 是按下\n cnt += 1\n T=d11.temperature() # return the temperature\n H=d11.humidity() # return the humidity\n print(\"{0:4.2f} 度, {1:4.2f} %, {2:2d}\".format(T,H,cnt))\n params = { \"value1\": T,\n \"value2\": H,\n \"value3\": cnt}\n WEBHOOK_SEND = WEBHOOK_URL + urlencode(params)\n print(\"送出Email!\")\n print(WEBHOOK_SEND)\n print(\".\",end=\"\")\n utime.sleep(10)", "sub_path": "source/iftttIn.py", "file_name": "iftttIn.py", "file_ext": "py", "file_size_in_byte": 1122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "xtools.connect_wifi_led", "line_number": 8, "usage_type": "call"}, {"api_name": "dht.DHT11", "line_number": 11, "usage_type": "call"}, {"api_name": "machine.Pin", "line_number": 11, "usage_type": "call"}, {"api_name": "machine.Pin", "line_number": 12, "usage_type": "call"}, {"api_name": "machine.Pin.IN", "line_number": 12, "usage_type": "attribute"}, {"api_name": "machine.Pin.PULL_UP", "line_number": 12, "usage_type": "attribute"}, {"api_name": "machine.Pin", "line_number": 14, "usage_type": "call"}, {"api_name": "machine.Pin.OUT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.KEY", "line_number": 15, "usage_type": "attribute"}, {"api_name": "machine.Pin", "line_number": 20, "usage_type": "call"}, {"api_name": "machine.Pin.OUT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "urlencode.urlencode", "line_number": 37, "usage_type": "call"}, {"api_name": "utime.sleep", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "519186892", "text": "from django.apps import AppConfig\n\n\nclass IssuesConfig(AppConfig):\n name = \"issues\"\n\n\nbuyers_beforeSort = [\n \"한펠사무실\",\n \"한펠공장\",\n \"(주)허니텍\",\n \"대한산업\",\n \"허니텍\",\n \"153양봉원\",\n \"가야농산\",\n \"강남양봉산업(천지)\",\n \"강원허니원영농조합\",\n \"강진양봉원\",\n \"거림양봉원\",\n \"거창양봉원\",\n \"경남양봉산업\",\n \"경주양봉조합\",\n \"고령양봉조합\",\n \"광주축협\",\n \"광주퓨리나\",\n \"구강회\",\n \"국화양봉원(올레)\",\n \"권혁순\",\n \"그린제너텍\",\n \"금호양봉원\",\n \"금호양봉원(남평)\",\n \"기언농장\",\n \"김상옥\",\n \"김수연\",\n \"김학은\",\n \"꿀벌동물병원\",\n \"꿀벌랜드\",\n \"꿀사랑협동조합\",\n \"남한강농장\",\n \"농심양봉산업\",\n \"농업회사법인화광코리아\",\n \"농협\",\n \"농협경제지주\",\n \"뉴트리온\",\n \"다인종합유통\",\n \"대성물산\",\n \"대전양봉원\",\n \"대주산업\",\n \"대한사료\",\n \"대한양봉진흥농원\",\n \"도흥농장\",\n \"동아원\",\n \"동원양봉원\",\n \"동화미생물연구소\",\n \"리브랩스\",\n \"멍이네약용곤충농원\",\n \"메디바이오넷\",\n \"메인양봉원\",\n \"메지온\",\n \"명작소컨설팅\",\n \"모던팜스\",\n \"문이네농장\",\n \"미래부\",\n \"미래축산\",\n \"미소팜\",\n \"바비스팩토리\",\n \"바우와우코리아\",\n \"박창섭\",\n \"박충호\",\n \"배가네양봉원\",\n \"배영갑\",\n \"배홍열\",\n \"백제양봉원\",\n \"보성파니피카\",\n \"부부양봉원\",\n \"부활양봉산업\",\n \"비트올\",\n \"산내양봉원\",\n \"산수기연\",\n \"산에들양봉원\",\n \"상주양봉원\",\n \"상주중앙양봉원\",\n \"서울사료\",\n \"서울축협\",\n \"서주양봉원\",\n \"선비벌꿀영농조합\",\n \"선진\",\n \"설봉영농조합법인\",\n \"설종수\",\n \"세미휘드텍\",\n \"세인리소스\",\n \"소대섭\",\n \"소브산칼륨\",\n \"수협사료\",\n \"순천양봉원\",\n \"신동아양봉산업\",\n \"심정희\",\n \"써미텍\",\n \"씨티씨바이오\",\n \"씨티씨바이오애니멀헬스\",\n \"아이원푸드\",\n \"아크\",\n \"안동양봉원\",\n \"애니포크\",\n \"엄병준\",\n \"에스웜\",\n \"에이디고창\",\n \"에이비알\",\n \"에이치디씨\",\n \"에이치엔에프\",\n \"에이티면역\",\n \"에이티바이오\",\n \"엔에이씨코리아\",\n \"여주꿀벌농원\",\n \"영덕양봉원\",\n \"영암매력한우TMR\",\n \"영주벌꿀\",\n \"예천양봉원\",\n \"오네스토펫푸드\",\n \"오션\",\n \"올바른\",\n \"완주양봉원\",\n \"우리와\",\n \"우성사료\",\n \"우성양봉원\",\n \"원삼양봉원\",\n \"원익진\",\n \"원주농협자재센터\",\n \"원트라\",\n \"웨스포피드\",\n \"유니바이오\",\n \"유성바이오\",\n \"유태민\",\n \"윤석민\",\n \"윤시호\",\n \"이경택\",\n \"이교춘\",\n \"이길수\",\n \"이노바텍\",\n \"이범희\",\n \"이영우\",\n \"이용성\",\n \"이지팜스\",\n \"이진혁\",\n \"이한석\",\n \"이현우피드\",\n \"이화팜텍\",\n \"자립농장\",\n \"자연양봉산업\",\n \"장수양봉원\",\n \"장창순\",\n \"전두천\",\n \"정선철\",\n \"정씨네양봉원\",\n \"정안양봉원\",\n \"정의수\",\n \"제이앤비\",\n \"제일사료\",\n \"조득희\",\n \"조성화\",\n \"조창하\",\n \"지부장\",\n \"참존농장\",\n \"천안시보조사업\",\n \"천일양봉원\",\n \"천지단향양봉원\",\n \"천호갑\",\n \"청호물산\",\n \"충주양봉원\",\n \"칠곡양봉영농조합\",\n \"칠곡제일양봉원\",\n \"칼스엔비티\",\n \"코리아펫푸드\",\n \"태형농장\",\n \"토비이앤텍\",\n \"티에이치이\",\n \"팜스코\",\n \"팜스코전남동부특약점\",\n \"펫퍼스\",\n \"펫퍼스 바이오켐\",\n \"평화양봉원\",\n \"퓨로바이오\",\n \"퓨리나\",\n \"피앤씨코리아\",\n \"피앤피에드텍\",\n \"하나로미네\",\n \"하나양봉원\",\n \"하선사료\",\n \"하스프\",\n \"하이독\",\n \"한국스마트사료\",\n \"한국양봉농협\",\n \"한국양봉산업\",\n \"한국양봉자재마트\",\n \"한남양봉원\",\n \"한닭\",\n \"한상열\",\n \"한양사료\",\n \"한일사료\",\n \"한탑\",\n \"해남양봉원\",\n \"해밀펫푸드\",\n \"현철농장\",\n \"호승글로벌\",\n \"화진벌꿀\",\n \"횡성양봉원\",\n \"효광에이에프\",\n \"효성양봉원\",\n \"BayerYakuhin,Ltd.\",\n \"DH바이탈피드\",\n \"DS피드\",\n \"Elanco\",\n \"ELANCOJAPAN\",\n \"ENT\",\n \"JKINTERMATIONAL\",\n \"L&P\",\n \"OEM\",\n \"VAC\",\n \"로버트(코코넛분말)\",\n \"안종훈(코코넛분말)\",\n \"맥주효모(중국)\",\n \"맥주효모(베트남)\",\n \"이덕호(대두박)\",\n \"구정본(젤라틴)\",\n \"민경신(젤라틴)\",\n \"야신(젤라틴)\",\n \"휘황(젤라틴)\",\n \"젤라틴\",\n \"변성타피오카\",\n \"팽화미\",\n \"등외\",\n \"밀가루\",\n \"미역분말\",\n \"바나나분말\",\n \"트리미딩\",\n \"약재\",\n \"구연산\",\n \"솔빈산칼륨\",\n \"프로피온산칼슘\",\n \"소이코밀\",\n \"ISP\",\n \"멀티락\",\n \"씨센스프리미엄\",\n \"디텍\",\n \"케르세틴\",\n \"렌틸콩\",\n \"커피화분\",\n \"인도화분\",\n \"파쇄미\",\n \"MSM\",\n \"황산칼슘\",\n \"신한프리믹스\",\n \"기타\",\n \"벌통(유디)\",\n \"벌통\",\n \"소초광\",\n \"대두박\",\n \"에이엔씨인터내셔널\",\n \"국민은행\",\n \"기업은행\",\n \"양봉기자재\",\n \"단미사료협회\",\n \"당사장님\",\n \"유승진대표님\",\n \"도드람양돈서비스\",\n \"CLA\",\n \"소야그린텍\",\n \"엠보스\",\n \"신한은행\",\n \"엠포피드\",\n \"유경지대\",\n \"윤병수회계사무소\",\n \"충남도청\",\n \"필리핀(수출)\",\n \"수출\",\n \"자몽세이프\",\n]\n\nbuyers = sorted(buyers_beforeSort)", "sub_path": "issues/apps.py", "file_name": "apps.py", "file_ext": "py", "file_size_in_byte": 6009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.apps.AppConfig", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "2129319", "text": "from __future__ import division\n'''\n@author Adrian Oeftiger\n@date 26.05.2014\n@copyright CERN\n'''\n\n# @TODO\n# think about flexible design to separate numerical methods\n# and physical parameters (as before for the libintegrators.py)\n# while satisfying this design layout.\n# currently: only Euler Cromer supported in RFSystems\n\n\nimport numpy as np\n\nfrom abc import ABCMeta, abstractmethod\nfrom scipy.constants import c, e\n\nsin = np.sin\ncos = np.cos\n\nclass LongitudinalMap(object):\n \"\"\"\n A longitudinal map represents a longitudinal dynamical element\n (e.g. a kick or a drift...), i.e. an abstraction of a cavity\n of an RF system etc.\n LongitudinalMap objects can compose a longitudinal one turn map!\n Definitions of various orders of the slippage factor eta(delta)\n for delta = (p - p0) / p0 should be implemented in this class.\n Any derived objects will access self.eta(delta, gamma).\n\n Note: the momentum compaction factors are defined by the change of radius\n \\Delta R / R0 = \\sum_i \\\\alpha_i * \\delta^(i + 1)\n hence yielding expressions for the higher slippage factor orders\n \\Delta w / w0 = \\sum_j \\eta_j * \\delta^(i + 1)\n (for the revolution frequency w)\n \"\"\"\n __metaclass__ = ABCMeta\n\n def __init__(self, alpha_array):\n \"\"\"\n The length of the momentum compaction factor array /alpha_array/\n defines the order of the slippage factor expansion.\n \"\"\"\n self.alpha_array = alpha_array\n\n @abstractmethod\n def track(self, beam):\n pass\n\n def eta(self, delta, gamma):\n \"\"\"\n Depending on the number of entries in self.alpha_array the\n according order of \\eta = \\sum_i \\eta_i * \\delta^i where\n \\delta = \\Delta p / p0 will be included in this gathering function.\n\n Note: Please implement higher slippage factor orders as static methods\n with name _eta where is the order of delta in eta(delta)\n and with signature (alpha_array, beam).\n \"\"\"\n eta = 0\n for i in xrange( len(self.alpha_array) ): # order = len - 1\n eta_func = getattr(self, '_eta' + str(i))\n eta_i = eta_func(self.alpha_array, gamma)\n eta += eta_i * (delta ** i)\n return eta\n\n @staticmethod\n def _eta0(alpha_array, gamma):\n return alpha_array[0] - gamma ** -2\n\nclass Drift(LongitudinalMap):\n r\"\"\"\n The drift (i.e. Delta z) of the particle's z coordinate is given by\n the (separable) Hamiltonian derived by dp (defined by (p - p0) / p0).\n\n self.length is the drift length,\n self.shrinkage_p_increment being non-zero includes the shrinking\n ratio \\beta_{n+1} / \\beta_n (see MacLachlan 1989 in FN-529),\n it is usually neglected. [Otherwise it may continuously be\n adapted by the user according to the total momentum increment.]\n If it is not neglected, the beta factor ratio would yield\n (\\beta + \\Delta \\beta) / \\beta =\n = 1 - \\Delta \\gamma / (\\beta^2 * \\gamma^2)\n resp. = 1 - p_increment / (\\gamma^3 * p0)\n since p_increment = \\gamma * m * c / \\beta * \\Delta gamma .\n \"\"\"\n\n def __init__(self, alpha_array, length, shrinkage_p_increment=0):\n super(Drift, self).__init__(alpha_array)\n self.length = length\n self.shrinkage_p_increment = shrinkage_p_increment\n\n def track(self, beam):\n beta_ratio = 1 - self.shrinkage_p_increment / (beam.gamma**3 * beam.p0)\n beam.z = (beta_ratio * beam.z -\n self.eta(beam.dp, beam.gamma) * beam.dp * self.length)\n\nclass Kick(LongitudinalMap):\n \"\"\"\n The Kick class represents the kick by a single RF element\n in a ring! The kick (i.e. Delta dp) of the particle's dp\n coordinate is given by the (separable) Hamiltonian derived\n by z, i.e. the force.\n\n self.p_increment is the momentum step per turn of the\n synchronous particle, it can be continuously adjusted externally\n by the user to reflect different slopes in the dipole field ramp.\n\n self.phi_offset reflects an offset of the cavity's reference system,\n this can be tweaked externally by the user for simulating RF system\n ripple and the like. Include the pi offset for the right RF voltage\n gradient here.\n\n (self._phi_acceleration adds to the offset as well but should\n be used internally in the module (e.g. by RFSystems) for\n acceleration purposes. It may be used for synchronisation with the\n momentum updating by self.p_increment via self.calc_phi_0(beam),\n thus readjusting the zero-crossing of this sinosoidal kick.\n This requires a convention how to mutually displace the Kick\n phases to each other w.r.t. to their contribution to acceleration.)\n \"\"\"\n\n def __init__(self, alpha_array, circumference, harmonic, voltage,\n phi_offset=0, p_increment=0):\n super(Kick, self).__init__(alpha_array)\n self.circumference = circumference\n self.harmonic = harmonic\n self.voltage = voltage\n self.phi_offset = phi_offset\n self.p_increment = p_increment\n self._phi_acceleration = 0\n\n def track(self, beam):\n amplitude = e * self.voltage / (beam.beta * c)\n phi = self._phi(beam.z)\n\n delta_p = beam.dp * beam.p0\n delta_p += amplitude * (sin(phi) - sin(self.calc_phi_0(beam))) #- self.p_increment\n beam.p0 += self.p_increment\n beam.dp = delta_p / beam.p0\n\n def potential(self, z, beam, phi_0=None):\n \"\"\"The contribution of this kick to the overall potential V(z).\"\"\"\n amplitude = e * self.voltage / (beam.p0 * 2 * np.pi * self.harmonic)\n if phi_0 is None:\n phi_0 = self.calc_phi_0(beam)\n phi = self._phi(z)\n modulation = cos(phi) - cos(phi_0) + (phi - phi_0) * sin(phi_0)\n return amplitude * modulation\n\n def _phi(self, z):\n theta = (2 * np.pi / self.circumference) * z\n return self.harmonic * theta + self.phi_offset + self._phi_acceleration\n\n def Qs(self, beam):\n '''\n Synchrotron tune derived from the linearized Hamiltonian\n\n .. math::\n H = -1 / 2 * eta * beta * c * delta ** 2\n + e * V / (p0 * 2 * np.pi * h) *\n * (np.cos(phi) - np.cos(dphi) + (phi - dphi) * np.sin(dphi))\n NOTE: This function only returns the synchroton tune effectuated\n by this single Kick instance, any contribution from other Kick\n objects is not taken into account! (I.e. in general, this\n calculated value is wrong for multi-harmonic RF systems.)\n '''\n Qs = np.sqrt(e * self.voltage * np.abs(self.eta(0, beam.gamma)) *\n self.harmonic / (2 * np.pi * beam.p0 * beam.beta * c))\n return Qs\n\n def calc_phi_0(self, beam):\n \"\"\"The phase deviation from the unaccelerated case\n calculated via the momentum step self.p_increment\n per turn. It includes the jump in the e.o.m.\n (via sign(eta)) at transition energy:\n gamma < gamma_transition <==> phi_0 ~ pi\n gamma > gamma_transition <==> phi_0 ~ 0\n In the case of only one Kick element in the ring, this phase\n deviation coincides with the synchronous phase!\n \"\"\"\n if self.p_increment == 0 and self.voltage == 0:\n return 0\n deltaE = self.p_increment * c * beam.beta / beam.gamma\n phi_rel = np.arcsin(deltaE / (e * self.voltage))\n\n if self.eta(0, beam.gamma) < 0:\n # return np.sign(deltaE) * np.pi - phi_rel\n return np.pi - phi_rel\n else:\n return phi_rel\n\n # sgn_eta = np.sign(self.eta(0, beam.gamma))\n # return np.arccos(\n # sgn_eta * np.sqrt(1 - (deltaE / (e * self.voltage)) ** 2))\n\n\nclass LongitudinalOneTurnMap(LongitudinalMap):\n \"\"\"\n A longitudinal one turn map tracks over a complete turn.\n Any inheriting classes guarantee to provide a self.track(beam) method that\n tracks around the whole ring!\n\n LongitudinalOneTurnMap classes possibly comprise several\n LongitudinalMap objects.\n \"\"\"\n\n __metaclass__ = ABCMeta\n\n def __init__(self, alpha_array, circumference):\n \"\"\"LongitudinalOneTurnMap objects know their circumference:\n this is THE ONE place to store the circumference in the simulations!\"\"\"\n super(LongitudinalOneTurnMap, self).__init__(alpha_array)\n self.circumference = circumference\n\n @abstractmethod\n def track(self, beam):\n \"\"\"\n Contract: advances the longitudinal coordinates\n of the beam over a full turn / circumference.\n \"\"\"\n pass\n\nclass RFSystems(LongitudinalOneTurnMap):\n \"\"\"\n With one RFSystems object in the ring layout (with all Kick\n objects located at the same longitudinal position), the\n longitudinal separatrix function is exact and makes a valid\n local statement about stability!\n \"\"\"\n\n def __init__(self, circumference, harmonic_list, voltage_list,\n phi_offset_list, alpha_array, p_increment=0, shrinking=False):\n \"\"\"\n The first entry in harmonic_list, voltage_list and\n phi_offset_list defines the parameters for the one\n accelerating Kick object (i.e. the accelerating RF system).\n For several accelerating Kick objects one would have to\n extend this class and settle for the relative phases\n between the Kick objects! (For one accelerating Kick object,\n all the other Kick objects' zero crossings are displaced by\n the negative phase shift induced by the accelerating Kick.)\n\n The length of the momentum compaction factor array alpha_array\n defines the order of the slippage factor expansion.\n (See the LongitudinalMap class for further details.)\n\n RFSystems comprises a half the circumference drift,\n then all the kicks by the RF Systems in one location,\n then the remaining half the circumference drift.\n This Verlet algorithm (\"leap-frog\" featuring O(n_turn^2) as\n opposed to symplectic Euler-Cromer with O(n_turn)) makes\n sure that the longitudinal phase space is read out in\n a symmetric way (otherwise phase space should be tilted\n at the entrance or exit of the cavity / kick location!\n cf. discussions with Christian Carli).\n\n The boolean parameter shrinking determines whether the\n shrinkage ratio \\\\beta_{n+1} / \\\\beta_n should be taken\n into account during the second Drift.\n (See the Drift class for further details.)\n\n - self.p_increment is the momentum step per turn of the\n synchronous particle, it can be continuously adjusted to\n reflect different slopes in the dipole magnet strength ramp.\n (See the Kick class for further details.)\n - self.kicks is a list of the Kick objects (defined by the\n respective lists in the constructor)\n - self.accelerating_kick returns the first Kick object in\n self.kicks which carries the only p_increment != 0\n - self.elements is comprised of a half turn Drift, self.kicks,\n and another half turn Drift\n - self.fundamental_kick returns the Kick object with the lowest\n harmonic of the revolution frequency\n \"\"\"\n\n super(RFSystems, self).__init__(alpha_array, circumference)\n\n if not len(harmonic_list) == len(voltage_list) == len(phi_offset_list):\n print (\"Warning: parameter lists for RFSystems \" +\n \"do not have the same length!\")\n\n self._shrinking = shrinking\n self.kicks = []\n for h, V, dphi in zip(harmonic_list, voltage_list, phi_offset_list):\n kick = Kick(alpha_array, self.circumference, h, V, dphi)\n self.kicks.append(kick)\n self.elements = ( [Drift(alpha_array, self.circumference / 2)]\n + self.kicks\n + [Drift(alpha_array, self.circumference / 2)]\n )\n self.accelerating_kick = self.kicks[0]\n self.p_increment = p_increment\n self.fundamental_kick = min(self.kicks, key=lambda kick: kick.harmonic)\n\n def track(self, beam):\n if self.p_increment:\n betagamma_old = beam.betagamma\n for longMap in self.elements:\n longMap.track(beam)\n if self.p_increment:\n self._shrink_transverse_emittance(\n beam, np.sqrt(betagamma_old / beam.betagamma))\n\n @staticmethod\n def _shrink_transverse_emittance(beam, geo_emittance_factor):\n \"\"\"accounts for the transverse geometrical emittance shrinking\"\"\"\n beam.x *= geo_emittance_factor\n beam.xp *= geo_emittance_factor\n beam.y *= geo_emittance_factor\n beam.yp *= geo_emittance_factor\n\n @property\n def p_increment(self):\n return self.accelerating_kick.p_increment\n @p_increment.setter\n def p_increment(self, value):\n self.accelerating_kick.p_increment = value\n if self._shrinking:\n self.elements[-1].shrinkage_p_increment = value\n\n\n def potential(self, z, beam):\n \"\"\"\n The potential well of the RF system.\n \"\"\"\n phi_0 = self.accelerating_kick.calc_phi_0(beam)\n h1 = self.accelerating_kick.harmonic\n def fetch_potential(kick):\n phi_acc_individual = -kick.harmonic / h1 * phi_0\n if kick is not self.accelerating_kick:\n kick._phi_acceleration = phi_acc_individual\n return kick.potential(z, beam)\n potential_list = map(fetch_potential, self.kicks)\n return sum(potential_list)\n\n def hamiltonian(self, z, dp, beam):\n \"\"\"\n The full separable Hamiltonian of the RF system.\n Its zero value is located at the fundamental separatrix\n (between bound and unbound motion).\n \"\"\"\n kinetic = -0.5 * self.eta(dp, beam.gamma) * beam.beta * c * dp ** 2\n return kinetic + self.potential(z, beam)\n\n def separatrix(self, z, beam):\n \"\"\"\n Returns the separatrix delta_sep = (p - p0) / p0 for the\n synchronous particle (since eta depends on delta, inverting\n the separatrix equation 0 = H(z_sep, dp_sep)\n becomes inexplicit in general).\n \"\"\"\n return np.sqrt(2 / (beam.beta * c * self.eta(0, beam.gamma))\n * self.potential(z, beam))\n\n def is_in_separatrix(self, z, dp, beam):\n \"\"\"\n Returns boolean whether this coordinate is located\n strictly inside the separatrix.\n \"\"\"\n return hamiltonian(z, dp, beam) < 0\n\nclass LinearMap(LongitudinalOneTurnMap):\n '''\n Linear Map represented by a Courant-Snyder transportation matrix.\n self.alpha is the linear momentum compaction factor.\n '''\n\n def __init__(self, circumference, alpha, Qs):\n \"\"\"alpha is the linear momentum compaction factor,\n Qs the synchroton tune.\"\"\"\n self.circumference = circumference\n self.alpha = alpha\n self.Qs = Qs\n\n def track(self, beam):\n\n eta = self.alpha - beam.gamma ** -2\n\n omega_0 = 2 * np.pi * beam.beta * c / self.circumference\n omega_s = self.Qs * omega_0\n\n dQs = 2 * np.pi * self.Qs\n cosdQs = cos(dQs)\n sindQs = sin(dQs)\n\n z0 = beam.z\n dp0 = beam.dp\n\n beam.z = z0 * cosdQs - eta * c / omega_s * dp0 * sindQs\n beam.dp = dp0 * cosdQs + omega_s / eta / c * z0 * sindQs\n\n", "sub_path": "trackers/simple_long_tracker.py", "file_name": "simple_long_tracker.py", "file_ext": "py", "file_size_in_byte": 15481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.sin", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 21, "usage_type": "attribute"}, {"api_name": "abc.ABCMeta", "line_number": 39, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 48, "usage_type": "name"}, {"api_name": "scipy.constants.e", "line_number": 136, "usage_type": "name"}, {"api_name": "scipy.constants.c", "line_number": 136, "usage_type": "name"}, {"api_name": "scipy.constants.e", "line_number": 146, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 154, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 170, "usage_type": "call"}, {"api_name": "scipy.constants.e", "line_number": 170, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 171, "usage_type": "attribute"}, {"api_name": "scipy.constants.c", "line_number": 171, "usage_type": "name"}, {"api_name": "scipy.constants.c", "line_number": 186, "usage_type": "name"}, {"api_name": "numpy.arcsin", "line_number": 187, "usage_type": "call"}, {"api_name": "scipy.constants.e", "line_number": 187, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 191, "usage_type": "attribute"}, {"api_name": "abc.ABCMeta", "line_number": 210, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 218, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 305, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 345, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 355, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 355, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 382, "usage_type": "attribute"}, {"api_name": "scipy.constants.c", "line_number": 382, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 385, "usage_type": "attribute"}, {"api_name": "scipy.constants.c", "line_number": 392, "usage_type": "name"}, {"api_name": "scipy.constants.c", "line_number": 393, "usage_type": "name"}]} +{"seq_id": "467552728", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom .import views\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('',views.home,name='home'),\n path('about/',views.about,name='about'),\n path('add_stock/',views.add_stock,name='add_stock'),\n path('delete/',views.delete,name='delete'),\n path('delete_stock',views.delete_stock,name='delete_stock')\n\n]\n", "sub_path": "Testing/Demo1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"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"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "242465262", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom tornado import gen\n\nfrom base_app.classes.debug import Debug\nfrom base_app.models.mongodb.user.general_info.general_info import UserModel\nfrom user_app.handlers.base import BaseHandler, authentication\n\n__author__ = 'Morteza'\n\n\nclass ChangeLineHeightHandler(BaseHandler):\n @gen.coroutine\n @authentication()\n def post(self, *args):\n try:\n line_height = self.get_argument('line_height', 'low')\n UserModel(_id=self.current_user).update_line_height(line_height)\n self.update_full_current_user()\n self.status = True\n except:\n Debug.get_exception(sub_system='admin', severity='error', tags='search_news_sidebar')\n self.write(self.result)\n", "sub_path": "user_app/handlers/line_height.py", "file_name": "line_height.py", "file_ext": "py", "file_size_in_byte": 767, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "user_app.handlers.base.BaseHandler", "line_number": 12, "usage_type": "name"}, {"api_name": "base_app.models.mongodb.user.general_info.general_info.UserModel", "line_number": 18, "usage_type": "call"}, {"api_name": "base_app.classes.debug.Debug.get_exception", "line_number": 22, "usage_type": "call"}, {"api_name": "base_app.classes.debug.Debug", "line_number": 22, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 13, "usage_type": "name"}, {"api_name": "user_app.handlers.base.authentication", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "245005664", "text": "#Thanks to Ruizhu Xiong for providing the code.\n\nimport tweepy, xlsxwriter\nfrom datetime import datetime\n \n#####################################################################################################################\n#Here is your consumer keys and access tokens. You should use your own corresponding to your Tweeter Developer Account.\n#####################################################################################################################\nconsumer_key = \"XXXX\"\nconsumer_secret = \"XXXX\"\naccess_token = \"XXXX\"\naccess_token_secret = \"XXXX\"\n \nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth,wait_on_rate_limit=True,wait_on_rate_limit_notify=True)\n\n####################################################################################################################\n#Here you can configure the spreadsheet name and which sheet to use.\n####################################################################################################################\noutBook = xlsxwriter.Workbook(\"data_key_mybodymychoice_Jun29toJul1.xlsx\")\noutSheet = outBook.add_worksheet(\"Sheet1\")\n\ncount = 0 \noutSheet.write(0,0,'userName')\noutSheet.write(0,1,'screenName')\noutSheet.write(0,2,'userLocation')\noutSheet.write(0,3,'tweetText')\noutSheet.write(0,4,'tweetId')\noutSheet.write(0,5,'favorites')\noutSheet.write(0,6,'in_reply_to_user')\noutSheet.write(0,7,'creationTime')\noutSheet.write(0,8,'geo')\noutSheet.write(0,9,'source')\noutSheet.write(0,10,'retweetedTo')\noutSheet.write(0,11,'retweetCounts')\noutSheet.write(0,12,'user_descrip')\noutSheet.write(0,13,'followers') \noutSheet.write(0,16,'user_url')\noutSheet.write(0,17,'user_ctime')\n#outSheet.write(0,18,'hashtags')\n#outSheet.write(0,20,'')\n#outSheet.write(0,21,'')\n#outSheet.write(0,22,'')\n\nsearch_words = [\"#covid-19\", \"covid19\", \"coronavirus\",\"#Covid-19\",\"#covid19\"]\n\ntweet = tweepy.Cursor(api.search,\n q=\"my body my choice\", ####### This is the keyword used for searching the tweets.\n count=100,\n #geocode=\"29.424349,-98.491142,50km\", ####### This is tailored for San Antonio area.\n since='2020-06-29', ####### Date from.\n until='2020-07-01', ####### Date to.\n result_type='recent',\n include_entities=True,\n monitor_rate_limit=True, \n wait_on_rate_limit=True,\n sleep_on_rate_limits=True,\n tweet_mode='extended',\n lang=\"en\").items()\n\ntry: \n for status in tweet:\n \n tweets = tweet.next()\n \n count += 1 \n\n print(type(status), status)\n userName = status.user.name\n screenName = status.user.screen_name\n userLocation = status.user.location\n user_descrip = status.user.description\n followers = status.user.followers_count\n friends = status.user.friends_count\n created_at = status.created_at\n user_url = status.user.url\n user_ctime = status.user.created_at\n #hashtags = status.entities['hashtags']\n \n created_at_formatted = datetime.strftime(created_at,'%a %b %d %H:%M:%S %z %Y')\n user_ctime_formatted = datetime.strftime(user_ctime,'%a %b %d %H:%M:%S %z %Y')\n \n #tweetText = status.extended_tweet['entities']\n tweetText = status.full_text\n tweetId = status.id_str \n favorites = status.favorite_count \n in_reply_to_user = ''\n if type(status.in_reply_to_user_id_str) == str or type(status.in_reply_to_user_id_str) == type(None): \n in_reply_to_user = status.in_reply_to_user_id_str\n else:\n print(type(status.in_reply_to_user_id_str))\n retweetCounts = 0 \n retweetedTo = ''\n try:\n retweetCounts = status.retweet_count \n retweetedTo = status.retweeted_status.user.screen_name\n except Exception as e: \n retweetCounts = 0 \n retweetedTo = ''\n\n creationTime = created_at_formatted #status.created_at\n geo = status.coordinates\n source = status.source\n\n outSheet.write(count,0,userName)\n outSheet.write(count,1,screenName)\n outSheet.write(count,2,userLocation)\n outSheet.write(count,3,tweetText)\n outSheet.write(count,4,tweetId)\n outSheet.write(count,5,favorites)\n outSheet.write(count,6,in_reply_to_user)\n outSheet.write(count,7,creationTime)\n try:\n outSheet.write(count,8,geo)\n except:\n print('Exception writing geo ', geo)\n outSheet.write(count,9,source)\n outSheet.write(count,10,retweetedTo)\n outSheet.write(count,11,retweetCounts)\n outSheet.write(count,12,user_descrip)\n outSheet.write(count,13,followers)\n outSheet.write(count,14,friends)\n outSheet.write(count,15,created_at_formatted)\n outSheet.write(count,16,user_url)\n outSheet.write(count,17,user_ctime_formatted)\n# outSheet.write(count,18,hashtags)\n# outSheet.write(count,20,source_url)\n# outSheet.write(count,21,source_url)\n# outSheet.write(count,22,source_url)\nexcept Exception as e: \n print('Exception caught in Tweepy: ', e)\n\noutBook.close()\n", "sub_path": "import_tweets.py", "file_name": "import_tweets.py", "file_ext": "py", "file_size_in_byte": 5215, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 14, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 17, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 22, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "434115846", "text": "from datetime import date\n\nclass Index( object ):\n def __init__(self, fileName, name):\n self.fileName = fileName\n self.name = name\n self.loadIndex()\n\n def loadIndex( self ):\n f = open( self.fileName )\n line = f.readline()\n self.ano = int(line[ len(line) - line[::-1].find('/') : -1 ])\n self.index = []\n while 1:\n try:\n line = f.next()\n self.index.append( line.replace(',','.').strip('\\n') )\n except:\n break\n\n\nclass Index2( object ):\n def __init__(self, fileName, name):\n self.fileName = fileName\n self.name = name\n self.today = date.today().toordinal()\n self.loadIndex()\n\n def loadIndex( self ):\n f = open( self.fileName )\n self.index = []\n self.time = []\n while 1:\n try:\n line = f.next().split('\\t')\n data = line[0].split('/')\n day = date(int(data[2]), int(data[1]), int(data[0])).toordinal()\n self.time.append( day )\n self.index.append( float(line[1].strip('\\n').replace(',','.')) )\n except:\n break\n\ndef dateTicks(x, pos):\n 'The two args are the value and tick position'\n day = date.fromordinal(int(x))\n return day.year\n\n\n\n \nif __name__ == '__main__':\n from matplotlib.ticker import FuncFormatter\n import matplotlib.pyplot as plt\n \n selic = Index2( 'data/SELIC2.txt', 'SELIC' )\n ipca = Index2( 'data/IPCA2.txt', 'IPCA' )\n cdi = Index2( 'data/CDI2.txt', 'CDI' )\n dolar = Index2( 'data/DOLAR.txt', 'DOLAR' )\n\n indexes = [selic, ipca, cdi, dolar]\n\n formatter = FuncFormatter(dateTicks)\n fig, ax = plt.subplots()\n fig.subplots_adjust(left=0.2, bottom=None, right=None, top=None)\n ax.xaxis.set_major_formatter(formatter)\n\n for index in indexes:\n plt.plot(index.time, index.index, label=index.name)\n plt.grid(True)\n plt.legend(loc=0)\n plt.show()\n\n", "sub_path": "Index.py", "file_name": "Index.py", "file_ext": "py", "file_size_in_byte": 2013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "datetime.date.today", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 26, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.date.fromordinal", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FuncFormatter", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "185171213", "text": "import logging\nimport time\nfrom datetime import timedelta\n\nfrom humanize import precisedelta\nfrom selenium.common.exceptions import NoSuchElementException, ElementClickInterceptedException\n\nfrom data.db import db_session\nfrom data.models.product import Product\nfrom data.parsers.commonParser import CommonParser\nfrom data.exceptions import InvalidRegionError\n\n\nclass Dixy(CommonParser):\n \"\"\"Class that consists of products data of Dixy market in particular region and updates them\"\"\"\n\n url: str = 'https://dixy.ru/catalog/'\n interval: int = 3600\n\n def __init__(self, region: str = 'Санкт-Петербург'):\n super(Dixy, self).__init__(region)\n\n def select_region(self):\n error_msg = f'Failed to find {self.region} in region buttons [IN {self}]'\n\n self.driver.get(self.url)\n region_select_menu = self.driver.find_element_by_class_name('icon-arrow-down')\n if not region_select_menu:\n return logging.error(message=f'Failed to get region select menu [IN {self}]')\n region_select_menu.click()\n region_buttons = self.driver.find_elements_by_xpath('//div[@class=\"simplebar-content\"]/ul//li/a')\n if not region_buttons:\n logging.error(message=error_msg)\n raise InvalidRegionError(error_msg)\n try:\n region_button = list(filter(lambda reg: reg.text.lower() == self.region.lower(),\n region_buttons))[0]\n except IndexError:\n logging.error(error_msg)\n raise InvalidRegionError(error_msg)\n region_button.click()\n print('\\nSET REGION\\n')\n return True\n\n def scroll_to_bottom(self, tag: str, class_: str):\n elem = True\n been_toggled = False\n while elem:\n try:\n elem = self.driver.find_element_by_xpath(f'//{tag}[@class=\"{class_}\"]')\n except NoSuchElementException:\n elem = None\n if not elem and not been_toggled:\n return logging.warning(message='Failed to scroll to bottom', market=self)\n elif elem:\n been_toggled = True\n try:\n elem.click()\n except ElementClickInterceptedException:\n print('I AM STUCK')\n continue\n\n def update_data(self, init_call=False):\n print('\\nSTARTED UPDATING DATA\\n')\n if not init_call:\n self.driver.refresh()\n\n self.scroll_to_bottom('a', 'btn view-more')\n\n checked = []\n idx_to = len(self.products)\n\n session = db_session.create_session()\n\n products = self.driver.find_elements_by_class_name('dixyCatalogItem ')\n for product in products:\n try:\n pic_block = product.find_element_by_class_name('dixyModal__pic'\n ).find_element_by_tag_name('img')\n title = pic_block.get_attribute('alt').replace('\\xa0', ' ')\n try:\n price = int(product.find_element_by_xpath('.//p[@itemprop=\"price\"]'\n ).get_attribute('content'))\n except ValueError:\n continue\n checked = self.merge_product(title, price, checked)\n prod = Product(title=title, price=price, img=pic_block.get_attribute('src'))\n session.add(prod)\n session.commit()\n self.products.append(prod)\n session.merge(self)\n session.commit()\n except NoSuchElementException as e:\n logging.warning(msg=f'{e.msg} [IN {self}]')\n\n for i in range(idx_to):\n if i not in checked:\n session.delete(self.products.pop(i))\n\n session.commit()\n session.close()\n\n self.set_refresh_process()\n\n def __repr__(self):\n return f'Дикси. {self.region[0].upper() + self.region[1:]}'\n\n\nif __name__ == '__main__':\n start_time = time.time()\n dixy = Dixy('Ленинградская область')\n print('\\n'.join([', '.join([f'{key}: {val}' for key, val in [item for item in product.items()]])\n for product in dixy.get_data()]))\n seconds, microseconds = map(int, '{0:.2f}'.format(time.time() - start_time).split('.'))\n print(f'Time passed: {precisedelta(timedelta(seconds=seconds, microseconds=microseconds))}')\n", "sub_path": "data/parsers/dixy.py", "file_name": "dixy.py", "file_ext": "py", "file_size_in_byte": 4497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "data.parsers.commonParser.CommonParser", "line_number": 14, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 33, "usage_type": "call"}, {"api_name": "data.exceptions.InvalidRegionError", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 39, "usage_type": "call"}, {"api_name": "data.exceptions.InvalidRegionError", "line_number": 40, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 51, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 54, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.ElementClickInterceptedException", "line_number": 59, "usage_type": "name"}, {"api_name": "data.db.db_session.create_session", "line_number": 73, "usage_type": "call"}, {"api_name": "data.db.db_session", "line_number": 73, "usage_type": "name"}, {"api_name": "data.models.product.Product", "line_number": 87, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 93, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 94, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "time.time", "line_number": 114, "usage_type": "call"}, {"api_name": "humanize.precisedelta", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "478463634", "text": "# input\n# 4 5\n# 00110\n# 00011\n# 11111\n# 00000\n\n# sol(DFS)\n# N, M을 공백으로 구분하여 입력받기\nn, m = map(int, input().split())\n\n# 2차원 리스트의 맵 정보 입력받기\ngraph = []\nfor i in range(n):\n graph.append(list(map(int, input())))\n\n# DFS로 특정한 노드 방문한 뒤에 연결된 모든 노드들도 방문\ndef dfs(x, y):\n # 주어진 범위를 벗어나는 경우에는 즉시 종료\n if x <= -1 or x >= n or y <= -1 or y >= m:\n return False\n # 현재 노드를 아직 방문하지 않았다면\n if graph[x][y] == 0:\n # 해당 노드 방문 처리\n graph[x][y] = 1\n # 상, 하, 좌, 우의 위치도 모두 재귀적으로 호출\n dfs(x - 1, y)\n dfs(x, y - 1)\n dfs(x + 1, y)\n dfs(x, y + 1)\n return True # 최종적인 결과는 True. 앞의 dfs문은 방문 여부를 업데이트 하기 위함\n return False\n\n# 모든 노드(위치)에 대하여 음료수 채우기\nresult = 0\nfor i in range(n):\n for j in range(m):\n # 현재 위치에서 DFS 수행\n if dfs(i, j) == True:\n result += 1\n\nprint(result) # 정답 출력\n\n\n\n# sol(BFS)\n# Reference) https://daphne-dev.github.io/2020/10/05/coding-test-003/\nfrom collections import deque\nn, m = map(int, input().split())\n\ngraph = []\nfor i in range(n):\n graph.append(list(map(int, input())))\n\ndx = [1, -1, 0, 0]\ndy = [0, 0, 1, -1]\n\ndef bfs(x, y):\n if graph[x][y] == 1:\n return False\n queue = deque()\n queue.append((x, y))\n\n while queue:\n x, y = queue.popleft()\n graph[x][y] = 1\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if 0 <= nx < n and 0 <= ny < m and not graph[nx][ny]:\n queue.append((nx, ny))\n return True\n\nresult = 0\nfor i in range(n):\n for j in range(m):\n if bfs(i, j):\n result += 1\n\nprint(result)", "sub_path": "이것이 취업을 위한 코딩테스트다 with 파이썬/DFS_BFS/음료수 얼려 먹기.py", "file_name": "음료수 얼려 먹기.py", "file_ext": "py", "file_size_in_byte": 1920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.deque", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "24994351", "text": "from django.db import models\n\n\nclass Choice(models.Model):\n previous = models.ForeignKey('self', null=True, on_delete=models.CASCADE, \n related_name='paths', verbose_name='poprzednia decyzja')\n title = models.CharField(null=False, max_length=100, verbose_name='tytuł')\n is_root = models.BooleanField(default=False)\n\n #horizontal position\n index = models.IntegerField(null=False, verbose_name='indeks')\n\n class Meta:\n verbose_name = 'decyzja'\n ordering = ['index']\n \n\nclass Step(models.Model):\n index = models.IntegerField(null=False, verbose_name='indeks')\n choice = models.ForeignKey(Choice, on_delete=models.CASCADE, null=False, \n related_name='steps', verbose_name='decyzja')\n title = models.CharField(null=False, max_length=100, verbose_name='tytuł')\n description = models.TextField(null=True, verbose_name='opis')\n attachment = models.CharField(null=True, max_length=200, verbose_name='załącznik')\n\n class Meta:\n verbose_name = 'krok'\n ordering = ['index']\n\n\nclass StepAttachment(models.Model):\n step = models.OneToOneField(Step, on_delete=models.CASCADE, null=False, verbose_name='krok')\n image = models.ImageField(null=False, verbose_name='zdjęcie', upload_to='steps')\n\n @property\n def image_url(self):\n return self.image.url if self.image else None\n\n class Meta:\n verbose_name = 'załącznik do kroku'", "sub_path": "src/steps/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.models.Model", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 4, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 5, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 5, "usage_type": "attribute"}, {"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.BooleanField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.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.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.models.ImageField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "537515113", "text": "import tensorflow as tf\nimport numpy as np\nimport core as cr\nfrom buffer import replay_buffer\nfrom ou_noise import OU_noise\nfrom tensorboardX import SummaryWriter\n\nclass DDPG:\n def __init__(self):\n self.sess = tf.Session()\n self.memory = replay_buffer(max_length=1e5)\n self.tau = 0.995\n self.gamma = 0.99\n self.state_size = 33\n self.output_size = 4\n self.action_limit = 1.0\n self.hidden = [400, 300]\n self.batch_size = 100\n self.pi_lr = 1e-4\n self.q_lr = 1e-4\n self.noise = OU_noise(self.output_size, 1)\n\n self.x_ph, self.a_ph, self.x2_ph, self.r_ph, self.d_ph = \\\n cr.placeholders(self.state_size, self.output_size, self.state_size, None, None)\n\n with tf.variable_scope('main'):\n self.pi, self.q, self.q_pi = cr.mlp_actor_critic(self.x_ph,\n self.a_ph, self.hidden, activation=tf.nn.relu, output_activation=tf.tanh,\n output_size=self.output_size, action_limit=self.action_limit)\n\n with tf.variable_scope('target'):\n self.pi_targ, _, self.q_pi_targ = cr.mlp_actor_critic(self.x2_ph,\\\n self.a_ph, self.hidden, activation=tf.nn.relu, output_activation=tf.tanh,\n output_size=self.output_size, action_limit=self.action_limit)\n\n self.target = tf.stop_gradient(self.r_ph + self.gamma * (1 - self.d_ph) * self.q_pi_targ)\n self.pi_loss = -tf.reduce_mean(self.q_pi)\n self.v_loss = tf.reduce_mean((self.q - self.target) ** 2) * 0.5\n self.pi_optimizer = tf.train.AdamOptimizer(self.pi_lr)\n self.v_optimizer = tf.train.AdamOptimizer(self.q_lr)\n self.pi_train = self.pi_optimizer.minimize(self.pi_loss, var_list=cr.get_vars('main/pi'))\n self.v_train = self.v_optimizer.minimize(self.v_loss, var_list=cr.get_vars('main/q'))\n\n self.target_update = tf.group([tf.assign(v_targ, self.tau * v_targ + (1 - self.tau) * v_main)\n for v_main, v_targ in zip(cr.get_vars('main'), cr.get_vars('target'))])\n\n self.target_init = tf.group([tf.assign(v_targ, v_main)\n for v_main, v_targ in zip(cr.get_vars('main'), cr.get_vars('target'))])\n\n self.sess.run(tf.global_variables_initializer())\n\n self.sess.run(self.target_init)\n\n def update(self):\n data = self.memory.get_sample(sample_size=self.batch_size)\n feed_dict = {\n self.x_ph : data['state'],\n self.x2_ph : data['next_state'],\n self.a_ph : data['action'],\n self.r_ph : data['reward'],\n self.d_ph : data['done']\n }\n\n q_loss, _ = self.sess.run([self.v_loss, self.v_train], feed_dict=feed_dict)\n pi_loss, _, _ = self.sess.run([self.pi_loss, self.pi_train, self.target_update], feed_dict=feed_dict)\n\n return q_loss, pi_loss\n\n def get_action(self, state, epsilon):\n a = self.sess.run(self.pi, feed_dict={self.x_ph: [state]})\n a += epsilon * self.noise.sample()\n return np.clip(a, -self.action_limit, self.action_limit)[0]\n\n def test(self):\n env = gym.make('Pendulum-v0')\n while True:\n state = env.reset()\n done = False\n while not done:\n env.render()\n action = self.get_action(state, 0)\n next_state, _, done,_ = env.step(action)\n state = next_state\n\n def run(self):\n from mlagents.envs import UnityEnvironment\n\n writer = SummaryWriter('runs/ddpg')\n num_worker = 20\n state_size = 33\n output_size = 4\n epsilon = 1.0\n ep = 0\n train_size = 5\n\n env = UnityEnvironment(file_name='env/training', worker_id=0)\n default_brain = env.brain_names[0]\n brain = env.brains[default_brain]\n initial_observation = env.reset()\n\n step = 0\n score = 0\n\n while True:\n ep += 1\n env_info = env.reset()\n states = np.zeros([num_worker, state_size])\n terminal = False\n self.noise.reset()\n if epsilon > 0.001:\n epsilon = -ep * 0.005 + 1.0\n while not terminal:\n step += 1\n\n actions = [self.get_action(s, epsilon) for s in states]\n env_info = env.step(actions)[default_brain]\n\n next_states = env_info.vector_observations\n rewards = env_info.rewards\n dones = env_info.local_done\n\n terminal = dones[0]\n\n for s, ns, r, d, a in zip(states, next_states, rewards, dones, actions):\n self.memory.append(s, ns, r, d, a)\n\n score += sum(rewards)\n\n states = next_states\n\n if step % train_size == 0:\n self.update()\n\n if ep < 1000:\n print('episode :' ,ep, '| score : ', score, '| epsilon :', epsilon)\n writer.add_scalar('data/reward', score, ep)\n writer.add_scalar('data/epsilon', epsilon, ep)\n writer.add_scalar('data/memory_size', len(self.memory.memory), ep)\n score = 0\n \nif __name__ == '__main__':\n agent = DDPG()\n agent.run()", "sub_path": "ddpg.py", "file_name": "ddpg.py", "file_ext": "py", "file_size_in_byte": 5290, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tensorflow.Session", "line_number": 10, "usage_type": "call"}, {"api_name": "buffer.replay_buffer", "line_number": 11, "usage_type": "call"}, {"api_name": "ou_noise.OU_noise", "line_number": 21, "usage_type": "call"}, {"api_name": "core.placeholders", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 26, "usage_type": "call"}, {"api_name": "core.mlp_actor_critic", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.tanh", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 31, "usage_type": "call"}, {"api_name": "core.mlp_actor_critic", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.tanh", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.stop_gradient", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 40, "usage_type": "attribute"}, {"api_name": "core.get_vars", "line_number": 41, "usage_type": "call"}, {"api_name": "core.get_vars", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 44, "usage_type": "call"}, {"api_name": "core.get_vars", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 47, "usage_type": "call"}, {"api_name": "core.get_vars", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 88, "usage_type": "call"}, {"api_name": "mlagents.envs.UnityEnvironment", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "{'UnityEnvironment': 'mlagents.envs.UnityEnvironment'}", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "71182920", "text": "#!/usr/local/bin/python3\n#\n# linter.py\n# cs1570-grader\n#\n# Created by Illya Starikov on 06/04/2017\n# Copyright 2017. Illya Starikov. All rights reserved.\n#\n\nimport re\nimport sys\nimport csv\n\nfrom itertools import islice\n\nextensions = [\"hpp\", \"cpp\", \"h\"]\n\n# These sections specify what the rules are and how the work\n# Every rule is specified as an enum, then stored in a dictionary\n# The enum is the key, and a (regular expression, description) is the value\n# A regular expression is used to specify how to match against the enum,\n# a description is used to print out what the violation is\n\n# Note if the regex is $a, this is a character after the end of the line\n# If you think that's impossible, you're correct. This is used to specify\n# a rule that's too complicated to be checked line by line (further in the script)\n# and has it's own dedicated function.\n\n\ndef enum(*sequential, **named):\n enums = dict(zip(sequential, range(len(sequential))), **named)\n return type('Enum', (), enums)\n\n\n# If a new rule appears, simply add to the enum and the regex to the rules section\nRuleTypes = enum('HEADER', 'DOCUMENTATION', 'HEADER_GAURDS_MATCHING', 'HEADER_GAURDS_NAMING', 'SWITCH_DEFAULT', 'FUNCTIONS', 'COLUMN', 'BRACES', 'TABS', \"CONSTANTS\")\n\n\nrules = {\n RuleTypes.HEADER: (\"$a\", \"Missing Header\"),\n RuleTypes.DOCUMENTATION: (\"$a\", \"Missing Documentation\"),\n RuleTypes.FUNCTIONS: (\"$a\", \"Return Statements < Functions\"),\n RuleTypes.HEADER_GAURDS_MATCHING: (\"$a\", \"Header Gaurds Don't Match\"),\n RuleTypes.HEADER_GAURDS_NAMING: (\"$a\", \"Header Gaurds Are Incorrect Format\"),\n RuleTypes.SWITCH_DEFAULT: (\"$a\", \"No Default in Switch Case\"),\n RuleTypes.COLUMN: (\".{80}\\S\", \"80 Column Rule\"),\n RuleTypes.BRACES: (\"[^\\s].*([^\\s*=\\s*]{|}[^\\s*while.*])\", \"Brace Not On Newline\"),\n RuleTypes.TABS: (\"\\A\\t\", \"Tabs\"),\n RuleTypes.CONSTANTS: (\"const\\s+([a-zA-Z]|_)([a-zA-Z]|[0-9]|_)*\\s+(([a-zA-Z]|_)([a-zA-Z]|[0-9]|_)*|\\s*,\\s*)*([a-zA-Z]|_)([A-Z]|[0-9]|_)*[a-z]+([A-Z]|[0-9]|_)*(\\s*=\\s*.+)*;\", \"Non-Uppercase Constants\")\n}\n\n\n# Args: a list of the form (rule enum, line number)\n# Prints all violations in markdown form. Why markdown?\n# Easily exportable to other formats.\ndef printOutViolations(filename, violations):\n violations.sort()\n\n keys = [i[0] for i in violations]\n violationsPrinted = {x: (0, False) for x in list(rules.keys())}\n\n print(\"## {filename}\".format(filename=basename(filename)))\n\n for rule, line in violations:\n if keys.count(rule) > 10 and not violationsPrinted[rule][1]:\n print(\"\\n**{violation}** *({count} Violations, Omitting After Five)*\\n\".format(count=keys.count(rule), violation=rules[rule][1]))\n elif not violationsPrinted[rule][1]:\n print(\"\\n**{violation}**\\n\".format(violation=rules[rule][1]))\n\n if violationsPrinted[rule][0] < 5:\n print('- Line {line}: `{violatingLine}`\\n'.format(line=line, violatingLine=stripExcessSpace(getLine(filename, line))))\n\n violationsPrinted[rule] = (violationsPrinted[rule][0] + 1, True)\n\n\ndef exportToCSV(filename, violations):\n violations.sort()\n\n keys = [i[0] for i in violations]\n keys = removeDuplicates(keys)\n\n toExport = [filename]\n toExport += [rules[i][1] for i in keys]\n\n csvFile = open(\"violations.csv\", 'w')\n wr = csv.writer(csvFile)\n wr.writerow(toExport)\n\n\n# Checks against all the regexes in the regex rules\n# Args: Line (string) that is to be checked, and line number (int)\n# Returns a list of the form (rule enum, line number)\n# This list method is the standard for all violations\ndef checkAgainstRules(line, number):\n violations = []\n\n for rule, (regex, description) in rules.items():\n pattern = re.compile(regex)\n\n if pattern.search(line):\n violations.append((rule, number + 1))\n\n return violations\n\n\n# Arg: a string to specify the filename\n# Return an array to be added to the list of the\ndef checkHeaderComments(filename):\n authorFound = False\n filenameFound = False\n\n with open(filename) as fh:\n # We assume the header to be in the top 10 lines of the file\n for line in list(islice(fh, 8)):\n # The regex basically looks for something beginning with //,*, or a spaces\n\n if re.search(\"(\\/\\/|\\*|\\s)+.*(File|file|.hpp|.cpp|.h)\", line):\n filenameFound = True\n if re.search(\"(Author|author)\", line):\n authorFound = True\n\n fh.close()\n if authorFound or filenameFound:\n return []\n else:\n return [(RuleTypes.HEADER, 0)]\n\n\n# Arg: a string to specify the filename\n# Will go through and attempt to count the number of lines in the file\n# Uses heuristic to see if there are roughly enough comments to satisfy\n# a decent description of the files\ndef checkForDocumentation(filename):\n totalLinesOfComments = 0\n\n entireFile = getEntireFile(filename)\n # This covers the /* ... */ comments\n allCommentPattern = re.compile('\\/\\*[\\S\\s]*\\*\\/')\n comments = allCommentPattern.findall(entireFile)\n\n if comments != []:\n for comment in comments:\n if comment.count('\\n') == 0:\n totalLinesOfComments += 1\n else:\n # Just a simple check to see if there's comments preceding */ or after /*\n if re.search(\"\\/\\*\\S+\", comment):\n totalLinesOfComments += 1\n if re.search(\"\\S+\\*\\/\", comment):\n totalLinesOfComments += 1\n\n # We subtract one empty lines after * (this also matches the /* \\n)\n totalLinesOfComments += comment.count('\\n') - len(re.findall('\\*\\s*\\n', comment))\n\n # This covers // comments\n allSingleLineComments = re.compile('\\/\\/.+')\n totalLinesOfComments += len(allSingleLineComments.findall(entireFile))\n\n # A good hueristic for documentation is 3 lines of code for every function\n if 3 * (numberOfFunctions(filename))[1] > totalLinesOfComments:\n newRule = \"Missing Documentation ({functionCount} Functions, {commentCount} Lines of Comments)\".format(functionCount=numberOfFunctions(filename)[1], commentCount=totalLinesOfComments)\n rules[RuleTypes.DOCUMENTATION] = (\"$a\", newRule)\n return [(RuleTypes.DOCUMENTATION, 0)]\n else:\n return []\n\n\n# Arg: a string to specify the filename\n# Verifies every function has a return statement\ndef verifyReturnStatements(filename):\n if re.search(\".*.cpp\", filename):\n fh = open(filename)\n totalNumberOfReturnStatements = 0\n\n for line in fh:\n if re.search(\"\\s*return\\s*;\\s*\", line):\n totalNumberOfReturnStatements += 1\n\n fh.close()\n\n if totalNumberOfReturnStatements < numberOfFunctions(filename):\n return [(RuleTypes.FUNCTIONS, 0)]\n\n return []\n\n\n# Args: a string to specify the filename\n# Verifies that every switch has a default case\n# as specified in the styleguide\ndef checkForDefaultInSwitch(filename):\n entireFile = getEntireFile(filename)\n violations = []\n\n pattern = re.compile('switch\\s*\\(.*\\)\\s*\\{[^\\{;]+\\}')\n switchCases = pattern.findall(entireFile)\n lineNumbers = [m.start(0) for m in pattern.finditer(entireFile)]\n\n for switch, line in zip(switchCases, lineNumbers):\n if not re.search('\\s*default:\\s+', switch):\n # the line of Nth character is used because the iterator returns what character\n # number is in the file. There should be a more effecient way, but Meh\n violations.append((RuleTypes.SWITCH_DEFAULT, lineOfNthCharacter(filename, line)))\n\n return violations\n\n\n# Args: a string to specify the filename\n# Verifies that header gaurds both:\n# 1) are named the same\n# 2) have the format of FILENAME_EXTENSION\ndef checkHeaderGaurds(filename):\n entireFile = getEntireFile(filename)\n violations = []\n\n # If not a header, disregard\n if re.search(\".*.(h|hpp)\", filename):\n # Match against the header gaurds, assuming there are only one\n pattern = re.compile('#ifndef\\s*(.*)\\n#define\\s*(.*)')\n headerGaurds = pattern.search(entireFile)\n\n if headerGaurds:\n ifNotDefine = headerGaurds.group(1)\n define = headerGaurds.group(2)\n\n # If not defined the same\n if ifNotDefine != define:\n violations.append((RuleTypes.HEADER_GAURDS_MATCHING, findFirstOccurenceInFile(filename, ifNotDefine)))\n\n # If not in the format FILENAME_EXTENSION\n if stripExcessSpace(define) not in stripExcessSpace(filename.replace(\".\", \"_\").upper()):\n violations.append((RuleTypes.HEADER_GAURDS_NAMING, findFirstOccurenceInFile(filename, define)))\n\n return violations\n\n\n# Args: someTuple is, well, a tuple\n# Returns te last element in the said tuple\ndef lastElement(someTuple):\n return someTuple[len(someTuple) - 1]\n\n\n# Args: a string to specify the filename\n# Returns what line the Nth character in the string is\n# This is literally going to hurt python coders\n# but I have no idea of a better way to do it\ndef lineOfNthCharacter(filename, characterCount):\n entireFile = getEntireFile(filename)\n\n if len(entireFile) < characterCount:\n return None\n\n count = 1\n\n for index, character in enumerate(entireFile):\n if character == '\\n':\n count += 1\n if index + 1 >= characterCount:\n return count\n\n\n# Args: string for filename, string token\n# Searches in the file line by line and returns\n# The first occurence of said token is returned\ndef findFirstOccurenceInFile(filename, token):\n fh = open(filename)\n\n for index, line in enumerate(fh):\n if token in line:\n fh.close()\n return index + 1\n\n fh.close()\n return None\n\n\n# Arg: a string to specify the filename\n# Counts and returns an integer specifying how many function there are in said file\ndef numberOfFunctions(filename):\n entireFile = getEntireFile(filename)\n\n pattern = re.compile(\n '(([a-zA-Z]|_)([a-zA-Z]|[0-9]|_)*)\\s+(([a-zA-Z]|_)([a-zA-Z]|[0-9]|_)*\\s*::\\s*)?([a-zA-Z]|_)([a-zA-Z]|[0-9]|_)*\\(([a-zA-Z]|[0-9]|_|\\[.*|\\]|\\&|\\s|,)*\\)\\s*(.)'\n )\n allFunctions = pattern.findall(entireFile)\n\n definitions = list(filter(lambda x: lastElement(x) == '{', allFunctions))\n prototypes = list(filter(lambda x: lastElement(x) == ';', allFunctions))\n\n return (len(definitions), len(prototypes))\n\n\n# Args: a string filename and the line for which you want returned\n# Navigates to the line, and returns the string up to and including \\n\ndef getLine(filename, lineNumber):\n fh = open(filename)\n\n for index, line in enumerate(fh):\n if index + 1 == lineNumber:\n fh.close()\n return line\n\n fh.close()\n return \"\"\n\n\n# Args: A string\n# Will strip all special space characters (\\n, \\t, \\r) from beginning and end\ndef stripExcessSpace(string):\n removedBeginningSpace = re.sub('^\\s*', '', string)\n removedBeginningAndEndingSpace = re.sub('\\s*$', '', removedBeginningSpace)\n return removedBeginningAndEndingSpace\n\n\n# http://stackoverflow.com/questions/480214/how-do-you-remove-duplicates-from-a-list-in-whilst-preserving-order\ndef removeDuplicates(seq):\n seen = set()\n seen_add = seen.add\n return [x for x in seq if not (x in seen or seen_add(x))]\n\n\n# Args: a string to specify the filename\n# gets the entirety of a the file, and returns said file as a string\ndef getEntireFile(filename):\n fh = open(filename)\n fileAsString = \"\"\n\n for line in fh:\n fileAsString += line\n\n fh.close()\n return fileAsString\n\n\ndef filesToGrade():\n files = []\n\n for argument in sys.argv:\n if re.search(\".+\\.(.+)\", argument):\n pattern = re.compile(\".+\\.(.+)\")\n extension = pattern.search(argument).group(1)\n\n if extension in extensions:\n files.append(argument)\n\n return files\n\n\ndef basename(filename):\n pattern = re.compile(\"(.*\\/)*(.+.)\")\n return pattern.search(filename).group(2)\n\n\ndef main():\n nonLineByLineRules = [checkHeaderComments, checkForDocumentation, checkHeaderGaurds, checkForDefaultInSwitch]\n allViolations = []\n\n for fileToGrade in filesToGrade():\n fh = open(fileToGrade)\n\n violations = []\n\n for function in nonLineByLineRules:\n additionalViolations = function(fileToGrade)\n\n if additionalViolations != []:\n violations += additionalViolations\n\n for index, line in enumerate(fh):\n additionalViolations = checkAgainstRules(line, index)\n\n if additionalViolations != []:\n violations += additionalViolations\n\n allViolations += violations\n\n if violations != []:\n printOutViolations(fileToGrade, violations)\n\n if \"--csv\" in sys.argv:\n exportToCSV(sys.argv[1], allViolations)\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "stylechecker.py", "file_name": "stylechecker.py", "file_ext": "py", "file_size_in_byte": 12884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "csv.writer", "line_number": 86, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 98, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 114, "usage_type": "call"}, {"api_name": "re.search", "line_number": 117, "usage_type": "call"}, {"api_name": "re.search", "line_number": 119, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 138, "usage_type": "call"}, {"api_name": "re.search", "line_number": 147, "usage_type": "call"}, {"api_name": "re.search", "line_number": 149, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 153, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 156, "usage_type": "call"}, {"api_name": "re.search", "line_number": 171, "usage_type": "call"}, {"api_name": "re.search", "line_number": 176, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 194, "usage_type": "call"}, {"api_name": "re.search", "line_number": 199, "usage_type": "call"}, {"api_name": "re.search", "line_number": 216, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 218, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 281, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 309, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 310, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 337, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 338, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 339, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 349, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 379, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 380, "usage_type": "attribute"}]} +{"seq_id": "273524845", "text": "import logging\nimport os\nimport platform\n\nimport mxnet as mx\nimport mxnet.gluon as gluon\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom core import FocalLoss, HuberLoss\nfrom core import TargetGenerator, Prediction\nfrom core import Voc_2007_AP\nfrom core import box_resize\nfrom core import plot_bbox\nfrom core import testdataloader\n\nlogfilepath = \"\" # 따로 지정하지 않으면 terminal에 뜸\nif os.path.isfile(logfilepath):\n os.remove(logfilepath)\nlogging.basicConfig(filename=logfilepath, level=logging.INFO)\n\n\ndef run(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225],\n load_name=\"512_512_ADAM_PRES_18\", load_period=10, GPU_COUNT=0,\n test_weight_path=\"weights\",\n test_dataset_path=\"Dataset/test\",\n test_save_path=\"result\",\n test_graph_path=\"test_Graph\",\n foreground_iou_thresh=0.5,\n background_iou_thresh=0.4,\n num_workers=4,\n show_flag=True,\n save_flag=True,\n decode_number=5000,\n multiperclass=True,\n nms_thresh=0.5,\n nms_topk=500,\n except_class_thresh=0.05,\n plot_class_thresh=0.5):\n if GPU_COUNT <= 0:\n ctx = mx.cpu(0)\n elif GPU_COUNT > 0:\n ctx = mx.gpu(0)\n\n # 운영체제 확인\n if platform.system() == \"Linux\":\n logging.info(f\"{platform.system()} OS\")\n elif platform.system() == \"Windows\":\n logging.info(f\"{platform.system()} OS\")\n else:\n logging.info(f\"{platform.system()} OS\")\n\n if GPU_COUNT > 0:\n free_memory, total_memory = mx.context.gpu_memory_info(0)\n free_memory = round(free_memory / (1024 * 1024 * 1024), 2)\n total_memory = round(total_memory / (1024 * 1024 * 1024), 2)\n logging.info(f'Running on {ctx} / free memory : {free_memory}GB / total memory {total_memory}GB')\n else:\n logging.info(f'Running on {ctx}')\n\n logging.info(f\"test {load_name}\")\n netheight = int(load_name.split(\"_\")[0])\n netwidth = int(load_name.split(\"_\")[1])\n if not isinstance(netheight, int) and not isinstance(netwidth, int):\n logging.info(\"height is not int\")\n logging.info(\"width is not int\")\n raise ValueError\n else:\n logging.info(f\"network input size : {(netheight, netwidth)}\")\n\n try:\n test_dataloader, test_dataset = testdataloader(path=test_dataset_path,\n input_size=(netheight, netwidth),\n num_workers=num_workers,\n mean=mean, std=std)\n except Exception:\n logging.info(\"The dataset does not exist\")\n exit(0)\n\n weight_path = os.path.join(test_weight_path, load_name)\n sym = os.path.join(weight_path, f'{load_name}-symbol.json')\n params = os.path.join(weight_path, f'{load_name}-{load_period:04d}.params')\n\n test_update_number_per_epoch = len(test_dataloader)\n if test_update_number_per_epoch < 1:\n logging.warning(\" test batch size가 데이터 수보다 큼 \")\n exit(0)\n\n num_classes = test_dataset.num_class # 클래스 수\n name_classes = test_dataset.classes\n\n logging.info(\"symbol model test\")\n try:\n net = gluon.SymbolBlock.imports(sym,\n ['data'],\n params, ctx=ctx)\n except Exception:\n # DEBUG, INFO, WARNING, ERROR, CRITICAL 의 5가지 등급\n logging.info(\"loading symbol weights 실패\")\n exit(0)\n else:\n logging.info(\"loading symbol weights 성공\")\n\n net.hybridize(active=True, static_alloc=True, static_shape=True)\n\n confidence_loss = FocalLoss(alpha=0.25, # 논문에서 가장 좋다고 한 숫자\n gamma=2, # 논문에서 가장 좋다고 한 숫자\n sparse_label=True,\n from_sigmoid=False,\n batch_axis=None,\n num_class=num_classes,\n reduction=\"sum\",\n exclude=False)\n\n localization_loss = HuberLoss(rho=1,\n batch_axis=None,\n reduction=\"sum\",\n exclude=False)\n\n targetgenerator = TargetGenerator(foreground_iou_thresh=foreground_iou_thresh,\n background_iou_thresh=background_iou_thresh)\n\n prediction = Prediction(\n from_sigmoid=False,\n num_classes=num_classes,\n decode_number=decode_number,\n nms_thresh=nms_thresh,\n nms_topk=nms_topk,\n except_class_thresh=except_class_thresh,\n multiperclass=multiperclass)\n\n precision_recall = Voc_2007_AP(iou_thresh=0.5, class_names=name_classes)\n\n ground_truth_colors = {}\n for i in range(num_classes):\n ground_truth_colors[i] = (0, 0, 1)\n\n conf_loss_sum = 0\n loc_loss_sum = 0\n\n for image, label, name, origin_image, origin_box in tqdm(test_dataloader):\n _, height, width, _ = origin_image.shape\n logging.info(f\"real input size : {(height, width)}\")\n origin_image = origin_image.asnumpy()[0]\n origin_box = origin_box.asnumpy()[0]\n\n image = image.as_in_context(ctx)\n label = label.as_in_context(ctx)\n gt_boxes = label[:, :, :4]\n gt_ids = label[:, :, 4:5]\n\n cls_preds, box_preds, anchors = net(image)\n ids, scores, bboxes = prediction(cls_preds, box_preds, anchors)\n\n precision_recall.update(pred_bboxes=bboxes,\n pred_labels=ids,\n pred_scores=scores,\n gt_boxes=gt_boxes,\n gt_labels=gt_ids)\n\n bbox = box_resize(bboxes[0], (netwidth, netheight), (width, height))\n ground_truth = plot_bbox(origin_image, origin_box[:, :4], scores=None, labels=origin_box[:, 4:5], thresh=None,\n reverse_rgb=True,\n class_names=test_dataset.classes, absolute_coordinates=True,\n colors=ground_truth_colors)\n plot_bbox(ground_truth, bbox, scores=scores[0], labels=ids[0], thresh=plot_class_thresh,\n reverse_rgb=False,\n class_names=test_dataset.classes, absolute_coordinates=True,\n image_show=show_flag, image_save=save_flag, image_save_path=test_save_path, image_name=name[0])\n\n cls_targets, box_targets = targetgenerator(anchors, gt_boxes, gt_ids)\n except_ignore_samples = cls_targets > -1\n positive_samples = cls_targets > 0\n positive_numbers = positive_samples.sum()\n\n conf_loss = confidence_loss(cls_preds, cls_targets, except_ignore_samples.expand_dims(axis=-1))\n conf_loss = mx.nd.divide(conf_loss, positive_numbers + 1)\n conf_loss_sum += conf_loss.asscalar()\n\n loc_loss = localization_loss(box_preds, box_targets, positive_samples.expand_dims(axis=-1))\n loc_loss_sum += loc_loss.asscalar()\n\n # epoch 당 평균 loss\n test_conf_loss_mean = np.divide(conf_loss_sum, test_update_number_per_epoch)\n test_loc_loss_mean = np.divide(loc_loss_sum, test_update_number_per_epoch)\n test_total_loss_mean = test_conf_loss_mean + test_loc_loss_mean\n\n logging.info(\n f\"test confidence loss : {test_conf_loss_mean} / test localization loss : {test_loc_loss_mean} / test total loss : {test_total_loss_mean}\")\n\n AP_appender = []\n round_position = 2\n class_name, precision, recall, true_positive, false_positive, threshold = precision_recall.get_PR_list()\n for j, c, p, r in zip(range(len(recall)), class_name, precision, recall):\n name, AP = precision_recall.get_AP(c, p, r)\n logging.info(f\"class {j}'s {name} AP : {round(AP * 100, round_position)}%\")\n AP_appender.append(AP)\n mAP_result = np.mean(AP_appender)\n\n logging.info(f\"mAP : {round(mAP_result * 100, round_position)}%\")\n precision_recall.get_PR_curve(name=class_name,\n precision=precision,\n recall=recall,\n threshold=threshold,\n AP=AP_appender, mAP=mAP_result, folder_name=test_graph_path)\n\n\nif __name__ == \"__main__\":\n run(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225],\n load_name=\"512_512_ADAM_PRES_18\", load_period=100, GPU_COUNT=0,\n test_weight_path=\"weights\",\n test_dataset_path=\"Dataset/test\",\n test_save_path=\"result\",\n test_graph_path=\"test_Graph\",\n foreground_iou_thresh=0.5,\n background_iou_thresh=0.4,\n num_workers=4,\n show_flag=True,\n save_flag=True,\n decode_number=5000,\n multiperclass=True,\n nms_thresh=0.5,\n nms_topk=500,\n except_class_thresh=0.05,\n plot_class_thresh=0.5)\n", "sub_path": "RETINA/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 8982, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.isfile", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "mxnet.cpu", "line_number": 42, "usage_type": "call"}, {"api_name": "mxnet.gpu", "line_number": 44, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 48, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 52, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 52, "usage_type": "call"}, {"api_name": "mxnet.context.gpu_memory_info", "line_number": 55, "usage_type": "call"}, {"api_name": "mxnet.context", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 60, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 67, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 70, "usage_type": "call"}, {"api_name": "core.testdataloader", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 93, "usage_type": "call"}, {"api_name": "mxnet.gluon.SymbolBlock.imports", "line_number": 95, "usage_type": "call"}, {"api_name": "mxnet.gluon.SymbolBlock", "line_number": 95, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 95, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 103, "usage_type": "call"}, {"api_name": "core.FocalLoss", "line_number": 107, "usage_type": "call"}, {"api_name": "core.HuberLoss", "line_number": 116, "usage_type": "call"}, {"api_name": "core.TargetGenerator", "line_number": 121, "usage_type": "call"}, {"api_name": "core.Prediction", "line_number": 124, "usage_type": "call"}, {"api_name": "core.Voc_2007_AP", "line_number": 133, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 142, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 144, "usage_type": "call"}, {"api_name": "core.box_resize", "line_number": 162, "usage_type": "call"}, {"api_name": "core.plot_bbox", "line_number": 163, "usage_type": "call"}, {"api_name": "core.plot_bbox", "line_number": 167, "usage_type": "call"}, {"api_name": "mxnet.nd.divide", "line_number": 178, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 186, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 189, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 199, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "387425953", "text": "#!/usr/bin/python\n\"\"\"\nAtta main\n\"\"\"\nimport platform\nimport sys\nimport os\n\nfrom atta.tools.internal.Misc import AttaClassOrModule\nfrom atta.tools.Misc import isstring\nfrom atta import Atta, LogLevel, Properties, OS, Dict\n\ndef _ParseArgv(argv):\n import argparse\n\n argsParser = argparse.ArgumentParser(\n prog = Atta.name,\n formatter_class = argparse.RawDescriptionHelpFormatter,\n description = Atta.name\n + ' v' + Atta.version\n + os.linesep\n + Atta.description,\n epilog = '''\nMore options are available in the file ''' + Dict.attaPropsFileName + '''\nwhich is searched in the following locations and in such order:\n\n the Atta directory,\n the user's home directory,\n the directory of the project.\n\nOptions read later override previously set.\nOptions from the command line overrides all others.\n\nBugs, comments and suggestions please report on page\\nhttps://github.com/boguslawski-piotr/Atta/issues\n'''\n )\n\n buildGroup = argsParser.add_argument_group('build')\n buildGroup.add_argument(\n Dict.target, nargs = '*', default = '',\n help = ''\n )\n buildGroup.add_argument(\n '-f', nargs = 1, default = [Dict.defaultBuildFileName], metavar = 'file',\n help = 'use given buildfile'\n )\n\n paramGroup = argsParser.add_argument_group('parameters')\n paramGroup.add_argument(\n '-D', action = 'append', metavar = 'name=value',\n help = \"insert the 'value' to the list of environment variables (accessible through Project.env) under the name 'name' always converted to uppercase.\"\n )\n\n logGroup = argsParser.add_argument_group('logging')\n logGroup.add_argument(\n '-d', action = 'store_true',\n help = 'set debug mode (shortcut for -ll 0)')\n logGroup.add_argument(\n '-v', action = 'store_true',\n help = 'set verbose mode (shortcut for -ll 1)')\n logGroup.add_argument(\n '-q', action = 'store_true',\n help = 'be quiet (shortcut for -ll 3)')\n logGroup.add_argument(\n '-scs', action = 'store_true',\n help = 'show call stack (traceback) on error')\n\n args, argv = argsParser.parse_known_args(argv)\n if argv:\n print((Atta.name + ': error: unrecognized arguments: %s\\n' % ' '.join(argv)))\n argsParser.print_help()\n return None\n\n return args\n\ndef _PutArgsIntoProps(args, props):\n if args.D:\n for D in args.D:\n try:\n D = D.split('=')\n props['D' + D[0]] = '1' if len(D) <= 1 else D[1]\n except Exception:\n continue\n\n if args.scs: props['scs'] = args.scs\n if args.d: props['d'] = args.d\n if args.v: props['v'] = args.v\n if args.q: props['q'] = args.q\n\ndef _Dump():\n Atta.Log('*** Atta', level = LogLevel.DEBUG)\n Atta.Log('Platform.platform = ' + platform.platform(), level = LogLevel.DEBUG)\n Atta.Log('Platform.system = ' + platform.system(), level = LogLevel.DEBUG)\n Atta.Log('Python.version = ' + platform.python_version(), level = LogLevel.DEBUG)\n Atta.Log('Atta.version = ' + Atta.version, level = LogLevel.DEBUG)\n Atta.Log('Atta.version = ' + str(Atta.version), level = LogLevel.DEBUG)\n Atta.Log('Atta.dirName = ' + Atta.dirName, level = LogLevel.DEBUG)\n Atta.Log('***', level = LogLevel.DEBUG)\n\ndef Main():\n # Setup environment\n minPythonVersion = '2.7.0'\n if int(platform.python_version().replace('.', '')) < int(minPythonVersion.replace('.', '')):\n print(('Wrong version of Python. Requires {0}+ and {1} were detected.'.format(minPythonVersion, platform.python_version())))\n return 1\n\n Atta.dirName = os.path.dirname(os.path.realpath(sys.argv[0]))\n argv = sys.argv[1:]\n environ = os.environ\n\n # Load settings (part 1).\n\n try:\n # Load the settings from the Atta directory (global settings)\n # and the user's home directory (user settings).\n # User settings override the global settings.\n gprops = None\n uprops = None\n try: gprops = Properties.Open(os.path.join(Atta.dirName, Dict.attaPropsFileName), True)\n except Exception: pass\n try: uprops = Properties.Open(os.path.join(os.path.expanduser('~'), Dict.attaPropsFileName), True)\n except Exception: pass\n props = gprops.ItemsAsDict()\n if uprops:\n props.update(uprops.ItemsAsDict())\n Atta._SetProps(props)\n except Exception:\n pass\n\n # Parse command line arguments.\n\n args = _ParseArgv(argv)\n if args is None:\n return 1\n\n buildFileName = args.f[0]\n\n # Load settings (part 2).\n\n try:\n # Load the settings from build directory (project settings).\n # Project settings override the global and user settings.\n buildDirName = os.path.dirname(os.path.realpath(buildFileName))\n if buildDirName != Atta.dirName:\n pprops = Properties.Open(os.path.join(buildDirName, Dict.attaPropsFileName), True)\n Atta.Props().update(pprops.ItemsAsDict())\n except Exception:\n pass\n\n # Command line settings override the global, user and project settings.\n\n props = Atta.Props()\n _PutArgsIntoProps(args, props)\n\n # Handle settings.\n\n def Bool(v):\n if isstring(v):\n v = v.lower()\n return v == '1' or v == Dict.true or v == Dict.yes\n return bool(v)\n\n # -Dname=value definitions\n for N, V in list(props.items()):\n if N.startswith('D'):\n environ[N[1:]] = '1' if len(V) <= 0 else V\n\n Atta.Logger().SetLevel(props.get('ll', LogLevel.Default()))\n\n if Bool(props.get('d')):\n Atta.Logger().SetLevel(LogLevel.DEBUG)\n if Bool(props.get('v')):\n Atta.Logger().SetLevel(LogLevel.VERBOSE)\n if Bool(props.get('q')):\n Atta.Logger().SetLevel(LogLevel.WARNING)\n\n lc = props.get('lc')\n if lc:\n lc = AttaClassOrModule(lc)\n __import__(OS.Path.RemoveExt(lc))\n Atta.Logger().SetImpl(lc)\n\n llc = OS.Path.AsList(props.get('llc'), ',')\n for c in llc:\n c = AttaClassOrModule(c)\n __import__(OS.Path.RemoveExt(c))\n Atta.Logger().RegisterListener(c)\n\n javac = props.get('javac')\n if javac:\n from atta.tasks.Javac import Javac\n Javac.SetDefaultCompilerImpl(javac)\n javarc = props.get('javarc')\n if javarc:\n from atta.tasks.Javac import Javac\n Javac.SetDefaultRequiresCompileImpl(javarc)\n\n _Dump()\n Atta.Log(\"args = {0}\".format(args), level = LogLevel.DEBUG)\n Atta.Log('***', level = LogLevel.DEBUG)\n\n # Run project\n\n try:\n from atta.Project import Project\n Project()._Run(environ, buildFileName, args.target)\n return 0\n\n except Exception as E:\n if props.get('scs') or Atta.LogLevel() <= LogLevel.VERBOSE:\n import traceback\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lines = traceback.extract_tb(exc_traceback)\n lines = lines[-5:] # only last five\n for line in lines:\n print(('%s: %d' % (line[0], line[1])))\n print((' %s' % line[3]))\n print('')\n for line in traceback.format_exception_only(exc_type, exc_value):\n print(line)\n else:\n Atta.Log(E, level = LogLevel.ERROR)\n return 1\n\nif __name__ == \"__main__\":\n sys.exit(Main())\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "atta.Atta.name", "line_number": 17, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 17, "usage_type": "name"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 18, "usage_type": "attribute"}, {"api_name": "atta.Atta.name", "line_number": 19, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 19, "usage_type": "name"}, {"api_name": "atta.Atta.version", "line_number": 20, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 20, "usage_type": "name"}, {"api_name": "os.linesep", "line_number": 21, "usage_type": "attribute"}, {"api_name": "atta.Atta.description", "line_number": 22, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 22, "usage_type": "name"}, {"api_name": "atta.Dict.attaPropsFileName", "line_number": 24, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 24, "usage_type": "name"}, {"api_name": "atta.Dict.target", "line_number": 40, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 40, "usage_type": "name"}, {"api_name": "atta.Dict.defaultBuildFileName", "line_number": 44, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 44, "usage_type": "name"}, {"api_name": "atta.Atta.name", "line_number": 70, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 70, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 91, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 91, "usage_type": "name"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 91, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 91, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 92, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 92, "usage_type": "name"}, {"api_name": "platform.platform", "line_number": 92, "usage_type": "call"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 92, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 92, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 93, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 93, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 93, "usage_type": "call"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 93, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 93, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 94, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 94, "usage_type": "name"}, {"api_name": "platform.python_version", "line_number": 94, "usage_type": "call"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 94, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 94, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 95, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 95, "usage_type": "name"}, {"api_name": "atta.Atta.version", "line_number": 95, "usage_type": "attribute"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 95, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 95, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 96, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 96, "usage_type": "name"}, {"api_name": "atta.Atta.version", "line_number": 96, "usage_type": "attribute"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 96, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 96, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 97, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 97, "usage_type": "name"}, {"api_name": "atta.Atta.dirName", "line_number": 97, "usage_type": "attribute"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 97, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 97, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 98, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 98, "usage_type": "name"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 98, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 98, "usage_type": "name"}, {"api_name": "platform.python_version", "line_number": 103, "usage_type": "call"}, {"api_name": "platform.python_version", "line_number": 104, "usage_type": "call"}, {"api_name": "atta.Atta.dirName", "line_number": 107, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 107, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 107, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 109, "usage_type": "attribute"}, {"api_name": "atta.Properties.Open", "line_number": 119, "usage_type": "call"}, {"api_name": "atta.Properties", "line_number": 119, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "atta.Atta.dirName", "line_number": 119, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 119, "usage_type": "name"}, {"api_name": "atta.Dict.attaPropsFileName", "line_number": 119, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 119, "usage_type": "name"}, {"api_name": "atta.Properties.Open", "line_number": 121, "usage_type": "call"}, {"api_name": "atta.Properties", "line_number": 121, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 121, "usage_type": "call"}, {"api_name": "atta.Dict.attaPropsFileName", "line_number": 121, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 121, "usage_type": "name"}, {"api_name": "atta.Atta._SetProps", "line_number": 126, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 126, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 143, "usage_type": "call"}, {"api_name": "atta.Atta.dirName", "line_number": 144, "usage_type": "attribute"}, {"api_name": "atta.Atta", "line_number": 144, "usage_type": "name"}, {"api_name": "atta.Properties.Open", "line_number": 145, "usage_type": "call"}, {"api_name": "atta.Properties", "line_number": 145, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "atta.Dict.attaPropsFileName", "line_number": 145, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 145, "usage_type": "name"}, {"api_name": "atta.Atta.Props", "line_number": 146, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 146, "usage_type": "name"}, {"api_name": "atta.Atta.Props", "line_number": 152, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 152, "usage_type": "name"}, {"api_name": "atta.tools.Misc.isstring", "line_number": 158, "usage_type": "call"}, {"api_name": "atta.Dict.true", "line_number": 160, "usage_type": "attribute"}, {"api_name": "atta.Dict", "line_number": 160, "usage_type": "name"}, {"api_name": "atta.Dict.yes", "line_number": 160, "usage_type": "attribute"}, {"api_name": "atta.Atta.Logger", "line_number": 168, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 168, "usage_type": "name"}, {"api_name": "atta.LogLevel.Default", "line_number": 168, "usage_type": "call"}, {"api_name": "atta.LogLevel", "line_number": 168, "usage_type": "name"}, {"api_name": "atta.Atta.Logger", "line_number": 171, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 171, "usage_type": "name"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 171, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 171, "usage_type": "name"}, {"api_name": "atta.Atta.Logger", "line_number": 173, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 173, "usage_type": "name"}, {"api_name": "atta.LogLevel.VERBOSE", "line_number": 173, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 173, "usage_type": "name"}, {"api_name": "atta.Atta.Logger", "line_number": 175, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 175, "usage_type": "name"}, {"api_name": "atta.LogLevel.WARNING", "line_number": 175, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 175, "usage_type": "name"}, {"api_name": "atta.tools.internal.Misc.AttaClassOrModule", "line_number": 179, "usage_type": "call"}, {"api_name": "atta.OS.Path.RemoveExt", "line_number": 180, "usage_type": "call"}, {"api_name": "atta.OS.Path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "atta.OS", "line_number": 180, "usage_type": "name"}, {"api_name": "atta.Atta.Logger", "line_number": 181, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 181, "usage_type": "name"}, {"api_name": "atta.OS.Path.AsList", "line_number": 183, "usage_type": "call"}, {"api_name": "atta.OS.Path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "atta.OS", "line_number": 183, "usage_type": "name"}, {"api_name": "atta.tools.internal.Misc.AttaClassOrModule", "line_number": 185, "usage_type": "call"}, {"api_name": "atta.OS.Path.RemoveExt", "line_number": 186, "usage_type": "call"}, {"api_name": "atta.OS.Path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "atta.OS", "line_number": 186, "usage_type": "name"}, {"api_name": "atta.Atta.Logger", "line_number": 187, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 187, "usage_type": "name"}, {"api_name": "atta.tasks.Javac.Javac.SetDefaultCompilerImpl", "line_number": 192, "usage_type": "call"}, {"api_name": "atta.tasks.Javac.Javac", "line_number": 192, "usage_type": "name"}, {"api_name": "atta.tasks.Javac.Javac.SetDefaultRequiresCompileImpl", "line_number": 196, "usage_type": "call"}, {"api_name": "atta.tasks.Javac.Javac", "line_number": 196, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 199, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 199, "usage_type": "name"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 199, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 199, "usage_type": "name"}, {"api_name": "atta.Atta.Log", "line_number": 200, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 200, "usage_type": "name"}, {"api_name": "atta.LogLevel.DEBUG", "line_number": 200, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 200, "usage_type": "name"}, {"api_name": "atta.Project.Project", "line_number": 206, "usage_type": "call"}, {"api_name": "atta.Atta.LogLevel", "line_number": 210, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 210, "usage_type": "name"}, {"api_name": "atta.LogLevel.VERBOSE", "line_number": 210, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 210, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 212, "usage_type": "call"}, {"api_name": "traceback.extract_tb", "line_number": 213, "usage_type": "call"}, {"api_name": "traceback.format_exception_only", "line_number": 219, "usage_type": "call"}, {"api_name": "atta.Atta.Log", "line_number": 222, "usage_type": "call"}, {"api_name": "atta.Atta", "line_number": 222, "usage_type": "name"}, {"api_name": "atta.LogLevel.ERROR", "line_number": 222, "usage_type": "attribute"}, {"api_name": "atta.LogLevel", "line_number": 222, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 226, "usage_type": "call"}]} +{"seq_id": "623094010", "text": "# Copyright 2017 Neural Networks and Deep Learning lab, MIPT\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 itertools\nimport re\nfrom logging import getLogger\nfrom typing import Tuple, List, Optional, Union, Dict, Any\nfrom collections import namedtuple\n\nimport nltk\n\nfrom deeppavlov.core.common.registry import register\nfrom deeppavlov.core.models.component import Component\nfrom deeppavlov.core.models.serializable import Serializable\nfrom deeppavlov.core.common.file import read_json\nfrom deeppavlov.models.kbqa.template_matcher import TemplateMatcher\nfrom deeppavlov.models.kbqa.entity_linking import EntityLinker\nfrom deeppavlov.models.kbqa.wiki_parser import WikiParser\nfrom deeppavlov.models.kbqa.rel_ranking_infer import RelRankerInfer\nfrom deeppavlov.models.kbqa.rel_ranking_bert_infer import RelRankerBertInfer\nfrom deeppavlov.models.kbqa.utils import \\\n extract_year, extract_number, order_of_answers_sorting, make_combs, fill_query\n\nlog = getLogger(__name__)\n\n\n@register('query_generator')\nclass QueryGenerator(Component, Serializable):\n \"\"\"\n This class takes as input entity substrings, defines the template of the query and\n fills the slots of the template with candidate entities and relations.\n \"\"\"\n\n def __init__(self, template_matcher: TemplateMatcher,\n linker_entities: EntityLinker,\n linker_types: EntityLinker,\n wiki_parser: WikiParser,\n rel_ranker: Union[RelRankerInfer, RelRankerBertInfer],\n load_path: str,\n rank_rels_filename_1: str,\n rank_rels_filename_2: str,\n sparql_queries_filename: str,\n entities_to_leave: int = 5,\n rels_to_leave: int = 7,\n return_answers: bool = False, **kwargs) -> None:\n \"\"\"\n\n Args:\n template_matcher: component deeppavlov.models.kbqa.template_matcher\n linker_entities: component deeppavlov.models.kbqa.entity_linking for linking of entities\n linker_types: component deeppavlov.models.kbqa.entity_linking for linking of types\n wiki_parser: component deeppavlov.models.kbqa.wiki_parser\n rel_ranker: component deeppavlov.models.kbqa.rel_ranking_infer\n load_path: path to folder with wikidata files\n rank_rels_filename_1: file with list of rels for first rels in questions with ranking \n rank_rels_filename_2: file with list of rels for second rels in questions with ranking\n sparql_queries_filename: file with sparql query templates\n entities_to_leave: how many entities to leave after entity linking\n rels_to_leave: how many relations to leave after relation ranking\n sparql_queries_filename: file with a dict of sparql queries\n return_answers: whether to return answers or candidate answers\n **kwargs:\n \"\"\"\n super().__init__(save_path=None, load_path=load_path)\n self.template_matcher = template_matcher\n self.linker_entities = linker_entities\n self.linker_types = linker_types\n self.wiki_parser = wiki_parser\n self.rel_ranker = rel_ranker\n self.rank_rels_filename_1 = rank_rels_filename_1\n self.rank_rels_filename_2 = rank_rels_filename_2\n self.rank_list_0 = []\n self.rank_list_1 = []\n self.entities_to_leave = entities_to_leave\n self.rels_to_leave = rels_to_leave\n self.sparql_queries_filename = sparql_queries_filename\n self.return_answers = return_answers\n\n self.load()\n\n def load(self) -> None:\n with open(self.load_path / self.rank_rels_filename_1, 'r') as fl1:\n lines = fl1.readlines()\n self.rank_list_0 = [line.split('\\t')[0] for line in lines]\n\n with open(self.load_path / self.rank_rels_filename_2, 'r') as fl2:\n lines = fl2.readlines()\n self.rank_list_1 = [line.split('\\t')[0] for line in lines]\n\n self.template_queries = read_json(self.load_path / self.sparql_queries_filename)\n\n def save(self) -> None:\n pass\n\n def __call__(self, question_batch: List[str],\n template_type_batch: List[str],\n entities_from_ner_batch: List[List[str]],\n types_from_ner_batch: List[List[str]]) -> List[Union[List[Tuple[str, Any]], List[str]]]:\n\n candidate_outputs_batch = []\n for question, template_type, entities_from_ner, types_from_ner in \\\n zip(question_batch, template_type_batch, entities_from_ner_batch, types_from_ner_batch):\n\n candidate_outputs = []\n self.template_num = template_type\n\n replace_tokens = [(' - ', '-'), (' .', ''), ('{', ''), ('}', ''), (' ', ' '), ('\"', \"'\"), ('(', ''),\n (')', ''), ('–', '-')]\n for old, new in replace_tokens:\n question = question.replace(old, new)\n\n entities_from_template, types_from_template, rels_from_template, rel_dirs_from_template, \\\n query_type_template = self.template_matcher(question)\n self.template_num = query_type_template\n\n log.debug(f\"question: {question}\\n\")\n log.debug(f\"template_type {self.template_num}\")\n\n if entities_from_template or types_from_template:\n entity_ids = self.get_entity_ids(entities_from_template, \"entities\")\n type_ids = self.get_entity_ids(types_from_template, \"types\")\n log.debug(f\"entities_from_template {entities_from_template}\")\n log.debug(f\"types_from_template {types_from_template}\")\n log.debug(f\"rels_from_template {rels_from_template}\")\n log.debug(f\"entity_ids {entity_ids}\")\n log.debug(f\"type_ids {type_ids}\")\n\n candidate_outputs = self.find_candidate_answers(question, entity_ids, type_ids, rels_from_template,\n rel_dirs_from_template)\n\n if not candidate_outputs and entities_from_ner:\n log.debug(f\"(__call__)entities_from_ner: {entities_from_ner}\")\n log.debug(f\"(__call__)types_from_ner: {types_from_ner}\")\n entity_ids = self.get_entity_ids(entities_from_ner, \"entities\")\n type_ids = self.get_entity_ids(types_from_ner, \"types\")\n log.debug(f\"(__call__)entity_ids: {entity_ids}\")\n log.debug(f\"(__call__)type_ids: {type_ids}\")\n self.template_num = template_type[0]\n log.debug(f\"(__call__)self.template_num: {self.template_num}\")\n candidate_outputs = self.find_candidate_answers(question, entity_ids[:2], type_ids)\n candidate_outputs_batch.append(candidate_outputs)\n if self.return_answers:\n answers = self.rel_ranker(question_batch, candidate_outputs_batch)\n log.debug(f\"(__call__)answers: {answers}\")\n return answers\n else:\n log.debug(f\"(__call__)candidate_outputs_batch: {[output[:5] for output in candidate_outputs_batch]}\")\n return candidate_outputs_batch\n\n def get_entity_ids(self, entities: List[str], what_to_link: str) -> List[List[str]]:\n entity_ids = []\n for entity in entities:\n entity_id = []\n if what_to_link == \"entities\":\n entity_id, confidences = self.linker_entities(entity)\n if what_to_link == \"types\":\n entity_id, confidences = self.linker_types(entity)\n entity_ids.append(entity_id[:15])\n return entity_ids\n\n def find_candidate_answers(self, question: str,\n entity_ids: List[List[str]],\n type_ids: List[List[str]],\n rels_from_template: Optional[List[Tuple[str]]] = None,\n rel_dirs_from_template: Optional[List[str]] = None) -> List[Tuple[str]]:\n candidate_outputs = []\n log.debug(f\"(find_candidate_answers)self.template_num: {self.template_num}\")\n templates = [template for num, template in self.template_queries.items() if\n template[\"template_num\"] == self.template_num]\n templates = [template for template in templates if (template[\"exact_entity_type_match\"] and\n template[\"entities_and_types_num\"] == [len(entity_ids),\n len(type_ids)])\n or not template[\"exact_entity_type_match\"]]\n if not templates:\n return candidate_outputs\n if rels_from_template is not None:\n query_template = {}\n for template in templates:\n if template[\"rel_dirs\"] == rel_dirs_from_template:\n query_template = template\n if query_template:\n candidate_outputs = self.query_parser(question, query_template, entity_ids, type_ids,\n rels_from_template)\n else:\n for template in templates:\n candidate_outputs = self.query_parser(question, template, entity_ids, type_ids, rels_from_template)\n if candidate_outputs:\n return candidate_outputs\n\n if not candidate_outputs:\n log.debug(f\"(find_candidate_answers)templates: {templates}\")\n alternative_templates = templates[0][\"alternative_templates\"]\n for template in alternative_templates:\n candidate_outputs = self.query_parser(question, template, entity_ids, type_ids, rels_from_template)\n return candidate_outputs\n\n log.debug(\"candidate_rels_and_answers:\\n\" + '\\n'.join([str(output) for output in candidate_outputs[:5]]))\n\n return candidate_outputs\n\n def query_parser(self, question: str, query_info: Dict[str, str],\n entity_ids: List[List[str]], type_ids: List[List[str]],\n rels_from_template: Optional[List[Tuple[str]]] = None) -> List[Tuple[str]]:\n candidate_outputs = []\n question_tokens = nltk.word_tokenize(question)\n query = query_info[\"query_template\"].lower().replace(\"wdt:p31\", \"wdt:P31\")\n rels_for_search = query_info[\"rank_rels\"]\n query_seq_num = query_info[\"query_sequence\"]\n return_if_found = query_info[\"return_if_found\"]\n log.debug(f\"(query_parser)quer: {query}, {rels_for_search}, {query_seq_num}, {return_if_found}\")\n query_triplets = re.findall(\"{[ ]?(.*?)[ ]?}\", query)[0].split(' . ')\n log.debug(f\"(query_parser)query_triplets: {query_triplets}\")\n query_triplets = [triplet.split(' ')[:3] for triplet in query_triplets]\n query_sequence_dict = {num: triplet for num, triplet in zip(query_seq_num, query_triplets)}\n query_sequence = []\n for i in range(1, max(query_seq_num) + 1):\n query_sequence.append(query_sequence_dict[i])\n log.debug(f\"(query_parser)query_sequence: {query_sequence}\")\n triplet_info_list = [(\"forw\" if triplet[2].startswith('?') else \"backw\", search_source)\n for search_source, triplet in zip(rels_for_search, query_triplets) if\n search_source != \"do_not_rank\"]\n log.debug(f\"(query_parser)rel_directions: {triplet_info_list}\")\n entity_ids = [entity[:self.entities_to_leave] for entity in entity_ids]\n entity_combs = make_combs(entity_ids, permut=True)\n log.debug(f\"(query_parser)entity_combs: {entity_combs[:3]}\")\n type_combs = make_combs(type_ids, permut=False)\n log.debug(f\"(query_parser)type_combs: {type_combs[:3]}\")\n if rels_from_template is not None:\n rels = rels_from_template\n else:\n rels = [self.find_top_rels(question, entity_ids, triplet_info)\n for triplet_info in triplet_info_list]\n log.debug(f\"(query_parser)rels: {rels}\")\n rels_from_query = [triplet[1] for triplet in query_triplets if triplet[1].startswith('?')]\n answer_ent = re.findall(\"select [\\(]?([\\S]+) \", query)\n order_info_nt = namedtuple(\"order_info\", [\"variable\", \"sorting_order\"])\n order_variable = re.findall(\"order by (asc|desc)\\((.*)\\)\", query)\n answers_sorting_order = order_of_answers_sorting(question)\n if order_variable:\n order_info = order_info_nt(order_variable[0][1], answers_sorting_order)\n else:\n order_info = order_info_nt(None, None)\n log.debug(f\"question, order_info: {question}, {order_info}\")\n filter_from_query = re.findall(\"contains\\((\\?\\w), (.+?)\\)\", query)\n log.debug(f\"(query_parser)filter_from_query: {filter_from_query}\")\n\n year = extract_year(question_tokens, question)\n number = extract_number(question_tokens, question)\n log.debug(f\"year {year}, number {number}\")\n if year:\n filter_info = [(elem[0], elem[1].replace(\"n\", year)) for elem in filter_from_query]\n elif number:\n filter_info = [(elem[0], elem[1].replace(\"n\", number)) for elem in filter_from_query]\n else:\n filter_info = [elem for elem in filter_from_query if elem[1] != \"n\"]\n log.debug(f\"(query_parser)filter_from_query: {filter_from_query}\")\n rel_combs = make_combs(rels, permut=False)\n import datetime\n start_time = datetime.datetime.now()\n for combs in itertools.product(entity_combs, type_combs, rel_combs):\n query_hdt_seq = [\n fill_query(query_hdt_elem, combs[0], combs[1], combs[2]) for query_hdt_elem in query_sequence]\n candidate_output = self.wiki_parser(\n rels_from_query + answer_ent, query_hdt_seq, filter_info, order_info)\n candidate_outputs += [combs[2][:-1] + output for output in candidate_output]\n if return_if_found and candidate_output:\n return candidate_outputs\n log.debug(f\"(query_parser)loop time: {datetime.datetime.now() - start_time}\")\n log.debug(f\"(query_parser)final outputs: {candidate_outputs[:3]}\")\n\n return candidate_outputs\n\n def find_top_rels(self, question: str, entity_ids: List[List[str]], triplet_info: namedtuple) -> List[str]:\n ex_rels = []\n direction, source = triplet_info\n if source == \"wiki\":\n for entity_id in entity_ids:\n for entity in entity_id[:self.entities_to_leave]:\n ex_rels += self.wiki_parser.find_rels(entity, direction)\n ex_rels = list(set(ex_rels))\n ex_rels = [rel.split('/')[-1] for rel in ex_rels]\n elif source == \"rank_list_1\":\n ex_rels = self.rank_list_0\n elif source == \"rank_list_2\":\n ex_rels = self.rank_list_1\n scores = self.rel_ranker.rank_rels(question, ex_rels)\n top_rels = [score[0] for score in scores]\n return top_rels[:self.rels_to_leave]\n", "sub_path": "deeppavlov/models/kbqa/query_generator.py", "file_name": "query_generator.py", "file_ext": "py", "file_size_in_byte": 15655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 35, "usage_type": "call"}, {"api_name": "deeppavlov.core.models.component.Component", "line_number": 39, "usage_type": "name"}, {"api_name": "deeppavlov.core.models.serializable.Serializable", "line_number": 39, "usage_type": "name"}, {"api_name": "deeppavlov.models.kbqa.template_matcher.TemplateMatcher", "line_number": 45, "usage_type": "name"}, {"api_name": "deeppavlov.models.kbqa.entity_linking.EntityLinker", "line_number": 46, "usage_type": "name"}, {"api_name": "deeppavlov.models.kbqa.entity_linking.EntityLinker", "line_number": 47, "usage_type": "name"}, {"api_name": "deeppavlov.models.kbqa.wiki_parser.WikiParser", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 49, "usage_type": "name"}, {"api_name": "deeppavlov.models.kbqa.rel_ranking_infer.RelRankerInfer", "line_number": 49, "usage_type": "name"}, {"api_name": "deeppavlov.models.kbqa.rel_ranking_bert_infer.RelRankerBertInfer", "line_number": 49, "usage_type": "name"}, {"api_name": "deeppavlov.core.common.file.read_json", "line_number": 101, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 108, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 161, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 173, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 213, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 214, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 214, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 214, "usage_type": "name"}, {"api_name": "nltk.word_tokenize", "line_number": 216, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 222, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.make_combs", "line_number": 235, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.make_combs", "line_number": 237, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 246, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 247, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 248, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.order_of_answers_sorting", "line_number": 249, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 255, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.extract_year", "line_number": 258, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.extract_number", "line_number": 259, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.make_combs", "line_number": 268, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 270, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 270, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 271, "usage_type": "call"}, {"api_name": "deeppavlov.models.kbqa.utils.fill_query", "line_number": 273, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 279, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 279, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 284, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 284, "usage_type": "name"}, {"api_name": "deeppavlov.core.common.registry.register", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "413478819", "text": "\n# coding: utf-8\n\n# # Stage 4 Data Analysis on the Integrated Table\n\n# First, we repeat what we did in stage 3 and get a set of matching tuples.\n\n\nimport pandas as pd\nimport py_entitymatching as em\nA = em.read_csv_metadata('./TableA_lower.csv', key = 'ID')\nB = em.read_csv_metadata('./TableB_lower.csv', key = 'ID')\n\nblock_f = em.get_features_for_blocking(A, B)\nblock_t = em.get_tokenizers_for_blocking()\nblock_s = em.get_sim_funs_for_blocking()\nr = em.get_feature_fn('jaccard(dlm_dc0(ltuple[\"Title\"]), dlm_dc0(rtuple[\"Title\"]))', block_t, block_s)\nem.add_feature(block_f, 'Title_Title_jac_dlm_dc0_dlm_dc0', r)\n\nob = em.OverlapBlocker()\nC = ob.block_tables(A, B, 'Author', 'Author', \n l_output_attrs=['Title','Author','Publication','Format','ISBN','Series', 'Physical Details'], \n r_output_attrs=['Title','Author','Publication','Format','ISBN','Series', 'Physical Details'], \n overlap_size = 2)\n\nD = ob.block_candset(C, 'Title', 'Title', overlap_size = 4)\nlabel_S = pd.read_csv('./Set_G.csv')\n# em.copy_properties(S, label_S)\nem.set_property(label_S, 'key', '_id')\nem.set_property(label_S, 'fk_ltable', 'ltable_ID')\nem.set_property(label_S, 'fk_rtable', 'rtable_ID')\nlabel_S_rtable = em.read_csv_metadata('./label_S_rtable.csv')\nlabel_S_ltable = em.read_csv_metadata('./label_S_ltable.csv')\nem.set_property(label_S, 'rtable', label_S_rtable)\nem.set_property(label_S, 'ltable', label_S_ltable)\nlabel_S['ltable_Title'] = label_S['ltable_Title'].apply(lambda x: x.lower())\nlabel_S['rtable_Title'] = label_S['rtable_Title'].apply(lambda x: x.lower())\nlabel_S['ltable_Author'] = label_S['ltable_Author'].apply(lambda x: x.lower())\nlabel_S['rtable_Author'] = label_S['rtable_Author'].apply(lambda x: x.lower())\nlabel_S['ltable_Publication'] = label_S['ltable_Publication'].apply(lambda x: x.lower())\nlabel_S['rtable_Publication'] = label_S['rtable_Publication'].apply(lambda x: x.lower())\nlabel_S['ltable_Format'] = label_S['ltable_Format'].apply(lambda x: x.lower())\nlabel_S['rtable_Format'] = label_S['rtable_Format'].apply(lambda x: x.lower())\nlabel_S['ltable_Series'] = label_S['ltable_Series'].apply(lambda x: x.lower())\nlabel_S['rtable_Series'] = label_S['rtable_Series'].apply(lambda x: x.lower())\nmatch_f = em.get_features_for_matching(A, B)\nmatch_t = em.get_tokenizers_for_matching()\nmatch_s = em.get_sim_funs_for_matching()\nf1 = em.get_feature_fn('jaccard(dlm_dc0(ltuple[\"Title\"]), dlm_dc0(rtuple[\"Title\"]))', match_t, match_s)\n# f2 = em.get_feature_fn('jaccard(dlm_dc0(ltuple[\"Author\"]), dlm_dc0(rtuple[\"Author\"]))', match_t, match_s)\nf3 = em.get_feature_fn('jaccard(dlm_dc0(ltuple[\"Publication\"]), dlm_dc0(rtuple[\"Publication\"]))', match_t, match_s)\nf4 = em.get_feature_fn('jaccard(dlm_dc0(ltuple[\"Series\"]), dlm_dc0(rtuple[\"Series\"]))', match_t, match_s)\nem.add_feature(match_f, 'Title_Title_jac_dlm_dc0_dlm_dc0', f1)\n# em.add_feature(match_f, 'Author_Author_jac_dlm_dc0_dlm_dc0', f2)\nem.add_feature(match_f, 'Publication_Publication_jac_dlm_dc0_dlm_dc0', f3)\nem.add_feature(match_f, 'Series_Series_jac_dlm_dc0_dlm_dc0', f4)\n\n\n# Add blackbox feature\n\nimport re\n# for Roman numerals matching\ndef Title_Title_blackbox_1(x, y):\n \n # get name attribute\n x_title = x['Title']\n y_title = y['Title']\n# regex_roman = '\\s+[MDCLXVI]+\\s+'\n regex_roman = '\\s+[mdclxvi]+($|\\s+)'\n x_match = None\n y_match = None\n if re.search(regex_roman, x_title):\n x_match = re.search(regex_roman, x_title).group(0).strip()\n if re.search(regex_roman, y_title):\n y_match = re.search(regex_roman, y_title).group(0).strip()\n\n if x_match is None or y_match is None:\n return False\n else:\n return x_match == y_match\n\nem.add_blackbox_feature(match_f, 'blackbox_1', Title_Title_blackbox_1)\n\n\n# for number matching (e.g. 6th edition)\ndef Title_Title_blackbox_2(x, y):\n # x, y will be of type pandas series\n \n x_title = x['Title']\n y_title = y['Title']\n regex_number = '\\s+(\\d+)\\s*th'\n x_match = None\n y_match = None\n if re.search(regex_number, x_title):\n x_match = re.search(regex_number, x_title).group(1)\n if re.search(regex_number, y_title):\n y_match = re.search(regex_number, y_title).group(1)\n\n if x_match is None or y_match is None:\n return False\n else:\n return x_match == y_match\n\nem.add_blackbox_feature(match_f, 'blackbox_2', Title_Title_blackbox_2)\n\n# for number matching (e.g. 6th edition)\nfrom fuzzywuzzy import fuzz\ndef Author_Author_blackbox_3(x, y):\n # x, y will be of type pandas series\n \n x_author = x['Author']\n y_author = y['Author']\n return fuzz.token_set_ratio(x_author, y_author)/100.0\n \nem.add_blackbox_feature(match_f, 'blackbox_3', Author_Author_blackbox_3)\nmatch_f = match_f[(match_f['left_attribute'] != 'ID') & (match_f['left_attribute'] != 'ISBN')]\nmatch_f = match_f[(match_f['left_attribute'] != 'Format') & (match_f['left_attribute'] != 'Series')]\nH = em.extract_feature_vecs(label_S, feature_table=match_f, attrs_after=['label'])\n# RF\nrf = em.RFMatcher(n_estimators = 300,max_depth = 300, name='RF')\nrf.fit(table=H, \n exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'label'], \n target_attr='label')\nH_test = em.extract_feature_vecs(D, feature_table=match_f)\npred_table = rf.predict(table= H_test, \n exclude_attrs=['_id', 'ltable_ID', 'rtable_ID'], \n target_attr='predicted_labels', \n return_probs=True, \n probs_attr='proba', \n append=True)\n# eval_summary = em.eval_matches(pred_table, 'label', 'predicted_labels')\n# eval_summary\n\n\n# In[14]:\n\n\npred_rows = pred_table[pred_table['predicted_labels'] == 1]\n\n\n# ## Merge the tables\n# \n# Here we obtained the matching set based on the prediction.\n# \n# \n# There are 1337 matched books in total.\n\nmatched_set = D[D['_id'].isin(pred_rows['_id'])]\nmatched_set\nmatched_set.shape\nmatched_id = matched_set[['ltable_ID', 'rtable_ID']]\n\n\n# We put the id of matching tuples in to dictionaries.\n\n# AB_list=[['ltable_ID','rtable_ID'] for x in pred_table if predicted_labels==1]\nAB_list = matched_id.values.tolist()\nAB_dict = {item[0]: item[1] for item in AB_list}\nBA_dict = {item[1]: item[0] for item in AB_list}\n\nlen(AB_dict.keys())\n\n\n# Here we merge the matching tuples.\n# \n# For each field of a matching tuple, we simply keep the one with longer string length.\n\ndf = matched_set.iloc[:, 3:10]\n\ndf = df.rename(columns={'ltable_Title' : 'Title',\n 'ltable_Author' : 'Author',\n 'ltable_Publication' : 'Publication',\n 'ltable_Format' : 'Format',\n 'ltable_ISBN' : 'ISBN',\n 'ltable_Series' : 'Series',\n 'ltable_Physical Details' : 'Physical Details'\n })\n\nattr=['Title','Author','Publication','Format','ISBN','Series', 'Physical Details']\ni = 0\nfor index, r in matched_set.iterrows():\n for a in attr:\n left_name = 'ltable_' + a\n right_name = 'rtable_' + a\n# print(index,' ' ,attr.index(a))\n if len(str(r[left_name])) < len(str(r[right_name])):\n \n df.iloc[i, attr.index(a)] = r[right_name]\n else:\n df.iloc[i, attr.index(a)] = r[left_name]\n i += 1\n\n\n# We added an Source attribute to indicate the source of a tuple.\n\ndf['Source'] = 'ab'\nA['Source'] = 'a'\nB['Source'] = 'b'\n\n\n# After merging the matching set, we concat the set with the original table A and B.\n\nset_merged = pd.concat([df, A[~A['ID'].isin(AB_dict.keys())].iloc[:,1:], B[~B['ID'].isin(BA_dict.keys())].iloc[:,1:]])\nA[~A['ID'].isin(AB_dict.keys())].shape\nB[~B['ID'].isin(BA_dict.keys())].shape\nset_merged\n\n\n# Final merged set contains 9350 tuples in total.\n\n# The final set contains 9350 tuples in total.\nset_merged.shape\n\nset_merged[set_merged['Source'] == 'a'].shape\n\n\n# We save the set to Table_E.csv.\nset_merged.to_csv('Table_E.csv', sep=',')\n\n", "sub_path": "stage4/merge.py", "file_name": "merge.py", "file_ext": "py", "file_size_in_byte": 7968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "py_entitymatching.read_csv_metadata", "line_number": 11, "usage_type": "call"}, {"api_name": "py_entitymatching.read_csv_metadata", "line_number": 12, "usage_type": "call"}, {"api_name": "py_entitymatching.get_features_for_blocking", "line_number": 14, "usage_type": "call"}, {"api_name": "py_entitymatching.get_tokenizers_for_blocking", "line_number": 15, "usage_type": "call"}, {"api_name": "py_entitymatching.get_sim_funs_for_blocking", "line_number": 16, "usage_type": "call"}, {"api_name": "py_entitymatching.get_feature_fn", "line_number": 17, "usage_type": "call"}, {"api_name": "py_entitymatching.add_feature", "line_number": 18, "usage_type": "call"}, {"api_name": "py_entitymatching.OverlapBlocker", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "py_entitymatching.set_property", "line_number": 29, "usage_type": "call"}, {"api_name": "py_entitymatching.set_property", "line_number": 30, "usage_type": "call"}, {"api_name": "py_entitymatching.set_property", "line_number": 31, "usage_type": "call"}, {"api_name": "py_entitymatching.read_csv_metadata", "line_number": 32, "usage_type": "call"}, {"api_name": "py_entitymatching.read_csv_metadata", "line_number": 33, "usage_type": "call"}, {"api_name": "py_entitymatching.set_property", "line_number": 34, "usage_type": "call"}, {"api_name": "py_entitymatching.set_property", "line_number": 35, "usage_type": "call"}, {"api_name": "py_entitymatching.get_features_for_matching", "line_number": 46, "usage_type": "call"}, {"api_name": "py_entitymatching.get_tokenizers_for_matching", "line_number": 47, "usage_type": "call"}, {"api_name": "py_entitymatching.get_sim_funs_for_matching", "line_number": 48, "usage_type": "call"}, {"api_name": "py_entitymatching.get_feature_fn", "line_number": 49, "usage_type": "call"}, {"api_name": "py_entitymatching.get_feature_fn", "line_number": 51, "usage_type": "call"}, {"api_name": "py_entitymatching.get_feature_fn", "line_number": 52, "usage_type": "call"}, {"api_name": "py_entitymatching.add_feature", "line_number": 53, "usage_type": "call"}, {"api_name": "py_entitymatching.add_feature", "line_number": 55, "usage_type": "call"}, {"api_name": "py_entitymatching.add_feature", "line_number": 56, "usage_type": "call"}, {"api_name": "re.search", "line_number": 72, "usage_type": "call"}, {"api_name": "re.search", "line_number": 73, "usage_type": "call"}, {"api_name": "re.search", "line_number": 74, "usage_type": "call"}, {"api_name": "re.search", "line_number": 75, "usage_type": "call"}, {"api_name": "py_entitymatching.add_blackbox_feature", "line_number": 82, "usage_type": "call"}, {"api_name": "re.search", "line_number": 94, "usage_type": "call"}, {"api_name": "re.search", "line_number": 95, "usage_type": "call"}, {"api_name": "re.search", "line_number": 96, "usage_type": "call"}, {"api_name": "re.search", "line_number": 97, "usage_type": "call"}, {"api_name": "py_entitymatching.add_blackbox_feature", "line_number": 104, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz.token_set_ratio", "line_number": 113, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz", "line_number": 113, "usage_type": "name"}, {"api_name": "py_entitymatching.add_blackbox_feature", "line_number": 115, "usage_type": "call"}, {"api_name": "py_entitymatching.extract_feature_vecs", "line_number": 118, "usage_type": "call"}, {"api_name": "py_entitymatching.RFMatcher", "line_number": 120, "usage_type": "call"}, {"api_name": "py_entitymatching.extract_feature_vecs", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 203, "usage_type": "call"}]} +{"seq_id": "348814522", "text": "import inspect\nfrom typing import Dict, Any, List, Tuple, Optional\n\nfrom .base_logger import _BaseLogger, _BulkBaseLogger\nfrom .globals import Globals\nfrom ..constants import AWS_REGION, SOURCE_CODE\nfrom ..log import LOG\nfrom ..models import LambdaContext\n\n\nclass LambdaLogger(_BaseLogger):\n \"\"\"\n This class can be used to decorate a handler for a Python lambda function.\n\n For decorating multiple lambda handlers, it is useful to use the method\n `decorate_all_functions`.\n\n See the documentation in the base class (:class:`_BaseLogger`) for more\n info.\n\n \"\"\"\n FUNCTION_TYPE = 'Lambda'\n\n _decorated = []\n\n def decorate_all_functions(self, enabled_lvl: Optional[str] = None,\n ses_identity: Optional[str] = None,\n teams_email: Optional[str] = None,\n dev_emails: Optional[str] = None):\n \"\"\"\n Decorates all functions (assumed to be lambda functions) in the calling\n module with the wrapper provided by :class:`LambdaLogger`\n\n See documentation under the constructor for :class:`_BaseLogger`\n (e.g. `BaseLogger()`) for info on parameters.\n\n This method call is ideally made after all desired functions to be\n decorated are implemented in the caller module. See below for an\n example.\n\n\n module_a.py:\n\n def f1(): # This function will be decorated\n ...\n\n def f2(): # This is also decorated\n ...\n\n LambdaLogger().decorate_all_functions(teams_email='abc123.my.domain@amer.teams.ms')\n\n def f3(): # This function won't be decorated as it's defined later\n ...\n\n \"\"\"\n # Copy over global defaults\n Globals.enabled_lvl = enabled_lvl\n Globals.ses_identity = ses_identity\n Globals.teams_email = teams_email\n Globals.dev_emails = dev_emails\n\n caller = inspect.stack()[1]\n caller_module_locals = caller[0].f_locals\n caller_module_name = caller_module_locals['__name__']\n\n # Decorate all functions in module\n decorated_functions = []\n for attr, fn in caller_module_locals.items():\n if self._should_decorate_fn(caller_module_name, fn):\n # Decorate the function if its local to the caller module\n caller_module_locals[attr] = self.__call__(fn)\n decorated_functions.append(attr)\n\n LOG.debug(\n 'Successfully decorated %d lambda functions: %s',\n len(decorated_functions), decorated_functions)\n\n @classmethod\n def _should_decorate_fn(cls, module_name: str, fn: Any) -> bool:\n \"\"\"\n Confirm that a function is defined in the module and has not been\n previously decorated.\n\n Return a boolean indicating whether the function should be decorated.\n \"\"\"\n if not callable(fn) or fn.__module__ != module_name:\n return False\n\n key = f'{module_name}.{fn.__name__}'\n\n if key in cls._decorated:\n return False\n\n cls._decorated.append(key)\n return True\n\n def _set_context(self, func, *args, **kwargs):\n try:\n # Context is generally passed as 2nd argument for lambda functions,\n # but to be safe we use the last positional argument instead.\n self.context = args[-1]\n except IndexError:\n # Not a regular lambda function - perhaps a local function used\n # for testing purposes.\n self.context: Any = LambdaContext(func.__name__)\n\n def _get_context_and_links(self) -> Tuple[Dict[str, Any], List[Dict[str, str]]]:\n links = []\n aws_root = 'https://console.aws.amazon.com'\n\n if isinstance(self.context, LambdaContext):\n # Mock lambda context (lambda function has an empty or missing\n # context argument)\n return {'Function Name': self.context.function_name}, links\n\n log_group_name = self.context.log_group_name\n log_stream_name = self.context.log_stream_name\n\n account_id = self.context.invoked_function_arn.split(':')[4]\n account_name = self._get_account_name()\n\n context = {'Function Name': self.context.function_name,\n 'Request Id': self.context.aws_request_id,\n 'Account Name': account_name,\n 'Account Id': account_id}\n\n if SOURCE_CODE:\n links.append({'location': SOURCE_CODE, 'text': 'Link to Source'})\n\n links.append({\n 'location': f'{aws_root}/lambda/home?region={AWS_REGION}#/'\n f'functions/{self.context.function_name}',\n 'text': 'Link to Lambda'\n })\n\n links.append({\n 'location': f'{aws_root}/cloudwatch/home?region={AWS_REGION}#logEventViewer:'\n f'group={log_group_name};stream={log_stream_name}',\n 'text': 'Link to Logs'\n })\n\n return context, links\n\n\nclass BulkLambdaLogger(LambdaLogger, _BulkBaseLogger):\n \"\"\"\n This class can be used to decorate a handler for a Python lambda function.\n\n The `Bulk` logger implementation will send templated emails in bulk,\n e.g. via the ``ses:SendBulkTemplatedEmail`` API call. Use this\n implementation when it is expected that multiple logs will be sent to Teams\n or Outlook, as there will be a performance increase when using a `Bulk`\n logger.\n\n For decorating multiple lambda handlers, it is useful to use the method\n `decorate_all_functions`.\n\n See the documentation in the base class (:class:`_BaseLogger`) for more\n info.\n\n \"\"\"\n", "sub_path": "aws_teams_logger/loggers/lambda_logger.py", "file_name": "lambda_logger.py", "file_ext": "py", "file_size_in_byte": 5686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "base_logger._BaseLogger", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 29, "usage_type": "name"}, {"api_name": "globals.Globals.enabled_lvl", "line_number": 57, "usage_type": "attribute"}, {"api_name": "globals.Globals", "line_number": 57, "usage_type": "name"}, {"api_name": "globals.Globals.ses_identity", "line_number": 58, "usage_type": "attribute"}, {"api_name": "globals.Globals", "line_number": 58, "usage_type": "name"}, {"api_name": "globals.Globals.teams_email", "line_number": 59, "usage_type": "attribute"}, {"api_name": "globals.Globals", "line_number": 59, "usage_type": "name"}, {"api_name": "globals.Globals.dev_emails", "line_number": 60, "usage_type": "attribute"}, {"api_name": "globals.Globals", "line_number": 60, "usage_type": "name"}, {"api_name": "inspect.stack", "line_number": 62, "usage_type": "call"}, {"api_name": "log.LOG.debug", "line_number": 74, "usage_type": "call"}, {"api_name": "log.LOG", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 105, "usage_type": "name"}, {"api_name": "models.LambdaContext", "line_number": 105, "usage_type": "call"}, {"api_name": "models.LambdaContext", "line_number": 111, "usage_type": "argument"}, {"api_name": "constants.SOURCE_CODE", "line_number": 127, "usage_type": "name"}, {"api_name": "constants.SOURCE_CODE", "line_number": 128, "usage_type": "name"}, {"api_name": "constants.AWS_REGION", "line_number": 131, "usage_type": "name"}, {"api_name": "constants.AWS_REGION", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 107, "usage_type": "name"}, {"api_name": "base_logger._BulkBaseLogger", "line_number": 145, "usage_type": "name"}]} +{"seq_id": "255406647", "text": "from django.shortcuts import render, get_object_or_404, Http404, HttpResponseRedirect\nfrom .models import Category, ContentType, ContentBase, Author\nfrom .forms import ReccommendContentForm\nfrom django.core.urlresolvers import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom accounts.models import UserContentItem, UserProfile\nfrom django.contrib.auth.models import User\nfrom events.models import Event\n\n\n\ndef reccommend_content(request):\n\ttry:\n\t\tthe_user = request.user\n\t\tif request.method == 'GET':\n\t\t\tform_r = ReccommendContentForm()\n\t\telse:\n\t\t\tform_r = ReccommendContentForm(request.POST)\n\t\t\tif form_r.is_valid():\n\t\t\t\tlink = form_r.cleaned_data['link']\n\t\t\t\tcomment = form_r.cleaned_data['comment']\n\t\t\t\tis_visible = form_r.cleaned_data['is_visible']\n\t\t\t\tcontent = Author.objects.create(author = the_user, is_visible = is_visible, link = link, comment = comment)\n\t\t\t\treturn HttpResponseRedirect(reverse('homepage'))\n\t\treturn form_r\n\texcept:\n\t\treturn HttpResponseRedirect(reverse('login'))\n\t\n\ndef homepage(request):\n\tcontents = ContentBase.objects.get_featured()\n\tmonth_course = ContentBase.objects.get(month_course = 'yes')\n\tcategories = Category.objects.all()\n\tcontent_types = ContentType.objects.all()\n\ttry: \n\t\tthe_user = request.user\n\t\tu = UserProfile.objects.get(user = the_user)\n\texcept:\n\t\tu = None \n\tform_r = reccommend_content(request)\n\tcontext = {\"month_course\" : month_course, \"u\" : u, \"contents\" : contents, \"categories\" : categories, \"content_types\" : content_types, \"form_r\" : form_r}\n\ttemplate_name = 'home.html'\n\treturn render(request, template_name, context)\n\n\ndef category_content(request, slug_cat):\n\tmonth_course = ContentBase.objects.get(month_course = 'yes')\n\ttry:\n\t\tobj_cat = get_object_or_404(Category, slug = slug_cat)\n\texcept:\n\t\tpass\n\n\ttry: \n\t\tthe_user = request.user\n\t\tu = UserProfile.objects.get(user = the_user)\n\texcept:\n\t\tu = None \n\tcategories = Category.objects.all()\n\tcontent_types = ContentType.objects.all()\n\tform_r = reccommend_content(request)\n\tqueryset = ContentBase.objects.filter(category = obj_cat).order_by('created_date')\n\tcontext = {\"month_course\" : month_course, \"u\" : u, \"form_r\" : form_r, \"obj_cat\" : obj_cat, \"queryset\" : queryset, \"categories\" : categories, \"content_types\" : content_types}\n\ttemplate_name = 'video/category_content.html'\n\treturn render(request, template_name, context)\n\ndef content_type_content(request, slug_con):\n\tmonth_course = ContentBase.objects.get(month_course = 'yes')\n\tobj = get_object_or_404(ContentType, slug = slug_con)\n\tcategories = Category.objects.all()\n\tcontent_types = ContentType.objects.all()\n\tform_r = reccommend_content(request)\n\ttry: \n\t\tthe_user = request.user\n\t\tu = UserProfile.objects.get(user = the_user)\n\texcept:\n\t\tu = None \n\tqueryset = ContentBase.objects.filter(content_type = obj).order_by('created_date')\n\tcontext = {\"month_course\" : month_course, \"u\" : u, \"form_r\" : form_r, \"obj\" : obj, \"queryset\" : queryset, \"categories\" : categories, \"content_types\" : content_types}\n\ttemplate_name = 'video/content_type_content.html'\n\treturn render(request, template_name, context)\n\ndef content_list(request, slug_cat, slug_con):\n\tmonth_course = ContentBase.objects.get(month_course = 'yes')\n\ttry:\n\t\tobj_cat = get_object_or_404(Category, slug = slug_cat)\n\texcept:\n\t\tpass\n\ttry:\n\t\tobj_con = get_object_or_404(ContentType, slug = slug_con)\n\texcept:\n\t\tpass\n\n\ttry: \n\t\tthe_user = request.user\n\t\tu = UserProfile.objects.get(user = the_user)\n\texcept:\n\t\tu = None \n\tcategories = Category.objects.all()\n\tcontent_types = ContentType.objects.all()\n\tform_r = reccommend_content(request)\n\tqueryset = ContentBase.objects.filter(category = obj_cat, content_type = obj_con).order_by('created_date')\n\tcontext = {\"month_course\" : month_course, \"u\" : u, \"form_r\" : form_r, \"obj_cat\" : obj_cat, \"obj_con\" : obj_con, \"queryset\" : queryset, \"categories\" : categories, \"content_types\" : content_types}\n\ttemplate_name = 'video/content_list.html'\n\treturn render(request, template_name, context)\n\ndef search(request):\n\ttry: \n\t\tthe_user = request.user\n\t\tu = UserProfile.objects.get(user = the_user)\n\texcept:\n\t\tu = None\n\ttry:\n\t\tq = request.GET.get('q')\n\texcept:\n\t\tq = None\n\tif q:\n\t\tcontents = ContentBase.objects.filter(title__icontains = q)\n\t\tcategories = Category.objects.all()\n\t\tcontent_types = ContentType.objects.all()\n\t\tmessage = \"No results found\"\n\t\tcontext = {\"u\" : u, \"contents\" : contents, \"message\" : message, \"query\" : q, \"categories\" : categories, \"content_types\" : content_types}\n\t\ttemplate_name = 'search.html'\n\t\treturn render(request, template_name, context)\n\telse:\n\t\treturn Http404\n\ndef content_detail(request, slug_con):\n\tcategories = Category.objects.all()\n\tform_r = reccommend_content(request)\n\tcontent = ContentBase.objects.get(slug = slug_con)\n\ttry:\n\t\ttemp = content.reccommend_by.author.id\n\t\tprint(content.reccommend_by.author.id)\n\t\tuser = User.objects.get(id = temp)\n\texcept:\n\t\tpass\n\ttry: \n\t\tthe_user = request.user\n\t\tu = UserProfile.objects.get(user = the_user)\n\texcept:\n\t\tu = None\n\t\n\tcontent_temp = str((content.get_content_type()))\n\trelated_items = ContentBase.objects.filter(content_type = content_temp)[:5]\n\n\tcontext = {\"u\" : u, \"form_r\" : form_r, \"categories\" : categories, \"content\" : content, \"related_items\" : related_items}\n\ttemplate_name = 'video/video_detail.html'\n\treturn render(request, template_name, context)\n\ndef basepage(request):\n\tcourses = ContentBase.objects.filter(month_course = 1)\n\tevent = Event.objects.filter(month_event = 1)\n\tprint(event)\n\tcontext = {\"courses\" : courses, \"event\" : event}\n\ttemplate_name = 'base.html'\n\treturn render(request, template_name, context)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "videos/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "forms.ReccommendContentForm", "line_number": 16, "usage_type": "call"}, {"api_name": "forms.ReccommendContentForm", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Author.objects.create", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 27, "usage_type": "call"}, {"api_name": "models.ContentBase.objects.get_featured", "line_number": 31, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 31, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 33, "usage_type": "name"}, {"api_name": "models.ContentType.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "models.ContentType.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.ContentType", "line_number": 34, "usage_type": "name"}, {"api_name": "accounts.models.UserProfile.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "accounts.models.UserProfile", "line_number": 37, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "models.ContentBase.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 49, "usage_type": "argument"}, {"api_name": "accounts.models.UserProfile.objects.get", "line_number": 55, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "accounts.models.UserProfile", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 58, "usage_type": "name"}, {"api_name": "models.ContentType.objects.all", "line_number": 59, "usage_type": "call"}, {"api_name": "models.ContentType.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.ContentType", "line_number": 59, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 61, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "models.ContentBase.objects.get", "line_number": 67, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 68, "usage_type": "call"}, {"api_name": "models.ContentType", "line_number": 68, "usage_type": "argument"}, {"api_name": "models.Category.objects.all", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 69, "usage_type": "name"}, {"api_name": "models.ContentType.objects.all", "line_number": 70, "usage_type": "call"}, {"api_name": "models.ContentType.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.ContentType", "line_number": 70, "usage_type": "name"}, {"api_name": "accounts.models.UserProfile.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "accounts.models.UserProfile", "line_number": 74, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.filter", "line_number": 77, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 77, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "models.ContentBase.objects.get", "line_number": 83, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 83, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 85, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 85, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 89, "usage_type": "call"}, {"api_name": "models.ContentType", "line_number": 89, "usage_type": "argument"}, {"api_name": "accounts.models.UserProfile.objects.get", "line_number": 95, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "accounts.models.UserProfile", "line_number": 95, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 98, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 98, "usage_type": "name"}, {"api_name": "models.ContentType.objects.all", "line_number": 99, "usage_type": "call"}, {"api_name": "models.ContentType.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "models.ContentType", "line_number": 99, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 104, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects.get", "line_number": 109, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "accounts.models.UserProfile", "line_number": 109, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.filter", "line_number": 117, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 117, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 118, "usage_type": "name"}, {"api_name": "models.ContentType.objects.all", "line_number": 119, "usage_type": "call"}, {"api_name": "models.ContentType.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "models.ContentType", "line_number": 119, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 123, "usage_type": "call"}, {"api_name": "django.shortcuts.Http404", "line_number": 125, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 128, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 128, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.get", "line_number": 130, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 130, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 134, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 134, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 134, "usage_type": "name"}, {"api_name": "accounts.models.UserProfile.objects.get", "line_number": 139, "usage_type": "call"}, {"api_name": "accounts.models.UserProfile.objects", "line_number": 139, "usage_type": "attribute"}, {"api_name": "accounts.models.UserProfile", "line_number": 139, "usage_type": "name"}, {"api_name": "models.ContentBase.objects.filter", "line_number": 144, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 144, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 148, "usage_type": "call"}, {"api_name": "models.ContentBase.objects.filter", "line_number": 151, "usage_type": "call"}, {"api_name": "models.ContentBase.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "models.ContentBase", "line_number": 151, "usage_type": "name"}, {"api_name": "events.models.Event.objects.filter", "line_number": 152, "usage_type": "call"}, {"api_name": "events.models.Event.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "events.models.Event", "line_number": 152, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "147968844", "text": "import matplotlib\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.backends.backend_tkagg import (\n FigureCanvasTkAgg, NavigationToolbar2Tk)\nfrom matplotlib.figure import Figure\n\nfrom numpy import equal\n#plt.style.use('seaborn-white')\n\nclass Plot2D:\n\n def __init__(self, xAxis, yAxis, view, slice, p):\n self.view = view\n self.slice = slice\n self.p = p\n\n #matplotlib colormap that's being used by the colorbar and the contourf\n self.color = 'viridis'\n \n self.xAxis = xAxis\n self.yAxis = yAxis\n\n self.sensors = {}\n self.obstacles = {}\n\n self.fig = Figure()\n self.ax = self.fig.add_subplot(111)\n\n #adds a colorbar to the graph, with a temp array to make it go fully from 0 to 1\n data = [[0, 1],[0, 1]]\n cax = self.ax.imshow(data, cmap=self.color)\n cbar = self.fig.colorbar(cax, ticks=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])\n cbar.ax.set_yticklabels(['0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1'])\n\n self.updateDimensions()\n \n #adds the labels, specific per side from which it's viewed\n if self.view == 0:\n self.fig.suptitle(\"IDW Hydrogen concentration, p: \" + str(self.p) + \", at height: \" + str(self.slice))\n self.fig.supxlabel(\"Length\")\n self.fig.supylabel(\"Width\")\n elif self.view == 1:\n self.fig.suptitle(\"IDW Hydrogen concentration, p: \" + str(self.p) + \", at width: \" + str(self.slice))\n self.fig.supxlabel(\"Length\")\n self.fig.supylabel(\"Height\")\n else:\n self.fig.suptitle(\"IDW Hydrogen concentration, p: \" + str(self.p) + \", at length: \" + str(self.slice))\n self.fig.supxlabel(\"Width\")\n self.fig.supylabel(\"Height\")\n \n\n def updateRoom(self, xAxis, yAxis):\n self.xAxis = xAxis\n self.yAxis = yAxis\n \n def addSensor(self, sensorId, x, y, z):\n self.sensors[int(sensorId)] = {}\n self.sensors[int(sensorId)]['x'] = x\n self.sensors[int(sensorId)]['y'] = y\n self.sensors[int(sensorId)]['z'] = z\n self.sensors[int(sensorId)]['value'] = 0.\n \n def updateSensor(self, sensorId, x, y, z):\n self.sensors[int(sensorId)]['x'] = x\n self.sensors[int(sensorId)]['y'] = y\n self.sensors[int(sensorId)]['z'] = z\n\n def addObstacle(self, obstacleId, x1, y1, z1, x2, y2, z2):\n self.obstacles[obstacleId] = {}\n self.obstacles[obstacleId]['positions'] = (x1,y1,z1)\n self.obstacles[obstacleId]['sizes'] = (x2,y2,z2)\n\n def updateObstacle(self, obstacleId, x1, y1, z1, x2, y2, z2):\n self.obstacles[obstacleId]['positions'] = (x1,y1,z1)\n self.obstacles[obstacleId]['sizes'] = (x2,y2,z2)\n \n def updateSensorData(self, sensorId, sensorValue):\n self.sensors[int(sensorId)]['value'] = sensorValue\n \n #creates the meshgrid arrays for the contourf method, linspace creates an array from 0 to the room length, in 50 steps\n def updateDimensions(self):\n self.x = np.linspace(0, self.xAxis, 50)\n self.y = np.linspace(0, self.yAxis, 50)\n self.X, self.Y = np.meshgrid(self.x, self.y)\n\n # sets the axis to the correct length and sets the aspect ratio to an equally spaced x and y axis\n def setRoomAxis(self):\n self.ax.set_xlim([0, self.xAxis])\n self.ax.set_ylim([0, self.yAxis])\n self.ax.set_aspect('equal', 'box')\n \n # method to draw data, fills the 2d array \"Z\" with the corresponding values and contourf draws the image\n # if/else to catch empty sensor list, otherwise calcPointValue would error with a divide by 0\n def plotData(self):\n Z = []\n if self.sensors != {}:\n for indey, yC in enumerate(self.y):\n Z.append([])\n for xC in self.x:\n arr = Z[int(indey)] \n arr.append(self.calcPointValue(xC, yC))\n else:\n Z = self.X\n self.ax.contourf(self.X, self.Y, Z, 50, cmap=self.color, vmin=0, vmax=1)\n\n #method used to animate the plot. \"i\" is the frame number given by the animate function, but not needed here\n def animate(self, i):\n self.ax.clear()\n self.setRoomAxis()\n self.plotData()\n\n def setSlice(self, slice):\n self.slice = slice\n self.updateTitle()\n\n def setP(self, p):\n self.p = p\n self.updateTitle()\n \n #updates the title above the plot to correctly display the p value and the \"slice\"\n def updateTitle(self):\n if self.view == 0:\n self.fig.suptitle(\"IDW Hydrogen concentration, p: \" + str(self.p) + \", at height: \" + str(self.slice))\n elif self.view == 1:\n self.fig.suptitle(\"IDW Hydrogen concentration, p: \" + str(self.p) + \", at width: \" + str(self.slice))\n else:\n self.fig.suptitle(\"IDW Hydrogen concentration, p: \" + str(self.p) + \", at length: \" + str(self.slice))\n \n #returns the inverse distance weighted value of the x, y location in relation to the sensorvalues\n # p is an IDW variable that specifies the importance of the closest sensor. A larger p value means that closer sensors are more important\n def calcPointValue(self, x, y):\n \n A = 0\n B = 0\n for index, sensor in self.sensors.items():\n C = 1/np.power(self.distance(x, y, self.slice, [sensor['x'],sensor['y'],sensor['z']]), self.p)\n A += C*sensor['value']\n B += C\n\n return A / B\n\n #returns the distance between the x, y, z and a list of an x, y and z\n def distance(self, x, y, z, other):\n return np.sqrt(np.sum(np.square(np.array([x, y, z]) - np.array(other))))\n\n #returns the figure that contains the plot\n def getFig(self):\n return self.fig\n\n\n\nclass Plot3D:\n # initialize the room \n def __init__(self, l, w, h):\n self.sensors = {}\n self.obstacles = {}\n self.fig = Figure(facecolor='xkcd:brown', dpi=100)\n\n self.ax = self.fig.add_subplot(111, projection='3d')\n self.fig.tight_layout()\n self.l = l\n self.w = w\n self.h = h\n\n self.ax.grid(False)\n self.ax.set_facecolor('xkcd:brown')\n self.updateRoom(l, w, h)\n \n '''returns a Figure that is holded by the Plot3D object'''\n def getFig(self):\n return self.fig\n\n '''takes int, int, int and updates the dimensions on the Plot3D object to set te room size'''\n def updateRoom(self, l, w, h):\n self.l = l\n self.w = w\n self.h = h\n\n\n '''takes int, int, int and updates the plot room size'''\n def setRoomAxis(self, l, w, h):\n self.ax.grid(False)\n self.ax.set_facecolor('xkcd:brown')\n self.ax.set_xlim([0, l])\n self.ax.set_ylim([0, w])\n self.ax.set_zlim([0, h])\n self.ax.set_box_aspect(aspect=(l, w, h))\n\n '''takes int, int, int, int and adds it to a list of sensors'''\n def addSensor(self, sensorId, x, y, z):\n self.sensors[int(sensorId)] = {}\n self.sensors[int(sensorId)]['x'] = x\n self.sensors[int(sensorId)]['y'] = y\n self.sensors[int(sensorId)]['z'] = z\n self.sensors[int(sensorId)]['value'] = 0.\n \n '''plots all sensors on the Plot3D object'''\n def plotSensors(self):\n for sensorId, sensorData in self.sensors.items():\n if sensorData['value'] < 0.1:\n self.ax.plot(sensorData['x'], sensorData['y'], sensorData['z'], 'o',color='#95A5A6',markersize=5)\n if sensorData['value'] > 0.1:\n self.ax.plot(sensorData['x'], sensorData['y'], sensorData['z'], 'o',color='g',markersize=10)\n if sensorData['value'] > 0.2:\n self.ax.plot(sensorData['x'], sensorData['y'], sensorData['z'], 'o',color='b',markersize=20)\n if sensorData['value'] > 0.4:\n self.ax.plot(sensorData['x'], sensorData['y'], sensorData['z'], 'o',color='#FF5733',markersize=40)\n if sensorData['value'] > 0.6:\n self.ax.plot(sensorData['x'], sensorData['y'], sensorData['z'], 'o',color='r',markersize=60)\n \n\n '''takes int, int, int, int and updates the sensor with the given sensor id in the list with sensors'''\n def updateSensor(self, sensorId, x, y, z):\n self.sensors[int(sensorId)]['x'] = x\n self.sensors[int(sensorId)]['y'] = y\n self.sensors[int(sensorId)]['z'] = z\n\n '''takes int, int, int, int and adds it to a list of obstacles'''\n def addObstacle(self, obstacleId, x1, y1, z1, x2, y2, z2):\n self.obstacles[obstacleId] = {}\n self.obstacles[obstacleId]['positions'] = (x1,y1,z1)\n self.obstacles[obstacleId]['sizes'] = (x2,y2,z2)\n\n '''takes int, int, int, int and updates the obstacle with the given obstacle id in the list with obstacles'''\n def updateObstacle(self, obstacleId, x1, y1, z1, x2, y2, z2):\n self.obstacles[obstacleId]['positions'] = (x1,y1,z1)\n self.obstacles[obstacleId]['sizes'] = (x2,y2,z2)\n \n '''plots all obstacles on the Plot3D object'''\n def plotObstacles(self):\n for obstacleId, obstacle in self.obstacles.items():\n self.plotCubeAt(pos=obstacle['positions'], size=obstacle['sizes'], ax=self.ax)\n\n '''takes int, float and updates the value of a sensor with the given sensor id'''\n def updateSensorData(self, sensorId, sensorValue):\n self.sensors[int(sensorId)]['value'] = sensorValue\n\n '''takes a tuple(int,int,int), tuple(int,int,int) and generates data for plotting a obstacle'''\n def cuboid_data(self, o, size=(1,1,1)):\n l, w, h = size\n x = [[o[0], o[0] + l, o[0] + l, o[0], o[0]],\n [o[0], o[0] + l, o[0] + l, o[0], o[0]],\n [o[0], o[0] + l, o[0] + l, o[0], o[0]],\n [o[0], o[0] + l, o[0] + l, o[0], o[0]]]\n y = [[o[1], o[1], o[1] + w, o[1] + w, o[1]],\n [o[1], o[1], o[1] + w, o[1] + w, o[1]],\n [o[1], o[1], o[1], o[1], o[1]],\n [o[1] + w, o[1] + w, o[1] + w, o[1] + w, o[1] + w]]\n z = [[o[2], o[2], o[2], o[2], o[2]],\n [o[2] + h, o[2] + h, o[2] + h, o[2] + h, o[2] + h],\n [o[2], o[2], o[2] + h, o[2] + h, o[2]],\n [o[2], o[2], o[2] + h, o[2] + h, o[2]]]\n return np.array(x), np.array(y), np.array(z)\n\n '''takes a tuple(int,int,int), tuple(int,int,int), Axis object with the current plot info and plots a obstacle'''\n def plotCubeAt(self, pos=(0, 0, 0), size=(1, 1, 1), ax=None, **kwargs):\n # Plotting a cube element at position pos\n if ax !=None:\n X, Y, Z = self.cuboid_data(pos, size )\n plot = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, **kwargs)\n return plot\n\n '''method is used to animate continues data'''\n def animate(self, i):\n self.ax.clear()\n self.setRoomAxis(self.l, self.w, self.h)\n self.plotSensors()\n self.plotObstacles()\n", "sub_path": "Visualization.py", "file_name": "Visualization.py", "file_ext": "py", "file_size_in_byte": 11018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.figure.Figure", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.figure.Figure", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}]} +{"seq_id": "63252890", "text": "import cv2\n\n# Detector de faces\ndetectorFace = cv2.CascadeClassifier(\"haarcascade-frontalface-default.xml\")\n# Cria um reconhecedor com o eigenface\nreconhecedor = cv2.face.EigenFaceRecognizer_create()\n# Ler o classificador criado a partir das fotos tiradas - Foi criado no Treinamento.py\nreconhecedor.read(\"classificadorEigen.yml\")\n# Largura e altura das imagem\nlargura, altura = 220, 220\n# Criando uma fonte\nfont = cv2.FONT_HERSHEY_COMPLEX_SMALL\n# Fazendo a conexão com a Webcam\ncamera = cv2.VideoCapture(0)\n\nwhile (True):\n # Escrevendo nas variáveis a imagem lida pela Webcam\n conectado, imagem = camera.read()\n # Transformando a imagem capturada em escala de cinza\n imagemCinza = cv2.cvtColor(imagem, cv2.COLOR_BGR2GRAY)\n # Detectando as faces através do haarcascade e a imagem em escala de cinza\n facesDetectadas = detectorFace.detectMultiScale(imagemCinza, scaleFactor=1.5, minSize=(30,30))\n for (x, y, l, a) in facesDetectadas:\n # Redimensionando a imagem\n imagemFace = cv2.resize(imagemCinza[y:y + a, x:x + l], (largura, altura))\n # Colocando um retângulo na imagem para visualização\n cv2.rectangle(imagem, (x, y), (x + l, y + a), (0,0,255), 2)\n #\n id, confianca = reconhecedor.predict(imagemFace)\n nome = \"\"\n # Através do classificador obtemos o id da imagem reconhecida\n if id == 1:\n nome = 'Leandro'\n elif id == 2:\n nome = 'Adriana'\n\n # Quanto menor o valor de confiança melhor (Variável confiança- distância que a\n # imagem do classificador está da original)\n if confianca < 7000:\n # Escrevendo na imagem o nome\n cv2.putText(imagem, nome, (x,y +(a+30)), font, 2, (0,0,255))\n cv2.putText(imagem, str(confianca), (x,y + (a+50)), font, 1, (0,0,255))\n\n # Criando uma janela para mostrar a imagem com o título \"Face\"\n cv2.imshow(\"Face\", imagem)\n # Caso o usuário aperte a letra \"q\"\n if cv2.waitKey(1) == ord('q'):\n break\n\n\n# Limpa tudo que tinha criado anteriormente\ncamera.release()\ncv2.destroyAllWindows()\n", "sub_path": "Reconhecedor_eigenfaces.py", "file_name": "Reconhecedor_eigenfaces.py", "file_ext": "py", "file_size_in_byte": 2113, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.face.EigenFaceRecognizer_create", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.face", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX_SMALL", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "543604544", "text": "#!/usr/bin/env python\n\n\"\"\"\nTF-IDF methods\nComputes ranking based TF-IDF score for each term per conference\n\"\"\"\n\nfrom _analyzer import *\nfrom _builder import * \nimport math\n\n# setup logging\nlog = logging.getLogger('_tdidf')\nlogging.basicConfig(level=logging.DEBUG)\n\ndef computeIDF(keywords,filelist,filepath,category_type):\n\t\"\"\"\n\tCompute IDF for each keyword\n\t\"\"\"\n\tdoc_topics = []\n\tfor f in filelist:\n\t\ttopicsInCategory = dict()\n\t\t# logging.info(f)\n\t\twith open(filepath+'/'+f, 'r') as f2:\n\t\t\treader = csv.reader(f2,delimiter=',')\n\t\t\tfor index,row in enumerate(reader):\n\t\t\t\t# if(index>=500):\n\t\t\t\t# \t# Take up till 500? \n\t\t\t\t# \tbreak\n\t\t\t\ttopicsInCategory[row[0]]=row[1]\n\t\tdoc_topics.append(topicsInCategory)\n\tlogging.info('Total number of documents %d',len(doc_topics))\n\t# logging.info(topics)\n\tidf = dict()\n\tfor k in keywords:\n\t\tcount = 0 \n\t\tfor d in doc_topics:\n\t\t\tif(k in d):\n\t\t\t\tcount+=1\n\t\t\t\tcontinue\n\t\ttry:\n\t\t\tidf_score = math.log(1 + len(filelist) / count)\n\t\texcept:\n\t\t\tidf_score = math.log(1)\n\t\tidf[k]=idf_score\n\t#logging.info(idf)\n\treturn idf,doc_topics\n\ndef getDocIndex(fname):\n\t\"\"\"\n\tBuild document index from idx file\n\t\"\"\"\n\tdocIndex = [line.rstrip() for line in open(fname)]\n\treturn docIndex\n\ndef buildCategoricalVectors(idf,doc_topics,filelist,keywords,cat,save=True):\n\t\"\"\"\n\tBuilds Categorical Vectors\n\t\"\"\"\n\tvector = np.zeros((len(doc_topics),len(keywords)))\n\t# Should get a D by V matrix\n\n\tfor j,topic in enumerate(keywords):\n\t\tfor i, doc in enumerate(doc_topics):\n\t\t\tif topic in doc:\n\t\t\t\ttf = doc[topic]\n\t\t\t\tidf_score = idf[topic]\n\t\t\t\ttf_idf = float(tf) * idf_score\n\t\t\t\tvector[i][j] = tf_idf\n\t\t\telse:\n\t\t\t\tvector[i][j] = 0\n\tlogging.info(vector.shape)\n\tif(save):\n\t\tnp.save('Models/tfidf_'+cat+'_vectors.npy',vector)\n\t\tlogging.info(\"Saved to /Models\")\n\t\twith open('Models/tfidf_'+cat+'.idx','w') as f:\n\t\t\tfor f2 in filelist:\n\t\t\t\ttry:\n\t\t\t\t\toutput = f2[:f2.index('_')]\n\t\t\t\texcept:\n\t\t\t\t\toutput = f2\n\t\t\t\tf.write(output +'\\n')\n\treturn vector\n\ndef similarDocument(name,doc_index,model,topn=10):\n\tfrom scipy.spatial.distance import cosine\n\timport collections\n\timport operator\n\tif(name not in doc_index):\n\t\tlogging.info(\"Name of document does not exist\")\n\t\treturn False\n\tidx = doc_index.index(name)\n\tourVec = model[idx,:]\n\tlogging.info(ourVec.shape)\n\n\tdistances = dict()\n\tfor i in range(len(doc_index)):\n\t\tdist = cosine(ourVec,model[i][:])\n\t\tdistances[doc_index[i]]=(1-dist)\n\tod = collections.OrderedDict(sorted(distances.items(),reverse=True,key=operator.itemgetter(1)))\n\tlogging.info(od)\n\nif __name__=='__main__':\n\tcategory_type = 'booktitle'\n\tfilepath = 'Output/'+category_type\n\tkeywords = getKeywords()\n\tfilelist = readFileDirectory(filepath)\n\tfilelist = [x for x in filelist if x.endswith('_p.csv')]\n\tidf,doc_topics = computeIDF(keywords,filelist,filepath,category_type)\n\tdoc_index = getDocIndex('Models/tfidf_booktitle.idx')\n\tlogging.info(doc_index)\n\tmodel = buildCategoricalVectors(idf,doc_topics,filelist,keywords,category_type)\n\tsimilarDocument('SIGIR',doc_index,model)", "sub_path": "_tfidf.py", "file_name": "_tfidf.py", "file_ext": "py", "file_size_in_byte": 2977, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "math.log", "line_number": 42, "usage_type": "call"}, {"api_name": "math.log", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 98, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 100, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "425724205", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 18 09:38:11 2018\n\n@author: sanijo.durasevic\n\"\"\"\n\nimport DyMat as dm\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n\nfiles_list = ['0-300_oldCoeff_k_0.8', '0-300_oldCoeff_k_1.2']\n\ndef dy_mat_func(file):\n \n #for n in f.names():\n # print(n)\n \n f = dm.DyMatFile(file + \".mat\")\n\n time = f.abscissa('HC_B9.T', valuesOnly=True)\n B9_T = f.data('HC_B9.T')-273.15\n A7_T = f.data('HC_A7.T')-273.15\n A2_T = f.data('HC_A2.T')-273.15\n A11_T = f.data('HC_A11.T')-273.15\n B3_T = f.data('HC_B3.T')-273.15\n \n #speed\n v = f.data('Speed.y[1]')\n \n #Perscribed heat flow (conversion from W to kW)\n Q = f.data('timeTable.y[1]')/1000\n \n #Inlet and outlet temperature of HE or Radiator\n fi = f.data('fluid_inlet.y')\n fo = f.data('fluid_outlet.y')\n \n #Conversion of arrays to lists\n list1 = time.tolist()\n list2 = B9_T.tolist()\n list3 = A7_T.tolist()\n list4 = A2_T.tolist()\n list5 = A11_T.tolist()\n list6 = B3_T.tolist()\n\n list7 = fi.tolist()\n list8 = fo.tolist()\n \n list9 = v.tolist()\n list10 = Q.tolist()\n \n #Plots\n f, axarr = plt.subplots(2, 2, figsize=(14, 10))\n \n axarr[0, 0].plot(list1, list2, label='T$_{B9}$')\n axarr[0, 0].plot(list1, list3, label='T$_{A7}$')\n axarr[0, 0].plot(list1, list4, label='T$_{A2}$')\n axarr[0, 0].plot(list1, list5, label='T$_{A11}$')\n axarr[0, 0].plot(list1, list6, label='T$_{B3}$')\n axarr[0, 0].set_xlabel(\"Time [s]\", fontsize=10)\n axarr[0, 0].set_ylabel(\"Temperature $[^{\\circ}C]$\", fontsize=10)\n axarr[0, 0].autoscale(tight=True)\n axarr[0, 0].grid(True)\n axarr[0, 0].legend()\n #axarr[0, 0].set_title(file)\n \n axarr[0, 1].plot(list1, list7, label='T$_{fluid\\; inlet}$')\n axarr[0, 1].plot(list1, list8, label='T$_{fluid\\; outlet}$')\n axarr[0, 1].set_xlabel(\"Time [s]\", fontsize=10)\n axarr[0, 1].set_ylabel(\"Temperature $[^{\\circ}C]$\", fontsize=10)\n axarr[0, 1].autoscale(tight=True)\n axarr[0, 1].grid(True)\n axarr[0, 1].legend()\n #axarr[0, 1].set_title(file)\n \n axarr[1, 0].plot(list1, list9)\n axarr[1, 0].set_xlabel(\"Time [s]\", fontsize=10)\n axarr[1, 0].set_ylabel(\"Velocity [km/h]\", fontsize=10)\n axarr[1, 0].autoscale(tight=True)\n axarr[1, 0].grid(True)\n #axarr[1, 0].set_title(file)\n \n axarr[1, 1].plot(list1, list10) \n axarr[1, 1].set_xlabel(\"Time [s]\", fontsize=10)\n axarr[1, 1].set_ylabel(\"Q [kW]\", fontsize=10)\n axarr[1, 1].autoscale(tight=True)\n axarr[1, 1].grid(True)\n # axarr[1, 1].set_title(file)\n \n plt.subplots_adjust(wspace=0.25, hspace=0.25)\n plt.suptitle(file)\n plt.savefig(file + \"_COMPLETE.pdf\", format='pdf')\n plt.show()\n \n\nfor file in files_list:\n dy_mat_func(file)", "sub_path": "myDyMat2.py", "file_name": "myDyMat2.py", "file_ext": "py", "file_size_in_byte": 2814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "DyMat.DyMatFile", "line_number": 21, "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": "matplotlib.pyplot.subplots_adjust", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}]} +{"seq_id": "303759541", "text": "import sys\nfrom random import randrange\nfrom PyQt5.QtGui import QPainter, QColor\nfrom PyQt5.QtWidgets import QApplication, QMainWindow\nfrom UI import Ui_yellow\n\n\nclass Applicaton(QMainWindow, Ui_yellow):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.is_clicked = False\n self.btn_start.clicked.connect(self.btn_start_clicked)\n\n def paintEvent(self, event):\n qp = QPainter()\n qp.begin(self)\n if self.is_clicked:\n self.start(qp)\n qp.end()\n\n def btn_start_clicked(self):\n self.is_clicked = True\n self.repaint()\n\n def start(self, qp):\n for i in range(100):\n d = randrange(20, 100)\n qp.setBrush(QColor(randrange(255), randrange(255), randrange(255)))\n qp.drawEllipse(randrange(700), randrange(300), d, d)\n\n\ndef main():\n app = QApplication(sys.argv)\n ex = Applicaton()\n ex.show()\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 8, "usage_type": "name"}, {"api_name": "UI.Ui_yellow", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 16, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 29, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 29, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "163418392", "text": "'''''\n--> Package Information:\n --------------------\n This package enables creation of word clouds from analyzing the csv files of the comments \n retrieved of various videos and plotting the top twenty words as a word cloud of the \n respective videos\n \n--> Version Control:\n ----------------\n Source:\n -------\n version 1.0, Date: April 23, 2019 \n'''''\n\n# ------------< Importing packages >------------\n\nimport pandas as pd\nimport os\nfrom nltk.corpus import stopwords\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\nimport matplotlib.pyplot as plt\nfrom nltk.probability import *\n\n# ------------< Creating class >------------\nclass CreatingWordCloud:\n\n # ------------< Creating respective wordclouds according to path >------------\n def word_Cloud(self, path):\n top_20_words = self.getMostUsedWords(path)\n wordcloud = WordCloud(width=800, height=400, max_words=20, background_color=\"white\").generate_from_frequencies(top_20_words)\n plt.figure(figsize=(20, 10), facecolor=None)\n plt.tight_layout(pad=0)\n plt.axis(\"off\")\n save_with_name = os.path.basename(os.path.normpath(path))\n wordcloud.to_file(\"./data/word_clouds/word_cloud_\"+save_with_name+\".png\")\n\n # ------------< Getting top 20 most used words in the comments of videos >------------\n def getMostUsedWords(self, path):\n try:\n all = pd.read_csv(path)\n stop_eng = stopwords.words('english')\n customstopwords = []\n tokens = []\n sentences = []\n tokenizedSentences = []\n for txt in all.text:\n sentences.append(txt.lower())\n tokenized = [t.lower().strip(\":,.!?\") for t in txt.split()]\n tokens.extend(tokenized)\n tokenizedSentences.append(tokenized)\n hashtags = [w for w in tokens if w.startswith('#')]\n ghashtags = [w for w in tokens if w.startswith('+')]\n mentions = [w for w in tokens if w.startswith('@')]\n links = [w for w in tokens if w.startswith('http') or w.startswith('www')]\n filtered_tokens = [w for w in tokens if\n not w in stop_eng and not w in customstopwords and w.isalpha() and not len(\n w) < 3 and not w in hashtags and not w in ghashtags and not w in links and not w in mentions]\n wordFrequency = FreqDist(filtered_tokens)\n return wordFrequency\n except Exception as e:\n print(\"The exception occurred is: \\t{0}\".format(e))\n\n\n# ------------< Execution starts here >------------\n\nif __name__ == \"__main__\":\n # ------------< Paths to analyze >------------\n paths_to_analyze = [\"./csv_files/commentsCSV_PewTop20V.csv\",\n \"./csv_files/commentsCSV_Bitch Lasagna Song.csv\",\n \"./csv_files/commentsCSV_Congratulations.csv\",\n \"./csv_files/commentsCSV_Flare TV.csv\",\n \"./csv_files/commentsCSV_TsrTop20V.csv\",\n \"./csv_files/commentsCSV_Pewdiepie vs T Series.csv\"]\n # ------------< Creating object of class >------------\n obj = CreatingWordCloud()\n\n # ------------< Analysis begins >------------\n for path in paths_to_analyze:\n obj.word_Cloud(path)", "sub_path": "CreateWordCloud.py", "file_name": "CreateWordCloud.py", "file_ext": "py", "file_size_in_byte": 3353, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "wordcloud.WordCloud", "line_number": 30, "usage_type": "call"}, {"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.tight_layout", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 34, "usage_type": "call"}, {"api_name": "wordcloud.to_file", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 41, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "111631356", "text": "\"\"\"Road network flow maps\n\"\"\"\nimport os\nimport sys\nfrom collections import OrderedDict\n\nimport geopandas as gpd\nimport pandas as pd\nimport cartopy.crs as ccrs\nimport cartopy.io.shapereader as shpreader\nimport matplotlib.pyplot as plt\nfrom shapely.geometry import LineString\nfrom atra.utils import *\nfrom tqdm import tqdm\n\ndef assign_veh_to_roads(x,veh_list):\n \"\"\"Assign terrain as flat or mountain to national roads\n\n Parameters\n x - Pandas DataFrame of values\n - dia_hinh__ - String value of type of terrain\n\n Returns\n String value of terrain as flat or mountain\n \"\"\"\n veh_no = 0\n road_no = str(x.road_name).split(',')\n road_no = [int(y) for y in road_no if y.isdigit() is True] + [y for y in road_no if y.isdigit() is False]\n\n for vals in veh_list:\n rn = str(vals.ruta)\n if rn.isdigit() is True:\n rn = int(rn)\n\n if rn in road_no and x.prog_min >= vals.inicio and x.prog_max <= vals.fin:\n veh_no = 0.01*(vals.ca + vals.semi)*vals.tmd\n break\n\n\n return veh_no\n\ndef main():\n tqdm.pandas()\n config = load_config()\n data_path = config['paths']['data']\n incoming_data_path = config['paths']['incoming_data']\n road_file_path = os.path.join(data_path,'network','road_edges.shp')\n # road_file_path = os.path.join(config['paths']['data'], 'network',\n # 'road_edges.shp')\n\n road_file = gpd.read_file(road_file_path,encoding='utf-8')\n road_file = road_file[road_file['road_type'] == 'national']\n\n road_veh = pd.read_excel(os.path.join(incoming_data_path,'5','DNV_data_recieved_06082018','TMDA y Clasificación 2016.xlsx'),sheet_name='Clasificación 2016',skiprows=14,encoding='utf-8-sig').fillna(0)\n road_veh.columns = map(str.lower, road_veh.columns)\n road_veh = list(road_veh.itertuples(index=False))\n road_file['veh_no'] = road_file.progress_apply(lambda x: assign_veh_to_roads(x, road_veh), axis=1)\n\n road_file_path = os.path.join(incoming_data_path, '5','Lineas de deseo OD- 2014','3.6.1.9.asignacion_vial',\n 'Asignacion_2014_vial.shp')\n dnv_file = gpd.read_file(road_file_path,encoding='utf-8')\n dnv_file.columns = map(str.lower, dnv_file.columns)\n dnv_file['road_type'] = 'national'\n\n veh_df = [road_file,dnv_file]\n\n plot_sets = [\n {\n 'file_tag': 'veh_no',\n 'legend_label': \"AADT (vehicles/day)\",\n 'divisor': 1,\n 'columns': ['veh_no'],\n 'title_cols': ['Daily vehicle count'],\n 'significance':0\n },\n {\n 'file_tag': 'dnv_no',\n 'legend_label': \"AADT ('000 vehicles/year)\",\n 'divisor': 1000,\n 'columns': ['total_grup'],\n 'title_cols': ['Annual vehicle count'],\n 'significance':0\n },\n ]\n\n for ps in range(len(plot_sets)):\n plot_set = plot_sets[ps]\n mode_file = veh_df[ps]\n for c in range(len(plot_set['columns'])):\n # basemap\n proj_lat_lon = ccrs.PlateCarree()\n ax = get_axes()\n plot_basemap(ax, data_path)\n scale_bar(ax, location=(0.8, 0.05))\n plot_basemap_labels(ax, data_path, include_regions=False)\n\n # generate weight bins\n column = plot_set['columns'][c]\n weights = [\n record[column]\n for iter_, record in mode_file.iterrows()\n ]\n max_weight = max(weights)\n width_by_range = generate_weight_bins(weights, n_steps=7, width_step=0.02)\n\n road_geoms_by_category = {\n 'national': [],\n 'none': [],\n }\n\n column = plot_set['columns'][c]\n for iter_, record in mode_file.iterrows():\n cat = str(record['road_type'])\n if cat not in road_geoms_by_category:\n raise Exception\n geom = record.geometry\n val = record[column]\n if val == 0:\n cat = 'none'\n\n buffered_geom = None\n for (nmin, nmax), width in width_by_range.items():\n if nmin <= val and val < nmax:\n buffered_geom = geom.buffer(width)\n\n if buffered_geom is not None:\n road_geoms_by_category[cat].append(buffered_geom)\n else:\n print(\"Feature was outside range to plot\", iter_)\n\n styles = OrderedDict([\n ('national', Style(color='#e41a1c', zindex=9, label='National')), # red\n ('none', Style(color='#969696', zindex=6, label='No value'))\n ])\n\n for cat, geoms in road_geoms_by_category.items():\n cat_style = styles[cat]\n ax.add_geometries(\n geoms,\n crs=proj_lat_lon,\n linewidth=0,\n facecolor=cat_style.color,\n edgecolor='none',\n zorder=cat_style.zindex\n )\n\n x_l = -62.4\n x_r = x_l + 0.4\n base_y = -42.1\n y_step = 0.8\n y_text_nudge = 0.2\n x_text_nudge = 0.2\n\n ax.text(\n x_l,\n base_y + y_step - y_text_nudge,\n plot_set['legend_label'],\n horizontalalignment='left',\n transform=proj_lat_lon,\n size=10)\n\n divisor = plot_set['divisor']\n significance_ndigits = plot_set['significance']\n max_sig = []\n for (i, ((nmin, nmax), line_style)) in enumerate(width_by_range.items()):\n if round(nmin/divisor, significance_ndigits) < round(nmax/divisor, significance_ndigits):\n max_sig.append(significance_ndigits)\n elif round(nmin/divisor, significance_ndigits+1) < round(nmax/divisor, significance_ndigits+1):\n max_sig.append(significance_ndigits+1)\n elif round(nmin/divisor, significance_ndigits+2) < round(nmax/divisor, significance_ndigits+2):\n max_sig.append(significance_ndigits+2)\n else:\n max_sig.append(significance_ndigits+3)\n\n significance_ndigits = max(max_sig)\n\n for (i, ((nmin, nmax), width)) in enumerate(width_by_range.items()):\n y = base_y - (i*y_step)\n line = LineString([(x_l, y), (x_r, y)]).buffer(width)\n ax.add_geometries(\n [line],\n crs=proj_lat_lon,\n linewidth=0,\n edgecolor='#000000',\n facecolor='#000000',\n zorder=2)\n if nmin == max_weight:\n value_template = '>{:.' + str(significance_ndigits) + 'f}'\n label = value_template.format(\n round(max_weight/divisor, significance_ndigits))\n else:\n value_template = '{:.' + str(significance_ndigits) + \\\n 'f}-{:.' + str(significance_ndigits) + 'f}'\n label = value_template.format(\n round(nmin/divisor, significance_ndigits), round(nmax/divisor, significance_ndigits))\n\n ax.text(\n x_r + x_text_nudge,\n y - y_text_nudge,\n label,\n horizontalalignment='left',\n transform=proj_lat_lon,\n size=10)\n\n plt.title('AADT - {}'.format(plot_set['title_cols'][c]), fontsize=10)\n legend_from_style_spec(ax, styles)\n output_file = os.path.join(\n config['paths']['figures'],\n 'road_traffic-map-{}-{}.png'.format(plot_set['file_tag'], column))\n save_fig(output_file)\n plt.close()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "src/atra/plot/road_dnv_estimates.py", "file_name": "road_dnv_estimates.py", "file_ext": "py", "file_size_in_byte": 8033, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tqdm.tqdm.pandas", "line_number": 43, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "geopandas.read_file", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "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": "geopandas.read_file", "line_number": 61, "usage_type": "call"}, {"api_name": "cartopy.crs.PlateCarree", "line_number": 91, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 91, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 131, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "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": "matplotlib.pyplot.close", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}]} +{"seq_id": "313257186", "text": "# Write your model configuration here\n\nfrom datetime import datetime\n\nDATETIME_NOW = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\nSEQUENCE_LEN = 1000\nEPOCH = 100\nSPLIT_RATIO = 0.2 # Validation dataset as a %\nBATCH_SIZE = 16\nSEED = 2019", "sub_path": "rnn-model-many2one/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "datetime.datetime.now", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "465709628", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 5 11:23:34 2020\n\n@author: premchand\n\"\"\"\nimport torch\nimport numpy as np\nimport matlab.engine\n\nclass Environment():\n def __init__(self, num_steps, actions, proc_num, device = \"cuda\"):\n self.num_steps = num_steps\n # self.init_state = init_state\n self.actions = actions # dictionary of indexed primitives (in degs)\n # self.p_st_foot = p_st_foot\n self.p = proc_num\n self.device = device\n \n \n def gen_data(self, policy, seed, torch_seed):\n try:\n eng = matlab.engine.start_matlab()\n except matlab.engine.EngineError: \n eng = matlab.engine.start_matlab()\n eng.cd('/home/rhaegar/Github/3D_biped_updated/')\n eng.addpath(eng.genpath(eng.pwd()))\n \n params = eng.gen_params(seed, nargout = 1)\n \n x2m = params['x0']\n init_state = params['init_state']\n # self.init_state + params[\"w_s\"] # add random noise to the initial state\n t = matlab.double([0])\n p_st_foot = params['p_st_foot']\n # actions = {} # define actions as an empty dictionary \n k = 1 # step counter\n # d = 0\n cost1, cost2, cost = 0, 0, 0\n max_dev = 0\n is_fallen = False\n prim_seq = []\n eng.workspace['simout'] = []\n \n while(k <= self.num_steps and is_fallen == False):\n # pass the state vector to the policy \n res = policy(torch.tensor(init_state).view(1,20).to(self.device))[0]\n # choose the largest componet of res\n res = int(res.max(0).indices)\n # pick action according to the largest component\n turn = self.actions[str(res)] # int value of turn in degrees (hope so) \n prim_seq.append(turn)\n beta = eng.compute_beta(matlab.double([turn]), nargout = 1)\n \n out1 = eng.left_stance_transition1(t, x2m, beta, p_st_foot, params, nargout = 5)\n x1m = out1[0]\n te1 = out1[1]\n p_stance_footR = out1[2]\n is_fallen = out1[3]\n simL = out1[4]\n t_float = np.float32(t)\n if is_fallen == False and (te1-t_float) > 0.1:\n Fxl = simL['Fx']\n Fyl = simL['Fy']\n cost1 += simL['cost']\n max_dev = max(max_dev,simL['max_dev'])\n out2 = eng.right_stance_transition1(te1, x1m, beta, p_stance_footR, params, nargout = 5)\n x2m = out2[0]\n te2 = out2[1]\n p_stance_footL = out2[2]\n dist = np.linalg.norm(p_stance_footR)\n is_fallen = out2[3]\n simR = out2[4]\n if is_fallen == False and (te2 - te1) > 0.1:\n Fxr = simR['Fx']\n Fyr = simR['Fy']\n cost2 += simR['cost']\n max_dev = max(max_dev,simR['max_dev'])\n dist = np.linalg.norm(p_stance_footL)\n \n Fx = Fxl + Fxr\n Fy = Fyl + Fyr\n else:\n cost2 += 0\n is_fallen = True\n break\n else:\n is_fallen = True\n cost1 += 0\n break\n init_state = np.concatenate((x2m, Fx, Fy) ,axis = None)\n init_state = np.float32(init_state)\n t = te2\n p_st_foot = p_stance_footL\n cost = (cost1 + cost2)/k**2\n k = k+1\n \n \n # if k<=25:\n # max_dev = 1\n print('Policy seed: {}, Env: {}, steps: {}, dist: {:.3f}, max_dev: {:.3f}, cost: {:.3f}'.format(seed, \n torch_seed, k, dist, max_dev,\n # d, \n # cost))\n max_dev/dist))\n \n \n eng.quit()\n # return cost\n return max_dev/dist", "sub_path": "biped_gen_data.py", "file_name": "biped_gen_data.py", "file_ext": "py", "file_size_in_byte": 3995, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matlab.engine.engine.start_matlab", "line_number": 24, "usage_type": "call"}, {"api_name": "matlab.engine.engine", "line_number": 24, "usage_type": "attribute"}, {"api_name": "matlab.engine", "line_number": 24, "usage_type": "name"}, {"api_name": "matlab.engine.engine", "line_number": 25, "usage_type": "attribute"}, {"api_name": "matlab.engine", "line_number": 25, "usage_type": "name"}, {"api_name": "matlab.engine.engine.start_matlab", "line_number": 26, "usage_type": "call"}, {"api_name": "matlab.engine.engine", "line_number": 26, "usage_type": "attribute"}, {"api_name": "matlab.engine", "line_number": 26, "usage_type": "name"}, {"api_name": "matlab.engine.double", "line_number": 35, "usage_type": "call"}, {"api_name": "matlab.engine", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 48, "usage_type": "call"}, {"api_name": "matlab.engine.double", "line_number": 54, "usage_type": "call"}, {"api_name": "matlab.engine", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "259848659", "text": "import random\nimport json\nimport copy\nimport re\nimport numpy as np\n\n\ndef remove_brackets(x):\n y = x\n if x[0] == \"(\" and x[-1] == \")\":\n x = x[1:-1]\n flag = True\n count = 0\n for s in x:\n if s == \")\":\n count -= 1\n if count < 0:\n flag = False\n break\n elif s == \"(\":\n count += 1\n if flag:\n return x\n return y\n\n\ndef check_bracket(x, english=False):\n if english:\n for idx, s in enumerate(x):\n if s == '[':\n x[idx] = '('\n elif s == '}':\n x[idx] = ')'\n s = x[0]\n idx = 0\n if s == \"(\":\n flag = 1\n temp_idx = idx + 1\n while flag > 0 and temp_idx < len(x):\n if x[temp_idx] == \")\":\n flag -= 1\n elif x[temp_idx] == \"(\":\n flag += 1\n temp_idx += 1\n if temp_idx == len(x):\n x = x[idx + 1:temp_idx - 1]\n elif x[temp_idx] != \"*\" and x[temp_idx] != \"/\":\n x = x[idx + 1:temp_idx - 1] + x[temp_idx:]\n while True:\n y = len(x)\n for idx, s in enumerate(x):\n if s == \"+\" and idx + 1 < len(x) and x[idx + 1] == \"(\":\n flag = 1\n temp_idx = idx + 2\n while flag > 0 and temp_idx < len(x):\n if x[temp_idx] == \")\":\n flag -= 1\n elif x[temp_idx] == \"(\":\n flag += 1\n temp_idx += 1\n if temp_idx == len(x):\n x = x[:idx + 1] + x[idx + 2:temp_idx - 1]\n break\n elif x[temp_idx] != \"*\" and x[temp_idx] != \"/\":\n x = x[:idx + 1] + x[idx + 2:temp_idx - 1] + x[temp_idx:]\n break\n if y == len(x):\n break\n return x\n\n lx = len(x)\n for idx, s in enumerate(x):\n if s == \"[\":\n flag_b = 0\n flag = False\n temp_idx = idx\n while temp_idx < lx:\n if x[temp_idx] == \"]\":\n flag_b += 1\n elif x[temp_idx] == \"[\":\n flag_b -= 1\n if x[temp_idx] == \"(\" or x[temp_idx] == \"[\":\n flag = True\n if x[temp_idx] == \"]\" and flag_b == 0:\n break\n temp_idx += 1\n if not flag:\n x[idx] = \"(\"\n x[temp_idx] = \")\"\n continue\n if s == \"(\":\n flag_b = 0\n flag = False\n temp_idx = idx\n while temp_idx < lx:\n if x[temp_idx] == \")\":\n flag_b += 1\n elif x[temp_idx] == \"(\":\n flag_b -= 1\n if x[temp_idx] == \"[\":\n flag = True\n if x[temp_idx] == \")\" and flag_b == 0:\n break\n temp_idx += 1\n if not flag:\n x[idx] = \"[\"\n x[temp_idx] = \"]\"\n return x\n\n\n# Return a list of indexes, one for each word in the sentence, plus EOS\ndef indexes_from_sentence(lang, sentence, tree=False):\n res = []\n # if \"[SOS]\" in lang.index2word and not tree:\n # res.append(lang.word2index[\"[SOS]\"])\n for word in sentence:\n if len(word) == 0:\n continue\n if word in lang.word2index:\n res.append(lang.word2index[word])\n else:\n res.append(lang.word2index[\"[UNK]\"])\n if \"[EOS]\" in lang.index2word and not tree:\n res.append(lang.word2index[\"[EOS]\"])\n return res\n\n\ndef indexes_from_constants(lang, word_list):\n res = []\n # if \"[SOS]\" in lang.index2word and not tree:\n # res.append(lang.word2index[\"[SOS]\"])\n for word in word_list:\n if len(word) == 0:\n continue\n if word in lang.word2index:\n res.append(lang.word2index[word])\n return res\n\n\n# Pad a with the PAD symbol\ndef pad_seq(seq, seq_len, max_length, pad_token=0):\n seq += [pad_token for _ in range(max_length - seq_len)]\n return seq\n\n\n# 用于获取等式中没有出现在输出字典中的数字\ndef get_num_stack(eq, output_lang, num_pos):\n num_stack = []\n for word in eq:\n temp_num = []\n flag_not = True\n if word not in output_lang.index2word:\n flag_not = False\n for i, j in enumerate(num_pos):\n if j == word:\n temp_num.append(i)\n if not flag_not and len(temp_num) != 0: # 数字/符号不在词表中，但在等式中出现\n num_stack.append(temp_num)\n if not flag_not and len(temp_num) == 0: # 数字/符号不在词表中，且不在等式中出现\n num_stack.append([_ for _ in range(len(num_pos))])\n num_stack.reverse()\n return num_stack\n\n\n# 将模型输出的表达式(id表示)转换为真正human可读的表达式\ndef convert_expression_list(expression, output_lang, num_list, num_stack=None):\n max_index = output_lang.n_words\n res = []\n for i in expression:\n # if i == 0:\n # return res\n if i < max_index - 1:\n idx = output_lang.index2word[i]\n if idx[0] == \"N\":\n if int(idx[1:]) >= len(num_list):\n return None\n res.append(num_list[int(idx[1:])])\n else:\n res.append(idx)\n else:\n pos_list = num_stack.pop()\n c = num_list[pos_list[0]]\n res.append(c)\n return res\n\n\ndef get_pretrained_embedding_weight(word2vec_path, lang, dims=300):\n lines = open(word2vec_path, 'r', encoding=\"utf-8\").readlines()\n word2vec_dict = {}\n for line in lines[1:]:\n segs = line.split()\n key = []\n key_end = 0\n for idx, seg in enumerate(segs):\n try:\n float(seg)\n key_end = idx\n break\n except:\n key.append(seg)\n if key_end == 0:\n key = segs[0]\n value = [float(seg) for seg in segs[1:]]\n else:\n key = ''.join(key)\n value = [float(seg) for seg in segs[key_end:]]\n word2vec_dict[key] = value\n\n special_tokens = [\"[PAD]\", \"[NUM]\", \"[UNK]\", \"[SOS]\", \"[EOS]\"]\n vocab_size = len(lang.index2word)\n embedding_weight = np.zeros((vocab_size, dims))\n for idx, word in enumerate(lang.index2word):\n if word in word2vec_dict.keys():\n embedding_weight[idx] = word2vec_dict[word]\n else:\n if word in special_tokens:\n embedding_weight[idx] = np.random.uniform(-1, 1, dims)\n else:\n embedding_weight[idx] = np.zeros((dims))\n for t in word:\n if t in word2vec_dict.keys():\n embedding_weight[idx] += word2vec_dict[t]\n else:\n embedding_weight[idx] += np.random.uniform(-1, 1, dims)\n embedding_weight[idx] /= len(word)\n\n return embedding_weight\n\ndef read_json(path):\n with open(path,'r') as f:\n file = json.load(f)\n return file\n\ndef change_num(num):\n new_num = []\n for item in num:\n if '/' in item:\n new_str = item.split(')')[0]\n new_str = new_str.split('(')[1]\n a = float(new_str.split('/')[0])\n b = float(new_str.split('/')[1])\n value = a/b\n new_num.append(value)\n elif '%' in item:\n value = float(item[0:-1])/100\n new_num.append(value)\n else:\n new_num.append(float(item))\n return new_num\n\n\n# num net graph\ndef get_lower_num_graph(max_len, sentence_length, num_list, id_num_list,contain_zh_flag=True):\n diag_ele = np.zeros(max_len)\n num_list = change_num(num_list)\n for i in range(sentence_length):\n diag_ele[i] = 1\n graph = np.diag(diag_ele)\n if not contain_zh_flag:\n return graph\n for i in range(len(id_num_list)):\n for j in range(len(id_num_list)):\n if float(num_list[i]) <= float(num_list[j]):\n graph[id_num_list[i]][id_num_list[j]] = 1\n else:\n graph[id_num_list[j]][id_num_list[i]] = 1\n return graph\n\n\ndef get_greater_num_graph(max_len, sentence_length, num_list, id_num_list,contain_zh_flag=True):\n diag_ele = np.zeros(max_len)\n num_list = change_num(num_list)\n for i in range(sentence_length):\n diag_ele[i] = 1\n graph = np.diag(diag_ele)\n if not contain_zh_flag:\n return graph\n for i in range(len(id_num_list)):\n for j in range(len(id_num_list)):\n if float(num_list[i]) > float(num_list[j]):\n graph[id_num_list[i]][id_num_list[j]] = 1\n else:\n graph[id_num_list[j]][id_num_list[i]] = 1\n return graph\n\n\n# attribute between graph\ndef get_attribute_between_graph(input_batch, max_len, id_num_list, sentence_length, quantity_cell_list, contain_zh_flag=True):\n diag_ele = np.zeros(max_len)\n for i in range(sentence_length):\n diag_ele[i] = 1\n graph = np.diag(diag_ele)\n #quantity_cell_list = quantity_cell_list.extend(id_num_list)\n if not contain_zh_flag:\n return graph\n for i in id_num_list:\n for j in quantity_cell_list:\n if i < max_len and j < max_len and j not in id_num_list and abs(i-j) < 4:\n graph[i][j] = 1\n graph[j][i] = 1\n for i in quantity_cell_list:\n for j in quantity_cell_list:\n if i < max_len and j < max_len:\n if input_batch[i] == input_batch[j]:\n graph[i][j] = 1\n graph[j][i] = 1\n return graph\n\n\n# quantity between graph\ndef get_quantity_between_graph(max_len, id_num_list, sentence_length, quantity_cell_list,contain_zh_flag=True):\n diag_ele = np.zeros(max_len)\n for i in range(sentence_length):\n diag_ele[i] = 1\n graph = np.diag(diag_ele)\n #quantity_cell_list = quantity_cell_list.extend(id_num_list)\n if not contain_zh_flag:\n return graph\n for i in id_num_list:\n for j in quantity_cell_list:\n if i < max_len and j < max_len and j not in id_num_list and abs(i-j) < 4:\n graph[i][j] = 1\n graph[j][i] = 1\n for i in id_num_list:\n for j in id_num_list:\n graph[i][j] = 1\n graph[j][i] = 1\n return graph\n\n\n# quantity cell graph\ndef get_quantity_cell_graph(max_len, id_num_list, sentence_length, quantity_cell_list,contain_zh_flag=True):\n diag_ele = np.zeros(max_len)\n for i in range(sentence_length):\n diag_ele[i] = 1\n graph = np.diag(diag_ele)\n #quantity_cell_list = quantity_cell_list.extend(id_num_list)\n if not contain_zh_flag:\n return graph\n for i in id_num_list:\n for j in quantity_cell_list:\n if i < max_len and j < max_len and j not in id_num_list and abs(i-j) < 4:\n graph[i][j] = 1\n graph[j][i] = 1\n return graph\n\n\ndef get_single_batch_graph(input_batch, input_length,group,num_value,num_pos):\n batch_graph = []\n max_len = max(input_length)\n for i in range(len(input_length)):\n input_batch_t = input_batch[i]\n sentence_length = input_length[i]\n quantity_cell_list = group[i]\n num_list = num_value[i]\n id_num_list = num_pos[i]\n graph_newc = get_quantity_cell_graph(max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_greater = get_greater_num_graph(max_len, sentence_length, num_list, id_num_list)\n graph_lower = get_lower_num_graph(max_len, sentence_length, num_list, id_num_list)\n graph_quanbet = get_quantity_between_graph(max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_attbet = get_attribute_between_graph(input_batch_t, max_len, id_num_list, sentence_length, quantity_cell_list)\n #graph_newc1 = get_quantity_graph1(input_batch_t, max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_total = [graph_newc.tolist(),graph_greater.tolist(),graph_lower.tolist(),graph_quanbet.tolist(),graph_attbet.tolist()]\n batch_graph.append(graph_total)\n batch_graph = np.array(batch_graph)\n return batch_graph\n\n\ndef get_single_example_graph(input_batch, input_length, group, num_value, num_pos):\n batch_graph = []\n max_len = input_length\n sentence_length = input_length\n quantity_cell_list = group\n num_list = num_value\n id_num_list = num_pos\n graph_newc = get_quantity_cell_graph(max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_quanbet = get_quantity_between_graph(max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_attbet = get_attribute_between_graph(input_batch, max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_greater = get_greater_num_graph(max_len, sentence_length, num_list, id_num_list)\n graph_lower = get_greater_num_graph(max_len, sentence_length, num_list, id_num_list)\n #graph_newc1 = get_quantity_graph1(input_batch, max_len, id_num_list, sentence_length, quantity_cell_list)\n graph_total = [graph_newc.tolist(),graph_greater.tolist(),graph_lower.tolist(),graph_quanbet.tolist(),graph_attbet.tolist()]\n batch_graph.append(graph_total)\n batch_graph = np.array(batch_graph)\n return batch_graph\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": "src/data_utils.py", "file_name": "data_utils.py", "file_ext": "py", "file_size_in_byte": 13600, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.zeros", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 224, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 384, "usage_type": "call"}]} +{"seq_id": "310781347", "text": "import os\nimport sys, getopt\nfrom io import StringIO\n\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfpage import PDFPage\n\n#Convert the pdf cv to text\ndef pdf_to_text(file_name, pages=None):\n #In case there are mutiple pages\n if not pages:\n pagenumber = set()\n else:\n pagenumber = set(pages)\n\n output = StringIO()\n manager = PDFResourceManager()\n converter = TextConverter(manager, output, laparams=LAParams())\n interpreter = PDFPageInterpreter(manager, converter)\n\n input_file = open(file_name, 'rb')\n for page in PDFPage.get_pages(input_file, pagenumber):\n interpreter.process_page(page)\n input_file.close()\n converter.close()\n text = output.getvalue()\n output.close\n return text\n\n\n#the pdf resumes are to be stored in the Candidates directory.\n\nresume_directory = \"Candidates\"\ntext_directory = \"Candidates_txt\"\n\nos.mkdir(text_directory)\n\ndirectory = os.fsencode(resume_directory)\n\n#loops through the directory and converts each resume into a text file to be stored in the text directory.\n#This is also to ensure that the main directory in untouched.\nfor file in os.listdir(directory):\n filename = os.fsdecode(file)\n if filename.endswith(\".pdf\"):\n filepath = os.path.join(resume_directory, filename)\n text = pdf_to_text(filepath)\n targetpath = os.path.join(text_directory, filename)\n text_file = open(targetpath, \"w\")\n text_file.write(text)\n text_file.close()\n else:\n continue\n", "sub_path": "pdf2text.py", "file_name": "pdf2text.py", "file_ext": "py", "file_size_in_byte": 1631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "io.StringIO", "line_number": 18, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFResourceManager", "line_number": 19, "usage_type": "call"}, {"api_name": "pdfminer.converter.TextConverter", "line_number": 20, "usage_type": "call"}, {"api_name": "pdfminer.layout.LAParams", "line_number": 20, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFPageInterpreter", "line_number": 21, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage.get_pages", "line_number": 24, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage", "line_number": 24, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.fsencode", "line_number": 40, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.fsdecode", "line_number": 45, "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": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "266977678", "text": "#\n# Copyright (c) 2017-2022 Amazon.com, Inc. or its affiliates. All Rights\n# Reserved.\n# Copyright (c) 2081 Cisco Systems, Inc. All rights reserved.\n#\n# Additional copyrights may follow\n#\n\nimport argparse\nimport logging\nimport os\nimport json\nimport hashlib\nimport time\nimport datetime\nimport shutil\nimport subprocess\nimport fileinput\nimport Coverity\nimport BuilderUtils\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom git import Repo, exc\nfrom enum import Enum\n\n\ndef compute_hashes(filename):\n \"\"\"Helper function to compute MD5 and SHA1 hashes\"\"\"\n retval = {}\n md5 = hashlib.md5()\n sha1 = hashlib.sha1()\n sha256 = hashlib.sha256()\n with open(filename, 'rb') as f:\n while True:\n data = f.read(64 * 1024)\n if not data:\n break\n md5.update(data)\n sha1.update(data)\n sha256.update(data)\n retval['md5'] = md5.hexdigest()\n retval['sha1'] = sha1.hexdigest()\n retval['sha256'] = sha256.hexdigest()\n return retval\n\n\n# a note on paths used in the Builder...\n#\n# config['scratch_path']\t: \n# config['project_path']\t: /\n# current_build['build_root']\t: //-/\n# current_build['source_tree']\t: //-/[repo]\nclass Builder(object):\n \"\"\"Build one or more branches of a git repo\n\n Core class of a nightly build system (possibly to be extended into\n a release build system as well). User callable functions are the\n object constructor as well as run()\n\n \"\"\"\n\n _base_options = { 'email_log_level' : 'INFO',\n 'console_log_level' : 'CRITICAL',\n 'scratch_path' : '${TMPDIR}' }\n\n class BuildResult(Enum) :\n SUCCESS = 1\n FAILED = 2\n SKIPPED = 3\n\n\n def __init__(self, config, filer):\n \"\"\"Create a Builder object\n\n Create a builder object, which will build most simple\n projects. Projects with more complicated needs will likely\n want to override the add_arguments(), call(), and\n find_build_artifacts() functions. In the case of\n add_arguments() and call(), it is highly recommended\n that functions provided by a subclass of Builder call into the\n Builder functions to do the actual work.\n\n \"\"\"\n self._logger = None\n self._current_build = {}\n self._config = self._base_options.copy()\n self._config.update(config)\n self._filer = filer\n self._parser = argparse.ArgumentParser(description='Nightly build script for Open MPI related projects')\n self.add_arguments(self._parser)\n # copy arguments into options, assuming they were specified\n for key, value in vars(self._parser.parse_args()).items():\n if not value == None:\n self._config[key] = value\n # special case hack... expand out scratch_path\n self._config['scratch_path'] = os.path.expandvars(self._config['scratch_path'])\n self._config['project_path'] = os.path.join(self._config['scratch_path'],\n self._config['project_short_name'])\n self._config['builder_tools'] = os.path.dirname(os.path.realpath(__file__))\n\n # special hack for OMPI being inconsistent in short names....\n if not 'project_very_short_name' in self._config:\n self._config['project_very_short_name'] = self._config['project_short_name']\n\n if not os.path.exists(self._config['scratch_path']):\n os.makedirs(self._config['scratch_path'])\n\n # logging initialization. Logging will work after this point.\n self._logger = logging.getLogger(\"Builder\")\n # while we use the handler levels to limit output, the\n # effective level is the lowest of the handlers and the base\n # logger output. There's a switch in the output function of\n # the call() utility to dump all output on debug, so be a\n # little careful about setting debug level output on the\n # logger to avoid that path being activated all the time.\n if self._config['console_log_level'] == 'DEBUG' or self._config['email_log_level'] == 'DEBUG':\n self._logger.setLevel(logging.DEBUG)\n else:\n self._logger.setLevel(logging.INFO)\n\n ch = logging.StreamHandler()\n ch.setLevel(self._config['console_log_level'])\n ch.setFormatter(logging.Formatter('%(levelname)s: %(message)s'))\n self._logger.addHandler(ch)\n\n self._config['log_file'] = os.path.join(self._config['scratch_path'],\n 'builder-output-%d.log' % (int(time.time())))\n\n self._fh = logging.FileHandler(self._config['log_file'], 'w')\n self._fh.setLevel(self._config['email_log_level'])\n self._fh.setFormatter(logging.Formatter('%(message)s'))\n self._logger.addHandler(self._fh)\n\n\n def __del__(self):\n # delete the log file, since it doesn't auto-clean (we're only\n # using it for email, so no one will miss it)\n if self._logger != None:\n self._logger.removeHandler(self._fh)\n self._fh.close()\n os.remove(self._config['log_file'])\n\n\n def add_arguments(self, parser):\n \"\"\"Add options for command line arguments\n\n Called during initialization of the class in order to add any\n required arguments to the options parser. Builder classes can\n provide their own add_arguments call, but should call the base\n add_arguments() in order to get the base set of options added\n to the parser.\n\n \"\"\"\n self._parser.add_argument('--console-log-level',\n help='Console Log level (default: CRITICAL).', type=str,\n choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'])\n self._parser.add_argument('--email-log-level',\n help='Email Log level (default: INFO).', type=str,\n choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'])\n self._parser.add_argument('--scratch-path',\n help='Directory to use as base of build tree.',\n type=str)\n\n\n def run(self):\n \"\"\"Do all the real work of the Builder\n\n Other than __init__(), this is the real API for the Builder\n class. This function will execute every build described by\n the configuration passed to __init__(). Internally, it uses a\n helper function run_single_build() to execute each build. The\n only real logic in this function (other than iterating over\n keys and calling single_build) is to write the summary output\n / send emails).\n\n \"\"\"\n self._logger.info(\"Branches: %s\", str(self._config['branches'].keys()))\n good_builds = []\n failed_builds = []\n skipped_builds = []\n\n for branch_name in self._config['branches']:\n try:\n result = self.run_single_build(branch_name)\n if result == Builder.BuildResult.SUCCESS:\n good_builds.append(branch_name)\n elif result == Builder.BuildResult.FAILED:\n failed_builds.append(branch_name)\n elif result == Builder.BuildResult.SKIPPED:\n skipped_builds.append(branch_name)\n except Exception as e:\n self._logger.error(\"run_single_build(%s) threw exception %s: %s\" %\n (branch_name, str(type(e)), str(e)))\n failed_builds.append(branch_name)\n # if run_single_build throws an exception, we should\n # not continue trying to run, but should just do the\n # cleanup work\n break\n\n # Generate results output for email\n body = \"Successful builds: %s\\n\" % (str(good_builds))\n body += \"Skipped builds: %s\\n\" % (str(skipped_builds))\n body += \"Failed builds: %s\\n\" % (str(failed_builds))\n if len(failed_builds) > 0:\n subject = \"%s nightly build: FAILURE\" % (self._config['project_name'])\n else:\n subject = \"%s nightly build: SUCCESS\" % (self._config['project_name'])\n body += \"\\n=== Build output ===\\n\\n\"\n body += open(self._config['log_file'], 'r').read()\n body += \"\\nYour friendly daemon,\\nCyrador\\n\"\n\n msg = MIMEText(body)\n msg['Subject'] = subject\n msg['From'] = self._config['email_from']\n msg['To'] = self._config['email_dest']\n\n s = smtplib.SMTP('localhost')\n s.sendmail(self._config['email_from'], [self._config['email_dest']], msg.as_string())\n s.quit()\n\n\n def run_single_build(self, branch_name):\n \"\"\"Run a single branch build\n\n All the logic required to run a single build. This function\n should not raise an exception unless all follow-on builds\n should be skipped.\n\n \"\"\"\n self._logger.info(\"\\nStarting build for \" + branch_name)\n self._current_build = { \"status\" : 0,\n \"branch_name\" : branch_name }\n retval = Builder.BuildResult.SUCCESS\n\n remote_repository = self._config['repository']\n\n now = time.time()\n self._current_build['build_unix_time'] = int(now)\n self._current_build['build_time'] = self.generate_build_time(now)\n build_root = os.path.join(self._config['project_path'],\n branch_name + \"-\" + self._current_build['build_time'])\n source_tree = os.path.join(build_root,\n os.path.basename(remote_repository))\n\n self._current_build['remote_repository'] = remote_repository\n self._current_build['build_root'] = build_root\n self._current_build['source_tree'] = source_tree\n self._current_build['branch'] = branch_name\n\n build_history = self.get_build_history()\n if len(build_history) > 0:\n # this is really kind of awful, but build_history keys are\n # unix timestamps of the build. Find the last timestamp,\n # and that's the last build. Then look at the revision to\n # get the revision id of that build.\n last_version = build_history[sorted(build_history.keys())[-1:][0]]['revision']\n else:\n last_version = ''\n\n self.prepare_source_tree()\n try:\n if last_version == self._current_build['revision']:\n self._logger.info(\"Build for revision %s already exists, skipping.\",\n self._current_build['revision'])\n retval = Builder.BuildResult.SKIPPED\n else:\n self._logger.info(\"Found new revision %s\",\n self._current_build['revision'])\n\n self.update_version_file()\n self.build()\n self.find_build_artifacts()\n self.publish_build_artifacts()\n if ('coverity' in self._config['branches'][branch_name]\n and self._config['branches'][branch_name]['coverity']\n and len(self._current_build['artifacts']) > 0):\n try:\n Coverity.run_coverity(self._logger,\n self._current_build['build_root'],\n os.path.join(self._current_build['source_tree'],\n next(iter(self._current_build['artifacts'].keys()))),\n self._config['coverity'])\n except Exception as e:\n self._logger.error(\"ERROR: Coverity submission failed: %s\"\n % (str(e)))\n else:\n self._logger.info(\"Successfully submitted Coverity build\")\n self._logger.info(\"%s build of revision %s completed successfully\" %\n (branch_name, self._current_build['revision']))\n except Exception as e:\n self._logger.error(\"FAILURE: %s: %s\"\n % (str(type(e)), str(e)))\n self.publish_failed_build()\n retval = Builder.BuildResult.FAILED\n finally:\n self.cleanup()\n self.remote_cleanup(build_history)\n return retval\n\n\n def generate_build_time(self, build_unix_time):\n \"\"\"Helper function to format time strings from unix time\"\"\"\n return datetime.datetime.utcfromtimestamp(build_unix_time).strftime(\"%Y%m%d%H%M\")\n\n\n def generate_build_history_filename(self, branch_name, build_unix_time, revision):\n \"\"\"Helper function to build filename\n\n The build history file represents a single build, and has to\n have an agreed-upon naming convention between both the builder\n script and the web pages that will consume the output.\n Override if build--.json\n is not sufficient for your project.\n\n \"\"\"\n build_time = self.generate_build_time(build_unix_time)\n return os.path.join(self._config['branches'][branch_name]['output_location'],\n \"build-%s-%s-%s-%s.json\" % (self._config['project_short_name'],\n branch_name,\n build_time,\n revision))\n\n\n def get_build_history(self):\n \"\"\"Helper function to list all known builds for the current branch\n\n Pull all known builds from the remote storage and return an\n array of the build history objects for the current branch.\n Returns an empty list if there are no known builds for the\n current branch.\n\n \"\"\"\n branch_name = self._current_build['branch_name']\n dirname = self._config['branches'][branch_name]['output_location']\n builds = self._filer.file_search(dirname, \"build-*.json\")\n build_history = {}\n for build in builds:\n self._logger.debug(\"looking at data file %s\" % build)\n stream = self._filer.download_to_stream(build)\n data = json.load(stream)\n if not 'build_unix_time' in data:\n continue\n if not 'branch' in data:\n continue\n data_build_unix_time = data['build_unix_time']\n data_branch_name = data['branch']\n if data_branch_name == branch_name:\n build_history[data_build_unix_time] = data\n return build_history\n\n\n def prepare_source_tree(self):\n \"\"\"Build a local source tree for the current branch\n\n Builds the current tree, including building all parent\n directories, checks out the source for the current branch, and\n sets _current_build['revision'] to the revision of the HEAD\n for the current branch.\n\n \"\"\"\n branch_name = self._current_build['branch_name']\n remote_repository = self._current_build['remote_repository']\n source_tree = self._current_build['source_tree']\n branch = self._current_build['branch']\n\n # assume that the build tree doesn't exist. Makedirs will\n # throw an exception if it does.\n self._logger.debug(\"Making build tree: \" + os.path.dirname(source_tree))\n os.makedirs(os.path.dirname(source_tree))\n\n # get an up-to-date git repository\n self._logger.debug(\"Cloning from \" + remote_repository)\n repo = Repo.clone_from(remote_repository, source_tree)\n\n # switch to the right branch and reset the HEAD to be\n # origin//HEAD\n self._logger.debug(\"Switching to branch: \" + branch)\n if not branch in repo.heads:\n repo.git.checkout(branch)\n repo.head.reference = repo.refs[branch]\n\n # And pull in all the right submodules\n repo.git.submodule('update', '--init', '--recursive')\n\n # wish I could figure out how to do this without resorting to\n # shelling out to git :/\n self._current_build['revision'] = repo.git.rev_parse(repo.head.object.hexsha, short=7)\n\n\n def update_version_file(self):\n \"\"\"Hook to update version file if needed before the actual build step.\n\n Most projects have custom methods of updating the version used\n by the build process before making a nightly tarball (so that\n different revisions are evident by the tarball name / build\n version). Projects should provide a customized version of\n this function if necessary. Default action is to do\n nothing.\n\n \"\"\"\n pass\n\n\n def build(self):\n \"\"\"Execute building the tarball.\n\n Most projects have a helper script for building tarballs. If\n the key 'tarball_builder' is present in the config, this\n function will execute the tarball_builder. Otherwise, it will\n run autoreconf -if; ./configure ; make distcheck.\n\n \"\"\"\n branch_name = self._current_build['branch_name']\n source_tree = self._current_build['source_tree']\n cwd = os.getcwd()\n os.chdir(source_tree)\n try:\n if 'tarball_builder' in self._config:\n self.call(self._config['tarball_builder'], build_call=True)\n else:\n self.call([\"autoreconf\", \"-if\"], build_call=True)\n self.call([\"./configure\"], build_call=True)\n self.call([\"make\", \"distcheck\"], build_call=True)\n finally:\n os.chdir(cwd)\n\n\n def call(self, args, log_name=None, build_call=False, env=None):\n \"\"\"Modify shell executable string before calling\n\n Some projects (like Open MPI) use shell modules to configure\n the environment properly for a build. The easiest way to\n support that use case is a shell wrapper function that\n properly configures the environment. This function provides a\n hook which can be used to add the shell wrapper function into\n the call arguments, resulting in the build system having the\n right environment at execution time. The default is to call\n args directly.\n\n \"\"\"\n if log_name == None:\n log_file = args[0]\n else:\n log_file = log_name\n log_file=os.path.join(self._current_build['build_root'], log_file + \"-output.txt\")\n BuilderUtils.logged_call(args, log_file=log_file, env=env)\n\n\n def find_build_artifacts(self):\n \"\"\"Pick up any build artifacts from the build step\n\n Returns a list of file names relative to source_tree of the\n build artifacts from the build step. The\n Builder.find_build_artifacts() implementation will search for\n any .tar.gz and .tar.bz2 files in the top level of the build\n tree. Overload if the project builder can be more\n specific.\n\n \"\"\"\n self._current_build['artifacts'] = {}\n source_tree = self._current_build['source_tree']\n for file in os.listdir(source_tree):\n if file.endswith(\".tar.gz\") or file.endswith(\".tar.bz2\"):\n filename = os.path.join(source_tree, file)\n info = os.stat(filename)\n hashes = compute_hashes(filename)\n self._current_build['artifacts'][file] = {}\n self._current_build['artifacts'][file]['sha1'] = hashes['sha1']\n self._current_build['artifacts'][file]['sha256'] = hashes['sha256']\n self._current_build['artifacts'][file]['md5'] = hashes['md5']\n self._current_build['artifacts'][file]['size'] = info.st_size\n self._logger.debug(\"Found artifact %s, size: %d, md5: %s, sha1: %s sha256: %s\"\n % (file, info.st_size, hashes['md5'], hashes['sha1'], hashes['sha256']))\n\n\n def publish_build_artifacts(self):\n \"\"\"Publish any successful build artifacts\n\n Publish any build artifacts found by find_build_artifacts and\n create / publish the build history blob for the artifacts.\n This function also creates the \"latest_snapshot.txt\" file,\n on the assumption that the current build is, in fact, the latest.\n\n \"\"\"\n branch_name = self._current_build['branch_name']\n\n build_data = {}\n build_data['branch'] = self._current_build['branch']\n build_data['valid'] = True\n build_data['revision'] = self._current_build['revision']\n build_data['build_unix_time'] = self._current_build['build_unix_time']\n build_data['delete_on'] = 0\n build_data['files'] = {}\n\n for build in self._current_build['artifacts']:\n local_filename = os.path.join(self._current_build['source_tree'],\n build)\n remote_filename = os.path.join(self._config['branches'][branch_name]['output_location'],\n build)\n self._logger.debug(\"Publishing file %s (local: %s, remote: %s)\" %\n (build, local_filename, remote_filename))\n self._filer.upload_from_file(local_filename, remote_filename)\n build_data['files'][build] = self._current_build['artifacts'][build]\n\n datafile = self.generate_build_history_filename(self._current_build['branch_name'],\n self._current_build['build_unix_time'],\n self._current_build['revision'])\n self._filer.upload_from_stream(datafile, json.dumps(build_data), {'Cache-Control' : 'max-age=600'})\n\n latest_filename = os.path.join(self._config['branches'][branch_name]['output_location'],\n 'latest_snapshot.txt')\n version_string = self._current_build['version_string'] + '\\n'\n self._filer.upload_from_stream(latest_filename, version_string, {'Cache-Control' : 'max-age=600'} )\n\n\n def update_build_history(self, build_history):\n \"\"\"Update any build histories that need expiring\n\n Deletion of build histories / artifacts is a two step process.\n First, when there are more config['than max_count'] builds\n found, the oldest N are expired to get under max_count.\n Expired builds are not immediately deleted. Instead, they\n have their valid field set to false and a delete_on time set\n to 24 hours from now. This is to give the web front end time\n to see the update and stop publishing the now-expired builds.\n Second, builds with a delete_on time in the past are deleted\n from the remote archive. This function handles moving builds\n from \"valid\" to \"expired\", and remote_cleanup() handles the\n deletion case.\n\n \"\"\"\n branch_name = self._current_build['branch_name']\n\n # set builds past max_count to invalid and set an expiration\n # if one isn't already set. Note that this isn't quite right,\n # as we'll count already invalid builds against max_count, but\n # unless builds are added to the build_history out of order\n # (which would be an entertaining causality problem), the\n # effect is the same, and this is way less code.\n if 'max_count' in self._config['branches'][branch_name]:\n max_count = self._config['branches'][branch_name]['max_count']\n else:\n max_count = 10\n builds = sorted(build_history[branch_name]['builds'].keys())\n if len(builds) > max_count:\n expire_builds = builds[max_count:]\n for key in expire_builds:\n if not build_history[branch_name]['builds'][key]['valid']:\n continue\n build_history[branch_name]['builds'][key]['valid'] = False\n build_history[branch_name]['builds'][key]['delete_on'] = 12\n self._logger.debug(\"Expiring build %s\" % (key))\n\n\n def publish_failed_build(self):\n \"\"\"Deal with a failed build\n\n Builds fail. It happens to the best of us. This function is\n called when something in the build failed (any step, from code\n checkout to finding build artifacts). This function will\n create a tarball of the build directory and publish it so that\n future generations may see what went wrong and learn from our\n mistakes. After making a tarball of the directory, it uploads\n the tarball to the remote storage and sets\n ._current_build['failed tarball'] to the name (relative to\n ._config['failed_build_prefix'] where the tarball was\n uploaded.\n\n \"\"\"\n if not 'failed_build_prefix' in self._config:\n self._logger.warn(\"failed_build_prefix not set in config; not saving failed build info\")\n return\n\n branch_name = self._current_build['branch_name']\n self._logger.debug(\"publishing failed build for %s\" % (branch_name))\n failed_tarball_name = \"%s-%s-%s-failed.tar.gz\" % (self._config['project_short_name'],\n branch_name,\n self._current_build['build_time'])\n failed_tarball_path = os.path.join(self._config['project_path'],\n failed_tarball_name)\n cwd = os.getcwd()\n os.chdir(self._current_build['build_root'])\n try:\n self.call([\"tar\", \"czf\", failed_tarball_path, \".\"],\n log_name=\"failed-tarball-tar\")\n finally:\n os.chdir(cwd)\n remote_filename = os.path.join(self._config['failed_build_prefix'],\n failed_tarball_name)\n\n self._filer.upload_from_file(failed_tarball_path, remote_filename)\n os.remove(failed_tarball_path)\n\n self._logger.warn('Build artifacts available at: %s' %\n (self._config['failed_build_url'] + remote_filename))\n\n\n def cleanup(self):\n \"\"\"Clean up after ourselves\n\n If your builder subclass does anything crazy in the previous\n steps, override here. Otherwise, deleting everything in the\n build directory should be sufficient.\n\n \"\"\"\n dirpath = self._config['project_path']\n self._logger.debug(\"Deleting directory: %s\" % (dirpath))\n # deal with \"make distcheck\"'s stupid permissions. Exception\n # handling is inside the loop so that we do not skip some\n # files on an error. os.chmod will throw an error if it\n # tries to follow a dangling symlink.\n for root, dirs, files in os.walk(dirpath):\n for momo in dirs:\n try:\n os.chmod(os.path.join(root, momo), 0o700)\n except:\n pass\n for momo in files:\n try:\n os.chmod(os.path.join(root, momo), 0o700)\n except:\n pass\n shutil.rmtree(dirpath)\n\n\n def remote_cleanup(self, build_history):\n \"\"\"Clean up old builds on remote storage\"\"\"\n now = int(time.time())\n branch_name = self._current_build['branch_name']\n\n # set builds past max_count to invalid and set an expiration\n # if one isn't already set. Note that this isn't quite right,\n # as we'll count already invalid builds against max_count, but\n # unless builds are added to the build_history out of order\n # (which would be an entertaining causality problem), the\n # effect is the same, and this is way less code. Also, this\n # is a little racy as hell, given there's no locking on\n # simultaneous builds, but the worst case should be that the\n # server ends up with a few too many valid builds.\n if 'max_count' in self._config['branches'][branch_name]:\n max_count = self._config['branches'][branch_name]['max_count']\n else:\n max_count = 10\n builds = sorted(build_history.keys())\n if len(builds) > max_count:\n builds = builds[0:len(builds) - max_count]\n for key in builds:\n if not build_history[key]['valid']:\n continue\n build_history[key]['valid'] = False\n # expire in one day\n build_history[key]['delete_on'] = now + (24 * 60 * 60)\n self._logger.debug(\"Expiring build %s\" % (key))\n filename = self.generate_build_history_filename(build_history[key]['branch'],\n build_history[key]['build_unix_time'],\n build_history[key]['revision'])\n self._filer.upload_from_stream(filename,\n json.dumps(build_history[key]), {'Cache-Control' : 'max-age=600'})\n\n for build in build_history.keys():\n delete_on = build_history[build]['delete_on']\n if delete_on != 0 and delete_on < int(time.time()):\n self._logger.debug(\"Removing build %s\" % (build))\n for name in build_history[build]['files'].keys():\n dirname = self._config['branches'][branch_name]['output_location']\n pathname = os.path.join(dirname, name)\n self._logger.debug(\"Removing file %s\" % (pathname))\n self._filer.delete(pathname)\n datafile = self.generate_build_history_filename(build_history[build]['branch'],\n build_history[build]['build_unix_time'],\n build_history[build]['revision'])\n self._logger.debug(\"Removing data file %s\" % (datafile))\n self._filer.delete(datafile)\n\n # as a (maybe temporary?) hack, generate md5sum.txt and\n # sha1sum.txt files for all valid builds. Do this in\n # remote_cleanup rather than update_build_history so that it\n # gets regenerated whenever files go invalid/removed, rather\n # than just when new builds are created.\n md5sum_string = ''\n sha1sum_string = ''\n for build in build_history.keys():\n if not build_history[build]['valid']:\n continue\n for filename in build_history[build]['files'].keys():\n filedata = build_history[build]['files'][filename]\n md5sum_string += '%s %s\\n' % (filedata['md5'], filename)\n sha1sum_string += '%s %s\\n' % (filedata['sha1'], filename)\n output_base = self._config['branches'][branch_name]['output_location']\n self._filer.upload_from_stream(os.path.join(output_base, 'md5sums.txt'),\n md5sum_string)\n self._filer.upload_from_stream(os.path.join(output_base, 'sha1sums.txt'),\n sha1sum_string)\n", "sub_path": "nightly-tarball/Builder.py", "file_name": "Builder.py", "file_ext": "py", "file_size_in_byte": 31407, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "hashlib.md5", "line_number": 30, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 31, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 32, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 66, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.expandvars", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"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.dirname", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 106, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 117, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 119, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 121, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 123, "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": "time.time", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 129, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 131, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 141, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 212, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 217, "usage_type": "call"}, {"api_name": "time.time", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path", "line_number": 240, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "Coverity.run_coverity", "line_number": 278, "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": "datetime.datetime.utcfromtimestamp", "line_number": 303, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 303, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path", "line_number": 369, "usage_type": "attribute"}, {"api_name": "git.Repo.clone_from", "line_number": 373, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 373, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 415, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 416, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path", "line_number": 445, "usage_type": "attribute"}, {"api_name": "BuilderUtils.logged_call", "line_number": 446, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 462, "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.stat", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 496, "usage_type": "call"}, {"api_name": "os.path", "line_number": 496, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 498, "usage_type": "call"}, {"api_name": "os.path", "line_number": 498, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 508, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 510, "usage_type": "call"}, {"api_name": "os.path", "line_number": 510, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 579, "usage_type": "call"}, {"api_name": "os.path", "line_number": 579, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 581, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 582, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 587, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 588, "usage_type": "call"}, {"api_name": "os.path", "line_number": 588, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 592, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 612, "usage_type": "call"}, {"api_name": "os.chmod", "line_number": 615, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 615, "usage_type": "call"}, {"api_name": "os.path", "line_number": 615, "usage_type": "attribute"}, {"api_name": "os.chmod", "line_number": 620, "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": "shutil.rmtree", "line_number": 623, "usage_type": "call"}, {"api_name": "time.time", "line_number": 628, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 658, "usage_type": "call"}, {"api_name": "time.time", "line_number": 662, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 666, "usage_type": "call"}, {"api_name": "os.path", "line_number": 666, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 690, "usage_type": "call"}, {"api_name": "os.path", "line_number": 690, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 692, "usage_type": "call"}, {"api_name": "os.path", "line_number": 692, "usage_type": "attribute"}]} +{"seq_id": "408309056", "text": "# -*- coding: utf-8 -*-\r\nimport pandas as pd\r\nimport numpy as np\r\nimport datetime\r\nfrom pathlib import Path\r\nhome = str(Path.home())\r\nPATH = home + \"\\\\Desktop\\\\ee\\\\\"\r\n\r\nRECOMMEND_LIST = PATH + \"dataFile\\\\food_recommendV2.xlsx\"\r\nOUTPUT = PATH + \"dataFile\\\\output_log.xlsx\"\r\nFOOD_DATA = PATH + \"dataFile\\\\food_data.xlsx\"\r\n\r\ndef pick_random_from_list(list1):\r\n \"from list1 pick random value\"\r\n randIndex = np.random.randint(0,len(list1))\r\n return list1[randIndex]\r\n\r\ndef yes_or_no_loop(data):\r\n \"append column names from data\"\r\n data_category_list=[]\r\n for name in data:\r\n data_category_list.append(name)\r\n noChoiceList = []\r\n choice = 0\r\n print(\"\\n음식을 추천해드립니다!\\n\")\r\n \r\n while(choice == 0):\r\n pickedCategory = pick_random_from_list(data_category_list)\r\n categoryData = data[pickedCategory]\r\n categoryData = categoryData.dropna()\r\n randomFood = list(categoryData.sample(1))[0]\r\n \r\n #음식리스트 전부가 마음에 안들경우 출력\r\n if len(noChoiceList) == data.count().sum():\r\n print(\"더 이상 추천해줄 음식이 없습니다.\")\r\n choice = \"EXIT\"\r\n break\r\n \r\n #골랐던 음식이 마음에 안든 경우 이를 제외 추천\r\n if randomFood in noChoiceList:\r\n continue\r\n \r\n print(pickedCategory,\"-\",randomFood)\r\n while(True):\r\n choice = input(\"Yes or No? \")\r\n choice = choice.upper()\r\n if choice in [\"Y\",\"YES\",\"EXIT\"]:\r\n break\r\n elif choice in [\"N\", \"NO\"]:\r\n print()\r\n choice = 0\r\n noChoiceList.append(randomFood)\r\n break\r\n else:\r\n print(\"Wrong Input\")\r\n if choice == \"EXIT\":\r\n randomFood = \"Dump\"\r\n return randomFood, noChoiceList\r\n\r\ndef pick_main(id_code):\r\n recommend_data = pd.read_excel(RECOMMEND_LIST)\r\n food_data = pd.read_excel(FOOD_DATA)\r\n try:\r\n log_data= pd.read_excel(OUTPUT) \r\n except:\r\n log_data = pd.DataFrame(columns=[\"id_code\",\"food_id\",\"choice\",\"time\",\"gps\",\"weather\",\"temp\"])\r\n randomFood, noChoiceList = yes_or_no_loop(recommend_data)\r\n for food in noChoiceList:\r\n food_id = food_data.loc[food_data[\"food\"]==food][\"food_id\"].values[0]\r\n log_data = log_data.append({\"id_code\":id_code, \"food_id\":food_id, \"choice\":0, \"time\":datetime.datetime.now(), \"gps\":np.nan, \"weather\":np.nan, \"temp\":np.nan},ignore_index=True)\r\n if randomFood == \"Dump\":\r\n pass\r\n else:\r\n food_id = food_data.loc[food_data[\"food\"]==randomFood][\"food_id\"].values[0]\r\n log_data = log_data.append({\"id_code\":id_code, \"food_id\":food_id, \"choice\":1, \"time\":datetime.datetime.now(), \"gps\":np.nan, \"weather\":np.nan, \"temp\":np.nan},ignore_index=True)\r\n #return log_data\r\n log_data.to_excel(OUTPUT, index=None)\r\n #return log_data\r\n#pick_main(1)\r\n", "sub_path": "pickFunctionV3.py", "file_name": "pickFunctionV3.py", "file_ext": "py", "file_size_in_byte": 2976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pathlib.Path.home", "line_number": 6, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 70, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 75, "usage_type": "attribute"}]} +{"seq_id": "232121133", "text": "\nimport logging\nimport os\nimport datetime\nimport sys\n\nfrom eventlet.green import urllib2\nfrom ptvalidator.runtime.configuration import ConfigurationManager\nfrom ptvalidator.runtime.utility import create_file_with_folder\n\nlogger = logging.getLogger()\n\n\ndef init_logger():\n if not logger.handlers:\n # global settings\n formatter = logging.Formatter(\n '%(asctime)s - %(levelname)s - %(message)s')\n logger.setLevel(logging.DEBUG)\n # Add console handler\n console_log_handler = init_stream_log_handler()\n console_log_handler.setFormatter(formatter)\n logger.addHandler(console_log_handler)\n # Add file handler\n file_log_handler = init_file_log_handler()\n file_log_handler.setFormatter(formatter)\n logger.addHandler(file_log_handler)\n\n\ndef init_stream_log_handler():\n console_log_handler = logging.StreamHandler(sys.stdout)\n return console_log_handler\n\n\ndef init_file_log_handler():\n config = ConfigurationManager()\n log_folder_path = os.path.join(os.getcwd(), '/log')\n if config.current.ServiceDefault.log_path is not None:\n log_folder_path = config.current.ServiceDefault.log_path\n file_name = 'ptvalidator.log'\n log_file = os.path.join(log_folder_path, file_name)\n file_log_handler = logging.handlers.TimedRotatingFileHandler(log_file, when='midnight')\n file_log_handler.suffix = '%Y%m%d'\n return file_log_handler\n", "sub_path": "web/ptvalidator/runtime/log.py", "file_name": "log.py", "file_ext": "py", "file_size_in_byte": 1437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ptvalidator.runtime.configuration.ConfigurationManager", "line_number": 36, "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": "os.getcwd", "line_number": 37, "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": "logging.handlers.TimedRotatingFileHandler", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 42, "usage_type": "attribute"}]} +{"seq_id": "5177252", "text": "from typing import List\nfrom typing import Optional\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution:\n def is_num(self, s: str):\n try:\n float(s)\n return True\n except ValueError:\n pass\n try:\n import unicodedata\n unicodedata.numeric(s)\n return True\n except Exception:\n pass\n return False\n def evalRPN(self, tokens: List[str]) -> int:\n st = []\n for c in tokens:\n if self.is_num(c):\n st.append(int(c))\n else:\n num2 = st.pop(-1)\n num1 = st.pop(-1)\n if c == '+':\n st.append(num1+ num2)\n if c == '-':\n st.append(num1 - num2)\n if c == '*':\n st.append(num1 * num2)\n if c == '/':\n st.append(int(num1 / num2))\n return st[0]\n\n def generateParenthesis(self, n: int) -> List[str]:\n ret = []\n tmp = []\n def dfs(left, right):\n if len(tmp) == 2 * n:\n ret.append(''.join(tmp))\n return\n if left < n:\n tmp.append('(')\n dfs(left+1, right)\n tmp.pop(-1)\n if left > right:\n tmp.append(')')\n dfs(left, right+1)\n tmp.pop(-1)\n dfs(0, 0)\n return ret\n \n def generateParenthesis2(self, n: int) -> List[str]:\n dp = [[] for _ in range(n+1)]\n dp[0].append('')\n\n for i in range(1, n+1):\n for p in range(i):\n L = dp[p]\n R = dp[i-1-p]\n for l in L:\n for r in R:\n dp[i].append(f'({l}){r}')\n return dp[n]\n \n def generateParenthesis3(self, n: int) -> List[str]:\n mem = [None for _ in range(n+1)]\n mem[0] = [\"\"]\n\n def recur(m):\n if mem[m] != None:\n return mem[m]\n \n tmp = []\n for c in range(m):\n for l in recur(c):\n for r in recur(n-1-c):\n tmp.append(f'({l}){r}')\n mem[m] = tmp\n return tmp\n recur(n)\n return mem[n]\n\n # No15 三数之和\n def threeSum(self, nums: List[int]) -> List[List[int]]:\n ret = []\n nums.sort()\n for k in range(len(nums)-2):\n if k != 0 and nums[k] == nums[k-1]:\n continue\n i = k + 1\n j = len(nums) - 1\n while i < j:\n if nums[i] + nums[j] + nums[k] < 0:\n i += 1\n while i < j and nums[i] == nums[i-1]:\n i += 1\n elif nums[i] + nums[j] + nums[k] > 0:\n j -= 1\n while i < j and nums[j] == nums[j+1]:\n j -= 1\n else:\n ret.append([nums[i], nums[j], nums[k]])\n i, j = i + 1, j - 1\n while i < j and nums[i] == nums[i-1]:\n i += 1\n while i < j and nums[j] == nums[j+1]:\n j += 1\n return ret\n\n\n # No98 验证二叉搜索树\n def isValidBST(self, root: Optional[TreeNode]) -> bool:\n if not root or (not root.left and not root.right):\n return True\n \n l, r = self.isValidBST(root.left), self.isValidBST(root.right)\n if not l or not r:\n return False\n \n \n def findm(r, mode):\n if mode == 0: # find most left one\n while r.left:\n r = r.left\n else:\n while r.right:\n r = r.right\n return r.val\n \n l_m = findm(root.left, 1)\n r_m = findm(root.right, 0)\n return l_m <= root.val and root.val <= r_m\n \n\n # No322. 零钱兑换\n def coinChange(self, coins: List[int], amount: int) -> int:\n ret = float('inf')\n tmp = []\n coins.sort()\n def search():\n nonlocal ret\n if sum(tmp) == amount:\n ret = min(ret, len(tmp))\n \n if sum(tmp) > amount:\n return\n\n for i in range(len(coins)):\n tmp.append(coins[i])\n search()\n tmp.pop(-1)\n \n search()\n return ret if ret != float('inf') else -1\n \n\n \n\n\n\nso = Solution()\n# so.evalRPN(\n# [\"10\",\"6\",\"9\",\"3\",\"+\",\"-11\",\"*\",\"/\",\"*\",\"17\",\"+\",\"5\",\"+\"])\n# so.generateParenthesis3(2)\nso.threeSum(\n[-2,0,0,2,2])", "sub_path": "leetcode/7_13.py", "file_name": "7_13.py", "file_ext": "py", "file_size_in_byte": 4832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unicodedata.numeric", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 145, "usage_type": "name"}]} +{"seq_id": "327665943", "text": "# Copyright 2017 The TensorFlow 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# ==============================================================================\n\"\"\"Densenet Training.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom absl import app\nfrom absl import flags\nimport tensorflow as tf # TF2\nimport tensorflow_datasets as tfds\nfrom tensorflow_examples.models.densenet import densenet\nassert tf.__version__.startswith('2')\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_integer('buffer_size', 50000, 'Shuffle buffer size')\nflags.DEFINE_integer('batch_size', 64, 'Batch Size')\nflags.DEFINE_integer('epochs', 1, 'Number of epochs')\nflags.DEFINE_boolean('enable_function', True, 'Enable Function?')\nflags.DEFINE_string('data_dir', None, 'Directory to store the dataset')\nflags.DEFINE_string('mode', 'from_depth', 'Deciding how to build the model')\nflags.DEFINE_integer('depth_of_model', 3, 'Number of layers in the model')\nflags.DEFINE_integer('growth_rate', 12, 'Filters to add per dense block')\nflags.DEFINE_integer('num_of_blocks', 3, 'Number of dense blocks')\nflags.DEFINE_integer('output_classes', 10, 'Number of classes in the dataset')\nflags.DEFINE_integer('num_layers_in_each_block', -1,\n 'Number of layers in each dense block')\nflags.DEFINE_string('data_format', 'channels_last',\n 'channels_last or channels_first')\nflags.DEFINE_boolean('bottleneck', True, 'Add bottleneck blocks between layers')\nflags.DEFINE_float(\n 'compression', 0.5,\n 'reducing the number of inputs(filters) to the transition block.')\nflags.DEFINE_float('weight_decay', 1e-4, 'weight decay')\nflags.DEFINE_float('dropout_rate', 0., 'dropout rate')\nflags.DEFINE_boolean(\n 'pool_initial', False,\n 'If True add a conv => maxpool block at the start. Used for Imagenet')\nflags.DEFINE_boolean('include_top', True, 'Include the classifier layer')\nflags.DEFINE_string('train_mode', 'custom_loop',\n 'Use either \"keras_fit\" or \"custom_loop\"')\nflags.DEFINE_integer('num_gpu', 1, 'Number of GPUs to use')\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n\nCIFAR_MEAN = [125.3, 123.0, 113.9]\nCIFAR_STD = [63.0, 62.1, 66.7]\n\nHEIGHT = 32\nWIDTH = 32\n\n\nclass Preprocess(object):\n \"\"\"Preprocess images.\n\n Args:\n data_format: channels_first or channels_last\n \"\"\"\n\n def __init__(self, data_format, train):\n self.data_format = data_format\n self.train = train\n\n def __call__(self, image, label):\n image = tf.cast(image, tf.float32)\n\n if self.train:\n image = tf.image.random_flip_left_right(image)\n image = self.random_jitter(image)\n\n image = (image - CIFAR_MEAN) / CIFAR_STD\n\n if self.data_format == 'channels_first':\n image = tf.transpose(image, [2, 0, 1])\n\n return image, label\n\n def random_jitter(self, image):\n # add 4 pixels on each side; image_size == (36 x 36)\n image = tf.image.resize_image_with_crop_or_pad(\n image, HEIGHT + 8, WIDTH + 8)\n\n image = tf.image.random_crop(image, size=[HEIGHT, WIDTH, 3])\n\n return image\n\n\ndef create_dataset(buffer_size, batch_size, data_format, data_dir=None):\n \"\"\"Creates a tf.data Dataset.\n\n Args:\n buffer_size: Shuffle buffer size.\n batch_size: Batch size\n data_format: channels_first or channels_last\n data_dir: directory to store the dataset.\n\n Returns:\n train dataset, test dataset, metadata\n \"\"\"\n\n preprocess_train = Preprocess(data_format, train=True)\n preprocess_test = Preprocess(data_format, train=False)\n\n dataset, metadata = tfds.load(\n 'cifar10', data_dir=data_dir, as_supervised=True, with_info=True)\n train_dataset, test_dataset = dataset['train'], dataset['test']\n\n train_dataset = train_dataset.map(\n preprocess_train, num_parallel_calls=AUTOTUNE)\n train_dataset = train_dataset.shuffle(buffer_size).batch(batch_size)\n train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)\n\n test_dataset = test_dataset.map(\n preprocess_test, num_parallel_calls=AUTOTUNE).batch(batch_size)\n test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)\n\n return train_dataset, test_dataset, metadata\n\n\nclass Train(object):\n \"\"\"Train class.\n\n Args:\n epochs: Number of epochs\n enable_function: If True, wraps the train_step and test_step in tf.function\n model: Densenet model.\n num_gpu: Number of GPUs.\n \"\"\"\n\n def __init__(self, epochs, enable_function, model, num_gpu):\n self.epochs = epochs\n self.enable_function = enable_function\n self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n from_logits=True)\n self.optimizer = tf.keras.optimizers.SGD(learning_rate=0.1,\n momentum=0.9, nesterov=True)\n self.train_loss_metric = tf.keras.metrics.Mean(name='train_loss')\n self.train_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(\n name='train_accuracy')\n self.test_loss_metric = tf.keras.metrics.Mean(name='test_loss')\n self.test_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy(\n name='test_accuracy')\n self.model = model\n self.num_gpu = num_gpu\n\n def decay(self, epoch):\n if epoch < 150:\n return 0.1 * self.num_gpu\n if epoch >= 150 and epoch < 225:\n return 0.01 * self.num_gpu\n if epoch >= 225:\n return 0.001 * self.num_gpu\n\n def train_step(self, inputs):\n \"\"\"One train step.\n\n Args:\n inputs: one batch input.\n \"\"\"\n image, label = inputs\n with tf.GradientTape() as tape:\n predictions = self.model(image, training=True)\n loss = self.loss_object(label, predictions)\n loss += sum(self.model.losses)\n gradients = tape.gradient(loss, self.model.trainable_variables)\n self.optimizer.apply_gradients(zip(gradients,\n self.model.trainable_variables))\n\n self.train_loss_metric(loss)\n self.train_acc_metric(label, predictions)\n\n def test_step(self, inputs):\n \"\"\"One test step.\n\n Args:\n inputs: one batch input.\n \"\"\"\n image, label = inputs\n predictions = self.model(image, training=False)\n loss = self.loss_object(label, predictions)\n loss += sum(self.model.losses)\n\n self.test_loss_metric(loss)\n self.test_acc_metric(label, predictions)\n\n def custom_loop(self, train_iterator, test_iterator,\n num_train_steps_per_epoch, num_test_steps_per_epoch,\n strategy):\n \"\"\"Custom training and testing loop.\n\n Args:\n train_iterator: Training iterator created using strategy\n test_iterator: Testing iterator created using strategy\n num_train_steps_per_epoch: number of training steps in an epoch.\n num_test_steps_per_epoch: number of test steps in an epoch.\n strategy: Distribution strategy\n\n Returns:\n train_loss, train_accuracy, test_loss, test_accuracy\n \"\"\"\n\n # this code is expected to change.\n def distributed_train():\n return strategy.experimental_run(self.train_step, train_iterator)\n\n def distributed_test():\n return strategy.experimental_run(self.test_step, test_iterator)\n\n if self.enable_function:\n distributed_train = tf.function(distributed_train)\n distributed_test = tf.function(distributed_test)\n\n for epoch in range(self.epochs):\n self.optimizer.learning_rate = self.decay(epoch)\n\n train_iterator.initialize()\n for _ in range(num_train_steps_per_epoch):\n distributed_train()\n\n test_iterator.initialize()\n for _ in range(num_test_steps_per_epoch):\n distributed_test()\n\n template = ('Epoch: {}, Train Loss: {}, Train Accuracy: {}, '\n 'Test Loss: {}, Test Accuracy: {}')\n\n print(\n template.format(epoch, self.train_loss_metric.result(),\n self.train_acc_metric.result(),\n self.test_loss_metric.result(),\n self.test_acc_metric.result()))\n\n if epoch != self.epochs - 1:\n self.train_loss_metric.reset_states()\n self.train_acc_metric.reset_states()\n self.test_loss_metric.reset_states()\n self.test_acc_metric.reset_states()\n\n return (self.train_loss_metric.result().numpy(),\n self.train_acc_metric.result().numpy(),\n self.test_loss_metric.result().numpy(),\n self.test_acc_metric.result().numpy())\n\n\ndef run_main(argv):\n \"\"\"Passes the flags to main.\n\n Args:\n argv: argv\n \"\"\"\n del argv\n kwargs = {\n 'epochs': FLAGS.epochs,\n 'enable_function': FLAGS.enable_function,\n 'buffer_size': FLAGS.buffer_size,\n 'batch_size': FLAGS.batch_size,\n 'mode': FLAGS.mode,\n 'depth_of_model': FLAGS.depth_of_model,\n 'growth_rate': FLAGS.growth_rate,\n 'num_of_blocks': FLAGS.num_of_blocks,\n 'output_classes': FLAGS.output_classes,\n 'num_layers_in_each_block': FLAGS.num_layers_in_each_block,\n 'data_format': FLAGS.data_format,\n 'bottleneck': FLAGS.bottleneck,\n 'compression': FLAGS.compression,\n 'weight_decay': FLAGS.weight_decay,\n 'dropout_rate': FLAGS.dropout_rate,\n 'pool_initial': FLAGS.pool_initial,\n 'include_top': FLAGS.include_top,\n 'train_mode': FLAGS.train_mode,\n 'num_gpu': FLAGS.num_gpu\n }\n main(**kwargs)\n\n\ndef main(epochs,\n enable_function,\n buffer_size,\n batch_size,\n mode,\n growth_rate,\n output_classes,\n depth_of_model=None,\n num_of_blocks=None,\n num_layers_in_each_block=None,\n data_format='channels_last',\n bottleneck=True,\n compression=0.5,\n weight_decay=1e-4,\n dropout_rate=0.,\n pool_initial=False,\n include_top=True,\n train_mode='custom_loop',\n data_dir=None,\n num_gpu=1):\n\n devices = ['/device:GPU:{}'.format(i) for i in range(num_gpu)]\n strategy = tf.distribute.MirroredStrategy(devices)\n\n with strategy.scope():\n model = densenet.DenseNet(\n mode, growth_rate, output_classes, depth_of_model, num_of_blocks,\n num_layers_in_each_block, data_format, bottleneck, compression,\n weight_decay, dropout_rate, pool_initial, include_top)\n\n trainer = Train(epochs, enable_function, model, num_gpu)\n\n train_dataset, test_dataset, metadata = create_dataset(\n buffer_size, batch_size, data_format, data_dir)\n\n num_train_steps_per_epoch = metadata.splits[\n 'train'].num_examples // batch_size\n num_test_steps_per_epoch = metadata.splits[\n 'test'].num_examples // batch_size\n\n train_iterator = strategy.make_dataset_iterator(train_dataset)\n test_iterator = strategy.make_dataset_iterator(test_dataset)\n\n print('Training...')\n if train_mode == 'custom_loop':\n return trainer.custom_loop(train_iterator,\n test_iterator,\n num_train_steps_per_epoch,\n num_test_steps_per_epoch,\n strategy)\n elif train_mode == 'keras_fit':\n raise ValueError(\n '`tf.distribute.Strategy` does not support subclassed models yet.')\n else:\n raise ValueError(\n 'Please enter either \"keras_fit\" or \"custom_loop\" as the argument.')\n\n\nif __name__ == '__main__':\n app.run(run_main)\n", "sub_path": "tensorflow_examples/models/densenet/distributed_train.py", "file_name": "distributed_train.py", "file_ext": "py", "file_size_in_byte": 11790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tensorflow.__version__.startswith", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 26, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 28, "usage_type": "attribute"}, {"api_name": "absl.flags", "line_number": 28, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 30, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 30, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 31, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 31, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 32, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 32, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 33, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 33, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 34, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 34, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 35, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 35, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 36, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 36, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 37, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 37, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 38, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 38, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 39, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 39, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 40, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 40, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 42, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 42, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 44, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 44, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_float", "line_number": 45, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 45, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_float", "line_number": 48, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 48, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_float", "line_number": 49, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 49, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 50, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 50, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 53, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 53, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 54, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 54, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 56, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 56, "usage_type": "name"}, {"api_name": "tensorflow.data", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_flip_left_right", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.transpose", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.image.resize_image_with_crop_or_pad", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_crop", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.load", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.SGD", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.SparseCategoricalAccuracy", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.SparseCategoricalAccuracy", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 224, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 225, "usage_type": "call"}, {"api_name": "tensorflow.distribute.MirroredStrategy", "line_number": 312, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 312, "usage_type": "attribute"}, {"api_name": "tensorflow_examples.models.densenet.densenet.DenseNet", "line_number": 315, "usage_type": "call"}, {"api_name": "tensorflow_examples.models.densenet.densenet", "line_number": 315, "usage_type": "name"}, {"api_name": "absl.app.run", "line_number": 349, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 349, "usage_type": "name"}]} +{"seq_id": "402991433", "text": "import os\nimport logging\n\nfrom gym.envs.registration import register\n\nos.environ[\"PYGAME_HIDE_SUPPORT_PROMPT\"] = \"yes please\"\nLOG_DIR = \"logs\"\nif not os.path.isdir(LOG_DIR):\n os.mkdir(LOG_DIR)\n# Init and setup the root logger\nlogging.basicConfig(filename=LOG_DIR + '/macad-gym.log', level=logging.DEBUG)\n\n_AVAILABLE_ENVS = {\n 'HomoNcomIndePOIntrxMASS3CTWN3-v0': {\n \"entry_point\":\n \"macad_gym.envs:HomoNcomIndePOIntrxMASS3CTWN3\",\n \"description\":\n \"Homogenous, Non-communicating, Independed, Partially-\"\n \"Observable Intersection Multi-Agent scenario with \"\n \"Stop-Sign, 3 Cars in Town3, version 0\"\n },\n 'HeteNcomIndePOIntrxMATLS1B2C1PTWN3-v0': {\n \"entry_point\":\n \"macad_gym.envs:HeteNcomIndePOIntrxMATLS1B2C1PTWN3\",\n \"description\":\n \"Heterogeneous, Non-communicating, Independent,\"\n \"Partially-Observable Intersection Multi-Agent\"\n \" scenario with Traffic-Light Signal, 1-Bike, 2-Car,\"\n \"1-Pedestrian in Town3, version 0\"\n }\n}\n\nfor id, val in _AVAILABLE_ENVS.items():\n register(id=id, entry_point=val.get(\"entry_point\"))\n\n\ndef list_available_envs():\n print(\"Environment-ID: Short-description\")\n import pprint\n available_envs = {}\n for env_id, val in _AVAILABLE_ENVS.items():\n available_envs[env_id] = val.get(\"description\")\n pprint.pprint(available_envs)\n", "sub_path": "src/macad_gym/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "gym.envs.registration.register", "line_number": 34, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "253049670", "text": "#!/usr/bin/python\nimport httplib2\nimport json\nimport xmljson\nh = httplib2.Http(\".cache\")\nresp, content = h.request(\"http://scpark1.tiesv.org/fid-parkingmanagement\", \n \"POST\", body=\" \\\n \\\n \\\n \\\n \\\n Driver-1 \\\n \\\n \\\n \\\n \\\n \\\n\", \n headers={'content-type':'application/x-www-form-urlencoded'} )\nprint (content)\n\n\n\nfrom lxml.etree import fromstring, tostring\nxml = fromstring(content)\nprint (json.dumps(xmljson.parker.data(xml)))\n\n\n", "sub_path": "TIEHACK/findDriverByDriverId.py", "file_name": "findDriverByDriverId.py", "file_ext": "py", "file_size_in_byte": 557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "httplib2.Http", "line_number": 5, "usage_type": "call"}, {"api_name": "lxml.etree.fromstring", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "xmljson.parker.data", "line_number": 26, "usage_type": "call"}, {"api_name": "xmljson.parker", "line_number": 26, "usage_type": "attribute"}]} +{"seq_id": "276025867", "text": "#encoding: utf-8\r\nimport os.path, Tkinter, tkMessageBox\r\nfrom whoosh import index\r\nfrom whoosh.fields import Schema, TEXT, KEYWORD, ID, DATETIME\r\nfrom whoosh.analysis import StemmingAnalyzer\r\nfrom whoosh.qparser import QueryParser\r\nfrom whoosh.qparser.plugins import GtLtPlugin, FieldsPlugin, RangePlugin\r\n \r\n\r\n#Clase-------------------------------------------------------------------------------------------------------------------\r\nclass indice():\r\n def __init__(self, nombre_indice = 'index'):\r\n self.nombre_indice = nombre_indice\r\n \r\n try:\r\n self.indice = index.open_dir(nombre_indice)\r\n except WindowsError:\r\n self.indice = None\r\n \r\n self.schema = Schema(numero = ID(stored=True),\r\n remitente = ID(),\r\n destinatarios = KEYWORD(),\r\n fecha = DATETIME(),\r\n asunto = TEXT(analyzer=StemmingAnalyzer()),\r\n cuerpo = TEXT())\r\n \r\n \r\n def crear_indice(self):\r\n if not self.schema:\r\n tkMessageBox.showerror('Error', 'No existe ningún esquema.\\nPor favor, cree un esquema y reintente la búsqueda.')\r\n else:\r\n nombre_indice = self.nombre_indice\r\n if not os.path.exists(nombre_indice):\r\n os.mkdir(nombre_indice)\r\n \r\n indice = index.create_in(nombre_indice, self.schema)\r\n self.indice = indice\r\n \r\n \r\n def actualizar_indice(self):\r\n if not self.indice:\r\n tkMessageBox.showerror('Error', 'No existe ningún índice.\\nPor favor, cree un índice y reintente la búsqueda.')\r\n else:\r\n nombre_indice = self.nombre_indice\r\n ix = index.open_dir(nombre_indice)\r\n writer = ix.writer()\r\n \r\n correos = leer_correos()\r\n \r\n for correo in correos:\r\n writer.add_document(numero = correo[0], remitente = correo[1], destinatarios = correo[2], fecha = correo[3], \\\r\n asunto = correo[4], cuerpo = correo[5])\r\n \r\n writer.commit()\r\n \r\n \r\n def buscar_indice(self, campo, query):\r\n if not self.indice:\r\n tkMessageBox.showerror('Error', 'No existe ningún índice.\\nPor favor, cree un índice y reintente la b��squeda.')\r\n else:\r\n result = []\r\n indice = self.indice\r\n searcher = indice.searcher()\r\n \r\n qp = QueryParser(campo, schema=self.schema)\r\n qp.add_plugin(FieldsPlugin())\r\n qp.add_plugin(RangePlugin())\r\n qp.add_plugin(GtLtPlugin())\r\n q = qp.parse(unicode(query))\r\n \r\n with searcher as s:\r\n busqueda = s.search(q)\r\n result = [correo['numero'] for correo in busqueda]\r\n \r\n return result\r\n#------------------------------------------------------------------------------------------------------------------------\r\n \r\n\r\n#Métodos-----------------------------------------------------------------------------------------------------------------\r\n#Remitente, destinatario, fecha, asunto, cuerpo.\r\ndef leer_correo(correo):\r\n result= [unicode(correo.readline().strip()), unicode(correo.readline().strip()), unicode(correo.readline().strip()),\\\r\n unicode(correo.readline().strip()), unicode(correo.read())]\r\n \r\n return result\r\n \r\n\r\ndef leer_correos():\r\n numero = 1\r\n correo = True\r\n result = []\r\n \r\n while correo:\r\n try:\r\n with open('Correos/' + str(numero) + '.txt', 'r') as correo:\r\n c = leer_correo(correo)\r\n c.insert(0, unicode(numero))\r\n result.append(c)\r\n numero += 1\r\n except IOError:\r\n correo = None\r\n return result\r\n\r\n\r\ndef buscar_correos(tipo, practica):\r\n def callback():\r\n if tipo == 'Remitente':\r\n email = buscar_email_remitente(entry.get().strip())\r\n if not email:\r\n tkMessageBox.showinfo('Información', 'El nombre indicado no existe en la agenda.'\\\r\n + '\\nPor favor, reintente la búsqueda.')\r\n result = None\r\n else: \r\n result = practica.buscar_indice(tipo.lower(), email)\r\n else:\r\n busqueda = entry.get().strip()\r\n if tipo == 'Fecha':\r\n busqueda = 'fecha:' + busqueda\r\n result = practica.buscar_indice(tipo.lower(), busqueda)\r\n if not result:\r\n tkMessageBox.showinfo('Información', 'Su búsqueda no ha proporcionado resultados.'\\\r\n + '\\nPor favor, reintente la búsqueda.')\r\n \r\n if result:\r\n correos = listar_correos(result)\r\n mostrar_correos(correos)\r\n \r\n top = Tkinter.Toplevel(bg='white')\r\n pfont = 'Helvetica 10 bold'\r\n label = Tkinter.Label(top, text=tipo, font=pfont, bg='white')\r\n label.pack(side=Tkinter.LEFT)\r\n \r\n entry = Tkinter.Entry(top, width=50)\r\n entry.focus_set()\r\n entry.pack(side=Tkinter.LEFT)\r\n \r\n button = Tkinter.Button(top, text='Buscar', cursor='hand2', font=pfont, bg='white', command=callback)\r\n button.pack(side=Tkinter.LEFT)\r\n \r\n\r\ndef buscar_email_remitente(remitente):\r\n with open('Agenda/agenda.txt', 'r') as agenda:\r\n result = None\r\n email = agenda.readline()\r\n \r\n while email:\r\n nombre = agenda.readline().strip()\r\n \r\n if nombre == remitente:\r\n result = email\r\n break\r\n else:\r\n email = agenda.readline()\r\n \r\n return result\r\n\r\n\r\ndef listar_correos(numeros):\r\n result = []\r\n numeros.sort(key = lambda st: int(st))\r\n \r\n for numero in numeros:\r\n with open('Correos/' + str(numero) + '.txt', 'r') as correo:\r\n c = leer_correo(correo)\r\n c.insert(0, unicode(numero))\r\n result.append(c)\r\n \r\n return result\r\n\r\n\r\ndef mostrar_correos(correos):\r\n top = Tkinter.Toplevel(bg = 'white')\r\n \r\n scrollbar = Tkinter.Scrollbar(top)\r\n scrollbar.pack(side = Tkinter.RIGHT, fill=Tkinter.Y)\r\n \r\n texto = Tkinter.Text(top, height = 35, width = 67, yscrollcommand = scrollbar.set, wrap = Tkinter.WORD)\r\n \r\n opciones_titulo = {'foreground' : 'black', 'font' : 'helvetica 12 bold'}\r\n opciones_negrita = {'foreground' : 'black', 'font' : 'helvetica 10 bold'}\r\n opciones = {'foreground' : 'black', 'font' : 'helvetica 10'}\r\n \r\n for correo in correos: \r\n texto.insert('end', 'Correo ' + correo[0])\r\n cambiar_formato(texto, 'Correo ' + correo[0], 'correo' + correo[0], opciones_titulo)\r\n texto.insert('end', '\\n')\r\n \r\n texto.insert('end', 'Remitente: ')\r\n cambiar_formato(texto, 'Remitente: ', 'remitente' + correo[0], opciones_negrita)\r\n texto.insert('end', correo[1])\r\n cambiar_formato(texto, correo[1], 'texto_remitente' + correo[0], opciones)\r\n texto.insert('end', '\\n')\r\n \r\n texto.insert('end', 'Destinatarios: ')\r\n cambiar_formato(texto, 'Destinatarios: ', 'destinatarios' + correo[0], opciones_negrita)\r\n texto.insert('end', correo[2])\r\n cambiar_formato(texto, correo[2], 'texto_destinatarios' + correo[0], opciones)\r\n texto.insert('end', '\\n')\r\n \r\n texto.insert('end', 'Fecha: ')\r\n cambiar_formato(texto, 'Fecha: ', 'fecha' + correo[3], opciones_negrita)\r\n texto.insert('end', correo[3])\r\n cambiar_formato(texto, correo[3], 'texto_fecha' + correo[3], opciones)\r\n texto.insert('end', '\\n')\r\n \r\n texto.insert('end', 'Asunto: ')\r\n cambiar_formato(texto, 'Asunto: ', 'asunto' + correo[0], opciones_negrita)\r\n texto.insert('end', correo[4])\r\n cambiar_formato(texto, correo[4], 'texto_asunto' + correo[0], opciones)\r\n texto.insert('end', '\\n')\r\n \r\n texto.insert('end', correo[5] + '\\n\\n')\r\n \r\n texto.pack(side = Tkinter.BOTTOM, fill = Tkinter.BOTH, expand = Tkinter.TRUE)\r\n texto.config(state = 'disabled')\r\n texto.focus()\r\n \r\n scrollbar.config(command = texto.yview)\r\n\r\n\r\ndef cambiar_formato(widget, text, tag_name, dic):\r\n end_index = widget.index('end')\r\n start_index = \"%s-%sc\" % (end_index, len(text) + 1)\r\n widget.tag_add(tag_name, start_index, end_index)\r\n widget.tag_configure(tag_name, dic)\r\n#------------------------------------------------------------------------------------------------------------------------\r\n\r\n\r\n#Apartado gráfico--------------------------------------------------------------------------------------------------------\r\nroot = Tkinter.Tk()\r\nroot.title('Agenda')\r\npractica = indice()\r\n\r\ntitulos_font = 'Helvetica 10 bold'\r\n\r\nboton_crear_indice = Tkinter.Button(root, text='Crear indice', cursor='hand2', font=titulos_font, bg='white',\\\r\n command=practica.crear_indice)\r\nboton_crear_indice.pack(side=Tkinter.LEFT)\r\n\r\nboton_actualizar_indice = Tkinter.Button(root, text='Actualizar índice', cursor='hand2', font=titulos_font, bg='white',\\\r\n command=practica.actualizar_indice)\r\nboton_actualizar_indice.pack(side=Tkinter.LEFT)\r\n\r\nboton_mostrar_correos = Tkinter.Button(root, text='Buscar por remitente', cursor='hand2', font=titulos_font, bg='white',\\\r\n command=lambda: buscar_correos('Remitente', practica))\r\nboton_mostrar_correos.pack(side=Tkinter.LEFT)\r\n\r\nboton_buscar = Tkinter.Button(root, text='Buscar por asunto', cursor='hand2', font=titulos_font, bg='white',\\\r\n command=lambda: buscar_correos('Asunto', practica))\r\nboton_buscar.pack(side=Tkinter.LEFT)\r\n\r\nboton_buscar = Tkinter.Button(root, text='Buscar por fecha', cursor='hand2', font=titulos_font, bg='white',\\\r\n command=lambda: buscar_correos('Fecha', practica))\r\nboton_buscar.pack(side=Tkinter.LEFT)\r\n\r\nroot.mainloop()", "sub_path": "Practicas/practica6/practica6.py", "file_name": "practica6.py", "file_ext": "py", "file_size_in_byte": 10168, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "whoosh.index.open_dir", "line_number": 16, "usage_type": "call"}, {"api_name": "whoosh.index", "line_number": 16, "usage_type": "name"}, {"api_name": "whoosh.fields.Schema", "line_number": 20, "usage_type": "call"}, {"api_name": "whoosh.fields.ID", "line_number": 20, "usage_type": "call"}, {"api_name": "whoosh.fields.ID", "line_number": 21, "usage_type": "call"}, {"api_name": "whoosh.fields.KEYWORD", "line_number": 22, "usage_type": "call"}, {"api_name": "whoosh.fields.DATETIME", "line_number": 23, "usage_type": "call"}, {"api_name": "whoosh.fields.TEXT", "line_number": 24, "usage_type": "call"}, {"api_name": "whoosh.analysis.StemmingAnalyzer", "line_number": 24, "usage_type": "call"}, {"api_name": "whoosh.fields.TEXT", "line_number": 25, "usage_type": "call"}, {"api_name": "tkMessageBox.showerror", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.mkdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "name"}, {"api_name": "whoosh.index.create_in", "line_number": 36, "usage_type": "call"}, {"api_name": "whoosh.index", "line_number": 36, "usage_type": "name"}, {"api_name": "tkMessageBox.showerror", "line_number": 42, "usage_type": "call"}, {"api_name": "whoosh.index.open_dir", "line_number": 45, "usage_type": "call"}, {"api_name": "whoosh.index", "line_number": 45, "usage_type": "name"}, {"api_name": "tkMessageBox.showerror", "line_number": 59, "usage_type": "call"}, {"api_name": "whoosh.qparser.QueryParser", "line_number": 65, "usage_type": "call"}, {"api_name": "whoosh.qparser.plugins.FieldsPlugin", "line_number": 66, "usage_type": "call"}, {"api_name": "whoosh.qparser.plugins.RangePlugin", "line_number": 67, "usage_type": "call"}, {"api_name": "whoosh.qparser.plugins.GtLtPlugin", "line_number": 68, "usage_type": "call"}, {"api_name": "tkMessageBox.showinfo", "line_number": 110, "usage_type": "call"}, {"api_name": "tkMessageBox.showinfo", "line_number": 121, "usage_type": "call"}, {"api_name": "Tkinter.Toplevel", "line_number": 128, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 130, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 131, "usage_type": "attribute"}, {"api_name": "Tkinter.Entry", "line_number": 133, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 135, "usage_type": "attribute"}, {"api_name": "Tkinter.Button", "line_number": 137, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 138, "usage_type": "attribute"}, {"api_name": "Tkinter.Toplevel", "line_number": 172, "usage_type": "call"}, {"api_name": "Tkinter.Scrollbar", "line_number": 174, "usage_type": "call"}, {"api_name": "Tkinter.RIGHT", "line_number": 175, "usage_type": "attribute"}, {"api_name": "Tkinter.Y", "line_number": 175, "usage_type": "attribute"}, {"api_name": "Tkinter.Text", "line_number": 177, "usage_type": "call"}, {"api_name": "Tkinter.WORD", "line_number": 177, "usage_type": "attribute"}, {"api_name": "Tkinter.BOTTOM", "line_number": 214, "usage_type": "attribute"}, {"api_name": "Tkinter.BOTH", "line_number": 214, "usage_type": "attribute"}, {"api_name": "Tkinter.TRUE", "line_number": 214, "usage_type": "attribute"}, {"api_name": "Tkinter.Tk", "line_number": 230, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 236, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 238, "usage_type": "attribute"}, {"api_name": "Tkinter.Button", "line_number": 240, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 242, "usage_type": "attribute"}, {"api_name": "Tkinter.Button", "line_number": 244, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 246, "usage_type": "attribute"}, {"api_name": "Tkinter.Button", "line_number": 248, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 250, "usage_type": "attribute"}, {"api_name": "Tkinter.Button", "line_number": 252, "usage_type": "call"}, {"api_name": "Tkinter.LEFT", "line_number": 254, "usage_type": "attribute"}]} +{"seq_id": "289392919", "text": "__author__ = 'jim'\n\nimport re\n\nfrom scrapy.http import Request\n\nfrom alascrapy.items import ProductItem, ReviewItem, ProductIdItem\nfrom alascrapy.spiders.base_spiders.ala_spider import AlaSpider\nfrom alascrapy.lib.generic import get_full_url, date_format\n\n\nclass CrateandbarrelComSpider(AlaSpider):\n name = 'crateandbarrel_com'\n allowed_domains = ['crateandbarrel.com', 'bazaarvoice.com']\n start_urls = ['http://www.crateandbarrel.com/kitchen-and-food/appliances-electrics/',\n 'http://www.crateandbarrel.com/kitchen-and-food/coffee-and-tea/']\n\n bv_key = '3nrh8am0ufbc174ibar6c3jkj'\n bv_version = '5.4'\n\n def parse(self, response):\n category_urls = self.extract_list(response.xpath('//ul[@class=\"SuperCatNav\"]/li/a/@href'))\n\n for category_url in category_urls:\n category_url = get_full_url(response, category_url)\n yield Request(url=category_url, callback=self.parse_category)\n\n def parse_category(self, response):\n ocn = self.extract_all(response.xpath('//div[@id=\"SiteMapPath\"]/span//text()'))\n product_urls = self.extract_list(response.xpath(\n '//span[@class=\"product-thumb\"][following-sibling::div[@class=\"hwBottomHitArea\"]]/a/@href'))\n for product_url in product_urls:\n product_url = get_full_url(response, product_url)\n request = Request(url=product_url, callback=self.parse_product)\n request.meta['ocn'] = ocn\n yield request\n\n def parse_product(self, response):\n product = ProductItem()\n\n product['TestUrl'] = response.url\n product['OriginalCategoryName'] = response.meta['ocn']\n name = self.extract(response.xpath('//h1[@id=\"productNameHeader\"]/text()'))\n product['PicURL'] = self.extract(response.xpath('//img[@id=\"_imgLarge\"]/@src'))\n product['source_internal_id'] = self.extract(response.xpath('//span[@class=\"jsSwatchSku\"]/text()'))\n\n mpn = self.extract(response.xpath('//p[contains(text(),\"Item Number\")]/span/text()'))\n if mpn:\n product_id = ProductIdItem()\n product[\"ProductName\"] = name+' '+mpn\n product_id['ProductName'] = product[\"ProductName\"]\n product_id['source_internal_id'] = product['source_internal_id']\n product_id['ID_kind'] = \"MPN\"\n product_id['ID_value'] = mpn\n yield product\n yield product_id\n else:\n product[\"ProductName\"] = name\n yield product\n\n test_url = 'http://api.bazaarvoice.com/data/reviews.json?apiversion=%s&passkey=%s&Filter=ProductId:s%s' \\\n '&Sort=SubmissionTime:desc&Limit=100' % (self.bv_version, self.bv_key, product['source_internal_id'])\n\n request = Request(url=test_url, callback=self.parse_reviews)\n request.meta['product'] = product\n yield request\n\n @staticmethod\n def parse_reviews(response):\n reviews = re.findall(r'{\"TagDimensions\":(((?!(\"TagDimensions\")).)+)}', response.body)\n\n for item in reviews:\n review = item[0]\n user_review = ReviewItem()\n user_review['DBaseCategoryName'] = \"USER\"\n user_review['ProductName'] = response.meta['product']['ProductName']\n user_review['TestUrl'] = response.meta['product']['TestUrl']\n user_review['source_internal_id'] = response.meta['product']['source_internal_id']\n date = re.findall(r'\"SubmissionTime\":\"([\\d-]+)', review)\n user_review['TestDateText'] = date_format(date[0], \"%Y-%m-%d\")\n rate = re.findall(r'\"Rating\":([\\d])', review)\n user_review['SourceTestRating'] = rate[0]\n author = re.findall(r'\"UserNickname\":\"([^\"]+)', review)\n if author:\n user_review['Author'] = author[0]\n title = re.findall(r'\"Title\":\"([^\"]+)', review)\n if title:\n user_review['TestTitle'] = title[0]\n summary = re.findall(r'\"ReviewText\":\"([^\"]+)', review)\n if summary:\n user_review['TestSummary'] = summary[0]\n yield user_review\n", "sub_path": "alascrapy/spiders/crateandbarrel_com.py", "file_name": "crateandbarrel_com.py", "file_ext": "py", "file_size_in_byte": 4130, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "alascrapy.spiders.base_spiders.ala_spider.AlaSpider", "line_number": 12, "usage_type": "name"}, {"api_name": "alascrapy.lib.generic.get_full_url", "line_number": 25, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 26, "usage_type": "call"}, {"api_name": "alascrapy.lib.generic.get_full_url", "line_number": 33, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 34, "usage_type": "call"}, {"api_name": "alascrapy.items.ProductItem", "line_number": 39, "usage_type": "call"}, {"api_name": "alascrapy.items.ProductIdItem", "line_number": 49, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 64, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 70, "usage_type": "call"}, {"api_name": "alascrapy.items.ReviewItem", "line_number": 74, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 79, "usage_type": "call"}, {"api_name": "alascrapy.lib.generic.date_format", "line_number": 80, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 81, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 83, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 86, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "146758803", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport json\nimport os\nimport requests\nimport socket\nimport subprocess\nimport sys\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom ipaddress import ip_address\nfrom urllib.parse import urlparse\n\n__all__ = [\n 'ping21',\n]\n\n\ndef get_public_ip(uri):\n \"\"\"Return public IP address from URI.\"\"\"\n parsed = urlparse(uri)\n if not parsed.netloc:\n parsed = urlparse('http://' + uri)\n domain = parsed.netloc\n try:\n address = socket.gethostbyname(domain)\n except socket.gaierror:\n address = None\n else:\n if ip_address(address).is_private:\n address = None\n return address\n\n\ndef get_device_geoip():\n \"\"\"Return GeoIP data for IP address of this device.\"\"\"\n path = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), '.device-geoip.json')\n\n if os.path.isfile(path):\n path_date = datetime.fromtimestamp(os.path.getmtime(path))\n if datetime.now() - path_date < timedelta(hours=8):\n with open(path) as f:\n return json.loads(f.read())\n\n url = 'https://dazzlepod.com/ip/me.json'\n response = requests.get(url, timeout=10)\n geoip = response.json()\n with open(path, 'w') as f:\n f.write(json.dumps(geoip))\n\n return geoip\n\n\ndef ping21(uri, count=3, packet_size=64, timeout=3):\n ip = get_public_ip(uri)\n if ip is None:\n raise ValueError('Cannot resolve {} to a valid IP address'.format(uri))\n\n cmd = [\n '/bin/ping',\n '-c', str(count),\n '-s', str(packet_size),\n '-W', str(timeout),\n str(ip),\n ]\n\n try:\n output = subprocess.check_output(cmd).decode()\n except subprocess.CalledProcessError as err:\n raise ValueError('cmd={}: {}'.format(cmd, err))\n\n output = [line for line in output.split('\\n') if line]\n data = dict(ping=output, server=get_device_geoip())\n return data\n\n\nif __name__ == '__main__':\n url = 'dazzlepod.com'\n if len(sys.argv) > 1:\n url = sys.argv[1]\n data = ping21(url)\n print(json.dumps(data, indent=4, sort_keys=True))\n", "sub_path": "ping21.py", "file_name": "ping21.py", "file_ext": "py", "file_size_in_byte": 2119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "urllib.parse.urlparse", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.gethostbyname", "line_number": 27, "usage_type": "call"}, {"api_name": "socket.gaierror", "line_number": 28, "usage_type": "attribute"}, {"api_name": "ipaddress.ip_address", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.getmtime", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 70, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 82, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "333890077", "text": "import sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '.'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '../..'))\n\nimport pandas as pd\nimport numpy as np\nimport pythreejs\n\nfrom base import Structure\nfrom utils.matrices import roty, rotz, rotx\n\ncur_id = 0\n\nBBOX_COLORS = {\n 'Car': '#ff0000',\n 'Pedestrian': '#00ff00',\n 'Van': '#ff8800',\n 'Truck': '#ffff00',\n 'Cyclist': '#0000ff',\n 'Tram': '#00ffff',\n 'Person (sitting)': '#00ff88',\n 'Misc': '#88ff88'\n}\n\n\ndef corners_to_centered_bbox(box3d_corner):\n # center point is in the center of xy plane and in bottom of z plane\n # (N, 8, 3) -> (N, 7)\n assert box3d_corner.ndim == 3\n batch_size = box3d_corner.shape[0]\n\n xyz = np.mean(box3d_corner[:, :4, :], axis=1)\n\n h = abs(np.mean(box3d_corner[:, 4:, 2] -\n box3d_corner[:, :4, 2], axis=1, keepdims=True))\n\n w = (np.sqrt(np.sum((box3d_corner[:, 0, [0, 1]] - box3d_corner[:, 1, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 2, [0, 1]] - box3d_corner[:, 3, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 4, [0, 1]] - box3d_corner[:, 5, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 6, [0, 1]] - box3d_corner[:, 7, [0, 1]]) ** 2, axis=1, keepdims=True))) / 4\n\n l = (np.sqrt(np.sum((box3d_corner[:, 0, [0, 1]] - box3d_corner[:, 3, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 1, [0, 1]] - box3d_corner[:, 2, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 4, [0, 1]] - box3d_corner[:, 7, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 5, [0, 1]] - box3d_corner[:, 6, [0, 1]]) ** 2, axis=1, keepdims=True))) / 4\n\n theta = (np.arctan2(box3d_corner[:, 2, 1] - box3d_corner[:, 1, 1],\n box3d_corner[:, 2, 0] - box3d_corner[:, 1, 0]) +\n np.arctan2(box3d_corner[:, 3, 1] - box3d_corner[:, 0, 1],\n box3d_corner[:, 3, 0] - box3d_corner[:, 0, 0]) +\n np.arctan2(box3d_corner[:, 2, 0] - box3d_corner[:, 3, 0],\n box3d_corner[:, 3, 1] - box3d_corner[:, 2, 1]) +\n np.arctan2(box3d_corner[:, 1, 0] - box3d_corner[:, 0, 0],\n box3d_corner[:, 0, 1] - box3d_corner[:, 1, 1]))[:, np.newaxis] / 4\n\n return np.concatenate([xyz, l, w, h, theta], axis=1).reshape(batch_size, 7)\n\n\nclass BoundingBoxes(Structure):\n def __init__(self, points, calib, bboxes, ignore_empty_bboxes=False, corners=None, **kwargs):\n ''' corners: list of bounding boxes with corners in the following order\n bboxes: info about the original bounding box Object3d (coordinates of this object aren't always right, they're based on original bounding box, so corners object should be trusted if available)\n 6 -------- 7 z| x\n /| /| |/ \n 5 -------- 4 . y -- -- -y\n | | | | /\n . 2 -------- 3 -x \n |/ |/\n 1 -------- 0\n '''\n Structure.__init__(self, points=points, **kwargs)\n self.calib = calib\n self.bboxes = bboxes\n if len(self.bboxes) > 0:\n if corners is not None:\n self.corners = corners\n else:\n self._compute_corners(bboxes)\n\n self.corners = np.array(self.corners)\n self.centered_bbox = corners_to_centered_bbox(\n self.corners) # xyzlwhr\n self.bboxes = np.array(self.bboxes)\n\n if ignore_empty_bboxes is True:\n self.corners = self._filter_empty_bboxes(self.corners)\n else:\n self.corners = np.array([])\n self.centered_bbox = np.array([])\n\n def _filter_empty_bboxes(self, corners):\n # this code is here for reference, but in practice it's not efficient to calculate this each time we use the dataset\n # the right way to do it is to calculate it once and save the invalid bboxes in a file or remove them from the dataset directly\n # (unless the bounding boxes are dynamic)\n def point_inside_box(c, point):\n # https://math.stackexchange.com/questions/1472049/check-if-a-point-is-inside-a-rectangular-shaped-area-3d\n ax = c[2] - c[1]\n ay = c[0] - c[1]\n az = c[5] - c[1]\n\n px = np.dot(ax, point)\n py = np.dot(ay, point)\n pz = np.dot(az, point)\n\n cx = px >= np.dot(ax, c[1]) and px <= np.dot(ax, c[2])\n cy = py >= np.dot(ay, c[1]) and py <= np.dot(ay, c[0])\n cz = pz >= np.dot(az, c[1]) and pz <= np.dot(az, c[5])\n\n return cx and cy and cz\n\n def box_contains_points(box, points):\n for point in points:\n if point_inside_box(box, point):\n return True\n return False\n valid_corners = []\n for i, corner in enumerate(corners):\n if box_contains_points(corner, self._points) is True:\n valid_corners.append(i)\n\n return corners[valid_corners]\n\n def _compute_corners(self, bboxes):\n self.corners = []\n for obj in bboxes:\n # compute bboxes on REF COORD\n # compute rotational matrix around yaw axis\n R = roty(obj.ry)\n\n # 3d bounding box dimensions\n l = obj.l\n w = obj.w\n h = obj.h\n\n # 3d bounding box corners\n x_corners = [l / 2, l / 2, -l / 2, -\n l / 2, l / 2, l / 2, -l / 2, -l / 2]\n y_corners = [0, 0, 0, 0, -h, -h, -h, -h]\n z_corners = [w / 2, -w / 2, -w / 2,\n w / 2, w / 2, -w / 2, -w / 2, w / 2]\n\n # rotate and translate 3d bounding box\n corners_3d = np.dot(R, np.vstack(\n [x_corners, y_corners, z_corners]))\n # print corners_3d.shape\n corners_3d[0, :] = corners_3d[0, :] + obj.t[0]\n corners_3d[1, :] = corners_3d[1, :] + obj.t[1]\n corners_3d[2, :] = corners_3d[2, :] + obj.t[2]\n\n # project from REF COORD to VELO COORD\n vel_corners = self.calib.project_ref_to_velo(\n np.transpose(corners_3d))\n self.corners.append(vel_corners)\n\n def compute(self):\n \"\"\"ABC API\"\"\"\n global cur_id\n self.id = \"BBOXES({})\".format(cur_id)\n cur_id += 1\n\n def plot(self, scene):\n for cur in range(0, len(self.corners)):\n bbox_corners = self.corners[cur]\n bbox = self.bboxes[cur]\n lines = []\n for k in range(0, 4):\n i, j = k, (k + 1) % 4\n lines.append([(bbox_corners[i, 0], bbox_corners[i, 1], bbox_corners[i, 2]),\n (bbox_corners[j, 0], bbox_corners[j, 1], bbox_corners[j, 2])])\n\n i, j = k + 4, (k + 1) % 4 + 4\n lines.append([(bbox_corners[i, 0], bbox_corners[i, 1], bbox_corners[i, 2]),\n (bbox_corners[j, 0], bbox_corners[j, 1], bbox_corners[j, 2])])\n\n i, j = k, k + 4\n lines.append([(bbox_corners[i, 0], bbox_corners[i, 1], bbox_corners[i, 2]),\n (bbox_corners[j, 0], bbox_corners[j, 1], bbox_corners[j, 2])])\n\n for line in lines:\n try:\n color = BBOX_COLORS[bbox.type]\n except:\n color = '#88ff88'\n\n line_geometry = pythreejs.Geometry(\n vertices=line)\n drew_line = pythreejs.Line(\n geometry=line_geometry,\n material=pythreejs.LineBasicMaterial(color=color),\n type='LinePieces')\n scene.add(drew_line)\n\n def generate_subcloud(self, box_xyzlwh):\n box_x, box_y, box_z, box_l, box_w, box_h = box_xyzlwh\n\n def point_inside_box(point):\n on_l = (point[0] <= box_x + box_l /\n 2) and (point[0] >= box_x - box_l / 2)\n on_w = (point[1] <= box_y + box_w /\n 2) and (point[1] >= box_y - box_w / 2)\n on_h = (point[2] <= box_z + box_h /\n 2) and (point[2] >= box_z - box_h / 2)\n return (on_l and on_w and on_h)\n\n idx_to_include = []\n for idx in range(0, len(self.corners)):\n include_corner_set = True\n corner_set = self.corners[idx]\n for i in range(0, len(corner_set)):\n if not point_inside_box(corner_set[i]):\n include_corner_set = False\n break\n if include_corner_set:\n idx_to_include.append(idx)\n\n new_corners = []\n for idx in idx_to_include:\n corner = self.corners[idx].copy()\n corner -= np.array([box_x, box_y, box_z])\n new_corners.append(corner)\n\n structure = BoundingBoxes(points=self._points, bounds=self.bounds, calib=self.calib, origin=self.origin,\n bboxes=self.bboxes[idx_to_include], corners=new_corners, ignore_empty_bboxes=False)\n return structure\n\n def __str__(self):\n st = ''\n\n st += '{} bounding box/es'.format(len(self.corners))\n return st\n\n def __len__(self):\n return len(self.corners)\n\n def get_shape(self, corner):\n w = np.linalg.norm(corner[0] - corner[1])\n h = np.linalg.norm(corner[5] - corner[1])\n l = np.linalg.norm(corner[2] - corner[1])\n return (l, w, h)\n\n def get_shapes(self):\n shapes = []\n for corner in self.corners:\n shapes.append(self.get_shape(corner))\n\n return shapes\n\n\nif __name__ == '__main__':\n # test corners_to_centered_bbox\n # test bbox: bottom left corner on origin, width height and length of s, no angle\n s = 4\n corners = np.array([[[0, -s, 0], [0, 0, 0], [s, 0, 0], [s, -s, 0], \n [0, -s, s], [0, 0, s], [s, 0, s], [s, -s, s]]])\n print(corners_to_centered_bbox(corners))\n\n front_offset = 1\n corners = np.array([[[0, -s, 0], [0, 0, 0], [s, -front_offset, 0], [s, -s-front_offset, 0], \n [0, -s, s], [0, 0, s], [s, -front_offset, s], [s, -s-front_offset, s]]])\n print(corners_to_centered_bbox(corners))", "sub_path": "pyntcloud/structures/bounding_boxes.py", "file_name": "bounding_boxes.py", "file_ext": "py", "file_size_in_byte": 10497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "base.Structure", "line_number": 61, "usage_type": "name"}, {"api_name": "base.Structure.__init__", "line_number": 73, "usage_type": "call"}, {"api_name": "base.Structure", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.matrices.roty", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 154, "usage_type": "call"}, {"api_name": "pythreejs.Geometry", "line_number": 187, "usage_type": "call"}, {"api_name": "pythreejs.Line", "line_number": 189, "usage_type": "call"}, {"api_name": "pythreejs.LineBasicMaterial", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 239, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 240, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 260, "usage_type": "call"}]} +{"seq_id": "16827825", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas_datareader.data as web\nfrom datetime import datetime\nimport os\nimport csv\nimport os.path\nimport numpy as np\nimport pandas as pd\nfrom fbprophet import Prophet\nfrom sklearn import preprocessing\nfrom sklearn import linear_model\nfrom flask import request, redirect\nfrom flask import Flask, render_template\nfrom sklearn import model_selection\nfrom pathlib import Path\nfrom itertools import zip_longest\n\n\n\n\n\napp = Flask(__name__)\n@app.after_request\ndef add_header(response):\n response.headers['X-UA-Compatible'] = 'IE=Edge,chrome=1'\n response.headers['Cache-Control'] = 'public, max-age=0'\n return response\n \n@app.route(\"/\")\ndef first_page():\n tmp = Path(\"static/prophet.png\")\n tmp_csv = Path(\"static/numbers.csv\")\n if tmp.is_file():\n os.remove(tmp)\n if tmp_csv.is_file():\n os.remove(tmp_csv)\n return render_template(\"index.html\")\n\ndef read_data(symbol,start=datetime(2016, 1, 31),end=datetime(2019, 1, 31)):\n print(symbol)\n stockdata= web.DataReader(symbol, 'yahoo',start,end)\n return stockdata\n\ndef linear_reg_prepare_data(data,forecast_col='Close',pred_period=10,test_size=0):\n #creating new column called label with prediction period set as null\n label = data[forecast_col].shift(-pred_period)\n #creating the feature array as X: historical prices\n X = np.array(data[[forecast_col]]) \n #storing for predictions\n X_lately = X[-pred_period:] \n X = X[:-pred_period] \n label.dropna(inplace=True)\n # assigning Y\n y = np.array(label) \n X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, y, test_size=test_size) \n response = [X_train,X_test , Y_train, Y_test , X_lately];\n return response;\n\n@app.route(\"/plot\" , methods = ['POST', 'GET'] )\ndef main():\n if request.method == 'POST':\n stock = request.form['companyname']\n #hist_data = read_data(stock)\n hist_data=data\n # on the basis of closing prices only\n df = hist_data.filter(['Close'])\n # log of closing prices\n df['y'] = np.log(df['Close'])\n df['ds'] = df.index\n data_end = df['Close'][-1]\n #print(df)\n\n model1 = Prophet(daily_seasonality=True)\n model1.fit(df)\n\n pred_period=10\n\n future = model1.make_future_dataframe(periods=pred_period)\n forecast = model1.predict(future)\n\n #print(forecast)\n #print (forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())\n\n df.set_index('ds', inplace=True)\n forecast.set_index('ds', inplace=True)\n\n viz_df = df.join(forecast[['yhat', 'yhat_lower','yhat_upper']], how = 'outer')\n viz_df['yhat_scaled'] = np.exp(viz_df['yhat'])\n\n close_data = viz_df.Close\n forecasted_data = viz_df.yhat_scaled\n #print(close_data)\n print(forecasted_data[-10:])\n forecast1=forecasted_data[-10:].values\n X_train, X_test, Y_train, Y_test , X_lately =linear_reg_prepare_data(data,forecast_col='Close',pred_period=10,test_size=0.2)\n learner = linear_model.LinearRegression(); #initializing linear regression model\n\n learner.fit(X_train,Y_train) #training the linear regression model\n score=learner.score(X_test,Y_test)#testing the linear regression model\n\n forecast2= learner.predict(X_lately)\n forecast_en= (forecast1*0.4)+(forecast2*0.6)\n print(forecast_en)\n forecasted_data=forecasted_data.replace(forecasted_data[-10:].values,forecast_en)\n print(forecasted_data[-10:])\n date = future['ds']\n forecast_start = forecasted_data[-pred_period]\n print(forecasted_data[-10:])\n d = [date, close_data, forecasted_data]\n export_data = zip_longest(*d, fillvalue = '')\n with open('static/numbers.csv', 'w', encoding=\"ISO-8859-1\", newline='') as myfile:\n wr = csv.writer(myfile)\n wr.writerow((\"Date\", \"Actual\", \"Forecasted\"))\n wr.writerows(export_data)\n myfile.close()\n\n return render_template(\"plot.html\", original = round(original_end,2), forecast = round(forecast_start,2), stock_tinker = stock.upper())\n\nif __name__ == \"__main__\":\n app.run(debug=True, threaded=True)\n\n", "sub_path": "stock_price_prediction/prediction.py", "file_name": "prediction.py", "file_ext": "py", "file_size_in_byte": 4237, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Flask", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 39, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas_datareader.data.DataReader", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas_datareader.data", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 73, "usage_type": "call"}, {"api_name": "fbprophet.Prophet", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 101, "usage_type": "name"}, {"api_name": "itertools.zip_longest", "line_number": 115, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "312057743", "text": "from selenium import webdriver\nfrom scrapy.selector import Selector\nimport time\n\ndef zhihu_login_test():\n browser = webdriver.Chrome(executable_path='C:/opt/selenium/chromedriver.exe')\n\n browser.get(\"https://www.zhihu.com/#signin\")\n browser.find_element_by_css_selector(\"input[name='account']\").send_keys('13571899655')\n browser.find_element_by_css_selector(\"input[name='password']\").send_keys('hejunwei3269982')\n time.sleep(4)\n\n try:\n # browser.find_element_by_css_selector(\"input[name='captcha']\").send_keys('1234')\n browser.find_element_by_css_selector(\"button.sign-button\").click()\n selector = Selector(text=browser.page_source)\n print(browser.page_source)\n # browser.quit()\n except:\n zhihu_login_test()\n\n\ndef oschina_blog_test():\n browser = webdriver.Chrome(executable_path='C:\\opt\\selenium\\chromedriver.exe')\n browser.get(\"https://www.oschina.net/blog\")\n for i in range(3):\n browser.execute_script(\n \"\"\"\n window.scrollTo(0,document.body.scrollHeight);\n \n \"\"\"\n )\n time.sleep(2)\n\n\ndef weibo_login_test():\n chrome_opt = webdriver.ChromeOptions()\n prefs = {\"profile.managed_default_content_settings.images\":2}\n chrome_opt.add_experimental_option(\"prefs\",prefs)\n browser = webdriver.Chrome(executable_path='C:/opt/selenium/chromedriver.exe', chrome_options=chrome_opt)\n browser.get(\"https://weibo.com/login\")\n\n\ndef taobao_detail_test():\n browser = webdriver.PhantomJS(executable_path='C:/opt/phantomjs-2.1.1-windows/bin/phantomjs.exe')\n browser.get(\"https://detail.tmall.com/item.htm?id=41696362194&spm=a223v.7835278.t0.1.bHk1ax&pvid=716b6051-9fed-45f3-a1da-c2fe08e635ea&scm=1007.12144.81309.9011_8949\")\n print(browser.page_source)\n browser.quit()\n\n\ntaobao_detail_test()", "sub_path": "build/lib/ArticleSpider/utils/selenium_spider.py", "file_name": "selenium_spider.py", "file_ext": "py", "file_size_in_byte": 1835, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 11, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 16, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 37, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 40, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 40, "usage_type": "name"}, {"api_name": "selenium.webdriver.PhantomJS", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "573937451", "text": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# @file: scripts/kubuntu.py\n# Copyright (c) 2013 Korepwx. All rights reserved.\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Contributors:\n# Korepwx 2013-08-22\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Use of this source code is governed by BSD license that can be found in the\n# LICENSE file.\n\n'''\nThis script initializes basic packages for kubuntu.\n'''\n\nimport os\nimport re\nimport logging\nfrom scripts import require\nfrom core.util import *\nfrom core.osutil import *\n\ndef dist_upgrade():\n do('apt-get -y dist-upgrade')\n if (last_status == 0):\n logging.info('Upgrade system completed.')\n return True\n logging.error('Cannot upgrade system.')\n return False\n\ndef install_language_packs():\n do('apt-get install -y language-pack-zh-hans '\n 'language-pack-gnome-zh-hans '\n 'language-pack-kde-zh-hans '\n 'kde-l10n-zhcn')\n if (last_status == 0):\n logging.info('Language packs installed.')\n return True\n logging.error('Cannot install language packs.')\n return False\n\ndef install_fcitx_im():\n def alter_qt_config():\n cfg = QtConfig()\n fpath = os.path.join(USERHOME, '.config/Trolltech.conf')\n if (not os.path.isfile(fpath)):\n userwrite(fpath, '[qt]\\nDefaultInputMethod=fcitx\\n')\n return True\n cfg.read(fpath)\n cfg.set('qt', 'DefaultInputMethod', 'fcitx')\n with useropen(fpath, 'wb') as f:\n cfg.write(f)\n return True\n do('apt-get install -y im-switch fcitx fcitx-sunpinyin '\n 'fcitx-config-gtk fcitx-config-gtk2 fcitx-frontend-all '\n 'fcitx-modules fcitx-module-cloudpinyin '\n 'fcitx-module-kimpanel fcitx-tools libfcitx-qt5-0 '\n 'kde-config-fcitx'\n )\n if (last_status == 0):\n if (userdo('im-switch -s fcitx') == 0):\n alter_qt_config()\n logging.info('Fcitx input method installed.')\n return True\n logging.error('Cannot install fcitx input method.')\n return False\n\ndef add_ppa_repository():\n from scripts.nouveau import HAS_NVIDIA_CARD\n if (os.path.isfile('/etc/apt/sources.list.d/'\n 'fcitx-team-nightly-raring.list')):\n return True\n do('add-apt-repository -y ppa:blue-shell/firefox-kde')\n do('add-apt-repository -y ppa:fcitx-team/nightly')\n do('add-apt-repository -y ppa:kazam-team/stable-series')\n do('add-apt-repository -y ppa:tualatrix/ppa') # Ubuntu Tweak\n do('add-apt-repository -y ppa:webupd8team/sublime-text-3')\n if (do('apt-get update') != 0):\n logging.error('Cannot add ppa repositories.')\n return False\n logging.info('Added ppa repositories.')\n return True\n\ndef install_firefox():\n do('apt-get install -y firefox firefox-kde-support '\n 'firefox-locale-zh-hans')\n if (last_status != 0):\n logging.error('Cannot install firefox.')\n return False\n logging.info('Firefox installed.')\n return True\n\ndef config_desktop_fonts():\n import ConfigParser\n usermkdir(os.path.join(USERHOME, '.kdev'))\n usermkdir(os.path.join(USERHOME, '.kde/share'))\n usermkdir(os.path.join(USERHOME, '.kde/share/config'))\n cfgfile = os.path.join(USERHOME, '.kde/share/config/kdeglobals')\n if (not os.path.isfile(cfgfile)):\n userwrite(cfgfile, '')\n cfg = QtConfig()\n cfg.read(cfgfile)\n if (cfg.get('General', 'desktopFont', '').startswith('Sans Serif')):\n logging.info('Skip config desktop fonts.')\n return True\n sans12 = 'Sans Serif,12,-1,5,50,0,0,0,0,0'\n mono12 = 'Monospace,12,-1,5,50,0,0,0,0,0'\n sans11 = 'Sans Serif,11,-1,5,50,0,0,0,0,0'\n cfg.set('General', 'desktopFont', sans12)\n cfg.set('General', 'font', sans12)\n cfg.set('General', 'fixed', mono12)\n cfg.set('General', 'menuFont', sans12)\n cfg.set('General', 'smallestReadableFont', sans11)\n cfg.set('General', 'taskbarFont', sans12)\n cfg.set('General', 'toolBarFont', sans12)\n with useropen(cfgfile, 'wb') as f:\n cfg.write(f)\n logging.info('Desktop fonts configured.')\n return True\n\ndef install_fonts():\n do('apt-get install -y ttf-droid ttf-liberation ttf-wqy-microhei '\n 'ttf-dejavu')\n if (last_status != 0):\n logging.error('Cannot install fonts from apt.')\n return False\n if (not os.path.isdir('/usr/share/fonts/my')):\n os.mkdir('/usr/share/fonts/my')\n for n in listdata('fonts'):\n with opendata('fonts/' + n, 'rb') as f:\n with open('/usr/share/fonts/my/' + n, 'wb') as f2:\n f2.write(f.read())\n do('fc-cache -f')\n fonts_conf = readdata('fonts.conf')\n userwrite(os.path.join(USERHOME, '.fonts.conf'), fonts_conf)\n userwrite(os.path.join(USERHOME, '.fonts.conf.backup'), fonts_conf)\n logging.info('Fonts installed.')\n return True\n\ndef install_multimedia():\n do('apt-get purge -y dragonplayer')\n do('apt-get install -y smplayer vlc')\n if (last_status != 0):\n logging.error('Cannot install multimedia apps.')\n return False\n logging.info('Multimedia apps installed.')\n return True\n\ndef install_nice_apps():\n do('apt-get install -y kazam filezilla ubuntu-tweak sublime-text-installer '\n 'transmission-qt software-center')\n if (last_status != 0):\n logging.error('Cannot install several nice apps.')\n return False\n logging.info('Several nice apps installed.')\n return True\n\ndef install_extras():\n do('apt-get install -y appmenu-qt5 qtchooser ia32-libs yakuake p7zip-full '\n 'p7zip-rar rar unrar')\n if (last_status != 0):\n logging.error('Cannot install extra apps for kubuntu.')\n return False\n logging.info('Extra apps installed.')\n return True\n\n# ---- Main Routine ----\ndef execute():\n require('aptsrc')\n require('nouveau')\n sequence = [\n add_ppa_repository,\n dist_upgrade,\n install_language_packs,\n install_fonts,\n config_desktop_fonts,\n install_fcitx_im,\n install_firefox,\n install_multimedia,\n install_nice_apps,\n install_extras,\n ]\n for c in sequence:\n if (not c()):\n return False\n return True\n", "sub_path": "scripts/kubuntu.py", "file_name": "kubuntu.py", "file_ext": "py", "file_size_in_byte": 6396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.info", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 66, "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": "logging.error", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 105, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 119, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 138, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 145, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 147, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 154, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 156, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 163, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 165, "usage_type": "call"}, {"api_name": "scripts.require", "line_number": 170, "usage_type": "call"}, {"api_name": "scripts.require", "line_number": 171, "usage_type": "call"}]} +{"seq_id": "19886964", "text": "from django.test import TestCase, RequestFactory\nfrom django.http import HttpResponseRedirect, Http404\nfrom django.urls import reverse\nfrom django.views.generic import DetailView, TemplateView\nfrom django.core import mail, signing\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib.auth.models import AnonymousUser, User\nfrom django.contrib.messages.storage.fallback import FallbackStorage\nfrom django.forms import modelform_factory\nfrom django.conf import settings\nfrom rest_framework.viewsets import ModelViewSet\nfrom rest_framework.test import APIRequestFactory\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\nfrom rest_framework.permissions import IsAuthenticated\nfrom .util import ProfileMixin, APIProfileMixin\nfrom .factories import ProfileFactory, UserFactory\nfrom .models import Profile\nfrom projects.models import Project\nfrom projects.factories import ProjectFactory\nfrom organizations.factories import OrganizationFactory\nfrom organizations.models import Organization\nfrom annotations.models import Annotation\nfrom annotations.factories import AnnotationFactory\nfrom annotations.serializers import AnnotationSerializer\nfrom users.forms import UserForm\n\nclass ProfileMixinTestCase(TestCase):\n\n class ProfileMixinView(ProfileMixin, DetailView):\n model = Project\n queryset = Project.objects.all()\n template_name = \"test.html\"\n\n def handle_no_permission(self):\n #we subclass this so we can easily test it's being called\n raise PermissionDenied\n\n def setUp(self):\n self.factory = RequestFactory()\n self.profile = ProfileFactory()\n self.profile_2 = ProfileFactory(user=self.profile.user)\n self.other_profile = ProfileFactory()\n self.user = self.profile.user\n self.org = self.profile.organization\n self.view = self.ProfileMixinView()\n self.project = project = ProjectFactory(organization=self.org)\n self.view.kwargs = {'pk' : self.project.pk}\n\n def test_no_profile_calls_handle_no_permission(self):\n request = self.factory.get('/')\n request.user = AnonymousUser()\n\n self.view.request = request\n with self.assertRaises(PermissionDenied):\n self.view.dispatch(request)\n\n def test_profile(self):\n request = self.factory.get('/')\n request.user = self.user\n\n self.view.request = request\n self.view.dispatch(request)\n\n self.assertIn(self.profile, self.view.profiles)\n\n def test_as_user_sets_other_profile(self):\n other_project = ProjectFactory(organization=self.other_profile.organization)\n\n request = self.factory.get('/?user={}'.format(self.other_profile.user.pk))\n self.user.is_staff = True\n request.user = self.user\n\n self.view.kwargs = {'pk' : other_project.pk}\n\n self.view.request = request\n self.view.dispatch(request)\n self.assertEqual(self.other_profile.user, self.view.user)\n self.assertIn(self.other_profile, self.view.profiles)\n\n def test_as_profile_user_is_not_staff(self):\n \"\"\"should still return the users profile\"\"\"\n request = self.factory.get('/?user={}'.format(self.other_profile.pk))\n self.user.is_staff = False\n request.user = self.user\n\n self.view.request = request\n self.view.dispatch(request)\n\n self.assertNotEqual(self.other_profile.user, self.view.user)\n self.assertIn(self.profile, self.view.profiles)\n\n def test_user_has_no_profile_returns_redirect(self):\n request = self.factory.get('/')\n request.user = UserFactory()\n\n setattr(request, 'session', 'session')\n messages = FallbackStorage(request)\n setattr(request, '_messages', messages)\n self.view.request = request\n response = self.view.dispatch(request)\n\n self.assertIsInstance(response, HttpResponseRedirect)\n self.assertEqual(response.url, self.view.create_profile_url)\n\n def test_profile_in_context(self):\n request = self.factory.get('/')\n request.user = self.user\n self.view.request = request\n self.view.dispatch(request)\n context = self.view.get_context_data()\n\n self.assertIn(self.profile, context['profiles'])\n\n def test_profile_not_active(self):\n request = self.factory.get('/')\n request.user = self.user\n self.profile.is_active = False\n self.profile.save()\n self.view.request = request\n with self.assertRaises(Http404):\n self.view.dispatch(request)\n\n def test_org_not_active(self):\n request = self.factory.get('/')\n request.user = self.user\n self.org.is_active = False\n self.org.save()\n self.view.request = request\n with self.assertRaises(Http404):\n self.view.dispatch(request)\n\n def test_permission_can_edit_required(self):\n request = self.factory.get('/')\n request.user = self.user\n self.profile.can_edit = True\n self.profile.save()\n\n self.view.permissions_required = ['can_edit']\n self.view.request = request\n self.view.dispatch(request)\n ### nothing is raised so we're in the clear\n self.assertTrue(True)\n\n def test_permission_can_edit_required_profile_has_no_permission(self):\n request = self.factory.get('/')\n request.user = self.user\n self.profile.can_edit = False\n self.profile.save()\n\n self.view.permissions_required = ['can_edit']\n self.view.request = request\n with self.assertRaises(PermissionDenied):\n self.view.dispatch(request)\n\n def test_get_object_project_same_org(self):\n request = self.factory.get('/')\n request.user = self.user\n\n self.view.request = request\n self.view.dispatch(request)\n obj = self.view.get_object()\n self.assertEqual(obj, self.project)\n\n def test_get_object_project_diff_org_raises_404(self):\n request = self.factory.get('/')\n request.user = self.user\n project = ProjectFactory()\n self.view.kwargs = {'pk': project.pk}\n self.view.request = request\n with self.assertRaises(Http404):\n self.view.dispatch(request)\n\n def test_get_object_org_same_org(self):\n request = self.factory.get('/')\n request.user = self.user\n mixinView = self.ProfileMixinView\n\n self.view.queryset = Organization.objects.all()\n self.view.model = Organization\n self.view.kwargs = {'pk' : self.org.pk}\n self.view.request = request\n self.view.dispatch(request)\n obj = self.view.get_object()\n self.assertEqual(obj, self.org)\n\n def test_get_object_org_diff_org_raises_404(self):\n request = self.factory.get('/')\n request.user = self.user\n other_org = OrganizationFactory()\n self.view.model = Organization\n self.view.queryset = Organization.objects.all()\n self.view.kwargs = {'pk': other_org.pk}\n self.view.request = request\n with self.assertRaises(Http404):\n self.view.dispatch(request)\n\nclass APIProfileTestCase(TestCase):\n\n class ProfileMixinViewSet(APIProfileMixin, ModelViewSet):\n serializer_class = AnnotationSerializer\n queryset = Annotation.objects.all()\n\n ###subclass this so we can easily assert that it gets called\n def permission_denied(self, request, message=None):\n raise PermissionDenied\n\n def setUp(self):\n self.profile = ProfileFactory()\n self.other_profile = ProfileFactory()\n self.user = self.profile.user\n self.org = self.profile.organization\n self.view = self.ProfileMixinViewSet()\n self.factory = APIRequestFactory()\n self.project = ProjectFactory(organization=self.org)\n self.note = AnnotationFactory(project=self.project)\n\n def test_permission_denied_with_no_login(self):\n request = self.factory.get('/')\n request.user = None\n request.data = {'profile': self.profile.pk}\n self.org.is_active = False\n self.org.save()\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n with self.assertRaises(PermissionDenied):\n self.view.check_permissions(request)\n\n def test_get_profile(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile' : self.profile.pk}\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n self.view.check_permissions(request)\n self.assertEqual(self.view.profile, self.profile)\n\n def test_404_with_no_profile(self):\n \"\"\"raises a 404 if there is no profile\"\"\"\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {}\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n with self.assertRaises(Http404):\n self.view.check_permissions(request)\n\n def test_project_pk_gets_project(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile': self.profile.pk, 'project': self.project.pk}\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n self.view.check_permissions(request)\n self.assertEqual(self.view.project, self.project)\n\n def test_permission_denied_with_different_org_profile(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile': self.other_profile.pk}\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n with self.assertRaises(PermissionDenied):\n self.view.check_permissions(request)\n\n def test_permission_denied_with_inactive_profile(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile': self.profile.pk}\n self.profile.is_active = False\n self.profile.save()\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n with self.assertRaises(PermissionDenied):\n self.view.check_permissions(request)\n\n def test_permission_denied_with_inactive_org(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile': self.profile.pk}\n self.org.is_active = False\n self.org.save()\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n with self.assertRaises(PermissionDenied):\n self.view.check_permissions(request)\n\n def test_action_list_okay_without_can_edit(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile': self.profile.pk}\n self.profile.can_edit = False\n self.profile.save()\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n self.view.action = 'list'\n self.view.check_permissions(request)\n #no exception raised, everything looks okay\n self.assertTrue(True)\n\n def test_permission_denied_without_can_edit(self):\n request = self.factory.get('/')\n request.user = self.profile.user\n request.data = {'profile': self.profile.pk}\n self.profile.can_edit = False\n self.profile.save()\n self.view.kwargs = {'pk' : self.note.pk}\n self.view.request = request\n\n can_edit_actions = ['create', 'update', 'partial_update', 'destroy', 'change_page', 'duplicate', 'reorder', 'move', 'upload_screenshot']\n for action in can_edit_actions:\n self.view.action = action\n with self.assertRaises(PermissionDenied):\n self.view.check_permissions(request)\n\nclass InviteViewTestCase(TestCase):\n def setUp(self):\n self.profile = ProfileFactory(org_admin=True)\n self.org = self.profile.organization\n self.client.force_login(self.profile.user)\n self.url = reverse('profiles:invite', args=[self.org.pk])\n self.data = {'profile-can_edit' : True, 'user-first_name': 'Billy', 'user-last_name': 'Weinerschnitzel', 'user-email': 'bweinerschnitzel@gmail.com'}\n\n def test_get_invite_view_as_admin(self):\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, 200)\n\n def test_get_invite_view_not_admin(self):\n self.profile.org_admin = False\n self.profile.save()\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, 403)\n\n def test_profile_is_created(self):\n response = self.client.post(self.url, self.data)\n\n self.assertEqual(response.status_code, 302)\n self.assertEqual(response.url, reverse('organizations:detail', args=[self.org.pk])) ##we get redirected to the correct view\n new_user = User.objects.filter(first_name=\"Billy\").filter(last_name=\"Weinerschnitzel\").filter(email=\"bweinerschnitzel@gmail.com\").filter(username=\"bweinerschnitzel\")\n self.assertTrue(new_user.exists())\n\n new_user = new_user.first()\n new_profile = Profile.objects.filter(user=new_user).filter(organization=self.org).filter(can_edit=True)\n\n self.assertTrue(new_profile.exists())\n\n def test_profile_is_pending(self):\n \"\"\"make sure the new profile.is_pending == True\"\"\"\n self.client.post(self.url, self.data)\n new_profile = Profile.objects.filter(user__first_name=\"Billy\").filter(user__last_name=\"Weinerschnitzel\").filter(user__email=\"bweinerschnitzel@gmail.com\").filter(organization=self.org).first()\n self.assertTrue(new_profile.is_pending)\n\n def test_email_is_sent(self):\n self.client.post(self.url, self.data)\n self.assertEqual(len(mail.outbox), 1)\n\nclass ProfileActivationViewTestCase(TestCase):\n def setUp(self):\n self.profile = ProfileFactory(org_admin=True, is_pending=True, is_active=False)\n self.org = self.profile.organization\n self.salt = getattr(settings, 'REGISTRATION_SALT')\n self.key = signing.dumps(obj=self.profile.pk, salt=self.salt)\n\n def test_activate(self):\n key = self.key\n url = reverse('profiles:activate', args=[key])\n response = self.client.get(url)\n self.assertEqual(response.status_code, 302)\n self.assertEqual(response.url, reverse('projects:list'))\n self.profile.refresh_from_db()\n\n self.assertTrue(self.profile.is_active)\n self.assertFalse(self.profile.is_pending)\n\n def test_activate_bad_key(self):\n key = signing.dumps(obj=self.profile.pk + 1, salt=self.salt)\n url = reverse('profiles:activate', args=[key])\n response = self.client.get(url)\n self.assertEqual(response.status_code, 404)\n self.profile.refresh_from_db()\n\n self.assertFalse(self.profile.is_active)\n self.assertTrue(self.profile.is_pending)\n\n def test_activate_already_active(self):\n self.profile.is_active = True\n self.profile.is_pending = False\n self.profile.save()\n\n key = signing.dumps(obj=self.profile.pk, salt=self.salt)\n url = reverse('profiles:activate', args=[key])\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n\n self.profile.refresh_from_db()\n\n self.assertTrue(self.profile.is_active)\n self.assertFalse(self.profile.is_pending)\n", "sub_path": "webapp/profiles/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 15755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.test.TestCase", "line_number": 27, "usage_type": "name"}, {"api_name": "util.ProfileMixin", "line_number": 29, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 29, "usage_type": "name"}, {"api_name": "projects.models.Project", "line_number": 30, "usage_type": "name"}, {"api_name": "projects.models.Project.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "projects.models.Project.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "projects.models.Project", "line_number": 31, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 36, "usage_type": "name"}, {"api_name": "django.test.RequestFactory", "line_number": 39, "usage_type": "call"}, {"api_name": "factories.ProfileFactory", "line_number": 40, "usage_type": "call"}, {"api_name": "factories.ProfileFactory", "line_number": 41, "usage_type": "call"}, {"api_name": "factories.ProfileFactory", "line_number": 42, "usage_type": "call"}, {"api_name": "projects.factories.ProjectFactory", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 51, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 54, "usage_type": "argument"}, {"api_name": "projects.factories.ProjectFactory", "line_number": 67, "usage_type": "call"}, {"api_name": "factories.UserFactory", "line_number": 94, "usage_type": "call"}, {"api_name": "django.contrib.messages.storage.fallback.FallbackStorage", "line_number": 97, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 102, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 120, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 129, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 152, "usage_type": "argument"}, {"api_name": "projects.factories.ProjectFactory", "line_number": 167, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 170, "usage_type": "argument"}, {"api_name": "organizations.models.Organization.objects.all", "line_number": 178, "usage_type": "call"}, {"api_name": "organizations.models.Organization.objects", "line_number": 178, "usage_type": "attribute"}, {"api_name": "organizations.models.Organization", "line_number": 178, "usage_type": "name"}, {"api_name": "organizations.models.Organization", "line_number": 179, "usage_type": "name"}, {"api_name": "organizations.factories.OrganizationFactory", "line_number": 189, "usage_type": "call"}, {"api_name": "organizations.models.Organization", "line_number": 190, "usage_type": "name"}, {"api_name": "organizations.models.Organization.objects.all", "line_number": 191, "usage_type": "call"}, {"api_name": "organizations.models.Organization.objects", "line_number": 191, "usage_type": "attribute"}, {"api_name": "organizations.models.Organization", "line_number": 191, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 194, "usage_type": "argument"}, {"api_name": "django.test.TestCase", "line_number": 197, "usage_type": "name"}, {"api_name": "util.APIProfileMixin", "line_number": 199, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 199, "usage_type": "name"}, {"api_name": "annotations.serializers.AnnotationSerializer", "line_number": 200, "usage_type": "name"}, {"api_name": "annotations.models.Annotation.objects.all", "line_number": 201, "usage_type": "call"}, {"api_name": "annotations.models.Annotation.objects", "line_number": 201, "usage_type": "attribute"}, {"api_name": "annotations.models.Annotation", "line_number": 201, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 205, "usage_type": "name"}, {"api_name": "factories.ProfileFactory", "line_number": 208, "usage_type": "call"}, {"api_name": "factories.ProfileFactory", "line_number": 209, "usage_type": "call"}, {"api_name": "rest_framework.test.APIRequestFactory", "line_number": 213, "usage_type": "call"}, {"api_name": "projects.factories.ProjectFactory", "line_number": 214, "usage_type": "call"}, {"api_name": "annotations.factories.AnnotationFactory", "line_number": 215, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 226, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 247, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 267, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 279, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 291, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 319, "usage_type": "argument"}, {"api_name": "django.test.TestCase", "line_number": 322, "usage_type": "name"}, {"api_name": "factories.ProfileFactory", "line_number": 324, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 327, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 344, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 345, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 345, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 345, "usage_type": "name"}, {"api_name": "models.Profile.objects.filter", "line_number": 349, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 349, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 349, "usage_type": "name"}, {"api_name": "models.Profile.objects.filter", "line_number": 356, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 356, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 356, "usage_type": "name"}, {"api_name": "django.core.mail.outbox", "line_number": 361, "usage_type": "attribute"}, {"api_name": "django.core.mail", "line_number": 361, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 363, "usage_type": "name"}, {"api_name": "factories.ProfileFactory", "line_number": 365, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 367, "usage_type": "argument"}, {"api_name": "django.core.signing.dumps", "line_number": 368, "usage_type": "call"}, {"api_name": "django.core.signing", "line_number": 368, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 372, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 375, "usage_type": "call"}, {"api_name": "django.core.signing.dumps", "line_number": 382, "usage_type": "call"}, {"api_name": "django.core.signing", "line_number": 382, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 383, "usage_type": "call"}, {"api_name": "django.core.signing.dumps", "line_number": 396, "usage_type": "call"}, {"api_name": "django.core.signing", "line_number": 396, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 397, "usage_type": "call"}]} +{"seq_id": "113050311", "text": "from django.conf.urls import url\nfrom . import views\n\n\napp_name = 'games'\n\nurlpatterns = [\n # /games/\n url(r'^$', views.IndexView.as_view(), name='index'),\n\n # /games//\n url(r'^(?P[0-9]+)/$', views.DetailView.as_view(), name='detail'),\n\n # /games/add/\n url(r'^add/$', views.AddView.as_view(), name='add'),\n\n # /games//delete\n url(r'^(?P[0-9]+)/delete/$', views.DeleteView.as_view(), name='delete'),\n\n # /games//update\n url(r'^(?P[0-9]+)/update/$', views.UpdateView.as_view(), name='update'),\n]\n", "sub_path": "games/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 563, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "228013185", "text": "from flask import Flask, request, jsonify\nfrom flask import Flask, request, render_template, flash, redirect, url_for\nfrom flask_cors import CORS\nfrom flask_sqlalchemy import SQLAlchemy\nimport googlemaps\nfrom datetime import datetime\nimport uuid\nfrom os import environ\n\n\nmodel = None\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = environ.get('dbURL')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\ndb = SQLAlchemy(app)\nCORS(app)\n\nclass Product(db.Model):\n __tablename__ = 'postItem'\n \n productID = db.Column(db.String(100), primary_key=True)\n userID = db.Column(db.String(10), primary_key=True)\n productName = db.Column(db.String(100), nullable=False)\n productType = db.Column(db.String(20), nullable=False)\n productDesc = db.Column(db.String(500), nullable=True)\n productStatus = db.Column(db.String(20), nullable=False)\n meetup = db.Column(db.String(100), nullable=False)\n \n def __init__(self, productID, userID, productName, productType, productDesc, productStatus, meetup):\n self.productID = productID\n self.userID = userID\n self.productName = productName\n self.productType = productType\n self.productDesc = productDesc\n self.productStatus = productStatus\n self.meetup = meetup\n\n def json(self):\n return {\"productID\": self.productID, \"sellerID\": self.userID, \"productName\": self.productName, \"productType\": self.productType,\"productDesc\": self.productDesc, \"productStatus\": self.productStatus, \"meetup\": self.meetup }\n\n\n@app.route(\"/\")\ndef welcome():\n return \"Hello there, this is product microservice\"\n\n@app.route(\"/recent_products\")\ndef recent_products():\n all_products = Product.query.limit(20).all()\n return jsonify({\"all_products\": [product.json() for product in all_products]}) \n\n@app.route(\"/search_products\", methods=[\"POST\"])\ndef search_products():\n if request.method == \"POST\":\n content = request.json\n search_term = content['search_term']\n search_term = \"%{}%\".format(search_term)\n search_products = Product.query.filter(Product.productName.like(search_term)).all()\n if not search_products:\n return jsonify({\"message\": \"No product found with the search term\"})\n else:\n return jsonify({\"message\": \"product found\", \"search_products\": [product.json() for product in search_products]})\n\n\n@app.route(\"/getProductByUserId/\", methods=[\"GET\"])\ndef getProductByUserId(userID):\n # authenticate first\n all_products = Product.query.filter_by(userID=userID).all()\n return jsonify({\"message\": \"successful\", \"all_products\": [product.json() for product in all_products]})\n\n@app.route(\"/update_product_status\", methods=[\"GET\",\"POST\"])\ndef update_product_status():\n # change product status to pending\n # change successful bid status from pending to accepted, pending paynent, create new entry for transaction db\n # once transaction completed, change bid to successful and product status to closed\n if request.method == 'POST':\n content = request.json\n productID = content['productID']\n\n product_queried = Product.query.filter_by(productID =productID).first()\n product_queried.productStatus = 'closed'\n db.session.commit()\n\n \n return jsonify({\"message\": \"product status updated\"})\n\n@app.route(\"/get_product_info/\", methods=[\"GET\"])\ndef get_product_info_by_productID(productID):\n if request.method == 'GET': \n product = Product.query.filter_by(productID=productID).first()\n if product:\n return jsonify({\"message\": \"product found\", \"product\": [product.json()]})\n return jsonify({\"message\": \"product not found\" })\n\n\n\n\n@app.route(\"/post_new_product\", methods=[\"POST\",\"GET\"])\ndef post_new_product():\n if request.method == 'POST':\n content = request.json\n productName = content['productName']\n productType = content['productType']\n productDesc = content['productDesc']\n sellerID = content[\"userID\"]\n meetup = content['meetup']\n\n add_product = Product(str(uuid.uuid4())[:10], sellerID, productName, productType, productDesc, \"newly listed\", meetup)\n db.session.add(add_product)\n db.session.commit()\n # redirect to product page with status change\n return jsonify({\"message\": \"successfully added a new product\"})\n if request.method == 'GET':\n return \"This is a page to post new product\"\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5001, debug=True)\n", "sub_path": "docker/product_management_microservice/product.py", "file_name": "product.py", "file_ext": "py", "file_size_in_byte": 4573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "name"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 101, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "99302932", "text": "# --------------\n# import packages\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\nimport re\nfrom nltk.corpus import stopwords\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score ,confusion_matrix\n\n\n# Code starts here\n\n# load data\nnews = pd.read_csv(path)\n\n# subset data\nnews = news[['TITLE','CATEGORY']]\n\n# distribution of classes\ndist = news.CATEGORY.value_counts()\n\n# display class distribution\nprint(news.head(), dist, sep = '\\n \\n')\n\n\n# display data\nimport matplotlib.pyplot as plt\nplt.figure()\ndist.plot.bar()\n\n\n# --------------\n# Code starts here\n\n# stopwords \nstop = set(stopwords.words('english'))\n\n# retain only alphabets\nnews['TITLE'] = news['TITLE'].apply(lambda x: re.sub(\"[^a-zA-Z]\", \" \",x))\n\n# convert to lowercase and tokenize\nnews['TITLE'] = news['TITLE'].apply(lambda x: x.lower())\nnews['TITLE'] = news['TITLE'].apply(lambda x: x.split())\n\n# remove stopwords\nnews['TITLE'] = news['TITLE'].apply(lambda x: [i for i in x if i not in stop])\n\n# join list elements\nnews['TITLE'] = news['TITLE'].apply(lambda x: ' '.join(x))\n\n# split into training and test sets\nX_train, X_test, y_train, y_test = train_test_split(news.TITLE, news.CATEGORY, test_size = 0.2, random_state = 3)\n\n\n# Code ends here\n\n\n\n# --------------\n# initialize count vectorizer / tfidf vectorizer\ncount_vectorizer = CountVectorizer()\ntfidf_vectorizer = TfidfVectorizer(ngram_range = (1,3))\n\n# fit and transform with count vectorizer\nX_train_count = count_vectorizer.fit_transform(X_train)\nX_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n\n# Code ends here\n\n\n# --------------\n# Code starts here\n\n# initialize multinomial naive bayes\nnb_1 = MultinomialNB()\nnb_2 = MultinomialNB()\n\n# fit on count vectorizer training data\nnb_1.fit(X_train_count, y_train)\n\n# fit on tfidf vectorizer training data\nnb_2.fit(X_train_tfidf, y_train)\n\n# accuracy with count vectorizer\nacc_count_nb = accuracy_score(nb_1.predict(X_test_count), y_test)\n\n# accuracy with tfidf vectorizer\nacc_tfidf_nb = accuracy_score(nb_2.predict(X_test_tfidf), y_test)\n\n# display accuracies\nprint(acc_count_nb, acc_tfidf_nb)\n\n# Code ends here\n\n\n# --------------\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# initialize logistic regression / fit on training data\nlogreg_1 = OneVsRestClassifier(LogisticRegression(random_state = 10)).fit(X_train_count, y_train)\nlogreg_2 = OneVsRestClassifier(LogisticRegression(random_state = 10)).fit(X_train_tfidf, y_train)\n\n# accuracy with count / tfidf\ny_pred_count_logreg = logreg_1.predict(X_test_count)\nacc_count_logreg = accuracy_score(y_test,y_pred_count_logreg)\n\ny_pred_tfidf_logreg = logreg_2.predict(X_test_tfidf)\nacc_tfidf_logreg = accuracy_score(y_test,y_pred_tfidf_logreg)\n\nprint(\"Accuracy of Logistic Regression Classifier with Count Vector processed Data: {}\\nAccuracy of Logistic Regression Classifier with TF-IDF processed Data: {}\".format(acc_count_logreg, acc_tfidf_logreg))\n\n# display accuracies\nimport matplotlib.pyplot as plt\nplt.figure()\nplt.bar([1,2,3,4], [acc_count_nb, acc_tfidf_nb, acc_count_logreg, acc_tfidf_logreg], tick_label = [\"NB-Count\", \"NB-TF-IDF\",\"LogReg-Count\", \"LogReg-TF-IDF\"])\nplt.show()\n\n\n\n\n", "sub_path": "classify-the-news-articles(nlp)/code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 3433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 43, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 43, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 83, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 95, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.multiclass.OneVsRestClassifier", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.multiclass.OneVsRestClassifier", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 113, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "239868205", "text": "from selenium import webdriver\nfrom selenium.common.exceptions import TimeoutException,NoSuchElementException\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom threading import Thread\nimport re,time,random\n# 引入 ActionChains 类\nfrom selenium.webdriver.common.action_chains import ActionChains\nFile_Path=r'C:\\Users\\Administrator\\Desktop\\mm.txt'\ncompile1=re.compile('100.00%')\ncompile2=re.compile('已完成：')\ncompile3=re.compile(r'[\\r\\n]+')\ncompile_ksxx=re.compile('开始学习')\ncompile_jxxx=re.compile('继续学习')\nurl=\"http://www.fsa.gov.cn/zxHome/index.html\"\ndef open_file(file):\n with open(file,'r') as f:\n data=f.read()\n return data\nclass Study(Thread):\n def __init__(self,data,addr=url):\n Thread.__init__(self)\n self.data=data\n self.url=addr\n self.flag=1\n self.driver=None\n self.wait=None\n self.main_handle=None\n def waiting(self):\n self.driver.implicitly_wait(10)\n def login(self):\n time.sleep(random.uniform(1, 3))\n self.driver = webdriver.Firefox()\n self.wait = WebDriverWait(self.driver, 10)\n self.driver.implicitly_wait(4)\n self.driver.get(self.url)\n print('填充id,pw：', self.data)\n self.driver.find_element_by_id('wlxy_username').send_keys(self.data[0])\n self.driver.find_element_by_id('wlxy_password').send_keys(self.data[1])\n\n self.driver.find_element_by_id('checkCode').click()\n text = self.driver.find_element_by_id('checkCode').text\n print('填充验证码：', text)\n self.driver.find_element_by_id('wlxy_code').send_keys(text)\n self.driver.find_element_by_class_name('btn_index_login').click()\n self.waiting()\n self.main_handle = self.driver.current_window_handle\n def check_ndbx(self):\n print(self.data[0],'check ndbxk')\n self.driver.find_element_by_class_name('ndbxk').click()\n self.waiting()\n elements = self.driver.find_elements_by_class_name('index_dt')\n n = 0\n for i in elements:\n if compile_ksxx.search(i.text) or compile_jxxx.search(i.text):\n n += 1\n if n == 0:\n self.flag = 0\n print(self.data[0],'年度必修已经学完....')\n else:\n print(self.data[0],'年度必修。。。。')\n def chose_xxk_or_bxk(self):\n if self.flag:\n try:\n self.driver.find_element_by_class_name('ndbxk').click()\n except NoSuchElementException:\n self.driver.find_element_by_class_name('ndbxk cur').click()\n print(self.data[0],'click ndbxk')\n else:\n self.driver.switch_to.window(self.main_handle)\n self.driver.refresh()\n self.waiting()\n try:\n self.driver.find_element_by_class_name('xxk').click()\n except NoSuchElementException:\n self.driver.find_element_by_class_name('xxk cur').click()\n print(self.data[0], 'click xxk')\n time.sleep(1)\n def start_learn(self):\n try:\n self.waiting()\n self.driver.find_element_by_link_text('继续学习').click()\n except NoSuchElementException:\n self.waiting()\n self.driver.find_element_by_link_text('开始学习').click()\n\n def switch_handle(self):\n time.sleep(3)\n all_handles = self.driver.window_handles\n print(self.data[0],'handles:', all_handles)\n for handle in all_handles:\n if handle != self.main_handle:\n self.driver.switch_to.window(handle)\n try:\n self.driver.find_element_by_link_text('点这里').click()\n print(self.data[0],'路过点这里')\n except NoSuchElementException:\n print(self.data[0],'continue')\n #系列课程里的选择\n def chose_kcml(self):\n self.waiting()\n print(self.data[0],'find kcml ')\n elements = self.driver.find_elements_by_id('kcml')\n for i in elements:\n s = compile3.split(i.text)\n print(self.data[0],'list:', s)\n for n in s:\n is_over = compile1.search(n)\n is_link = compile2.search(n)\n if is_link:\n if not is_over:\n try:\n print('正在播放：',str(n))\n p=self.wait.until(EC.element_to_be_clickable((By.PARTIAL_LINK_TEXT, str(n[:-10]))))\n self.driver.execute_script(\"arguments[0].click();\", p)\n p.click()\n p.click()\n except Exception as e :\n print('......',e)\n break\n def play(self):\n self.waiting()\n data = self.driver.find_element_by_id('playBtn').get_attribute('title')\n print(data)\n p = self.driver.find_element_by_id('playBtn')\n try:\n self.driver.execute_script(\"arguments[0].click();\", p)\n p = self.wait.until(EC.element_to_be_clickable((By.ID, 'playBtn')))\n p.click()\n except Exception:\n p.click()\n count = random.randint(10,20)\n for i in range(count):\n print('%d,还剩:%d分钟' % (count, (count - i)))\n time.sleep(60)\n print('close', self.driver.current_window_handle)\n self.driver.close()\n\n def run(self):\n self.login()\n self.check_ndbx()\n #必修课是否有学完 flag=0 表示必修课已经都学完了\n while 1:\n self.chose_xxk_or_bxk()\n self.start_learn()\n self.switch_handle()\n self.chose_kcml()\n self.play()\n self.driver.switch_to.window(self.main_handle)\n self.driver.refresh()\n self.waiting()\n self.chose_xxk_or_bxk()\nif __name__=='__main__':\n\n # 打开文件\n users=open_file(File_Path)\n #多线程\n threads=[]\n for i in re.split(r'[\\r\\n]+',users):\n if i:\n user=re.split(r'[\\s]+',i)\n if '#'in i:\n continue\n print('user:', user)\n t=Study(user,)\n threads.append(t)\n for t in threads:\n t.start()\n for t in threads:\n t.join()\n\n print('all process end...........')\n", "sub_path": "网络干部学院.py", "file_name": "网络干部学院.py", "file_ext": "py", "file_size_in_byte": 6492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 21, "usage_type": "name"}, {"api_name": "threading.Thread.__init__", "line_number": 23, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 23, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 67, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 76, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 84, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 98, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 115, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 115, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.PARTIAL_LINK_TEXT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 115, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 129, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 129, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 129, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 129, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 133, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 136, "usage_type": "call"}, {"api_name": "re.split", "line_number": 160, "usage_type": "call"}, {"api_name": "re.split", "line_number": 162, "usage_type": "call"}]} +{"seq_id": "324030026", "text": "from selenium import webdriver\nurl=\"https://www.up-rera.in/frm_sanitize_prj_search.aspx?regid=918\"\ndriver=webdriver.Chrome(\"/Users/prakhargarg/Desktop/chromedriver\")\ndriver.get(url)\nlink = driver.find_element_by_link_text(\"Click Here To View Complete Project Details\")\nlink.click()\nwindow_after = driver.window_handles[1]\ndriver.switch_to.window(window_after)\ndriver.implicitly_wait(5)\nrows=len(driver.find_elements_by_xpath('//*[@id=\"ShowTableApartment\"]/tr'))\ncols=len(driver.find_elements_by_xpath('//*[@id=\"ShowTableApartment\"]/tr[1]/th'))\nprint(rows)\nprint(cols)\nfor r in range(2,rows+1):\n for c in range(1,cols+1):\n value=driver.find_element_by_xpath('//*[@id=\"ShowTableApartment\"]/tr[\"+str(r)+\"]/td[\"+str(c)+\"]').text\n print(value, end=' ')\ndriver.quit()\n", "sub_path": "scrapy_file/selenium_for_loop_problem.py", "file_name": "selenium_for_loop_problem.py", "file_ext": "py", "file_size_in_byte": 781, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 3, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "455352425", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreate average temperature map 2013\r\n\r\n@author: Jordan Capnerhurst 2016\r\n\"\"\"\r\n\r\n# import things\r\nfrom mpl_toolkits.basemap import Basemap\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport netCDF4\r\nfrom mpl_toolkits.basemap import maskoceans\r\n\r\n# Set colour map\r\ncool = cm = plt.get_cmap('jet')\r\n\r\n# Import data\r\nm1 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/JAN.nc', 'r') # January\r\nm2 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/FEB.nc', 'r') # Febuary\r\nm3 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/MAR.nc', 'r') # March\r\nm4 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/APR.nc', 'r') # April\r\nm5 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/MAY.nc', 'r') # May\r\nm6 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/JUN.nc', 'r') # June\r\nm7 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/JUL.nc', 'r') # July\r\nm8 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/AUG.nc', 'r') # August\r\nm9 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/SEP.nc', 'r') # September\r\nm10 = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/OCT.nc', 'r') # October\r\nmnov = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/NOV.nc', 'r') # November\r\nmdec = netCDF4.Dataset('O:/Honours_data/OEHYear/Complete year/DEC.nc', 'r') # Decmber\r\n\r\n\r\n# Import variables\r\n# Store_bio\r\n\r\n# [ time, source, lon, lat ] Jan\r\n# plot Daily average\r\nm11 = m1.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm12 = m1.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\njan = np.concatenate((m11, m12), axis=1)/81\r\n\r\n# average\r\njanav = np.mean(jan, axis=(0, 1))\r\n\r\njanavres = np.reshape(janav, 3600)\r\n\r\n# Reshape for month\r\njanres = jan.reshape(744, 60, 60) \r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt11 = m1.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt12 = m1.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\njant = np.concatenate((mt11, mt12), axis=1)\r\n\r\n# average\r\njantav = np.mean(jant, axis=(0, 1))\r\n\r\njantavres = np.reshape(jantav, 3600)\r\n\r\n# Reshape for month\r\njantres = jant.reshape(744, 60, 60) \r\n\r\nstdfebt = np.std(jantres, axis=(1, 2))\r\n\r\njantresav = np.mean(jantres, axis=(1,2))\r\n\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011\r\n# plot Daily average\r\nm21 = m2.variables['store_Bio'][:, 14:23, 0, 22:36, 25:45]\r\nm22 = m2.variables['store_Bio'][:, 0:14, 0, 22:36, 25:45]\r\n\r\n# concatenate back to 24 hour period\r\nfeb = np.concatenate((m21, m22), axis=1)/81\r\n\r\n# average\r\nfebav = np.mean(feb, axis=(0, 1))\r\n\r\nfebavres = np.reshape(febav, 280)\r\n\r\n# Reshape for month\r\n#febres = feb.reshape(672, 60, 60) \r\n\r\n#stdfeb = np.std(febres, axis=(1, 2))\r\n\r\n#febresav = np.mean(febres, axis=(1,2))\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt21 = m2.variables['temp_a'][:, 14:23, 0, 22:36, 25:45]\r\nmt22 = m2.variables['temp_a'][:, 0:14, 0, 22:36, 25:45]\r\n\r\n# concatenate back to 24 hour period\r\nfebt = np.concatenate((mt21, mt22), axis=1)\r\n\r\n# average\r\nfebtav = np.mean(febt, axis=(0, 1))\r\n\r\nfebtavres = np.reshape(febtav, 280)\r\n\r\n# Reshape for month\r\n#febtres = febt.reshape(672, 60, 60) \r\n\r\n#stdfebt = np.std(febtres, axis=(1, 2))\r\n\r\n#febtresav = np.mean(febtres, axis=(1,2))\r\n\r\n# plot correlation\r\nplt.figure(figsize=(15, 5))\r\nplt.plot(febtavres, linestyle='-', linewidth=2, c='r',\r\n label='Average Ambient Temperature ($^o$c)')\r\n\r\nplt.plot(febavres, linestyle='-', linewidth=2, c='r',\r\n label='Average Ambient Temperature ($^o$c)')\r\n \r\n\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] Mar\r\n# plot Daily average\r\nm31 = m3.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm32 = m3.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nmar = np.concatenate((m31, m32), axis=1)/81\r\n\r\n# average\r\nmarav = np.mean(mar, axis=(0, 1))\r\n\r\n# Reshape for month\r\nmarres = mar.reshape(744, 60, 60) \r\n\r\nmaravres = marav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt31 = m3.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt32 = m3.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nmart = np.concatenate((mt31, mt32), axis=1)\r\n\r\n# average\r\nmartav = np.mean(mart, axis=(0, 1))\r\n\r\nmartavres = np.reshape(martav, 3600)\r\n\r\n# Reshape for month\r\nmartres = mart.reshape(744, 60, 60) \r\n\r\nstdfebt = np.std(martres, axis=(1, 2))\r\n\r\nmartresav = np.mean(martres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] Apr\r\n# plot Daily average\r\nm41 = m4.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm42 = m4.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\napr = np.concatenate((m41, m42), axis=1)/81\r\n\r\n# average\r\naprav = np.mean(apr, axis=(0, 1))\r\n\r\n# Reshape for month\r\naprres = apr.reshape(720, 60, 60) \r\n\r\napravres = aprav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt41 = m4.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt42 = m4.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\naprt = np.concatenate((mt41, mt42), axis=1)\r\n\r\n# average\r\naprtav = np.mean(aprt, axis=(0, 1))\r\n\r\naprtavres = np.reshape(aprtav, 3600)\r\n\r\n# Reshape for month\r\naprtres = aprt.reshape(720, 60, 60) \r\n\r\nstdaprt = np.std(aprtres, axis=(1, 2))\r\n\r\naprtresav = np.mean(aprtres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] May\r\n# plot Daily average\r\nm51 = m5.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm52 = m5.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nmay = np.concatenate((m51, m52), axis=1)/81\r\n\r\n# average\r\nmayav = np.mean(may, axis=(0, 1))\r\n\r\n# Reshape for month\r\nmayres = may.reshape(744, 60, 60) \r\n\r\nmayavres = mayav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt51 = m5.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt52 = m5.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nmayt = np.concatenate((mt51, mt52), axis=1)\r\n\r\n# average\r\nmaytav = np.mean(mayt, axis=(0, 1))\r\n\r\nmaytavres = np.reshape(maytav, 3600)\r\n\r\n# Reshape for month\r\nmaytres = mayt.reshape(744, 60, 60) \r\n\r\nstdmayt = np.std(maytres, axis=(1, 2))\r\n\r\nmaytresav = np.mean(maytres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] june\r\n# plot Daily average\r\nm61 = m6.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm62 = m6.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\njun = np.concatenate((m61, m62), axis=1)/81\r\n\r\n# average\r\njunav = np.mean(jun, axis=(0, 1))\r\n\r\n# Reshape for month\r\njunres = jun.reshape(720, 60, 60) \r\n\r\njunavres = junav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt61 = m6.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt62 = m6.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\njunt = np.concatenate((mt61, mt62), axis=1)\r\n\r\n# average\r\njuntav = np.mean(junt, axis=(0, 1))\r\n\r\njuntavres = np.reshape(juntav, 3600)\r\n\r\n# Reshape for month\r\njuntres = junt.reshape(720, 60, 60) \r\n\r\nstdjunt = np.std(juntres, axis=(1, 2))\r\n\r\njuntresav = np.mean(juntres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] July\r\n# plot Daily average\r\nm71 = m7.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm72 = m7.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\njul = np.concatenate((m71, m72), axis=1)/81\r\n\r\n# average\r\njulav = np.mean(jul, axis=(0, 1))\r\n\r\n# Reshape for month\r\njulres = jul.reshape(720, 60, 60) \r\n\r\njulavres = julav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt71 = m7.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt72 = m7.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\njult = np.concatenate((mt71, mt72), axis=1)\r\n\r\n# average\r\njultav = np.mean(jult, axis=(0, 1))\r\n\r\njultavres = np.reshape(jultav, 3600)\r\n\r\n# Reshape for month\r\njultres = jult.reshape(720, 60, 60) \r\n\r\nstdjult = np.std(jultres, axis=(1, 2))\r\n\r\njultresav = np.mean(jultres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] aug\r\n# plot Daily average\r\nm81 = m8.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm82 = m8.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\naug = np.concatenate((m81, m82), axis=1)/81\r\n\r\n# average\r\naugav = np.mean(aug, axis=(0, 1))\r\n\r\n# Reshape for month\r\naugres = aug.reshape(744, 60, 60) \r\n\r\naugavres = augav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt81 = m8.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt82 = m8.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\naugt = np.concatenate((mt81, mt82), axis=1)\r\n\r\n# average\r\naugtav = np.mean(augt, axis=(0, 1))\r\n\r\naugtavres = np.reshape(augtav, 3600)\r\n\r\n# Reshape for month\r\naugtres = augt.reshape(744, 60, 60) \r\n\r\nstdaugt = np.std(augtres, axis=(1, 2))\r\n\r\naugtresav = np.mean(augtres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] sep\r\n# plot Daily average\r\nm91 = m9.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm92 = m9.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nsep = np.concatenate((m91, m92), axis=1)/81\r\n\r\n# average\r\nsepav = np.mean(sep, axis=(0, 1))\r\n\r\n# Reshape for month\r\nsepres = sep.reshape(720, 60, 60) \r\n\r\nsepavres = sepav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt91 = m9.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt92 = m9.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nsept = np.concatenate((mt91, mt92), axis=1)\r\n\r\n# average\r\nseptav = np.mean(sept, axis=(0, 1))\r\n\r\nseptavres = np.reshape(septav, 3600)\r\n\r\n# Reshape for month\r\nseptres = sept.reshape(720, 60, 60) \r\n\r\nstdsept = np.std(septres, axis=(1, 2))\r\n\r\nseptresav = np.mean(septres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] oct\r\n# plot Daily average\r\nm101 = m10.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm102 = m10.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\noct = np.concatenate((m101, m102), axis=1)/81\r\n\r\n# average\r\noctav = np.mean(oct, axis=(0, 1))\r\n\r\n# Reshape for month\r\noctres = oct.reshape(696, 60, 60) \r\n\r\noctavres = octav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt101 = m10.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt102 = m10.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\noctt = np.concatenate((mt101, mt102), axis=1)\r\n\r\n# average\r\nocttav = np.mean(octt, axis=(0, 1))\r\n\r\nocttavres = np.reshape(octtav, 3600)\r\n\r\n# Reshape for month\r\nocttres = octt.reshape(696, 60, 60) \r\n\r\nstdoctt = np.std(octtres, axis=(1, 2))\r\n\r\nocttresav = np.mean(octtres, axis=(1,2))\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] nov\r\n# plot Daily average\r\nm111 = mnov.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm112 = mnov.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nnov = np.concatenate((m111, m112), axis=1)/81\r\n\r\n# average\r\nnovav = np.mean(nov, axis=(0, 1))\r\n\r\n# Reshape for month\r\nnovres = nov.reshape(720, 60, 60) \r\n\r\nnovavres = novav.reshape(3600)\r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt111 = mnov.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt112 = mnov.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\nnovt = np.concatenate((mt111, mt112), axis=1)\r\n\r\n# average\r\nnovtav = np.mean(novt, axis=(0, 1))\r\n\r\nnovtavres = np.reshape(novtav, 3600)\r\n\r\n# Reshape for month\r\nnovtres = novt.reshape(720, 60, 60) \r\n\r\nstdnovt = np.std(novtres, axis=(1, 2))\r\n\r\nnovtresav = np.mean(novtres, axis=(1,2))\r\n\r\n\r\n##############################################################################\r\n\r\n# [ time, source, lon, lat ] dec\r\n# plot Daily average\r\nm121 = mdec.variables['store_Bio'][:, 14:24, 0, :, :]\r\nm122 = mdec.variables['store_Bio'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\ndec = np.concatenate((m121, m122), axis=1)/81\r\n\r\n# average\r\ndecav = np.mean(dec, axis=(0, 1))\r\n\r\n# Reshape for month\r\ndecres = dec.reshape(720, 60, 60)\r\n\r\ndecavres = decav.reshape(3600) \r\n\r\n###########################################\r\n\r\n# [ time, source, lon, lat ] Feb Bigger due to comparison to 2011 ~~~ Temp\r\n# plot Daily average\r\nmt121 = mdec.variables['temp_a'][:, 14:24, 0, :, :]\r\nmt122 = mdec.variables['temp_a'][:, 0:14, 0, :, :]\r\n\r\n# concatenate back to 24 hour period\r\ndect = np.concatenate((mt121, mt122), axis=1)\r\n\r\n# average\r\ndectav = np.mean(dect, axis=(0, 1))\r\n\r\ndectavres = np.reshape(dectav, 3600)\r\n\r\n# Reshape for month\r\ndectres = dect.reshape(720, 60, 60) \r\n\r\nstddect = np.std(dectres, axis=(1, 2))\r\n\r\ndectresav = np.mean(dectres, axis=(1,2))\r\n\r\n################################################################################\r\n###########\r\n# concatanate all months \r\n\r\n#year = np.concatenate((janres,febres,marres,aprres,mayres,junres,julres,augres,sepres,octres,novres,decres))\r\n\r\n##############################################################################\r\n# Define map parameters\r\n\r\nmax = 9\r\nmin = 0\r\ntrans = 0.5\r\nface1 =dectav\r\nface= (face1)/face1.max()\r\n##############################################################################\r\n\r\n# Other variables\r\nat = (m2.variables['lndtype'][0, :, :])\r\nst = (m2.variables['soiltype'][0, :, :])\r\nlai = (m1.variables['lai'][0, :, :])\r\nt = (m2.variables['temp_a'][:, 0:24, 0, :, :])\r\ng = (m2.variables['skin_temp'][:, 0:24, :, :])\r\n\r\n\r\n'''\r\n# Mean over array\r\nv1 = m2con.mean(axis=(0, 1))\r\nt1 = t.mean(axis=(0, 1))\r\ng1 = g.mean(axis=(0, 1))\r\n#cc1 = cc.mean(axis=(0, 1))\r\n#cr1 = cr.mean(axis=(0, 1))\r\n#ft1 = cr.mean(axis=(0, 1))\r\n'''\r\n\r\ndatalats = m2.variables['lat'][:] \r\ndatalons = m2.variables['lon'][:]\r\n\r\n# Map\r\n# for 'low', not a numeral 1\r\nmap = Basemap(projection='merc', lat_0=-33, lon_0=151,\r\n resolution='h', area_thresh=0.1,\r\n llcrnrlon=147.804, llcrnrlat=-36.7246, # Lower left corner\r\n urcrnrlon=153.114, urcrnrlat=-31.4146) # Upper Right corner\r\n\r\n\r\nplt.figure(figsize=(10, 10))\r\n\r\nmlons, mlats = np.meshgrid(datalons, datalats)\r\n\r\n# Mask oceans and dams\r\nmocedata = maskoceans(mlons, mlats, octav, inlands=True, resolution='h',\r\n grid=1.25)\r\n\r\n# Colour mesh\r\nmap.pcolormesh(mlons, mlats, mocedata, latlon=True, zorder=1, vmin=min, vmax=max, \r\n cmap=cool)\r\n\r\n\r\nmap.drawstates(color='white', linewidth=3)\r\nmap.drawcoastlines(color='white', linewidth=3)\r\n# map.drawcountries()\r\n# map.fillcontinents('white')\r\nmap.drawmapboundary()\r\n# map.drawrivers(color='black', linewidth=2)\r\n# map.shadedrelief()\r\n\r\n\r\n# City markers and names\r\nlons = [151.2070, 150.8931, 151.7789, 149.1287]\r\nlats = [-33.8675, -34.4250, -32.9267, -35.2820]\r\nx, y = map(lons, lats)\r\nmap.plot(x, y, 'ro', markersize=15)\r\n\r\nlabels = [' Sydney', 'Wollongong', ' Newcastle', ' Canberra']\r\nfor label, xpt, ypt in zip(labels, x, y):\r\n plt.text(xpt-90000, ypt+10000, label, color='white')\r\nfontsize = 14\r\n\r\n# North Arrow\r\nlons = [152.7]\r\nlats = [-36.5]\r\nx, y = map(lons, lats)\r\nmap.plot(x, y, 'w^', markersize=30)\r\n\r\n# Add scale bar\r\nmap.drawmapscale(151.5, -36.4, 151, -34, 50, barstyle='simple', units='km',\r\n fontsize=15, yoffset=None, labelstyle='simple', fontcolor='w',\r\n fillcolor1='w', fillcolor2='w', ax=None, format='%d',\r\n zorder=None)\r\n\r\nlabels = ['North', '', ' ', ' ']\r\nfor label, xpt, ypt in zip(labels, x, y):\r\n plt.text(xpt-20000, ypt+30000, label, color ='white')\r\nfontsize = 40\r\n\r\n# Draw Meridians and labels\r\nmap.drawmeridians(np.arange(0, 360, 1), labels=[0, 0, 0, 1], fontsize=10,\r\n color='white', linewidth=2)\r\nmap.drawparallels(np.arange(-90, 90, 1), labels=[1, 0, 0, 0], fontsize=10,\r\n color='white', linewidth=2)\r\n\r\n\r\n# Add colour bar\r\ncol = map.pcolormesh(mlons, mlats, mocedata,\r\n latlon=True, zorder=1, vmin=min, vmax=max,\r\n cmap=cool) \r\n\r\ncb = map.colorbar(col, \"right\", size=\"5%\", pad=\"2%\")\r\ncb.set_label('LAI ($m^2$/$m^2$)', fontsize=15)\r\n\r\nplt.title(\"CTM Domain 3 3x3 Leaf Area Index\\\r\n\\n Sydney Metropolitan Region Feburary 2011\", fontsize=10)\r\n\r\n\r\n\r\nplt.show()\r\n# plt.savefig('./bmap_syd.png')\r\n", "sub_path": "Temp map CT2013 D1.py", "file_name": "Temp map CT2013 D1.py", "file_ext": "py", "file_size_in_byte": 17799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.pyplot.get_cmap", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "netCDF4.Dataset", "line_number": 19, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 20, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 21, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 22, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 23, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 24, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 26, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 27, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 28, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 29, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "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": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 377, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 480, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 485, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 501, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 521, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 526, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 528, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 569, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 575, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 575, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 577, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.maskoceans", "line_number": 580, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 605, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 605, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 622, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 622, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 626, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 628, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 640, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 640, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 645, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 645, "usage_type": "name"}]} +{"seq_id": "442292524", "text": "from flask import Flask, request, jsonify\nimport pickle\nimport numpy as np\n\n# загружаем модель из файла\nwith open('models/model_2.pkl', 'rb') as pkl_file:\n model = pickle.load(pkl_file)\n\n# создаём приложение\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n msg = \"Тестовое сообщение. Сервер запущен!\"\n return msg\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n features = np.array(request.json)\n features = features.reshape(1, 4)\n prediction = model.predict(features)\n return jsonify({'prediction': prediction[0]})\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000)", "sub_path": "Unit_7_restart/web/app/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pickle.load", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "355568546", "text": "# coding: utf-8\n\n\n\"\"\"Provides container image retrieval.\n\nThe images are downloaded from a registry.\nThen, they are tagged, saved on the disk and optionally compressed.\n\nAll of these actions are done by a single task.\n\"\"\"\n\n\nimport operator\nimport os\nfrom pathlib import Path\nfrom typing import Any, Optional, List\n\nfrom buildchain import docker_command\nfrom buildchain import config\nfrom buildchain import types\n\nfrom . import image\n\n\nclass RemoteImage(image.ContainerImage):\n \"\"\"A remote container image to download.\"\"\"\n\n def __init__(\n self,\n registry: str,\n name: str,\n version: str,\n digest: str,\n destination: Path,\n save_as_tar: bool=False,\n remote_name: Optional[str]=None,\n **kwargs: Any\n ):\n \"\"\"Initialize a remote container image.\n\n Arguments:\n registry: registry where the image is\n name: image name\n version: image version\n digest: image digest\n destination: save location for the image\n save_as_tar: save the image as a tar archive?\n remote_name: image name in the registry\n\n Keyword Arguments:\n They are passed to `Target` init method.\n \"\"\"\n self._registry = registry\n self._digest = digest\n self._remote_name = remote_name or name\n self._use_tar = save_as_tar\n kwargs.setdefault('task_dep', []).append('check_for:skopeo')\n super().__init__(\n name=name, version=version,\n destination=destination,\n **kwargs\n )\n self._targets = [self.filepath]\n\n registry = property(operator.attrgetter('_registry'))\n digest = property(operator.attrgetter('_digest'))\n\n @property\n def fullname(self) -> str:\n \"\"\"Complete image name.\n\n Usable by `docker` commands.\n \"\"\"\n return '{obj.registry}/{obj._remote_name}@{obj.digest}'.format(\n obj=self\n )\n\n @property\n def basicname(self) -> str:\n \"\"\"Base image name (no digest).\n\n Usable by `docker` commands.\n \"\"\"\n return '{obj.registry}/{obj._remote_name}:{obj.version}'.format(\n obj=self\n )\n\n\n @property\n def repository(self) -> str:\n \"\"\"Base image name (no digest).\n\n Usable by `docker` commands.\n \"\"\"\n return '{obj.registry}/{obj._remote_name}'.format(\n obj=self\n )\n\n\n @property\n def filepath(self) -> Path:\n \"\"\"Path to the file tracked on disk.\"\"\"\n if self._use_tar:\n return self.dest_dir/'{obj.name}-{obj.version}{ext}'.format(\n obj=self, ext='.tar'\n )\n # Just to keep track of something on disk.\n return self.dirname/'manifest.json'\n\n @property\n def task(self) -> types.TaskDict:\n task = self.basic_task\n task.update({\n 'title': lambda _: self.show('PULL IMG'),\n 'doc': 'Download {} container image.'.format(self.name),\n 'uptodate': [True],\n })\n docker_pull = docker_command.DockerPull(\n self.repository,\n self.digest\n )\n docker_tag = docker_command.DockerTag(\n self.repository,\n self.fullname,\n self.version\n )\n docker_save = docker_command.DockerSave(\n self.basicname,\n Path(os.path.join(\n self.dest_dir,\n '{}-{}.tar'.format(self.name, self.version)\n ))\n )\n if self._use_tar:\n task.update({\n 'actions': [docker_pull, docker_tag, docker_save],\n })\n else:\n task.update({\n 'actions': [self.mkdirs, self._skopeo_copy()],\n 'clean': [self.clean],\n })\n return task\n\n def _skopeo_copy(self) -> List[str]:\n \"\"\"Return the command line to execute skopeo copy.\"\"\"\n cmd = [\n config.ExtCommand.SKOPEO.value, '--override-os', 'linux',\n '--insecure-policy', 'copy', '--format', 'v2s2'\n ]\n if not self._use_tar:\n cmd.append('--dest-compress')\n cmd.append('docker://{}'.format(self.fullname))\n cmd.append(self._skopeo_dest())\n return cmd\n\n def _skopeo_dest(self) -> str:\n \"\"\"Return the destination, formatted for skopeo copy.\"\"\"\n if self._use_tar:\n return 'docker-archive:{}'.format(self.filepath)\n return 'dir:{}'.format(self.dirname)\n", "sub_path": "buildchain/buildchain/targets/remote_image.py", "file_name": "remote_image.py", "file_ext": "py", "file_size_in_byte": 4583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pathlib.Path", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 37, "usage_type": "name"}, {"api_name": "operator.attrgetter", "line_number": 65, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 66, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 101, "usage_type": "name"}, {"api_name": "buildchain.docker_command.DockerPull", "line_number": 118, "usage_type": "call"}, {"api_name": "buildchain.docker_command", "line_number": 118, "usage_type": "name"}, {"api_name": "buildchain.docker_command.DockerTag", "line_number": 122, "usage_type": "call"}, {"api_name": "buildchain.docker_command", "line_number": 122, "usage_type": "name"}, {"api_name": "buildchain.docker_command.DockerSave", "line_number": 127, "usage_type": "call"}, {"api_name": "buildchain.docker_command", "line_number": 127, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "buildchain.types.TaskDict", "line_number": 111, "usage_type": "attribute"}, {"api_name": "buildchain.types", "line_number": 111, "usage_type": "name"}, {"api_name": "buildchain.config.ExtCommand", "line_number": 148, "usage_type": "attribute"}, {"api_name": "buildchain.config", "line_number": 148, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 145, "usage_type": "name"}]} +{"seq_id": "123685583", "text": "#!/usr/bin/env python3\n\n################################################\n### Load Dependencies\nimport re\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.transforms import Bbox, BboxTransformFrom, blended_transform_factory, CompositeGenericTransform\n\n################################################\n### Classes\n\nclass PointTransform(CompositeGenericTransform):\n \"\"\"\n Create a new imperial-unit coordinate system around a given anchor.\n \"\"\"\n\n def __init__(self, object=None, anchor:str='bl', system:str='12pt'):\n \n if isinstance(object, plt.Figure):\n fig = object\n elif hasattr(object,'figure'):\n fig = object.figure\n else:\n raise Exception('Cannot find figure from object')\n \n self.obj = object\n self.fig = fig\n self.system = str(system)\n self.anchor = anchor\n \n self.fig_pos = fig.bbox._points\n self.obj_pos = self.get_object_position()\n \n bbox = self.get_bbox() \n super().__init__(BboxTransformFrom(bbox), self.fig.transFigure)\n\n def get_object_position(self):\n \"\"\"docstring\"\"\"\n \n obj_pos = self.fig_pos\n \n if isinstance(self.obj, plt.Axes):\n axis_position = self.obj.get_position()\n obj_pos = self.fig.transFigure.transform(axis_position)\n \n return obj_pos\n \n def get_bbox(self):\n \n anchors = {\n 'bl' : lambda x: x[0],\n 'tl' : lambda x: x.diagonal(),\n 'tr' : lambda x: x[1],\n 'br' : lambda x: x.flatten()[[2,1]],\n }\n\n # scale = self.fig._dpi / (72 / self.system)\n scale = self.get_scale()\n points = (self.fig_pos - anchors[self.anchor](self.obj_pos)) / scale\n bbox = Bbox(points)\n return bbox\n \n def get_scale(self):\n \n string = self.system\n dpi = self.fig._dpi\n \n res = re.match(\"([0-9.]+)(\\w+)\", string)\n if res is not None:\n spacing,unit,*_ = res.groups()\n spacing = float(spacing)\n \n if unit in ['pt', 'point', 'points']:\n val = dpi * spacing / 72\n elif unit in ['pc','pica','picas']:\n val = dpi * spacing / 6\n elif unit in ['in', 'inch', 'inches']:\n val = dpi * spacing\n elif unit in ['mm']:\n val = dpi * spacing / 25.4\n elif unit in ['cm']:\n val = dpi * spacing / 2.54\n else:\n raise Exception(f\"'{string}' is not a valid argument for spacing\")\n \n return val\n\ndef transform_factory(object=None, system='figure', anchor='bl'):\n \n fig = None\n ob = None\n \n # Deal with arguments \n if object is None:\n fig = plt.gcf()\n ob = fig\n elif isinstance(object, plt.Figure):\n fig = object\n ob = fig\n elif hasattr(object, 'figure'):\n ob = object\n fig = object.figure\n else:\n raise Exception('Invalid object passed')\n \n if isinstance(system, str):\n system = [system]\n n = min(2, len(system))\n \n transforms = []\n for i in range(n):\n syst = system[i]\n \n if syst == 'figure':\n trans = fig.transFigure\n elif syst in ['ax','axes','axis']:\n trans = ob.transAxes\n elif syst == 'data':\n trans = ob.transData\n elif syst in ['pc','pica','picas']:\n trans = PointTransform(object=ob, anchor=anchor, system='12pt')\n elif syst in ['in', 'inch', 'inches']:\n trans = PointTransform(object=ob, anchor=anchor, system='1in')\n elif syst in ['pt', 'point', 'points']:\n trans = PointTransform(object=ob, anchor=anchor, system='1pt')\n else:\n trans = PointTransform(object=ob, anchor=anchor, system=syst)\n \n transforms.append(trans)\n\n if len(transforms) == 2:\n transform = blended_transform_factory(*transforms)\n elif len(transforms) == 1:\n transform = transforms[0]\n \n return transform\n \ndef decorator_custom_transform(func):\n \n def wrapper(self, *args, system=None, anchor='bl', **kwargs):\n \n if system is not None:\n trans = transform_factory(object=self, system=system, anchor=anchor)\n kwargs.update({'transform': trans})\n \n # output = func(self, *args, **kwargs, transform=trans)\n output = func(self, *args, **kwargs)\n # print(f'Ran {func.__name__} function')\n\n return output\n \n return wrapper", "sub_path": "mpltransform/mpltransform.py", "file_name": "mpltransform.py", "file_ext": "py", "file_size_in_byte": 4670, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.transforms.CompositeGenericTransform", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.transforms.BboxTransformFrom", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 43, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.transforms.Bbox", "line_number": 61, "usage_type": "call"}, {"api_name": "re.match", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 98, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.transforms.blended_transform_factory", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "642403571", "text": "#!/usr/bin/python3\n\"\"\"\n9-sates - starts a flask application\n\"\"\"\nfrom flask import Flask, render_template\nfrom models import storage\n\napp = Flask(__name__)\n\n\n@app.route('/states/', strict_slashes=False)\ndef get_states():\n \"\"\" displays an html page with states \"\"\"\n states = storage.all('State').values()\n state_count = len(states)\n\n return render_template('9-states.html', states=states,\n state_count=state_count)\n\n\n@app.route('/states/', strict_slashes=False)\ndef state_cities(id):\n \"\"\" displays an html page with a single state \"\"\"\n states = storage.all('State')\n if id is None:\n return render_template('9-states.html', states=states.values)\n\n state = \"\"\n cities = []\n for k in states.keys():\n if id == k:\n state = states.get(k)\n cities = state.cities\n return render_template('9-states.html', state=state, cities=cities)\n\n\n@app.teardown_appcontext\ndef teardown(self):\n \"\"\" closes storage session \"\"\"\n storage.close()\n\nif __name__ == \"__main__\":\n \"\"\" runs application only if not being imported \"\"\"\n app.run(host='0.0.0.0', port=5000)\n", "sub_path": "web_flask/9-states.py", "file_name": "9-states.py", "file_ext": "py", "file_size_in_byte": 1149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 24, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "models.storage.close", "line_number": 40, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "572877148", "text": "import os.path\nimport logging\nimport sys\nsys.path.append(os.path.abspath(os.path.join('0','../Databaselayer')))\nfrom LoginClass import LoginClass\n\nsys.path.append(os.path.abspath(os.path.join('0', '../extensions')))\nfrom extensions import mysql\nfrom extensions_logging import logmyerror\n\n\nclass LoginMyClass:\n def __init__(self,email,loggedIn, firstName, typeOfUser,value,result):\n self.email = email\n self.loggedIn = loggedIn\n self.firstName = firstName\n self.typeOfUser = typeOfUser\n self.value = value\n self.result = result\n\t\n def getMyLoginDetails(self):\n try:\n if (self.value == ''):\n getlogindetails = LoginClass(mysql,self.email,'', '', '', '')\n self.loggedIn, self.firstName, self.typeOfUser = getlogindetails.getLoginDetails()\n return (self.loggedIn, self.firstName, self.typeOfUser)\n except Exception as e:\n excep_msg = \"Error occured in method getMyLoginDetails method\"\n level = logging.getLogger().getEffectiveLevel()\n logmyerror.loadMyExceptionInDb(level,excep_msg,e)\n logging.info(excep_msg, exc_info=True)\n", "sub_path": "Businesslayer/LoginMyClass.py", "file_name": "LoginMyClass.py", "file_ext": "py", "file_size_in_byte": 1183, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 4, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 4, "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.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "LoginClass.LoginClass", "line_number": 24, "usage_type": "call"}, {"api_name": "extensions.mysql", "line_number": 24, "usage_type": "argument"}, {"api_name": "logging.getLogger", "line_number": 29, "usage_type": "call"}, {"api_name": "extensions_logging.logmyerror.loadMyExceptionInDb", "line_number": 30, "usage_type": "call"}, {"api_name": "extensions_logging.logmyerror", "line_number": 30, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "593329721", "text": "from flask import request, jsonify\nfrom sqlalchemy.orm.exc import NoResultFound\nfrom sqlalchemy.exc import IntegrityError\nimport webapp2.api as API\n\n\nclass LangTranslateViewMixin( object ):\n def __init__( self ):\n self.registerRoute( '', self.recordDelete, methods = [ 'DELETE' ] )\n return\n\n def recordDelete( self, id, **kwargs ):\n self.checkAuthentication()\n if 'locker' in kwargs:\n locker = kwargs[ 'locker' ]\n\n else:\n locker = self._lock_cls.locked( int( id ) )\n\n API.app.logger.debug( 'MIXIN DELETE: {} {} by {}'.format( self._uri, locker.data, locker.user ) )\n record = self._model_cls.query.get( locker.id )\n if self._lock:\n API.recordTracking.delete( self._model_cls.__tablename__,\n locker.id,\n record.dictionary,\n locker.user )\n\n API.app.logger.debug( 'Deleting record: {}'.format( record ) )\n API.db.session.delete( record )\n API.app.logger.debug( 'Commit delete' )\n message = ''\n try:\n API.db.session.commit()\n result = True\n\n except IntegrityError:\n message = 'Could not delete due relations still exists'\n result = False\n\n API.app.logger.debug( 'recordDelete() => {} {}'.format( result, record ) )\n return jsonify( ok = result, reason = message ), 200 if result else 409\n", "sub_path": "backend/language_translates/view_mixin.py", "file_name": "view_mixin.py", "file_ext": "py", "file_size_in_byte": 1501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "webapp2.api.app.logger.debug", "line_number": 20, "usage_type": "call"}, {"api_name": "webapp2.api.app", "line_number": 20, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 20, "usage_type": "name"}, {"api_name": "webapp2.api.recordTracking.delete", "line_number": 23, "usage_type": "call"}, {"api_name": "webapp2.api.recordTracking", "line_number": 23, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 23, "usage_type": "name"}, {"api_name": "webapp2.api.app.logger.debug", "line_number": 28, "usage_type": "call"}, {"api_name": "webapp2.api.app", "line_number": 28, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 28, "usage_type": "name"}, {"api_name": "webapp2.api.db.session.delete", "line_number": 29, "usage_type": "call"}, {"api_name": "webapp2.api.db", "line_number": 29, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 29, "usage_type": "name"}, {"api_name": "webapp2.api.app.logger.debug", "line_number": 30, "usage_type": "call"}, {"api_name": "webapp2.api.app", "line_number": 30, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 30, "usage_type": "name"}, {"api_name": "webapp2.api.db.session.commit", "line_number": 33, "usage_type": "call"}, {"api_name": "webapp2.api.db", "line_number": 33, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 33, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.IntegrityError", "line_number": 36, "usage_type": "name"}, {"api_name": "webapp2.api.app.logger.debug", "line_number": 40, "usage_type": "call"}, {"api_name": "webapp2.api.app", "line_number": 40, "usage_type": "attribute"}, {"api_name": "webapp2.api", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "524666391", "text": "import cv2\n#Reading image from disk\ndog = cv2.imread('dog.jpg')\n#Resizing image to make it smaller\ndog = cv2.resize(dog, (300,280) )\n#Applying different blur functions with 7*7 filter\nimg_0 = cv2.blur(dog, ksize = (7, 7))\nimg_1 = cv2.GaussianBlur(dog, (7, 7), 0)\nimg_2 = cv2.medianBlur(dog, 7)\nimg_3 = cv2.bilateralFilter(dog, 7, 75, 75)\n#Displaying resultant images\ncv2.imshow('Original', dog)\ncv2.imshow('Blur', img_0)\ncv2.imshow('Gaussian Blur', img_1)\ncv2.imshow('Median Blur', img_2)\ncv2.imshow('Bilateral Filter', img_3)\n#Waits for a user to press key for exit\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "sub_path": "Blurring and Smoothing/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "cv2.imread", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.blur", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "612142221", "text": "\n\n# http://qiita.com/Kawa1128/items/9aa0091a8157816401f9\n# sudo apt-get install libxml2-dev libxslt-dev\n# pip install pyquery\n\nfrom urlparse import urljoin\nfrom pprint import pprint\nfrom pyquery import PyQuery as pq\n\nif __name__ == '__main__':\n url = 'http://www.yahoo.co.jp'\n dom = pq(url)\n result = set()\n for img in dom('img').items():\n img_url = img.attr['src']\n if img_url.startswith('http'):\n result.add(img_url)\n else:\n result.add(urljoin(url, img_url))\n pprint(result)\n ", "sub_path": "sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pyquery.PyQuery", "line_number": 13, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 20, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "314974097", "text": "#!/usr/bin/env python\n# coding: utf-8\n#\n# Usage: \n# Author: viking(auimoviki@gmail.com)\n\nimport math\nimport logging\nimport mxnet as mx\n\nfrom .utils import tokenize, Vocab\nfrom .corpusiter import NceCorpusIter\n\n\nclass NceCorpus(object):\n def __init__(self, ftrain, ftest, fvalid, vocab:Vocab=None):\n self.logger = logging.getLogger('NceCorpus') \n\n update_vocab = vocab is None\n self.data_train, self.vocab = tokenize(ftrain, vocab, update_vocab=update_vocab, eos=True)\n self.data_valid, _ = tokenize(fvalid, vocab, eos=True)\n self.data_test, _ = tokenize(ftest, vocab, eos=True)\n\n self._test_iter = None\n self._train_iter = None\n self._valid_iter = None\n\n self.wrdfrq = []\n self.negdis = []\n self.negative = []\n\n self.build_negative()\n\n\n def build_negative(self):\n total_wrd = 0\n\n # build negdis for train corpus\n self.wrdfrq = [0.0]*len(self.vocab)\n for idx in self.data_train: \n self.wrdfrq[idx] += 1\n total_wrd += 1\n\n total_cnt = 0\n self.negdis = [0]*len(self.vocab)\n for idx,cnt in enumerate(self.wrdfrq):\n self.wrdfrq[idx] /= total_wrd\n if idx 70:#如果相似度大于80\n if name == 'pms':\n print(\"欢迎%s !\" % name)\n time.sleep(3)\n if name == 'lsd':\n print(\"欢迎%s !\" % name)\n time.sleep(3)\n return name\n else:\n print(\"对不起，我不认识你！\")\n name = 'Unknow'\n return 0\n if result['error_msg'] == 'pic not has face':\n print('检测不到人脸')\n OLED.fail('人脸')\n time.sleep(2)\n return -2\n else:\n print(str(result['error_code'])+' ' + str(result['error_code']))\n return -1\n#人脸识别 识别成功返回1 识别失败返回0\ndef identify():\n print('开始识别')\n getimage()#拍照\n img = transimage()#转换照片格式\n res = go_api(img)#将转换了格式的图片上传到百度云\n if res == 0 or res == -1:\n print(\"关门\")\n else:\n print(\"开门\")\n return res\n", "sub_path": "Raspberry/face_identification.py", "file_name": "face_identification.py", "file_ext": "py", "file_size_in_byte": 2196, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "aip.AipFace", "line_number": 12, "usage_type": "call"}, {"api_name": "picamera.PiCamera", "line_number": 15, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "OLED.fail", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "417110921", "text": "#02/14/2020\r\n#Web Scraper for Betting Sites\r\n\r\nimport bs4\r\nimport json\r\nfrom urllib.request import urlopen as uOpen\r\nfrom bs4 import BeautifulSoup as soup\r\nimport requests\r\nimport time\r\nfrom datetime import date\r\n\r\ndef websiteScraping(league):\r\n containers = []\r\n i = 0\r\n game = []\r\n team_name = ''\r\n name_ret = False\r\n\r\n # here we are simply accesing the website and taking all the data from the html\r\n if(league == \"NCAA\"):\r\n url = \"https://www.betonline.ag/sportsbook\"\r\n elif(league == \"hockey\"):\r\n url = \"https://www.betonline.ag/sportsbook/hockey/nhl\"\r\n aClient = requests.get(url)\r\n\r\n # here is where the html is parsed\r\n page_soup = soup(aClient.text, \"html.parser\")\r\n\r\n # here we will find and all the games (basketball) that have not yet occurred\r\n # we will pair the teams that are playing against eachother in a list which will be \r\n # stored in another list\r\n\r\n for entry in page_soup.find_all(\"td\", {\"class\":\"col_teamname bdevtt\"}):\r\n for char in entry:\r\n team_name += char\r\n team_name = team_name[:-1]\r\n if(i >= 2):\r\n containers.append(game)\r\n game = []\r\n i = 0\r\n game.append(team_name)\r\n team_name = ''\r\n i += 1\r\n containers.append(game)\r\n\r\n # here, like the last for loop, we will be collecting the point spreads and odds\r\n # associated with every team\r\n\r\n j = 0\r\n spread_count = 0\r\n teamOneS = ''\r\n teamTwoS = ''\r\n point_spreads = ''\r\n game_spread = ()\r\n for entry in page_soup.find_all(\"td\", {\"class\":\"odds bdevtt displayOdds\"}):\r\n point_spreads = ''\r\n for char in entry:\r\n if(j % 2 == 0):\r\n point_spreads += char\r\n spread_count += 1\r\n if(spread_count == 1):\r\n teamOneS += point_spreads\r\n #print(teamOneS)\r\n elif(spread_count == 2):\r\n teamTwoS += point_spreads\r\n game_spread = (teamOneS, teamTwoS)\r\n #print(game_spread)\r\n #print(game_spread)\r\n containers[(j//4)%1].append(game_spread)\r\n game_spread = ()\r\n teamOneS = ''\r\n teamTwoS = ''\r\n spread_count = 0\r\n j += 1\r\n\r\n # like the last two loops, this one scrapes data but of projected score differences\r\n\r\n point_difs = ''\r\n k = 0\r\n dif_count = 0\r\n teamOneDif = ''\r\n teamTwoDif = ''\r\n team_dif = ()\r\n for entry in page_soup.find_all(\"td\", {\"class\":\"hdcp bdevtt\"}):\r\n point_difs = ''\r\n for var in entry:\r\n if(k % 2 == 0):\r\n point_difs += var\r\n dif_count += 1\r\n if(dif_count == 1):\r\n teamOneDif += point_difs\r\n elif(dif_count == 2):\r\n teamTwoDif += point_difs\r\n team_dif = (teamOneDif, teamTwoDif)\r\n containers[(k//4)].append(team_dif)\r\n team_dif = ()\r\n teamOneDif = ''\r\n teamTwoDif = ''\r\n dif_count = 0\r\n k += 1\r\n\r\n # we now need to acces the date that the games are occurring on so the betting\r\n # is able to be kept live. insert(0, x)\r\n\r\n date_ret = False\r\n date = page_soup.find_all(\"td\", {\"class\":\"bdt\"})\r\n date = str(date)\r\n str_date = ''\r\n for let in date:\r\n if(let == \">\"):\r\n date_ret = True\r\n continue\r\n elif(let == \"<\"):\r\n date_ret = False\r\n continue\r\n if(date_ret == True):\r\n str_date += let\r\n # this allows us to remove some characters that are not letters or useful \r\n # information (extra whitespace and spare bracket)\r\n str_date = str_date[1:-2]\r\n containers.insert(0, str_date)\r\n\r\n # next, we nood two access all the game times and save them to their respective lists\r\n\r\n time_stamp = ''\r\n ite = 1\r\n for time in page_soup.find_all(\"td\", {\"class\":\"col_time bdevtt\"}):\r\n for char in time:\r\n time_stamp = char\r\n containers[ite].append(time_stamp)\r\n ite += 1\r\n return containers \r\n\r\ndef timeConversion(container):\r\n time_str = ''\r\n time_int = 0\r\n game_times = []\r\n time_since_epoch = []\r\n for i in range(1, len(container)):\r\n time_str = container[i][len(container[i]) - 1]\r\n time_str = time_str[:2] + time_str[3:6]\r\n time_int = int(time_str)\r\n game_times.append(time_int)\r\n\r\n for j in range(0, len(game_times)):\r\n if(game_times[j] < 1200):\r\n game_times[j] = game_times[j] + 1200\r\n \r\n date = container[0]\r\n\r\n seconds = time.time()\r\n local_time = time.localtime(seconds)\r\n hour = local_time.tm_hour\r\n minute = local_time.tm_min\r\n curr_time = (hour*100) + minute\r\n \r\n for time_slot in game_times:\r\n if(curr_time < time_slot):\r\n timeTilGame = time_slot - curr_time\r\n elif(curr_time > time_slot):\r\n timeTilGame = 24 - (curr_time - time_slot)\r\n \r\n hoursTilGame = timeTilGame//100\r\n minutesTilGame = timeTilGame % 100\r\n\r\n # this gives the current time of the next most recent game in terms of seconds \r\n # since epoch.\r\n finalTimeTilGame = seconds + (60 * minutesTilGame) + (3600 * hoursTilGame)\r\n time_since_epoch.append(finalTimeTilGame)\r\n\r\n # we need to make it so that if the finalTimeTilGame is never equal to or less than\r\n # the current time (meaning that the game ahs started), that we remove it from wherever\r\n # it is being stored (probably the json file), because betting on that game, will be frozen\r\n return time_since_epoch\r\n", "sub_path": "Web Scraping/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 106, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 107, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 109, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 149, "usage_type": "name"}, {"api_name": "time.time", "line_number": 151, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "59375693", "text": "\"\"\"Runs FFN inference by batching over all subvolumes in a bounding box.\n\nInference is performed with lots of threads.\n\nThis script supports multiple inference requests at once, as long\nas they share a model (and they are forced to share a global bbox\nand subvolume structure). Each subvolume will have each inference\nrequest run on it in the order they were provided (in SERIAL!)\n(The use case is to do forward inference and then backwards inference,\nand of course the latter of those requires the results of the former,\nhence SERIAL!). Really the only thing allowed is different seed\npolicies (IE PEAKS AND REVERSE!).\n\nThese are supported by launching a runner per inference request,\nbut making sure that the runners all share an executor and session\nand model etc.\n\"\"\"\nimport itertools\nimport logging\nfrom multiprocessing.pool import ThreadPool\nimport numpy as np\nimport os\nimport subprocess\nimport time\n\nfrom absl import app\nfrom absl import flags\nfrom google.protobuf import text_format\nimport h5py\nfrom tensorflow import gfile\n\nfrom ffn.utils import bounding_box_pb2\nfrom ffn.utils import bounding_box\nfrom ffn.inference import inference_pb2\nfrom ffn.inference import inference\n\n\nflags.DEFINE_string(\n 'inference_requests',\n None,\n 'One or more InferenceRequest pbtxt filenames, space separated.'\n)\nflags.DEFINE_string(\n 'bounding_box',\n None,\n 'BoundingBox pbtxt file. Optional: if not supplied, will look '\n 'at the inference request, open the raw data, and use its bbox.',\n)\nflags.DEFINE_integer('subvolume_size', -1, '')\nflags.DEFINE_integer('subvolume_overlap', 48, '')\n\nflags.DEFINE_integer('rank', -1, 'My worker id.')\nflags.DEFINE_integer('nworkers', -1, 'Number of workers.')\n\nflags.DEFINE_integer(\n 'nslurmworkers',\n -1,\n 'Overrides rank/nworkers to parallelize on Slurm.',\n)\nflags.DEFINE_string(\n 'srunflags',\n \"-u -p gpu --gres=gpu:1 -J inf{rank}\",\n \"Flags for srun if --nslurmworkers set. \"\n \"Will be formatted with `rank=FLAGS.rank`.\",\n)\n\nflags.DEFINE_boolean(\n 'advise',\n False,\n 'Don\\'t run anything, just give advice.'\n)\n\n\nFLAGS = flags.FLAGS\n\n\ndef _thread_main(runners_and_bbox):\n thread_start = time.time()\n runners, bbox = runners_and_bbox\n logging.info(\n f'Thread calling run at (zyx)={bbox.start}, '\n f'(dzyx)={bbox.size}.'\n )\n for irun, runner in enumerate(runners):\n logging.info('Thread starting run %d/%d', irun + 1, len(runners))\n logging.info('')\n runner.run(bbox.start, bbox.size)\n logging.info('Thread finished run %d/%d', irun + 1, len(runners))\n return time.time() - thread_start\n\n\ndef get_requests():\n \"\"\"Load and return the FLAGS.inference_requests\"\"\"\n requests = []\n for infreq_filename in FLAGS.inference_requests.split():\n request = inference_pb2.InferenceRequest()\n with open(infreq_filename) as infreq_f:\n text_format.Parse(infreq_f.read(), request)\n if not gfile.Exists(request.segmentation_output_dir):\n gfile.MakeDirs(request.segmentation_output_dir)\n requests.append(request)\n return requests\n\n\ndef get_outer_bbox(request):\n outer_bbox_pb = bounding_box_pb2.BoundingBox()\n if FLAGS.bounding_box:\n with open(FLAGS.bounding_box) as bbox_f:\n text_format.Parse(bbox_f.read(), outer_bbox_pb)\n outer_bbox = bounding_box.BoundingBox(outer_bbox_pb)\n else:\n logging.info(\n \"You didn't give a bounding box. Trying to figure it out from \"\n \"the data.\"\n )\n path, dset = request.image.hdf5.split(\":\")\n with h5py.File(path, \"r\") as f:\n size_zyx = f[dset].shape\n outer_bbox = bounding_box.BoundingBox(start=(0, 0, 0), size=size_zyx)\n return outer_bbox\n\n\ndef infer():\n requests = get_requests()\n print(\"Launching. Requests are:\")\n for request in requests:\n print(request)\n\n # Some asserts to make sure things don't go haywire\n batch_size = requests[0].batch_size\n assert all(r.batch_size == batch_size for r in requests)\n concurrent_requests = requests[0].concurrent_requests\n assert all(r.concurrent_requests == concurrent_requests for r in requests)\n model_name = requests[0].model_name\n assert all(r.model_name == model_name for r in requests)\n model_args = requests[0].model_args\n assert all(r.model_args == model_args for r in requests)\n model_checkpoint_path = requests[0].model_checkpoint_path\n assert all(\n r.model_checkpoint_path == model_checkpoint_path for r in requests\n )\n\n # Global bounding box ---------------------------------------------\n outer_bbox = get_outer_bbox(requests[0])\n print(outer_bbox)\n\n # Subvolumes ------------------------------------------------------\n if FLAGS.subvolume_size < 0:\n svsize = outer_bbox.size\n else:\n svsize = [FLAGS.subvolume_size] * 3\n\n print('Using subvolume size', svsize)\n print('Using overlap', [FLAGS.subvolume_overlap] * 3)\n svcalc = bounding_box.OrderlyOverlappingCalculator(\n outer_bbox,\n svsize,\n [FLAGS.subvolume_overlap] * 3,\n )\n nsb = svcalc.num_sub_boxes()\n print(svcalc)\n print('Total nsb:', nsb, 'Along axes:', svcalc.total_sub_boxes_xyz)\n subvols = list(svcalc.generate_sub_boxes())\n print('The boxes:\\n\\t', '\\n\\t'.join(str(s) for s in subvols))\n print('The slices:\\n\\t', '\\n\\t'.join(str(s.to_slice()) for s in subvols))\n\n # If we are worker i of many, take the ith subvol\n # only when (i mod nworkers) == rank\n # TODO: This method suffers from load balance problems.\n # However, parallelizing better is a tricky problem.\n if FLAGS.nworkers > 0 and FLAGS.rank >= 0:\n assert FLAGS.rank < FLAGS.nworkers\n subvols = itertools.islice(subvols, FLAGS.rank, nsb, FLAGS.nworkers)\n # Compute correct number of subvolumes for this worker\n # nsb // nworkers can give the wrong answer.\n nsb = len(range(*slice(FLAGS.rank, nsb, FLAGS.nworkers).indices(nsb)))\n\n # Figure out how many threads\n if concurrent_requests < 0:\n concurrent_requests = nsb\n elif concurrent_requests < requests[0].batch_size:\n raise ValueError(\n 'Please let concurrent_requests < 0 or '\n 'concurrent_requests >= batch_size.'\n )\n # Update infreq so runner doesn't freak out if something changed.\n for request in requests:\n request.concurrent_requests = concurrent_requests\n\n # Initialize inference runner -------------------------------------\n runner0 = inference.Runner()\n runner0.start(\n requests[0],\n executor_expected_clients=len(requests) * nsb,\n )\n runners = [runner0]\n for request in requests[1:]:\n runner = inference.Runner()\n runner.start(\n request,\n session=runner0.session,\n model=runner0.model,\n executor=runner0.executor,\n )\n runners.append(runner)\n\n # Main loop -------------------------------------------------------\n # Log some details\n if FLAGS.nworkers > 0 and FLAGS.rank >= 0:\n logging.info(\n f'Hi. This is rank {FLAGS.rank} out of '\n f'{FLAGS.nworkers} workers.'\n )\n logging.info(f'Launching worker threads.')\n logging.info(\n f'{nsb} subvolumes to get through with '\n f'{concurrent_requests} workers sharing '\n f'{batch_size} slots, and {len(requests)} '\n f'infreqs to run per subvol.'\n )\n\n # Start threads\n start_time = time.time()\n job_args = ((runners, sv) for sv in subvols)\n dts = []\n with ThreadPool(concurrent_requests) as pool:\n for dt in pool.imap_unordered(_thread_main, job_args):\n dts.append(dt)\n logging.info(\n f'gathered run in {dt:0.3f}s. '\n f'nrun: {len(dts)}. expected total: {nsb}'\n )\n logging.info(f'total nrun: {len(dts)}. expected: {nsb}')\n end_time = time.time()\n\n # Log time info to see how bad the load balance is.\n time_hrs = (end_time - start_time) / 60 / 60\n min_per_seg = (end_time - start_time) / 60 / len(dts)\n logging.info(f'Took {time_hrs:0.3f} hours.')\n logging.info(f'That\\'s {min_per_seg:0.3f} mins per subvolume.')\n\n logging.info('Timing from inside the threads. All in minutes.')\n dts = np.array(dts) / 60\n logging.info(\n f'min={dts.min():0.3f}, mean={dts.mean():0.3f}, '\n f'max={dts.max():0.3f}. std={dts.std()}.'\n )\n\n # Save counters for fun -------------------------------------------\n for runner, request in zip(runners, requests):\n counter_path = os.path.join(\n request.segmentation_output_dir, 'counters.txt'\n )\n if not gfile.Exists(counter_path):\n runner.counters.dump(counter_path)\n\n # Clean up --------------------------------------------------------\n logging.info('Done. Stopping executor.')\n del runner\n\n\ndef launch_slurm_jobs():\n # build an srun command for each rank\n print(\"Launching slurm jobs for infreqs\", FLAGS.inference_requests)\n argvs = [\n [\n \"srun\",\n *FLAGS.srunflags.format(rank=i).split(),\n \"python\",\n __file__,\n f\"--rank={i}\",\n f\"--nworkers={FLAGS.nslurmworkers}\",\n \"--inference_requests\",\n FLAGS.inference_requests,\n \"--subvolume_size\",\n str(FLAGS.subvolume_size),\n \"--subvolume_overlap\",\n str(FLAGS.subvolume_overlap),\n ] + (\n [\"--bounding_box\", FLAGS.bounding_box]\n if FLAGS.bounding_box\n else []\n )\n for i in range(FLAGS.nslurmworkers)\n ]\n\n # launch processes and wait for them.\n procs = [subprocess.Popen(argv) for argv in argvs]\n while None in [proc.poll() for proc in procs]:\n time.sleep(10.0)\n print(\"Return codes:\", [p.returncode for p in procs])\n\n\ndef advise():\n \"\"\"Reports the number of subvolumes per FLAGS.\"\"\"\n requests = get_requests()\n outer_bbox = get_outer_bbox(requests[0])\n if FLAGS.subvolume_size < 0:\n svsize = outer_bbox.size\n else:\n svsize = [FLAGS.subvolume_size] * 3\n svcalc = bounding_box.OrderlyOverlappingCalculator(\n outer_bbox, svsize, [FLAGS.subvolume_overlap] * 3\n )\n nsb = svcalc.num_sub_boxes()\n print(f'num subvols={nsb}.')\n\n\ndef main(unused_argv):\n if FLAGS.advise:\n advise()\n elif FLAGS.nslurmworkers > 0:\n launch_slurm_jobs()\n else:\n infer()\n\n\nif __name__ == '__main__':\n app.run(main)\n", "sub_path": "run_batch_inference.py", "file_name": "run_batch_inference.py", "file_ext": "py", "file_size_in_byte": 10587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "absl.flags.DEFINE_string", "line_number": 38, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 38, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 43, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 43, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 49, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 49, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 50, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 50, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 52, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 52, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 53, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 53, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 55, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 55, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 60, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 60, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 67, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 67, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS", "line_number": 74, "usage_type": "attribute"}, {"api_name": "absl.flags", "line_number": 74, "usage_type": "name"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "ffn.inference.inference_pb2.InferenceRequest", "line_number": 96, "usage_type": "call"}, {"api_name": "ffn.inference.inference_pb2", "line_number": 96, "usage_type": "name"}, {"api_name": "google.protobuf.text_format.Parse", "line_number": 98, "usage_type": "call"}, {"api_name": "google.protobuf.text_format", "line_number": 98, "usage_type": "name"}, {"api_name": "tensorflow.gfile.Exists", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 99, "usage_type": "name"}, {"api_name": "tensorflow.gfile.MakeDirs", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 100, "usage_type": "name"}, {"api_name": "ffn.utils.bounding_box_pb2.BoundingBox", "line_number": 106, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box_pb2", "line_number": 106, "usage_type": "name"}, {"api_name": "google.protobuf.text_format.Parse", "line_number": 109, "usage_type": "call"}, {"api_name": "google.protobuf.text_format", "line_number": 109, "usage_type": "name"}, {"api_name": "ffn.utils.bounding_box.BoundingBox", "line_number": 110, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box", "line_number": 110, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 112, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 117, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box.BoundingBox", "line_number": 119, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box", "line_number": 119, "usage_type": "name"}, {"api_name": "ffn.utils.bounding_box.OrderlyOverlappingCalculator", "line_number": 155, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box", "line_number": 155, "usage_type": "name"}, {"api_name": "itertools.islice", "line_number": 173, "usage_type": "call"}, {"api_name": "ffn.inference.inference.Runner", "line_number": 191, "usage_type": "call"}, {"api_name": "ffn.inference.inference", "line_number": 191, "usage_type": "name"}, {"api_name": "ffn.inference.inference.Runner", "line_number": 198, "usage_type": "call"}, {"api_name": "ffn.inference.inference", "line_number": 198, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 210, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 214, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 215, "usage_type": "call"}, {"api_name": "time.time", "line_number": 223, "usage_type": "call"}, {"api_name": "multiprocessing.pool.ThreadPool", "line_number": 226, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 229, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 233, "usage_type": "call"}, {"api_name": "time.time", "line_number": 234, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 239, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 240, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 243, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Exists", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 254, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 258, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 288, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 290, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box.OrderlyOverlappingCalculator", "line_number": 302, "usage_type": "call"}, {"api_name": "ffn.utils.bounding_box", "line_number": 302, "usage_type": "name"}, {"api_name": "absl.app.run", "line_number": 319, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 319, "usage_type": "name"}]} +{"seq_id": "130297499", "text": "import pickle\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection\nimport matplotlib.pyplot as plt\n\ndef run_file(sim_name, tmp_path, synodic_period):\n\n # READ OUTPUT FILES\n mesh_file = open(\"{}{}.mesh.p\".format(tmp_path, sim_name), 'rb')\n node_list, face_list, cell_list = pickle.load(mesh_file)\n output_file = open(\"{}{}.output.p\".format(tmp_path, sim_name), 'rb')\n time_output_list, cell_temperature_output_list, surface_temperature_output_list, surface_irradiance_output_list \\\n = pickle.load(output_file)\n\n # PLOT TEMPERATURE VS DEPTH\n plt.figure(2)\n time_list = []\n n = 1\n time_output_list = np.array(time_output_list)/synodic_period\n num_steps_diurnal = np.int64(np.ceil(len(time_output_list)/n))\n end_index = np.int64(len(time_output_list))\n start_index = end_index - num_steps_diurnal\n step = np.int64(np.ceil(num_steps_diurnal/12))\n time_bounds = np.linspace(n-1, n, 13)\n for current_time in time_bounds[0:time_bounds.shape[0]-1]:\n temp_list = list()\n z_list = list()\n time_difference = abs(time_output_list - current_time)\n time_index = np.argmin(time_difference)\n for cell_index in range(0, len(cell_list)):\n temp_list.append(cell_temperature_output_list[time_index][cell_index])\n z_list.append(cell_list[cell_index].center_coord[2])\n temp_list.append(surface_temperature_output_list[time_index][0])\n z_list.append(np.float64(0))\n sorted_temp = [x for _,x in sorted(zip(z_list, temp_list))]\n sorted_z = np.sort(z_list)\n plt.plot(sorted_temp, -sorted_z, linewidth=1)\n time_list.append(\"{:2.2f}\".format((current_time-(n-1))*24))\n plt.ylabel('Depth (m)')\n plt.xlabel('Temperature (K)')\n plt.legend(time_list)\n plt.ylim([0, 0.5])\n plt.gca().invert_yaxis()\n\n # PLOT TIME VS SURFACE TEMPERATURE\n n=1\n plt.figure(3)\n for i in range(0, 1):\n plt.plot(np.array(time_output_list)-n+1, np.array(surface_temperature_output_list)[:, i],'black')\n plt.xlabel(\"Time (lunar cycles)\")\n plt.ylabel(\"Surface Temperature (K)\")\n plt.xlim([0, 1])\n plt.show()\n\n", "sub_path": "src/modules/plot_output_figures.py", "file_name": "plot_output_figures.py", "file_ext": "py", "file_size_in_byte": 2227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pickle.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"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.xlim", "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"}]} +{"seq_id": "589289274", "text": "from scipy.cluster.vq import kmeans, vq\r\nfrom numpy import array, reshape, zeros\r\nimport cv2\r\nimport numpy as np\r\n\r\nvqclst = [2, 10, 64]\r\n\r\n#读入图片数据\r\ndata = cv2.imread('/Users/yangboqing/Downloads/timg.jpg').astype(float)\r\n(height, width, channel) = data.shape\r\n\r\ndata = reshape(data, (height * width, channel))\r\n\r\nfor k in vqclst:\r\n print('Generating vq-%d...' % k)\r\n (centroids, distor) = kmeans(data, k) #输出有两个,第一个是聚类中心(centroids),第二个是损失distortion,即聚类后各数据点到其聚类中心的距离的加和\r\n (code, distor) = vq(data, centroids) #根据聚类中心将所有数据进行分类。输出同样有两个:第一个是各个数据属于哪一类的label,第二个和kmeans的第二个输出是一样的,都是distortion\r\n #print('distor: %.6f' % distor.sum())\r\n im_vq = centroids[code, :]\r\n img = reshape(im_vq, (height, width, channel))\r\n #产生进行压缩后的图片\r\n cv2.imwrite('result-%d.jpg' % k, img)\r\n #展示进行压缩后的图片\r\n img = img.astype(np.uint8) #float的矩阵并不是归一化后的矩阵并不能保证元素范围一定就在0-1之间，所以要进行强制类型转换\r\n cv2.imshow('im%d' % k,img)\r\n cv2.waitKey(0)\r\n print(k,\"finish\")", "sub_path": "第二次实验/k-means.py", "file_name": "k-means.py", "file_ext": "py", "file_size_in_byte": 1293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "cv2.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 12, "usage_type": "call"}, {"api_name": "scipy.cluster.vq.kmeans", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.cluster.vq.vq", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "412117186", "text": "from __future__ import division\nimport time\nimport pandas as pd\nimport numpy as np\nimport datetime \nfrom datetime import datetime, date\nimport glob\nimport netCDF4 as nc\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport time\n\n#####################get SMAP paths#############################################################\ntic= time.time()\nfile= \"/home/cx43/cee690-07/data/2018fire/2018fire.csv\"\ndataset= pd.read_csv(file)\n#print(data.shape) #(322, 9)-> 313\ndf = pd.DataFrame(dataset)\n\ntime_csv= df['date']\ntime_tmp1= pd.Series.to_string(time_csv,index= False)\n#print(time.size,len(tmp)) #313 #9389 w/ index -> 7511 w/o index\nformat= ' %m/%d/%Y %I:%M:%S %p'\ntime_tmp2= time_tmp1.split('\\n')\n#print(time_tmp2) #' 11/14/2018 2:30:00 PM'\n\ntime_list= ['013000','043000','073000','103000','133000','163000','193000','223000']\nformat_time= '%H%M%S'\nnew_time={}\nnew={}\nfor i in range(7):\n new_time[i]= datetime.strptime(time_list[i],format_time)\n new[i]= new_time[i].time()\n\nstrs= glob.glob(\"/stor/tyche/hydro/private/cx43/CEE675_2019Fall/project2/smap/data/*.h5\")\nfiles= sorted(strs)\n\nFILES=[]\nTIME=[]\nk=0\nfor tmp in time_tmp2:\n new_date= datetime.strptime(tmp, format)\n #print(new_date.strptime(format))\n tmp1= new_date.date()\n tmp2= new_date.time()\n #print('Date:', new_date.date()) #Date: 2018-11-18\n #print('Time:', new_date.time()) #Time: 21:00:00\n date_tmp= tmp1.strftime('%Y%m%d')\n time_tmp= tmp2.strftime('%H%M%S')\n for single_file in files:\n tmp_file= ('/stor/tyche/hydro/private/cx43/CEE675_2019Fall/project2/smap/data/SMAP_L4_SM_gph_%sT223000_Vv4030_001.h5' % date_tmp)\n if tmp_file== single_file: FILES.append(date_tmp)\n #print(len(FILES)) #306\n\n delta={}\n for i in range(7):\n delta[i]= datetime.combine(date.min, new[i]) - datetime.combine(date.min, tmp2)\n threshold= delta[i].total_seconds()\n if threshold<5400.0: \n new_tmp= new[i].strftime('%H%M%S')\n TIME.append(new_tmp)\n k += 1\n#print(len(TIME),TIME,k) #len=1530,k= 306\nNEW_TIME= TIME[::5]\n#print(NEW_TIME,len(NEW_TIME)) #LEN= 306\n\nSMAP_FILE=[]\nfor i in range(len(NEW_TIME)):\n tmp_file= ('/stor/tyche/hydro/private/cx43/CEE675_2019Fall/project2/smap/data/SMAP_L4_SM_gph_%sT%s_Vv4030_001.h5' % (FILES[i],NEW_TIME[i]))\n SMAP_FILE.append(tmp_file)\n#print(len(SMAP_FILE)) #306\nSMAP= sorted(SMAP_FILE)\n#print(SMAP)\ntmp= time.time()-tic\nprint(\"checkpoint 1 = \",tmp) #0.3s\n\n############################append SMAP sm_surface into FIRE DATA#################################\n\"\"\"tic= time.time()\nvar= 'sm_surface'\nSM=[]\ntmp_ilat= df['ilat']\ntmp_ilon= df['ilon']\nilat= np.array(tmp_ilat)\nilon= np.array(tmp_ilon)\n\ni=0\nfor path in SMAP:\n fp = xr.open_dataset(path,group='Geophysical_Data' )\n tmp_data= fp[var][:] #globally, (1624, 3856) -> y,x\n data= np.array(tmp_data)\n smap_tmp= data[ilat[i],ilon[i]]\n SM.append(smap_tmp) # len(SM)= 306\n i +=1\ntmp= time.time()-tic\nprint(\"checkpoint 2 = \",tmp) #87s\n\ntic= time.time()\ndf['sm_surface'] = SM\ndf.to_csv(file)\nprint(df.head())\ntmp= time.time()-tic\nprint(\"checkpoint 3 = \",tmp) # 0.05s\"\"\"\n\n############################append SMAP T,windSPEED into FIRE DATA#################################\ntic= time.time()\nvars= ['surface_temp','windspeed_lowatmmodlay']\nST=[]\nWSP=[]\nTMP=[]\ntmp_ilat= df['ilat']\ntmp_ilon= df['ilon']\nilat= np.array(tmp_ilat)\nilon= np.array(tmp_ilon)\ni=0\nfor path in SMAP:\n fp = xr.open_dataset(path,group='Geophysical_Data' )\n for var in vars:\n tmp_data= fp[var][:]\n data= np.array(tmp_data)\n smap_tmp= data[ilat[i],ilon[i]]\n TMP.append(smap_tmp)\n i +=1\n#print(len(TMP)) #612\ntmp= time.time()-tic\nprint(\"checkpoint 4 = \",tmp) #123s\n\ntic= time.time()\nST= TMP[:306:]\nWSP = TMP[306::]\ndf['sTemp'] = ST\ndf['Wind'] = WSP\ndf.to_csv(file)\ntmp= time.time()-tic\nprint(\"checkpoint 5 = \",tmp) ", "sub_path": "sm_t_wind.py", "file_name": "sm_t_wind.py", "file_ext": "py", "file_size_in_byte": 3824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.Series.to_string", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 21, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.date.min", "line_number": 57, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 57, "usage_type": "name"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "time.time", "line_number": 105, "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": "xarray.open_dataset", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "time.time", "line_number": 124, "usage_type": "call"}, {"api_name": "time.time", "line_number": 127, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "8454442", "text": "import pandas as pd\nfrom os.path import expanduser, join\nimport datetime as dt\n\nimport matplotlib.pyplot as plt\n\n'''\nreference link: http://quantsoftware.gatech.edu/MC1-Project-1\ntest case: http://quantsoftware.gatech.edu/MC1-Project-1-Test-Cases-spr2016\n\n(reference code: \nhttps://stackoverflow.com/questions/9622163/save-plot-to-image-file-instead-of-displaying-it-using-matplotlib)\n\n'''\n\n\ndef symbol_to_path(symbol):\n return join(expanduser(\"~/github/python/finance\"), \"{}.csv\".format(symbol))\n\n\ndef get_trade_dates(start, end):\n dates = pd.date_range(start, end)\n df = pd.DataFrame(index=dates)\n index_file = symbol_to_path('SPY')\n df_tmp = pd.read_csv(index_file, parse_dates=True, index_col='Date',\n usecols=['Date','Adj Close'], na_values=['nan'])\n df = df.join(df_tmp)\n df.dropna(inplace=True)\n return df.index\n\n\ndef get_data(symbols, dates):\n\n df_final = pd.DataFrame(index=dates)\n\n for symbol in symbols:\n file_path = symbol_to_path(symbol)\n df_tmp = pd.read_csv(file_path, parse_dates=True, index_col='Date',\n usecols=['Date','Adj Close'], na_values=['nan'])\n df_tmp = df_tmp.rename(columns={'Adj Close': symbol})\n df_final = df_final.join(df_tmp)\n\n if df_final.isnull().values.any():\n fill_missing_values(df_final)\n\n return df_final\n\ndef fill_missing_values(df_data):\n df_data.fillna(method='ffill', inplace=True)\n df_data.fillna(method='bfill', inplace=True)\n\ndef normalize_data(df):\n return df/df.iloc[0]\n\ndef compute_daily_returns(df):\n daily_rets = (df/df.shift(1)) - 1\n daily_rets.iloc[0] = 0\n return daily_rets\n\n\ndef assess_portfolio(sd, ed, syms, allocs, sv, rfr=0.0, sf=252, gen_plot=False):\n '''\n\n :param sd: A datetime object that represents the start date\n :param ed: A datetime object that represents the end date\n :param syms: A list of 2 or more symbols that make up the portfolio\n :param allocs: A list of 2 or more allocations to the stocks, must sum to 1.0\n :param sv: start value of the portfolio\n :param rfr: The risk free return per sample period (single value, not array)\n that does not change for the entire date range\n :param sf: Sampleing frequency per year\n :param gen_plot: if False, do not create any output,\n if True, it is OK to output a plot such as plot.png\n :return: Cumulative return, Average daily return, standard deviation of daily return,\n Sharp ratio, End value of portfolio\n (cr, adr, sddr, sr, ev)\n '''\n\n dates = get_trade_dates(sd, ed)\n\n prices = get_data(syms, dates)\n\n norm = normalize_data(prices)\n\n pos_val = norm * allocs * sv\n\n port_val = pos_val.sum(axis=1)\n\n daily_rets = compute_daily_returns(port_val)\n daily_rets = daily_rets[1:]\n\n cr = port_val[-1]/port_val[0] - 1\n\n adr, sddr = daily_rets.mean(), daily_rets.std()\n\n dailyrfr = ((1.0+rfr)**(1./sf))-1\n sr = ((daily_rets - dailyrfr).mean()/sddr)*(sf**(1./2))\n ev = port_val[-1]\n\n if gen_plot:\n ax = normalize_data(port_val).plot(title='Daily portfolio value vs. S&P 500', label='Portfolio')\n SPY = get_data(['SPY'], dates = dates)\n normed_SPY = normalize_data(SPY)\n normed_SPY.plot(label='SPY', ax=ax)\n ax.set_xlabel('date')\n ax.set_ylabel('price')\n ax.legend(loc='best')\n plt.savefig('plot.png')\n # plt.show()\n \n print(\"Start Date:\", sd)\n print(\"End Date:\", ed)\n print(\"Symbols:\", syms)\n print(\"Allocations:\", allocs)\n print(\"Starting Portfolio Value:\", sv)\n print(\"Cumulative Return:\", cr)\n print(\"Average Daily Return:\", adr)\n print(\"Sharpe Ratio:\", sr)\n print(\"Volatility (stdev of daily returns):\", sddr)\n print(\"Ending Portfolio Value:\", ev)\n \n\n return cr, adr, sddr, sr, ev\n\n\ndef test_run():\n assess_portfolio(sd=dt.datetime(2006, 1, 3), ed=dt.datetime(2008, 1, 2), \\\n syms=['MMM', 'MO', 'MSFT', 'INTC'], \\\n allocs=[0.0, 0.9, 0.1, 0.0], \\\n sv=1000000, rfr=0.0, sf=252.0, \\\n gen_plot=True)\n\nif __name__ == \"__main__\":\n test_run()", "sub_path": "homework/finance/5_portfolio_stat.py", "file_name": "5_portfolio_stat.py", "file_ext": "py", "file_size_in_byte": 4211, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "137046075", "text": "import heapq\nfrom collections import Counter, namedtuple\n\n\"\"\"\nThis program implements Huffman encoding with the help of Python heap.\n\"\"\"\n\n\nclass Node(namedtuple(\"Node\", [\"left\", \"right\"])):\n def walk(self, code, acc):\n self.left.walk(code, acc + '0')\n self.right.walk(code, acc + '1')\n\n\nclass Leaf(namedtuple(\"Leaf\", [\"char\"])):\n def walk(self, code, acc):\n code[self.char] = acc or '0'\n\n\ndef huffman_encode(s):\n code = {}\n h = []\n\n for ch, freq in Counter(s).items():\n h.append((freq, len(h), Leaf(ch)))\n\n heapq.heapify(h)\n\n count = len(h)\n while len(h) > 1:\n freq1, _c1, left = heapq.heappop(h)\n freq2, _c2, right = heapq.heappop(h)\n heapq.heappush(h, (freq1 + freq2, count, Node(left, right)))\n count += 1\n\n if h:\n [(_freq, _count, root)] = h\n root.walk(code, \"\")\n\n return code\n\n\ndef huffman_decode(encoded, code):\n key = ''\n decoded = ''\n decode_code = {key: ch for ch, key in code.items()}\n for s in encoded:\n key += s\n if key in decode_code:\n decoded += decode_code[key]\n key = ''\n\n return decoded\n\n\ndef main():\n s = input()\n code = huffman_encode(s)\n encoded = \"\".join(code[ch] for ch in s)\n print(len(s), len(encoded))\n for key in sorted(code):\n print(f'{code[key]}: {key}')\n print(encoded)\n\n\ndef test(n_iter):\n import random as r\n import string\n import sys\n\n for _ in range(n_iter):\n length = r.randint(0, 32)\n s = \"\".join(r.choice(string.ascii_letters) for _ in range(length))\n code = huffman_encode(s)\n encoded = \"\".join(code[ch] for ch in s)\n try:\n decoded = huffman_decode(encoded, code)\n assert s == decoded\n except AssertionError:\n sys.exit(f'{s}, {decoded}')\n print('ALL OK')\n\n\nif __name__ == \"__main__\":\n # test(1000)\n main()\n", "sub_path": "algorithms/greedy_algorithms/huffman_encoding.py", "file_name": "huffman_encoding.py", "file_ext": "py", "file_size_in_byte": 1915, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.namedtuple", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 24, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 27, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 31, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 32, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 33, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 73, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "399923273", "text": "import praw\nimport sys\n\nargument = sys.argv\nif len(argument) < 3:\n print(\"no. :(\")\n print(\"Syntax: reddithtml.py \")\n sys.exit()\ncomments = input(\"Comments? \")\nf = open(\"reddit.html\", \"w\")\nf.write(\"\")\nf.close()\nf = open(\"reddit.html\", \"a\")\nf.write(\"Reddit\")\nf.close()\nr = praw.Reddit(user_agent='my_cool_application')\n\ncount = int(argument[2])\n\nsubreddit = r.get_subreddit(argument[1])\n\nfor submission in subreddit.get_new(limit=count):\n title = submission.title.encode('ascii', 'ignore')\n print(title)\n post_text = submission.selftext.encode('ascii', 'ignore')\n if post_text == \"\":\n print(\" No text.\")\n continue\n comments = submission.comments.encode('ascii', 'ignore')\n print(comments)\n #if len(post_text) > 5000:\n # print(\" \" + str(len(post_text)) + \" characters is too much text.\")\n # continue\n\n #print(post_text)\n f = open(\"reddit.html\", \"a\")\n f.write(\"

\")\n f.write(title + \"


\")\n f.write('

' + post_text + \"



\")\n f.close()\n\nf = open(\"reddit.html\", \"a\")\nf.write(\"\")\nf.close()", "sub_path": "reddithtml.py", "file_name": "reddithtml.py", "file_ext": "py", "file_size_in_byte": 1187, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.argv", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 8, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "514632189", "text": "import json\nimport datetime\nfrom os.path import basename, isdir, normpath\nfrom denite.util import expand, path2project\n\n\nclass Projectile(object):\n def __init__(self, nvim):\n self._nvim = nvim\n self._data_dir = self._nvim.eval('g:projectile#data_dir')\n\n def auto_add_project(self):\n data_file = expand(self._data_dir + '/projects.json')\n boofer = self._nvim.current.buffer.name\n pj_root = path2project(self._nvim, boofer, '.git,.hg,.svn')\n\n is_pj = (isdir(\"{}/.git\".format(pj_root))\n or isdir(\"{}/.hg\".format(pj_root))\n or isdir(\"{}/.svn\".format(pj_root)))\n if is_pj:\n is_new_pj = True\n with open(data_file, 'r') as g:\n try:\n json_info = json.load(g)\n except json.JSONDecodeError:\n json_info = []\n\n projects = json_info[:]\n for i in range(len(projects)):\n if projects[i]['root'] == pj_root:\n is_new_pj = False\n break\n\n if is_new_pj:\n pj_name = basename(normpath(pj_root))\n new_data = {\n 'name': pj_name,\n 'root': pj_root,\n 'timestamp': str(datetime.datetime.now().isoformat()),\n 'description': '',\n 'vcs': is_pj\n }\n\n projects.append(new_data)\n with open(data_file, 'w') as f:\n json.dump(projects, f, indent=2)\n", "sub_path": "rplugin/python3/projectile/projectile.py", "file_name": "projectile.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "denite.util.expand", "line_number": 13, "usage_type": "call"}, {"api_name": "denite.util.path2project", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 19, "usage_type": "call"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "164298668", "text": "#!/usr/bin/env python\n\n# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n\n\"\"\"\nExamples to show how to create EventHubProducerClient and EventHubConsumerClient that connect to custom endpoint.\n\"\"\"\n\nimport os\nfrom azure.eventhub import EventHubProducerClient, EventHubConsumerClient, EventData\n\nCONNECTION_STR = os.environ[\"EVENT_HUB_CONN_STR\"]\nEVENTHUB_NAME = os.environ['EVENT_HUB_NAME']\n# The custom endpoint address to use for establishing a connection to the Event Hubs service,\n# allowing network requests to be routed through any application gateways\n# or other paths needed for the host environment.\nCUSTOM_ENDPOINT_ADDRESS = 'sb://:'\n# The optional absolute path to the custom certificate file used by client to authenticate the\n# identity of the connection endpoint in the case that endpoint has its own issued CA.\n# If not set, the certifi library will be used to load certificates.\nCUSTOM_CA_BUNDLE_PATH = ''\n\n\ndef producer_connecting_to_custom_endpoint():\n producer_client = EventHubProducerClient.from_connection_string(\n conn_str=CONNECTION_STR,\n eventhub_name=EVENTHUB_NAME,\n custom_endpoint_address=CUSTOM_ENDPOINT_ADDRESS,\n connection_verify=CUSTOM_CA_BUNDLE_PATH,\n )\n\n with producer_client:\n # Without specifying partition_id or partition_key\n # the events will be distributed to available partitions via round-robin.\n event_data_batch = producer_client.create_batch()\n event_data_batch.add(EventData('Single message'))\n producer_client.send_batch(event_data_batch)\n print(\"Send a message.\")\n\n\ndef on_event(partition_context, event):\n # Put your code here.\n # If the operation is i/o intensive, multi-thread will have better performance.\n print(\"Received event from partition: {}.\".format(partition_context.partition_id))\n\n\ndef consumer_connecting_to_custom_endpoint():\n consumer_client = EventHubConsumerClient.from_connection_string(\n conn_str=CONNECTION_STR,\n consumer_group='$Default',\n eventhub_name=EVENTHUB_NAME,\n custom_endpoint_address=CUSTOM_ENDPOINT_ADDRESS,\n connection_verify=CUSTOM_CA_BUNDLE_PATH,\n )\n\n try:\n with consumer_client:\n consumer_client.receive(\n on_event=on_event,\n starting_position=\"-1\", # \"-1\" is from the beginning of the partition.\n )\n except KeyboardInterrupt:\n print('Stopped receiving.')\n\n\nproducer_connecting_to_custom_endpoint()\nconsumer_connecting_to_custom_endpoint()\n", "sub_path": "sdk/eventhub/azure-eventhub/samples/sync_samples/connection_to_custom_endpoint_address.py", "file_name": "connection_to_custom_endpoint_address.py", "file_ext": "py", "file_size_in_byte": 2901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "azure.eventhub.EventHubProducerClient.from_connection_string", "line_number": 28, "usage_type": "call"}, {"api_name": "azure.eventhub.EventHubProducerClient", "line_number": 28, "usage_type": "name"}, {"api_name": "azure.eventhub.EventData", "line_number": 39, "usage_type": "call"}, {"api_name": "azure.eventhub.EventHubConsumerClient.from_connection_string", "line_number": 51, "usage_type": "call"}, {"api_name": "azure.eventhub.EventHubConsumerClient", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "585708170", "text": "#-*-coding:utf-8-*-\n# https://make-muda.net/2017/10/5645/\nfrom websocket_server import WebsocketServer\n\nIP='127.0.0.1'\nPORT=6700\n\ndef new_client(client, server):\n print('New client {}:{} has joined.'.format(client['address'][0], client['address'][1]))\n \ndef message_received(client, server, message):\n print(message)\n\nserver = WebsocketServer(host=IP, port=PORT)\nserver.set_fn_new_client(new_client)\nserver.set_fn_message_received(message_received)\nserver.run_forever()\n", "sub_path": "python/sample_server.py", "file_name": "sample_server.py", "file_ext": "py", "file_size_in_byte": 476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "websocket_server.WebsocketServer", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "181819154", "text": "# Copyright 2016 Google Inc.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\nimport os\r\nimport urllib\r\nimport webapp2\r\n\r\nimport jinja2\r\nfrom google.appengine.ext import ndb\r\nfrom google.appengine.api import memcache\r\nimport blog_db\r\nimport json\r\nimport logging\r\nimport time\r\n\r\nJINJA_ENVIRONMENT = jinja2.Environment(\r\n loader=jinja2.FileSystemLoader('templates/'),\r\n extensions=['jinja2.ext.autoescape'],\r\n autoescape=True)\r\nPOSTS = \"posts\"\r\nquery_time = time.time()\r\nclass BlogPage_handler(webapp2.RequestHandler):\r\n def get(self):\r\n global query_time\r\n posts = memcache.get(POSTS)\r\n if posts is None:\r\n # read DB\r\n logging.info(\"DB query\")\r\n query_time = time.time()\r\n posts = blog_db.Post.query().fetch()\r\n memcache.set(POSTS,posts)\r\n \r\n template = JINJA_ENVIRONMENT.get_template(\"blog.html\")\r\n query_interval = time.time() - query_time\r\n self.response.write(template.render(posts=posts,query_interval=\"%.1f\" % query_interval))\r\n\r\n# for blog mainpage json\r\nclass BlogPage_json_handler(webapp2.RequestHandler):\r\n def get(self):\r\n posts = [p.to_dict() for p in blog_db.Post.query().fetch()]\r\n for p in posts:\r\n if p.get(\"created\"):\r\n p[\"created\"] = str(p[\"created\"])\r\n self.response.headers['Content-Type'] = 'application/json'\r\n self.response.write(json.dumps(posts))\r\n \r\nclass Newpost_handler(webapp2.RequestHandler):\r\n def get(self):\r\n template = JINJA_ENVIRONMENT.get_template(\"newpost.html\")\r\n template_values = {}\r\n self.response.write(template.render(template_values))\r\n def post(self):\r\n template = JINJA_ENVIRONMENT.get_template(\"newpost.html\")\r\n subject = self.request.get(\"subject\")\r\n content = self.request.get(\"content\")\r\n if not subject or not content:\r\n error = \"Please submit correct form...\"\r\n self.response.write(template.render(error=error))\r\n return\r\n # submit to database\r\n newpost = blog_db.Post(subject=subject,content=content)\r\n newpost.put()\r\n # clear memcache\r\n memcache.delete(POSTS)\r\n \r\n #add new post and query time to single post memcache\r\n if not memcache.add(str(newpost.key.id), (newpost,time.time())):\r\n logging.error(\"id already exist for new post\")\r\n self.redirect('/blog/'+str(newpost.key.id()))\r\n\r\nclass Post_id_handler(webapp2.RequestHandler):\r\n def get(self,post_id):\r\n #post = blog_db.Post.get_by_id(int(post_id))\r\n # get single post and query time\r\n singlepost_cached = memcache.get(post_id)\r\n prev_qeury_time = None\r\n post = None\r\n if not singlepost_cached:\r\n logging.info(\"get single post with DB query\")\r\n post = blog_db.Post.get_by_id(int(post_id))\r\n prev_qeury_time = time.time()\r\n memcache.set(post_id,(post,prev_qeury_time))\r\n else:\r\n post, prev_qeury_time = singlepost_cached\r\n template = JINJA_ENVIRONMENT.get_template(\"single_post.html\")\r\n if not post:\r\n self.response.write(template.render(error=\"Can't find the post with this id!\"))\r\n return\r\n self.response.write(template.render(subject=post.subject,\r\n content=post.content,query_interval=\"%.1f\" % (time.time()-prev_qeury_time)))\r\n \r\nclass Post_id_json_handler(webapp2.RequestHandler):\r\n def get(self,post_str):\r\n post_id = post_str.split(\".\")[0]\r\n post = blog_db.Post.get_by_id(int(post_id))\r\n post = post.to_dict()\r\n if post and post.get(\"created\"):\r\n post[\"created\"] = str(post[\"created\"])\r\n self.response.headers['Content-Type'] = 'application/json'\r\n self.response.write(json.dumps(post))\r\n \r\nclass Flush_handler(webapp2.RequestHandler):\r\n def get(self): \r\n memcache.flush_all()\r\n self.redirect('/blog')\r\n \r\n \r\n \r\n\r\n", "sub_path": "prob7_wiki/blog.py", "file_name": "blog.py", "file_ext": "py", "file_size_in_byte": 4536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "jinja2.Environment", "line_number": 27, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 33, "usage_type": "attribute"}, {"api_name": "google.appengine.api.memcache.get", "line_number": 36, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 36, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 40, "usage_type": "call"}, {"api_name": "blog_db.Post.query", "line_number": 41, "usage_type": "call"}, {"api_name": "blog_db.Post", "line_number": 41, "usage_type": "attribute"}, {"api_name": "google.appengine.api.memcache.set", "line_number": 42, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 42, "usage_type": "name"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 49, "usage_type": "attribute"}, {"api_name": "blog_db.Post.query", "line_number": 51, "usage_type": "call"}, {"api_name": "blog_db.Post", "line_number": 51, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 56, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 58, "usage_type": "attribute"}, {"api_name": "blog_db.Post", "line_number": 72, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache.delete", "line_number": 75, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 75, "usage_type": "name"}, {"api_name": "google.appengine.api.memcache.add", "line_number": 78, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 78, "usage_type": "name"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 79, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 82, "usage_type": "attribute"}, {"api_name": "google.appengine.api.memcache.get", "line_number": 86, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 86, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 90, "usage_type": "call"}, {"api_name": "blog_db.Post.get_by_id", "line_number": 91, "usage_type": "call"}, {"api_name": "blog_db.Post", "line_number": 91, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache.set", "line_number": 93, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 93, "usage_type": "name"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 103, "usage_type": "attribute"}, {"api_name": "blog_db.Post.get_by_id", "line_number": 106, "usage_type": "call"}, {"api_name": "blog_db.Post", "line_number": 106, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 111, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 113, "usage_type": "attribute"}, {"api_name": "google.appengine.api.memcache.flush_all", "line_number": 115, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 115, "usage_type": "name"}]} +{"seq_id": "495797147", "text": "from workflow.blocks.blocks_pallet import GroupType\nfrom workflow.blocks.generic import GenericBlock, save_params_actions_list, execute_block_actions_list\nfrom wrappers.sk_classifiers import apply_ncf_classifier\n\n__author__ = 'pavel'\n\nfrom workflow.blocks.fields import InputBlockField, ParamField, InputType, FieldType, ActionsList, OutputBlockField\nfrom webapp.tasks import wrapper_task\n\nclass NCF(GenericBlock):\n block_group = GroupType.CLASSIFIER\n\n block_base_name = \"NCF\"\n name = \"Network-Constrained Forest\"\n\n classifier_name = \"ncf\"\n\n is_abstract = False\n\n is_block_supports_auto_execution = True\n\n # Block behavior\n _block_actions = ActionsList([])\n _block_actions.extend(save_params_actions_list)\n _block_actions.extend(execute_block_actions_list)\n\n gene2gene = InputBlockField(name=\"gene2gene\", order_num=30,\n required_data_type=\"BinaryInteraction\",\n required=True)\n miRNA2gene = InputBlockField(name=\"miRNA2gene\", order_num=31,\n required_data_type=\"BinaryInteraction\",\n required=True)\n\n # User defined parameters\n # Input ports definition\n _m_train_es = InputBlockField(name=\"mRNA_train_es\", order_num=10,\n required_data_type=\"ExpressionSet\",\n required=True)\n _m_test_es = InputBlockField(name=\"mRNA_test_es\", order_num=20,\n required_data_type=\"ExpressionSet\",\n required=True)\n _mi_train_es = InputBlockField(name=\"miRNA_train_es\", order_num=21,\n required_data_type=\"ExpressionSet\",\n required=True)\n _mi_test_es = InputBlockField(name=\"miRNA_test_es\", order_num=22,\n required_data_type=\"ExpressionSet\",\n required=True)\n\n\n # Provided outputs\n _result = OutputBlockField(name=\"result\", field_type=FieldType.CUSTOM,\n provided_data_type=\"ClassifierResult\", init_val=None)\n\n n_estimators = ParamField(\n name=\"n_estimators\",\n title=\"The number of trees in the forest\",\n input_type=InputType.TEXT,\n field_type=FieldType.INT,\n init_val=\"1000\",\n order_num=41\n )\n\n walk_max_length = ParamField(\n name=\"walk_max_length\",\n title=\"Walk max length\",\n input_type=InputType.TEXT,\n field_type=FieldType.INT,\n init_val=\"10\",\n order_num=50\n )\n\n criterion = ParamField(\n name=\"criterion\",\n title=\"The function to measure the quality of a split\",\n input_type=InputType.SELECT,\n field_type=FieldType.STR,\n order_num=60,\n options={\n \"inline_select_provider\": True,\n \"select_options\": [\n [\"gini\", \"Gini impurity\"],\n [\"entropy\", \"Information gain\"]\n ]\n }\n )\n\n eps = ParamField(\n name=\"eps\",\n title=\"Eps\",\n input_type=InputType.TEXT,\n field_type=FieldType.FLOAT,\n init_val=\"0.01\",\n order_num=70\n )\n\n max_depth = ParamField(\n name=\"max_depth\",\n title=\"The maximum depth of the tree\",\n input_type=InputType.TEXT,\n field_type=FieldType.INT,\n init_val=\"2\",\n order_num=80\n )\n\n min_samples_split = ParamField(\n name=\"min_samples_split\",\n title=\"The minimum number of samples to split an internal node\",\n input_type=InputType.TEXT,\n field_type=FieldType.INT,\n init_val=\"2\",\n order_num=90,\n )\n\n min_samples_leaf = ParamField(\n name=\"min_samples_leaf\",\n title=\"The minimum number of samples to be at a leaf node\",\n input_type=InputType.TEXT,\n field_type=FieldType.INT,\n init_val=\"2\",\n order_num=100\n )\n\n bootstrap = ParamField(\n name=\"bootstrap\",\n title=\"bootstrap\",\n input_type=InputType.CHECKBOX,\n field_type=FieldType.BOOLEAN,\n required=False,\n order_num=110\n )\n\n def __init__(self, *args, **kwargs):\n super(NCF, self).__init__(*args, **kwargs)\n\n self.celery_task = None\n self.classifier_options = {}\n self.fit_options = {}\n\n def execute(self, exp, *args, **kwargs):\n self.set_out_var(\"result\", None)\n self.collect_options()\n\n mRNA_train_es = self.get_input_var(\"mRNA_train_es\")\n mRNA_test_es = self.get_input_var(\"mRNA_test_es\")\n\n miRNA_train_es = self.get_input_var(\"miRNA_train_es\")\n miRNA_test_es = self.get_input_var(\"miRNA_test_es\")\n\n self.celery_task = wrapper_task.s(\n apply_ncf_classifier,\n exp=exp, block=self,\n\n mRNA_train_es=mRNA_train_es, mRNA_test_es=mRNA_test_es,\n miRNA_train_es=miRNA_train_es, miRNA_test_es=miRNA_test_es,\n\n classifier_name=self.classifier_name,\n classifier_options=self.classifier_options,\n fit_options=self.fit_options,\n\n base_folder=exp.get_data_folder(),\n base_filename=\"%s_%s\" % (self.uuid, self.classifier_name),\n )\n exp.store_block(self)\n self.celery_task.apply_async()\n\n def success(self, exp, result, *args, **kwargs):\n # We store obtained result as an output variable\n self.set_out_var(\"result\", result)\n exp.store_block(self)\n\n def reset_execution(self, exp, *args, **kwargs):\n self.clean_errors()\n # self.get_scope().remove_temp_vars()\n self.set_out_var(\"result\", None)\n exp.store_block(self)\n\n def get_option_safe(self, name, target_type=None):\n if hasattr(self, name):\n raw = getattr(self, name)\n if raw:\n if target_type:\n try:\n return target_type(raw)\n except:\n pass\n else:\n return raw\n return None\n\n def collect_option_safe(self, name, target_type=None, target_name=None):\n value = self.get_option_safe(name, target_type)\n # from celery.contrib import rdb; rdb.set_trace()\n if value:\n if target_name:\n self.classifier_options[target_name] = value\n else:\n self.classifier_options[name] = value\n return value\n\n def collect_options(self):\n self.classifier_options[\"gene2gene\"] = self.get_input_var(\"gene2gene\")\n self.classifier_options[\"miRNA2gene\"] = self.get_input_var(\"miRNA2gene\")\n self.classifier_options['walk_lengths'] = range(1, int(self.walk_max_length))\n self.collect_option_safe(\"eps\")\n self.collect_option_safe(\"n_estimators\", int)\n # self.collect_option_safe(\"max_features\")\n self.collect_option_safe(\"max_depth\", int)\n self.collect_option_safe(\"min_samples_leaf\", int)\n self.collect_option_safe(\"min_samples_split\", int)\n self.classifier_options[\"bootstrap\"] = self.bootstrap\n", "sub_path": "mixgene_project/workflow/blocks/classifiers/ncf.py", "file_name": "ncf.py", "file_ext": "py", "file_size_in_byte": 7136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "workflow.blocks.generic.GenericBlock", "line_number": 10, "usage_type": "name"}, {"api_name": "workflow.blocks.blocks_pallet.GroupType.CLASSIFIER", "line_number": 11, "usage_type": "attribute"}, {"api_name": "workflow.blocks.blocks_pallet.GroupType", "line_number": 11, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ActionsList", "line_number": 23, "usage_type": "call"}, {"api_name": "workflow.blocks.generic.save_params_actions_list", "line_number": 24, "usage_type": "argument"}, {"api_name": "workflow.blocks.generic.execute_block_actions_list", "line_number": 25, "usage_type": "argument"}, {"api_name": "workflow.blocks.fields.InputBlockField", "line_number": 27, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputBlockField", "line_number": 30, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputBlockField", "line_number": 36, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputBlockField", "line_number": 39, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputBlockField", "line_number": 42, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputBlockField", "line_number": 45, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.OutputBlockField", "line_number": 51, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.FieldType.CUSTOM", "line_number": 51, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 51, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 54, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.TEXT", "line_number": 57, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 57, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.INT", "line_number": 58, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 58, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 63, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.TEXT", "line_number": 66, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 66, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.INT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 67, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 72, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.SELECT", "line_number": 75, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 75, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.STR", "line_number": 76, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 76, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 87, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.TEXT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 90, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.FLOAT", "line_number": 91, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 91, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 96, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.TEXT", "line_number": 99, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 99, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.INT", "line_number": 100, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 100, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 105, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.TEXT", "line_number": 108, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 108, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.INT", "line_number": 109, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 109, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 114, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.TEXT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 117, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.INT", "line_number": 118, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 118, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.ParamField", "line_number": 123, "usage_type": "call"}, {"api_name": "workflow.blocks.fields.InputType.CHECKBOX", "line_number": 126, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.InputType", "line_number": 126, "usage_type": "name"}, {"api_name": "workflow.blocks.fields.FieldType.BOOLEAN", "line_number": 127, "usage_type": "attribute"}, {"api_name": "workflow.blocks.fields.FieldType", "line_number": 127, "usage_type": "name"}, {"api_name": "webapp.tasks.wrapper_task.s", "line_number": 149, "usage_type": "call"}, {"api_name": "wrappers.sk_classifiers.apply_ncf_classifier", "line_number": 150, "usage_type": "argument"}, {"api_name": "webapp.tasks.wrapper_task", "line_number": 149, "usage_type": "name"}]} +{"seq_id": "557490342", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/geobaldi/src/ripiu/public/github/djangocms_aoxomoxoa/ripiu/djangocms_aoxomoxoa/templatetags/aoxomoxoa_filters.py\n# Compiled at: 2018-04-03 09:24:46\n# Size of source mod 2**32: 953 bytes\nfrom django.template import Library\nregister = Library()\n\n@register.filter(name='get_size')\ndef get_size(thumb_opt):\n return (thumb_opt.width, thumb_opt.height)\n\n\n@register.filter(name='get_alt')\ndef get_alt(instance):\n if hasattr(instance, 'image'):\n return instance.alt_text or instance.image.default_alt_text\n else:\n if hasattr(instance, 'picture'):\n if 'alt' in instance.attributes:\n if instance.attributes['alt']:\n return instance.attributes['alt']\n return instance.picture.default_alt_text\n return 'PUPPA!'\n\n\n@register.filter(name='get_caption')\ndef get_caption(instance):\n if hasattr(instance, 'image'):\n return instance.caption_text or instance.image.default_caption\n if hasattr(instance, 'picture'):\n return instance.caption_text or instance.picture.default_caption", "sub_path": "pycfiles/ripiu.djangocms_aoxomoxoa-0.0.4-py3-none-any/aoxomoxoa_filters.cpython-36.py", "file_name": "aoxomoxoa_filters.cpython-36.py", "file_ext": "py", "file_size_in_byte": 1231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.template.Library", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "352271244", "text": "#! /usr/bin/env python\n\"\"\"bro %s -- compression/decompression utility using the Brotli algorithm.\"\"\"\n\nfrom __future__ import print_function\nimport getopt\nimport sys\nimport os\nimport brotli\nimport platform\n\n__usage__ = \"\"\"\\\nUsage: bro [--force] [--decompress] [--input filename] [--output filename]\n [--mode 'text'|'font'] [--transform]\"\"\"\n\n__version__ = '0.1'\n\n\nBROTLI_MODES = {\n 'text': brotli.MODE_TEXT,\n 'font': brotli.MODE_FONT\n}\n\n\ndef get_binary_stdio(stream):\n \"\"\" Return the specified standard input, output or errors stream as a\n 'raw' buffer object suitable for reading/writing binary data from/to it.\n \"\"\"\n assert stream in ['stdin', 'stdout', 'stderr'], \"invalid stream name\"\n stdio = getattr(sys, stream)\n if sys.version_info[0] < 3:\n if sys.platform == 'win32':\n # set I/O stream binary flag on python2.x (Windows)\n runtime = platform.python_implementation()\n if runtime == \"PyPy\":\n # the msvcrt trick doesn't work in pypy, so I use fdopen\n mode = \"rb\" if stream == \"stdin\" else \"wb\"\n stdio = os.fdopen(stdio.fileno(), mode, 0)\n else:\n # this works with CPython -- untested on other implementations\n import msvcrt\n msvcrt.setmode(stdio.fileno(), os.O_BINARY)\n return stdio\n else:\n # get 'buffer' attribute to read/write binary data on python3.x\n if hasattr(stdio, 'buffer'):\n return stdio.buffer\n else:\n orig_stdio = getattr(sys, \"__%s__\" % stream)\n return orig_stdio.buffer\n\n\ndef main(args):\n\n options = parse_options(args)\n\n if options.infile:\n if not os.path.isfile(options.infile):\n print('file \"%s\" not found' % options.infile, file=sys.stderr)\n sys.exit(1)\n with open(options.infile, \"rb\") as infile:\n data = infile.read()\n else:\n if sys.stdin.isatty():\n # interactive console, just quit\n usage()\n infile = get_binary_stdio('stdin')\n data = infile.read()\n\n if options.outfile:\n if os.path.isfile(options.outfile) and not options.force:\n print('output file exists')\n sys.exit(1)\n outfile = open(options.outfile, \"wb\")\n else:\n outfile = get_binary_stdio('stdout')\n\n try:\n if options.decompress:\n data = brotli.decompress(data)\n else:\n data = brotli.compress(data, options.mode, options.transform)\n except brotli.error as e:\n print('[ERROR] %s: %s' % (e, options.infile or 'sys.stdin'),\n file=sys.stderr)\n sys.exit(1)\n\n outfile.write(data)\n outfile.close()\n\n\ndef parse_options(args):\n try:\n raw_options, dummy = getopt.gnu_getopt(\n args, \"?hdi:o:fm:t\",\n [\"help\", \"decompress\", \"input=\", \"output=\", \"force\", \"mode=\",\n \"transform\"])\n except getopt.GetoptError as e:\n print(e, file=sys.stderr)\n usage()\n options = Options(raw_options)\n return options\n\n\ndef usage():\n print(__usage__, file=sys.stderr)\n sys.exit(1)\n\n\nclass Options(object):\n\n def __init__(self, raw_options):\n self.decompress = self.force = self.transform = False\n self.infile = self.outfile = None\n self.mode = BROTLI_MODES['text']\n for option, value in raw_options:\n if option in (\"-h\", \"--help\"):\n print(__doc__ % (__version__))\n print(\"\\n%s\" % __usage__)\n sys.exit(0)\n elif option in ('-d', '--decompress'):\n self.decompress = True\n elif option in ('-i', '--input'):\n self.infile = value\n elif option in ('-o', '--output'):\n self.outfile = value\n elif option in ('-f', '--force'):\n self.force = True\n elif option in ('-m', '--mode'):\n value = value.lower()\n if value not in ('text', 'font'):\n print('mode \"%s\" not recognized' % value, file=sys.stderr)\n usage()\n self.mode = BROTLI_MODES[value]\n elif option in ('-t', '--transform'):\n self.transform = True\n\n\nif __name__ == '__main__':\n main(sys.argv[1:])\n", "sub_path": "python/bro.py", "file_name": "bro.py", "file_ext": "py", "file_size_in_byte": 4329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "brotli.MODE_TEXT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "brotli.MODE_FONT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 31, "usage_type": "attribute"}, {"api_name": "platform.python_implementation", "line_number": 33, "usage_type": "call"}, {"api_name": "os.fdopen", "line_number": 37, "usage_type": "call"}, {"api_name": "msvcrt.setmode", "line_number": 41, "usage_type": "call"}, {"api_name": "os.O_BINARY", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.stdin.isatty", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 72, "usage_type": "call"}, {"api_name": "brotli.decompress", "line_number": 79, "usage_type": "call"}, {"api_name": "brotli.compress", "line_number": 81, "usage_type": "call"}, {"api_name": "brotli.error", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 84, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 85, "usage_type": "call"}, {"api_name": "getopt.gnu_getopt", "line_number": 93, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 119, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 131, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 139, "usage_type": "attribute"}]} +{"seq_id": "586408255", "text": "import os\nimport glob\nfrom concurrent.futures import ThreadPoolExecutor\n\nimport cv2\nimport numpy as np\n\nfrom pipeline import CameraCalibration, BirdsEyeView, ImageSection, Point\n\nfrom pipeline import lab_enhance_yellow\n\nRETURN = 13\nSPACE = 32\nBACKSPACE = 8\n\nWINDOW_WIDTH = 16\nWINDOW_HEIGHT = 16\n\n# PATH = 'harder_challenge_video.mp4'\n# PATH = 'challenge_video.mp4'\nPATH = 'project_video.mp4'\n\n\ndef main():\n negatives = [np.float32(cv2.imread(path, cv2.IMREAD_GRAYSCALE)) / 255\n for path in glob.glob(os.path.join('templates', '**', 'negative-*.png'))]\n positives = [np.float32(cv2.imread(path, cv2.IMREAD_GRAYSCALE)) / 255\n for path in glob.glob(os.path.join('templates', '**', 'positive-*.png'))]\n\n cc = CameraCalibration.from_pickle('calibration.pkl')\n\n section = ImageSection(\n top_left=Point(x=580, y=461.75),\n top_right=Point(x=702, y=461.75),\n bottom_right=Point(x=1013, y=660),\n bottom_left=Point(x=290, y=660),\n )\n\n bev = BirdsEyeView(section,\n section_width=3.6576, # one lane width in meters\n section_height=2 * 13.8826) # two dash distances in meters\n\n cap = cv2.VideoCapture(PATH)\n length = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n current_pos, last_pos = 0, None\n\n window = 'Video'\n cv2.namedWindow(window, cv2.WINDOW_KEEPRATIO)\n\n pe = ThreadPoolExecutor(max_workers=8)\n\n while True:\n current_pos = min(length, max(0, current_pos))\n cap.set(cv2.CAP_PROP_POS_FRAMES, current_pos)\n print('Frame: {}'.format(current_pos))\n ret, img = cap.retrieve()\n if not ret:\n print('End of video.')\n break\n\n img, _ = cc.undistort(img, False)\n warped = bev.warp(img)\n gray, _ = lab_enhance_yellow(warped, normalize=True)\n gray_orig = gray.copy()\n\n cv2.imwrite('sample.jpg', img)\n cv2.imwrite('sample-warped.jpg', warped)\n\n mode = cv2.TM_CCOEFF\n gray = cv2.GaussianBlur(gray, (9, 9), 0)\n\n def filter(template):\n m = cv2.matchTemplate(gray, template, mode)\n m[m < 0] = 0\n return m\n\n pos_matched = pe.map(filter, positives)\n neg_matched = pe.map(filter, negatives)\n\n pos_sum = np.zeros_like(gray)\n for result in pos_matched:\n pos_sum[8:745+8, 8:285+8] += result\n pos_sum /= len(positives)\n\n neg_sum = np.zeros_like(gray)\n for result in neg_matched:\n neg_sum[8:745 + 8, 8:285 + 8] += result\n neg_sum /= len(negatives)\n\n mask = (1 - neg_sum) * pos_sum\n mask[mask < 0] = 0\n mask = cv2.normalize(mask, 1, cv2.NORM_MINMAX)\n\n mask = cv2.GaussianBlur(mask, (5, 5), 0)\n mask[mask < 0.05] = 0\n mask = cv2.normalize(mask, 1, cv2.NORM_MINMAX)\n\n img = np.hstack([warped.astype(np.float32) / 255.,\n cv2.cvtColor(gray_orig, cv2.COLOR_GRAY2BGR),\n cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR),\n cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)])\n cv2.imshow(window, img)\n key = cv2.waitKey(0)\n if key == 27:\n break\n if key == SPACE:\n current_pos += 1\n elif key == BACKSPACE:\n current_pos -= 1\n elif key == RETURN:\n current_pos += 60\n\n cap.release()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "test_templates.py", "file_name": "test_templates.py", "file_ext": "py", "file_size_in_byte": 3430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.float32", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pipeline.CameraCalibration.from_pickle", "line_number": 30, "usage_type": "call"}, {"api_name": "pipeline.CameraCalibration", "line_number": 30, "usage_type": "name"}, {"api_name": "pipeline.ImageSection", "line_number": 32, "usage_type": "call"}, {"api_name": "pipeline.Point", "line_number": 33, "usage_type": "call"}, {"api_name": "pipeline.Point", "line_number": 34, "usage_type": "call"}, {"api_name": "pipeline.Point", "line_number": 35, "usage_type": "call"}, {"api_name": "pipeline.Point", "line_number": 36, "usage_type": "call"}, {"api_name": "pipeline.BirdsEyeView", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.WINDOW_KEEPRATIO", "line_number": 48, "usage_type": "attribute"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_POS_FRAMES", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pipeline.lab_enhance_yellow", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.matchTemplate", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 92, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 99, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 100, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "481365098", "text": "from django.http import HttpResponse\r\nfrom django.shortcuts import *\r\nfrom django.views.decorators import csrf\r\nfrom untitled1.chatbot import ChaBot\r\n\r\n#form\r\n\r\nhandler = ChaBot()\r\n\r\n\r\n# 用ajax重写\r\n# def search(re):\r\n# ctx = {}\r\n# print('Post:'+str(re.POST))\r\n# if re.POST:\r\n# ctx['rlt'] = handler.chat_main(re.POST['q'])\r\n# return render(re,'Question.html',ctx)\r\n\r\ndef search(re) :\r\n return render(re,'Question.html')\r\n\r\n@csrf.csrf_exempt\r\ndef answer(re):\r\n print(re.body)\r\n quest = re.POST.get('q')\r\n print(quest)\r\n if re.method ==\"POST\":\r\n ans = handler.chat_main(str(quest))\r\n return HttpResponse(ans)\r\n\r\n\r\n\r\ndef demo_ajax(request):\r\n return render(request, 'demo_ajax.html')\r\n\r\ndef demo_add(request):\r\n a=request.GET['a']\r\n b=request.GET['b']\r\n\r\n if request.is_ajax():\r\n ajax_string = 'ajax request: '\r\n else:\r\n ajax_string = 'not ajax request: '\r\n\r\n c = int(a) + int(b)\r\n r = HttpResponse(ajax_string + str(c))\r\n return r", "sub_path": "untitled1/untitled1/question.py", "file_name": "question.py", "file_ext": "py", "file_size_in_byte": 1023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "untitled1.chatbot.ChaBot", "line_number": 8, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 29, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.views.decorators.csrf", "line_number": 22, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "496704738", "text": "import tensorflow as tf\nfrom fabot.custom.memnet.data_utils import MemNetT5DataAdapter, MemNetT6DataAdapter\nfrom data.feature_factory import T6Featurizer, T5Featurizer\nfrom data.database import BabiDB\nfrom globals import BABI_T6_KB_FILE, BABI_T6_TRN_FILE, BABI_T6_DEV_FILE, BABI_T6_TST_FILE, \\\n PERSISTED_MEMNET_PATH, BABI_T5_TRN_FILE, BABI_T5_DEV_FILE, \\\n BABI_T5_TST_FILE, BABI_T5_TST_OOV_FILE\nfrom rasa_core.policies import Policy\nfrom rasa_core.events import ActionExecuted\nfrom rasa_core.actions.action import ACTION_LISTEN_NAME\nfrom numpy import mean, exp, argmax\nfrom os.path import join, isfile, exists\nfrom os import mkdir\nimport logging\nimport pickle\nimport argparse\nfrom data.babi_reader import BabiReader\nfrom copy import copy\nimport re\nimport json\n\nlogger = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.DEBUG)\n\n\ndef softmax(z):\n e_x = exp(z - max(z))\n return list(e_x / e_x.sum())\n\n\ndef batch_matmul(x, A):\n \"\"\"\n Apply tf.matmul with a matrix on a tensor with rank higher than 2\n This can be done with scan, but this performs better\n (https://stackoverflow.com/questions/38235555/tensorflow-matmul-of-input-matrix-with-batch-data/43829731#43829731)\n :param x: first tensor in the dot product\n :param A: matrix (second element in the dot product). Its first dimension must be equal to the last dimension of x\n :return: a tensor that preserves all of x' dimensions except the last. Dot product with A is applied to each element\n in the last dimension of x. Every other dimension is preserved\n \"\"\"\n prod = tf.reshape(x, [-1, x.shape[-1]])\n prod = tf.matmul(prod, A)\n final_shape = tf.shape(x)[:-1]\n final_shape = tf.concat([final_shape, [tf.shape(A)[-1]]], 0)\n return tf.reshape(prod, final_shape)\n\n\nclass MemoryNetwork(object):\n\n def __init__(self,\n num_actions,\n utterance_len,\n embedding_size,\n mem_size=10,\n hops=3,\n var_init=tf.random_normal_initializer(stddev=0.1),\n model_name=\"MemoryNetwork\",\n optimizer=tf.train.AdamOptimizer(learning_rate=1e-4, epsilon=1e-8),\n clip_norm=15.,\n keep_prob=1.):\n self.h = [[0] * utterance_len] # used during online test\n self.hops = hops\n self.mem_size = mem_size\n self.h_len = utterance_len\n # placeholders\n # [batch, mem_size, utter_len]\n self.history = tf.placeholder(tf.float32, [None, None, utterance_len], name=\"history\")\n self.queries = tf.placeholder(tf.float32, [None, utterance_len], name=\"queries\") # [batch, query_len]\n self.labels = tf.placeholder(tf.float32, [None, num_actions], name=\"labels\") # [batch, num_actions]\n self.keep_prob = tf.placeholder(tf.float32, [], name='keep_probability')\n self.train_keep_prob = keep_prob\n\n # model parameters\n with tf.variable_scope(model_name):\n self.A = [tf.Variable(initial_value=var_init([utterance_len, embedding_size]), name=\"A\" + str(i))\n for i in range(hops+1)]\n # self.C = tf.Variable(initial_value=var_init([h_utterance_len, embedding_size]), name=\"C\")\n self.W = tf.Variable(initial_value=var_init([embedding_size, num_actions]), name=\"W\")\n self.H = tf.Variable(initial_value=var_init([embedding_size, embedding_size]), name=\"H\")\n self.embedding_size = embedding_size\n\n # define graph\n u = tf.matmul(self.queries, self.A[0]) # [batch, embedding_size]\n for i in range(hops):\n \"\"\"we need a matmul on each [mem_size, utter_len] element of history with A. scan takes each element\n (across 0th dim) of history and applies the lambda function (matmul) to it. Argument x is the element\n currently processed of history and a is the result of the previous call (the first time, a equals\n initializer, or history[0] if no initializer present). Since the final value is a concatenation of\n each call, then each must return a value of same shape. Hence, not providing the initializer causes\n an exception since result[0] = history[0] which has different shape than matmul(history[i], A).\n For result[0] to be lambda(_, history[0]), there must be an initializer, even if not used, so giving a\n z ero matrix with the right dimensions. These are two other ways to achieve the same:\n m = batch_matmul(self.history, self.A) # [batch, mem_size, embedding_size] # more efficient but ugly\n m = tf.scan(lambda a, x: tf.matmul(x, self.A), self.history,\n initializer=tf.matmul(self.history[0], self.A)) # crashes with empty batches\n m = tf.scan(lambda a, x: tf.matmul(x, self.A), self.history, # won't crash with empty batches\n initializer=tf.matmul(tf.zeros(shape=tf.gather(tf.shape(self.history), [1, 2])), self.A))\n Finally, decided to do it the cleanest way possible: with matmul, so long as dimensions are compatible:\n for A*B, A[-1] = B[-2] (i.e. the inner-most dimensions of A and B must be valid for matrix \n multiplication). The rest of the dimensions, must be identical (since in the end, we are just \n multiplying a bunch of matrices, each dimension besides the inner most is just one more bag of such \n matrices into the bunch) A having rank [20, 30, 10, 15] * B having rank [20, 30, 15, 50] produces A*B \n having rank [20, 30, 10, 50]. Hence, only the last dimension of A is lost, as this operation can be \n understood as embedding that last dimension to the space of the last dimension of B (size 50).\n Since A is missing a dimension equal to mem_size, we just add it with expand_dims and use tile to repeat\n A across mem_size rows (tile requires number of times each dimension gets repeated, hence the 1's) \n \"\"\"\n # [batch, mem_size, embedding_size]\n m = tf.matmul(self.history, tf.tile(tf.expand_dims(self.A[i], 0), [tf.shape(self.history)[0], 1, 1]))\n \"\"\"This is like doing softmax in np like this:\n soft = np.exp(p) # where p is a matrix\n sum = np.sum(soft, axis=1) # sum across columns, you get a vector\n soft /= sum[:, None] # divide matrix by vector. The None dim is shorthand to add expanded dim in np\n so that the / broadcasts automatically (just like *)\n \"\"\"\n # the extra dim added to u causes a broadcast at m * u. reduce_sum just cause no easy dot product in tf\n p = tf.nn.softmax(tf.reduce_sum(m * tf.expand_dims(u, 1), -1)) # [batch, mem]\n c = tf.matmul(self.history, tf.tile(tf.expand_dims(self.A[i+1], 0), [tf.shape(self.history)[0], 1, 1]))\n # c = tf.matmul(self.history, tf.tile(tf.expand_dims(self.C, 0), [tf.shape(self.history)[0], 1, 1]))\n # multiply each of the [batch, mem] c embeddings by scalar p to get weighted average output embedding\n o = tf.reduce_sum(tf.expand_dims(p, -1) * c, axis=1) # [batch, embedding_size]\n u = o + tf.matmul(u, self.H)\n tf.summary.tensor_summary('my_tensor_summary', p)\n self.logits = tf.nn.dropout(tf.matmul((o + u), self.W), self.keep_prob) # [batch, actions]\n self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.labels, logits=self.logits))\n self.output_p = tf.nn.softmax(self.logits)\n self.pred_action = tf.arg_max(self.output_p, dimension=0)\n self.accuracy = tf.reduce_mean(tf.cast(\n tf.equal(tf.argmax(self.logits, axis=1), tf.argmax(self.labels, axis=1)),\n tf.float32))\n\n grads, vars = zip(*optimizer.compute_gradients(self.loss))\n grads_clipped, _ = tf.clip_by_global_norm(grads, clip_norm=clip_norm)\n self.train_op = optimizer.apply_gradients(zip(grads_clipped, vars))\n\n self.sess = tf.Session()\n self.summary = tf.summary.merge_all()\n self.summary_writer = tf.summary.FileWriter('misc_experiments/logs/', self.sess.graph)\n self.sess.run(tf.global_variables_initializer())\n self.step = 0\n self.p = p\n\n def prediction(self, history, query):\n summary_str, prediction = self.sess.run([self.summary, self.output_p], feed_dict={self.history: [history],\n self.queries: [query],\n self.keep_prob: 1})\n self.summary_writer.add_summary(summary_str, self.step)\n self.step += 1\n self.summary_writer.flush()\n return prediction\n\n def train_step(self, history_batch, query_batch, label_batch):\n pred, loss, _ = self.sess.run([self.pred_action, self.loss, self.train_op],\n feed_dict={self.history: history_batch,\n self.queries: query_batch,\n self.labels: label_batch,\n self.keep_prob: self.train_keep_prob})\n return pred, loss\n\n def predict_turn(self, x):\n \"\"\"predicts given one turn, keeping track of history\n :param x: featurized turn\"\"\"\n prediction = self.sess.run(self.output_p, feed_dict={self.history: [self.h if len(self.h) > 0 else\n [[0] * self.h_len]],\n self.queries: [x],\n self.keep_prob: 1})\n self.h.append(x)\n # if len(self.h) > self.mem_size:\n # self.h = self.h[::-1][:self.mem_size][::-1]\n return prediction.squeeze()\n\n def reset_conversation_state(self):\n self.h = []\n\n def persist(self, path):\n config = {'hops': self.hops, 'num_actions': int(self.labels.shape[-1]), 'mem_size': self.mem_size,\n 'train_dropout': self.train_keep_prob, 'input_dim': int(self.queries.shape[-1]),\n 'embedding_size': self.embedding_size}\n if not exists(path):\n mkdir(path)\n with open(join(path, 'config.json'), 'w') as fh:\n json.dump(config, indent=2, fp=fh)\n saver = tf.train.Saver()\n saver.save(self.sess, path, global_step=0)\n logging.info('successfully persisted the model at {}'.format(path))\n\n @staticmethod\n def load(path):\n with open(join(path, 'config.json')) as fh:\n config = json.load(fh)\n model = MemoryNetwork(num_actions=config['num_actions'], utterance_len=config['input_dim'],\n embedding_size=config['embedding_size'], mem_size=config['mem_size'], hops=config['hops'],\n keep_prob=config['train_dropout'])\n saver = tf.train.Saver()\n ckpt = tf.train.get_checkpoint_state(path)\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(model.sess, ckpt.model_checkpoint_path)\n logging.info('successfully restored Memory Network model from {}'.format(path))\n else:\n logging.error('persisted model not found')\n return model\n\n\nclass MemNetPolicy(Policy):\n \"\"\"\n Rasa wrapper around the memory network\n \"\"\"\n def __init__(self, history, query, label, batch_indexes, encoder, model=None, embedding_size=10, hops=3,\n var_init=tf.random_normal_initializer(stddev=0.1), model_name='MemoryNetwork'):\n \"\"\"\n Sets or creates a model and sets the training data\n :param history: Iterable[List[List[float]]]. Training data history component. A list where each element refers\n to a data point. Each element is a list of memories, each one a list of numbers comprising the featurization of\n a memory. All such featurizations must have identical length and there can't be empty lists (an empty history\n should be comprised of at least 1 fully 0-padded memory). History can't be empty.\n :param query: Iterable[List[float]]. Training data query component. A list where each element refers to a data\n point. Each element is a list of numbers comprising the featurization of a memory. All such featurizations must\n have identical length. query can't be empty and should have same length as history.\n :param label: Iterable[List[float]]. Training data label component. A list where each element refers to a data\n point. Each element is a list of numbers comprising the 1-hot encoding of a label. All such encodings must have\n length equal to total number of actions. label can't be empty and should have same length as history and query.\n :param batch_indexes: Iterable[Tuple[int, int]]. Indexes of start and end elements of each batch. Training data\n will be split in batches according to these indexes, so the ith batch goes from data[batch_indexes[i][0]] to\n data[batch_indexes[i][1]] (non inclusive)\n :param encoder: MemNetDataAdapter object. It's encode function will be called during inference to featurize\n user input\n :param model: a MemoryNetwork object, compatible with the dimensions of the training data (e.g. length of\n memories, number of actions). If not provided, one will be instantiated, inferring the length of each memory,\n length of queries and number of actions from the provided training data.\n :param embedding_size: int, size of memory and query embeddings for the Memory Network\n :param hops: int, number of hops (i.e. memory checks) of the Memory Network\n :param var_init: tf.keras.initializers.Initializer, model variable initializer\n :param model_name: str, name of the model. It identifies the variable scope\n \"\"\"\n super(MemNetPolicy, self).__init__(None, None) # rasa crap\n assert len(history) > 0, 'Memory network received empty training data'\n assert len(history) == len(query) and len(query) == len(label), 'Different sizes of history ({}), queries ' \\\n '({}) and labels ({}) provided to the ' \\\n 'MemoryNetwork'.format(len(history),\n len(query),\n len(label))\n self.history = history\n self.queries = query\n self.labels = label\n self.batch_indexes = list(batch_indexes)\n self.current_epoch = 0\n self.model_name = model_name\n self.sess = tf.Session()\n self.mem_size = max(len(h) for h in self.history)\n self.encoder = encoder\n num_actions = len(label[0]) # trn data can't be empty\n utterance_len = len(history[0][0]) # at least 1 padded memory is in history, always\n if model:\n assert isinstance(model, MemoryNetwork), 'expected a MemoryNetwork as model. Got {}'.format(type(model))\n MemNetPolicy._check_data_model_compatibility(model, num_actions, utterance_len)\n self.model = model\n else:\n self.model = MemoryNetwork(num_actions=num_actions, utterance_len=utterance_len,\n embedding_size=embedding_size, mem_size=self.mem_size, hops=hops,\n var_init=var_init, model_name=model_name)\n\n @staticmethod\n def _check_data_model_compatibility(model, num_actions, h_utterance_len):\n model_num_actions = int(model.labels.shape[-1])\n model_utterance_len = int(model.history.shape[-1])\n model_query_len = int(model.queries.shape[-1])\n assert model_num_actions == num_actions, 'number of actions defined in training data ({}) differs from ' \\\n 'the one defined in the model ({})'.format(num_actions,\n model_num_actions)\n assert model_utterance_len == h_utterance_len, 'length of history utterances defined in training data ' \\\n '({}) differs from the one defined in the model ({})'. \\\n format(h_utterance_len, model_utterance_len)\n assert model_query_len == model_utterance_len, 'length of queries defined in training data ({}) differs from ' \\\n 'the one defined in the model ({})'.format(model_utterance_len,\n model_query_len)\n\n def reset(self):\n self.encoder._reset()\n\n def predict_action_probabilities(self, tracker, domain):\n if isinstance(tracker.events[-1], ActionExecuted):\n # dead simple patch to force a listen after every action, cause fuck rasa\n p = [0.0] * len(domain.action_names)\n p[domain.action_map[ACTION_LISTEN_NAME][0]] = 1\n return p\n query, history = self.encoder.encode(tracker, self.mem_size)\n predictions = self.sess.run(self.model.output_p, feed_dict={self.model.history: [history],\n self.model.queries: [query],\n self.model.keep_prob: 1})\n predictions = [0.0, 0.0] + list(predictions[0]) # leading 0s due to action_listen and action_restart\n # hardcoded rules\n predictions = self.apply_domain_rules(predictions, tracker, domain)\n return predictions\n\n def apply_domain_rules(self, p, tracker, domain):\n \"\"\"\n Called after the Memory Network made its prediction. Subclasses can override this method to apply domain\n specific rules to this vector before, potentially redifining the predicted action\n :param p: probability vector over actions\n :param tracker: rasa tracker object\n :param domain: rasa domain object\n :return: probability vector after applying the domain specific rules\n \"\"\"\n return p\n\n def train(self, training_data, domain, **kwargs):\n \"\"\"\n Creates the model (only if it is not created yet) and trains it.\n :param training_data: not used, kept only for Rasa compatibility\n :param domain: rasa Domain object\n :param kwargs: Following keys are optional, along with their default values:\n mn_training_data: training data. If provided, it overrides the current training data of the policy.\n Dictionary with the keys 'history', 'query' and 'label' (for the respective components of the training data) and\n 'batch_indexes' (see __init__ documentation). Their dimensions (history utterance length, query length and\n number of actions) must match those defined in the model.\n mn_dev_data: development data. If provided, development data accuracy and loss will be printed along with\n training statistics. It must follow the same conditions applicable for mn_training_data\n mn_keep_prob: keep probability for dropout, defaults to 1\n mn_epochs: training epochs of the memory network, defaults to 50\n mn_clip_norm: maximum allowed norm of the gradients during training, defaults to 25\n mn_print_cycle: number of batches to process before each print of training statistics, defaults to 100\n \"\"\"\n keep_prob = kwargs['mn_keep_prob'] if 'mn_keep_prob' in kwargs else 1\n epochs = kwargs['mn_epochs'] if 'mn_epochs' in kwargs else 50\n clip_norm = kwargs['mn_clip_norm'] if 'mn_clip_norm' in kwargs else 25\n print_cycle = kwargs['mn_print_cycle'] if 'mn_print_cycle' in kwargs else 100\n if 'mn_training_data' in kwargs:\n assert 'history' in kwargs['mn_training_data'], 'no history in the mn_training_data dictionary'\n assert 'query' in kwargs['mn_training_data'], 'no queries in the mn_training_data dictionary'\n assert 'label' in kwargs['mn_training_data'], 'no labels in the mn_training_data dictionary'\n assert 'batch_indexes' in kwargs['mn_training_data'], 'no batch indexes in the mn_training_data dictionary'\n num_actions = len(kwargs['mn_training_data']['label'][0])\n h_utterance_len = len(kwargs['mn_training_data']['history'][0][0])\n query_len = len(kwargs['mn_training_data']['query'][0])\n MemNetPolicy._check_data_model_compatibility(self.model, num_actions, h_utterance_len, query_len)\n self.history = kwargs['mn_training_data']['history']\n self.queries = kwargs['mn_training_data']['query']\n self.labels = kwargs['mn_training_data']['label']\n self.batch_indexes = list(kwargs['mn_training_data']['batch_indexes'])\n if 'mn_dev_data' in kwargs:\n assert 'history' in kwargs['mn_dev_data'], 'no history in the mn_dev_data dictionary'\n assert 'query' in kwargs['mn_dev_data'], 'no queries in the mn_dev_data dictionary'\n assert 'label' in kwargs['mn_dev_data'], 'no labels in the mn_dev_data dictionary'\n if 'batch_indexes' not in kwargs['mn_dev_data']:\n logger.info('no batch indexes in dev data. Using a single batch')\n dev_batch_indexes = [(0, len(kwargs['mn_dev_data']['query']))]\n else:\n dev_batch_indexes = list(kwargs['mn_dev_data']['batch_indexes'])\n num_actions_dev = len(kwargs['mn_dev_data']['label'][0])\n h_utterance_len_dev = len(kwargs['mn_dev_data']['history'][0][0])\n MemNetPolicy._check_data_model_compatibility(self.model, num_actions_dev, h_utterance_len_dev)\n # all set. Aan de slag\n logging.info(\n 'starting training\\nConfig:\\nhops: {}\\nactions: {}\\nhistory utterance length: {}\\nquery length: {}\\n'\n 'embedding size: {}\\nbatch size: {}\\nmax memory: {}\\nkeep prob: {}\\nepochs: {}\\n'\n 'gradient clip norm: {}\\n'.format(\n self.model.hops, int(self.model.labels.shape[-1]), int(self.model.history.shape[-1]),\n int(self.model.queries.shape[-1]), self.model.embedding_size, len(self.batch_indexes),\n max(len(h) for h in self.history), keep_prob, epochs, clip_norm))\n optimizer = tf.train.AdamOptimizer(learning_rate=1e-4, epsilon=1e-8)\n loss_op = self.model.loss\n grads, vars = zip(*optimizer.compute_gradients(loss_op))\n grads_clipped, _ = tf.clip_by_global_norm(grads, clip_norm=clip_norm)\n train_op = optimizer.apply_gradients(zip(grads_clipped, vars))\n self.sess.run(tf.global_variables_initializer())\n for epoch in range(epochs):\n trn_accuracies = []\n for i, (b_start, b_end) in enumerate(self.batch_indexes):\n feed_dict = {self.model.history: self.history[b_start:b_end],\n self.model.queries: self.queries[b_start:b_end],\n self.model.labels: self.labels[b_start:b_end],\n self.model.keep_prob: keep_prob\n }\n loss, _ = self.sess.run([loss_op, train_op], feed_dict=feed_dict)\n feed_dict[self.model.keep_prob] = 1\n accuracy = self.sess.run(self.model.accuracy, feed_dict=feed_dict)\n if i % print_cycle == 0:\n logging.info('epoch: {}\\tbatch: {}\\tloss: {}\\taccuracy:{}'.format(epoch, i, loss, accuracy))\n trn_accuracies.append(accuracy)\n if 'mn_dev_data' in kwargs:\n dev_accuracies, dev_losses = [], []\n for i, (b_start, b_end) in enumerate(dev_batch_indexes):\n dev_accuracy, dev_loss = self.sess.run(\n [self.model.accuracy, loss_op],\n feed_dict={\n self.model.history: kwargs['mn_dev_data']['history'][b_start:b_end],\n self.model.queries: kwargs['mn_dev_data']['query'][b_start:b_end],\n self.model.labels: kwargs['mn_dev_data']['label'][b_start:b_end],\n self.model.keep_prob: 1})\n dev_accuracies.append(dev_accuracy)\n dev_losses.append(dev_loss)\n logging.info('epoch: {}\\tdev loss: {}\\tdev accuracy: {}\\ttrain accuracy: {}'.format(\n epoch,\n mean(dev_losses),\n mean(dev_accuracies),\n mean(trn_accuracies)\n ))\n else:\n logging.info('epoch: {}\\ttrain accuracy: {}'.format(epoch, mean(trn_accuracies)))\n self.current_epoch += epochs + 1\n\n def persist(self, path):\n raise NotImplementedError\n\n @classmethod\n def load(cls, path, featurizer, max_history):\n raise NotImplementedError\n\n\nclass MemNetT5Policy(MemNetPolicy):\n def persist(self, path):\n # persist metadata\n if not exists(path):\n mkdir(path)\n with open(join(path, 'MemoryNetwork_metadata.pickle'), 'wb') as metadata_fh:\n pickle.dump((self.history, self.queries, self.labels, self.batch_indexes, self.model.hops,\n self.model.embedding_size, self.current_epoch, self.model_name), metadata_fh)\n # persist model variables\n saver = tf.train.Saver()\n saver.save(self.sess, join(path, \"MemoryNetwork\"))\n\n @classmethod\n def load(cls, path, featurizer, max_history):\n # restore metadata\n with open(join(path, 'MemoryNetwork_metadata.pickle'), 'rb') as metadata_fh:\n history, queries, labels, batch_indexes, hops, embedding_size, current_epoch, model_name = \\\n pickle.load(metadata_fh)\n num_actions = len(labels[0])\n utterance_len = len(history[0][0])\n mem_size = max(len(h) for h in history)\n\n # restore model\n tf.reset_default_graph()\n model = MemoryNetwork(num_actions, utterance_len, embedding_size, mem_size, hops=hops)\n mem_net_policy = cls(history, queries, labels, batch_indexes, model=model,\n embedding_size=embedding_size, hops=hops, encoder=MemNetT5DataAdapter())\n mem_net_policy.current_epoch = current_epoch\n\n saver = tf.train.Saver()\n saver.restore(mem_net_policy.sess, join(path, \"MemoryNetwork\"))\n logger.info('successfully restored model')\n return mem_net_policy\n\n\nclass MemNetT6Policy(MemNetPolicy):\n\n def __init__(self, *args, **kwargs):\n self.db = BabiDB(BABI_T6_KB_FILE)\n super(MemNetT6Policy, self).__init__(*args, **kwargs)\n\n def persist(self, path):\n # persist metadata\n if not exists(path):\n mkdir(path)\n with open(join(path, 'MemoryNetwork_metadata.pickle'), 'wb') as metadata_fh:\n pickle.dump((self.history, self.queries, self.labels, self.batch_indexes, self.model.hops,\n self.model.embedding_size, self.current_epoch, self.model_name, self.encoder.path,\n self.encoder.kb_filename, self.encoder.vocab_filename, self.encoder.w2v_model_filename),\n metadata_fh)\n # persist model variables\n saver = tf.train.Saver()\n saver.save(self.sess, join(path, \"MemoryNetwork\"))\n\n @classmethod\n def load(cls, path, featurizer, max_history):\n # restore metadata\n with open(join(path, 'MemoryNetwork_metadata.pickle'), 'rb') as metadata_fh:\n history, queries, labels, batch_indexes, hops, embedding_size, current_epoch, model_name, nlu_path, \\\n kb_filename, vocab_filename, w2v_model_filename = pickle.load(metadata_fh)\n num_actions = len(labels[0])\n utterance_len = len(history[0][0])\n mem_size = max(len(h) for h in history)\n # restore model\n tf.reset_default_graph()\n model = MemoryNetwork(num_actions, utterance_len, embedding_size, mem_size, hops=hops)\n mem_net_policy = cls(history, queries, labels, batch_indexes, model=model,\n embedding_size=embedding_size, hops=hops, encoder=MemNetT6DataAdapter(nlu_path,\n kb_filename,\n vocab_filename,\n w2v_model_filename))\n mem_net_policy.current_epoch = current_epoch\n\n saver = tf.train.Saver()\n saver.restore(mem_net_policy.sess, join(path, \"MemoryNetwork\"))\n logger.info('successfully restored model')\n return mem_net_policy\n\n def apply_domain_rules(self, p, tracker, domain):\n \"\"\"\n Called after the Memory Network made its prediction. Subclasses can override this method to apply domain\n specific rules to this vector before, potentially redifining the predicted action\n :param p: probability vector over actions\n :param tracker: rasa tracker object\n :param domain: rasa domain object\n :return: probability vector after applying the domain specific rules\n \"\"\"\n # action = domain.action_for_index(argmax(p)).name()\n # entities = {e: v for e, v in tracker.current_slot_values().items() if v}\n # if action == 'api_call':\n # num_results = self.db.num_results(**entities)\n # if num_results == 0: # don't issue api_call\n # p[domain.index_for_action(action)] = 0\n # p = softmax(p)\n return p\n\n\ndef get_args():\n parser = argparse.ArgumentParser(\n description='train or test a MemoryNetwork for bAbI tasks 5 or 6')\n parser.add_argument('--job', choices=['train', 'test'], required=True,\n help='train the network or test an already trained one. Mandatory')\n parser.add_argument('--task', choices=['5', '6'], required=True, help='bAbI task, must be t5 or t6. Mandatory')\n parser.add_argument('--entities', choices=['regex', 'nlu'], required=True,\n help='regex if you want to use basic pattern match to find entities. nlu if you want to use '\n 'Rasa NLU instead. Mandatory')\n parser.add_argument('--features', choices=['williams', 'rasa'], required=True)\n parser.add_argument('--bot-prev', choices=['online', 'offline', 'no'], required=True,\n help=\"'online' to use the actual bot last prediction as a feature. 'offline' to use the ground \"\n \"truth previous prediction instead. 'no' to not use that feature at all\")\n parser.add_argument('--oov', action='store_true', default=False, help='use OOV test file for task 5')\n return parser.parse_args()\n\n\ndef format_babi_data(filename, featurizer):\n \"\"\"\n produces bAbI data in a format suitable for the memory network. Each conversation provides as many training examples\n as turns. Turn i produces a training example that contains the conversation history up to turn i-1, the user\n utterance from turn i and the bot action at turn i\n :param filename: bAbI conversation filename\n :param featurizer: featurizer object\n :return: List of training examples with labels. Each element is a Dictionary['history': h, 'query': q, 'label': l,\n 'entities': e]\n h: List, h[i] is the featurized data point at turn i in the conversation\n q: vector of features representing the current turn (in the same format as the elements of h)\n l: 1 hot vector indicating the bot action at the current turn\n e: Dictionary with the value of each entity at that point in the conversation (None, for those with no value set)\n \"\"\"\n data = []\n\n def prev_bot_utter():\n return '' if i == 0 else story[i-1]['bot']\n for si, story in enumerate(BabiReader.babi_dialogue_iterator(filename)):\n logger.info('featurizing story {}'.format(si))\n h = []\n for i, turn in enumerate(story):\n x = featurizer.featurize(user_text=turn['human'], prev_bot_text=prev_bot_utter(), turn=i)\n data.append({'history': copy(h), 'query': x, 'label': featurizer.bot_features(turn['bot']),\n 'entities': copy(featurizer.slot_values)})\n h.append(x)\n featurizer.reset()\n return data\n\n\ndef build_batches(filename, featurizer, batch_size=32, max_memory_size=8):\n \"\"\"\n Produces batch indexes of points with similar length of history and adds padding so that the history component of\n all points in a batch have the same length\n :param filename: bAbI conversation filename\n :param featurizer: featurizer object\n :param batch_size: number of elements in a batch, unless there are not enough elements, then a batch will have the\n remaining points, with len < batch_size\n :param max_memory_size: max number of previous utterances (bot and user alike) to keep in history for a single\n data point\n :return: history, query, label, entities, batch_indexes. Each one is a list. The ith element of all lists is refers\n to one data point. The lists are sorted by decreasing length of history and adding padding so that all histories in\n the same batch have equal length. batch_indexes is an iterable of Tuple[start, end], where end is not part of the\n current batch but of the next only (i.e. end_i = start_i+1 for all i except the last). Do note the end index of the\n last batch is not checked against the number of elements in the data, therefore this number can be higher.\n This should not be a problem if data is split using the data[start:end] slicing format\n \"\"\"\n data = format_babi_data(filename=filename, featurizer=featurizer)\n max_memory_size = min(max_memory_size, max(len(x['history']) for x in data)) # no point in max_mem > longest h\n batch_size = min(batch_size, len(data))\n data.sort(key=lambda x: len(x['history']), reverse=True)\n history, query, label, entities = [], [], [], []\n batch_memory_size = max_memory_size # redundant, just to comply with PEP8 (batch_mem could be reference before bla)\n for i, x in enumerate(data):\n h, q, l, e = x.values()\n if i % batch_size == 0: # new batch. history length of this first point in the batch determines memory_size\n batch_memory_size = max(1, min(max_memory_size, len(h))) # memory in [1, max_memory_size]\n h = h[::-1][:batch_memory_size][::-1] # take only last memory_size sentences (flip, cut at mem size, flip back)\n pad_size = max(0, batch_memory_size - len(h)) # pad h\n for _ in range(pad_size): # empty histories will thus consist of 1 0 padded memory\n h.append([0] * featurizer.feature_len())\n history.append(h)\n query.append(q)\n label.append(l)\n entities.append(e)\n batch_indexes = list(zip(range(0, len(data) - batch_size, batch_size), range(batch_size, len(data), batch_size)))\n return history, query, label, entities, batch_indexes\n\n\ndef train_t6():\n logging.info(\n 'starting training\\nConfig:\\nhops: {}\\nactions: {}\\nhistory utterance length: {}\\n'\n 'embedding size: {}\\nbatch size: {}\\nmemory size: {}\\nepochs: {}\\ngradient clip norm: {}\\n'\n 'keep prob: {}\\n'.format(hops, num_actions, h_len, embedding_size, batch, mem_size, epochs,\n clip_norm,\n keep_prob))\n saved_batches = 'fabot/custom/memnet/saved_data/t6_trn_memnet_{bot_prev}_{ent}_{feats}_data.pickle'.format(\n bot_prev=args.bot_prev if args.bot_prev != 'online' else 'offline', ent=args.entities, feats=args.features)\n if isfile(saved_batches):\n with open(saved_batches, 'rb') as batches_fh:\n trn_history, trn_query, trn_label, trn_entities, batch_indexes = pickle.load(batches_fh)\n else:\n print('train batches data not found, now recreating it')\n trn_history, trn_query, trn_label, trn_entities, batch_indexes = build_batches(filename=BABI_T6_TRN_FILE,\n featurizer=featurizer,\n batch_size=batch,\n max_memory_size=mem_size)\n print('saving data')\n with open(saved_batches, 'wb') as batches_fh:\n pickle.dump((trn_history, trn_query, trn_label, trn_entities, batch_indexes), batches_fh)\n print('saved')\n saved_batches = 'fabot/custom/memnet/saved_data/t6_dev_memnet_{bot_prev}_{ent}_{feats}_data.pickle'.format(\n bot_prev=args.bot_prev if args.bot_prev != 'online' else 'offline', ent=args.entities, feats=args.features)\n if isfile(saved_batches):\n with open(saved_batches, 'rb') as batches_fh:\n dev_history, dev_query, dev_label, dev_entities, dev_batch_indexes = pickle.load(batches_fh)\n else:\n print('dev batches data not found, now recreating it')\n dev_history, dev_query, dev_label, dev_entities, dev_batch_indexes = build_batches(\n filename=BABI_T6_DEV_FILE, featurizer=featurizer, batch_size=100, max_memory_size=mem_size)\n print('saving data')\n with open(saved_batches, 'wb') as batches_fh:\n pickle.dump((dev_history, dev_query, dev_label, dev_entities, dev_batch_indexes), batches_fh)\n print('saved')\n\n model = MemoryNetwork(num_actions=num_actions, utterance_len=h_len,\n embedding_size=embedding_size, mem_size=mem_size, hops=hops, keep_prob=keep_prob,\n clip_norm=clip_norm)\n highest_dev_acc = 0\n chances2improve = 5\n stop = False\n for epoch in range(epochs):\n if stop:\n break\n batch_indexes = list(batch_indexes)\n trn_accuracies = []\n for i, (b_start, b_end) in enumerate(batch_indexes):\n pred, loss = model.train_step(history_batch=trn_history[b_start:b_end],\n query_batch=trn_query[b_start:b_end],\n label_batch=trn_label[b_start:b_end])\n accuracy = model.sess.run(model.accuracy, feed_dict={model.history: trn_history[b_start:b_end],\n model.queries: trn_query[b_start:b_end],\n model.labels: trn_label[b_start:b_end],\n model.keep_prob: 1})\n if i % print_cycle == 0:\n logging.info('epoch: {}\\tbatch: {}\\tloss: {}\\taccuracy:{}'.format(epoch, i, loss, accuracy))\n trn_accuracies.append(accuracy)\n dev_accuracies, dev_losses = [], []\n for i, (b_start, b_end) in enumerate(dev_batch_indexes):\n _, dev_loss = model.train_step(history_batch=dev_history[b_start:b_end],\n query_batch=dev_query[b_start:b_end],\n label_batch=dev_label[b_start:b_end])\n dev_accuracy = model.sess.run(model.accuracy, feed_dict={model.history: dev_history[b_start:b_end],\n model.queries: dev_query[b_start:b_end],\n model.labels: dev_label[b_start:b_end],\n model.keep_prob: 1})\n dev_accuracies.append(dev_accuracy)\n dev_losses.append(dev_loss)\n dev_acc = mean(dev_accuracies)\n if dev_acc >= highest_dev_acc:\n highest_dev_acc = dev_acc\n chances2improve = 5\n model.persist(persisted_path)\n else:\n chances2improve -= 1\n if chances2improve == 0:\n logger.info('no improvement in dev in more the last 5 epochs, stopping now with the best model so far')\n stop = True\n logging.info('epoch: {}\\tdev loss: {}\\tdev accuracy: {}\\ttrain accuracy: {}'.format(\n epoch,\n mean(dev_losses),\n dev_acc,\n mean(trn_accuracies)\n ))\n\n\ndef train(task='5'):\n if task == '5':\n trn_file = BABI_T5_TRN_FILE\n dev_file = BABI_T5_DEV_FILE\n else:\n trn_file = BABI_T6_TRN_FILE\n dev_file = BABI_T6_DEV_FILE\n logging.info(\n 'starting training\\nConfig:\\nhops: {}\\nactions: {}\\nhistory utterance length: {}\\n'\n 'embedding size: {}\\nbatch size: {}\\nmemory size: {}\\nepochs: {}\\ngradient clip norm: {}\\n'\n 'keep prob: {}\\n'.format(hops, num_actions, h_len, embedding_size, batch, mem_size, epochs,\n clip_norm,\n keep_prob))\n saved_batches = 'fabot/custom/memnet/saved_data/t{task}_trn_memnet_{bot_prev}_{ent}_{feats}_data.pickle'.format(\n bot_prev=args.bot_prev if args.bot_prev != 'online' else 'offline', task=task, ent=args.entities,\n feats=args.features)\n if isfile(saved_batches):\n with open(saved_batches, 'rb') as batches_fh:\n trn_history, trn_query, trn_label, trn_entities, batch_indexes = pickle.load(batches_fh)\n else:\n print('train batches data not found, now recreating it')\n trn_history, trn_query, trn_label, trn_entities, batch_indexes = build_batches(filename=trn_file,\n featurizer=featurizer,\n batch_size=batch,\n max_memory_size=mem_size)\n print('saving data')\n with open(saved_batches, 'wb') as batches_fh:\n pickle.dump((trn_history, trn_query, trn_label, trn_entities, batch_indexes), batches_fh)\n print('saved')\n saved_batches = 'fabot/custom/memnet/saved_data/t{task}_dev_memnet_{bot_prev}_{ent}_{feats}_data.pickle'.format(\n bot_prev=args.bot_prev if args.bot_prev != 'online' else 'offline', task=task, ent=args.entities,\n feats=args.features)\n if isfile(saved_batches):\n with open(saved_batches, 'rb') as batches_fh:\n dev_history, dev_query, dev_label, dev_entities, dev_batch_indexes = pickle.load(batches_fh)\n else:\n print('dev batches data not found, now recreating it')\n dev_history, dev_query, dev_label, dev_entities, dev_batch_indexes = build_batches(\n filename=dev_file, featurizer=featurizer, batch_size=100, max_memory_size=mem_size)\n print('saving data')\n with open(saved_batches, 'wb') as batches_fh:\n pickle.dump((dev_history, dev_query, dev_label, dev_entities, dev_batch_indexes), batches_fh)\n print('saved')\n\n model = MemoryNetwork(num_actions=num_actions, utterance_len=h_len,\n embedding_size=embedding_size, mem_size=mem_size, hops=hops, keep_prob=keep_prob,\n clip_norm=clip_norm)\n highest_dev_acc = 0\n chances2improve = 5\n stop = False\n for epoch in range(epochs):\n if stop:\n break\n batch_indexes = list(batch_indexes)\n trn_accuracies = []\n for i, (b_start, b_end) in enumerate(batch_indexes):\n pred, loss = model.train_step(history_batch=trn_history[b_start:b_end],\n query_batch=trn_query[b_start:b_end],\n label_batch=trn_label[b_start:b_end])\n accuracy = model.sess.run(model.accuracy, feed_dict={model.history: trn_history[b_start:b_end],\n model.queries: trn_query[b_start:b_end],\n model.labels: trn_label[b_start:b_end],\n model.keep_prob: 1})\n if i % print_cycle == 0:\n logging.info('epoch: {}\\tbatch: {}\\tloss: {}\\taccuracy:{}'.format(epoch, i, loss, accuracy))\n trn_accuracies.append(accuracy)\n dev_accuracies, dev_losses = [], []\n for i, (b_start, b_end) in enumerate(dev_batch_indexes):\n _, dev_loss = model.train_step(history_batch=dev_history[b_start:b_end],\n query_batch=dev_query[b_start:b_end],\n label_batch=dev_label[b_start:b_end])\n dev_accuracy = model.sess.run(model.accuracy, feed_dict={model.history: dev_history[b_start:b_end],\n model.queries: dev_query[b_start:b_end],\n model.labels: dev_label[b_start:b_end],\n model.keep_prob: 1})\n dev_accuracies.append(dev_accuracy)\n dev_losses.append(dev_loss)\n dev_acc = mean(dev_accuracies)\n if dev_acc > highest_dev_acc:\n highest_dev_acc = dev_acc\n chances2improve = 5\n model.persist(persisted_path)\n else:\n chances2improve -= 1\n if chances2improve == 0:\n logger.info('no improvement in dev in more the last 5 epochs, stopping now with the best model so far')\n stop = True\n logging.info('epoch: {}\\tdev loss: {}\\tdev accuracy: {}\\ttrain accuracy: {}'.format(\n epoch,\n mean(dev_losses),\n dev_acc,\n mean(trn_accuracies)\n ))\n\n\ndef bot_prev_utter(story, turn, prev_pred):\n if turn == 0:\n return ''\n elif args.bot_prev == 'offline':\n return story[turn - 1]['bot']\n elif args.bot_prev == 'online':\n return prev_pred\n elif args.bot_prev == 'no':\n return prev_pred # just to update the current rest correctly\n else:\n raise ValueError('bot-prev argument should be online, offline or no. Received {}'.format(args.bot_prev))\n\n\ndef test(task='6'):\n model = MemoryNetwork.load(persisted_path)\n results = []\n total_act_matches, total_literal_matches = 0, 0\n perfect_act_dialogs, perfect_literal_dialogs = 0, 0\n if task == '6':\n tst_file = BABI_T6_TST_FILE\n total_turns = 11237\n total_dialogs = 1117\n else: # i.e. 5\n tst_file = BABI_T5_TST_FILE if not args.oov else BABI_T5_TST_OOV_FILE\n total_turns = 18398 if not args.oov else 18368\n total_dialogs = 1000\n for story in BabiReader.babi_dialogue_iterator(tst_file):\n featurizer.reset()\n story_results = []\n dialog_act_matches, dialog_literal_matches = 0, 0\n h = []\n prev_pred = '' # initial value for the previous prediction\n for i, turn in enumerate(story):\n x = featurizer.featurize(user_text=turn['human'], prev_bot_text=bot_prev_utter(story, i, prev_pred), turn=i)\n prediction = model.prediction(history=h if len(h) > 0 else [[0] * featurizer.feature_len()], query=x)\n\n turn_results = dict()\n turn_results['human'] = turn['human']\n turn_results['target'] = turn['bot']\n actual_da = featurizer.get_bot_act(turn['bot'])\n predicted_da = featurizer.id2act(argmax(prediction))\n prev_pred = featurizer.act2pattern(predicted_da)[1].format(**featurizer.slots())\n turn_results['actual'] = prev_pred\n turn_results['literal_match'] = re.match(pattern=turn_results['actual'],\n string=turn_results['target']) is not None\n turn_results['act_match'] = actual_da == predicted_da\n story_results.append(turn_results)\n\n h.append(x)\n # if len(h) > mem_size:\n # h = h[::-1][:mem_size][::-1]\n\n dialog_act_matches += int(turn_results['act_match'])\n dialog_literal_matches += int(turn_results['literal_match'])\n total_act_matches += dialog_act_matches\n total_literal_matches += dialog_literal_matches\n perfect_act_dialogs += int(dialog_act_matches == len(story))\n perfect_literal_dialogs += int(dialog_literal_matches == len(story))\n results.append(story_results)\n with open('fabot/custom/memnet/results/tst_t{task}_memnet_{bot_prev}_{ent}_{feats}_{oov}results.json'.format(\n task=task, ent=args.entities, feats=args.features, bot_prev=args.bot_prev, oov='oov_' if args.oov else ''),\n 'w') as fh:\n json.dump(results, fh, indent=2)\n logging.info('test act match results:\\n'\n 'accuracy: {}/{} ({:.2%})\\tperfect dialogs: {}/{} ({})'.format(\n total_act_matches, total_turns, total_act_matches / total_turns, perfect_act_dialogs, total_dialogs,\n perfect_act_dialogs / total_dialogs))\n logging.info('test literal match results:\\n'\n 'accuracy: {}/{} ({:.2%})\\tperfect dialogs: {}/{} ({})'.format(\n total_literal_matches, total_turns, total_literal_matches / total_turns, perfect_literal_dialogs, total_dialogs,\n perfect_literal_dialogs / total_dialogs))\n\n\nif __name__ == '__main__':\n args = get_args()\n if args.task == '6':\n if args.features == 'williams':\n features = {'use_bow': True, 'use_turn': True, 'use_bot_utter': args.bot_prev != 'no',\n 'use_embeddings': True, 'use_intent': False, 'use_nlu_entity_extractor': args.entities == 'nlu',\n 'use_entities': True, 'use_context': True}\n else: # Rasa\n features = {'use_bow': False, 'use_turn': True, 'use_bot_utter': args.bot_prev != 'no',\n 'use_embeddings': False, 'use_intent': True, 'use_nlu_entity_extractor': args.entities == 'nlu',\n 'use_entities': True, 'use_context': True}\n persisted_path = PERSISTED_MEMNET_PATH.format(\n bot_prev=args.bot_prev if args.bot_prev != 'online' else 'offline', task=args.task, ent=args.entities,\n feats=args.features)\n featurizer = T6Featurizer(**features)\n num_actions = T6Featurizer.num_actions()\n h_len = featurizer.feature_len()\n\n print_cycle = 100\n hops = 2\n embedding_size = 100\n batch = 32\n mem_size = 9\n epochs = 35\n clip_norm = 1.\n keep_prob = 0.8\n if args.job == 'train':\n train(task=args.task)\n if args.job == 'test':\n test(args.task)\n if args.task == '5':\n if args.features == 'williams':\n features = {'use_bow': True, 'use_turn': True, 'use_bot_utter': args.bot_prev != 'no',\n 'use_embeddings': True, 'use_intent': False, 'use_nlu_entity_extractor': args.entities == 'nlu',\n 'use_entities': True, 'use_context': False, 'use_oov': args.oov}\n else: # Rasa\n features = {'use_bow': False, 'use_turn': True, 'use_bot_utter': args.bot_prev != 'no',\n 'use_embeddings': False, 'use_intent': True, 'use_nlu_entity_extractor': args.entities == 'nlu',\n 'use_entities': True, 'use_context': False, 'use_oov': args.oov}\n featurizer = T5Featurizer(**features)\n persisted_path = PERSISTED_MEMNET_PATH.format(\n bot_prev=args.bot_prev if args.bot_prev != 'online' else 'offline', task=args.task, ent=args.entities,\n feats=args.features)\n num_actions = T5Featurizer.num_actions()\n h_len = featurizer.feature_len()\n\n print_cycle = 100\n hops = 2\n embedding_size = 100\n batch = 32\n mem_size = 9\n epochs = 35\n clip_norm = 1.\n keep_prob = 0.8\n if args.job == 'train':\n train(task=args.task)\n if args.job == 'test':\n test(args.task)\n\n", "sub_path": "babit6/fabot/custom/memnet/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 52948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.random_normal_initializer", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.tile", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.tile", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.summary.tensor_summary", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.softmax", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 126, "usage_type": "attribute"}, {"api_name": "tensorflow.arg_max", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_global_norm", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 138, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 179, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 183, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "json.load", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 194, "usage_type": "attribute"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 195, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 195, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 198, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 200, "usage_type": "call"}, {"api_name": "rasa_core.policies.Policy", "line_number": 204, "usage_type": "name"}, {"api_name": "tensorflow.random_normal_initializer", "line_number": 209, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 248, "usage_type": "call"}, {"api_name": "rasa_core.events.ActionExecuted", "line_number": 281, "usage_type": "argument"}, {"api_name": "rasa_core.actions.action.ACTION_LISTEN_NAME", "line_number": 284, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 353, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 360, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 360, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_global_norm", "line_number": 363, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 365, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 378, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 396, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 399, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 413, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 414, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 415, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 416, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 419, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 419, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 425, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 427, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 433, "usage_type": "call"}, {"api_name": "fabot.custom.memnet.data_utils.MemNetT5DataAdapter", "line_number": 436, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 439, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 439, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 440, "usage_type": "call"}, {"api_name": "data.database.BabiDB", "line_number": 448, "usage_type": "call"}, {"api_name": "globals.BABI_T6_KB_FILE", "line_number": 448, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 453, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 454, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 455, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 456, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 461, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 461, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 462, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 467, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 469, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 474, "usage_type": "call"}, {"api_name": "fabot.custom.memnet.data_utils.MemNetT6DataAdapter", "line_number": 477, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 483, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 483, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 484, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 508, "usage_type": "call"}, {"api_name": "data.feature_factory", "line_number": 538, "usage_type": "name"}, {"api_name": "data.babi_reader.BabiReader.babi_dialogue_iterator", "line_number": 542, "usage_type": "call"}, {"api_name": "data.babi_reader.BabiReader", "line_number": 542, "usage_type": "name"}, {"api_name": "data.feature_factory.append", "line_number": 547, "usage_type": "call"}, {"api_name": "data.feature_factory", "line_number": 547, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 547, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 548, "usage_type": "call"}, {"api_name": "data.feature_factory", "line_number": 551, "usage_type": "name"}, {"api_name": "data.feature_factory", "line_number": 571, "usage_type": "name"}, {"api_name": "data.feature_factory", "line_number": 572, "usage_type": "name"}, {"api_name": "data.feature_factory", "line_number": 573, "usage_type": "argument"}, {"api_name": "data.feature_factory.sort", "line_number": 574, "usage_type": "call"}, {"api_name": "data.feature_factory", "line_number": 574, "usage_type": "name"}, {"api_name": "data.feature_factory", "line_number": 577, "usage_type": "argument"}, {"api_name": "data.feature_factory", "line_number": 589, "usage_type": "argument"}, {"api_name": "logging.info", "line_number": 594, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 602, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 604, "usage_type": "call"}, {"api_name": "globals.BABI_T6_TRN_FILE", "line_number": 607, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 613, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 617, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 619, "usage_type": "call"}, {"api_name": "globals.BABI_T6_DEV_FILE", "line_number": 623, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 626, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 649, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 662, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 672, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 674, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 676, "usage_type": "call"}, {"api_name": "globals.BABI_T5_TRN_FILE", "line_number": 682, "usage_type": "name"}, {"api_name": "globals.BABI_T5_DEV_FILE", "line_number": 683, "usage_type": "name"}, {"api_name": "globals.BABI_T6_TRN_FILE", "line_number": 685, "usage_type": "name"}, {"api_name": "globals.BABI_T6_DEV_FILE", "line_number": 686, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 687, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 696, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 698, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 707, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 712, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 714, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 721, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 744, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 757, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 769, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 771, "usage_type": "call"}, {"api_name": "globals.BABI_T6_TST_FILE", "line_number": 794, "usage_type": "name"}, {"api_name": "globals.BABI_T5_TST_FILE", "line_number": 798, "usage_type": "name"}, {"api_name": "globals.BABI_T5_TST_OOV_FILE", "line_number": 798, "usage_type": "name"}, {"api_name": "data.babi_reader.BabiReader.babi_dialogue_iterator", "line_number": 801, "usage_type": "call"}, {"api_name": "data.babi_reader.BabiReader", "line_number": 801, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 815, "usage_type": "call"}, {"api_name": "re.match", "line_number": 818, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 837, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 838, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 842, "usage_type": "call"}, {"api_name": "globals.PERSISTED_MEMNET_PATH.format", "line_number": 859, "usage_type": "call"}, {"api_name": "globals.PERSISTED_MEMNET_PATH", "line_number": 859, "usage_type": "name"}, {"api_name": "data.feature_factory.T6Featurizer", "line_number": 862, "usage_type": "call"}, {"api_name": "data.feature_factory.T6Featurizer.num_actions", "line_number": 863, "usage_type": "call"}, {"api_name": "data.feature_factory.T6Featurizer", "line_number": 863, "usage_type": "name"}, {"api_name": "data.feature_factory.T5Featurizer", "line_number": 887, "usage_type": "call"}, {"api_name": "globals.PERSISTED_MEMNET_PATH.format", "line_number": 888, "usage_type": "call"}, {"api_name": "globals.PERSISTED_MEMNET_PATH", "line_number": 888, "usage_type": "name"}, {"api_name": "data.feature_factory.T5Featurizer.num_actions", "line_number": 891, "usage_type": "call"}, {"api_name": "data.feature_factory.T5Featurizer", "line_number": 891, "usage_type": "name"}]} +{"seq_id": "247204635", "text": "import pygame.midi as m\ndef main():\n m.init()\n i_num = m.get_count()\n for i in range(i_num):\n print(i)\n print(m.get_device_info(i))\n m.quit()\n exit()\n\nif __name__==\"__main__\":\n main()\n", "sub_path": "midi-dev.py", "file_name": "midi-dev.py", "file_ext": "py", "file_size_in_byte": 216, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.midi.init", "line_number": 3, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 3, "usage_type": "name"}, {"api_name": "pygame.midi.get_count", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 4, "usage_type": "name"}, {"api_name": "pygame.midi.get_device_info", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 7, "usage_type": "name"}, {"api_name": "pygame.midi.quit", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "227820336", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# @Time : 2022/11/28 15:20\n# @Author : ZANWEB\n# @File : DfMainDailyReportCode in PyCharm\n# @IDE : PyCharm\n# @Function :\nimport sys\n\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import QEvent, pyqtSlot, Qt, pyqtSignal\nfrom PyQt5.QtGui import QColor, QBrush\nfrom PyQt5.QtWidgets import QDialog, QApplication, QWidget, QMessageBox, QTableWidgetItem, QLineEdit, QCheckBox, \\\n QTableWidget, QVBoxLayout, QPushButton, QMainWindow\n\nfrom DBbase import dbFunctions\nfrom QtExt.ExtWidget import ExtendedTableWidget\nfrom df_main_daily_report import ReportDailyUI\nfrom DBbase.dbFunctions import (df_t_shift_read, df_t_emp_read, df_t_wc_read,\n df_t_capacity_read, df_t_mhr_read, t_mhr_many_insert,\n t_records_many_insert, df_t_record_read, t_mhr_update_many, t_record_update_many,\n t_record_del_many)\nfrom QtExt.ExtFun import is_decimal, diff_2_dic\n\n\nclass MyDialog(QDialog):\n def __init__(self, data):\n super().__init__()\n\n self._signal = pyqtSignal(list)\n self.btn_ok = None\n self.table = None\n self.data = data\n self.init_ui()\n\n def init_ui(self):\n self.setWindowTitle('人员列表')\n v_box = QVBoxLayout()\n self.resize(400, 500)\n self.table = QTableWidget()\n self.table.setColumnCount(len(self.data[0])+1)\n self.table.setRowCount(len(self.data))\n\n headers = []\n for i in range(len(self.data[0])):\n item = QTableWidgetItem(str(list(self.data[0].keys())[i]))\n headers.append(item)\n self.table.setHorizontalHeaderItem(i, item)\n self.table.setHorizontalHeaderItem(len(self.data[0]), QTableWidgetItem('选择'))\n\n for i in range(len(self.data)):\n for j in range(len(headers)):\n item = QTableWidgetItem(str(list(self.data[i].values())[j]))\n self.table.setItem(i, j, item)\n\n check_box_item = QTableWidgetItem()\n check_box_item.setFlags(Qt.ItemIsUserCheckable | Qt.ItemIsEnabled)\n check_box_item.setCheckState(Qt.Unchecked)\n self.table.setItem(i, j + 1, check_box_item)\n # set checkbox to column 1 and right-align text of all columns except the first one\n # if j == 0:\n # self.table.setItem(i, j + 1, check_box_item)\n # continue\n self.btn_ok = QPushButton('确定')\n v_box.addWidget(self.table)\n v_box.addWidget(self.btn_ok)\n self.btn_ok.clicked.connect(self.dlg_close)\n self.setLayout(v_box)\n self.show()\n\n def dlg_close(self):\n obj = self.get_checked_rows()\n # self._signal.emit(obj)\n self.close()\n\n def get_selected_rows(self):\n selected_rows = []\n for item_ in self.table.selectedItems():\n row = item_.row()\n if row not in selected_rows:\n selected_rows.append(row)\n return selected_rows\n\n def get_checked_rows(self):\n checked_id = []\n num = self.table.rowCount()\n for i in range(num):\n if self.table.item(i, 2).checkState():\n checked_id.append(self.table.item(i, 0).text())\n\n return checked_id\n\n\nclass DailyReport(QDialog, ReportDailyUI):\n def __init__(self, _user_info):\n super(DailyReport, self).__init__()\n self.emp = None\n self.old_records = None\n self.old_mhr = None\n self.capacity_info = None\n self.wc = None\n self.user_info = _user_info\n self.setup_ui(self)\n self.connect_()\n self.data_init_()\n\n def data_init_(self):\n shift = df_t_shift_read(self.user_info, ['name'])\n shift = [x['name'] for x in shift]\n self.edit_shift.addItems(shift)\n emp = df_t_emp_read(self.user_info, ['id', 'name'])\n self.emp = emp\n emp = ['|'.join([str(x['id']), x['name'].strip()]) for x in emp]\n self.edit_emp.addItems(emp)\n self.edit_emp.clearEditText()\n wc = df_t_wc_read(self.user_info, ['name'])\n wc = [x['name'].strip() for x in wc]\n self.edit_wc.addItems(wc)\n self.edit_wc.clearEditText()\n self.edit_wc.installEventFilter(self)\n\n def connect_(self):\n self.edit_emp.activated.connect(self.edit_emp_activated)\n self.edit_wc.currentTextChanged.connect(self.edit_wc_text_changed)\n self.btn_save.clicked.connect(self.save)\n self.edit_table.rowInserted.connect(self.inserted_row_triggered)\n # self.btn_query.clicked.connect(self.query)\n self.check_vendor.stateChanged.connect(lambda: (self.edit_vendors.setEnabled(self.check_vendor.checkState()),\n self.btn_select_vendors.setEnabled(\n self.check_vendor.checkState())))\n self.edit_vendors.editingFinished.connect(self.check_vendor_exist)\n self.edit_scaner.editingFinished.connect(self.scanned)\n self.btn_select_vendors.clicked.connect(self.on_btn_select_vendors_clicked)\n\n def on_btn_select_vendors_clicked(self):\n # 选择辅助工\n # create an instance of our custom dialog window class and pass it the sample data\n my_dialog = MyDialog(self.emp)\n my_dialog.btn_ok.clicked.connect(self.emmit_)\n # show the dialog window modally (i.e. user cannot interact with parent window until this is closed)\n\n result = my_dialog.exec_()\n select_rows = my_dialog.get_checked_rows()\n # print(result, select_rows)\n str_id = ','.join(select_rows)\n self.edit_vendors.setText(str_id)\n\n def emmit_(self):\n print('ok')\n\n @pyqtSlot()\n def scanned(self):\n # 检查是否符合扫描条件\n empty_key, info_ = self.check_main_keys()\n if empty_key:\n QMessageBox.warning(self, '警告', '请先填入必要信息.')\n # 判断扫描的数据是否符合条件\n txt = self.edit_scaner.text()\n if not txt:\n # QMessageBox.warning(self, '警告', '没有扫描数据.')\n self.edit_scaner.setFocus()\n return\n else:\n tmp = {}\n show_fields = ['*']\n params_name = ['FA_JOB']\n params_type = ['%s']\n if txt.find(':'):\n if txt.find('|') >= 0:\n txt_ = txt.split('|')\n params_name = ['BATCH_ID', 'SORT_ID', 'FA_JOB']\n params_type = ['%s', '%s', '%s']\n params = (txt_[0], txt_[1], txt_[2])\n else:\n params = txt\n try:\n rs = dbFunctions.t_oracle_read(self.user_info, show_fields, params_name, params_type, params)\n tmp['so'] = rs[0]['ORDER_NUM']\n tmp['batch'] = rs[0]['BATCH_ID']\n tmp['sort'] = rs[0]['SORT_ID']\n tmp['fa_job'] = rs[0]['FA_JOB']\n tmp['fa_qty'] = rs[0]['FA_QTY']\n tmp['rest_qty'] = int(tmp['fa_qty'] - (rs[0]['daily_report_qty'] if rs[0]['daily_report_qty'] else 0))\n tmp['unit_length'] = rs[0]['UNIT_LENGTH']\n tmp['unit_weight'] = float(rs[0]['UNIT_WEIGHT'])\n tmp['t_weight'] = tmp['unit_weight'] * tmp['rest_qty'] / 1000\n tmp['t_length'] = tmp['unit_length'] * tmp['rest_qty'] / 1000\n tmp['pkg'] = rs[0]['BUNDLE'] if rs[0]['BUNDLE'] else '0'\n except Exception as e:\n QMessageBox.warning(self, '警告:', f'未查询到扫描的信息!\\n{e}')\n return\n\n elif txt.find('|'):\n txt_1 = txt.split('|')\n tmp['batch'] = txt_1[0]\n tmp['so'] = txt_1[1]\n tmp['wo'] = txt_1[2]\n else:\n tmp['so'] = txt\n\n # edit_table 新增一行\n if len(tmp.keys()) > 1:\n num = self.edit_table.rowCount()\n if self.edit_table.item(num - 1, 6):\n self.edit_table.insertRow(num)\n # 按数据内容, 分类填入edit_tale\n last_row = self.edit_table.rowCount() - 1\n self.edit_table.setItem(last_row, 0, QTableWidgetItem(str(tmp['so'])))\n self.edit_table.setItem(last_row, 1, QTableWidgetItem(str(tmp['batch'])))\n self.edit_table.setItem(last_row, 2, QTableWidgetItem(str(tmp['pkg'])))\n self.edit_table.setItem(last_row, 3, QTableWidgetItem(str(tmp['t_length'])))\n self.edit_table.setItem(last_row, 4, QTableWidgetItem(str(tmp['t_weight'])))\n self.edit_table.setItem(last_row, 6, QTableWidgetItem(str(tmp['fa_job'])))\n self.edit_table.setItem(last_row, 7, QTableWidgetItem(str(tmp['sort'])))\n elif len(tmp.keys()) == 1:\n num = self.edit_table.rowCount()\n self.edit_table.inserRow(num)\n last_row = self.edit_table.rowCount() - 1\n self.edit_table.setItem(last_row, 0, QTableWidgetItem(str(tmp['so'])))\n else:\n return\n\n self.edit_table.resizeColumnsToContents()\n\n print(self.edit_scaner.text())\n self.edit_scaner.setText('')\n self.edit_scaner.setFocus()\n\n @pyqtSlot()\n def check_vendor_exist(self):\n if self.edit_vendors.text():\n txt = self.edit_vendors.text().strip().strip(',')\n vendors_ = []\n if txt.find(','):\n vendors_ = txt.split(',')\n else:\n vendors_[0] = txt\n emp_ids = [x['id'] for x in self.emp]\n for vendor in vendors_:\n if vendor == '':\n QMessageBox.warning(self, '警告:', f'输入有误, {vendor}.!\\n请重新输入!')\n self.edit_vendors.setFocus()\n elif int(vendor) not in emp_ids:\n QMessageBox.warning(self, '警告:', f'{vendor} 不在员工列表中!\\n请重新输入!')\n self.edit_vendors.setFocus()\n else:\n QMessageBox.warning(self, '警告:', '请正确输入辅助工编号!')\n self.edit_vendors.setFocus()\n\n @pyqtSlot()\n def query(self):\n # Daily query\n print('query')\n\n @pyqtSlot(int)\n def inserted_row_triggered(self, row):\n item_1 = QTableWidgetItem()\n item_1.setFlags(QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)\n self.edit_table.setItem(row, 5, item_1)\n\n @pyqtSlot()\n def save(self):\n # 检查has_vendors勾选, 但没有写辅助工编号的情况:\n if self.check_vendor.isChecked():\n if not self.edit_vendors.text():\n QMessageBox.warning(self, '警告:', '你勾选了有辅助工, 请输入辅助工的工号!')\n self.edit_vendors.setFocus()\n return\n mhr_m = {\n 'date': self.edit_date.date().toPyDate().strftime('%Y-%m-%d'),\n 'emp': int(self.edit_emp.currentText().split('|')[0]),\n 'shift': self.edit_shift.currentText(),\n 'wc': self.edit_wc.currentText()\n }\n # 检查获取列表内容\n if self.capacity_info[0]['unit'] and self.capacity_info[0]['unit'] != ' ':\n details = []\n t = self.edit_table\n r = t.rowCount()\n c = t.columnCount()\n for row in range(r):\n for col in range(c):\n if t.item(row, col) is None:\n QMessageBox.warning(self, '数据:', '请先正确添加数据!')\n t.setCurrentCell(row, col)\n return\n if col in [0, 1]:\n cell_content = t.item(row, col)\n cell_text = cell_content.text()\n if not cell_text.isdigit():\n QMessageBox.warning(self, '数据:', '请检查数据是否正确!')\n t.setCurrentCell(row, col)\n return\n else:\n if col in [3, 4]:\n cell_content = t.item(row, col)\n cell_text = cell_content.text()\n if self.capacity_info[0]['unit'] == '吨' or self.capacity_info[0]['is_line']:\n if not (is_decimal(cell_text) or cell_text.isdigit()):\n QMessageBox.warning(self, '数据:', '请检查数据是否正确!')\n t.setCurrentCell(row, col)\n return\n else:\n if not cell_text.isdigit():\n QMessageBox.warning(self, '数据:', '请检查数据是否正确!')\n t.setCurrentCell(row, col)\n return\n for row in range(r):\n detail_ = {\n 'so': int(t.item(row, 0).text()),\n 'batch_sort': int(t.item(row, 1).text()),\n 'pkg_part': t.item(row, 2).text(),\n 'length': float(t.item(row, 3).text()),\n 'wt_qty': (float(t.item(row, 4).text())\n if self.capacity_info[0]['unit'] == '吨' or\n self.capacity_info[0]['is_line'] else int(t.item(row, 4).text())),\n 'fa_job': t.item(row, 6).text(),\n 'sort': t.item(row, 7).text(),\n 'auto': int(t.item(row, 5).text()) if t.item(row, 5).text() else ''\n }\n detail = {**mhr_m, **detail_}\n details.append(detail)\n lengths = [d['length'] for d in details]\n total_lengths = sum(lengths)\n wts = [d['wt_qty'] for d in details]\n total_wts = sum(wts)\n if self.capacity_info[0]['is_line']:\n self.edit_vol.setText(str(total_lengths))\n self.edit_tons.setText(str(total_wts))\n else:\n if self.capacity_info[0]['unit'] == '米':\n self.edit_vol.setText(str(total_lengths))\n else:\n self.edit_vol.setText(str(total_wts))\n\n mhr_o = {\n 'net': float(self.edit_net.text()),\n 'self_maintenance': float(self.edit_self_maintenance.text()),\n 'maintenance': float(self.edit_maintenance.text()),\n 'debug': float(self.edit_debug.text()),\n 'material': float(self.edit_material.text()),\n 'others': float(self.edit_others.text()),\n 'is_main': self.check_main.checkState(),\n 'is_weekend': self.check_weekend.checkState(),\n 'has_vendor': self.check_vendor.checkState(),\n 'vol': float(self.edit_vol.text() if self.edit_vol.text() else 0.0),\n 'tons': float(self.edit_tons.text()) if self.edit_tons.isEnabled() else 0.0,\n # 'tons': float(self.edit_tons.text()),\n 'capacity_update': self.capacity_info[0]['capacity_update'],\n 'remark': self.edit_remark.text(),\n 'waiting': float(self.edit_waiting.text()),\n 'vendors': self.edit_vendors.text() if self.edit_vendors.text() else None\n }\n mhr = {**mhr_m, **mhr_o}\n # 1. 检查是否有在数据库中\n # 1.1 如果有, update (mhr, records)\n # 1.2 如果没有, insert\n # 1.3 还要考虑, records中部分有, 部分没有的情况)\n\n # 处理mhr信息\n if self.old_mhr:\n # 比较\n dict1 = self.old_mhr[0]\n dict2 = mhr\n diff = diff_2_dic(dict1, dict2)\n if diff:\n set_names_ = [x[0] for x in diff]\n condition_names_ = list(mhr_m.keys())\n values_ = [x[2] for x in diff]\n condition_values_ = [mhr_m[x] for x in condition_names_]\n values_.extend(condition_values_)\n values_ = [tuple(values_)]\n result = t_mhr_update_many(self.user_info, set_names_, condition_names_, values_)\n if result == -1:\n QMessageBox.warning(self, '数据库:', 'mhr数据未入库!')\n return\n else:\n # 导入数据库\n r_mhr = t_mhr_many_insert(self.user_info, [mhr])\n if r_mhr == -1:\n QMessageBox.warning(self, '导入数据库错误:', '未能导入工时信息!')\n return\n # 处理数据表信息:\n if self.capacity_info[0]['unit'] and self.capacity_info[0]['unit'] != ' ':\n if self.old_records:\n new_records = [x for x in details if not x['auto']]\n old_records = [x for x in details if x['auto']]\n auto_in_now = [x['auto'] for x in old_records]\n auto_in_old = [x['auto'] for x in self.old_records]\n auto_to_del = [x for x in auto_in_old if x not in auto_in_now]\n # 删除\n if auto_to_del:\n values_ = [tuple(auto_to_del)]\n result = t_record_del_many(self.user_info, ['auto'], values_)\n if result == -1:\n QMessageBox.warning(self, '删除��据错误:', '未能删除数据!')\n return\n # 添加\n if new_records:\n result = t_records_many_insert(self.user_info, new_records)\n if result == -1:\n QMessageBox.warning(self, '导入数据错误:', '未能导入新工单数据!')\n return\n # 更新\n if auto_in_now:\n set_names_ = [x for x in list(old_records[0].keys()) if x != 'auto']\n condition_names_ = ['auto']\n # del old_records['auto']\n # values_ = [tuple(x.values()) for x in old_records]\n records_ = old_records\n values_ = [tuple(x.values()) for x in records_]\n # values_ = [tuple(x) for x in auto_in_now]\n result = t_record_update_many(self.user_info, set_names_, condition_names_, values_)\n if result == -1:\n QMessageBox.warning(self, '更新数据库错误:', '未能更新工单信息!')\n return\n pass\n else:\n r_record = t_records_many_insert(self.user_info, details)\n if r_record == -1:\n QMessageBox.warning(self, '导入数据库错误:', '未能导入工单信息!')\n return\n\n self.edit_table.setRowCount(0)\n self.edit_table.setColumnCount(0)\n\n records_value = self.get_records()\n if records_value:\n keys_to_delete = ['date', 'emp', 'shift', 'wc']\n for record in records_value:\n for index, key_ in enumerate(keys_to_delete):\n del record[key_]\n self.detail_format()\n self.fill_records(records_value)\n self.old_records = records_value\n\n QMessageBox.warning(self, '恭喜:', '数据入库成功!')\n\n def eventFilter(self, obj, event):\n if obj == self.edit_wc:\n if event.type() == QEvent.FocusOut:\n self.edit_wc_lost_focus()\n return QWidget.eventFilter(self, obj, event)\n\n def edit_wc_lost_focus(self):\n if self.capacity_info:\n vol = self.capacity_info[0]['unit']\n self.label_vol.setText(vol)\n std_ton = self.capacity_info[0]['std_ton']\n if std_ton:\n self.edit_tons.setEnabled(True)\n else:\n self.edit_tons.setEnabled(False)\n\n def get_mhr(self):\n params = (\n self.edit_date.date().toPyDate().strftime('%Y-%m-%d'),\n int(self.edit_emp.currentText().split('|')[0]) if self.edit_emp.currentText() else None,\n self.edit_shift.currentText(),\n self.edit_wc.currentText())\n result = df_t_mhr_read(self.user_info, ['*'], ['date', 'emp', 'shift', 'wc'], ['%s', '%s', '%s', '%s'], params)\n return result\n\n def get_records(self):\n params = (\n self.edit_date.date().toPyDate().strftime('%Y-%m-%d'),\n int(self.edit_emp.currentText().split('|')[0]) if self.edit_emp.currentText() else None,\n self.edit_shift.currentText(),\n self.edit_wc.currentText())\n result = df_t_record_read(self.user_info, ['*'], ['date', 'emp', 'shift', 'wc'], ['%s', '%s', '%s', '%s'],\n params)\n return result\n\n def fill_mhr(self, mhr_):\n for key in mhr_[0].keys():\n ob = self.findChild(QLineEdit, key)\n if ob:\n if ob.isEnabled():\n ob.setText(str(mhr_[0][key]))\n ob = self.findChild(QCheckBox, key)\n if ob:\n ob.setChecked(mhr_[0][key])\n\n def fill_records(self, records):\n row = len(records)\n column = len(records[0])\n self.edit_table.setRowCount(row)\n self.edit_table.setColumnCount(column)\n brush = QBrush()\n brush.setColor(QColor(55, 55, 55))\n brush.setStyle(Qt.SolidPattern)\n for row, item in enumerate(records):\n for col, key in enumerate(item):\n value = item[key]\n if key == 'auto':\n item_1 = QTableWidgetItem(str(value))\n item_1.setFlags(QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)\n item_1.setBackground(brush)\n self.edit_table.setItem(row, col, item_1)\n elif key != '吨' or key != 'is_line':\n if col == 4:\n self.edit_table.setItem(row, col, QTableWidgetItem(str(value)))\n else:\n self.edit_table.setItem(row, col, QTableWidgetItem(str(value)))\n\n else:\n self.edit_table.setItem(row, col, QTableWidgetItem(str(value)))\n\n def check_main_keys(self):\n mhr_m = {\n 'date': self.edit_date.date().toPyDate().strftime('%Y-%m-%d'),\n 'emp': int(self.edit_emp.currentText().split('|')[0]) if self.edit_emp.currentText() else '',\n 'shift': self.edit_shift.currentText(),\n 'wc': self.edit_wc.currentText() if self.edit_wc.currentText() else ''\n }\n\n empty_keys = [k for k, v in mhr_m.items() if not bool(v)]\n return empty_keys, mhr_m\n\n @pyqtSlot()\n def edit_wc_text_changed(self):\n self.mhr_format()\n self.wc = self.edit_wc.currentText()\n self.capacity_info = df_t_capacity_read(self.user_info,\n ['*'],\n ['wc', 'capacity_update'],\n ['%s', '%d'],\n (self.wc, self.edit_date.text())\n )\n self.detail_format()\n main_value = self.get_mhr()\n if main_value:\n self.fill_mhr(main_value)\n self.old_mhr = main_value\n\n records_value = self.get_records()\n if records_value:\n keys_to_delete = ['date', 'emp', 'shift', 'wc']\n for record in records_value:\n for index, key_ in enumerate(keys_to_delete):\n del record[key_]\n self.fill_records(records_value)\n self.old_records = records_value\n\n def mhr_format(self):\n for ob in self.findChildren(QLineEdit):\n if ob.objectName() in ['vol', 'tons', 'remark', 'vendors']:\n ob.setText('')\n elif ob.objectName() in ['others', 'material', 'debug', 'maintenance', 'self_maintenance', 'net']:\n ob.setText('0.0')\n else:\n pass\n for ob in self.findChildren(QCheckBox):\n if ob.objectName() == 'is_main':\n ob.setChecked(True)\n elif ob.objectName() in ['is_weekend', 'has_vendor']:\n ob.setChecked(False)\n else:\n pass\n ob = self.findChild(ExtendedTableWidget, \"table\")\n if ob:\n ob.setRowCount(0)\n ob.clear()\n\n def detail_format(self):\n self.edit_table.setRowCount(0)\n self.edit_table.clear()\n if self.capacity_info:\n if self.capacity_info[0]['is_line']:\n head_label = ['so', 'batch', 'pkg', 'length', 'wt', 'auto', 'fa_job', 'sort']\n self.edit_table.setRowCount(1)\n self.edit_table.setColumnCount(len(head_label))\n self.edit_table.setHorizontalHeaderLabels(head_label)\n item_1 = QTableWidgetItem()\n item_1.setFlags(QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)\n self.edit_table.setItem(0, 5, item_1)\n else:\n head_label = ['so', 'batch', 'part', 'length', 'qty/wt', 'auto', 'fa_job', 'sort']\n self.edit_table.setRowCount(1)\n self.edit_table.setColumnCount(len(head_label))\n self.edit_table.setHorizontalHeaderLabels(head_label)\n item_1 = QTableWidgetItem()\n item_1.setFlags(QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled)\n self.edit_table.setItem(0, 5, item_1)\n\n @pyqtSlot()\n def edit_emp_activated(self):\n pass\n\n def edit_date_clicked(self):\n self.cal_date.show()\n self.cal_date.clicked[QtCore.QDate].connect(self.show_date)\n date = self.cal_date.selectedDate()\n self.edit_date.setText(date.toString(\"yyyy-MM-dd\"))\n\n def show_date(self, date):\n self.edit_date.setText(date.toString('yyyy-MM-dd'))\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n user_info = {\n 'server': '127.0.0.1\\\\stlsojsvr04',\n 'database': 'DFactory',\n 'account': 'zyq',\n 'password': 'zyq123'\n }\n window = DailyReport(user_info)\n window.show()\n sys.exit(app.exec())\n", "sub_path": "DfMainDailyReportCode.py", "file_name": "DfMainDailyReportCode.py", "file_ext": "py", "file_size_in_byte": 26572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidget", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsUserCheckable", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEnabled", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.Unchecked", "line_number": 58, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 64, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 94, "usage_type": "name"}, {"api_name": "df_main_daily_report.ReportDailyUI", "line_number": 94, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.df_t_shift_read", "line_number": 108, "usage_type": "call"}, {"api_name": "DBbase.dbFunctions.df_t_emp_read", "line_number": 111, "usage_type": "call"}, {"api_name": "DBbase.dbFunctions.df_t_wc_read", "line_number": 116, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 156, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.t_oracle_read", "line_number": 177, "usage_type": "call"}, {"api_name": "DBbase.dbFunctions", "line_number": 177, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 190, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 190, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 208, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 210, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 211, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 213, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 214, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 219, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 151, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 241, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 241, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 244, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 244, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 247, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 247, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 229, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 250, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 257, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 258, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 258, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 255, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 266, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 266, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 284, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 284, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 291, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 291, "usage_type": "name"}, {"api_name": "QtExt.ExtFun.is_decimal", "line_number": 299, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 300, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 300, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 305, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 305, "usage_type": "name"}, {"api_name": "QtExt.ExtFun.diff_2_dic", "line_number": 365, "usage_type": "call"}, {"api_name": "DBbase.dbFunctions.t_mhr_update_many", "line_number": 373, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 375, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 375, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.t_mhr_many_insert", "line_number": 379, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 381, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 381, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.t_record_del_many", "line_number": 394, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 396, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 396, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.t_records_many_insert", "line_number": 400, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 402, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 402, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.t_record_update_many", "line_number": 413, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 415, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 415, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.t_records_many_insert", "line_number": 419, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 421, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 421, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 437, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 437, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 261, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QEvent.FocusOut", "line_number": 441, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QEvent", "line_number": 441, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget.eventFilter", "line_number": 443, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 443, "usage_type": "name"}, {"api_name": "DBbase.dbFunctions.df_t_mhr_read", "line_number": 461, "usage_type": "call"}, {"api_name": "DBbase.dbFunctions.df_t_record_read", "line_number": 470, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 476, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 480, "usage_type": "argument"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 489, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 490, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.SolidPattern", "line_number": 491, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 491, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 496, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 497, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 497, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 502, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 504, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 507, "usage_type": "call"}, {"api_name": "DBbase.dbFunctions.df_t_capacity_read", "line_number": 524, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 520, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 546, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 553, "usage_type": "argument"}, {"api_name": "QtExt.ExtWidget.ExtendedTableWidget", "line_number": 560, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 574, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 575, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 575, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 582, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 583, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 583, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 586, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 592, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 592, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 601, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 601, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 610, "usage_type": "call"}]} +{"seq_id": "614636550", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport time\n\nt1 = time.time()\n\nprint(\"Hello\")\nprint(np.sin(3.14))\ntime.sleep(5.2)\nt2 = time.time()-t1\n\nprint(f'time without plots {t2}')\n\nx = np.linspace(0,20,1000)\nplt.plot(x,np.sin(x))\nplt.show()\n\n# plt.plot(np.cos(x),np.sin(x))\n# plt.show()", "sub_path": "python/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "time.time", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 8, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 9, "usage_type": "call"}, {"api_name": "time.time", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "458227776", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nfrom hashlib import md5\nfrom pathlib import Path\nfrom pwnutils.lib.config import SETTING\n\n\nCACHE_DIR = Path(SETTING[\"cache\"][\"dir\"])\n\nif not CACHE_DIR.is_dir():\n CACHE_DIR.mkdir(parents=True, exist_ok=True)\n\n\nclass Cache(object):\n def __init__(self):\n self.cache_dir = CACHE_DIR\n self.cache_file = None\n self.info = dict()\n self.info_str = str()\n self.hash = str()\n self.handler = None\n self.result = str()\n\n def _hash(self, text):\n h = md5()\n h.update(text)\n return h.hexdigest()\n\n def calc(self):\n pass\n\n def save(self):\n pass\n\n def search(self):\n pass\n\n\nclass CmdCache(Cache):\n def __init__(self, cmd: list, handler, **kwargs):\n super(CmdCache, self).__init__()\n\n self.cmd = list()\n self.handler = handler\n\n self._parse_cmd(cmd, **kwargs)\n self._dump_info()\n\n def _parse_cmd(self, cmd, **kwargs):\n \"\"\"\n kwargs:\n $filename?: like filename1, filename2 ...\n \"\"\"\n\n # ban same arg\n _args = [x for x in cmd if x.startswith(\"$\")]\n assert len(_args) == len(set(_args))\n\n # parse args start with \"$\"\n for i in range(len(cmd)):\n c = cmd[i]\n if c.startswith(\"$\"):\n arg = c[1:]\n cmd[i] = kwargs[arg]\n self.info[arg] = kwargs[arg]\n\n self.cmd = cmd\n\n def _dump_info(self):\n res = list()\n\n for k, v in self.info.items():\n if k.startswith(\"filename\"):\n # calc file hash\n h = self._hash(open(v, \"rb\").read())\n res.append(f\"{k}={h}\")\n else:\n res.append(f\"{k}={v}\")\n\n res.sort()\n res = \"\\n\".join(res)\n\n self.info_str = res\n return res\n\n def calc(self):\n # hash(cmd + info_str)\n text = \" \".join(self.cmd) + \"\\n\" + self.info_str\n self.hash = self._hash(text.encode())\n return self.hash\n\n def run(self): # handler wrapper\n self.result = self.handler(self.cmd)\n return self.result\n\n def save(self): # save result to file\n self.calc()\n self.cache_file = self.cache_dir / self.hash\n\n J = dict()\n J[\"result\"] = self.result\n J[\"cmd\"] = self.cmd\n J[\"info\"] = self.info\n J[\"info_str\"] = self.info_str\n\n json.dump(J, open(self.cache_file, \"w\"))\n\n def search(self):\n self.calc()\n self.cache_file = self.cache_dir / self.hash\n\n try:\n J = json.load(open(self.cache_file, \"r\"))\n self.result = J[\"result\"]\n return self.result\n except:\n return None\n", "sub_path": "lib/cache/cache.py", "file_name": "cache.py", "file_ext": "py", "file_size_in_byte": 2777, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pathlib.Path", "line_number": 10, "usage_type": "call"}, {"api_name": "pwnutils.lib.config.SETTING", "line_number": 10, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 108, "usage_type": "call"}, {"api_name": "json.load", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "51740172", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # 오토인코더로 이미지의 특징을 추출하기\n\nimport torch\nfrom torch import nn, optim\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nimport numpy as np\nimport pandas as pd\n\nfrom torch.utils.data import DataLoader, Dataset\nfrom PIL import Image\n\n\n# 하이퍼파라미터\nEPOCH = 10\nBATCH_SIZE = 1\ndimension = 3\nimage_size = (28, 28, 3)\n\nUSE_CUDA = torch.cuda.is_available()\nDEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\nprint(\"Using Device:\", DEVICE)\ncsv_file_name = 'data/example.csv'\n\nclass SeedDataset(Dataset):\n def __init__(self, dataframe, root='data/img'):\n self.dataframe = dataframe\n self.root = root\n\n def __len__(self):\n return len(self.dataframe)\n\n def __getitem__(self, i):\n file_name, label = self.dataframe.loc[i]['name'], self.dataframe.loc[i]['label']\n img_dir = self.root + file_name\n r_im = Image.open(img_dir).resize((image_size[0], image_size[1]))\n r_im = np.array(r_im)\n data = torch.FloatTensor(r_im)\n label = torch.FloatTensor(label)\n return data, label\n\n def get_sample(self, size):\n file_names, labels = self.dataframe.loc[0:size-1]['name'], self.dataframe.loc[0:4]['label']\n img_dir = self.root + file_names[0]\n r_im = Image.open(img_dir).resize((image_size[0], image_size[1]))\n data = [np.array(r_im)]\n\n for file_name in file_names[1:]:\n img_dir = self.root + file_name\n r_im = Image.open(img_dir).resize((image_size[0], image_size[1]))\n r_im = np.array(r_im)\n data = np.concatenate((data, [r_im]), axis=0)\n\n data = torch.FloatTensor(data)\n return data, torch.tensor(labels.values.astype(np.float32))\n\n\n\n\n\ntrainset = pd.read_csv(csv_file_name)\ntrainset = SeedDataset(trainset, root='data/img/')\n\ntrain_loader = torch.utils.data.DataLoader(\n dataset = trainset,\n batch_size = BATCH_SIZE,\n shuffle = True,\n num_workers = 2\n)\n\n\nclass ConvAutoencoder(nn.Module):\n def __init__(self):\n super(ConvAutoencoder, self).__init__()\n global dimension\n\n self.encoder = nn.Sequential(\n nn.Conv2d(3, 16, 3, padding=1), # batch x 16 x 28 x 28\n nn.ReLU(),\n nn.BatchNorm2d(16),\n nn.Conv2d(16, 32, 3, padding=1), # batch x 32 x 28 x 28\n nn.ReLU(),\n nn.BatchNorm2d(32),\n nn.Conv2d(32, 64, 3, padding=1), # batch x 32 x 28 x 28\n nn.ReLU(),\n nn.BatchNorm2d(64),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(64, 128, 3, padding=1), # batch x 64 x 14 x 14\n nn.ReLU(),\n nn.BatchNorm2d(128),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(128, 256, 3, padding=1), # batch x 64 x 7 x 7\n nn.ReLU()\n )\n self.decoder = nn.Sequential(\n nn.ConvTranspose2d(256, 128, 3, 2, 1, 1), # batch x 128 x 14 x 14\n nn.ReLU(),\n nn.BatchNorm2d(128),\n nn.ConvTranspose2d(128, 64, 3, 1, 1), # batch x 64 x 14 x 14\n nn.ReLU(),\n nn.BatchNorm2d(64),\n nn.ConvTranspose2d(64, 16, 3, 1, 1), # batch x 16 x 14 x 14\n nn.ReLU(),\n nn.BatchNorm2d(16),\n nn.ConvTranspose2d(16, 3, 3, 2, 1, 1), # batch x 1 x 28 x 28\n nn.ReLU()\n )\n\n def forward(self, x):\n x = x.view(-1, image_size[2], image_size[0], image_size[1])\n encoded = self.encoder(x)\n decoded = self.decoder(encoded)\n decoded = decoded.view(-1, image_size[0], image_size[1], image_size[2])\n return encoded, decoded\n\n\nautoencoder = ConvAutoencoder().to(DEVICE)\noptimizer = torch.optim.Adam(autoencoder.parameters(), lr=0.005)\ncriterion = nn.MSELoss()\n\n\n# 원본 이미지를 시각화 하기 (첫번째 열)\nview_data, _ = trainset.get_sample(5)\nview_data = view_data.view(-1, image_size[0]*image_size[1]*image_size[2])\nview_data = view_data.type(torch.FloatTensor)/255.\n\n\ndef train(autoencoder, train_loader):\n autoencoder.train()\n for step, (x, label) in enumerate(train_loader):\n x = x.to(DEVICE)\n y = x.to(DEVICE)\n\n encoded, decoded = autoencoder(x)\n\n loss = criterion(decoded, y)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n if step %10 == 0:\n print('step',step, 'loss',loss.item())\n\n\nfor epoch in range(1, EPOCH+1):\n train(autoencoder, train_loader)\n\n # 디코더에서 나온 이미지를 시각화 하기 (두번째 열)\n test_x = view_data.to(DEVICE)\n _, decoded_data = autoencoder(test_x)\n\n # 원본과 디코딩 결과 비교해보기\n f, a = plt.subplots(2, 5, figsize=(5, 2))\n print(\"[Epoch {}]\".format(epoch))\n for i in range(5):\n img = np.reshape(np.array(view_data[i]),(image_size[0], image_size[1],image_size[2]))\n a[0][i].imshow(img)\n a[0][i].set_xticks(()); a[0][i].set_yticks(())\n\n for i in range(5):\n img = np.reshape(decoded_data.to(\"cpu\").data.numpy()[i], (image_size[0], image_size[1],image_size[2]))\n a[1][i].imshow(img)\n a[1][i].set_xticks(()); a[1][i].set_yticks(())\n plt.show()\n\n\n# # 잠재변수 들여다보기\n\n# 잠재변수를 3D 플롯으로 시각화\nview_data, labels = trainset.get_sample(200)\nview_data = view_data.view(-1, image_size[0] * image_size[1] *image_size[2])\nview_data = view_data.type(torch.FloatTensor)/255.\nlabels = labels.numpy()\ntest_x = view_data.to(DEVICE)\nencoded_data, _ = autoencoder(test_x)\nencoded_data = encoded_data.to(\"cpu\")\n\n\nCLASSES = {\n 0: 'T-shirt/top',\n 1: 'Trouser',\n 2: 'Pullover',\n 3: 'Dress',\n 4: 'Coat',\n 5: 'Sandal',\n 6: 'Shirt',\n 7: 'Sneaker',\n 8: 'Bag',\n 9: 'Ankle boot'\n}\n\nif dimension == 2:\n fig = plt.figure(figsize=(10,8))\n ax = fig.add_subplot()\n X = encoded_data.data[:, 0].numpy()\n Y = encoded_data.data[:, 1].numpy()\n\n\n for x, y, s in zip(X, Y, labels):\n name = CLASSES[s]\n color = cm.rainbow(int(255*s/9))\n ax.text(x, y, name, backgroundcolor=color)\n\n ax.set_xlim(X.min(), X.max())\n ax.set_ylim(Y.min(), Y.max())\n plt.show()\nelse: # dimension == 3\n fig = plt.figure(figsize=(10, 8))\n ax = Axes3D(fig)\n X = encoded_data.data[:, 0].numpy()\n Y = encoded_data.data[:, 1].numpy()\n Z = encoded_data.data[:, 2].numpy()\n\n\n for x, y, z, s in zip(X, Y, Z, labels):\n name = CLASSES[s]\n color = cm.rainbow(int(255*s/9))\n ax.text(x, y, z, name, backgroundcolor=color)\n\n\n ax.set_xlim(X.min(), X.max())\n ax.set_ylim(Y.min(), Y.max())\n ax.set_zlim(Z.min(), Z.max())\n plt.show()\n\n\n\n\n", "sub_path": "conv_autoencoder.py", "file_name": "conv_autoencoder.py", "file_ext": "py", "file_size_in_byte": 6731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.cuda.is_available", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 55, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "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.ReLU", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "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.ReLU", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "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.ReLU", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 130, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 176, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.cm.rainbow", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.cm.rainbow", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}]} +{"seq_id": "125338512", "text": "import subprocess as sp\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport vplanet\nimport vplot as vpl\nimport os\nimport matplotlib.patches as mpatches\nimport sys\nimport scipy.ndimage\nfrom matplotlib.pyplot import figure\nimport matplotlib.lines as mlines\nimport bigplanet as bp\nimport pathlib\nfrom itertools import chain\n\n\ndest = ['../DynamicCases/CaseA/KDwarf/','../DynamicCases/CaseA/GDwarf/','../DynamicCases/CaseA/FDwarf/']\nstar = ['K Dwarf','G Dwarf','F Dwarf']\nnum = 10000\n\nfig, axs = plt.subplots(3,1,figsize=(9,7))\nfig.subplots_adjust(top=0.851,bottom=0.098,left=0.085,right=0.98,hspace=0.839,wspace=0.2)\n\nfor x in range(len(dest)):\n\n num = int(num)\n data = np.zeros(151)\n avg_count = np.zeros(151)\n icecount = 0\n \n file = bp.BPLFile( dest +\"/\" + dest + \".bpa\")\n\n icebeltL = bp.ExtractColumn(file,'earth:IceBeltLand:final')\n icebeltS = bp.ExtractColumn(file,'earth:IceBeltSea:final')\n northCapL = bp.ExtractColumn(file,'earth:IceCapNorthLand:final')\n northCapS = bp.ExtractColumn(file,'earth:IceCapNorthSea:final')\n southCapL = bp.ExtractColumn(file,'earth:IceCapSouthLand:final')\n southCapS = bp.ExtractColumn(file,'earth:IceCapSouthSea:final')\n\n earth_icefree = bp.ExtractColumn(file,'earth:IceFree:final')\n snowballL = bp.ExtractColumn(file,'earth:SnowballLand:final')\n snowballS = bp.ExtractColumn(file,'earth:SnowballSea:final')\n \n\n if (\n icebeltL == 1 and icebeltS == 0 and southCapS == 0 and\n southCapL == 0 and northCapS == 0 and northCapL == 0 and\n snowballL == 0 and snowballS == 0\n ):\n if icecount <= 70:\n lats = bp.ExtractUniqueValues(file,'earth:Latitude:climate')\n times = bp.ExtractColumn(file,'earth:Time:forward')\n ice = bp.ExtractColumn(file,'earth:IceHeight:climate')\n \n nlats = len(lats)\n ntimes = len(times)\n\n ice = np.reshape(ice,(ntimes,nlats))\n ice_last = ice[-1]\n\n data += ((ice_last.T)/1000)\n indi = axs[x].plot(lats,((ice_last.T)/1000), color = 'gray', alpha = 0.25)\n icecount += 1\n\n for z in range(data.size):\n avg_count[z] = data[z]/icecount\n\n avg_plot = axs[x].plot(lats,avg_count, color = 'black', linewidth = 4)\n axs[x].plot([-80,-90],[0,0], color = 'black', linewidth = 4)\n axs[x].plot([80,90],[0,0], color = 'black', linewidth = 4)\n\n indi_leg = mlines.Line2D([],[],color = 'gray',linewidth = 3 ,label = 'Individual Cases', alpha = 0.25)\n avg_leg = mlines.Line2D([],[],color = 'black',linewidth = 4,label = 'Average')\n\n axs[x].set_xlim(-90,90)\n axs[0].set_ylim(0.0,5.0)\n axs[1].set_ylim(0.0,5.0)\n axs[2].set_ylim(0.0,5.0)\n\n axs[0].set_yticks([0.0,2.5,5.0])\n axs[1].set_yticks([0.0,2.5,5.0])\n axs[2].set_yticks([0.0,2.5,5.0])\n\n axs[0].set_title(\"K Dwarf\", fontsize = 16)\n axs[1].set_title(\"G Dwarf\", fontsize = 16)\n axs[2].set_title(\"F Dwarf\", fontsize = 16)\n\n axs[x].set_xlabel(r'Latitude [$^\\circ$]', fontsize = 12)\n axs[x].set_ylabel(\"Ice Height [km]\", fontsize = 12)\n\n axs[0].legend(handles = [indi_leg,avg_leg], fontsize=14, loc = 'upper left',\n bbox_to_anchor=(0, 1.75, 1, 0.102),ncol=2, mode=\"expand\", borderaxespad=0,edgecolor='k')\n\n\nplt.tight_layout()\nif (sys.argv[1] == 'pdf'):\n plt.savefig('BeltHeight' + '.pdf')\nif (sys.argv[1] == 'png'):\n plt.savefig('BeltHeight' + '.png')\n\nplt.show()\nplt.close()\n", "sub_path": "BeltHeight/makeplot.py", "file_name": "makeplot.py", "file_ext": "py", "file_size_in_byte": 3463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "bigplanet.BPLFile", "line_number": 32, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 34, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 35, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 36, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 37, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 38, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 39, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 41, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 42, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 43, "usage_type": "call"}, {"api_name": "bigplanet.ExtractUniqueValues", "line_number": 52, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 53, "usage_type": "call"}, {"api_name": "bigplanet.ExtractColumn", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 97, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "526913130", "text": "from sklearn.datasets import load_iris\niris_dataset = load_iris()\n\nfrom sklearn.model_selection import train_test_split\n# Train data 75% , Test data 25% , 난수 생성으로 섞어서 나눈다.\nX_train, X_test, Y_train, Y_test = train_test_split(iris_dataset['data'], iris_dataset['target'], random_state= 0 )\n\nfrom sklearn.neighbors import KNeighborsClassifier\nknn = KNeighborsClassifier(n_neighbors=1)\n\nknn.fit(X_train, Y_train)\n''' 결과 \nKNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n weights='uniform')\n \n '''\nimport numpy as np\nX_new = np.array([[5, 2.9, 1, 0.2]])\nprediction = knn.predict(X_new)\nprint(\"예측: {}\".format(prediction))\nprint(\"예측한 타깃의 이름: {}\".format(iris_dataset['target_names'][prediction]))\n\n''' 모델 평가'''\ny_pred = knn.predict(X_test)\nprint(\"테스트 세트에 대한 예측값: \\n{}\".format(y_pred))\nprint(\"테스트 세트의 정확도: {:.2f}\".format(np.mean(y_pred==Y_test)))\n# KNN 객체의 score 메소드로 테스트 세트의 정확도를 계산 할 수 있다.\nprint(\"테스트 세트의 정확도: {:.2f}\".format(knn.score(X_test, Y_test)))\n\n\n\n\n\n", "sub_path": "파이썬 라이브러리를 활용한 머신러닝/iris KNN.py", "file_name": "iris KNN.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 2, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "609015426", "text": "from typing import Any, Dict\n\nfrom ....models.models import Committee\nfrom ....permissions.management_levels import OrganizationManagementLevel\nfrom ....shared.schema import id_list_schema\nfrom ...generics.create import CreateAction\nfrom ...util.default_schema import DefaultSchema\nfrom ...util.register import register_action\nfrom .committee_common_mixin import CommitteeCommonCreateUpdateMixin\n\n\n@register_action(\"committee.create\")\nclass CommitteeCreate(CommitteeCommonCreateUpdateMixin, CreateAction):\n \"\"\"\n Action to create committees.\n \"\"\"\n\n model = Committee()\n schema = DefaultSchema(Committee()).get_create_schema(\n required_properties=[\"organization_id\", \"name\"],\n optional_properties=[\n \"description\",\n \"organization_tag_ids\",\n \"forward_to_committee_ids\",\n \"receive_forwardings_from_committee_ids\",\n ],\n additional_optional_fields={\"manager_ids\": id_list_schema},\n )\n permission = OrganizationManagementLevel.CAN_MANAGE_ORGANIZATION\n\n def update_instance(self, instance: Dict[str, Any]) -> Dict[str, Any]:\n instance = super().update_instance(instance)\n if \"manager_ids\" in instance:\n self.apply_instance(instance)\n self.update_managers(instance)\n return instance\n", "sub_path": "openslides_backend/action/actions/committee/create.py", "file_name": "create.py", "file_ext": "py", "file_size_in_byte": 1313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "committee_common_mixin.CommitteeCommonCreateUpdateMixin", "line_number": 13, "usage_type": "name"}, {"api_name": "generics.create.CreateAction", "line_number": 13, "usage_type": "name"}, {"api_name": "models.models.Committee", "line_number": 18, "usage_type": "call"}, {"api_name": "util.default_schema.DefaultSchema", "line_number": 19, "usage_type": "call"}, {"api_name": "models.models.Committee", "line_number": 19, "usage_type": "call"}, {"api_name": "shared.schema.id_list_schema", "line_number": 27, "usage_type": "name"}, {"api_name": "permissions.management_levels.OrganizationManagementLevel.CAN_MANAGE_ORGANIZATION", "line_number": 29, "usage_type": "attribute"}, {"api_name": "permissions.management_levels.OrganizationManagementLevel", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 31, "usage_type": "name"}, {"api_name": "util.register.register_action", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "110753977", "text": "import torch\nimport torchvision\nfrom torchvision.transforms import ToPILImage\nimport torchaudio\nimport pandas as pd\nimport numpy as np\nfrom Audio2Video.models.dfcvae import DFCVAE\nfrom Audio2Video.models.SpeechVAE import SpeechVAE_Pair\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Runing on {}\".format(device))\n\ntopil = ToPILImage()\n\nclass AudioProcessor():\n def __init__(self, config):\n self.visual_model = DFCVAE(\n in_channels=config['model_params']['in_channels'],\n latent_dim=config['model_params']['latent_dim']\n ).to(device)\n\n self.audio_model = SpeechVAE_Pair(config['n_FBrank'], config['z_dim'], torch.nn.ReLU(True)).to(device)\n\n self.visual_model.load_state_dict(torch.load(config['visual_model_pth'], map_location=torch.device(device)))\n self.audio_model.load_state_dict(torch.load(config['audio_model_pth'], map_location=torch.device(device)))\n\n self.visual_model.eval()\n self.audio_model.eval()\n\n def process(self, data):\n with torch.no_grad():\n data = torch.tensor(data).to(device)\n # shape: F x T -> T x 1 x 1 x F\n data = data.permute(1, 0)\n data = data.view(1, 1, 20, 80) * 1.5 - 140\n #data = data.unsqueeze(0)\n #print(data.shape)\n latent_mel = self.audio_model.encode(data)\n output = self.visual_model.decode(latent_mel[0][0]) / 2 + 0.5\n\n #print(output.shape)\n #output = output[-1].permute(1, 2, 0).squeeze().detach().numpy() / 2 + 0.5\n #output = np.clip(output, 0, 1)\n output = np.array(topil(output[-1].detach()))\n\n return output\n", "sub_path": "inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 1712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.device", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 13, "usage_type": "call"}, {"api_name": "Audio2Video.models.dfcvae.DFCVAE", "line_number": 17, "usage_type": "call"}, {"api_name": "Audio2Video.models.SpeechVAE.SpeechVAE_Pair", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "225268604", "text": "\"\"\"ビューの自作の簡単な例\"\"\"\nfrom django.shortcuts import render\n\nfrom .models import Article\n\n\ndef year_archive(request, year):\n \"\"\"年間の記事一覧\"\"\"\n article_list = Article.objects.filter(pub_date__year=year) # 対象年を取得\n # viewに設定するコンテキストを設定\n context = {\n 'year': year,\n 'article_list': article_list\n }\n return render(request, 'news/year_archive.html', context)\n\n\ndef month_archive(request, month):\n \"\"\"月間の記事一覧\"\"\"\n article_list = Article.objects.filter(pub_date__month=month)\n context = {\n 'month': month,\n 'article_list': article_list\n }\n return render(request, 'news/month_archive.html', context)\n\n\ndef article_detail(request, year, month):\n \"\"\"記事詳細\"\"\"\n article_list = Article.objects.filter(pub_date__year=year, pub_date__month=month) # and条件にて検索\n context = {\n 'year': year,\n 'month': month,\n 'article_list': article_list\n }\n return render(request, 'news/article_detail.html', context)\n", "sub_path": "app/example/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1080, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "models.Article.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Article.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Article.objects.filter", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "354623212", "text": "#-------------------------------------------------------------------------\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\nfrom nose import with_setup\nfrom nose.tools import raises\nfrom Kqlmagic.constants import Constants\nfrom Kqlmagic.kql_magic import Kqlmagic as Magic\nfrom textwrap import dedent\nimport os.path\nimport re\nimport tempfile\n\nip = get_ipython() # pylint: disable=E0602\n\ndef setup():\n magic = Magic(shell=ip)\n ip.register_magics(magic)\n\ndef _setup():\n pass\n\ndef _teardown():\n pass\n\nTEST_URI_SCHEMA_NAME = \"kusto\"\n\nquery1 = \"-conn=$TEST_CONNECTION_STR let T = view () { datatable(n:long, name:string)[1,'foo',2,'bar'] }; T\"\n\nversion_command = \"--version\"\nversion_pw_command = version_command + \" -pw\"\nversion_expected_pattern = r'Kqlmagic version: [0-9]+\\.[0-9]+\\.[0-9]+'\n\n@with_setup(_setup, _teardown)\ndef test_ok():\n assert True\n\n@with_setup(_setup, _teardown)\ndef test_version():\n result = ip.run_line_magic('kql', version_command)\n version_str = str(result)\n print(version_str)\n expected_pattern = r'^' + version_expected_pattern + r'$'\n assert re.search(expected_pattern , version_str)\n\n@with_setup(_setup, _teardown)\ndef test_version_pw_button():\n result = ip.run_line_magic('kql', version_pw_command)\n pw_html_str = result._repr_html_()\n print(pw_html_str)\n assert re.search(r'popup version ', pw_html_str)\n\n@with_setup(_setup, _teardown)\ndef test_version_pw_file():\n result = ip.run_line_magic('kql', version_pw_command)\n pw_html_str = result._repr_html_()\n print(pw_html_str)\n f = re.search(r'kql_MagicLaunchWindowFunction\\(\\'(.+?)\\'\\,', pw_html_str) \n file_path = f.group(1)\n print(file_path)\n version_file = open(file_path) \n version_html_str = ''\n for line in version_file:\n version_html_str += line\n print(version_html_str)\n expected_pattern = r'^

' + version_expected_pattern + r'

$'\n assert re.search(expected_pattern , version_html_str)\n\n# def test_fail():\n# assert False \n\n@with_setup(_setup, _teardown)\ndef test_query():\n result = ip.run_line_magic('kql', query1)\n print(result)\n assert result[0][0] == 1\n assert result[1]['name'] == 'bar'\n", "sub_path": "azure/tests/test_basics.py", "file_name": "test_basics.py", "file_ext": "py", "file_size_in_byte": 2426, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "Kqlmagic.kql_magic.Kqlmagic", "line_number": 19, "usage_type": "call"}, {"api_name": "nose.with_setup", "line_number": 36, "usage_type": "call"}, {"api_name": "re.search", "line_number": 46, "usage_type": "call"}, {"api_name": "nose.with_setup", "line_number": 40, "usage_type": "call"}, {"api_name": "re.search", "line_number": 53, "usage_type": "call"}, {"api_name": "re.search", "line_number": 54, "usage_type": "call"}, {"api_name": "nose.with_setup", "line_number": 48, "usage_type": "call"}, {"api_name": "re.search", "line_number": 61, "usage_type": "call"}, {"api_name": "re.search", "line_number": 70, "usage_type": "call"}, {"api_name": "nose.with_setup", "line_number": 56, "usage_type": "call"}, {"api_name": "nose.with_setup", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "405670700", "text": "\n# coding: utf-8\n\nfrom naiveAI import AgentPER, NMnaive\nimport tensorflow as tf\nimport numpy as np\nfrom copy import deepcopy\nfrom buffer import SimpleMahjongBufferPER\nimport MahjongPy as mp\nfrom wrapper import EnvMahjong\nimport scipy.io as sio\nfrom datetime import datetime\n\nnow = datetime.now()\ndatetime_str = now.strftime(\"%Y%m%d-%H%M%S\")\n\ngraphs = [tf.Graph(), tf.Graph(), tf.Graph(), tf.Graph() ]\n\n\nenv = EnvMahjong()\n\nagents = [AgentPER(nn=NMnaive(graphs[i], agent_no=i), memory=SimpleMahjongBufferPER(size=1024), greedy=10.0 ** np.random.uniform(-1, 1)) for i in range(4)]\n\n#### Note:\n\n# This is for AI agents those only cares about itself, i.e., no defense. Therefore, there is no negative reward.\n\n# Also, 能和则和，能立直则立直\n\n\n\nn_games = 1000000\n\nprint(\"Start!\")\n\nfor n in range(n_games):\n\n if n % 10000 == 0:\n for i in range(4):\n agents[i].nn.save(model_dir=\"Agent{}-\".format(i) + datetime_str + \"-Game{}\".format(\n n)) # save network parameters every 10000 episodes\n print(\"\\r Game {}\".format(n), end='')\n\n episode_dones = [[], [], [], []]\n episode_states = [[], [], [], []]\n episode_rewards = [[], [], [], []]\n\n done = 0\n # policies = np.zeros([4,], dtype=np.int32)\n actions = np.zeros([4, ], dtype=np.int32)\n rs = np.zeros([4, ], dtype=np.float32)\n\n this_states = env.reset() ## for all players\n\n next_aval_states = deepcopy(this_states)\n next_states = [[], [], [], []]\n\n step = 0\n\n while not done and step < 10000:\n\n who, what = env.who_do_what()\n\n ## make selection\n if what == \"play\":\n\n ######################## 能和则和，能立直则立直 ##############\n aval_actions = env.t.get_self_actions()\n good_actions = []\n for a in range(len(aval_actions)):\n if aval_actions[a].action == mp.Action.Riichi:\n good_actions.append(a)\n\n if aval_actions[a].action == mp.Action.Tsumo:\n good_actions.append(a)\n ##########################################################\n\n next_aval_states = env.get_aval_next_states(who) ## for a single player\n next_aval_states = np.reshape(next_aval_states, [-1, 34, 4, 1])\n\n if len(good_actions) > 0:\n good_actions = np.reshape(good_actions, [-1, ])\n a_in_good_as, policy = agents[who].select(next_aval_states[good_actions])\n action = good_actions[a_in_good_as]\n else:\n action, policy = agents[who].select(next_aval_states)\n\n next_states[who], r, done, _ = env.step_play(action, playerNo=who)\n\n next_states[who] = env.get_state_(who)\n\n episode_dones[who].append(done)\n episode_states[who].append(this_states[who])\n episode_rewards[who].append(max(0., r))\n\n # agents[who].learn()\n\n this_states[who] = deepcopy(next_states[who])\n\n elif what == \"response\":\n next_aval_states_all = []\n policies = [[], [], [], []]\n for i in range(4):\n next_aval_states = env.get_aval_next_states(i)\n next_aval_states = np.reshape(next_aval_states, [-1, 34, 4, 1])\n next_aval_states_all.append(next_aval_states)\n\n ######################## 能和则和，能立直则立直 ##############\n aval_actions = env.t.get_response_actions()\n good_actions = []\n for a in range(len(aval_actions)):\n if aval_actions[a].action == mp.Action.Ron:\n good_actions.append(a)\n\n if aval_actions[a].action == mp.Action.ChanKan:\n good_actions.append(a)\n\n if aval_actions[a].action == mp.Action.ChanAnKan:\n good_actions.append(a)\n ##########################################################\n if len(good_actions) > 0:\n good_actions = np.reshape(good_actions, [-1, ])\n a_in_good_as, policies[i] = agents[i].select(\n np.reshape(next_aval_states[good_actions], [-1, 34, 4, 1]))\n actions[i] = good_actions[a_in_good_as]\n else:\n actions[i], policies[i] = agents[i].select(np.reshape(next_aval_states, [-1, 34, 4, 1]))\n\n next_states[i], rs[i], done, _ = env.step_response(actions[i], playerNo=i)\n\n ## Note: next_states is agent's prediction, but not the true one\n\n # table change after all players making actions\n\n for i in range(4):\n next_states[i] = env.get_state_(i)\n episode_dones[i].append(done)\n episode_states[i].append(this_states[i])\n episode_rewards[i].append(max(0., rs[i]))\n # agents[i].learn()\n\n ## next step\n for i in range(4):\n this_states[i] = deepcopy(next_states[i])\n\n step += 1\n\n # print(\"Game {}, step {}\".format(n, step))\n # print(env.get_phase_text())\n\n if done:\n final_score_change = env.get_final_score_change()\n for i in range(4):\n episode_states[i].append(env.get_state_(i))\n\n if len(episode_dones[i]) >= 1: # if not 1st turn end\n episode_dones[i][-1] = 1\n\n #### Disable the following line if not care others\n # episode_rewards[i][-1] = final_score_change[i]\n ##################################################\n\n if not np.max(final_score_change) == 0: ## score change\n for i in range(4):\n agents[i].remember_episode(episode_states[i], episode_rewards[i], episode_dones[i],\n weight=np.max(final_score_change))\n print(' ')\n print(env.t.get_result().result_type)\n else:\n if np.random.rand() < 0.1: ## no score change\n for i in range(4):\n agents[i].remember_episode(episode_states[i], episode_rewards[i], episode_dones[i], weight=0)\n print(' ')\n print(env.t.get_result().result_type)\n\n for n_train in range(5):\n for i in range(4):\n agents[i].learn(env.symmetric_hand, episode_start=128, care_others=False)\n\ndata = {\"rons\": env.rons}\nsio.savemat(\"./PERrons\" + datetime_str + \".mat\", data)\n\n", "sub_path": "AI/AI_PER.py", "file_name": "AI_PER.py", "file_ext": "py", "file_size_in_byte": 6673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.Graph", "line_number": 17, "usage_type": "call"}, {"api_name": "wrapper.EnvMahjong", "line_number": 20, "usage_type": "call"}, {"api_name": "naiveAI.AgentPER", "line_number": 22, "usage_type": "call"}, {"api_name": "naiveAI.NMnaive", "line_number": 22, "usage_type": "call"}, {"api_name": "buffer.SimpleMahjongBufferPER", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 51, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 55, "usage_type": "call"}, {"api_name": "MahjongPy.Action", "line_number": 71, "usage_type": "attribute"}, {"api_name": "MahjongPy.Action", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 82, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 105, "usage_type": "call"}, {"api_name": "MahjongPy.Action", "line_number": 112, "usage_type": "attribute"}, {"api_name": "MahjongPy.Action", "line_number": 115, "usage_type": "attribute"}, {"api_name": "MahjongPy.Action", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 127, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 170, "usage_type": "attribute"}, {"api_name": "scipy.io.savemat", "line_number": 181, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "464730511", "text": "from django import forms\nfrom django.core.validators import MaxLengthValidator\nfrom django.forms import ModelForm\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom .models import Image, Recipe, Step, IngredientPart\n\n\n#===============================================================================\nclass RecipeModelForm(forms.ModelForm):\n\n #---------------------------------------------------------------------------\n def __init__(self, *args, **kwargs):\n super(RecipeModelForm, self).__init__(*args, **kwargs)\n\n # Modify fields\n for field in iter(self.fields):\n self.fields[field].widget.attrs.update({\n 'class': 'form-control',\n 'required': False,\n })\n\n if field in ['title', 'last_name']:\n self.fields[field].widget.attrs.update({\n 'required': True,\n })\n\n #---------------------------------------------------------------------------\n class Meta:\n model = Recipe\n fields = ('title', 'intro', 'timeTotal','timeActive','serves','note','tip'\n )\n labels = {\n 'title': _('Title'),\n 'intro': _('Introduction (optional)'),\n 'timeTotal': _('Time Total'),\n 'timeActive': _('Time Active'),\n 'serves': _('Serves'),\n 'note': _('Note (optional)'),\n 'tip': _('Tip (optional)'),\n }\n\n\n#===============================================================================\nclass IngredientPartModelForm(forms.ModelForm):\n\n #---------------------------------------------------------------------------\n def __init__(self, *args, **kwargs):\n super(IngredientPartModelForm, self).__init__(*args, **kwargs)\n\n # Modify fields\n for field in iter(self.fields):\n self.fields[field].widget.attrs.update({\n 'class': 'form-control',\n 'required': True,\n })\n\n\n #---------------------------------------------------------------------------\n class Meta:\n model = IngredientPart\n fields = ('text',)\n labels = {\n 'text': _('Text'),\n }\n\n\n#===============================================================================\nclass StepModelForm(forms.ModelForm):\n\n #---------------------------------------------------------------------------\n def __init__(self, *args, **kwargs):\n super(StepModelForm, self).__init__(*args, **kwargs)\n\n # Modify fields\n for field in iter(self.fields):\n self.fields[field].widget.attrs.update({\n 'class': 'form-control',\n 'required': True,\n })\n\n\n #---------------------------------------------------------------------------\n class Meta:\n model = Step\n fields = ('text',)\n labels = {\n 'text': _('Text'),\n }\n\n#\n# #===============================================================================\n# class PracImagewhForm(forms.ModelForm):\n#\n# #---------------------------------------------------------------------------\n# class Meta:\n# model = Practitioner\n# fields = (\"imagewh\", 'croppingwh')\n# labels = {\n# 'imagewh': _('Image'),\n# 'croppingwh': _('Cropping'),\n# }\n#\n# #===============================================================================\n# class FacImgwwwhForm(forms.ModelForm):\n#\n# #---------------------------------------------------------------------------\n# class Meta:\n# model = Facility\n# fields = (\"imgwwwh\", 'croppingwwwh')\n# labels = {\n# 'imgwwwh': _('Background Image'),\n# 'croppingwwwh': _('Cropping'),\n# }\n#\n#\n# #===============================================================================\n# class FacImagewhForm(forms.ModelForm):\n#\n# #---------------------------------------------------------------------------\n# class Meta:\n# model = Facility\n# fields = (\"imagewh\", 'croppingwh')\n# labels = {\n# 'imagewh': _('Logo Image'),\n# 'croppingwh': _('Cropping'),\n# }\n#\n#\n# #===============================================================================\n# class FacilityModelAbout(ModelForm):\n#\n# #NOTE For some reason if the text area is already longer than max_len\n# # and remains unchanged, it lets it pass. I don't know why or how to fix,\n# # bu not likely to happen IRL so I'll let it be.\n#\n# #---------------------------------------------------------------------------\n# def __init__(self, *args, **kwargs):\n# super(FacilityModelAbout, self).__init__(*args, **kwargs)\n#\n# if self.instance:\n# highest_privilege = self.instance.highest_privilege\n# if str(highest_privilege) == \"2\":\n# max_len = MAXABOUTL2\n# elif str(highest_privilege) == \"3\":\n# max_len = MAXABOUTL3\n# else:\n# max_len = None\n# else:\n# max_len = None\n#\n# for field in iter(self.fields):\n# self.fields[field].widget.attrs.update({\n# \"MAXLENGTH\": str(max_len),\n# 'class': 'form-control',\n# 'required': False,\n# })\n#\n# #---------------------------------------------------------------------------\n# class Meta:\n# model = Facility\n# #fields = '__all__'\n# fields =['about']\n# labels = {\n# 'about': _('About'),\n# }\n#\n# #===============================================================================\n# class FacilityModelVideo(ModelForm):\n#\n# #---------------------------------------------------------------------------\n# def __init__(self, *args, **kwargs):\n# super(FacilityModelVideo, self).__init__(*args, **kwargs)\n#\n# for field in iter(self.fields):\n# self.fields[field].widget.attrs.update({\n# 'class': 'form-control',\n# 'required': False,\n# })\n#\n# #---------------------------------------------------------------------------\n# class Meta:\n# model = Facility\n# #fields = '__all__'\n#\n# fields =['video_url']\n# labels = {\n# 'video_url': _('Video URL'),\n# }\n#\n# #===============================================================================\n# class FacilityModelFormBasic(ModelForm):\n#\n# #---------------------------------------------------------------------------\n# def __init__(self, *args, **kwargs):\n# super(FacilityModelFormBasic, self).__init__(*args, **kwargs)\n# LANG_CHOICES = []\n# for item in Language.objects.all():\n# choice_tup = (item.pk, item)\n# LANG_CHOICES.append(choice_tup)\n# if item.slug == \"eng\":\n# initial_lan = choice_tup\n#\n#\n# # Defining fields\n# #this was overriding facility lnguages, for some reason bc of initial?\n# # self.fields[\"languages\"] = (forms.CharField(\n# # widget=forms.SelectMultiple(choices=LANG_CHOICES, attrs={'style': 'max-height:50px;'}),\n# # initial=initial_lan,\n# # label = 'Languages (optional; select all that apply)',)\n# # )\n#\n# self.fields[\"site\"] = (forms.CharField(\n# initial = \"http://\",\n# label = 'Website',\n# required=True)\n# )\n#\n#\n# for field in iter(self.fields):\n# self.fields[field].widget.attrs.update({\n# 'class': 'form-control',\n# 'required': True,\n# })\n#\n# self.fields['accreds'].widget.attrs.update({\n# 'required': False,\n# 'style': 'max-height:50px;'\n# })\n#\n# self.fields['languages'].widget.attrs.update({\n# 'required': False,\n# })\n#\n# #---------------------------------------------------------------------------\n# class Meta:\n# model = Facility\n# #fields = '__all__'\n#\n# fields =['name', 'email', 'site', 'phone', 'languages', 'accreds']\n# labels = {\n# 'name': _('Name'),\n# 'email': _('Email Address (optional)'),\n# #'site': _('Website'),\n# 'phone': _('Phone Number'),\n# 'languages': _('Languages (optional; select all that apply)'),\n# 'accreds': _('Accreditation (optional; select all that apply)'),\n# }\n#\n#\n# #===============================================================================\n# class FacilityFormAddress(forms.Form):\n#\n# #---------------------------------------------------------------------------\n# def __init__(self, *args, **kwargs):\n# fac = kwargs.pop('fac')\n# if fac:\n# addyl1data = fac.addyl1\n# citydata = fac.city.name\n# if fac.city and fac.city.region:\n# regiondata = (fac.city.region.pk, fac.city.region.name)\n# else:\n# regiondata = None\n# if fac.city and fac.city.country:\n# countrydata = (fac.city.country.pk, fac.city.country.name) or None\n# else:\n# countrydata = None\n# else:\n# addyl1data = None\n# citydata = None\n# regiondata = None\n# countrydata = None\n#\n# super(FacilityFormAddress, self).__init__(*args, **kwargs)\n#\n# # Defining fields\n# self.fields[\"addyl1\"] = (forms.CharField(\n# widget=forms.TextInput(),\n# label = 'Address',\n# initial = addyl1data,\n# required=True,\n# ))\n# self.fields[\"city\"] = (forms.CharField(\n# widget=forms.TextInput(),\n# label = 'City',\n# initial = citydata,\n# required=True,\n# ))\n# self.fields[\"region\"] = (forms.CharField(\n# widget=forms.SelectMultiple(choices = Region.objects.all().values_list('id','name')),\n# label = 'State/Province (optional)',\n# initial = regiondata,\n# required=False,\n# ))\n# self.fields[\"country\"] = (forms.CharField(\n# widget=forms.SelectMultiple(choices = Country.objects.all().values_list('id','name')),\n# label = 'Country',\n# initial = countrydata,\n# required=True,\n# ))\n#\n# # Modify fields\n# for field in iter(self.fields):\n# self.fields[field].widget.attrs.update({\n# 'class': 'form-control',\n# #'required': True,\n# #'required': False,\n# })\n#\n#\n#\n# #===============================================================================\n# class CountrySortForm(forms.Form):\n# \"\"\"Used in destinations_all, to sort countries\"\"\"\n#\n# sort_options = (('-facility_no', \"Number of Facilities: Most to Least\"),\n# ('facility_no', \"Number of Facilities: Least to Most\"),\n# ('name', \"Country Name: A-Z\"),\n# ('-name', \"Country Name: Z-A\"),\n# )\n# country_options = [(None, \"----\")] + list(Country.objects.filter(facility_no__gte=1).exclude(imgwwwh__exact='').values_list('slug','name'))\n#\n# sortby = forms.ChoiceField(choices=sort_options, required=False, label='Sort By', initial=sort_options[0])\n# country = forms.ChoiceField(choices = country_options, required=False, label='Select Country',)\n#\n# def __init__(self, *args, **kwargs):\n# super(CountrySortForm, self).__init__(*args, **kwargs)\n#\n# for field in iter(self.fields):\n# self.fields[field].widget.attrs.update({\n# 'class': 'form-control',\n# })\n#\n# #===============================================================================\n# class OfferSortForm(forms.Form):\n# \"\"\"Used in facility_profile, to sort offers\"\"\"\n#\n# def __init__(self, *args, **kwargs):\n# facility = kwargs.pop('fac')\n# treatment_options = [(None, \"All\")] + list(Treatment.objects.filter(offer__facility = facility ).distinct().values_list('pk','name'))\n# sort_options = (\n# ('name', \"Procedure Name\"),\n# ('price', \"Price: Lowest Fist\"),\n# ('-price', \"Price: Highest First\"),\n# )\n#\n# super(OfferSortForm, self).__init__(*args, **kwargs)\n#\n# self.fields[\"sortby\"] = (forms.ChoiceField(\n# widget=forms.Select(),\n# choices=sort_options,\n# label = 'Sort By',\n# initial = sort_options[0],\n# required=False,\n# ))\n#\n# self.fields[\"treatment\"] = (forms.ChoiceField(\n# widget=forms.Select(),\n# choices = treatment_options,\n# label = 'Treatment',\n# initial = treatment_options[0],\n# required=False,\n# ))\n#\n# for field in iter(self.fields):\n# self.fields[field].widget.attrs.update({\n# 'class': 'form-control form-control sortform',\n# 'style':'min-width:150px;'\n# })\n", "sub_path": "bookbuild/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 13768, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.forms.ModelForm", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Recipe", "line_number": 30, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 35, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 38, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 39, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 40, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}, {"api_name": "models.IngredientPart", "line_number": 61, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 69, "usage_type": "name"}, {"api_name": "models.Step", "line_number": 85, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "117218063", "text": "from django.conf.urls import url\nfrom django.views.generic.base import RedirectView\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.mainpage),\n url(r'^uploadtext/$', views.uploadedFile),\n url(r'^servertext/$', views.serverFile),\n url(r'^annotate/$', views.annotateSentence),\n url(r'^maps/(.+)$', views.map),\n url(r'^mapdata/(.+)$', views.mapdata),\n\turl(r'^getcoords/$', views.getCoordsView),\n]", "sub_path": "mapsite/oldnerui/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "301904476", "text": "import time\nimport argparse\nimport numpy as np\n\nfrom utils import *\nfrom gym_pybullet_drones.envs.BaseAviary import DroneModel, Physics\nfrom gym_pybullet_drones.envs.CtrlAviary import CtrlAviary\nfrom gym_pybullet_drones.control.DSLPIDControl import DSLPIDControl\nfrom gym_pybullet_drones.utils.Logger import Logger\n\nif __name__ == \"__main__\":\n\n #### Define and parse (optional) arguments for the script ##########################################\n parser = argparse.ArgumentParser(description='Downwash example script using CtrlAviary and DSLPIDControl')\n parser.add_argument('--drone', default=DroneModel.CF2X, type=lambda model: DroneModel[model], help='Drone model (default: CF2X)', metavar='')\n parser.add_argument('--num_drones', default=2, type=int, help='Number of drones (default: 2)', metavar='')\n parser.add_argument('--physics', default=Physics.PYB_GND_DRAG_DW,type=lambda phy: Physics[phy], help='Physics updates (default: PYB_GND_DRAG_DW)', metavar='')\n parser.add_argument('--gui', default=True, type=str2bool, help='Whether to use PyBullet GUI (default: True)', metavar='')\n parser.add_argument('--record_video', default=False, type=str2bool, help='Whether to record a video (default: False)', metavar='')\n parser.add_argument('--simulation_freq_hz', default=240, type=int, help='Simulation frequency in Hz (default: 240)', metavar='')\n parser.add_argument('--control_freq_hz', default=48, type=int, help='Control frequency in Hz (default: 48)', metavar='')\n parser.add_argument('--duration_sec', default=10, type=int, help='Duration of the simulation in seconds (default: 10)', metavar='')\n ARGS = parser.parse_args()\n\n #### Initialize the simulation #####################################################################\n INIT_XYZS = np.array([[.5,0,1],[-.5,0,.5]])\n env = CtrlAviary(drone_model=ARGS.drone, num_drones=ARGS.num_drones, initial_xyzs=INIT_XYZS, physics=ARGS.physics,\n neighbourhood_radius=10, freq=ARGS.simulation_freq_hz, gui=ARGS.gui, record=ARGS.record_video, obstacles=True)\n\n #### Initialize the trajectories ###################################################################\n PERIOD = 10; NUM_WP = ARGS.control_freq_hz*PERIOD; TARGET_POS = np.zeros((NUM_WP,2))\n for i in range(NUM_WP): TARGET_POS[i,:] = [0.5*np.cos(2*np.pi*(i/NUM_WP)), 0]\n wp_counters = np.array([ 0, int(NUM_WP/2) ])\n\n #### Initialize the logger #########################################################################\n logger = Logger(logging_freq_hz=ARGS.simulation_freq_hz, num_drones=ARGS.num_drones, duration_sec=ARGS.duration_sec)\n\n #### Initialize the controllers ####################################################################\n ctrl = [DSLPIDControl(env) for i in range(ARGS.num_drones)]\n\n #### Run the simulation ############################################################################\n CTRL_EVERY_N_STEPS= int(np.floor(env.SIM_FREQ/ARGS.control_freq_hz))\n action = { str(i): np.array([0,0,0,0]) for i in range(ARGS.num_drones) }\n START = time.time()\n for i in range(ARGS.duration_sec*env.SIM_FREQ):\n\n #### Step the simulation ###########################################################################\n obs, reward, done, info = env.step(action)\n\n #### Compute control at the desired frequency @@@@@#################################################\n if i%CTRL_EVERY_N_STEPS==0:\n\n #### Compute control for the current way point #####################################################\n for j in range(ARGS.num_drones):\n action[str(j)], _, _ = ctrl[j].computeControlFromState(control_timestep=CTRL_EVERY_N_STEPS*env.TIMESTEP, state=obs[str(j)][\"state\"],\n target_pos=np.hstack([ TARGET_POS[wp_counters[j],:], INIT_XYZS[j,2] ]))\n\n #### Go to the next way point and loop #############################################################\n for j in range(ARGS.num_drones): wp_counters[j] = wp_counters[j] + 1 if wp_counters[j]<(NUM_WP-1) else 0\n\n #### Log the simulation ############################################################################\n for j in range(ARGS.num_drones): logger.log(drone=j, timestamp=i/env.SIM_FREQ, state= obs[str(j)][\"state\"], control=np.hstack([ TARGET_POS[wp_counters[j],:], INIT_XYZS[j,2], np.zeros(9) ]))\n\n #### Printout ######################################################################################\n if i%env.SIM_FREQ==0: env.render()\n\n #### Sync the simulation ###########################################################################\n if ARGS.gui: sync(i, START, env.TIMESTEP)\n\n #### Close the environment #########################################################################\n env.close()\n\n #### Save the simulation results ###################################################################\n logger.save()\n\n #### Plot the simulation results ###################################################################\n logger.plot()\n", "sub_path": "examples/downwash.py", "file_name": "downwash.py", "file_ext": "py", "file_size_in_byte": 5475, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "gym_pybullet_drones.envs.BaseAviary.DroneModel.CF2X", "line_number": 15, "usage_type": "attribute"}, {"api_name": "gym_pybullet_drones.envs.BaseAviary.DroneModel", "line_number": 15, "usage_type": "name"}, {"api_name": "gym_pybullet_drones.envs.BaseAviary.Physics.PYB_GND_DRAG_DW", "line_number": 17, "usage_type": "attribute"}, {"api_name": "gym_pybullet_drones.envs.BaseAviary.Physics", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "gym_pybullet_drones.envs.CtrlAviary.CtrlAviary", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "gym_pybullet_drones.utils.Logger.Logger", "line_number": 36, "usage_type": "call"}, {"api_name": "gym_pybullet_drones.control.DSLPIDControl.DSLPIDControl", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "283897612", "text": "# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api\nfrom datetime import datetime\n\nclass helpdesk_ticket(models.Model):\n _name = 'helpdesk.ticket.email'\n\n ticket_id = fields.Many2one('helpdesk.ticket', ondelete='set null', string='Email Id')\n email_id = fields.Many2one('mail.message')\n subject = fields.Char('Subject')\n body = fields.Text(\"Body\")\n date = fields.Date('Date')\n status = fields.Char('Status')\n\n @api.onchange('email_id')\n def onchange_email_id(self):\n if self.email_id:\n self.subject = self.email_id.subject or ''\n self['body'] = self.email_id.body\n self.date = datetime.strptime(self.email_id.date, '%Y-%m-%d %H:%M:%S').date()\n\n\n @api.multi\n def action_reply(self):\n return {\n 'type': 'ir.actions.act_window',\n 'res_model': 'mail.inbox',\n 'view_type': 'form',\n 'view_mode': 'form',\n 'target': 'new',\n 'context': {\n 'email_to':True,\n 'default_email_to': self.email_id.email_from,\n 'default_model': 'helpdesk.ticket',\n 'default_res_id': self.ticket_id.id,\n\t\t'body_html': self.body,\n 'subject': self.subject\n }\n\n }\n\n @api.multi\n # def action_forward(self):\n # return {\n # 'type': 'ir.actions.act_window',\n # 'res_model': 'mail.inbox',\n # 'view_type': 'form',\n # 'view_mode': 'form',\n # 'target': 'new',\n # 'context': {\n # 'email_to': True,\n # 'default_model': 'helpdesk.ticket',\n # 'default_res_id': self.ticket_id.id,\n # }\n\n def action_forward(self):\n return {\n 'type': 'ir.actions.act_window',\n 'res_model': 'mail.inbox',\n 'view_mode': 'form',\n 'target': 'new',\n 'views': [[False, \"form\"]],\n 'flags': {'form': {'action_buttons': True, 'options': {'mode': 'edit'}}},\n 'context': {\n 'email_to': True,\n 'default_model': 'helpdesk.ticket',\n 'default_res_id': self.ticket_id.id,\n 'body_html': self.body,\n 'subject': self.subject\n },\n }\n", "sub_path": "beta-dev1/arf_modifier_fields/models/helpdesk_ticket_email.py", "file_name": "helpdesk_ticket_email.py", "file_ext": "py", "file_size_in_byte": 2326, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "odoo.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 6, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 9, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 9, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 10, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 10, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 11, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 11, "usage_type": "name"}, {"api_name": "odoo.fields.Text", "line_number": 12, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 13, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 14, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "odoo.api.onchange", "line_number": 16, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 16, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 24, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 24, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 43, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "505487144", "text": "import csv\nimport neopixel\nimport os.path\nfrom random import randint\nimport statistics\nimport time\n\nclass candle():\n\n ### File Locations ###\n flicker_burn_file = \"./candle1.csv\"\n normal_burn_file = \"./candle2.csv\"\n\n red = 0\n green = 0\n blue = 0\n\n # Declare our internal variables for the object.\n cycle_tracker = 0\n cycle_tracker_reset = 0\n flicker_brightness = []\n four_color_neopixels = False\n interval = \"\"\n last_update = 0\n neopixel_strip = 0\n normal_brightness = []\n pixel = 0\n\n # Initialize our state tracking variables.\n active_pattern = \"choose\"\n first_pass = True\n escape_from_flicker = False\n escape_from_normal = 0\n time_in_flicker = 0\n time_in_normal = 0\n\n # Initialize\n def __init__(\n self,\n pixel,\n neopixel_strip,\n interval = .0020,\n four_color_neopixels = False,\n red = 255,\n green = 127,\n blue = 10\n ):\n\n # Initialize the variables\n\n self.pixel = pixel\n self.neopixel_strip = neopixel_strip\n self.interval = interval\n self.red = red\n self.green = green\n self.blue = blue\n\n self.four_color_neopixels = four_color_neopixels\n\n # Generate the burn brightness pattern tuples.\n self.normal_brightness = self.read_csv_file(self.normal_burn_file)\n self.flicker_brightness = self.read_csv_file(self.flicker_burn_file)\n self.cycle_tracker_reset = len(self.normal_brightness) - 1\n\n def build_candle_array(self, data):\n '''\n The source data in the csv file only references the brightness of the\n candle, not the Red, Green and Blue brightness values. This function\n will calculate the Red, Green and Blue brightness value based on the\n brightness value in the \"data\" variable. The Red, Green, Blue data is\n saved as a tuple. The function returns a tuple of tuples to the main\n program.\n '''\n maplen = 200\n scale = 0.8 * maplen / statistics.median(data)\n temp_tuple = []\n for element in data:\n brightness = scale * element + 1\n current_red = int(self.red * brightness/256)\n if (current_red > 255):\n current_red = 255\n current_green = int(self.green * brightness/256)\n if (current_green > 255):\n current_green = 255\n current_blue = int(self.blue * brightness/256)\n if (current_blue > 255):\n current_blue = 255\n if (self.four_color_neopixels):\n temp_tuple.append((current_red, current_green, current_blue))\n else:\n temp_tuple.append((current_green, current_red, current_blue))\n return temp_tuple\n\n def read_csv_file(self, file_name):\n '''\n This function reads in the referenced .csv file, and returns a tuple\n of tuples to the main program. Each tuple represents a Red, Green and\n blue value, based on the pattern information contained in the .csv file\n '''\n data_tracker = []\n if os.path.isfile(file_name):\n with open(file_name) as csvfile:\n csv_reader = csv.reader(csvfile)\n for row in csv_reader:\n data_tracker.append(float(row[1]))\n else:\n print (\"Can't read: \", file_name)\n exit(1)\t\n final_tuple = self.build_candle_array(data_tracker)\n return final_tuple\n\n def cleanup(self):\n '''\n This method will set the red, green, blue, and optional White values\n for the current pixel to off.\n\n Useful for turning off a given pixel :)\n '''\n # Set the LED to black; i.e., turn off the current LED.\n if (self.four_color_neopixels):\n self.neopixel_strip[self.pixel] = (0,0,0,0)\n else:\n self.neopixel_strip[self.pixel] = (0,0,0)\n self.neopixel_strip.show()\n\n def update(self,chance_flicker):\n if (self.first_pass):\n rand_sleep = (randint(0,2)) + (randint(0,1000)/1000)\n self.last_update += rand_sleep\n self.first_pass = False\n if (time.time() - self.last_update) > self.interval:\n # Time to update\n self.last_update = time.time()\n if self.active_pattern == \"normal\":\n if self.time_in_normal == 0 and self.escape_from_normal == 0:\n self.escape_from_normal = randint(500,1500)\n # Pick a random starting point in the normal_brightness tuple\n self.cycle_tracker = randint(0,(len(self.normal_brightness) - 1))\n elif self.time_in_normal > self.escape_from_normal:\n self.time_in_normal = 0\n self.escape_from_normal = 0\n self.active_pattern = \"choose\"\n else:\n # Do Normal Burn stuff\n self.time_in_normal += 1\n self.normal()\n elif self.active_pattern == \"flicker\":\n # Do flicker Stuff\n if self.escape_from_flicker:\n self.escape_from_flicker = False\n self.active_pattern = \"choose\"\n else:\n self.flicker()\n elif self.active_pattern == \"choose\":\n # Choose your own adventure!\n if randint(1,10) > chance_flicker:\n self.active_pattern = \"flicker\"\n else:\n self.active_pattern = \"normal\"\n\n def normal(self):\n '''\n This function is responsible for iterating through the normal candle\n burn pattern. The pattern is a series of brightness value tuples,\n stored in the flicker_brightness variable. The cycle_tracker variable\n is a bookmark, indicating where the current point is in the playback of\n the pattern. If the tracker rolls over the total length of the tuple,\n the state variables are reset to starting conditions.\n '''\n if (self.four_color_neopixels):\n self.neopixel_strip[self.pixel] = (\n (self.normal_brightness[self.cycle_tracker][0]),\n (self.normal_brightness[self.cycle_tracker][1]),\n (self.normal_brightness[self.cycle_tracker][2]),\n 0\n )\n else:\n self.neopixel_strip[self.pixel] = (\n (self.normal_brightness[self.cycle_tracker][0]),\n (self.normal_brightness[self.cycle_tracker][1]),\n (self.normal_brightness[self.cycle_tracker][2])\n )\n self.neopixel_strip.show()\n self.cycle_tracker += 1\n if (self.cycle_tracker > self.cycle_tracker_reset):\n self.cycle_tracker = 0\n self.first_pass = True\n\n def flicker(self):\n '''\n This function is responsible for iterating through the flicker pattern.\n The pattern is a series of brightness value tuples, stored in the\n flicker_brightness variable. The cycle_tracker variable is a bookmark,\n indicating where the current point is in the playback of the pattern.\n If the tracker rolls over the total length of the tuple, the state\n variables are reset to starting conditions.\n '''\n if (self.four_color_neopixels):\n self.neopixel_strip[self.pixel] = (\n self.flicker_brightness[self.cycle_tracker][0],\n self.flicker_brightness[self.cycle_tracker][1],\n self.flicker_brightness[self.cycle_tracker][2],\n 0\n )\n else:\n self.neopixel_strip[self.pixel] = (\n self.flicker_brightness[self.cycle_tracker][0],\n self.flicker_brightness[self.cycle_tracker][1],\n self.flicker_brightness[self.cycle_tracker][2]\n )\n self.neopixel_strip.show()\n self.cycle_tracker += 1\n if (self.cycle_tracker > self.cycle_tracker_reset):\n self.cycle_tracker = 0\n self.first_pass = True\n self.escape_from_flicker = True\n", "sub_path": "tealight.py", "file_name": "tealight.py", "file_ext": "py", "file_size_in_byte": 8194, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "statistics.median", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 101, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 103, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 128, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 136, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 138, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "457345973", "text": "# for standard lib imports\nimport json\nimport datetime\n\n# for standard package imports\n\n# for specified package imports\nfrom website.datacenter.models import *\nfrom website.oss.core.log import logger\n\nBKC_PROJECT_BUILD_DEFAULT_CONFIG = {\n 'teamcity': {\n \"params\": {\n \"pwd\": \"eptvr26$\",\n \"user\": \"ccr\\\\sys_osshop\",\n \"server\": \"https://ubit-teamcity-sh.intel.com\"\n },\n \"type\": \"build\",\n \"proxy_type\": \"teamcity\"\n },\n 'teamcity_sh': {\n \"params\": {\n \"pwd\": \"eptvr26$\",\n \"user\": \"ccr\\\\sys_osshop\",\n \"server\": \"https://ubit-teamcity-sh.intel.com\"\n },\n \"type\": \"build\",\n \"proxy_type\": \"teamcity\"\n },\n 'teamcity_ba': {\n \"params\": {\n \"pwd\": \"eptvr26$\",\n \"user\": \"ccr\\\\sys_osshop\",\n \"server\": \"https://ubit-teamcity-ba.intel.com\"\n },\n \"type\": \"build\",\n \"proxy_type\": \"teamcity\"\n },\n 'jenkins': {\n \"params\": {\n \"pwd\": \"f09db9873111b8b55bf827241068356d\",\n \"user\": \"sys_degsi1\",\n \"server\": \"https://boss-jenkins.intel.com\"\n },\n \"type\": \"build\",\n \"proxy_type\": \"jenkins\"\n }\n}\n\nclass ExecFWType(object):\n BUILD = 'build'\n TEST = 'test'\n UPLOAD = 'upload'\n AUTO_CHECKIN = 'auto_checkin'\n\n\nclass ProxyType(object):\n TEAMCITY = 'teamcity'\n JENKINS = 'jenkins'\n CAF = 'caf'\n PERFORCE = 'p4'\n ATF = 'atf'\n IFWI = 'ifwi'\n\n\nclass ReportProxyType(object):\n JAMA = 'JAMA'\n GIT = 'GIT'\n\n class GitParams(object):\n LOCAL_PATH = 'local_path'\n MAIN_STREAM = 'main_stream'\n\n\nclass ProjConfigMgr(object):\n project = None\n project_config = None\n targets = None\n devBranch = None\n\n class ConfigDoesNotExist(Exception):\n pass\n\n def __init__(self, proj_id):\n self.project = Project.objects.using('oss').get(pk=proj_id)\n if not self.project.main_proj_id:\n self.project_config = json.loads(self.project.project_config)\n self.targets = json.loads(self.project.auto_config).get('targets', {}) if self.project.auto_config else {}\n self.devBranch = json.loads(self.project.auto_config).get('devBranch',\n {}) if self.project.auto_config else {}\n elif self.project.main_proj_id == -1:\n self.project_config = json.loads(self.project.project_config)\n sub_projects = Project.objects.using('oss').filter(main_proj_id=self.project.pk)\n self.targets = {}\n self.devBranch = {}\n for sub_project in sub_projects:\n if sub_project.auto_config:\n self.targets.update(json.loads(sub_project.auto_config).get('targets', {}))\n self.devBranch.update(json.loads(sub_project.auto_config).get('devBranch', {}))\n else:\n self.targets = json.loads(self.project.auto_config).get('targets', {}) if self.project.auto_config else {}\n self.devBranch = json.loads(self.project.auto_config).get('devBranch',\n {}) if self.project.auto_config else {}\n main_project = Project.objects.using('oss').get(pk=self.project.main_proj_id)\n self.project_config = json.loads(main_project.project_config)\n\n def _save_project_config_into_db(self):\n try:\n if not self.project.main_proj_id or self.project.main_proj_id == -1:\n self.project.project_config = json.dumps(self.project_config)\n self.project.save(using='oss')\n else:\n main_project = Project.objects.using('oss').get(pk=self.project.main_proj_id)\n main_project.project_config = json.dumps(self.project_config)\n main_project.save(using='oss')\n return True\n except Exception as e:\n logger.error('ProjConfigMgr._save_project_config_into_db: {}'.format(e))\n return False\n\n def get_targets(self):\n return self.targets\n\n def get_devBranch(self):\n return self.devBranch\n\n def save_targets(self, value):\n if type(value) != dict:\n logger.error('ProjConfigMgr.save_targets: targets value is not dictionary. value: {0}'.format(value))\n return False\n if self.project.main_proj_id == -1:\n logger.error(\n 'ProjConfigMgr.save_targets: Cannot save targets value into a main project. project_id: {0}'.format(\n self.project.proj_id))\n return False\n\n auto_config = json.loads(self.project.auto_config)\n auto_config['targets'] = value\n self.project.auto_config = json.dumps(auto_config)\n self.project.save(using='oss')\n logger.debug('Save targets: {0} into project: {1}'.format(value, self.project.proj_id))\n self.targets = value\n return True\n\n def save_devBranch(self, value):\n if type(value) != dict:\n logger.error('ProjConfigMgr.save_targets: targets value is not dictionary. value: {0}'.format(value))\n return False\n if self.project.main_proj_id == -1:\n logger.error(\n 'ProjConfigMgr.save_targets: Cannot save targets value into a main project. project_id: {0}'.format(\n self.project.proj_id))\n return False\n auto_config = json.loads(self.project.auto_config)\n auto_config['devBranch'] = value\n self.project.auto_config = json.dumps(auto_config)\n self.project.save(using='oss')\n logger.debug('Save targets: {0} into project: {1}'.format(value, self.project.proj_id))\n self.devBranch = value\n return True\n\n def get_specific_proxy_config(self, task_type, param_name=None):\n if task_type == 'upload' and param_name == 'xml':\n param_name = 'p4'\n ret = None\n for config in self.project_config['default_proxy_config']:\n if config['type'] == task_type and config['proxy_type'] == param_name:\n ret = config\n break\n return ret\n ret = None\n for config in self.project_config['default_proxy_config']:\n if config['type'] == task_type:\n ret = config\n break\n return ret\n\n def get_dev_specific_proxy_config(self, task_type):\n ret = None\n for config in self.project_config['default_proxy_config']:\n if config['type'] == task_type and config['proxy_type'] == \"p4\":\n ret = config\n break\n return ret\n\n def get_upload_config(self, proxy_type):\n ret = None\n for config in self.project_config['default_proxy_config']:\n if config['type'] == ExecFWType.UPLOAD and config['proxy_type'] == proxy_type:\n ret = config\n break\n return ret\n\n def get_build_config(self, proxy_type):\n ret = None\n for config in self.project_config['default_proxy_config']:\n if config['type'] == ExecFWType.BUILD and config['proxy_type'] == proxy_type:\n ret = config\n break\n return ret\n\n def save_specific_proxy_type(self, task_type, proxy_type):\n config = self.get_specific_proxy_config(task_type)\n config['proxy_type'] = proxy_type\n return self._save_project_config_into_db()\n\n def save_specific_proxy_config(self, task_type, param_name, param_value):\n if not self.validate_param_type(param_name, param_value):\n return False\n config = self.get_specific_proxy_config(task_type, param_name)\n config['params'][param_name] = param_value\n return self._save_project_config_into_db()\n\n def save_dev_specific_proxy_config(self, task_type, param_name, param_value):\n if not self.validate_dev_param_type(param_name, param_value):\n return False\n config = self.get_dev_specific_proxy_config(task_type)\n config['params'][param_name] = param_value\n return self._save_project_config_into_db()\n\n def save_upload_config(self, proxy_type, param_name, param_value):\n if not self.validate_param_type(param_name, param_value):\n return False\n config = self.get_upload_config(proxy_type)\n config['params'][param_name] = param_value\n return self._save_project_config_into_db()\n\n @staticmethod\n def validate_param_type(param_name, param_value):\n if param_name in (\"dev_xml\", 'xml', 'igredient_path'):\n if type(param_value) == list:\n return True\n else:\n logger.error(\n 'ProjConfigMgr.validate_param_type: invalid param_value type. name: {0}, value: {1}'.format(\n param_name, param_value))\n return False\n else:\n if isinstance(param_value, str):\n return True\n else:\n logger.error(\n 'ProjConfigMgr.validate_param_type: invalid param_value type. name: {0}, value: {1}'.format(\n param_name, param_value))\n return False\n\n @staticmethod\n def validate_dev_param_type(param_name, param_value):\n if param_name in ('dev_xml', 'igredient_path'):\n if type(param_value) == list:\n return True\n else:\n logger.error(\n 'ProjConfigMgr.validate_param_type: invalid param_value type. name: {0}, value: {1}'.format(\n param_name, param_value))\n return False\n else:\n if isinstance(param_value, str):\n return True\n else:\n logger.error(\n 'ProjConfigMgr.validate_param_type: invalid param_value type. name: {0}, value: {1}'.format(\n param_name, param_value))\n return False\n\n def get_p4_xml_config(self):\n ret = []\n p4_config = self.get_upload_config(ProxyType.PERFORCE)\n if p4_config:\n ret = p4_config['params']['xml']\n return ret\n\n def get_bat_report_config(self):\n ret = False\n bat_report_config = self.get_upload_config(ProxyType.ATF)\n if bat_report_config:\n try:\n bat = bat_report_config['params']['bat']\n if bat == u'true':\n ret = True\n except:\n ret = False\n return ret\n\n def get_bdba_report_config(self):\n ret = False\n bdba_report_config = self.get_upload_config(ProxyType.ATF)\n if bdba_report_config:\n try:\n bdba = bdba_report_config['params']['bdba']\n if bdba == 'true':\n ret = True\n except Exception as e:\n logger.info('%s no BDBA config' % self.project.name)\n ret = False\n return ret\n\n def get_dev_branch_config(self):\n ret = False\n dev_branch_config = self.get_upload_config(ProxyType.ATF)\n if dev_branch_config:\n try:\n dev_branch = dev_branch_config['params']['dev_branch']\n if dev_branch == u'true':\n ret = True\n except:\n ret = False\n return ret\n\n def get_auto_ing_config(self):\n ret = False\n dev_branch_config = self.get_upload_config(ProxyType.ATF)\n if dev_branch_config:\n try:\n dev_branch = dev_branch_config['params']['auto_ing']\n if dev_branch == u'1':\n ret = True\n except:\n ret = False\n return ret\n\n def get_dev_p4_xml_config(self):\n ret = []\n p4_config = self.get_upload_config(ProxyType.PERFORCE)\n if p4_config:\n ret = p4_config['params']['dev_xml']\n return ret\n\n def get_build_task_param_by_phase_and_target(self, phase, target):\n try:\n return self.targets[target][phase][0]\n except Exception as e:\n logger.error('ProjConfigMgr.get_build_task_param_by_phase_and_target: %s' % e)\n return ''\n\n def get_build_task_param_by_phase_and_devBranch(self, phase, devBranch):\n try:\n return self.devBranch[devBranch][phase][0]\n except Exception as e:\n logger.error('ProjConfigMgr.get_build_task_param_by_phase_and_devBranch: %s' % e)\n return ''\n\n def get_specific_report_proxy_config(self, proxy_type):\n if 'default_report_proxy_config' not in self.project_config:\n raise self.ConfigDoesNotExist(\n \"Project: {0} has no default report proxy config section.\".format(self.project.name))\n for now in self.project_config['default_report_proxy_config']:\n if now['type'].upper() == proxy_type.upper():\n return now['params']\n raise self.ConfigDoesNotExist(\n \"Project: {0} has no report proxy type: {1}\".format(self.project.name, proxy_type))\n\n def get_related_test_execution_proj_id(self):\n # This config only works for BIOS project to get the related IFWI project id and trigger test.\n return self.project_config.get('related_test_execution_proj_id', [])\n\n def get_preferred_build_target_name(self):\n # This config used to decide which build will be promoted when there are more than one build in one auto task.\n return self.project_config.get('preferred_build_target', '')\n\n def check_branch(self, branch_name, phase):\n if phase == 'nightly':\n enabled_branch = self.project_config.get('enabled_branch', None)\n if enabled_branch is None or not isinstance(enabled_branch, list):\n return True\n if branch_name in enabled_branch:\n return True\n else:\n return False\n return True\n\n def check_trigger_time_window(self, input_time=None):\n return True\n\n @staticmethod\n def create_default_bkc_target_config_dict(platform_name):\n return {\n platform_name: {\n \"0\": platform_name,\n \"checkin\": [\n \"\",\n []\n ],\n \"daily\": [\n \"\",\n []\n ]\n }\n }\n\n @staticmethod\n def get_bkc_target_config_from_old_target_dict(old_target_dict, new_platform_name):\n return {\n new_platform_name: {\n \"0\": new_platform_name,\n \"checkin\": old_target_dict['checkin'],\n \"daily\": old_target_dict['daily']\n }\n }\n\n @staticmethod\n def get_bkc_dev_config_from_old_dev_dict(old_target_dict, new_platform_name):\n return {\n new_platform_name: {\n \"0\": new_platform_name,\n \"checkin\": old_target_dict['checkin'],\n \"daily\": old_target_dict['daily']\n }\n }\n\n @staticmethod\n def create_default_bkc_devbranch_config_dict(platform_name):\n return {\n platform_name: {\n \"0\": platform_name,\n \"checkin\": [\n \"\",\n []\n ],\n \"daily\": [\n \"\",\n []\n ]\n }\n }\n\n\nclass ProjPhaseConfigMgr(ProjConfigMgr):\n phase_config = None\n\n def __init__(self, proj_id, phase):\n self.phase = phase\n ProjConfigMgr.__init__(self, proj_id)\n for config in self.project_config['phase_config']:\n if config['type'] == phase:\n self.phase_config = config\n break\n if not self.phase_config:\n raise Exception(\"Project: {0} dose not contain phase: {1}\".format(proj_id, phase))\n if not self.phase_config['task'].get('proxy_config'):\n self.phase_config['task']['proxy_config'] = self.project_config['default_proxy_config']\n\n def get_specific_proxy_config(self, task_type):\n ret = None\n for config in self.phase_config['task']['proxy_config']:\n if config['type'] == task_type:\n ret = config\n break\n if not ret:\n ret = ProjConfigMgr.get_specific_proxy_config(self, task_type)\n return ret\n\n def get_build_task_param_by_target(self, target):\n return self.get_build_task_param_by_phase_and_target(self.phase, target)\n\n def get_dev_build_task_param_by_target(self, dev):\n return self.get_build_task_param_by_phase_and_devBranch(self.phase, dev)\n\n def check_branch(self, branch_name, phase):\n enabled_branch = self.phase_config.get('enabled_branch', None)\n if enabled_branch is None or not isinstance(enabled_branch, list):\n return True\n\n if branch_name in enabled_branch:\n return True\n else:\n return False\n\n def check_trigger_time_window(self, input_time=None):\n # input_time should be datetime object\n time_window_config = self.phase_config.get('trigger_time_window', None)\n if not time_window_config:\n return True\n\n start_time = time_window_config.get(\"start_time\", '')\n end_time = time_window_config.get(\"end_time\", '')\n if not start_time or not end_time:\n return True\n\n if input_time:\n current_time = input_time\n else:\n current_time = datetime.datetime.now()\n\n current_time = int(current_time.strftime(\"%H%M\"))\n start_time = int(start_time.replace(':', ''))\n end_time = int(end_time.replace(':', ''))\n\n if start_time < end_time:\n if start_time <= current_time <= end_time:\n return True\n else:\n return False\n else:\n if current_time >= start_time or current_time <= end_time:\n return True\n else:\n return False\n\n\ndef get_proj_config_mgr(proj_id, phase):\n if phase in ('nightly', 'preflight', 'submit'):\n proj_config_mgr = ProjConfigMgr(proj_id)\n else:\n proj_config_mgr = ProjPhaseConfigMgr(proj_id, phase)\n logger.debug(\"phase_config:{}\".format(proj_config_mgr.phase_config))\n logger.debug(\"project_config:{}\".format(proj_config_mgr.project_config))\n logger.debug(\"targets:{}\".format(proj_config_mgr.targets))\n return proj_config_mgr\n", "sub_path": "oss/website/main/proj_config_mgr.py", "file_name": "proj_config_mgr.py", "file_ext": "py", "file_size_in_byte": 18628, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "json.loads", "line_number": 87, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 88, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 89, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 98, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 99, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 101, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 102, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 110, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 114, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 118, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 118, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 129, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 129, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 132, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 132, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 137, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 139, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger.debug", "line_number": 141, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 141, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 147, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 147, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 150, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 150, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 154, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 156, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger.debug", "line_number": 158, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 158, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 234, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 234, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 242, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 242, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 253, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 253, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 261, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 261, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.info", "line_number": 294, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 294, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 333, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 333, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.error", "line_number": 340, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 340, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 483, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 483, "usage_type": "attribute"}, {"api_name": "website.oss.core.log.logger.debug", "line_number": 506, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 506, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.debug", "line_number": 507, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 507, "usage_type": "name"}, {"api_name": "website.oss.core.log.logger.debug", "line_number": 508, "usage_type": "call"}, {"api_name": "website.oss.core.log.logger", "line_number": 508, "usage_type": "name"}]} +{"seq_id": "515563064", "text": "import flask\n\nfrom .. import auth\n\n\n@auth.bp.route(\"/get_username\",methods=(\"GET\",\"POST\"))\ndef get_username():\n # front-end\n if flask.request.method==\"GET\":\n if auth.check_client_session():\n return flask.redirect(\"/\")\n return flask.render_template(\n \"auth.html\",\n title=\"Forgot username\",\n action_name=\"submit\",\n ctrl_script_src=\"get_username.js\"\n )\n \n \n # back-end\n elif flask.request.method==\"POST\":\n form=flask.request.get_json()\n try:\n email=str(form[\"email\"]).lower()\n except (KeyError,TypeError):\n return \"{}\",400,{\"Content-Type\":\"application/json\"}\n \n if not(\n auth.check_email(email)\n ):\n return \"{}\",400,{\"Content-Type\":\"application/json\"}\n \n conn=auth.connectDB()\n cur=conn.cursor()\n \n cur.execute(\"select username from users where email=%s and status=%s limit 1;\",(email,\"verified\"))\n try:\n username=cur.fetchone()[0]\n except TypeError:\n pass\n else:\n auth.send_email(auth.NOREPLY,email,\"Your username at %s\"%auth.PROJECTNAME,\"

Your username at %s is:

%s


Best regards,

%s

\"%(auth.PROJECTNAME,username,auth.PROJECTNAME))\n \n conn.close()\n \n return \"{}\",{\"Content-Type\":\"application/json\"}", "sub_path": "src/auth/get_username.py", "file_name": "get_username.py", "file_ext": "py", "file_size_in_byte": 1421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.request", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request.get_json", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "369204920", "text": "import json\nimport numpy\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import MultipleLocator\n\nresult_file = \"./results.txt\"\n\n\ndef plot_line_chart(x, y, label, title):\n # plt.plot(x, y, 'r--', label=label)\n plt.scatter(x, y)\n plt.title(title)\n xlabel = 'Order'\n ylabel = 'Speed MB/s'\n if title == 'write_node_insert':\n ylabel = ' Million Ops'\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n\n # plt.legend()\n plt.savefig('./' + title + '.jpg')\n# print(dir(plt))\n #plt.show()\n plt.close()\n\n\ndef cal_logic_order(ts):\n return [ str(i) for i in range(len(ts))]\n\ndef cal_to_mb(nums, key):\n if key == 'write_node_insert':\n return [ (float(x)/1000000.0) for x in nums]\n return [ (float(x)/500.0) for x in nums]\n\ndef cal_proxy_mb(nums, key):\n if key == 'write_node_insert':\n return [ (float(x)/1000000.0) for x in nums]\n return nums\n\n\ndef cal_avg(durs, nums, key):\n dur = sum([float(d) for d in durs])\n num = sum([float(n) for n in nums])\n avg = num / dur * 1000\n return avg\n\nif __name__ == \"__main__\":\n f = open(result_file, \"r\")\n line = f.readline()\n result_str = json.loads(line)\n for key in result_str:\n if key in [\"query_node_insert\", \"reader_get_pulsar\", \"write_node_insert\", \"writer_get_pulsar\"]:\n plot_line_chart(cal_logic_order(result_str[key][\"InsertTime\"]), cal_to_mb(result_str[key][\"Speed\"], key), \"insert\", key)\n print(key, \" \", cal_to_mb([result_str[key][\"AvgSpeed\"]], key)[0])\n #print(key, \" \", cal_avg(result_str[key][\"DurationInMilliseconds\"], result_str[key][\"MsgLength\"], key))\n else:\n plot_line_chart(cal_logic_order(result_str[key][\"InsertTime\"]), result_str[key][\"ThroughputInMB\"], \"insert\", key)\n print(key, \" \", cal_to_mb([result_str[key][\"AvgSpeed\"]], key)[0])\n #print(key, \" \", result_str[key][\"AvgSpeed\"])\n", "sub_path": "plot_line_chart.py", "file_name": "plot_line_chart.py", "file_ext": "py", "file_size_in_byte": 1909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.pyplot.scatter", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "399981044", "text": "\"\"\"We will build a collection of 20 non-interannealing primers.\n\nDNA Chisel is not originally meant for creating collections of sequences\n(frameworks such as D-tailor were written with this purpose in mind), but\nit is still possible to create collections of inter-compatible sequences.\n\nHere we create 20 short sequences one after the other, while making sure\nthat each new primer is \"compatible\" with the other ones made so far.\nCompatibility in this example means low heterodimerization (=the primers\ncould be used together without undesired annealing between them).\n\n\"\"\"\n\nfrom dnachisel import DnaOptimizationProblem, random_dna_sequence\nfrom dnachisel.builtin_specifications import (\n AvoidHeterodimerization,\n EnforceGCContent,\n AvoidPattern,\n)\n\n\ndef create_new_primer(existing_primers):\n \"\"\"Create a new primer based on the primers created so far\"\"\"\n problem = DnaOptimizationProblem(\n sequence=random_dna_sequence(length=20),\n constraints=[\n AvoidHeterodimerization(existing_primers, tmax=3),\n AvoidPattern(\"3x3mer\"),\n AvoidPattern(\"4xG\"),\n ],\n objectives=[EnforceGCContent(target=0.6)],\n logger=None,\n )\n problem.resolve_constraints()\n problem.optimize()\n return problem.sequence\n\n\n# MAIN LOOP, WHERE PRIMERS ARE CREATED ONE BY ONE\n\nexisting_primers = []\nfor i in range(20):\n new_primer = create_new_primer(existing_primers)\n existing_primers.append(new_primer)\n\nprint(\"PRIMERS GENERATED: \\n\\n%s\\n\" % \"\\n\".join(existing_primers))\n\n# (OPTIONAL VERIFICATION OF THE COLLECTION)\n\nimport itertools\nimport primer3\nfrom dnachisel.biotools import gc_content\n\nmax_tm = max(\n primer3.calcHeterodimer(seq1, seq2).tm\n for seq1, seq2 in itertools.combinations(existing_primers, 2)\n)\nprint(\"Max Tm heterodimerization between any 2 primers: %.2f\" % max_tm)\n\ngc_contents = [gc_content(p) for p in existing_primers]\nprint(\"GC content range %.2f-%.2f\" % (min(gc_contents), max(gc_contents)))\n\n", "sub_path": "examples/common_scenarios/primers_collection.py", "file_name": "primers_collection.py", "file_ext": "py", "file_size_in_byte": 1990, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "dnachisel.DnaOptimizationProblem", "line_number": 24, "usage_type": "call"}, {"api_name": "dnachisel.random_dna_sequence", "line_number": 25, "usage_type": "call"}, {"api_name": "dnachisel.builtin_specifications.AvoidHeterodimerization", "line_number": 27, "usage_type": "call"}, {"api_name": "dnachisel.builtin_specifications.AvoidPattern", "line_number": 28, "usage_type": "call"}, {"api_name": "dnachisel.builtin_specifications.AvoidPattern", "line_number": 29, "usage_type": "call"}, {"api_name": "dnachisel.builtin_specifications.EnforceGCContent", "line_number": 31, "usage_type": "call"}, {"api_name": "primer3.calcHeterodimer", "line_number": 55, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 56, "usage_type": "call"}, {"api_name": "dnachisel.biotools.gc_content", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "571355507", "text": "from flask import render_template, redirect, url_for, abort, flash, request,\\\n current_app, make_response\nfrom flask.ext.login import login_required, current_user\nfrom . import main\nfrom .forms import EditProfileForm, EditProfileAdminForm, PostForm\nfrom ..models import Permission, Role, User, Post\nfrom ..decorators import admin_required, permission_required\n\n# def populate_blogform_choices(blog_form):\n# blog_form.authorid_select_field.choices = [(g.id, g.name) for g in users.query.order_by('id')]\n# blog_form.languageid_select_field.choices = [(g.languageid, g.language) for g in languages.query.order_by('languageid')]\n\n# @main.route('/relations')\n# def rel():\n# return render_template(\"relationships.html\")\n\n@main.route('/ehome')\ndef ehome():\n return render_template('home.html')\n\n@main.route('/about')\ndef about():\n return render_template(\"AboutUs.html\")\n\n@main.route('/pil')\ndef pil():\n return render_template(\"ParentInformationLetter.html\")\n\n@main.route('/approach')\ndef approach():\n return render_template(\"OurApproach.html\")\n\n@main.route('/adenviro')\ndef adenviro():\n return render_template(\"TodaysAdmissionEnvironment.html\")\n\n@main.route('/placement')\ndef placement():\n return render_template(\"PlacementServices.html\")\n\n@main.route('/testimonial')\ndef testimonial():\n return render_template(\"Testimonials.html\")\n\n@main.route('/contact')\ndef contact():\n return render_template(\"ContactUs.html\")\n\n@main.route('/gameplan')\ndef gameplan():\n return render_template(\"GamePlan.html\")\n\n@main.route('/secretstuff')\ndef secretstuff():\n return render_template(\"SecretStuff.html\")\n\n@main.route('/blogentry')\ndef blogentry():\n return render_template(\"BlogEntry.html\")\n\n\n\n\n\n\n# @main.route('/brdg')\n# def aboutUs():\n# return render_template('brdg.html')\n\n@main.route('/info')\ndef info():\n return render_template('RequestInformation.html')\n\n@main.route('/cinfo')\ndef cinfo():\n return render_template('Chinese/cRequestInformation.html')\n\n@main.route('/chineseAboutUs')\ndef aboutUsC():\n return render_template('Chinese/aboutUs.html') \n\n@main.route('/chinese')\ndef brdgC():\n return render_template('Chinese/brdg.html') \n\n@main.route('/', methods=['GET', 'POST'])\ndef index():\n form = PostForm()\n if current_user.can(Permission.WRITE_ARTICLES) and \\\n form.validate_on_submit():\n post = Post(body=form.body.data,\n author=current_user._get_current_object())\n db.session.add(post)\n return redirect(url_for('.index'))\n page = request.args.get('page', 1, type=int)\n show_followed = False\n if current_user.is_authenticated():\n show_followed = bool(request.cookies.get('show_followed', ''))\n if show_followed:\n query = current_user.followed_posts\n else:\n query = Post.query\n pagination = query.order_by(Post.timestamp.desc()).paginate(\n page, per_page=current_app.config['FLASKY_POSTS_PER_PAGE'],\n error_out=False)\n posts = pagination.items\n return render_template('index.html', form=form, posts=posts,\n show_followed=show_followed, pagination=pagination)\n\n\n@main.route('/user/')\ndef user(username):\n user = User.query.filter_by(username=username).first_or_404()\n page = request.args.get('page', 1, type=int)\n pagination = user.posts.order_by(Post.timestamp.desc()).paginate(\n page, per_page=current_app.config['FLASKY_POSTS_PER_PAGE'],\n error_out=False)\n posts = pagination.items\n return render_template('user.html', user=user, posts=posts,\n pagination=pagination)\n\n\n@main.route('/edit-profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n form = EditProfileForm()\n if form.validate_on_submit():\n current_user.name = form.name.data\n current_user.location = form.location.data\n current_user.about_me = form.about_me.data\n db.session.add(current_user)\n flash('Your profile has been updated.')\n return redirect(url_for('.user', username=current_user.username))\n form.name.data = current_user.name\n form.location.data = current_user.location\n form.about_me.data = current_user.about_me\n return render_template('edit_profile.html', form=form)\n\n\n@main.route('/edit-profile/', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef edit_profile_admin(id):\n user = User.query.get_or_404(id)\n form = EditProfileAdminForm(user=user)\n if form.validate_on_submit():\n user.email = form.email.data\n user.username = form.username.data\n user.confirmed = form.confirmed.data\n user.role = Role.query.get(form.role.data)\n user.name = form.name.data\n user.location = form.location.data\n user.about_me = form.about_me.data\n db.session.add(current_user)\n flash('The profile has been updated.')\n return redirect(url_for('.user', username=user.username))\n form.email.data = user.email\n form.username.data = user.username\n form.confirmed.data = user.confirmed\n form.role.data = user.role_id\n form.name.data = user.name\n form.location.data = user.location\n form.about_me.data = user.about_me\n return render_template('edit_profile.html', form=form, user=user)\n\n\n@main.route('/post/')\ndef post(id):\n post = Post.query.get_or_404(id)\n return render_template('post.html', posts=[post])\n\n@main.route('/post/new')\ndef newpost():\n return render_template('post.html', posts=[post])\n\n\n@main.route('/edit/', methods=['GET', 'POST'])\n@login_required\ndef edit(id):\n post = Post.query.get_or_404(id)\n if current_user != post.author and \\\n not current_user.can(Permission.ADMINISTER):\n abort(403)\n form = PostForm()\n if form.validate_on_submit():\n post.body = form.body.data\n db.session.add(post)\n flash('The post has been updated.')\n return redirect(url_for('.post', id=post.id))\n form.body.data = post.body\n return render_template('edit_post.html', form=form)\n\n\n@main.route('/follow/')\n@login_required\n@permission_required(Permission.FOLLOW)\ndef follow(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user.')\n return redirect(url_for('.index'))\n if current_user.is_following(user):\n flash('You are already following this user.')\n return redirect(url_for('.user', username=username))\n current_user.follow(user)\n flash('You are now following %s.' % username)\n return redirect(url_for('.user', username=username))\n\n\n@main.route('/unfollow/')\n@login_required\n@permission_required(Permission.FOLLOW)\ndef unfollow(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user.')\n return redirect(url_for('.index'))\n if not current_user.is_following(user):\n flash('You are not following this user.')\n return redirect(url_for('.user', username=username))\n current_user.unfollow(user)\n flash('You are not following %s anymore.' % username)\n return redirect(url_for('.user', username=username))\n\n\n@main.route('/followers/')\ndef followers(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user.')\n return redirect(url_for('.index'))\n page = request.args.get('page', 1, type=int)\n pagination = user.followers.paginate(\n page, per_page=current_app.config['FLASKY_FOLLOWERS_PER_PAGE'],\n error_out=False)\n follows = [{'user': item.follower, 'timestamp': item.timestamp}\n for item in pagination.items]\n return render_template('followers.html', user=user, title=\"Followers of\",\n endpoint='.followers', pagination=pagination,\n follows=follows)\n\n\n@main.route('/followed-by/')\ndef followed_by(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user.')\n return redirect(url_for('.index'))\n page = request.args.get('page', 1, type=int)\n pagination = user.followed.paginate(\n page, per_page=current_app.config['FLASKY_FOLLOWERS_PER_PAGE'],\n error_out=False)\n follows = [{'user': item.followed, 'timestamp': item.timestamp}\n for item in pagination.items]\n return render_template('followers.html', user=user, title=\"Followed by\",\n endpoint='.followed_by', pagination=pagination,\n follows=follows)\n\n\n@main.route('/all')\n@login_required\ndef show_all():\n resp = make_response(redirect(url_for('.index')))\n resp.set_cookie('show_followed', '', max_age=30*24*60*60)\n return resp\n\n\n@main.route('/followed')\n@login_required\ndef show_followed():\n resp = make_response(redirect(url_for('.index')))\n resp.set_cookie('show_followed', '1', max_age=30*24*60*60)\n return resp\n\n\n# @main.route('/contact')\n# @login_required\n# def show_ContactForm():\n# contact = ContactForm()\n# return render_template('contact.html', contact=contact)\n\n# @main.route('/add-language', methods=['GET', 'POST'])\n# @login_required\n# def add_language():\n# form = AddLanguageForm()\n# if form.validate_on_submit():\n# db.session.add(language)\n# db.session.commit()\n# flash('The language has been added.')\n# return redirect(url_for('main.index'))\n# flash('Language Inserted')\n# else:\n# flash('Insert my Language.')\n# return render_template(\"language.html\", form=form)\n# return redirect(url_for('index'))\n\n# @main.route('/add-relationType', methods=['GET', 'POST'])\n# @login_required\n# def add_relationType():\n# form = AddRelationTypeForm()\n# if form.validate_on_submit():\n# db.session.add(relationType)\n# db.session.commit()\n# flash('The relationType has been added.')\n# return redirect(url_for('main.index'))\n# flash('relationType Inserted')\n# else:\n# flash('Insert my relationType.')\n# return render_template(\"relationType.html\", form=form)\n# return redirect(url_for('index'))\n\n# @main.route('/blog', methods=['GET', 'POST'])\n# def postBlog():\n# id = None\n# blog_form = BlogForm()\n# # populate_blogform_choices(blog_form)\n# #This means that if we're not sending a post request then this if statement\n# #will always fail. So then we just move on to render the template normally.\n# if request.method == 'POST' and blog_form.validate():\n# #If we're making a post request and we passed all the validators then\n# #create a registered user model and push that model to the database.\n# flash('author_select_field')\n# blog_post = Posts(\n# title=blog_form.data['title_field'],\n# post=blog_form.data['post_field'],\n# authorid=blog_form.data['author_select_field'],\n# languageid=blog_form.data['language_select_field'],\n# # translate= blog_form.data['translate_field']\n# # translator = blog_form.data['translator_field']\n# trandate=blog_form.data['trandate_field'],\n# active=blog_form.data['active_field'])\n# db.session.add(blog_post)\n# db.session.commit()\n# return render_template(\n# template_name_or_list='successpost.html',\n# blog_form=blog_form)\n# return render_template(\n# template_name_or_list='blogpost.html',\n# blog_form=blog_form) \n", "sub_path": "app/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 11627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 84, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.can", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 89, "usage_type": "name"}, {"api_name": "models.Permission.WRITE_ARTICLES", "line_number": 89, "usage_type": "attribute"}, {"api_name": "models.Permission", "line_number": 89, "usage_type": "name"}, {"api_name": "models.Post", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user._get_current_object", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.is_authenticated", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.followed_posts", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 100, "usage_type": "name"}, {"api_name": "models.Post.query", "line_number": 102, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 102, "usage_type": "name"}, {"api_name": "models.Post.timestamp.desc", "line_number": 103, "usage_type": "call"}, {"api_name": "models.Post.timestamp", "line_number": 103, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 107, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 113, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "models.Post.timestamp.desc", "line_number": 115, "usage_type": "call"}, {"api_name": "models.Post.timestamp", "line_number": 115, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 119, "usage_type": "call"}, {"api_name": "forms.EditProfileForm", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.name", "line_number": 128, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.location", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.about_me", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user", "line_number": 131, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.name", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.location", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.about_me", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 124, "usage_type": "name"}, {"api_name": "models.User.query.get_or_404", "line_number": 144, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 144, "usage_type": "name"}, {"api_name": "forms.EditProfileAdminForm", "line_number": 145, "usage_type": "call"}, {"api_name": "models.Role.query.get", "line_number": 150, "usage_type": "call"}, {"api_name": "models.Role.query", "line_number": 150, "usage_type": "attribute"}, {"api_name": "models.Role", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user", "line_number": 154, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 141, "usage_type": "name"}, {"api_name": "decorators.admin_required", "line_number": 142, "usage_type": "name"}, {"api_name": "models.Post.query.get_or_404", "line_number": 169, "usage_type": "call"}, {"api_name": "models.Post.query", "line_number": 169, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 169, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 174, "usage_type": "call"}, {"api_name": "models.Post.query.get_or_404", "line_number": 180, "usage_type": "call"}, {"api_name": "models.Post.query", "line_number": 180, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 180, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user", "line_number": 181, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.can", "line_number": 182, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 182, "usage_type": "name"}, {"api_name": "models.Permission.ADMINISTER", "line_number": 182, "usage_type": "attribute"}, {"api_name": "models.Permission", "line_number": 182, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 183, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 191, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 178, "usage_type": "name"}, {"api_name": "models.User.query.filter_by", "line_number": 198, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 198, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 198, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 200, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 201, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 201, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.is_following", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.follow", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 205, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 206, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 207, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 207, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 195, "usage_type": "name"}, {"api_name": "decorators.permission_required", "line_number": 196, "usage_type": "call"}, {"api_name": "models.Permission.FOLLOW", "line_number": 196, "usage_type": "attribute"}, {"api_name": "models.Permission", "line_number": 196, "usage_type": "name"}, {"api_name": "models.User.query.filter_by", "line_number": 214, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 214, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 214, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 216, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 217, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 217, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.is_following", "line_number": 218, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 218, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 219, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 220, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 220, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.unfollow", "line_number": 221, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 221, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 211, "usage_type": "name"}, {"api_name": "decorators.permission_required", "line_number": 212, "usage_type": "call"}, {"api_name": "models.Permission.FOLLOW", "line_number": 212, "usage_type": "attribute"}, {"api_name": "models.Permission", "line_number": 212, "usage_type": "name"}, {"api_name": "models.User.query.filter_by", "line_number": 228, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 228, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 228, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 230, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 232, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 232, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 232, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 234, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 238, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 245, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 245, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 245, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 247, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 249, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 249, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 249, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 251, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 251, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 255, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 261, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 271, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 271, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 271, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 269, "usage_type": "name"}]} +{"seq_id": "286658954", "text": "#!/usr/bin/python3.2mu\n\n##----This module is to convert msgs to bytes, hex, base64, etc ----##\n\n# Type checking constants #\nimport sys\nimport os.path # To check if file exists\nimport base64\n\n__tstr = str(type(' '))\n__tint = str(type(5))\n__tfloat = str(type(5.43))\n__tbytes = str(\"\")\n__tbytearray = str(\"\")\nworkingmsg = \"\"\nbit8 = (128, 64, 32, 16, 8, 4, 2, 1)\nbit16 = (32768, 16384, 8192, 4096, 2048,\n 1024, 512, 256, 128, 64, 32, 16,\n 8, 4, 2, 1)\n\n\ndef byte_binary(num, byte):\n \"\"\"Number and byte iter\"\"\"\n result = []\n join = ''\n for f in byte:\n if num >= f:\n result.append(1)\n num = num - f\n else:\n result.append(0)\n for i in result:\n join = join + str(i)\n return join\n\n\ndef convert_binary(msg, byte):\n \"\"\"Message needs to be iterable ints.\nbyte bit8 or bit16\"\"\"\n msgb = \"\"\n for i in msg:\n if byte == 'bit8':\n msgb = msgb + byte_binary(i, bit8)\n if byte == 'bit16':\n msgb = msgb + byte_binary(i, bit16)\n return msgb\n\n\ndef msg_to_raw(msg, override=None):\n \"\"\"This is to convert msgs to raw format to work on.\nIt will check the type and convert accordingly with an option to override\n\nBelow are lists in override\nFor strings -\n \"\"\"\n\n if str(type(msg)) == __tstr:\n if override == None:\n msg = msg.encode()\n return msg\n elif override == 'hex':\n if msg[0:2] == '0x':\n msg = msg[2:]\n if msg[-1] == '\\n':\n msg = msg[:-1]\n msg = bytes.fromhex(msg)\n return msg\n elif override == 'base64':\n msg = base64.b64decode(msg)\n return msg\n elif str(type(msg)) == __tint:\n pass\n elif str(type(msg)) == __tfloat:\n print('You fed me a float!!!\\n\\n'\n 'What am I supposed to do with that?')\n return\n elif str(type(msg)) == __tbytes:\n # print('Msg is already in bytes')\n return msg\n elif str(type(msg)) == __tbytearray:\n print('Msg is a bytearray')\n return msg\n else:\n print('Unable to understand the type.\\nWas It Empty???\"')\n return\n\n\ndef raw_to_hexstr(msg):\n \"\"\"This is to convert raw to a hex string.\n0x will be placed at the beginning of the string\"\"\"\n j = ''\n for i in msg:\n if i < 16: # To add padding to bytes\n j += '0' + (hex(i)[2:])\n else:\n j += hex(i)[2:]\n j = '0x' + j\n return j\n\n\ndef check_for_file(file):\n \"\"\"This function is to check to see if a file exists and is readable\"\"\"\n return os.path.isfile(file)\n\n\nif __name__ == '__main__':\n try:\n m = sys.argv[1]\n n = msg_to_raw(m)\n print(n)\n except:\n print(\"Something went wrong. Exiting....\")\n", "sub_path": "Set3/Code/msg/convert.py", "file_name": "convert.py", "file_ext": "py", "file_size_in_byte": 2847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "base64.b64decode", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 103, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 108, "usage_type": "attribute"}]} +{"seq_id": "423095103", "text": "# -*- coding: utf-8 -*-\n\nNAMESPACE_URI1 = \"http://www.isotc211.org/2005/gmd\"\nNAMESPACE_URI2 = \"http://www.isotc211.org/2005/gco\"\nNAMESPACE_URI3 = \"http://www.opengis.net/gml\"\nNAMESPACE_URI4 = \"http://www.w3.org/2001/XMLSchema-instance\"\nNAMESPACE_URI5 = \"http://www.isotc211.org/2005/srv\"\nNAMESPACE_URI6 = \"http://www.w3.org/1999/xlink\"\n\nimport xml.dom.minidom\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtCore import QDate\nfrom PyQt5.QtWidgets import QCheckBox\nfrom functions import button_functions\nfrom functions.sql_functions import sql_valueRead\nfrom functions.button_functions import gl_categoryRolebox_changed\nfrom functions.button_functions import ai_aircraftRolebox_changed\nfrom functions.button_functions import qv_output_other\n\n\ntry:\n _encoding = QtWidgets.QApplication.UnicodeUTF8\n def _translate(context, text, disambig):\n return QtWidgets.QApplication.translate(context, text, disambig, _encoding)\nexcept AttributeError:\n def _translate(context, text, disambig):\n return QtWidgets.QApplication.translate(context, text, disambig)\n\n\ndef create_eufar_xml(self, out_file_name):\n doc = xml.dom.minidom.Document()\n doc_root = add_element(doc, \"gmd:MD_Metadata\", doc)\n doc_root.setAttribute(\"xmlns:gmd\", NAMESPACE_URI1)\n doc_root.setAttribute(\"xmlns:gco\", NAMESPACE_URI2)\n doc_root.setAttribute(\"xmlns:gml\", NAMESPACE_URI3)\n doc_root.setAttribute(\"xmlns:xsi\", NAMESPACE_URI4) \n doc_root.setAttribute(\"xmlns:srv\", NAMESPACE_URI5) \n doc_root.setAttribute(\"xmlns:xlink\", NAMESPACE_URI6) \n\n\n ############################\n # Identification Info\n ############################\n # Citation\n ########################\n identificationIdent1 = add_element(doc, \"gmd:identificationInfo\", doc_root)\n dataIdent1MD = add_element(doc, \"gmd:MD_DataIdentification\", identificationIdent1)\n citationIdent1 = add_element(doc, \"gmd:citation\", dataIdent1MD)\n citationIdent1CI = add_element(doc, \"gmd:CI_Citation\", citationIdent1)\n titleIdent1 = add_element(doc, \"gmd:title\", citationIdent1CI)\n add_element(doc, \"gco:CharacterString\", titleIdent1, self.id_resourceTitle_ln.text())\n identifierIdent1 = add_element(doc, \"gmd:identifier\", citationIdent1CI)\n identifierIdent1RS = add_element(doc, \"gmd:RS_Identifier\", identifierIdent1)\n codeIdent = add_element(doc, \"gmd:code\", identifierIdent1RS)\n add_element(doc, \"gco:CharacterString\", codeIdent, self.id_resourceIdent_ln.text())\n dateIdent1 = add_element(doc, \"gmd:date\", citationIdent1CI)\n dateIdent1CI = add_element(doc, \"gmd:CI_Date\", dateIdent1)\n dateIdent2 = add_element(doc, \"gmd:date\", dateIdent1CI)\n add_element(doc, \"gco:Date\", dateIdent2, self.tr_datePublication_do1.date().toString(Qt.ISODate))\n dateTypeIdent1 = add_element(doc, \"gmd:dateType\", dateIdent1CI)\n dateTypeCodeIdent1CI = add_element(doc, \"gmd:CI_DateTypeCode\", dateTypeIdent1, \"publication\")\n dateTypeCodeIdent1CI.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableS\"\n + \"tandards/ISO_19139_Schemas/resources/codelist/ML_gmxCodeli\"\n + \"sts.xml#CI_DateTypeCode\")\n dateTypeCodeIdent1CI.setAttribute(\"codeListValue\",\"publication\")\n dateIdent3 = add_element(doc, \"gmd:date\", citationIdent1CI)\n dateIdent2CI = add_element(doc, \"gmd:CI_Date\", dateIdent3)\n dateIdent4 = add_element(doc, \"gmd:date\", dateIdent2CI)\n add_element(doc, \"gco:Date\", dateIdent4, self.tr_dateRevision_do2.date().toString(Qt.ISODate))\n dateTypeIdent2 = add_element(doc, \"gmd:dateType\", dateIdent2CI)\n dateTypeCodeIdent2CI = add_element(doc, \"gmd:CI_DateTypeCode\", dateTypeIdent2, \"revision\")\n dateTypeCodeIdent2CI.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableS\"\n + \"tandards/ISO_19139_Schemas/resources/codelist/ML_gmxCodeli\"\n + \"sts.xml#CI_DateTypeCode\")\n dateTypeCodeIdent2CI.setAttribute(\"codeListValue\",\"revision\")\n dateIdent5 = add_element(doc, \"gmd:date\", citationIdent1CI)\n dateIdent3CI = add_element(doc, \"gmd:CI_Date\", dateIdent5)\n dateIdent6 = add_element(doc, \"gmd:date\", dateIdent3CI)\n add_element(doc, \"gco:Date\", dateIdent6, self.tr_dateCreation_do3.date().toString(Qt.ISODate))\n dateTypeIdent3 = add_element(doc, \"gmd:dateType\", dateIdent3CI)\n dateTypeCodeIdent3CI = add_element(doc, \"gmd:CI_DateTypeCode\", dateTypeIdent3, \"creation\")\n dateTypeCodeIdent3CI.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableS\"\n + \"tandards/ISO_19139_Schemas/resources/codelist/ML_gmxCodeli\"\n + \"sts.xml#CI_DateTypeCode\")\n dateTypeCodeIdent3CI.setAttribute(\"codeListValue\",\"creation\")\n\n ########################\n # Abstract\n ######################## \n abstractIdent = add_element(doc, \"gmd:abstract\", dataIdent1MD)\n add_element(doc, \"gco:CharacterString\", abstractIdent, self.id_resourceAbstract_ta.toPlainText())\n\n #######################\n # Topics\n #######################\n all_check_boxes = self.findChildren(QCheckBox)\n for check_box in all_check_boxes:\n if check_box.objectName()[0:3] == \"cl_\" and check_box.isChecked():\n query = sql_valueRead(self, 'topics', 'Text', check_box.text())\n topicIdent = add_element(doc, \"gmd:topicCategory\", dataIdent1MD)\n add_element(doc, \"gmd:MD_TopicCategoryCode\", topicIdent, query[0][1])\n\n #######################\n # Keywords\n #######################\n descriptiveKeywordIdent1 = add_element(doc, \"gmd:descriptiveKeywords\", dataIdent1MD)\n keywordIdent1MD = add_element(doc, \"gmd:MD_Keywords\", descriptiveKeywordIdent1)\n for check_box in all_check_boxes:\n if check_box.objectName()[0:3] == \"kw_\" and check_box.isChecked():\n query = sql_valueRead(self, 'scienceKeywords', 'Labels', check_box.objectName())\n keywordIdent1 = add_element(doc, \"gmd:keyword\", keywordIdent1MD)\n add_element(doc, \"gco:CharacterString\", keywordIdent1, query[0][1])\n thesaurusKeywordIdent1 = add_element(doc, \"gmd:thesaurusName\", keywordIdent1MD)\n citationKeywordIdent1CI = add_element(doc, \"gmd:CI_Citation\", thesaurusKeywordIdent1)\n titleKeywordIdent1 = add_element(doc, \"gmd:title\", citationKeywordIdent1CI)\n add_element(doc, \"gco:CharacterString\", titleKeywordIdent1, \"NASA/Global Change Master Director\"\n + \"y (GCMD) Earth Science Keywords. Version 8.0.0.0.0\")\n dateIdent7 = add_element(doc, \"gmd:date\", citationKeywordIdent1CI)\n dateIdent4CI = add_element(doc, \"gmd:CI_Date\", dateIdent7)\n dateIdent8 = add_element(doc, \"gmd:date\", dateIdent4CI)\n add_element(doc, \"gco:Date\", dateIdent8, \"2015-02-20\")\n dateTypeIdent4 = add_element(doc, \"gmd:dateType\", dateIdent4CI)\n dateTypeCodeIdent4CI = add_element(doc, \"gmd:CI_DateTypeCode\", dateTypeIdent4, \"revision\")\n dateTypeCodeIdent4CI.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableS\"\n + \"tandards/ISO_19139_Schemas/resources/codelist/ML_gmxCodeli\"\n + \"sts.xml#CI_DateTypeCode\")\n dateTypeCodeIdent4CI.setAttribute(\"codeListValue\", \"revision\")\n\n #######################\n # Location Extent\n #######################\n extentIdent1 = add_element(doc, \"gmd:extent\", dataIdent1MD)\n extentIdent1EX = add_element(doc, \"gmd:EX_Extent\", extentIdent1)\n extentDescription = add_element(doc, \"gmd:description\", extentIdent1EX)\n add_element(doc, \"gco:CharacterString\", extentDescription, self.gl_details_rl2.currentText())\n geographicIdent1 = add_element(doc, \"gmd:geographicElement\", extentIdent1EX)\n geographicIdent1EX = add_element(doc, \"gmd:EX_GeographicBoundingBox\", geographicIdent1)\n westBoundIdent = add_element(doc, \"gmd:westBoundLongitude\", geographicIdent1EX)\n add_element(doc, \"gco:Decimal\", westBoundIdent, self.gl_westBound_ln.text())\n eastBoundIdent = add_element(doc, \"gmd:eastBoundLongitude\", geographicIdent1EX)\n add_element(doc, \"gco:Decimal\", eastBoundIdent, self.gl_eastBound_ln.text())\n northBoundIdent = add_element(doc, \"gmd:northBoundLatitude\", geographicIdent1EX)\n add_element(doc, \"gco:Decimal\", northBoundIdent, self.gl_northBound_ln.text())\n southBoundIdent = add_element(doc, \"gmd:southBoundLatitude\", geographicIdent1EX)\n add_element(doc, \"gco:Decimal\", southBoundIdent, self.gl_southBound_ln.text())\n \n #######################\n # Temporal Extent\n #######################\n temporalIdent1 = add_element(doc, \"gmd:temporalElement\", extentIdent1EX)\n temporalIdent1EX = add_element(doc, \"gmd:EX_TemporalExtent\", temporalIdent1)\n extentIdent3 = add_element(doc, \"gmd:extent\", temporalIdent1EX)\n periodIdent1 = add_element(doc, \"gml:TimePeriod\", extentIdent3)\n periodIdent1.setAttribute(\"gml:id\",\"extent0\") \n add_element(doc, \"gml:beginPosition\", periodIdent1, self.tr_dateStart_do4.date().\n toString(Qt.ISODate))\n add_element(doc, \"gml:endPosition\", periodIdent1, self.tr_dateEnd_do5.date().\n toString(Qt.ISODate))\n if self.tr_tpex > 0:\n i = 0\n for item in self.tr_dtSt_1: # @UnusedVariable\n temporalIdent1 = add_element(doc, \"gmd:temporalElement\", extentIdent1EX)\n temporalIdent1EX = add_element(doc, \"gmd:EX_TemporalExtent\", temporalIdent1)\n extentIdent3 = add_element(doc, \"gmd:extent\", temporalIdent1EX)\n periodIdent1 = add_element(doc, \"gml:TimePeriod\", extentIdent3)\n periodIdent1.setAttribute(\"gml:id\",\"extent\" + str(i + 1))\n add_element(doc, \"gml:beginPosition\", periodIdent1, self.tr_dtSt_1[i].date().\n toString(Qt.ISODate))\n add_element(doc, \"gml:endPosition\", periodIdent1, self.tr_dtEd_1[i].date().\n toString(Qt.ISODate))\n i += 1\n \n #######################\n # Spatial Resolution\n #######################\n resolutionIdent1 = add_element(doc, \"gmd:spatialResolution\", dataIdent1MD)\n resolutionIdent1MD = add_element(doc, \"gmd:MD_Resolution\", resolutionIdent1)\n if self.gl_resolution_rl1.currentText() == 'Distance':\n distanceIdent1 = add_element(doc, \"gmd:distance\", resolutionIdent1MD)\n distanceIdent2 = add_element(doc, \"gco:Distance\", distanceIdent1, self.gl_resolution_ln.\n text())\n query = sql_valueRead(self, 'unit', 'Main', self.gl_unit_rl.currentText())\n distanceIdent2.setAttribute(\"uom\", query[0][1])\n elif self.gl_resolution_rl1.currentText() == 'Scale':\n scaleIdent1 = add_element(doc, \"gmd:equivalentScale\", resolutionIdent1MD)\n fractionIdent1MD = add_element(doc, \"gmd:MD_RepresentativeFraction\", scaleIdent1)\n denominatorIdent1 = add_element(doc, \"gmd:denominator\", fractionIdent1MD)\n add_element(doc, \"gco:Integer\", denominatorIdent1, self.gl_resolution_ln.text())\n \n ########################\n # Language\n ######################## \n query = sql_valueRead(self, 'languageRole', 'Id', self.id_resourceLang_rl2.currentText())\n languageIdent = add_element(doc, \"gmd:language\", dataIdent1MD)\n languageIdent2 = add_element(doc, \"gmd:LanguageCode\", languageIdent, query[0][1])\n languageIdent2.setAttribute(\"codeList\", \"http://www.loc.gov/standards/iso639-2/\")\n languageIdent2.setAttribute(\"codeListValue\", query[0][1])\n \n #######################\n # Resource Constraints\n #######################\n constraintIdent1 = add_element(doc, \"gmd:resourceConstraints\", dataIdent1MD)\n constraintIdent1MD = add_element(doc, \"gmd:MD_Constraints\", constraintIdent1)\n useIdent1 = add_element(doc, \"gmd:useLimitation\", constraintIdent1MD)\n add_element(doc, \"gco:CharacterString\", useIdent1, self.au_conditions_ta.toPlainText())\n if self.au_wn_2 > 0:\n i = 0\n for item in self.au_wn_con_ta: # @UnusedVariable\n useIdent1 = add_element(doc, \"gmd:useLimitation\", constraintIdent1MD)\n add_element(doc, \"gco:CharacterString\", useIdent1, self.au_wn_con_ta[i].toPlainText())\n i += 1\n constraintIdent2 = add_element(doc, \"gmd:resourceConstraints\", dataIdent1MD)\n legalIdent1MD = add_element(doc, \"gmd:MD_LegalConstraints\", constraintIdent2)\n accessIdent1 = add_element(doc, \"gmd:accessConstraints\", legalIdent1MD)\n restrictionIdent1MD = add_element(doc, \"gmd:MD_RestrictionCode\", accessIdent1, \"otherRestrictions\")\n restrictionIdent1MD.setAttribute(\"codeList\",\"http://standards.iso.org/ittf/PubliclyAvailableSta\"\n + \"ndards/ISO_19139_Schemas/resources/Codelist/gmxCodelists.xm\"\n + \"l#MD_RestrictionCode\")\n restrictionIdent1MD.setAttribute(\"codeListValue\",\"otherRestrictions\")\n accessIdent2 = add_element(doc, \"gmd:otherConstraints\", legalIdent1MD)\n add_element(doc, \"gco:CharacterString\", accessIdent2, self.au_limitations_ta.toPlainText())\n if self.au_wn_1 > 0:\n i = 0\n for item in self.au_wn_lim_ta: # @UnusedVariable\n accessIdent2 = add_element(doc, \"gmd:otherConstraints\", legalIdent1MD)\n add_element(doc, \"gco:CharacterString\", accessIdent2, self.au_wn_lim_ta[i].toPlainText())\n i += 1\n\n #######################\n # Resource Contacts\n #######################\n contactIdent1 = add_element(doc, \"gmd:pointOfContact\", dataIdent1MD)\n responsibleIdent1CI = add_element(doc, \"gmd:CI_ResponsibleParty\", contactIdent1)\n organisationIdent1 = add_element(doc, \"gmd:organisationName\", responsibleIdent1CI)\n add_element(doc, \"gco:CharacterString\", organisationIdent1, self.ro_responsibleParty_ln.text())\n contactIdent2 = add_element(doc, \"gmd:contactInfo\", responsibleIdent1CI)\n responsibleIdent2CI = add_element(doc, \"gmd:CI_Contact\", contactIdent2)\n addressIdent1 = add_element(doc, \"gmd:address\", responsibleIdent2CI)\n addressIdent1CI = add_element(doc, \"gmd:CI_Address\", addressIdent1)\n emailIdent1 = add_element(doc, \"gmd:electronicMailAddress\", addressIdent1CI)\n add_element(doc, \"gco:CharacterString\", emailIdent1, self.ro_responsibleEmail_ln.text())\n roleIdent1 = add_element(doc, \"gmd:role\", responsibleIdent1CI)\n query = sql_valueRead(self, 'languageRole', 'Id', self.ro_responsibleRole_rl1.currentText())\n roleCodeIdent1 = add_element(doc, \"gmd:CI_RoleCode\", roleIdent1, query[0][1])\n roleCodeIdent1.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableStandar\"\n + \"ds/ISO_19139_Schemas/resources/Codelist/gmxCodelists.xml#CI_Role\"\n + \"Code\")\n roleCodeIdent1.setAttribute(\"codeListValue\", query[0][1])\n if self.ro_roPy > 0:\n i = 0\n for item in self.ro_rlPy_ln: # @UnusedVariable\n contactIdent1 = add_element(doc, \"gmd:pointOfContact\", dataIdent1MD)\n responsibleIdent1CI = add_element(doc, \"gmd:CI_ResponsibleParty\", contactIdent1)\n organisationIdent1 = add_element(doc, \"gmd:organisationName\", responsibleIdent1CI)\n add_element(doc, \"gco:CharacterString\", organisationIdent1, self.ro_rlPy_ln[i].text())\n contactIdent2 = add_element(doc, \"gmd:contactInfo\", responsibleIdent1CI)\n responsibleIdent2CI = add_element(doc, \"gmd:CI_Contact\", contactIdent2)\n addressIdent1 = add_element(doc, \"gmd:address\", responsibleIdent2CI)\n addressIdent1CI = add_element(doc, \"gmd:CI_Address\", addressIdent1)\n emailIdent1 = add_element(doc, \"gmd:electronicMailAddress\", addressIdent1CI)\n add_element(doc, \"gco:CharacterString\", emailIdent1, self.ro_rlEm_ln[i].text())\n roleIdent1 = add_element(doc, \"gmd:role\", responsibleIdent1CI)\n query = sql_valueRead(self, 'languageRole', 'Id', self.ro_rlRl_ln[i].currentText())\n roleCodeIdent1 = add_element(doc, \"gmd:CI_RoleCode\", roleIdent1, query[0][1])\n roleCodeIdent1.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailabl\"\n + \"eStandards/ISO_19139_Schemas/resources/Codelist/gmxCodel\"\n + \"ists.xml#CI_RoleCode\")\n roleCodeIdent1.setAttribute(\"codeListValue\", query[0][1])\n i += 1\n \n \n ############################\n # Hierarchy Level\n ############################\n hierarchyLevel1 = add_element(doc, \"gmd:hierarchyLevel\", doc_root)\n scopeLevel1MD = add_element(doc, \"gmd:MD_ScopeCode\", hierarchyLevel1, self.id_resourceType_rl1.\n currentText().lower())\n scopeLevel1MD.setAttribute(\"codeList\",\"http://standards.iso.org/ittf/PubliclyAvailableStandards\"\n + \"/ISO_19139_Schemas/resources/Codelist/gmxCodelists.xml#MD_ScopeCode\")\n scopeLevel1MD.setAttribute(\"codeListValue\", self.id_resourceType_rl1.currentText().lower())\n \n \n ############################\n # Distribution Info\n ############################\n distributionInfo1 = add_element(doc, \"gmd:distributionInfo\", doc_root)\n distributionInfo1MD = add_element(doc, \"gmd:MD_Distribution\", distributionInfo1)\n transferInfo1 = add_element(doc, \"gmd:transferOptions\", distributionInfo1MD)\n transferInfo1MD = add_element(doc, \"gmd:MD_DigitalTransferOptions\", transferInfo1)\n onlineInfo1 = add_element(doc, \"gmd:onLine\", transferInfo1MD)\n onlineInfo1CI = add_element(doc, \"gmd:CI_OnlineResource\", onlineInfo1)\n linkageInfo1 = add_element(doc, \"gmd:linkage\", onlineInfo1CI)\n add_element(doc, \"gmd:URL\", linkageInfo1, self.id_resourceLocator_ln.text())\n \n \n ############################\n # Language Info\n ############################\n query = sql_valueRead(self, 'languageRole', 'Id', self.mm_language_rl1.currentText())\n languageInfo1 = add_element(doc, \"gmd:language\", doc_root)\n languageInfo2 = add_element(doc, \"gmd:LanguageCode\", languageInfo1, query[0][1])\n languageInfo2.setAttribute(\"codeList\",\"http://www.loc.gov/standards/iso639-2/\")\n languageInfo2.setAttribute(\"codeListValue\", query[0][1])\n\n\n ############################\n # Data Quality\n ############################\n qualityInfo1 = add_element(doc, \"gmd:dataQualityInfo\", doc_root)\n dataQuality1DQ = add_element(doc, \"gmd:DQ_DataQuality\", qualityInfo1)\n lineageQuality1 = add_element(doc, \"gmd:lineage\", dataQuality1DQ)\n lineageQuality1LI = add_element(doc, \"gmd:LI_Lineage\", lineageQuality1)\n statementQuality1 = add_element(doc, \"gmd:statement\", lineageQuality1LI)\n if self.qv_obsRadio.isChecked() == False and self.qv_insituRadio.isChecked() == False:\n statement = \"\"\n else:\n statement = self.save_statement()\n add_element(doc, \"gco:CharacterString\", statementQuality1, statement)\n reportQuality1 = add_element(doc, \"gmd:report\", dataQuality1DQ)\n domainConsistency1DQ = add_element(doc, \"gmd:DQ_DomainConsistency\", reportQuality1)\n resultQuality1 = add_element(doc, \"gmd:result\", domainConsistency1DQ)\n conformanceResult1DQ = add_element(doc, \"gmd:DQ_ConformanceResult\", resultQuality1)\n specificationQuality1 = add_element(doc, \"gmd:specification\", conformanceResult1DQ)\n citationQuality1CI = add_element(doc, \"gmd:CI_Citation\", specificationQuality1)\n titleQuality1 = add_element(doc, \"gmd:title\", citationQuality1CI)\n add_element(doc, \"gco:CharacterString\", titleQuality1, \"COMMISSION REGULATION (EC) No 1205/2008\"\n + \" of 3 December 2008 implementing Directive 2007/2/EC of the European Parliament \"\n + \"and of the Council as regards metadata\")\n dateQuality1 = add_element(doc, \"gmd:date\", citationQuality1CI)\n dateQuality1CI = add_element(doc, \"gmd:CI_Date\", dateQuality1)\n dateQuality2 = add_element(doc, \"gmd:date\", dateQuality1CI)\n add_element(doc, \"gco:Date\", dateQuality2, \"2008-12-04\")\n dateType1 = add_element(doc, \"gmd:dateType\", dateQuality1CI)\n dateTypeCode1 = add_element(doc, \"gmd:CI_DateTypeCode\", dateType1, \"publication\")\n dateTypeCode1.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableStandard\"\n + \"s/ISO_19139_Schemas/resources/codelist/ML_gmxCodelists.xml#CI_Dat\"\n + \"eTypeCode\")\n dateTypeCode1.setAttribute(\"codeListValue\", \"publication\")\n passQuality1 = add_element(doc, \"gmd:pass\", conformanceResult1DQ)\n add_element(doc, \"gco:Boolean\", passQuality1, \"True\")\n\n\n ############################\n # Aircraft and Instruments\n ############################\n if self.ai_otherAircraft == 0:\n query = sql_valueRead(self, 'languageRole', 'Id', self.ai_label_9.text())\n acquisitionInfo1 = add_element(doc, \"gmd:acquisitionInfo\", doc_root)\n aircraftInfo1 = add_element(doc, \"gmd:platformInfo\", acquisitionInfo1)\n aircraftInfo1AI = add_element(doc, \"gmd:PI_PlatformInfo\", aircraftInfo1)\n aircraftManufacturer = add_element(doc, \"gmd:platformManufacturer\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftManufacturer, self.ai_label_7.text())\n aircraftType = add_element(doc, \"gmd:platformType\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftType, self.ai_label_8.text())\n aircraftOperator = add_element(doc, \"gmd:platformOperator\", aircraftInfo1AI)\n if query:\n add_element(doc, \"gco:CharacterString\", aircraftOperator, query[0][1])\n else:\n add_element(doc, \"gco:CharacterString\", aircraftOperator, \"\")\n aircraftCountry = add_element(doc, \"gmd:platformCountry\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftCountry, self.ai_label_10.text())\n aircraftRegistration = add_element(doc, \"gmd:platformRegistration\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftRegistration, self.ai_label_11.text())\n else:\n acquisitionInfo1 = add_element(doc, \"gmd:acquisitionInfo\", doc_root)\n aircraftInfo1 = add_element(doc, \"gmd:platformInfo\", acquisitionInfo1)\n aircraftInfo1AI = add_element(doc, \"gmd:PI_PlatformInfo\", aircraftInfo1)\n aircraftManufacturer = add_element(doc, \"gmd:platformManufacturer\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftManufacturer, self.ai_manufacturer_ln.text())\n aircraftType = add_element(doc, \"gmd:platformType\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftType, self.ai_type_ln.text())\n aircraftOperator = add_element(doc, \"gmd:platformOperator\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftOperator, self.ai_operator_ln.text())\n aircraftCountry = add_element(doc, \"gmd:platformCountry\", aircraftInfo1AI)\n if self.ai_country_rl.currentText() != \"Make a choice...\":\n add_element(doc, \"gco:CharacterString\", aircraftCountry, self.ai_country_rl.currentText())\n else:\n add_element(doc, \"gco:CharacterString\", aircraftCountry, \"\")\n aircraftRegistration = add_element(doc, \"gmd:platformRegistration\", aircraftInfo1AI)\n add_element(doc, \"gco:CharacterString\", aircraftRegistration, self.ai_number_ln.text())\n if self.instModel_list:\n for i in range(0, len(self.instModel_list)):\n instrumentInfo1 = add_element(doc, \"gmd:instrumentInfo\", acquisitionInfo1)\n instrumentInfo1II = add_element(doc, \"gmd:II_InstrumentInfo\", instrumentInfo1)\n instrumentManufacturer = add_element(doc, \"gmd:instrumentManufacturer\", instrumentInfo1II)\n instrumentType = add_element(doc, \"gmd:instrumentType\", instrumentInfo1II)\n add_element(doc, \"gco:CharacterString\", instrumentManufacturer, self.instManufacturer_list[i])\n add_element(doc, \"gco:CharacterString\", instrumentType, self.instModel_list[i])\n else:\n instrumentInfo1 = add_element(doc, \"gmd:instrumentInfo\", acquisitionInfo1)\n instrumentInfo1II = add_element(doc, \"gmd:II_InstrumentInfo\", instrumentInfo1)\n instrumentManufacturer = add_element(doc, \"gmd:instrumentManufacturer\", instrumentInfo1II)\n instrumentType = add_element(doc, \"gmd:instrumentType\", instrumentInfo1II)\n add_element(doc, \"gco:CharacterString\", instrumentManufacturer, \"\")\n add_element(doc, \"gco:CharacterString\", instrumentType, \"\")\n\n\n ############################\n # Contact Info\n ############################\n contactContact1 = add_element(doc, \"gmd:contact\", doc_root)\n responsiblePartyInfo1CI = add_element(doc, \"gmd:CI_ResponsibleParty\", contactContact1)\n nameContact1 = add_element(doc, \"gmd:organisationName\", responsiblePartyInfo1CI)\n add_element(doc, \"gco:CharacterString\", nameContact1, self.mm_contactName_ln.text())\n infoContact1 = add_element(doc, \"gmd:contactInfo\", responsiblePartyInfo1CI)\n infoContact1CI = add_element(doc, \"gmd:CI_Contact\", infoContact1)\n addressContact1 = add_element(doc, \"gmd:address\", infoContact1CI)\n addressContact1CI = add_element(doc, \"gmd:CI_Address\", addressContact1)\n emailContact1 = add_element(doc, \"gmd:electronicMailAddress\", addressContact1CI)\n add_element(doc, \"gco:CharacterString\", emailContact1, self.mm_contactEmail_ln.text())\n roleContact1 = add_element(doc, \"gmd:role\", responsiblePartyInfo1CI)\n roleCodeContact1 = add_element(doc, \"gmd:CI_RoleCode\", roleContact1, \"pointOfContact\")\n roleCodeContact1.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvailableStand\"\n + \"ards/ISO_19139_Schemas/resources/Codelist/gmxCodelists.xml#CI_\"\n + \"RoleCode\")\n roleCodeContact1.setAttribute(\"codeListValue\", \"pointOfContact\")\n if self.mm_pofc > 0:\n i = 0\n for item in self.mm_conName_ln: # @UnusedVariable\n contactContact1 = add_element(doc, \"gmd:contact\", doc_root)\n responsiblePartyInfo1CI = add_element(doc, \"gmd:CI_ResponsibleParty\", contactContact1)\n nameContact1 = add_element(doc, \"gmd:organisationName\", responsiblePartyInfo1CI)\n add_element(doc, \"gco:CharacterString\", nameContact1, self.mm_conName_ln[i].text())\n infoContact1 = add_element(doc, \"gmd:contactInfo\", responsiblePartyInfo1CI)\n infoContact1CI = add_element(doc, \"gmd:CI_Contact\", infoContact1)\n addressContact1 = add_element(doc, \"gmd:address\", infoContact1CI)\n addressContact1CI = add_element(doc, \"gmd:CI_Address\", addressContact1)\n emailContact1 = add_element(doc, \"gmd:electronicMailAddress\", addressContact1CI)\n add_element(doc, \"gco:CharacterString\", emailContact1, self.mm_conEmail_ln[i].text())\n roleContact1 = add_element(doc, \"gmd:role\", responsiblePartyInfo1CI)\n roleCodeContact1 = add_element(doc, \"gmd:CI_RoleCode\", roleContact1, \"pointOfContact\")\n roleCodeContact1.setAttribute(\"codeList\", \"http://standards.iso.org/ittf/PubliclyAvaila\"\n + \"bleStandards/ISO_19139_Schemas/resources/Codelist/gmxC\"\n + \"odelists.xml#CI_RoleCode\")\n roleCodeContact1.setAttribute(\"codeListValue\", \"pointOfContact\")\n i += 1\n\n\n ############################\n # Metadata Date\n ############################\n dateStamp1 = add_element(doc, \"gmd:dateStamp\", doc_root)\n add_element(doc, \"gco:Date\", dateStamp1, self.mm_date_do1.date().toString(Qt.ISODate))\n\n \n ############################\n # File Creation\n ############################\n f = open(out_file_name, 'w')\n f.write(doc.toprettyxml())\n f.close()\n self.saved = True\n self.modified = False\n\n\ndef read_eufar_xml(self, in_file_name):\n currentIndex = self.tabWidget.currentIndex()\n f = open(in_file_name, 'r')\n doc = xml.dom.minidom.parse(f)\n\n\n ############################\n # Identification Info\n ############################\n # Citation\n ########################\n doc_root = get_element(doc, \"gmd:MD_Metadata\")\n identificationIdent1 = get_element(doc_root, \"gmd:identificationInfo\")\n dataIdent1MD = get_element(identificationIdent1, \"gmd:MD_DataIdentification\")\n citationIdent1 = get_element(dataIdent1MD, \"gmd:citation\")\n citationIdent1CI = get_element(citationIdent1, \"gmd:CI_Citation\")\n titleIdent1 = get_element(citationIdent1CI, \"gmd:title\")\n set_text_value(self.id_resourceTitle_ln, titleIdent1, \"gco:CharacterString\")\n identifierIdent1 = get_element(citationIdent1CI, \"gmd:identifier\")\n identifierIdent1RS = get_element(identifierIdent1, \"gmd:RS_Identifier\")\n codeIdent = get_element(identifierIdent1RS, \"gmd:code\")\n set_text_value(self.id_resourceIdent_ln, codeIdent, \"gco:CharacterString\")\n nodes = citationIdent1CI.getElementsByTagName(\"gmd:CI_Date\")\n elements = []\n for node in nodes:\n tmp1 = get_element(node, \"gmd:date\")\n elements.append(get_element_value(tmp1, \"gco:Date\"))\n self.tr_datePublication_do1.setDate(QDate.fromString(elements[0], Qt.ISODate))\n self.tr_dateRevision_do2.setDate(QDate.fromString(elements[1], Qt.ISODate))\n self.tr_dateCreation_do3.setDate(QDate.fromString(elements[2], Qt.ISODate))\n \n ########################\n # Abstract\n ########################\n abstractIdent = get_element(dataIdent1MD, \"gmd:abstract\")\n set_plainText_value(self.id_resourceAbstract_ta, abstractIdent, \"gco:CharacterString\")\n \n #######################\n # Topics\n #######################\n all_check_boxes = self.findChildren(QCheckBox)\n for widget in all_check_boxes:\n if widget.objectName()[0:3] == \"cl_\":\n for nodes in doc.getElementsByTagName(\"gmd:topicCategory\"):\n node = get_element_value(nodes, \"gmd:MD_TopicCategoryCode\")\n query = sql_valueRead(self, 'topics', 'Value', node)\n if query[0][0] == widget.text():\n widget.setChecked(True)\n\n #######################\n # Temporal Extent\n #######################\n self.tabWidget.setCurrentIndex(5)\n nodes = doc_root.getElementsByTagName(\"gml:TimePeriod\")\n starts = []\n ends = []\n for node in nodes:\n starts.append(get_element_value(node, \"gml:beginPosition\"))\n ends.append(get_element_value(node, \"gml:endPosition\"))\n self.tr_dateStart_do4.setDate(QDate.fromString(starts[0], Qt.ISODate))\n self.tr_dateEnd_do5.setDate(QDate.fromString(ends[0], Qt.ISODate))\n if len(nodes) > 1:\n i = 0\n for node in nodes[:-1]:\n button_functions.plusButton_3_clicked(self)\n self.tr_dtSt_1[i].setDate(QDate.fromString(starts[i + 1], Qt.ISODate))\n self.tr_dtEd_1[i].setDate(QDate.fromString(ends[i + 1], Qt.ISODate))\n i += 1\n\n #######################\n # Resource Constraints\n #######################\n self.tabWidget.setCurrentIndex(7)\n nodes = doc_root.getElementsByTagName(\"gmd:useLimitation\")\n set_plainText_value(self.au_conditions_ta, nodes[0], \"gco:CharacterString\")\n if len(nodes) > 1:\n i = 0\n for node in nodes[1:]:\n button_functions.plusButton_5_clicked(self)\n set_plainText_value(self.au_wn_con_ta[i], nodes[i + 1], \"gco:CharacterString\")\n i += 1\n nodes = doc_root.getElementsByTagName(\"gmd:otherConstraints\")\n set_plainText_value(self.au_limitations_ta, nodes[0], \"gco:CharacterString\")\n if len(nodes) > 1:\n i = 0\n for node in nodes[1:]:\n button_functions.plusButton_6_clicked(self)\n set_plainText_value(self.au_wn_lim_ta[i], nodes[i + 1], \"gco:CharacterString\")\n i += 1\n\n #######################\n # Resource Contacts\n #######################\n self.tabWidget.setCurrentIndex(8)\n nodes = doc_root.getElementsByTagName(\"gmd:pointOfContact\")\n responsibleIdent1CI = get_element(nodes[0], \"gmd:CI_ResponsibleParty\")\n organisationIdent1 = get_element(responsibleIdent1CI, \"gmd:organisationName\")\n set_text_value(self.ro_responsibleParty_ln, organisationIdent1, \"gco:CharacterString\")\n contactIdent2 = get_element(responsibleIdent1CI, \"gmd:contactInfo\", )\n responsibleIdent2CI = get_element(contactIdent2, \"gmd:CI_Contact\", )\n addressIdent1 = get_element(responsibleIdent2CI, \"gmd:address\", )\n addressIdent1CI = get_element(addressIdent1, \"gmd:CI_Address\", )\n emailIdent1 = get_element(addressIdent1CI, \"gmd:electronicMailAddress\", )\n set_text_value(self.ro_responsibleEmail_ln, emailIdent1, \"gco:CharacterString\")\n roleIdent1 = get_element(responsibleIdent1CI, \"gmd:role\", )\n combo_text = get_element_value(roleIdent1, \"gmd:CI_RoleCode\")\n query = sql_valueRead(self, 'languageRole', 'Short', combo_text)\n self.ro_responsibleRole_rl1.setCurrentIndex(self.ro_responsibleRole_rl1.findText(query[0][0]))\n if len(nodes) > 1:\n i = 0\n for node in nodes[1:]:\n button_functions.plusButton_7_clicked(self)\n responsibleIdent1CI = get_element(nodes[i + 1], \"gmd:CI_ResponsibleParty\")\n organisationIdent1 = get_element(responsibleIdent1CI, \"gmd:organisationName\")\n set_text_value(self.ro_rlPy_ln[i], organisationIdent1, \"gco:CharacterString\")\n contactIdent2 = get_element(responsibleIdent1CI, \"gmd:contactInfo\", )\n responsibleIdent2CI = get_element(contactIdent2, \"gmd:CI_Contact\", )\n addressIdent1 = get_element(responsibleIdent2CI, \"gmd:address\", )\n addressIdent1CI = get_element(addressIdent1, \"gmd:CI_Address\", )\n emailIdent1 = get_element(addressIdent1CI, \"gmd:electronicMailAddress\", )\n set_text_value(self.ro_rlEm_ln[i], emailIdent1, \"gco:CharacterString\")\n roleIdent1 = get_element(responsibleIdent1CI, \"gmd:role\", )\n combo_text = get_element_value(roleIdent1, \"gmd:CI_RoleCode\")\n query = sql_valueRead(self, 'languageRole', 'Short', combo_text)\n self.ro_rlRl_ln[i].setCurrentIndex(self.ro_rlRl_ln[i].findText(query[0][0]))\n i += 1\n \n #######################\n # Keywords\n #######################\n descriptiveKeywordIdent1 = get_element(dataIdent1MD, \"gmd:descriptiveKeywords\")\n keywordIdent1MD = get_element(descriptiveKeywordIdent1, \"gmd:MD_Keywords\")\n for widget in all_check_boxes:\n if widget.objectName()[0:3] == \"kw_\":\n query = sql_valueRead(self, 'scienceKeywords', 'Labels', widget.objectName())\n if query:\n for nodes in keywordIdent1MD.getElementsByTagName(\"gmd:keyword\"):\n node = get_element_value(nodes, \"gco:CharacterString\")\n if query[0][1] == node:\n widget.setChecked(True)\n\n #######################\n # Location Extent\n #######################\n extentIdent1 = get_element(dataIdent1MD, \"gmd:extent\")\n extentIdent1EX = get_element(extentIdent1, \"gmd:EX_Extent\")\n extentDescription = get_element(extentIdent1EX, \"gmd:description\")\n combo_text = get_element_value(extentDescription, \"gco:CharacterString\")\n if combo_text:\n query = sql_valueRead(self, 'emcLocations', 'Detail', combo_text)\n self.gl_category_rl1.setCurrentIndex(self.gl_category_rl1.findText(query[0][0]))\n gl_categoryRolebox_changed(self)\n self.gl_details_rl2.setCurrentIndex(self.gl_details_rl2.findText(query[0][1]))\n geographicIdent1 = get_element(extentIdent1EX, \"gmd:geographicElement\")\n geographicIdent1EX = get_element(geographicIdent1, \"gmd:EX_GeographicBoundingBox\")\n westBoundIdent = get_element(geographicIdent1EX, \"gmd:westBoundLongitude\")\n set_text_value(self.gl_westBound_ln, westBoundIdent, \"gco:Decimal\")\n eastBoundIdent = get_element(geographicIdent1EX, \"gmd:eastBoundLongitude\")\n set_text_value(self.gl_eastBound_ln, eastBoundIdent, \"gco:Decimal\")\n northBoundIdent = get_element(geographicIdent1EX, \"gmd:northBoundLatitude\")\n set_text_value(self.gl_northBound_ln, northBoundIdent, \"gco:Decimal\")\n southBoundIdent = get_element(geographicIdent1EX, \"gmd:southBoundLatitude\")\n set_text_value(self.gl_southBound_ln, southBoundIdent, \"gco:Decimal\")\n \n \n #######################\n # Spatial Resolution\n #######################\n resolutionIdent1 = get_element(dataIdent1MD, \"gmd:spatialResolution\")\n resolutionIdent1MD = get_element(resolutionIdent1, \"gmd:MD_Resolution\")\n test = resolutionIdent1MD.getElementsByTagName(\"gmd:equivalentScale\")\n if test:\n self.gl_resolution_rl1.setCurrentIndex(self.gl_resolution_rl1.findText(\"Scale\"))\n button_functions.gl_rolebox_changed(self)\n distscaleIdent1 = get_element(resolutionIdent1MD, \"gmd:equivalentScale\")\n fractionIdent1MD = get_element(distscaleIdent1, \"gmd:MD_RepresentativeFraction\")\n denominatorIdent1 = get_element(fractionIdent1MD, \"gmd:denominator\")\n set_text_value(self.gl_resolution_ln, denominatorIdent1, \"gco:Integer\")\n else:\n self.gl_resolution_rl1.setCurrentIndex(self.gl_resolution_rl1.findText(\"Distance\"))\n button_functions.gl_rolebox_changed(self)\n distscaleIdent1 = get_element(resolutionIdent1MD, \"gmd:distance\")\n set_text_value(self.gl_resolution_ln, distscaleIdent1, \"gco:Distance\")\n tmp = distscaleIdent1.getElementsByTagName(\"gco:Distance\")\n distanceIdent2 = tmp[0]\n distAttr = distanceIdent2.attributes[\"uom\"].value\n query = sql_valueRead(self, 'unit', 'Short', distAttr)\n self.gl_unit_rl.setCurrentIndex(self.gl_unit_rl.findText(query[0][0]))\n\n ########################\n # Language\n ######################## \n nodes = doc_root.getElementsByTagName(\"gmd:language\")\n combo_text = get_element_value(nodes[0], \"gmd:LanguageCode\")\n query = sql_valueRead(self, 'languageRole', 'Short', combo_text)\n self.id_resourceLang_rl2.setCurrentIndex(self.id_resourceLang_rl2.findText(query[0][0]))\n\n\n ############################\n # Hierarchy Level\n ############################\n hierarchyLevel = get_element(doc_root, \"gmd:hierarchyLevel\")\n combo_text = get_element_value(hierarchyLevel, \"gmd:MD_ScopeCode\")\n self.id_resourceType_rl1.setCurrentIndex(self.id_resourceType_rl1.findText(combo_text.title()))\n \n\n ############################\n # Distribution Info\n ############################\n distributionInfo1 = get_element(doc_root, \"gmd:distributionInfo\")\n distributionInfo1MD = get_element(distributionInfo1, \"gmd:MD_Distribution\")\n transferInfo1 = get_element(distributionInfo1MD, \"gmd:transferOptions\")\n transferInfo1MD = get_element(transferInfo1, \"gmd:MD_DigitalTransferOptions\")\n onlineInfo1 = get_element(transferInfo1MD, \"gmd:onLine\")\n onlineInfo1CI = get_element(onlineInfo1, \"gmd:CI_OnlineResource\")\n linkageInfo1 = get_element(onlineInfo1CI, \"gmd:linkage\")\n set_text_value(self.id_resourceLocator_ln, linkageInfo1, \"gmd:URL\")\n \n \n ############################\n # Language Info\n ############################\n nodes = doc_root.getElementsByTagName(\"gmd:language\")\n combo_text = get_element_value(nodes[1], \"gmd:LanguageCode\")\n query = sql_valueRead(self, 'languageRole', 'Short', combo_text)\n self.mm_language_rl1.setCurrentIndex(self.mm_language_rl1.findText(query[0][0]))\n \n \n ############################\n # Contact Info\n ############################\n self.tabWidget.setCurrentIndex(9)\n nodes = doc_root.getElementsByTagName(\"gmd:contact\")\n responsiblePartyInfo1CI = get_element(nodes[0], \"gmd:CI_ResponsibleParty\")\n nameContact1 = get_element(responsiblePartyInfo1CI, \"gmd:organisationName\")\n set_text_value(self.mm_contactName_ln, nameContact1, \"gco:CharacterString\")\n infoContact1 = get_element(responsiblePartyInfo1CI, \"gmd:contactInfo\")\n infoContact1CI = get_element(infoContact1, \"gmd:CI_Contact\")\n addressContact1 = get_element(infoContact1CI, \"gmd:address\")\n addressContact1CI = get_element(addressContact1, \"gmd:CI_Address\")\n emailContact1 = get_element(addressContact1CI, \"gmd:electronicMailAddress\")\n set_text_value(self.mm_contactEmail_ln, emailContact1, \"gco:CharacterString\")\n if len(nodes) > 1:\n i = 0\n for node in nodes[1:]:\n button_functions.plusButton_8_clicked(self)\n responsiblePartyInfo1CI = get_element(nodes[i + 1], \"gmd:CI_ResponsibleParty\")\n nameContact1 = get_element(responsiblePartyInfo1CI, \"gmd:organisationName\")\n set_text_value(self.mm_conName_ln[i], nameContact1, \"gco:CharacterString\")\n infoContact1 = get_element(responsiblePartyInfo1CI, \"gmd:contactInfo\")\n infoContact1CI = get_element(infoContact1, \"gmd:CI_Contact\")\n addressContact1 = get_element(infoContact1CI, \"gmd:address\")\n addressContact1CI = get_element(addressContact1, \"gmd:CI_Address\")\n emailContact1 = get_element(addressContact1CI, \"gmd:electronicMailAddress\")\n set_text_value(self.mm_conEmail_ln[i], emailContact1, \"gco:CharacterString\")\n i += 1\n \n\n ############################\n # Data Quality\n ############################\n self.tabWidget.setCurrentIndex(6)\n qualityInfo1 = get_element(doc_root, \"gmd:dataQualityInfo\")\n dataQuality1DQ = get_element(qualityInfo1, \"gmd:DQ_DataQuality\")\n lineageQuality1 = get_element(dataQuality1DQ, \"gmd:lineage\")\n lineageQuality1LI = get_element(lineageQuality1, \"gmd:LI_Lineage\")\n statementQuality1 = get_element(lineageQuality1LI, \"gmd:statement\")\n statement = get_element_value(statementQuality1, \"gco:CharacterString\")\n if statement != \"\":\n self.read_statement(statement)\n \n\n ############################\n # Aircraft and Instruments\n ############################\n self.tabWidget.setCurrentIndex(3)\n acquisitionInfo1 = get_element(doc_root, \"gmd:acquisitionInfo\")\n aircraftInfo1 = get_element(acquisitionInfo1, \"gmd:platformInfo\")\n aircraftInfo11AI = get_element(aircraftInfo1, \"gmd:PI_PlatformInfo\")\n aircraftRegistration = get_element(aircraftInfo11AI, \"gmd:platformRegistration\")\n aircraftManufacturer = get_element(aircraftInfo11AI, \"gmd:platformManufacturer\")\n aircraftType = get_element(aircraftInfo11AI, \"gmd:platformType\")\n aircraftOperator = get_element(aircraftInfo11AI, \"gmd:platformOperator\")\n manufacturer = get_element_value(aircraftManufacturer, \"gco:CharacterString\")\n atype = get_element_value(aircraftType, \"gco:CharacterString\")\n operator = get_element_value(aircraftOperator, \"gco:CharacterString\")\n areg = get_element_value(aircraftRegistration, \"gco:CharacterString\")\n if areg == None and operator == None and atype == None and manufacturer == None:\n self.ai_aircraft_rl1.setCurrentIndex(0)\n ai_aircraftRolebox_changed(self)\n elif areg != None and operator != None and atype != None and manufacturer != None:\n query = sql_valueRead(self, \"aircraftInformations\", \"Code\", areg)\n if query:\n self.ai_aircraft_rl1.setCurrentIndex(self.ai_aircraft_rl1.findText(query[0][0]))\n ai_aircraftRolebox_changed(self)\n else :\n aircraftCountry = get_element(aircraftInfo11AI, \"gmd:platformCountry\")\n country = get_element_value(aircraftCountry, \"gco:CharacterString\")\n self.ai_aircraft_rl1.setCurrentIndex(1)\n ai_aircraftRolebox_changed(self)\n self.ai_manufacturer_ln.setText(manufacturer)\n self.ai_type_ln.setText(atype)\n self.ai_operator_ln.setText(operator)\n self.ai_number_ln.setText(areg)\n self.ai_country_rl.setCurrentIndex(self.ai_country_rl.findText(country))\n else:\n aircraftCountry = get_element(aircraftInfo11AI, \"gmd:platformCountry\")\n country = get_element_value(aircraftCountry, \"gco:CharacterString\")\n self.ai_aircraft_rl1.setCurrentIndex(1)\n ai_aircraftRolebox_changed(self)\n self.ai_manufacturer_ln.setText(manufacturer)\n self.ai_type_ln.setText(atype)\n self.ai_operator_ln.setText(operator)\n self.ai_number_ln.setText(areg)\n self.ai_country_rl.setCurrentIndex(self.ai_country_rl.findText(country))\n nodes = doc_root.getElementsByTagName(\"gmd:instrumentInfo\")\n if len(nodes) > 0:\n i = 0\n for i in range(0, len(nodes)):\n instrument1AI = get_element(nodes[i], \"gmd:II_InstrumentInfo\")\n instrumentManufacturer = get_element(instrument1AI, \"gmd:instrumentManufacturer\")\n manufacturer = get_element_value(instrumentManufacturer, \"gco:CharacterString\")\n instrumentModel = get_element(instrument1AI, \"gmd:instrumentType\")\n model = get_element_value(instrumentModel, \"gco:CharacterString\")\n '''self.instModel_list.append(model)\n self.instManufacturer_list.append(manufacturer)'''\n button_functions.plusButton_4_clicked(self, manufacturer + \" - \" + model)\n \n \n ############################\n # Metadata Date\n ############################\n dateStamp1 = get_element(doc_root, \"gmd:dateStamp\")\n date = get_element_value(dateStamp1, \"gco:Date\")\n self.mm_date_do1.setDate(QDate.fromString(date, Qt.ISODate))\n\n self.tabWidget.setCurrentIndex(currentIndex)\n\n\ndef get_element(parent, element_name):\n return parent.getElementsByTagName(element_name)[0]\n\ndef get_element_value(parent, element_name):\n elements = parent.getElementsByTagName(element_name)\n if elements:\n element = elements[0]\n nodes = element.childNodes\n for node in nodes:\n if node.nodeType == node.TEXT_NODE:\n return node.data.strip()\n\ndef set_text_value(text_widget, parent, element_name):\n node_data = get_element_value(parent, element_name)\n if node_data:\n text_widget.setText(node_data)\n\ndef set_plainText_value(text_widget, parent, element_name):\n node_data = get_element_value(parent, element_name)\n if node_data:\n text_widget.setPlainText(node_data)\n\ndef add_element(doc, element_name, parent, value=None):\n new_element = doc.createElement(element_name)\n if value:\n new_text = doc.createTextNode(value)\n new_element.appendChild(new_text)\n parent.appendChild(new_element)\n return new_element\n\ndef save_statement_observation(self):\n statement = \"Earth observation/Remote sensing data|Name of calibration laboratory: \"\n statement = statement + str(self.qv_obsCalLabo_ln.text()) + \"|Date of radiometric calibration: \"\n statement = statement + str(self.qv_obsRadCal_dt.date().toString(Qt.ISODate)) + \"|Date of spectral calibration: \"\n statement = statement + str(self.qv_obsSpeCal_dt.date().toString(Qt.ISODate)) + \"|Number of spectral bands: \"\n statement = statement + str(self.qv_obsSpeBand_ln.text()) + \"|Overall heading / fligh direction (dd): \"\n statement = statement + str(self.qv_obsFltHdg_ln.text()) + \"|Overall altitude / average height ASL (m): \"\n statement = statement + str(self.qv_obsFltAlt_ln.text()) + \"|Solar zenith (dd): \"\n statement = statement + str(self.qv_obsSolZen_ln.text()) + \"|Solar azimuth (dd): \"\n statement = statement + str(self.qv_obsSolAzi_ln.text()) + \"|Report anomalies in data acquisition: \"\n statement = statement + str(self.qv_obsAnoAcq_ln.text()) + \"|Processing level: \"\n if self.qv_obsProLvl_rl.currentText() == \"Make a choice...\":\n choice = \"\"\n else:\n choice = self.qv_obsProLvl_rl.currentText()\n statement = statement + choice + \"|Dark current (DC) correction: \"\n statement = statement + getAnswer(self.qv_obsDrkCur_rd1, self.qv_obsDrkCur_rd2)\n statement = statement + \"|Aggregated interpolated pixel mask: \"\n statement = statement + getAnswer(self.qv_obsIntMask_rd1, self.qv_obsIntMask_rd2)\n statement = statement + \"|Aggregated bad pixel mask: \"\n statement = statement + getAnswer(self.qv_obsBadMask_rd1, self.qv_obsBadMask_rd2)\n statement = statement + \"|Saturated pixels / overflow: \"\n statement = statement + getAnswer(self.qv_obsSatPix_rd1, self.qv_obsSatPix_rd2)\n statement = statement + \"|Problems with affected by saturation in spatial/spectral neighbourhood: \"\n statement = statement + getAnswer(self.qv_obsSpeNeigh_rd1, self.qv_obsSpeNeigh_rd2)\n statement = statement + \"|Problems with position information / Interpolated position information: \"\n statement = statement + getAnswer(self.qv_obsPosInfo_rd1, self.qv_obsPosInfo_rd2)\n statement = statement + \"|Problems with attitude information / Interpolated attitude information: \"\n statement = statement + getAnswer(self.qv_obsAttInfo_rd1, self.qv_obsAttInfo_rd2)\n statement = statement + \"|Synchronization problems: \"\n statement = statement + getAnswer(self.qv_obsSynProb_rd1, self.qv_obsSynProb_rd2)\n statement = statement + \"|Interpolated pixels during geocoding: \"\n statement = statement + getAnswer(self.qv_obsIntGeo_rd1, self.qv_obsIntGeo_rd2)\n statement = statement + \"|Failure of atmospheric correction: \"\n statement = statement + getAnswer(self.qv_obsAtmCorr_rd1, self.qv_obsAtmCorr_rd2)\n statement = statement + \"|Cloud mask: \"\n statement = statement + getAnswer(self.qv_obsCldMask_rd1, self.qv_obsCldMask_rd2)\n statement = statement + \"|Cloud shadow mask: \"\n statement = statement + getAnswer(self.qv_obsShdMask_rd1, self.qv_obsShdMask_rd2)\n statement = statement + \"|Haze mask: \"\n statement = statement + getAnswer(self.qv_obsHazMask_rd1, self.qv_obsHazMask_rd2)\n statement = statement + \"|Critical terrain correction based on DEM roughness measure: \"\n statement = statement + getAnswer(self.qv_obsDEMMea_rd1, self.qv_obsDEMMea_rd2)\n statement = statement + \"|Critical terrain correction based on slope/local illumination angle: \"\n statement = statement + getAnswer(self.qv_obsIllAng_rd1, self.qv_obsIllAng_rd2)\n statement = statement + \"|Critical BRDF geometry based on sun-sensor-terrain geometry: \"\n statement = statement + getAnswer(self.qv_obsBRDFGeo_rd1, self.qv_obsBRDFGeo_rd2)\n statement = statement + \"|\"\n return statement\n\ndef save_statement_insitu(self):\n statement = \"Atmospheric/In-situ measurements|Link to the procedure's description: \"\n statement = statement + str(self.qv_insituCalDesc_ln.text()) + \"|Source of calibration constants: \"\n statement = statement + str(self.qv_insituCalCons_ln.text()) + \"|Source of calibration materials: \"\n statement = statement + str(self.qv_insituCalMat_ln.text()) + \"|Data converted to geophysical units: \"\n statement = statement + getAnswer(self.qv_insituGeoUnit_rd1, self.qv_insituGeoUnit_rd2) + \"|Output format: \"\n answer = \"\"\n if self.qv_insituOutFormat_rd1.isChecked() == True:\n answer = \"NetCDF\"\n elif self.qv_insituOutFormat_rd2.isChecked() == True:\n answer = \"HDF\"\n elif self.qv_insituOutFormat_rd3.isChecked() == True: \n answer = \"NASA/Ames\"\n elif self.qv_insituOutFormat_rd4.isChecked() == True: \n answer = \"Other/\" + self.qv_other_ln.text()\n statement = statement + answer + \"|Quality-control flagging applied to individual data points: \"\n statement = statement + str(self.qv_insituQuaFlag_ln.toPlainText()) + \"|Assumption: \"\n statement = statement + str(self.qv_insituAssumption_ln.toPlainText()) + \"|\"\n return statement\n\ndef read_statement_observation(self, statement):\n index1 = statement.find(\"Name of calibration laboratory: \")\n index11 = index1 + len(\"Name of calibration laboratory: \")\n index2 = statement.find(\"|Date of radiometric calibration: \")\n index22 = index2 + len(\"|Date of radiometric calibration: \")\n index3 = statement.find(\"|Date of spectral calibration: \")\n index33 = index3 + len(\"|Date of spectral calibration: \")\n index4 = statement.find(\"|Number of spectral bands: \")\n index44 = index4 + len(\"|Number of spectral bands: \")\n index5 = statement.find(\"|Overall heading / fligh direction (dd): \")\n index55 = index5 + len(\"|Overall heading / fligh direction (dd): \")\n index6 = statement.find(\"|Overall altitude / average height ASL (m): \")\n index66 = index6 + len(\"|Overall altitude / average height ASL (m): \")\n index7 = statement.find(\"|Solar zenith (dd): \")\n index77 = index7 + len(\"|Solar zenith (dd): \")\n index8 = statement.find(\"|Solar azimuth (dd): \")\n index88 = index8 + len(\"|Solar azimuth (dd): \")\n index9 = statement.find(\"|Report anomalies in data acquisition: \")\n index99 = index9 + len(\"|Report anomalies in data acquisition: \")\n index10 = statement.find(\"|Processing level: \")\n index100 = index10 + len(\"|Processing level: \")\n index12 = statement.find(\"|Dark current (DC) correction: \")\n index122 = index12 + len(\"|Dark current (DC) correction: \")\n index13 = statement.find(\"|Aggregated interpolated pixel mask: \")\n index133 = index13 + len(\"|Aggregated interpolated pixel mask: \")\n index14 = statement.find(\"|Aggregated bad pixel mask: \")\n index144 = index14 + len(\"|Aggregated bad pixel mask: \")\n index15 = statement.find(\"|Saturated pixels / overflow: \")\n index155 = index15 + len(\"|Saturated pixels / overflow: \")\n index16 = statement.find(\"|Problems with affected by saturation in spatial/spectral neighbourhood: \")\n index166 = index16 + len(\"|Problems with affected by saturation in spatial/spectral neighbourhood: \")\n index17 = statement.find(\"|Problems with position information / Interpolated position information: \")\n index177 = index17 + len(\"|Problems with position information / Interpolated position information: \")\n index18 = statement.find(\"|Problems with attitude information / Interpolated attitude information: \")\n index188 = index18 + len(\"|Problems with attitude information / Interpolated attitude information: \")\n index19 = statement.find(\"|Synchronization problems: \")\n index199 = index19 + len(\"|Synchronization problems: \")\n index20 = statement.find(\"|Interpolated pixels during geocoding: \")\n index200 = index20 + len(\"|Interpolated pixels during geocoding: \")\n index21 = statement.find(\"|Failure of atmospheric correction: \")\n index211 = index21 + len(\"|Failure of atmospheric correction: \")\n index23 = statement.find(\"|Cloud mask: \")\n index233 = index23 + len(\"|Cloud mask: \")\n index24 = statement.find(\"|Cloud shadow mask: \")\n index244 = index24 + len(\"|Cloud shadow mask: \")\n index25 = statement.find(\"|Haze mask: \")\n index255 = index25 + len(\"|Haze mask: \")\n index26 = statement.find(\"|Critical terrain correction based on DEM roughness measure: \")\n index266 = index26 + len(\"|Critical terrain correction based on DEM roughness measure: \")\n index27 = statement.find(\"|Critical terrain correction based on slope/local illumination angle: \")\n index277 = index27 + len(\"|Critical terrain correction based on slope/local illumination angle: \")\n index28 = statement.find(\"|Critical BRDF geometry based on sun-sensor-terrain geometry: \")\n index288 = index28 + len(\"|Critical BRDF geometry based on sun-sensor-terrain geometry: \")\n self.qv_obsCalLabo_ln.setText(statement[index11:index2])\n self.qv_obsRadCal_dt.setDate(QDate.fromString(statement[index22:index3], Qt.ISODate))\n self.qv_obsSpeCal_dt.setDate(QDate.fromString(statement[index33:index4], Qt.ISODate))\n self.qv_obsSpeBand_ln.setText(statement[index44:index5])\n self.qv_obsFltHdg_ln.setText(statement[index55:index6])\n self.qv_obsFltAlt_ln.setText(statement[index66:index7])\n self.qv_obsSolZen_ln.setText(statement[index77:index8])\n self.qv_obsSolAzi_ln.setText(statement[index88:index9])\n self.qv_obsAnoAcq_ln.setText(statement[index99:index10])\n if statement[index100:index12] != \"\":\n self.qv_obsProLvl_rl.setCurrentIndex(self.qv_obsProLvl_rl.findText(statement[index100:index12]))\n pushAnswer(self.qv_obsDrkCur_rd1, self.qv_obsDrkCur_rd2, statement[index122:index13])\n pushAnswer(self.qv_obsIntMask_rd1, self.qv_obsIntMask_rd2, statement[index133:index14])\n pushAnswer(self.qv_obsBadMask_rd1, self.qv_obsBadMask_rd2, statement[index144:index15])\n pushAnswer(self.qv_obsSatPix_rd1, self.qv_obsSatPix_rd2, statement[index155:index16])\n pushAnswer(self.qv_obsSpeNeigh_rd1, self.qv_obsSpeNeigh_rd2, statement[index166:index17])\n pushAnswer(self.qv_obsPosInfo_rd1, self.qv_obsPosInfo_rd2, statement[index177:index18])\n pushAnswer(self.qv_obsAttInfo_rd1, self.qv_obsAttInfo_rd2, statement[index188:index19])\n pushAnswer(self.qv_obsSynProb_rd1, self.qv_obsSynProb_rd2, statement[index199:index20])\n pushAnswer(self.qv_obsIntGeo_rd1, self.qv_obsIntGeo_rd2, statement[index200:index21])\n pushAnswer(self.qv_obsAtmCorr_rd1, self.qv_obsAtmCorr_rd2, statement[index211:index23])\n pushAnswer(self.qv_obsCldMask_rd1, self.qv_obsCldMask_rd2, statement[index233:index24])\n pushAnswer(self.qv_obsShdMask_rd1, self.qv_obsShdMask_rd2, statement[index244:index25])\n pushAnswer(self.qv_obsHazMask_rd1, self.qv_obsHazMask_rd2, statement[index255:index26])\n pushAnswer(self.qv_obsDEMMea_rd1, self.qv_obsDEMMea_rd2, statement[index266:index27])\n pushAnswer(self.qv_obsIllAng_rd1, self.qv_obsIllAng_rd2, statement[index277:index28])\n pushAnswer(self.qv_obsBRDFGeo_rd1, self.qv_obsBRDFGeo_rd2, statement[index288:-1])\n \ndef read_statement_insitu(self, statement):\n index1 = statement.find(\"Link to the procedure's description: \")\n index11 = index1 + len(\"Link to the procedure's description: \")\n index2 = statement.find(\"|Source of calibration constants: \")\n index22 = index2 + len(\"|Source of calibration constants: \")\n index3 = statement.find(\"|Source of calibration materials: \")\n index33 = index3 + len(\"|Source of calibration materials: \")\n index4 = statement.find(\"|Data converted to geophysical units: \")\n index44 = index4 + len(\"|Data converted to geophysical units: \")\n index5 = statement.find(\"|Output format: \")\n index55 = index5 + len(\"|Output format: \")\n index6 = statement.find(\"|Quality-control flagging applied to individual data points: \")\n index66 = index6 + len(\"|Quality-control flagging applied to individual data points: \")\n index7 = statement.find(\"|Assumption: \")\n index77 = index7 + len(\"|Assumption: \")\n self.qv_insituCalDesc_ln.setText(statement[index11:index2])\n self.qv_insituCalCons_ln.setText(statement[index22:index3])\n self.qv_insituCalMat_ln.setText(statement[index33:index4])\n pushAnswer(self.qv_insituGeoUnit_rd1, self.qv_insituGeoUnit_rd2, statement[index44:index5])\n format = statement[index55:index6] # @ReservedAssignment\n if format == \"NetCDF\":\n self.qv_insituOutFormat_rd1.setChecked(True)\n elif format == \"HDF\":\n self.qv_insituOutFormat_rd2.setChecked(True)\n elif format == \"NASA/Ames\":\n self.qv_insituOutFormat_rd3.setChecked(True)\n elif \"Other\" in format:\n self.qv_insituOutFormat_rd4.setChecked(True)\n qv_output_other(self)\n other_format = format[6:]\n self.qv_other_ln.setText(other_format)\n self.qv_insituQuaFlag_ln.setPlainText(statement[index66:index7])\n self.qv_insituAssumption_ln.setPlainText(statement[index77:-1]) \n\ndef getAnswer(radioButton1, radioButton2):\n answer = \"\"\n if radioButton1.isChecked() == True:\n answer = \"yes\"\n elif radioButton2.isChecked() == True:\n answer = \"no\"\n return answer\n\ndef pushAnswer(radioButton1, radioButton2, answer):\n if answer == \"yes\":\n radioButton1.setChecked(True)\n elif answer == \"no\":\n radioButton2.setChecked(True)\n ", "sub_path": "functions/eufar_metadata_xml.py", "file_name": "eufar_metadata_xml.py", "file_ext": "py", "file_size_in_byte": 59696, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication.translate", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication.translate", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 28, "usage_type": "name"}, {"api_name": "xml.dom.minidom.dom.minidom.Document", "line_number": 32, "usage_type": "call"}, {"api_name": "xml.dom.minidom.dom", "line_number": 32, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 60, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 60, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 70, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 70, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 80, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 97, "usage_type": "argument"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 100, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 157, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 157, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 159, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 159, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 169, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 169, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 171, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 171, "usage_type": "name"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 183, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 194, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 244, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 264, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 300, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 348, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 444, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 444, "usage_type": "name"}, {"api_name": "xml.dom.minidom.dom.minidom.parse", "line_number": 460, "usage_type": "call"}, {"api_name": "xml.dom.minidom.dom", "line_number": 460, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom", "line_number": 460, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 484, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 484, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 484, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 484, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 485, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 485, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 485, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 485, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 486, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 486, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 486, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 486, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 497, "usage_type": "argument"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 502, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 516, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 516, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 516, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 516, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 517, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 517, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 517, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 517, "usage_type": "name"}, {"api_name": "functions.button_functions.plusButton_3_clicked", "line_number": 521, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 521, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 522, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 522, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 522, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 522, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 523, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 523, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 523, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 523, "usage_type": "name"}, {"api_name": "functions.button_functions.plusButton_5_clicked", "line_number": 535, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 535, "usage_type": "name"}, {"api_name": "functions.button_functions.plusButton_6_clicked", "line_number": 543, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 543, "usage_type": "name"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 563, "usage_type": "call"}, {"api_name": "functions.button_functions.plusButton_7_clicked", "line_number": 568, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 568, "usage_type": "name"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 580, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 591, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 606, "usage_type": "call"}, {"api_name": "functions.button_functions.gl_categoryRolebox_changed", "line_number": 608, "usage_type": "call"}, {"api_name": "functions.button_functions.gl_rolebox_changed", "line_number": 630, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 630, "usage_type": "name"}, {"api_name": "functions.button_functions.gl_rolebox_changed", "line_number": 637, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 637, "usage_type": "name"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 643, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 651, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 681, "usage_type": "call"}, {"api_name": "functions.button_functions.plusButton_8_clicked", "line_number": 702, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 702, "usage_type": "name"}, {"api_name": "functions.button_functions.ai_aircraftRolebox_changed", "line_number": 746, "usage_type": "call"}, {"api_name": "functions.sql_functions.sql_valueRead", "line_number": 748, "usage_type": "call"}, {"api_name": "functions.button_functions.ai_aircraftRolebox_changed", "line_number": 751, "usage_type": "call"}, {"api_name": "functions.button_functions.ai_aircraftRolebox_changed", "line_number": 756, "usage_type": "call"}, {"api_name": "functions.button_functions.ai_aircraftRolebox_changed", "line_number": 766, "usage_type": "call"}, {"api_name": "functions.button_functions.plusButton_4_clicked", "line_number": 783, "usage_type": "call"}, {"api_name": "functions.button_functions", "line_number": 783, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 791, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 791, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 791, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 791, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 829, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 829, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 830, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 830, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 950, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 950, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 950, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 950, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate.fromString", "line_number": 951, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 951, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ISODate", "line_number": 951, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 951, "usage_type": "name"}, {"api_name": "functions.button_functions.qv_output_other", "line_number": 1005, "usage_type": "call"}]} +{"seq_id": "508478067", "text": "#!/usr/bin/env python\n# coding=utf-8\nfrom flask import Flask\n# from pymongo import MongoClient\nfrom flask import Flask, request, session, g, redirect, url_for, abort,\\\n render_template, flash\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef test():\n return render_template('index.html')\n\napp.run(port=5002, debug=True)\n\n#\n# def get_db():\n# client = MongoClient('127.0.0.1', 27037)\n# db_name = 'monitor'\n# db = client[db_name]\n# return db\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n\n", "sub_path": "web/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 562, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "613262720", "text": "from fpdf import FPDF\nimport os\nimport ocr2text2 as ocr2text\nfrom glob import glob\n\nfolder_img = \"img\"\nfolder_pdf = \"pdf\"\nfolder_output = \"txt\"\n\n\n# img to pdf\ndef group_imgs_by_name(file_name):\n # list_of_images = [\"img/topic1-1.jpg\", \"img/topic1-2.jpg\"]\n print(\"reading images:\" + os.path.join(folder_img, file_name, '*.*'))\n list_of_images = sorted(glob(os.path.join(folder_img, file_name + '*.*')), key=os.path.getmtime)\n print(list_of_images)\n path = \"\"\n if len(list_of_images) > 0:\n pdf = FPDF(orientation='L')\n pdf.compress = False\n for image in list_of_images:\n pdf.add_page()\n pdf.image(image, w=250)\n path = os.path.join(folder_pdf, file_name + \".pdf\")\n pdf.output(path, \"F\")\n print(folder_pdf + \"/\" + file_name + \".pdf\" + ' converted')\n return path\n\n\n# pdf to txt\ndef pdf_to_txt(path):\n count = 0\n dir_path = os.path.dirname(os.path.realpath(__file__))\n # print('Source file or folder of PDF(s) [' + dir_path + ']:')\n # print('(Press [Enter] for current working directory)')\n # source = input()\n # if source == '':\n # source = dir_path\n source = os.path.join(dir_path, path if path != '' else folder_pdf)\n print(source)\n # print('Destination folder for TXT [' + dir_path + ']:')\n # print('(Press [Enter] for current working directory)')\n # destination = input()\n # if destination == '':\n # destination = dir_path\n\n destination = os.path.join(dir_path, folder_output)\n\n if (os.path.exists(source)):\n if (os.path.isdir(source)):\n count = ocr2text.convert_recursive(source, destination, count)\n elif os.path.isfile(source):\n filepath, fullfile = os.path.split(source)\n filename, file_extension = os.path.splitext(fullfile)\n if (file_extension.lower() == '.pdf'):\n count = ocr2text.convert(source, os.path.join(destination, filename + '.txt'), count, 1)\n plural = 's'\n if count == 1:\n plural = ''\n print(str(count) + ' file' + plural + ' converted')\n else:\n print('The path ' + source + 'seems to be invalid')\n\n\nif __name__ == '__main__':\n # put images in /img folder and format the name into xxx-n.zzz;\n # eg: 1-1.jpg, 1-2.jpg;\n # all images matches {}-*.* ( \"{}-\" can be changed bellow ) will be merge into one file\n # for n in range(30):\n # pdf_to_txt(group_imgs_by_name(\"{}-\".format(n + 1)))\n pdf_to_txt(group_imgs_by_name('Screen Shot 2020-08-07 at 15.25.56'))\n", "sub_path": "images2pdf.py", "file_name": "images2pdf.py", "file_ext": "py", "file_size_in_byte": 2550, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "fpdf.FPDF", "line_number": 19, "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": "os.path.dirname", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 33, "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": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "ocr2text2.convert_recursive", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.split", "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": "ocr2text2.convert", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "105238174", "text": "# accounts/views/users.py\n\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth.models import update_last_login\nfrom django.db.models import F\nfrom django.http import JsonResponse\nfrom rest_framework import permissions, status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom rest_framework.generics import GenericAPIView\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.mixins import UpdateModelMixin\n\nfrom ..models import RoshamboUser as User, SkinsInventory, Skins, Stats, Wallet\nfrom ..serializers import UserSerializer, EditUserSerializer\n\n\n@api_view(['GET'])\ndef current_user(request):\n \"\"\"\n @auth-required: yes\n @method-supported: GET\n @GET: \n @return: \n keys:\n country_code\n email\n guild\n username\n id\n value: appropriate values corresponding to the keys.\n \"\"\"\n # TODO(benjibrandt): this is sorty janky to filter the entire user set...\n user = User.objects.filter(id=request.user.id).annotate(\n games_won=F('stats__games_won'), \n games_lost=F('stats__games_lost'),\n cash=F('wallet__cash')\n ).values('first_name', 'last_name', 'email', 'username', 'guild', 'games_won', 'games_lost', 'cash').first()\n return Response(user)\n\n\n@api_view(['GET'])\ndef users(request):\n \"\"\"\n @auth-required: yes\n @method-supported: GET\n @GET: \n @return: top 10 list of user infos.\n keys: games_won, games_lost, guild, username, is_active\n value: valid value to set the corresponding field to.\n \"\"\"\n users_info = list(User.objects.annotate(\n games_won=F('stats__games_won'), games_lost=F('stats__games_lost')\n ).values('username', 'guild', 'games_won', 'games_lost', 'is_active'))[:10]\n return JsonResponse(users_info, safe=False)\n\n\n@api_view(['GET'])\ndef active_users(request):\n \"\"\"\n @auth-required: yes\n @method-supported: GET\n @GET: \n @return: top 10 list of active user infos.\n keys: games_won, games_lost, guild, username\n value: valid value to set the corresponding field to.\n \"\"\"\n active_users = list(User.objects.filter(is_active=True).annotate(\n games_won=F('stats__games_won'), games_lost=F('stats__games_lost')\n ).values('username', 'guild', 'games_won', 'games_lost'))[:10]\n return Response(active_users)\n\n\nclass EditUser(GenericAPIView, UpdateModelMixin):\n \"\"\"\n @auth-required: yes\n @method-supported: PUT\n @PUT: \n @param user-field:\n key: username|email|password|country_code|guild\n value: valid value to set the corresponding field to.\n @return: the newly-updated user representation, as per GET /accounts/users/current/.\n \"\"\"\n queryset = User.objects.all()\n serializer_class = EditUserSerializer\n\n def get_object(self):\n queryset = self.filter_queryset(self.get_queryset())\n # make sure to catch 404's below\n obj = queryset.get(pk=self.request.user.id)\n self.check_object_permissions(self.request, obj)\n return obj\n\n def put(self, request, format='json'):\n return self.partial_update(request)\n \n\nclass Register(APIView):\n \"\"\"\n @auth-required: no\n @method-supported: POST\n @POST: \n @param user-field:\n keys: username, email, password, first_name, last_name, [country_code]\n value: valid value to set the corresponding field to.\n @return: \n Token: the valid token for the user session\n the newly-created user representation, as per GET /accounts/users/current/.\n \"\"\"\n # This permission class will overide the global permission class setting\n permission_classes = (permissions.AllowAny,)\n\n def post(self, request, format='json'):\n serializer = UserSerializer(data=request.data)\n if serializer.is_valid():\n user = serializer.save()\n if user:\n self._add_default_skin(user)\n self._initialize_stats_entries(user)\n self._initialize_wallet(user)\n update_last_login(request, user)\n token = Token.objects.create(user=user)\n json = serializer.data\n json['token'] = token.key\n return Response(json, status=status.HTTP_201_CREATED)\n else:\n return Response({'error': 'User creation failed due to an internal server error. Try again later.'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n error_message = {}\n for key, value in serializer.errors.items():\n error_message['error'] = '{}: {}'.format(key, value[0])\n return Response(error_message, status=status.HTTP_400_BAD_REQUEST)\n\n def _add_default_skin(self, user):\n skin = SkinsInventory.objects.get(skin=0)\n user_skin = Skins(user=user, active_skin=skin)\n user_skin.save() # must be saved before we can add to purchased_skins\n user_skin.purchased_skins.add(skin)\n user_skin.save()\n\n def _initialize_stats_entries(self, user):\n user_stats = Stats(user=user)\n user_stats.save()\n\n def _initialize_wallet(self, user):\n wallet = Wallet(user=user)\n wallet.save()\n\n\nclass Login(APIView):\n \"\"\"\n @auth-required: no\n @method-supported: POST\n @POST: \n @param user-field:\n keys: email, password\n value: valid value to set the corresponding field to.\n @return: \n Token: the valid token for the user session\n the user representation, as per GET /accounts/users/current/.\n \"\"\"\n # This permission class will overide the global permission class setting\n permission_classes = (permissions.AllowAny,)\n\n def post(self, request, format='json'):\n email = request.data.get(\"email\")\n password = request.data.get(\"password\")\n if email is None or password is None:\n return Response({'error': 'Please provide both email and password'},\n status=status.HTTP_400_BAD_REQUEST)\n user = authenticate(email=email, password=password)\n if user is not None:\n token = self._login(request, user, email)\n return Response({'token': token.key}, status=status.HTTP_200_OK)\n return Response({'error': 'Invalid Credentials'}, status=status.HTTP_404_NOT_FOUND)\n\n def _login(self, request, user, email):\n user_state = User.objects.get(email=email)\n user_state.is_active = True\n user_state.save()\n update_last_login(request, user)\n token, _ = Token.objects.get_or_create(user=user)\n return token\n\n@api_view(['POST'])\ndef logout(request, format=None): \n \"\"\"\n @auth-required: yes\n @method-supported: POST\n @POST: \n @return: HTTP 204 no content. Logout performed successfully.\n \"\"\"\n # simply delete the token to force a login \n request.user.is_active = False\n request.user.auth_token.delete() \n request.user.save()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n", "sub_path": "backend/accounts/views/users.py", "file_name": "users.py", "file_ext": "py", "file_size_in_byte": 7199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "models.RoshamboUser.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.RoshamboUser", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 19, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects.annotate", "line_number": 53, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.RoshamboUser", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 54, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 43, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.RoshamboUser", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 75, "usage_type": "name"}, {"api_name": "models.RoshamboUser.objects.all", "line_number": 85, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.RoshamboUser", "line_number": 85, "usage_type": "name"}, {"api_name": "serializers.EditUserSerializer", "line_number": 86, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 112, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 112, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 115, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.update_last_login", "line_number": 122, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects.create", "line_number": 123, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 123, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 126, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 126, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 126, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 128, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 132, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 132, "usage_type": "name"}, {"api_name": "models.SkinsInventory.objects.get", "line_number": 135, "usage_type": "call"}, {"api_name": "models.SkinsInventory.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "models.SkinsInventory", "line_number": 135, "usage_type": "name"}, {"api_name": "models.Skins", "line_number": 136, "usage_type": "call"}, {"api_name": "models.Stats", "line_number": 142, "usage_type": "call"}, {"api_name": "models.Wallet", "line_number": 146, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 150, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 163, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 163, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 169, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 170, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 170, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 171, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 174, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 174, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 174, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 175, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 175, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 175, "usage_type": "name"}, {"api_name": "models.RoshamboUser.objects.get", "line_number": 178, "usage_type": "call"}, {"api_name": "models.RoshamboUser.objects", "line_number": 178, "usage_type": "attribute"}, {"api_name": "models.RoshamboUser", "line_number": 178, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.update_last_login", "line_number": 181, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects.get_or_create", "line_number": 182, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 182, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 197, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 197, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 197, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 185, "usage_type": "call"}]} +{"seq_id": "187154537", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport logging\nimport os\nimport shutil\nimport signal\nimport subprocess\nimport sys\nimport tempfile\nimport unittest\n\nimport ffpuppet\nfrom .main import main\n\nlogging.basicConfig(level=logging.DEBUG if bool(os.getenv(\"DEBUG\")) else logging.INFO)\nlog = logging.getLogger(\"ffp_test\")\n\nCWD = os.path.realpath(os.path.dirname(__file__))\nTESTFF_BIN = os.path.join(CWD, \"testff\", \"testff.exe\") if sys.platform.startswith('win') else os.path.join(CWD, \"testff.py\")\nTESTMDSW_BIN = os.path.join(CWD, \"testmdsw\", \"testmdsw.exe\") if sys.platform.startswith('win') else os.path.join(CWD, \"testmdsw.py\")\n\nffpuppet.FFPuppet.MDSW_BIN = TESTMDSW_BIN\nffpuppet.FFPuppet.MDSW_MAX_STACK = 8\n\nclass TestCase(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n if sys.platform.startswith('win') and not os.path.isfile(TESTFF_BIN):\n raise EnvironmentError(\"testff.exe is missing see testff.py for build instructions\") # pragma: no cover\n if sys.platform.startswith('win') and not os.path.isfile(TESTMDSW_BIN):\n raise EnvironmentError(\"testmdsw.exe is missing see testmdsw.py for build instructions\") # pragma: no cover\n\n\nclass MainTests(TestCase):\n\n def setUp(self):\n self.tmpdir = tempfile.mkdtemp(prefix=\"ffp_test\")\n\n def tearDown(self):\n if os.path.isdir(self.tmpdir):\n shutil.rmtree(self.tmpdir)\n\n def test_01(self):\n \"test calling main with '-h'\"\n with self.assertRaises(SystemExit):\n main([\"-h\"])\n\n def test_02(self):\n \"test calling main with test binary/script\"\n out_logs = os.path.join(self.tmpdir, \"logs\")\n prefs = os.path.join(self.tmpdir, \"pref.js\")\n with open(prefs, \"w\") as prefs_fp:\n prefs_fp.write(\"//fftest_exit_code_0\\n\")\n main([TESTFF_BIN, \"-d\", \"-l\", out_logs, \"-p\", prefs])\n self.assertTrue(os.path.isdir(out_logs))\n self.assertGreater(len(os.listdir(out_logs)), 0)\n\n def test_03(self):\n \"test calling main with test binary/script\"\n prefs = os.path.join(self.tmpdir, \"pref.js\")\n with open(prefs, \"w\") as prefs_fp:\n prefs_fp.write(\"//fftest_big_log\\n\")\n main([TESTFF_BIN, \"-v\", \"-d\", \"-p\", prefs, \"--log-limit\", \"1\", \"-a\", \"blah_test\"])\n\n @unittest.skipIf(sys.platform.startswith('win'), \"This test is unsupported on Windows\")\n def test_04(self):\n \"test sending SIGINT\"\n prefs = os.path.join(self.tmpdir, \"pref.js\")\n with open(prefs, \"w\") as prefs_fp:\n # spam logs\n prefs_fp.write(\"//fftest_big_log\\n\")\n out_logs = os.path.join(self.tmpdir, \"logs\")\n with tempfile.TemporaryFile() as console:\n proc = subprocess.Popen(\n [sys.executable, \"-m\", \"ffpuppet\", TESTFF_BIN, \"-d\", \"-p\", prefs, \"-l\", out_logs],\n cwd=os.path.split(os.path.split(ffpuppet.__file__)[0])[0],\n stderr=console,\n stdout=console)\n while proc.poll() is None:\n console.seek(0)\n output = console.read()\n if b\"Running Firefox\" in output:\n break\n # verify we are in a good state otherwise display console output\n self.assertIn(b\"Running Firefox\", output)\n self.assertIsNone(proc.poll())\n proc.send_signal(signal.SIGINT)\n self.assertIsNotNone(proc.wait())\n console.seek(0)\n output = console.read()\n self.assertIn(b\"Ctrl+C detected\", output)\n self.assertIn(b\"Firefox process closed\", output)\n self.assertTrue(os.path.isdir(out_logs))\n self.assertGreater(len(os.listdir(out_logs)), 0)\n", "sub_path": "ffpuppet/test_main.py", "file_name": "test_main.py", "file_ext": "py", "file_size_in_byte": 3861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 21, "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": "sys.platform.startswith", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 22, "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": "ffpuppet.FFPuppet", "line_number": 24, "usage_type": "attribute"}, {"api_name": "ffpuppet.FFPuppet", "line_number": 25, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.platform.startswith", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 31, "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": "sys.platform.startswith", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 44, "usage_type": "call"}, {"api_name": "main.main", "line_number": 49, "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.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "main.main", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "main.main", "line_number": 66, "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": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tempfile.TemporaryFile", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 77, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "ffpuppet.__file__", "line_number": 79, "usage_type": "attribute"}, {"api_name": "signal.SIGINT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 97, "usage_type": "call"}, {"api_name": "unittest.skipIf", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 68, "usage_type": "attribute"}]} +{"seq_id": "512332207", "text": "import os\nimport numpy as np\nimport collections\nimport matplotlib.pyplot as plt\nimport pickle\nimport pyMRAW\nimport datetime\n\nfrom .methods import IDIMethod, SimplifiedOpticalFlow, GradientBasedOpticalFlow, LucasKanadeSc, LucasKanade\nfrom . import tools\n\n__version__ = '0.20'\n\navailable_method_shortcuts = [\n ('sof', SimplifiedOpticalFlow),\n ('lk', LucasKanade),\n ('lk_scipy', LucasKanadeSc)\n # ('gb', GradientBasedOpticalFlow)\n ]\n\n\nclass pyIDI:\n \"\"\"\n The pyIDI base class represents the video to be analysed.\n \"\"\"\n def __init__(self, cih_file):\n self.cih_file = cih_file\n if type(cih_file) == str:\n self.root = os.path.split(self.cih_file)[0]\n else:\n self.root = ''\n\n self.available_methods = dict([ \n (key, {\n 'IDIMethod': method,\n 'description': method.__doc__, \n })\n for key, method in available_method_shortcuts\n ])\n\n # Fill available methods into `set_method` docstring\n available_methods_doc = '\\n' + '\\n'.join([\n f\"'{key}' ({method_dict['IDIMethod'].__name__}): {method_dict['description']}\"\n for key, method_dict in self.available_methods.items()\n ])\n tools.update_docstring(self.set_method, added_doc=available_methods_doc)\n\n if type(cih_file) == str:\n # Load selected video\n self.mraw, self.info = pyMRAW.load_video(self.cih_file)\n self.N = self.info['Total Frame']\n self.image_width = self.info['Image Width']\n self.image_height = self.info['Image Height']\n \n elif type(cih_file) == np.ndarray:\n self.mraw = cih_file\n self.N = cih_file.shape[0]\n self.image_height = cih_file.shape[1]\n self.image_width = cih_file.shape[2]\n self.info = {}\n \n else:\n raise ValueError('`cih_file` must be either a cih filename or a 3D array (N_time, height, width)')\n\n\n def set_method(self, method, **kwargs):\n \"\"\"\n Set displacement identification method on video.\n To configure the method, use `method.configure()`\n\n Available methods:\n ---\n [Available method names and descriptions go here.]\n ---\n\n :param method: the method to be used for displacement identification.\n :type method: IDIMethod or str\n \"\"\"\n if isinstance(method, str) and method in self.available_methods.keys():\n self.method = self.available_methods[method]['IDIMethod'](self, **kwargs)\n elif callable(method) and hasattr(method, 'calculate_displacements'):\n try:\n self.method = method(self, **kwargs)\n except:\n raise ValueError(\"The input `method` is not a valid `IDIMethod`.\")\n else:\n raise ValueError(\"method must either be a valid name from `available_methods` or an `IDIMethod`.\")\n \n # Update `get_displacements` docstring\n tools.update_docstring(self.get_displacements, self.method.calculate_displacements)\n # Update `show_points` docstring\n if hasattr(self.method, 'show_points'):\n try:\n tools.update_docstring(self.show_points, self.method.show_points)\n except:\n pass\n\n\n def set_points(self, points=None, method=None, **kwargs):\n \"\"\"\n Set points that will be used to calculate displacements.\n If `points` is None and a `method` has aready been set on this `pyIDI` instance, \n the `method` object's `get_point` is used to get method-appropriate points.\n \"\"\"\n if points is None:\n if not hasattr(self, 'method'):\n if method is not None:\n self.set_method(method)\n else:\n raise ValueError(\"Invalid arguments. Please input points, or set the IDI method first.\")\n self.method.get_points(self, **kwargs) # get_points sets the attribute video.points \n else:\n self.points = np.asarray(points)\n\n\n def show_points(self, **kwargs):\n \"\"\"\n Show selected points on image.\n \"\"\"\n\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\n self.method.show_points(self, **kwargs)\n else:\n figsize = kwargs.get('figsize', (15, 5))\n cmap = kwargs.get('cmap', 'gray')\n marker = kwargs.get('marker', '.')\n color = kwargs.get('color', 'r')\n fig, ax = plt.subplots(figsize=figsize)\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\n ax.scatter(self.points[:, 1], self.points[:, 0], \n marker=marker, color=color)\n plt.grid(False)\n plt.show()\n\n\n def show_field(self, field, scale=1., width=0.5):\n \"\"\"\n Show displacement field on image.\n \n :param field: Field of displacements (number_of_points, 2)\n :type field: ndarray\n :param scale: scale the field, defaults to 1.\n :param scale: float, optional\n :param width: width of the arrow, defaults to 0.5\n :param width: float, optional\n \"\"\"\n max_L = np.max(field[:, 0]**2 + field[:, 1]**2)\n\n fig, ax = plt.subplots(1)\n ax.imshow(self.mraw[0], 'gray')\n for i, ind in enumerate(self.points):\n f0 = field[i, 0]\n f1 = field[i, 1]\n alpha = (f0**2 + f1**2) / max_L\n if alpha < 0.2:\n alpha = 0.2\n plt.arrow(ind[1], ind[0], scale*f1, scale*f0, width=width, color='r', alpha=alpha)\n\n\n def get_displacements(self, **kwargs):\n \"\"\"\n Calculate the displacements based on chosen method.\n\n Method docstring:\n ---\n Method is not set. Please use the `set_method` method.\n ---\n \"\"\"\n if hasattr(self, 'method'):\n self.method.calculate_displacements(self, **kwargs)\n self.displacements = self.method.displacements\n \n # auto-save and clearing temp files\n if hasattr(self.method, 'process_number'):\n if self.method.process_number == 0:\n if type(self.cih_file) == str:\n cih_file_ = os.path.split(self.cih_file)[-1].split('.')[0]\n auto_filename = f'{datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")}_{cih_file_}.pkl'\n else:\n auto_filename = f'{datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")}_displacements.pkl'\n \n self.save(auto_filename, root=self.root)\n self.method.clear_temp_files()\n \n return self.displacements\n else:\n raise ValueError('IDI method has not yet been set. Please call `set_method()` first.')\n\n\n def close_video(self):\n \"\"\"\n Close the .mraw video memmap.\n \"\"\"\n if hasattr(self, 'mraw'):\n self.mraw._mmap.close()\n del self.mraw\n\n\n def save(self, filename, root=''):\n \"\"\" Save computed displacements and other basic information.\n\n :param filename: Name of the file to save in.\n :param root: Root of the filename, defaults to ''\n \"\"\"\n full_filename = os.path.join(root, filename)\n out = {\n 'points': self.points,\n 'disp': self.displacements,\n 'first_image': self.mraw[0],\n 'info': self.info,\n 'cih_file': self.cih_file,\n 'settings': self.method.create_settings_dict()\n }\n pickle.dump(out, open(full_filename, 'wb'), protocol=-1)\n\n \n", "sub_path": "pyidi/pyidi.py", "file_name": "pyidi.py", "file_ext": "py", "file_size_in_byte": 7762, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "methods.SimplifiedOpticalFlow", "line_number": 15, "usage_type": "name"}, {"api_name": "methods.LucasKanade", "line_number": 16, "usage_type": "name"}, {"api_name": "methods.LucasKanadeSc", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pyMRAW.load_video", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.arrow", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 178, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "34069865", "text": "# Freddy @Terminal 1, Capital Airport, Beijing\n# Sep 2, 2017\nimport tensorflow as tf\nimport numpy as np\nfrom os import sys\nimport time\nimport cv2\nfrom read_las import read_las\n\nnp.seterr(divide='ignore', invalid='ignore')\n\ndef pixel_locator():\n '''\n f: focal distance, \n p: position of the camera, \n v: direction of the camera(from center of camera to focal point)\n x: point cloud\n '''\n x = tf.placeholder(tf.float32,[None,3])\n p = tf.placeholder(tf.float32,[None,3])\n v = tf.placeholder(tf.float32,[None,3])\n f = tf.placeholder(tf.float32,[None,1])\n divider = tf.reduce_sum( (p - x) * v , axis=0, keep_dims=True) + 1 \n result = f * v - f * f * f * (p + f * v - x) / divider\n\n return result, x, p, v,f\n\n\ndef timeit(func, *args, **kwargs):\n s = time.time()\n func(*args, **kwargs)\n e = time.time()\n print(\"USE <\\033[1;32m%.2f\\033[0m> s\" % (e - s))\n\ndef data_loader(address):\n print(\"load data...\")\n original_data = np.loadtxt(address)\n print(\"load complete\")\n print(\"------data stats------\")\n print(\"max x: %f max y: %f max z: %f\" % (max(original_data[:,0]),max(original_data[:,1]), max(original_data[:,2])))\n print(\"min x: %f min y: %f min z: %f\" % (min(original_data[:,0]),min(original_data[:,1]), min(original_data[:,2])))\n return original_data\n\n\ndef parameter_setting(original_data):\n photo_center = np.array([max(original_data[:,0])*1.2,max(original_data[:,1])*1.2, max(original_data[:,2])*20])\n focal_dir = np.array( [0 , 0 , max(original_data[:,2])*1.5 ] )\n focal_distance = (max(original_data[:,1]) - min(original_data[:,1]))*0.5\n return photo_center, focal_dir, focal_distance\n\ndef generate_photo(data, a, b, v, o):\n print(data[0],data[1],data[3], data[100],data[1000])\n photo = np.zeros([a, b, 3])\n xs = np.sum(data[:, :3] * o, axis=1)\n ys = np.sum(data[:, :3] * np.cross(o, v), axis=1)\n\n print(xs[0],xs[1],xs[3],xs[100],xs[1000])\n print(\"xs.max: \", xs.max)\n print(\"ys.max: \", ys.max)\n xs = (a - 1) * (xs - xs.min()) / (xs.max() - xs.min())\n ys = (b - 1) * (ys - ys.min()) / (ys.max() - ys.min())\n\n for i, (x,y) in enumerate(zip(xs, ys)):\n try:\n photo[int(x), int(y), :] = data[i, 3:6]\n # print(photo[int(x), int(y), :])\n print(\"success\")\n except:\n continue\n \n return photo\n'''\ndef generate_photo(data, a, b, v, o):\n photo = np.zeros([a, b, 3])\n print(data[:,:3].shape)\n\n print(o.shape)\n #print(v.shape)\n\n yd = np.cross(o,v)\n print(yd)\n print(yd.shape)\n xs = np.sum(data[:, :3] * o, axis=0)\n ys = np.sum(data[:, :3] * yd, axis=0)\n\n print(xs[0],xs[1],xs[2])\n print(\"xs.shape:\", xs.shape)\n xs = (a - 1) * (xs - xs.min()) / (xs.max() - xs.min())\n ys = (b - 1) * (ys - ys.min()) / (ys.max() - ys.min())\n print(\"-----point 1-----\")\n print(xs[0],xs[1],xs[2])\n for i, (x,y) in enumerate(zip(xs, ys)):\n photo[int(x), int(y), :] = data[i, 3:6]\n # print(photo[int(x), int(y), :])\n return photo\n\n\ndef generate_photo(data, a, b, v, o):\n #data:\n #a: length\n #b: width\n #v: direction of the camera(from center of camera to focal point)\n #o: positive direction of the photo\n #data = data.eval\n #v = v.eval\n #o = o.eval\n #photo = np.zeros([a, b, 3])\n photo = np.zeros([a, b, 3])\n #xs = np.sum(data[:, :3] * o, axis=1)\n xs = np.sum(data[:, :3] * o, axis=0)\n #print(\"xs.shape:\", xs.shape)\n\n #ys = np.sum(data[:, :3] * np.cross(o, v), axis=1)\n ys = np.sum(data[:, :3] * np.cross(o, v), axis=0)\n #ys = (b - 1) * (ys - ys.min()) / (ys.max() - ys.min())\n #xs = (a - 1) * (xs - xs.min()) / (xs.max() - xs.min())\n\n print(\"xs.shape:\", xs.shape)\n #photo = []\n for i in range(xs.shape[0]):\n #x_ = int(xs[i])\n #y_ = int(ys[i])\n #x_ = int(str(''.join(xs[i])))\n #y_ = int(str(''.join(ys[i])))\n photo[xs[i], ys[i], :] = data[i, 3:6]\n # print(photo[int(x), int(y), :])\n return photo\n'''\n#def str2vec(s):\n\ndef main():\n if \"txt\" in sys.argv[1]:\n original_data = data_loader(sys.argv[1])\n else:\n original_data = read_las(sys.argv[1])\n\n data,x,p,v,f = pixel_locator()\n\n photo_center, focal_dir, focal_distance = parameter_setting(original_data)\n \n init = tf.global_variables_initializer()\n \n with tf.Session() as sess:\n sess.run(init)\n point_num = len(original_data)\n photo_center = np.transpose(np.reshape(np.repeat(photo_center,point_num),(3,point_num)))\n focal_dir = np.transpose(np.reshape(np.repeat(focal_dir,point_num),(3,point_num)))\n focal_distance = np.transpose(np.reshape(np.repeat(focal_distance,point_num),(1,point_num)))\n \n print(\"photo_center.shape:\", photo_center.shape)\n print(\"focal_dir.shape: \",focal_dir.shape)\n print(\"focal_distance.shape: \",focal_distance.shape)\n \n print(\"to run...\")\n \n data = sess.run(data,feed_dict={\n x: original_data[:,:3] ,\n p: photo_center ,\n v: focal_dir,\n f: focal_distance }) # any data returned by sess.run is numpy.array\n print(\"max x: %f max y: %f max z: %f\" % (max(original_data[:,0]),max(original_data[:,1]), max(original_data[:,2])))\n print(max(data[:,0]), max(data[:,1]),max(data[:,2]))\n print(data[0],data[10],data[100])\n original_data[:,:3] = data\n\n\n #print(data.shape)\n #np.savetxt(sys.argv[2], original_data, fmt = \"%.3f %.3f %.3f %i %i %i %i\")\n #original_data = original_data.eval\n \n o = np.array([1,0,0])\n #v = v.eval\n #v = v[0].eval\n #print(\"v:\", v)\n #print(\"o:\", o)\n photo = generate_photo(original_data, 400,400,focal_dir,o)\n #print(photo.shape)\n print(photo)\n print(photo[0])\n cv2.imwrite(\"test.jpg\", photo)\n\n\nif __name__ == \"__main__\":\n #timeit(main,*args)\n main()\n", "sub_path": "TensorFlow-tutorial/pixel_locator.py", "file_name": "pixel_locator.py", "file_ext": "py", "file_size_in_byte": 5965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.seterr", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 55, "usage_type": "call"}, {"api_name": "os.sys.argv", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.sys", "line_number": 132, "usage_type": "name"}, {"api_name": "os.sys.argv", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.sys", "line_number": 133, "usage_type": "name"}, {"api_name": "read_las.read_las", "line_number": 135, "usage_type": "call"}, {"api_name": "os.sys.argv", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.sys", "line_number": 135, "usage_type": "name"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "81952811", "text": "import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport imghdr\n\n# wczytanie obrazu\npath = \"../standard_test_images/jetplane.tif\"\nimg = cv2.imread(path)\n\ncv2.imshow(\"tytul okna\", img) # stworzenie okna z obrazkiem\n\ncv2.waitKey() # ważna funkcja!!!\ncv2.destroyAllWindows() # zamknięcie wszystkich okien\n# Funkcja waitKey() odpowiada za obsługę okienek (ich rysowanie/odświeżanie/interakcję z użytkownikiem).\n# Funkcja zwraca kod klawisza który został naciśnięty.\n\n\nheight, width, channels = img.shape # zczytanie parametrów wysokosc, szerokosc ilosc kanałów\nprint(\"Wysokość: %s,\\nSzerokość: %s,\\nIlosc pikseli: %s,\\nIlosc kanałów: %s,\\nTyp danych obrazu: %s,\\nTyp pliku: %s\"\n % (height, width, img.size, channels, img.dtype, imghdr.what(path)))\n\n# stworzenie maski\nmask = np.zeros(img.shape[:2], np.uint8)\nmask[150:350, 100:400] = 255\nmasked_img = cv2.bitwise_and(img, img, mask=mask)\n\n# Wyliczenie histogramu bez użytej maski\nhist_full = cv2.calcHist([img], [0], None, [256], [0, 256])\nhist_mask = cv2.calcHist([img], [0], mask, [256], [0, 256])\nhist, bins = np.histogram(img.flatten(), 256, [0, 256])\n\n# wyświetlenie paru obrazków na jednym \"plot'cie\"\nplt.subplot(221), plt.imshow(img, 'gray')\nplt.subplot(222), plt.imshow(mask, 'gray')\nplt.subplot(223), plt.imshow(masked_img, 'gray')\nplt.subplot(224), plt.plot(hist_full), plt.plot(hist_full)\nplt.xlim([0, 256])\n\nplt.show()\n", "sub_path": "lab1/lab.py", "file_name": "lab.py", "file_ext": "py", "file_size_in_byte": 1428, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 13, "usage_type": "call"}, {"api_name": "imghdr.what", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_and", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "470675308", "text": "\"\"\" Module containing utility functions for intent operations\n\"\"\"\n\nfrom typing import List\nfrom copy import deepcopy\nfrom watson_developer_cloud import ConversationV1\n\ndef _load_intent_data(\n conversation: ConversationV1 = None,\n workspace_id: str = None,\n intent_data: List[dict] = None,\n config_data: dict = None):\n \"\"\" Add all the intent data to the target workspace\n\n parameters:\n conversation: instance of Conversation from WDC SDK\n workspace_id: target workspace id\n intent_data: list of intent dict objects\n config_data: Dict of configuration options\n clear_existing: will clear existing examples from target\n \"\"\"\n clear_existing = config_data['clear_existing'] \\\n if config_data is not None and \\\n 'clear_existing' in config_data.keys() else False\n if intent_data is None:\n return\n\n existing_intents = conversation.list_intents(workspace_id, export=True)\n\n for intent in intent_data:\n # check if there is an existing intent\n existing = None\n existing = _get_intent_from_export(\n intent['intent'],\n existing_intents\n )\n if existing is None:\n # intent does not exist in target, so free to create\n conversation.create_intent(\n workspace_id=workspace_id,\n intent=intent['intent'],\n examples=intent['examples'],\n description=intent['description']\n if 'description' in intent.keys()\n else None\n )\n else:\n if clear_existing:\n # intent exists in target, but it should be overwritten\n conversation.update_intent(\n workspace_id=workspace_id,\n intent=existing['intent'],\n new_examples=intent['examples'],\n new_description=intent['description']\n if 'description' in intent.keys()\n else None\n )\n else:\n # otherwise we need to merge\n existing = _merge_intents(existing, intent)\n conversation.update_intent(\n workspace_id=workspace_id,\n intent=existing['intent'],\n new_examples=existing['examples'],\n new_description=intent['description']\n if 'description' in intent.keys()\n else existing['description']\n )\ndef _merge_intents(existing, to_merge):\n \"\"\" Merge to_merge intent into existing\n\n parameters:\n existing: existing intent to merge into\n to_merge: intent to merge into existing\n \"\"\"\n existing_copy = deepcopy(existing)\n existing_examples = [x['text'] for x in existing_copy['examples']]\n merge_examples = [x['text'] for x in to_merge['examples']]\n\n example_set = set(existing_examples + merge_examples)\n\n existing_copy['examples'] = [{'text': x} for x in example_set]\n\n return existing_copy\n\ndef _get_intent_from_export(intent: str, export: dict):\n \"\"\" Find a matching intent in the results of a list intents export\n\n parameters:\n intent: the name of the intent to match\n export: the dict export of a list_intents operation\n\n returns: entity dict if found, else None\n\n \"\"\"\n try:\n matches = filter(lambda x: x['intent'].lower() == intent.lower(), export['intents'])\n return next(matches)\n except StopIteration:\n return None\n", "sub_path": "wcs_deployment_utils/intents/_util.py", "file_name": "_util.py", "file_ext": "py", "file_size_in_byte": 3531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "watson_developer_cloud.ConversationV1", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "583844945", "text": "# from inference_module import InferenceService\nimport itertools\nimport json\nimport os\n\nfrom punctuate.punctuate_text import Punctuation\nfrom srt.subtitle_generator import get_srt\n\n# from inverse_text_normalization.run_predict import inverse_normalize_text\nfrom lib.inference_lib import load_model_and_generator, get_results\nfrom model_item import ModelItem\n\n\nclass ModelService:\n\n def __init__(self, model_base_path, decoder_type, cuda, half):\n languages = os.environ.get('languages', ['all'])\n model_config_file_path = model_base_path + 'model_dict.json'\n if os.path.exists(model_config_file_path):\n with open(model_config_file_path, 'r') as f:\n model_config = json.load(f)\n else:\n raise Exception(f'Model configuration file is missing at {model_config_file_path}')\n self.model_items = {}\n self.cuda = cuda\n self.half = half\n self.supported_languages = list(model_config.keys())\n for language_code, path in model_config.items():\n if language_code in languages or 'all' in languages:\n path_split = path.split(\"/\")\n base_path = model_base_path[:-1] + \"/\".join(path_split[:-1])\n model_file_name = path_split[-1]\n model_item = ModelItem(base_path, model_file_name, language_code)\n\n model, generator = load_model_and_generator(model_item, self.cuda, decoder=decoder_type, half=self.half)\n model_item.set_model(model)\n model_item.set_generator(generator)\n self.model_items[language_code] = model_item\n print(f\"Loaded {language_code} model\")\n self.punc_models_dict = {'hi': Punctuation('hi'), 'en': Punctuation('en')}\n self.enabled_itn_lang_dict = {'hi': 1, 'en': 1}\n\n def transcribe(self, file_name, language, punctuate, itn):\n model_item = self.model_items[language]\n result = {}\n response = get_results(\n wav_path=file_name,\n dict_path=model_item.get_dict_file_path(),\n generator=model_item.get_generator(),\n use_cuda=self.cuda,\n model=model_item.get_model(),\n half=self.half\n )\n # result = self.inference.get_inference(file_name, language)\n result['transcription'] = response\n print(\"Before Punctuation**** \", result['transcription'])\n result['transcription'] = self.apply_punctuation(result['transcription'], language, punctuate)\n # result['transcription'] = self.apply_itn(result['transcription'], language, itn)\n print(\"After Punctuation**** \", result['transcription'])\n return result\n\n def get_srt(self, file_name, language, punctuate, itn):\n model_item = self.model_items[language]\n model = model_item.get_model()\n generator = model_item.get_generator()\n dict_file_path = model_item.get_dict_file_path()\n result = {}\n response = get_srt(file_name, model, generator, dict_file_path,\n os.path.dirname(__file__) + '/denoiser', audio_threshold=15,\n language=language, half=self.half)\n # result = self.inference.get_srt(file_name, language, os.path.dirname(__file__) + '/denoiser')\n response = [i.replace('\\n', ' ') for i in list(itertools.chain(*response)) if type(i) != bool]\n result['srt'] = ''.join(response)\n print(\"Before Punctuation**** \", result['srt'])\n result['srt'] = self.apply_punctuation(result['srt'], language, punctuate)\n # result['srt'] = self.apply_itn(result['srt'], language, itn)\n print(\"After Punctuation**** \", result['srt'])\n return result\n\n def apply_punctuation(self, text_to_punctuate, language, punctuate):\n result = text_to_punctuate\n if punctuate:\n punc_model_obj = self.punc_models_dict.get(language, None)\n if punc_model_obj != None:\n result = punc_model_obj.punctuate_text([text_to_punctuate])[0]\n return result\n\n # def apply_itn(self, text_to_itn, language, itn):\n # result = text_to_itn\n # if itn:\n # enabled_itn = self.enabled_itn_lang_dict.get(language, None)\n # if enabled_itn != None:\n # result = inverse_normalize_text([text_to_itn], language)[0]\n # return result\n", "sub_path": "model_service.py", "file_name": "model_service.py", "file_ext": "py", "file_size_in_byte": 4388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.environ.get", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "model_item.ModelItem", "line_number": 33, "usage_type": "call"}, {"api_name": "lib.inference_lib.load_model_and_generator", "line_number": 35, "usage_type": "call"}, {"api_name": "model_item.set_model", "line_number": 36, "usage_type": "call"}, {"api_name": "model_item.set_generator", "line_number": 37, "usage_type": "call"}, {"api_name": "punctuate.punctuate_text.Punctuation", "line_number": 40, "usage_type": "call"}, {"api_name": "lib.inference_lib.get_results", "line_number": 46, "usage_type": "call"}, {"api_name": "model_item.get_dict_file_path", "line_number": 48, "usage_type": "call"}, {"api_name": "model_item.get_generator", "line_number": 49, "usage_type": "call"}, {"api_name": "model_item.get_model", "line_number": 51, "usage_type": "call"}, {"api_name": "punctuate.punctuate_text", "line_number": 57, "usage_type": "argument"}, {"api_name": "model_item.get_model", "line_number": 64, "usage_type": "call"}, {"api_name": "model_item.get_generator", "line_number": 65, "usage_type": "call"}, {"api_name": "model_item.get_dict_file_path", "line_number": 66, "usage_type": "call"}, {"api_name": "srt.subtitle_generator.get_srt", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "itertools.chain", "line_number": 72, "usage_type": "call"}, {"api_name": "punctuate.punctuate_text", "line_number": 75, "usage_type": "argument"}, {"api_name": "punctuate.punctuate_text", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "155368569", "text": "#! /usr/bin/env python\n\"\"\"Author: Scott Staniewicz\nInput/Output functions for loading/saving SAR data in binary formats\nEmail: scott.stanie@utexas.edu\n\"\"\"\n\nfrom __future__ import division, print_function\nimport datetime\nimport fileinput\nimport glob\nimport math\nimport time\nimport json\nimport os\nimport re\nimport sys\nimport numpy as np\nimport warnings\n\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nimport h5py\nimport matplotlib.pyplot as plt\n\nimport apertools.utils\nimport apertools.parsers\nfrom .demloading import format_dem_rsc, load_dem_rsc, load_elevation\nfrom apertools.log import get_log\n\nlogger = get_log()\n\n# 2to3 compat.\ntry:\n basestring\nexcept NameError:\n basestring = str\n\n_take_looks = apertools.utils.take_looks\n\nFLOAT_32_LE = np.dtype(\" 1:\n try:\n return f[kwargs[\"dset\"]][:]\n except KeyError:\n print(\"sario.load for h5 requres `dset` kwarg\")\n raise\n else:\n return f[list(f)[0]][:]\n\n if ext in IMAGE_EXTS:\n try:\n from PIL import Image\n except ImportError:\n print(\"Need PIL installed to save as image. `pip install pillow`\")\n raise\n return np.array(\n Image.open(filename).convert(\"L\")\n ) # L for luminance == grayscale\n\n # Sentinel files should have .rsc file: check for dem.rsc, or elevation.rsc\n if rows is not None and cols is not None:\n rsc_data = {\"rows\": rows, \"cols\": cols, \"width\": cols, \"height\": rows}\n elif not rsc_file and os.path.exists(filename + \".rsc\"):\n rsc_file = filename + \".rsc\"\n elif not rsc_file and (\n ext in SENTINEL_EXTS or ext in ROI_PAC_EXTS or ext in BOOL_EXTS\n ):\n # Try harder for .rsc\n rsc_file = find_rsc_file(filename, verbose=verbose)\n\n if rsc_file and rsc_data is None:\n rsc_data = load_dem_rsc(rsc_file)\n\n if ext == \".grd\":\n ext = _get_full_grd_ext(filename)\n\n # UAVSAR files have an annotation file for metadata\n if not ann_info and not rsc_data and ext in UAVSAR_EXTS:\n try:\n u = apertools.parsers.Uavsar(filename, verbose=verbose)\n ann_info = u.parse_ann_file()\n except ValueError:\n try:\n u = apertools.parsers.UavsarInt(filename, verbose=verbose)\n ann_info = u.parse_ann_file()\n except ValueError:\n print(\"Failed loading ann_info\")\n pass\n\n if not ann_info and not rsc_data:\n raise ValueError(\"Need .rsc file or .ann file to load\")\n\n if ext in BOOL_EXTS:\n return _take_looks(\n load_bool(filename, arr=arr, rsc_data=rsc_data, rows=rows, cols=cols),\n *looks,\n )\n elif ext in STACKED_FILES:\n stacked = load_stacked_img(\n filename,\n arr=arr,\n rsc_data=rsc_data,\n ann_info=ann_info,\n rows=rows,\n cols=cols,\n **kwargs,\n )\n return stacked[..., ::downsample, ::downsample]\n elif is_complex(filename=filename, ext=ext):\n return _take_looks(\n load_complex(\n filename,\n arr=arr,\n ann_info=ann_info,\n rsc_data=rsc_data,\n rows=rows,\n cols=cols,\n ),\n *looks,\n )\n else:\n return _take_looks(\n load_real(\n filename,\n arr=arr,\n ann_info=ann_info,\n rsc_data=rsc_data,\n rows=rows,\n cols=cols,\n ),\n *looks,\n )\n\n\n# Make a shorter alias for load_file\nload = load_file\n\n\ndef _get_file_dtype(filename=None, ext=None):\n if ext is None:\n ext = apertools.utils.get_file_ext(filename)\n if ext in ELEVATION_EXTS:\n return np.int16\n elif ext in COMPLEX_EXTS:\n return np.complex64\n elif ext in REAL_EXTS:\n return np.float32\n else:\n raise NotImplementedError(\"Unknown file dtype for %s\" % ext)\n\n\ndef _get_full_grd_ext(filename):\n if any(\n e in filename for e in (\".int\", \".unw\", \".cor\", \".cc\", \".amp1\", \".amp2\", \".amp\")\n ):\n ext = \".\" + \".\".join(filename.split(\".\")[-2:])\n logger.info(\"Using %s for full grd extension\" % ext)\n return ext\n else:\n return \".grd\"\n\n\ndef find_files(directory, search_term):\n \"\"\"Searches for files in `directory` using globbing on search_term\n\n Path to file is also included.\n Returns in names sorted order.\n\n Examples:\n >>> import shutil, tempfile\n >>> temp_dir = tempfile.mkdtemp()\n >>> open(os.path.join(temp_dir, \"afakefile.txt\"), \"w\").close()\n >>> print('afakefile.txt' in find_files(temp_dir, \"*.txt\")[0])\n True\n >>> shutil.rmtree(temp_dir)\n \"\"\"\n return sorted(glob.glob(os.path.join(directory, search_term)))\n\n\ndef find_rsc_file(filename=None, directory=None, verbose=False):\n if filename:\n directory = os.path.split(os.path.abspath(filename))[0]\n # Should be just elevation.dem.rsc (for .geo folder) or dem.rsc (for igrams)\n possible_rscs = find_files(directory, \"*.rsc\")\n if verbose:\n logger.info(\"Searching %s for rsc files\", directory)\n logger.info(\"Possible rsc files:\")\n logger.info(possible_rscs)\n if len(possible_rscs) < 1:\n logger.info(\"No .rsc file found in %s\", directory)\n return None\n # raise ValueError(\"{} needs a .rsc file with it for width info.\".format(filename))\n elif len(possible_rscs) > 1:\n errmsg = \"multiple .rsc files directory: {}\".format(possible_rscs[:5])\n if filename is None:\n raise ValueError(errmsg)\n\n fileonly = os.path.split(os.path.abspath(filename))[1]\n rscbases = [os.path.split(r)[1] for r in possible_rscs]\n rscdirs = [os.path.split(r)[0] for r in possible_rscs]\n if any(r.startswith(fileonly) for r in rscbases): # Matching name\n possible_rscs = [\n os.path.join(b, r)\n for (b, r) in zip(rscdirs, rscbases)\n if r.startswith(fileonly)\n ]\n else:\n raise ValueError(errmsg)\n return apertools.utils.fullpath(possible_rscs[0])\n\n\ndef _get_file_rows_cols(rows=None, cols=None, ann_info=None, rsc_data=None):\n \"\"\"Wrapper function to find file width for different satellite types\"\"\"\n if rows is not None and cols is not None:\n return rows, cols\n elif (not rsc_data and not ann_info) or (rsc_data and ann_info):\n raise ValueError(\n \"needs either ann_info or rsc_data (but not both) to find number of cols\"\n )\n elif rsc_data:\n return rsc_data[\"file_length\"], rsc_data[\"width\"]\n elif ann_info:\n return ann_info[\"rows\"], ann_info[\"cols\"]\n\n\ndef _assert_valid_size(data, cols):\n \"\"\"Make sure the width of the image is valid for the data size\n\n Note that only width is considered- The number of rows is ignored\n \"\"\"\n error_str = \"Invalid number of cols (%s) for file size %s.\" % (cols, len(data))\n # math.modf returns (fractional remainder, integer remainder)\n assert math.modf(float(data.size) / cols)[0] == 0, error_str\n\n\ndef _load_binary1d(\n filename, arr=None, dtype=None, rows=None, cols=None, ann_info=None, rsc_data=None\n):\n if arr is not None:\n if dtype is not None:\n assert arr.dtype == np.dtype(dtype), f\"{arr.dtype} != {dtype}\"\n else:\n dtype = arr.dtype\n # https://github.com/numpy/numpy/blob/master/numpy/core/records.py#L896-L897\n with open(filename, \"rb\") as fd:\n fd.readinto(arr.data)\n data = arr\n else:\n data = np.fromfile(filename, dtype)\n\n rows, cols = _get_file_rows_cols(\n rows=rows, cols=cols, ann_info=ann_info, rsc_data=rsc_data\n )\n _assert_valid_size(data, cols)\n return data, rows, cols\n\n\ndef load_binary_img(\n filename, arr=None, rows=None, cols=None, ann_info=None, rsc_data=None, dtype=None\n):\n data, rows, cols = _load_binary1d(\n filename,\n dtype=dtype,\n arr=arr,\n rows=rows,\n cols=rows,\n ann_info=ann_info,\n rsc_data=rsc_data,\n )\n return data.reshape([-1, cols])\n\n\ndef load_real(filename, arr=None, rows=None, cols=None, ann_info=None, rsc_data=None):\n \"\"\"Reads in real 4-byte per pixel files\n\n Valid filetypes: See sario.REAL_EXTS\n\n Args:\n filename (str): path to the file to open\n arr (ndarray): pre-allocated array of the correct size/dtype\n rows (int): manually pass number of rows (overrides rsc/ann data)\n cols (int): manually pass number of cols (overrides rsc/ann data)\n rsc_data (dict): output from load_dem_rsc, gives width of file\n ann_info (dict): data parsed from UAVSAR annotation file\n\n Returns:\n ndarray: float32 values for the real 2D matrix\n\n \"\"\"\n return load_binary_img(\n filename,\n arr=arr,\n rows=rows,\n cols=cols,\n ann_info=ann_info,\n rsc_data=rsc_data,\n dtype=FLOAT_32_LE,\n )\n\n\ndef load_bool(filename, arr=None, rows=None, cols=None, ann_info=None, rsc_data=None):\n \"\"\"Load binary boolean image\n\n Args:\n filename (str): path to the file to open\n arr (ndarray): pre-allocated array of the correct size/dtype\n rows (int): manually pass number of rows (overrides rsc/ann data)\n cols (int): manually pass number of cols (overrides rsc/ann data)\n rsc_data (dict): output from load_dem_rsc, gives width of file\n ann_info (dict): data parsed from UAVSAR annotation file\n\n Returns:\n ndarray: imaginary numbers of the combined floats (dtype('complex64'))\n \"\"\"\n return load_binary_img(\n filename,\n arr=arr,\n rows=rows,\n cols=cols,\n ann_info=ann_info,\n rsc_data=rsc_data,\n dtype=np.bool,\n )\n\n\ndef load_complex(\n filename, arr=None, rows=None, cols=None, ann_info=None, rsc_data=None\n):\n \"\"\"Loads a Complex64 binary image\n\n Args:\n filename (str): path to the file to open\n arr (ndarray): pre-allocated array of the correct size/dtype\n rows (int): manually pass number of rows (overrides rsc/ann data)\n cols (int): manually pass number of cols (overrides rsc/ann data)\n rsc_data (dict): output from load_dem_rsc, gives width of file\n ann_info (dict): data parsed from UAVSAR annotation file\n\n Returns:\n ndarray: complex64 values for the real 2D matrix\n\n \"\"\"\n return load_binary_img(\n filename,\n arr=arr,\n rows=rows,\n cols=cols,\n ann_info=ann_info,\n rsc_data=rsc_data,\n dtype=COMPLEX_64_LE,\n )\n\n\ndef load_stacked_img(\n filename,\n arr=None,\n rows=None,\n cols=None,\n rsc_data=None,\n ann_info=None,\n return_amp=False,\n dtype=FLOAT_32_LE,\n **kwargs,\n):\n \"\"\"Helper function to load .unw and .cor files\n\n Format is two stacked matrices:\n [[first], [second]] where the first \"cols\" number of floats\n are the first matrix, next \"cols\" are second, etc.\n Also called BIL, Band Interleaved by Line\n See http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?topicname=BIL,_BIP,_and_BSQ_raster_files\n for explantion\n\n For .unw height files, the first is amplitude, second is phase (unwrapped)\n For .cc correlation files, first is amp, second is correlation (0 to 1)\n\n Args:\n filename (str): path to the file to open\n rows (int): manually pass number of rows (overrides rsc/ann data)\n cols (int): manually pass number of cols (overrides rsc/ann data)\n rsc_data (dict): output from load_dem_rsc, gives width of file\n return_amp (bool): flag to request the amplitude data to be returned\n\n Returns:\n ndarray: dtype=float32, the second matrix (height, correlation, ...) parsed\n if return_amp == True, returns two ndarrays stacked along axis=0\n \"\"\"\n data, rows, cols = _load_binary1d(\n filename,\n dtype=dtype,\n arr=arr,\n rows=rows,\n cols=rows,\n ann_info=ann_info,\n rsc_data=rsc_data,\n )\n\n first = data.reshape((rows, 2 * cols))[:, :cols]\n second = data.reshape((rows, 2 * cols))[:, cols:]\n if return_amp:\n return np.stack((first, second), axis=0)\n else:\n return second\n\n\ndef is_complex(filename=None, ext=None):\n \"\"\"Helper to determine if file data is real or complex\n\n Uses https://uavsar.jpl.nasa.gov/science/documents/polsar-format.html for UAVSAR\n Note: differences between 3 polarizations for .mlc files: half real, half complex\n \"\"\"\n if ext is None:\n ext = apertools.utils.get_file_ext(filename)\n\n if ext not in COMPLEX_EXTS and ext not in REAL_EXTS:\n raise ValueError(\n \"Invalid filetype for load_file: %s\\n \"\n \"Allowed types: %s\" % (ext, \" \".join(COMPLEX_EXTS + REAL_EXTS))\n )\n\n if ext in UAVSAR_POL_DEPENDENT:\n # Check if filename has one of the complex polarizations\n return any(pol in filename for pol in apertools.parsers.Uavsar.COMPLEX_POLS)\n else:\n return ext in COMPLEX_EXTS\n\n\ndef save(\n filename, data, normalize=True, cmap=\"gray\", preview=False, vmax=None, vmin=None\n):\n \"\"\"Save the numpy array in one of known formats\n\n Args:\n filename (str): Output path to save file in\n data (ndarray): matrix to save\n normalize (bool): scale array to [-1, 1]\n cmap (str, matplotlib.cmap): colormap (if output is png/jpg and will be plotted)\n preview (bool): for png/jpg, display the image before saving\n Returns:\n None\n\n Raises:\n NotImplementedError: if file extension of filename not a known ext\n \"\"\"\n\n def _is_little_endian():\n \"\"\"All UAVSAR data products save in little endian byte order\"\"\"\n return sys.byteorder == \"little\"\n\n def _force_float32(arr):\n if np.issubdtype(arr.dtype, np.floating):\n return arr.astype(FLOAT_32_LE)\n elif np.issubdtype(arr.dtype, np.complexfloating):\n return arr.astype(COMPLEX_64_LE)\n else:\n return arr\n\n ext = apertools.utils.get_file_ext(filename)\n if ext == \".rsc\":\n with open(filename, \"w\") as f:\n f.write(format_dem_rsc(data))\n return\n if ext == \".grd\":\n ext = _get_full_grd_ext(filename)\n if ext == \".png\": # TODO: or ext == '.jpg':\n # Normalize to be between 0 and 1\n if normalize:\n data = data / np.max(np.abs(data))\n vmin, vmax = -1, 1\n logger.info(\"previewing with (vmin, vmax) = (%s, %s)\" % (vmin, vmax))\n if preview:\n plt.imshow(data, cmap=cmap, vmin=vmin, vmax=vmax)\n plt.colorbar()\n plt.show(block=True)\n\n plt.imsave(\n filename, data, cmap=cmap, vmin=vmin, vmax=vmax, format=ext.strip(\".\")\n )\n\n elif ext in BOOL_EXTS:\n data.tofile(filename)\n elif (ext in COMPLEX_EXTS + REAL_EXTS + ELEVATION_EXTS) and (\n ext not in STACKED_FILES\n ):\n # If machine order is big endian, need to byteswap (TODO: test on big-endian)\n # TODO: Do we need to do this at all??\n if not _is_little_endian():\n data.byteswap(inplace=True)\n\n _force_float32(data).tofile(filename)\n elif ext in STACKED_FILES:\n if data.ndim != 3:\n raise ValueError(\"Need 3D stack ([amp, data]) to save.\")\n # first = data.reshape((rows, 2 * cols))[:, :cols]\n # second = data.reshape((rows, 2 * cols))[:, cols:]\n np.hstack((data[0], data[1])).astype(FLOAT_32_LE).tofile(filename)\n\n else:\n raise NotImplementedError(\"{} saving not implemented.\".format(ext))\n\n\ndef save_hgt(filename, amp_data, height_data):\n save(filename, np.stack((amp_data, height_data), axis=0))\n\n\ndef load_stack(file_list=None, directory=None, file_ext=None, **kwargs):\n \"\"\"Reads a set of images into a 3D ndarray\n\n Args:\n file_list (list[str]): list of file names to stack\n directory (str): alternative to file_name: path to a dir containing all files\n This will be loaded in ls-sorted order\n file_ext (str): If using `directory`, the ending type\n of files to read (e.g. '.unw')\n\n Returns:\n ndarray: 3D array of each file stacked\n 1st dim is the index of the image: stack[0, :, :]\n \"\"\"\n if file_list is None:\n if file_ext is None:\n raise ValueError(\"need file_ext if using `directory`\")\n else:\n file_list = find_files(directory, \"*\" + file_ext)\n\n # Test load to get shape\n test = load(file_list[0], **kwargs)\n nrows, ncols = test.shape\n dtype = test.dtype\n out = np.empty((len(file_list), nrows, ncols), dtype=dtype)\n\n # Now lazily load the files and store in pre-allocated 3D array\n file_gen = (load(filename, **kwargs) for filename in file_list)\n for idx, img in enumerate(file_gen):\n out[idx] = img\n\n return out\n\n\ndef get_full_path(directory=None, filename=None, full_path=None):\n if full_path:\n directory, filename = os.path.split(os.path.abspath(full_path))\n else:\n full_path = os.path.join(directory, os.path.split(filename)[1])\n return directory, filename, full_path\n\n\ndef load_deformation(\n igram_path=\".\", filename=\"deformation.h5\", full_path=None, n=None, dset=None\n):\n \"\"\"Loads a stack of deformation images from igram_path\n\n if using the \"deformation.npy\" version, igram_path must also contain\n the \"geolist.npy\" file\n\n Args:\n igram_path (str): directory of .npy file\n filename (str): default='deformation.npy', a .npy file of a 3D ndarray\n n (int): only load the last `n` layers of the stack\n\n Returns:\n tuple[ndarray, ndarray]: geolist 1D array, deformation 3D array\n \"\"\"\n igram_path, filename, full_path = get_full_path(igram_path, filename, full_path)\n\n if apertools.utils.get_file_ext(filename) == \".npy\":\n return _load_deformation_npy(\n igram_path=igram_path, filename=filename, full_path=full_path, n=n\n )\n elif apertools.utils.get_file_ext(filename) in (\".h5\", \"hdf5\"):\n return _load_deformation_h5(\n igram_path=igram_path,\n filename=filename,\n full_path=full_path,\n n=n,\n dset=dset,\n )\n else:\n raise ValueError(\"load_deformation only supported for .h5 or .npy\")\n\n\ndef _load_deformation_h5(\n igram_path=None, filename=None, full_path=None, n=None, dset=None\n):\n igram_path, filename, full_path = get_full_path(igram_path, filename, full_path)\n try:\n with h5py.File(full_path, \"r\") as f:\n if dset is None:\n dset = list(f)[0]\n if n is not None and n > 1:\n deformation = f[dset][-n:]\n else:\n deformation = f[dset][:]\n # geolist attr will be is a list of strings: need them as datetimes\n\n except (IOError, OSError) as e:\n logger.error(\"Can't load %s in path %s: %s\", filename, igram_path, e)\n return None, None\n try:\n geolist = load_geolist_from_h5(full_path, dset=dset)\n except Exception as e:\n logger.error(\n \"Can't load geolist from %s in path %s: %s\", filename, igram_path, e\n )\n geolist = None\n\n return geolist, deformation\n\n\ndef _load_deformation_npy(igram_path=None, filename=None, full_path=None, n=None):\n igram_path, filename, full_path = get_full_path(igram_path, filename, full_path)\n\n try:\n deformation = np.load(os.path.join(igram_path, filename))\n if n is not None:\n deformation = deformation[-n:]\n # geolist is a list of datetimes: encoding must be bytes\n geolist = np.load(\n os.path.join(igram_path, \"geolist.npy\"), encoding=\"bytes\", allow_pickle=True\n )\n except (IOError, OSError):\n logger.error(\"%s or geolist.npy not found in path %s\", filename, igram_path)\n return None, None\n\n return geolist, deformation\n\n\ndef load_geolist_from_h5(h5file, dset=None, parse=True):\n with h5py.File(h5file, \"r\") as f:\n if dset is None:\n geolist_str = f[GEOLIST_DSET][()].astype(str)\n else:\n geolist_str = f[dset].attrs[GEOLIST_DSET][()].astype(str)\n\n if parse:\n return parse_geolist_strings(geolist_str)\n else:\n return geolist_str\n\n\ndef load_intlist_from_h5(h5file, dset=None, parse=True):\n with h5py.File(h5file, \"r\") as f:\n date_pair_strs = f[INTLIST_DSET][:].astype(str)\n\n if parse:\n return parse_intlist_strings(date_pair_strs)\n else:\n return date_pair_strs\n\n\ndef parse_geolist_strings(geolist_str):\n return [_parse(g) for g in geolist_str]\n\n\ndef parse_intlist_strings(date_pairs):\n # If we passed filename YYYYmmdd_YYYYmmdd.int\n if isinstance(date_pairs, basestring):\n date_pairs = [date_pairs.strip(\".int\").split(\"_\")[:2]]\n return [(_parse(early), _parse(late)) for early, late in date_pairs]\n\n\ndef load_geolist_from_nc(ncfile, dim=\"date\", parse=True):\n import xarray as xr\n\n with xr.open_dataset(ncfile) as ds:\n dts = ds[dim].values.astype(\"datetime64[D]\")\n if parse is True:\n return dts.tolist()\n else:\n return [d.item().strftime().strftime(\"%Y%m%d\") for d in dts]\n\n\ndef _parse(datestr):\n return datetime.datetime.strptime(datestr, DATE_FMT).date()\n\n\ndef find_geos(directory=\".\", ext=\".geo\", parse=True, filename=None):\n \"\"\"Reads in the list of .geo files used, in time order\n\n Can also pass a filename containing .geo files as lines.\n\n Args:\n directory (str): path to the geolist file or directory\n ext (str): file extension when searching a directory\n parse (bool): output as parsed datetime tuples. False returns the filenames\n filename (string): name of a file with .geo filenames\n\n Returns:\n list[date]: the parse dates of each .geo used, in date order\n\n \"\"\"\n if filename is not None:\n with open(filename) as f:\n geo_file_list = f.read().splitlines()\n else:\n geo_file_list = find_files(directory, \"*\" + ext)\n\n if not parse:\n return geo_file_list\n\n # Stripped of path for parser\n geolist = [os.path.split(fname)[1] for fname in geo_file_list]\n if not geolist:\n return []\n # raise ValueError(\"No .geo files found in %s\" % directory)\n\n if re.match(r\"S1[AB]_\\d{8}\\.geo\", geolist[0]): # S1A_YYYYmmdd.geo\n # Note: will match even if file is S1A_YYYYmmdd.geo.vrt\n return sorted([_parse(_strip_geoname(geo)) for geo in geolist])\n elif re.match(r\"\\d{8}\", geolist[0]): # YYYYmmdd , just a date string\n return sorted([_parse(geo) for geo in geolist if geo])\n else: # Full sentinel product name\n return sorted(\n [apertools.parsers.Sentinel(geo).start_time.date() for geo in geolist]\n )\n\n\ndef _strip_geoname(name):\n \"\"\"Leaves just date from format S1A_YYYYmmdd.geo\"\"\"\n return (\n name.replace(\"S1A_\", \"\")\n .replace(\"S1B_\", \"\")\n .replace(\".geo\", \"\")\n .replace(\".vrt\", \"\")\n )\n\n\ndef find_igrams(directory=\".\", ext=\".int\", parse=True, filename=None):\n \"\"\"Reads the list of igrams to return dates of images as a tuple\n\n Args:\n directory (str): path to the igram directory\n ext (str): file extension when searching a directory\n parse (bool): output as parsed datetime tuples. False returns the filenames\n filename (str): name of a file with .geo filenames\n\n Returns:\n tuple(date, date) of (early, late) dates for all igrams (if parse=True)\n if parse=False: returns list[str], filenames of the igrams\n\n \"\"\"\n if filename is not None:\n with open(filename) as f:\n igram_file_list = f.read().splitlines()\n else:\n igram_file_list = find_files(directory, \"*\" + ext)\n\n if parse:\n igram_fnames = [os.path.split(f)[1] for f in igram_file_list]\n date_pairs = [intname.strip(\".int\").split(\"_\")[:2] for intname in igram_fnames]\n return parse_intlist_strings(date_pairs)\n else:\n return igram_file_list\n\n\ndef load_dem_from_h5(h5file=None, dset=\"dem_rsc\"):\n with h5py.File(h5file, \"r\") as f:\n return json.loads(f[dset][()])\n\n\ndef save_dem_to_h5(h5file, dem_rsc, dset_name=\"dem_rsc\", overwrite=True):\n if not check_dset(h5file, dset_name, overwrite):\n return\n\n with h5py.File(h5file, \"a\") as f:\n f[dset_name] = json.dumps(dem_rsc)\n\n\ndef save_geolist_to_h5(\n igram_path=None,\n out_file=None,\n dset_name=None,\n geo_date_list=None,\n overwrite=False,\n):\n if dset_name is not None:\n if not check_dset(out_file, dset_name, overwrite, attr_name=GEOLIST_DSET):\n return\n else:\n if not check_dset(out_file, GEOLIST_DSET, overwrite):\n return\n\n if geo_date_list is None:\n geo_date_list, _ = load_geolist_intlist(igram_path, parse=True)\n\n with h5py.File(out_file, \"a\") as f:\n # JSON gets messed from doing from julia to h5py for now\n # f[GEOLIST_DSET] = json.dumps(geolist_to_str(geo_date_list))\n if dset_name is not None:\n f[dset_name].attrs[GEOLIST_DSET] = geolist_to_str(geo_date_list)\n else:\n f[GEOLIST_DSET] = geolist_to_str(geo_date_list)\n\n\ndef save_intlist_to_h5(\n igram_path=None,\n dset_name=None,\n out_file=None,\n overwrite=False,\n int_date_list=None,\n):\n if not check_dset(out_file, INTLIST_DSET, overwrite):\n return\n\n if int_date_list is None:\n _, int_date_list = load_geolist_intlist(igram_path)\n\n logger.info(\"Saving igram dates to %s / %s\" % (out_file, INTLIST_DSET))\n with h5py.File(out_file, \"a\") as f:\n if dset_name is not None:\n f[dset_name].attrs[INTLIST_DSET] = intlist_to_str(int_date_list)\n else:\n f[INTLIST_DSET] = intlist_to_str(int_date_list)\n\n\ndef geolist_to_str(geo_date_list):\n return np.array([d.strftime(DATE_FMT) for d in geo_date_list]).astype(\"S\")\n\n\ndef intlist_to_str(int_date_list):\n \"\"\"Date pairs to Nx2 numpy array or strings\"\"\"\n return np.array(\n [(a.strftime(DATE_FMT), b.strftime(DATE_FMT)) for a, b in int_date_list]\n ).astype(\"S\")\n\n\ndef intlist_to_filenames(int_date_list, ext=\".int\"):\n \"\"\"Convert date pairs to list of string filenames\"\"\"\n return [\n \"{}_{}{ext}\".format(a.strftime(DATE_FMT), b.strftime(DATE_FMT), ext=ext)\n for a, b in int_date_list\n ]\n\n\ndef load_geolist_intlist(igram_dir, geo_dir=None, geolist_ignore_file=None, parse=True):\n \"\"\"Load the geo_date_list and int_date_list from a igram_dir with igrams\n\n if geo_dir is None, assumes that the .geo files are one diretory up from the igrams\n \"\"\"\n int_date_list = find_igrams(igram_dir, parse=parse)\n if geo_dir is None:\n geo_dir = apertools.utils.get_parent_dir(igram_dir)\n geo_date_list = find_geos(directory=geo_dir, parse=parse)\n\n if geolist_ignore_file is not None:\n ignore_filepath = os.path.join(igram_dir, geolist_ignore_file)\n geo_date_list, int_date_list = ignore_geo_dates(\n geo_date_list, int_date_list, ignore_file=ignore_filepath, parse=parse\n )\n return geo_date_list, int_date_list\n\n\ndef ignore_geo_dates(\n geo_date_list, int_date_list, ignore_file=\"geolist_missing.txt\", parse=True\n):\n \"\"\"Read extra file to ignore certain dates of interferograms\"\"\"\n ignore_geos = set(find_geos(filename=ignore_file, parse=parse))\n logger.info(\"Ignoring the following .geo dates:\")\n logger.info(sorted(ignore_geos))\n valid_geos = [g for g in geo_date_list if g not in ignore_geos]\n valid_igrams = [\n i for i in int_date_list if i[0] not in ignore_geos and i[1] not in ignore_geos\n ]\n return valid_geos, valid_igrams\n\n\ndef check_dset(h5file, dset_name, overwrite, attr_name=None):\n \"\"\"Returns false if the dataset exists and overwrite is False\n\n If overwrite is set to true, will delete the dataset to make\n sure a new one can be created\n \"\"\"\n with h5py.File(h5file, \"a\") as f:\n if attr_name is not None:\n if attr_name in f.get(dset_name, {}):\n logger.info(f\"{dset_name}:{attr_name} already exists in {h5file},\")\n if overwrite:\n logger.info(\"Overwrite true: Deleting.\")\n del f[dset_name].attrs[attr_name]\n else:\n logger.info(\"Skipping.\")\n return False\n else:\n if dset_name in f:\n logger.info(f\"{dset_name} already exists in {h5file},\")\n if overwrite:\n logger.info(\"Overwrite true: Deleting.\")\n del f[dset_name]\n else:\n logger.info(\"Skipping.\")\n return False\n\n return True\n\n\ndef load_mask(\n geo_date_list=None,\n perform_mask=True,\n deformation_filename=None,\n dset=None,\n mask_filename=\"masks.h5\",\n directory=None,\n):\n # TODO: Dedupe this from the insar one\n if not perform_mask:\n return np.ma.nomask\n\n if directory is not None:\n _, _, mask_full_path = get_full_path(\n directory=directory, filename=mask_filename\n )\n else:\n mask_full_path = mask_filename\n if not os.path.exists(mask_full_path):\n logger.warning(\"{} doesnt exist, not masking\".format(mask_full_path))\n return np.ma.nomask\n\n # If they pass a deformation .h5 stack, get only the dates actually used\n # instead of all possible dates stored in the mask stack\n if deformation_filename is not None:\n if directory is not None:\n deformation_filename = os.path.join(directory, deformation_filename)\n geo_date_list = load_geolist_from_h5(deformation_filename, dset=dset)\n\n # Get the indices of the mask layers that were used in the deformation stack\n all_geo_dates = load_geolist_from_h5(mask_full_path)\n if geo_date_list is None:\n used_bool_arr = np.full(len(all_geo_dates), True)\n else:\n used_bool_arr = np.array([g in geo_date_list for g in all_geo_dates])\n\n with h5py.File(mask_full_path) as f:\n # Maks a single mask image for any pixel that has a mask\n # Note: not using GEO_MASK_SUM_DSET since we may be sub selecting layers\n geo_dset = f[GEO_MASK_DSET]\n with geo_dset.astype(bool):\n stack_mask = np.sum(geo_dset[used_bool_arr, :, :], axis=0) > 0\n return stack_mask\n\n\ndef load_single_mask(\n int_date_string=None,\n date_pair=None,\n mask_filename=MASK_FILENAME,\n int_date_list=None,\n):\n \"\"\"Load one mask from the `mask_filename`\n\n Can either pass a tuple of Datetimes in date_pair, or a string like\n `20170101_20170104.int` or `20170101_20170303` to int_date_string\n \"\"\"\n if int_date_list is None:\n int_date_list = load_intlist_from_h5(mask_filename)\n\n if int_date_string is not None:\n # If the pass string with ., only take first part\n date_str_pair = int_date_string.split(\".\")[0].split(\"_\")\n date_pair = parse_intlist_strings([date_str_pair])[0]\n\n with h5py.File(mask_filename, \"r\") as f:\n idx = int_date_list.index(date_pair)\n dset = f[IGRAM_MASK_DSET]\n with dset.astype(bool):\n return dset[idx]\n\n\n# ######### GDAL FUNCTIONS ##############\n\n\ndef save_as_geotiff(outfile=None, array=None, rsc_data=None, nodata=0.0):\n \"\"\"Save an array to a GeoTIFF using gdal\n\n Ref: https://gdal.org/tutorials/raster_api_tut.html#using-create\n \"\"\"\n import gdal\n\n rows, cols = array.shape\n if rsc_data is not None and (\n rows != rsc_data[\"file_length\"] or cols != rsc_data[\"width\"]\n ):\n raise ValueError(\n \"rsc_data ({}, {}) does not match array shape: ({}, {})\".format(\n (rsc_data[\"file_length\"], rsc_data[\"width\"], rows, cols)\n )\n )\n\n driver = gdal.GetDriverByName(\"GTiff\")\n\n gdal_dtype = numpy_to_gdal_type(array.dtype)\n out_raster = driver.Create(\n outfile, xsize=cols, ysize=rows, bands=1, eType=gdal_dtype\n )\n\n if rsc_data is not None:\n # Set geotransform (based on rsc data) and projection\n out_raster.SetGeoTransform(rsc_to_geotransform(rsc_data))\n srs = gdal.osr.SpatialReference()\n srs.SetWellKnownGeogCS(\"WGS84\")\n out_raster.SetProjection(srs.ExportToWkt())\n\n band = out_raster.GetRasterBand(1)\n band.WriteArray(array)\n band.SetNoDataValue(nodata)\n band.FlushCache()\n band = None\n out_raster = None\n\n\ndef save_image_using_existing(arr, outname, input_fname, out_dtype=None, nodata=None):\n \"\"\"Writes out an array using the georeferencing data from `input_fname`\"\"\"\n import rasterio as rio\n\n with rio.open(input_fname) as src:\n if (src.height, src.width) != arr.shape:\n raise ValueError(\n f\"{input_fname} must be same size as arr to use georeference data\"\n )\n\n with rio.open(\n outname,\n \"w\",\n driver=src.driver,\n height=arr.shape[0],\n width=arr.shape[1],\n transform=src.transform,\n count=1,\n dtype=(out_dtype or arr.dtype),\n crs=src.crs,\n nodata=nodata,\n ) as dest:\n dest.write(arr, 1)\n\n\ndef save_as_vrt(\n filename=None,\n array=None,\n rows=None,\n cols=None,\n dtype=None,\n outfile=None,\n rsc_file=None,\n rsc_data=None,\n interleave=None,\n band=None,\n num_bands=None,\n):\n \"\"\"Save a VRT corresponding to a raw raster file\n\n VRT options:\n SourceFilename: The name of the raw file containing the data for this band.\n The relativeToVRT attribute can be used to indicate if the\n SourceFilename is relative to the .vrt file (1) or not (0).\n ImageOffset: The offset in bytes to the beginning of the first pixel of\n data of this image band. Defaults to zero.\n PixelOffset: The offset in bytes from the beginning of one pixel and\n the next on the same line. In packed single band data this will be\n the size of the dataType in bytes.\n LineOffset: The offset in bytes from the beginning of one scanline of data\n and the next scanline of data. In packed single band data this will\n be PixelOffset * rasterXSize.\n\n Ref: https://gdal.org/drivers/raster/vrt.html#vrt-descriptions-for-raw-files\n \"\"\"\n import gdal\n\n outfile = outfile or (filename + \".vrt\")\n if outfile is None:\n raise ValueError(\"Need outfile or filename to save\")\n\n # Get geotransform and project based on rsc data, or existing GDAL info\n if rsc_data is None:\n if rsc_file is None:\n # rsc_file = rsc_file if rsc_file else find_rsc_file(filename)\n try:\n ds = gdal.Open(filename)\n geotrans = ds.GetGeoTransform()\n srs = ds.GetSpatialRef()\n except:\n print(\n f\"Warning: Cant get geotransform from {filename}, no .rsc file or data given\"\n )\n geotrans = None\n srs = gdal.osr.SpatialReference()\n srs.SetWellKnownGeogCS(\"WGS84\")\n else:\n rsc_data = load(rsc_file)\n geotrans = rsc_to_geotransform(rsc_data)\n srs = gdal.osr.SpatialReference()\n srs.SetWellKnownGeogCS(\"WGS84\")\n\n if array is not None:\n dtype = array.dtype\n rows, cols = array.shape[-2:]\n\n if rsc_data is not None:\n rows, cols = _get_file_rows_cols(rsc_data=rsc_data)\n if array is not None:\n assert (rows, cols) == array.shape[-2:]\n\n if dtype is None:\n dtype = _get_file_dtype(filename)\n\n bytes_per_pix = np.dtype(dtype).itemsize\n total_bytes = os.path.getsize(filename)\n if interleave is None or num_bands is None:\n interleave, num_bands = get_interleave(filename, num_bands=num_bands)\n if band is None:\n # This will offset the start- only making the vrt point to phase\n band = 1 if apertools.utils.get_file_ext(filename) in STACKED_FILES else 0\n\n assert rows == int(total_bytes / bytes_per_pix / cols / num_bands), (\n f\"rows = total_bytes / bytes_per_pix / cols / num_bands : \"\n f\"{rows} = {total_bytes} / {bytes_per_pix} / {cols} / {num_bands} \"\n )\n # assert total_bytes == bytes_per_pix * rows * cols\n\n vrt_driver = gdal.GetDriverByName(\"VRT\")\n\n # out_raster = vrt_driver.Create(outfile, xsize=cols, ysize=rows, bands=1, eType=gdal_dtype)\n out_raster = vrt_driver.Create(outfile, xsize=cols, ysize=rows, bands=0)\n\n if geotrans is not None:\n out_raster.SetGeoTransform(geotrans)\n else:\n print(\"Warning: No GeoTransform could be made/set\")\n\n out_raster.SetProjection(srs.ExportToWkt())\n\n image_offset, pixel_offset, line_offset = get_offsets(\n dtype,\n interleave,\n band,\n cols,\n rows,\n num_bands,\n )\n options = [\n \"subClass=VRTRawRasterBand\",\n # split, since relative to file, so remove directory name\n \"SourceFilename={}\".format(os.path.split(filename)[1]),\n \"relativeToVRT=1\", # location of file: make it relative to the VRT file\n \"ImageOffset={}\".format(image_offset),\n \"PixelOffset={}\".format(pixel_offset),\n \"LineOffset={}\".format(line_offset),\n # 'ByteOrder=LSB'\n ]\n gdal_dtype = numpy_to_gdal_type(dtype)\n # print(\"gdal dtype\", gdal_dtype, dtype)\n out_raster.AddBand(gdal_dtype, options)\n out_raster = None # Force write\n\n # if geotrans is not None:\n # create_derived_band(outfile, rows, cols, geotrans, func=\"log10\")\n # create_derived_band(outfile, rows, cols, geotrans, func=\"phase\")\n return\n\n\ndef create_derived_band(\n src_filename, outfile=None, src_dtype=\"CFloat32\", desc=None, func=\"log10\"\n):\n import gdal\n\n # For new outfile, only have one .vrt extension\n if outfile is None:\n outfile = \"{}.{}.vrt\".format(src_filename.replace(\".vrt\", \"\"), func)\n desc = desc or \"{func} of {filename}\".format(func=func, filename=src_filename)\n srs = gdal.osr.SpatialReference()\n srs.SetWellKnownGeogCS(\"WGS84\")\n srs_string = srs.ExportToWkt()\n\n f_src = gdal.Open(src_filename)\n geotrans = f_src.GetGeoTransform()\n rows = f_src.RasterYSize\n cols = f_src.RasterXSize\n f_src = None\n\n derived_vrt_template = \"\"\"\n {srs} \n {geotrans} \n \n {desc} \n \n {src_filename}\n 1\n \n {func}\n {src_dtype}\n -inf\n \n\n\"\"\".format(\n src_filename=src_filename,\n geotrans=\",\".join((str(n) for n in geotrans)),\n srs=srs_string,\n rows=rows,\n cols=cols,\n desc=desc,\n func=func,\n src_dtype=src_dtype,\n )\n # colors=make_cmy_colortable())\n with open(outfile, \"w\") as f:\n f.write(derived_vrt_template)\n\n # Colors:\n # ds = gdal.Open(outfile)\n # colors = make_cmy_colortable()\n # band = ds.GetRasterBand(1)\n # band.SetRasterColorTable(colors)\n # band.SetRasterColorInterpretation(gdal.GCI_PaletteIndex)\n return\n\n\ndef get_interleave(filename, num_bands=None):\n \"\"\"Returns band interleave format, and number of bands\n\n Band interleaved by line (BIL), band interleaved by pixel (BIP), and band sequential (BSQ)\n https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/bil-bip-and-bsq-raster-files.htm\n \"\"\"\n if num_bands == 1:\n # 1 band is always same: its just all pixels in a row\n return \"BIP\", 1\n\n ext = apertools.utils.get_file_ext(filename)\n if ext in BIL_FILES:\n interleave, num_bands = \"BIL\", 2\n # TODO: the .amp files are actually BIP with 2 bands...\n elif ext in BIP_FILES:\n interleave, num_bands = \"BIP\", 1\n else:\n raise ValueError(\n \"Unknown band interleave format (BIP/BIL) for {}\".format(filename)\n )\n return interleave, num_bands\n\n\ndef get_offsets(dtype, interleave, band, width, length, num_bands):\n \"\"\"\n From ISCE Image.py\n \"\"\"\n bytes_per_pix = np.dtype(dtype).itemsize\n if interleave == \"BIL\":\n return (\n band * width * bytes_per_pix, # ImageOffset\n bytes_per_pix, # PixelOffset\n num_bands * width * bytes_per_pix, # LineOffset\n )\n elif interleave == \"BIP\":\n return (\n band * bytes_per_pix,\n num_bands * bytes_per_pix,\n num_bands * width * bytes_per_pix,\n )\n elif interleave == \"BSQ\":\n return (\n band * width * length * bytes_per_pix,\n bytes_per_pix,\n width * bytes_per_pix,\n )\n else:\n raise ValueError(\"Unknown interleave: %s\" % interleave)\n\n\ndef rsc_to_geotransform(rsc_data):\n\n # See here for geotransform info\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\n # NOTE: gdal standard is to reference pixel by top left corner,\n # while the SAR .rsc stuff wants center of pixel\n # Xgeo = GT(0) + Xpixel*GT(1) + Yline*GT(2)\n # Ygeo = GT(3) + Xpixel*GT(4) + Yline*GT(5)\n\n # So for us, this means we have\n # X0 = trans[0] + .5*trans[1] + (.5*trans[2])\n # Y0 = trans[3] + (.5*trans[4]) + .5*trans[5]\n # where trans[2], trans[4] are 0s for north-up rasters\n\n x_step = rsc_data[\"x_step\"]\n y_step = rsc_data[\"y_step\"]\n X0 = rsc_data[\"x_first\"] - 0.5 * x_step\n Y0 = rsc_data[\"y_first\"] - 0.5 * y_step\n return (X0, x_step, 0.0, Y0, 0.0, y_step)\n\n\ndef set_unit(filename, unit=\"cm\"):\n from osgeo import gdalconst\n import gdal\n\n go = gdal.Open(filename, gdalconst.GA_Update)\n b1 = go.GetRasterBand(1)\n b1.SetUnitType(unit)\n b1 = None\n go = None\n\n\ndef cmy_colors():\n # Default cyclic colormap from isce/mdx, provided by Piyush Agram, Jan 2020\n # generate the color list\n rgbs = np.zeros((256, 3), dtype=np.uint8)\n\n for kk in range(85):\n rgbs[kk, 0] = kk * 3\n rgbs[kk, 1] = 255 - kk * 3\n rgbs[kk, 2] = 255\n\n rgbs[85:170, 0] = rgbs[0:85, 2]\n rgbs[85:170, 1] = rgbs[0:85, 0]\n rgbs[85:170, 2] = rgbs[0:85, 1]\n\n rgbs[170:255, 0] = rgbs[0:85, 1]\n rgbs[170:255, 1] = rgbs[0:85, 2]\n rgbs[170:255, 2] = rgbs[0:85, 0]\n\n rgbs[255, 0] = 0\n rgbs[255, 1] = 255\n rgbs[255, 2] = 255\n\n rgbs = np.roll(rgbs, int(256 / 2 - 214), axis=0) # shift green to the center\n rgbs = np.flipud(\n rgbs\n ) # flip up-down so that orange is in the later half (positive)\n return rgbs\n\n\ndef make_cmy_colortable():\n import gdal\n\n # create color table\n colors = gdal.ColorTable()\n\n rgbs = cmy_colors()\n rgbs = [rgbs[0], rgbs[42], rgbs[84], rgbs[126], rgbs[168], rgbs[200], rgbs[255]]\n vals = np.linspace(-np.pi, np.pi, len(rgbs))\n # set color for each value\n # out = \"\\n\"\n for (val, (r, g, b)) in zip(vals, rgbs):\n print(int(val))\n colors.SetColorEntry(int(val), (r, g, b))\n # out += '\\n'.format(r, g, b)\n\n # out += \"\\n\"\n return colors\n # return out\n\n\ndef numpy_to_gdal_type(np_dtype):\n from osgeo import gdal_array, gdalconst\n\n if np.issubdtype(bool, np_dtype):\n return gdalconst.GDT_Byte\n # Wrap in np.dtype in case string is passed\n return gdal_array.NumericTypeCodeToGDALTypeCode(np.dtype(np_dtype))\n\n\ndef gdal_to_numpy_type(gdal_dtype=None, band=None):\n from osgeo import gdal_array\n\n if gdal_dtype is None:\n gdal_dtype = band.DataType\n return gdal_array.GDALTypeCodeToNumericTypeCode(gdal_dtype)\n\n\ndef find_looks_taken(\n igram_path,\n geo_path=None,\n igram_dem_file=\"dem.rsc\",\n geo_dem_file=\"elevation.dem.rsc\",\n):\n \"\"\"Calculates how many looks from .geo files to .int files\"\"\"\n if geo_path is None:\n geo_path = os.path.dirname(os.path.abspath(igram_path))\n\n geo_dem_rsc = load_dem_rsc(os.path.join(geo_path, geo_dem_file))\n\n igram_dem_rsc = load_dem_rsc(os.path.join(igram_path, igram_dem_file))\n\n row_looks = geo_dem_rsc[\"file_length\"] // igram_dem_rsc[\"file_length\"]\n col_looks = geo_dem_rsc[\"width\"] // igram_dem_rsc[\"width\"]\n return row_looks, col_looks\n\n\ndef calc_upsample_rate(rsc_filename=None):\n \"\"\"Find the rate of upsampling on an rsc file\n\n Args:\n rate (int): rate by which to upsample the DEM\n rsc_dict (str): Optional, the rsc data from Stitcher.create_dem_rsc()\n filepath (str): Optional, location of .dem.rsc file\n\n Note: Must supply only one of rsc_dict or rsc_filename\n\n Returns:\n tuple(float, float): (x spacing, y spacing)\n\n Raises:\n TypeError: if neither (or both) rsc_filename and rsc_dict are given\n\n \"\"\"\n rsc_dict = load_dem_rsc(filename=rsc_filename)\n default_spacing = 1.0 / 3600 # NASA SRTM uses 3600 pixels for 1 degree, or 30 m\n x_spacing = abs(rsc_dict[\"x_step\"])\n y_spacing = abs(rsc_dict[\"y_step\"])\n return default_spacing / x_spacing, default_spacing / y_spacing\n\n\n# TODO: put elsewhere\ndef to_datetimes(date_list):\n return [datetime.datetime(*d.timetuple()[:6]) for d in date_list]\n\n\ndef _get_latlon_arrs(h5_filename=None, dem_rsc_file=None, gdal_file=None, bbox=None):\n from apertools import latlon, sario\n\n if h5_filename is not None:\n lon_arr, lat_arr = latlon.grid(**load_dem_from_h5(h5_filename), sparse=True)\n elif dem_rsc_file is not None:\n lon_arr, lat_arr = latlon.grid(**sario.load(dem_rsc_file), sparse=True)\n elif gdal_file is not None:\n import rasterio as rio\n\n with rio.open(gdal_file) as src:\n rows, cols = src.shape\n max_len = max(rows, cols)\n lon_list, lat_list = src.xy(np.arange(max_len), np.arange(max_len))\n lon_arr = np.arange(lon_list[:cols])\n lat_arr = np.arange(lat_list[:rows])\n\n lon_arr, lat_arr = lon_arr.reshape(-1), lat_arr.reshape(-1)\n return lon_arr.reshape(-1), lat_arr.reshape(-1)\n\n\ndef _window_rowcol(lon_arr, lat_arr, bbox=None):\n if bbox is None:\n return (0, len(lat_arr)), (0, len(lon_arr))\n\n # TODO: this should def be in latlon\n left, bot, right, top = bbox\n lat_step = np.diff(lat_arr)[0]\n lon_step = np.diff(lon_arr)[0]\n row_top = np.clip(int(round(((top - lat_arr[0]) / lat_step))), 0, len(lat_arr))\n row_bot = np.clip(int(round(((bot - lat_arr[0]) / lat_step))), 0, len(lat_arr))\n col_left = np.clip(int(round(((left - lon_arr[0]) / lon_step))), 0, len(lon_arr))\n col_right = np.clip(int(round(((right - lon_arr[0]) / lon_step))), 0, len(lon_arr))\n # lat_arr = lon_arr[row_top:row_bot]\n # lon_arr = lon_arr[col_left:col_right]\n return (row_top, row_bot), (col_left, col_right)\n\n\ndef hdf5_to_netcdf(\n filename,\n stack_dset_list=[\"stack/1\"],\n stack_dim_list=[\"idx\"],\n outname=None,\n bbox=None,\n):\n \"\"\"Convert the stack in HDF5 to NetCDF with appropriate metadata\"\"\"\n import netCDF4 as nc\n\n if not filename.endswith(\".h5\"):\n raise ValueError(f\"{filename} must be an HDF5 file\")\n\n if outname is None:\n outname = filename.replace(\".h5\", \".nc\")\n if not outname.endswith(\".nc\"):\n raise ValueError(f\"{outname} must be an .nc filename\")\n\n with h5py.File(filename) as hf:\n # Get data and references from HDF% file\n\n # Just get one example for shape\n nstack, rows, cols = hf[stack_dset_list[0]].shape\n lon_arr, lat_arr = _get_latlon_arrs(h5_filename=filename, bbox=bbox)\n\n (row_top, row_bot), (col_left, col_right) = _window_rowcol(\n lon_arr, lat_arr, bbox=bbox\n )\n lat_arr = lat_arr[row_top:row_bot]\n lon_arr = lon_arr[col_left:col_right]\n rows, cols = len(lat_arr), len(lon_arr)\n\n stack_arrs = []\n for dset_name, stack_dim in zip(stack_dset_list, stack_dim_list):\n nstack, _, _ = hf[dset_name].shape\n if stack_dim == \"date\":\n try:\n geolist = load_geolist_from_h5(filename, dset=dset_name)\n except KeyError: # Give this one a shot too\n geolist = load_geolist_from_h5(filename)\n stack_dim_arr = to_datetimes(geolist)\n else:\n stack_dim_arr = np.arange(nstack)\n stack_arrs.append(stack_dim_arr)\n\n # TODO: store the int dates as dims... somehow\n\n print(\"Making dimensions and variables\")\n with nc.Dataset(outname, \"w\") as f:\n f.history = \"Created \" + time.ctime(time.time())\n\n f.createDimension(\"lat\", rows)\n f.createDimension(\"lon\", cols)\n # Could make this unlimited to add to it later?\n latitudes = f.createVariable(\"lat\", \"f4\", (\"lat\",), zlib=True)\n longitudes = f.createVariable(\"lon\", \"f4\", (\"lon\",), zlib=True)\n latitudes.units = \"degrees north\"\n longitudes.units = \"degrees east\"\n\n for dset_name, stack_dim_name, stack_arr in zip(\n stack_dset_list, stack_dim_list, stack_arrs\n ):\n dset = hf[dset_name]\n nstack, _, _ = dset.shape\n if stack_dim_name not in f.dimensions:\n f.createDimension(stack_dim_name, nstack)\n if stack_dim_name not in f.variables:\n if stack_dim_name == \"date\":\n idxs = f.createVariable(\n stack_dim_name, \"f4\", (stack_dim_name,), zlib=True\n )\n idxs.units = f\"days since {geolist[0]}\"\n else:\n idxs = f.createVariable(stack_dim_name, \"i4\", (stack_dim_name,))\n\n # Write data\n latitudes[:] = lat_arr\n longitudes[:] = lon_arr\n if stack_dim_name == \"date\":\n d2n = nc.date2num(stack_arr, units=idxs.units)\n idxs[:] = d2n\n else:\n idxs[:] = stack_arr\n\n # Finally, the actual stack\n # stackvar = rootgrp.createVariable(\"stack/1\", \"f4\", (\"date\", \"lat\", \"lon\"))\n print(f\"Writing {dset_name} data\")\n if hf[dset_name].dtype == np.dtype(\"bool\"):\n bool_type = \"i1\"\n # bool_type = f.createEnumType(\n # np.uint8, \"bool_t\", {\"FALSE\": 0, \"TRUE\": 1}\n # )\n dt = bool_type\n fill_value = 0\n else:\n dt = hf[dset_name].dtype\n fill_value = 0\n\n stackvar = f.createVariable(\n dset_name,\n dt,\n (stack_dim_name, \"lat\", \"lon\"),\n fill_value=fill_value,\n zlib=True,\n )\n d = dset[:, row_top:row_bot, col_left:col_right]\n print(f\"d shape: {d.shape}\")\n stackvar[:] = d\n\n\ndef testt(fn):\n # TODO: not quite working to add a colorbar to grayscale tif...\n import gdal\n\n ds = gdal.Open(fn, 1)\n band = ds.GetRasterBand(1)\n\n # create color table\n colors = gdal.ColorTable()\n\n # set color for each value\n colors.SetColorEntry(1, (112, 153, 89))\n colors.SetColorEntry(2, (242, 238, 162))\n colors.SetColorEntry(3, (242, 206, 133))\n colors.SetColorEntry(4, (194, 140, 124))\n colors.SetColorEntry(5, (214, 193, 156))\n\n # set color table and color interpretation\n band.SetRasterColorTable(colors)\n band.SetRasterColorInterpretation(gdal.GCI_PaletteIndex)\n\n\ndef make_unw_vrt(unw_filelist=None, directory=None, output=\"unw_stack.vrt\", ext=\".unw\"):\n import gdal\n\n if unw_filelist is None:\n unw_filelist = glob.glob(os.path.join(directory, \"*\" + ext))\n\n gdal.BuildVRT(output, unw_filelist, separate=True, srcNodata=\"nan 0.0\")\n # But we want the 2nd band (not an option on build for some reason)\n with fileinput.FileInput(output, inplace=True) as f:\n for line in f:\n print(\n line.replace(\n \"1\", \"2\"\n ),\n end=\"\",\n )\n", "sub_path": "apertools/sario.py", "file_name": "sario.py", "file_ext": "py", "file_size_in_byte": 57723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "warnings.filterwarnings", "line_number": 20, "usage_type": "call"}, {"api_name": "apertools.log.get_log", "line_number": 29, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 37, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.dtype", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 182, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 182, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 182, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 185, "usage_type": "call"}, {"api_name": "json.load", "line_number": 189, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 196, "usage_type": "call"}, {"api_name": "demloading.load_elevation", "line_number": 201, "usage_type": "call"}, {"api_name": "demloading.load_dem_rsc", "line_number": 203, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 222, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 222, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "demloading.load_dem_rsc", "line_number": 237, "usage_type": "call"}, {"api_name": "apertools.utils.parsers.Uavsar", "line_number": 245, "usage_type": "call"}, {"api_name": "apertools.utils.parsers", "line_number": 245, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 245, "usage_type": "name"}, {"api_name": "apertools.utils.parsers.UavsarInt", "line_number": 249, "usage_type": "call"}, {"api_name": "apertools.utils.parsers", "line_number": 249, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 249, "usage_type": "name"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 306, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 306, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 306, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 308, "usage_type": "attribute"}, {"api_name": "numpy.complex64", "line_number": 310, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 312, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path", "line_number": 347, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 363, "usage_type": "call"}, {"api_name": "os.path", "line_number": 363, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 363, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path", "line_number": 364, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "apertools.utils.utils.fullpath", "line_number": 374, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 374, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 374, "usage_type": "name"}, {"api_name": "math.modf", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 487, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 566, "usage_type": "call"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 578, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 578, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 578, "usage_type": "name"}, {"api_name": "apertools.utils.parsers", "line_number": 588, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 588, "usage_type": "name"}, {"api_name": "sys.byteorder", "line_number": 613, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 616, "usage_type": "call"}, {"api_name": "numpy.floating", "line_number": 616, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 618, "usage_type": "call"}, {"api_name": "numpy.complexfloating", "line_number": 618, "usage_type": "attribute"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 623, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 623, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 623, "usage_type": "name"}, {"api_name": "demloading.format_dem_rsc", "line_number": 626, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 633, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 633, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 637, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 637, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 638, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 638, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 639, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 639, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 641, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 641, "usage_type": "name"}, {"api_name": "numpy.hstack", "line_number": 661, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 668, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 695, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 707, "usage_type": "call"}, {"api_name": "os.path", "line_number": 707, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 707, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 709, "usage_type": "call"}, {"api_name": "os.path", "line_number": 709, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 709, "usage_type": "call"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 731, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 731, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 731, "usage_type": "name"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 735, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 735, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 735, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 752, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 779, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 779, "usage_type": "call"}, {"api_name": "os.path", "line_number": 779, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 783, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 784, "usage_type": "call"}, {"api_name": "os.path", "line_number": 784, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 794, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 807, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 830, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 839, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 839, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 867, "usage_type": "call"}, {"api_name": "os.path", "line_number": 867, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 872, "usage_type": "call"}, {"api_name": "re.match", "line_number": 875, "usage_type": "call"}, {"api_name": "apertools.utils.parsers.Sentinel", "line_number": 879, "usage_type": "call"}, {"api_name": "apertools.utils.parsers", "line_number": 879, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 879, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 914, "usage_type": "call"}, {"api_name": "os.path", "line_number": 914, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 922, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 923, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 930, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 931, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 951, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 974, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 982, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 987, "usage_type": "call"}, {"api_name": "apertools.utils.utils.get_parent_dir", "line_number": 1007, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 1007, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 1007, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1011, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1011, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 1038, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 1071, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1079, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1079, "usage_type": "attribute"}, {"api_name": "numpy.ma", "line_number": 1081, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1087, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1087, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 1093, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1095, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 1097, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1102, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 1125, "usage_type": "call"}, {"api_name": "gdal.GetDriverByName", "line_number": 1152, "usage_type": "call"}, {"api_name": "gdal.osr.SpatialReference", "line_number": 1162, "usage_type": "call"}, {"api_name": "gdal.osr", "line_number": 1162, "usage_type": "attribute"}, {"api_name": "rasterio.open", "line_number": 1178, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 1184, "usage_type": "call"}, {"api_name": "gdal.Open", "line_number": 1240, "usage_type": "call"}, {"api_name": "gdal.osr.SpatialReference", "line_number": 1248, "usage_type": "call"}, {"api_name": "gdal.osr", "line_number": 1248, "usage_type": "attribute"}, {"api_name": "gdal.osr.SpatialReference", "line_number": 1253, "usage_type": "call"}, {"api_name": "gdal.osr", "line_number": 1253, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 1268, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 1269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1269, "usage_type": "attribute"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 1274, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 1274, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 1274, "usage_type": "name"}, {"api_name": "gdal.GetDriverByName", "line_number": 1282, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 1305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1305, "usage_type": "attribute"}, {"api_name": "gdal.osr.SpatialReference", "line_number": 1332, "usage_type": "call"}, {"api_name": "gdal.osr", "line_number": 1332, "usage_type": "attribute"}, {"api_name": "gdal.Open", "line_number": 1336, "usage_type": "call"}, {"api_name": "apertools.utils.utils.get_file_ext", "line_number": 1389, "usage_type": "call"}, {"api_name": "apertools.utils.utils", "line_number": 1389, "usage_type": "attribute"}, {"api_name": "apertools.utils", "line_number": 1389, "usage_type": "name"}, {"api_name": "numpy.dtype", "line_number": 1406, "usage_type": "call"}, {"api_name": "gdal.Open", "line_number": 1454, "usage_type": "call"}, {"api_name": "osgeo.gdalconst.GA_Update", "line_number": 1454, "usage_type": "attribute"}, {"api_name": "osgeo.gdalconst", "line_number": 1454, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 1464, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 1464, "usage_type": "attribute"}, {"api_name": "numpy.roll", "line_number": 1483, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 1484, "usage_type": "call"}, {"api_name": "gdal.ColorTable", "line_number": 1494, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 1498, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 1498, "usage_type": "attribute"}, {"api_name": "numpy.issubdtype", "line_number": 1514, "usage_type": "call"}, {"api_name": "osgeo.gdalconst.GDT_Byte", "line_number": 1515, "usage_type": "attribute"}, {"api_name": "osgeo.gdalconst", "line_number": 1515, "usage_type": "name"}, {"api_name": "osgeo.gdal_array.NumericTypeCodeToGDALTypeCode", "line_number": 1517, "usage_type": "call"}, {"api_name": "osgeo.gdal_array", "line_number": 1517, "usage_type": "name"}, {"api_name": "numpy.dtype", "line_number": 1517, "usage_type": "call"}, {"api_name": "osgeo.gdal_array.GDALTypeCodeToNumericTypeCode", "line_number": 1525, "usage_type": "call"}, {"api_name": "osgeo.gdal_array", "line_number": 1525, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 1536, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1536, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 1536, "usage_type": "call"}, {"api_name": "demloading.load_dem_rsc", "line_number": 1538, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1538, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1538, "usage_type": "attribute"}, {"api_name": "demloading.load_dem_rsc", "line_number": 1540, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1540, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1540, "usage_type": "attribute"}, {"api_name": "demloading.load_dem_rsc", "line_number": 1564, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1573, "usage_type": "call"}, {"api_name": "apertools.latlon.grid", "line_number": 1580, "usage_type": "call"}, {"api_name": "apertools.latlon", "line_number": 1580, "usage_type": "name"}, {"api_name": "apertools.latlon.grid", "line_number": 1582, "usage_type": "call"}, {"api_name": "apertools.latlon", "line_number": 1582, "usage_type": "name"}, {"api_name": "apertools.sario.load", "line_number": 1582, "usage_type": "call"}, {"api_name": "apertools.sario", "line_number": 1582, "usage_type": "name"}, {"api_name": "rasterio.open", "line_number": 1586, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1589, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1590, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1591, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 1603, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 1604, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 1605, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 1606, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 1607, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 1608, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 1632, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1656, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 1662, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 1663, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1663, "usage_type": "call"}, {"api_name": "netCDF4.date2num", "line_number": 1693, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 1701, "usage_type": "call"}, {"api_name": "gdal.Open", "line_number": 1728, "usage_type": "call"}, {"api_name": "gdal.ColorTable", "line_number": 1732, "usage_type": "call"}, {"api_name": "gdal.GCI_PaletteIndex", "line_number": 1743, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 1750, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1750, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1750, "usage_type": "attribute"}, {"api_name": "gdal.BuildVRT", "line_number": 1752, "usage_type": "call"}, {"api_name": "fileinput.FileInput", "line_number": 1754, "usage_type": "call"}]} +{"seq_id": "88737146", "text": "from django.db.models import DecimalField, OuterRef, Subquery, Sum\nfrom django.db.models.functions import Cast\n\nfrom datahub.core.query_utils import (\n get_choices_as_case_expression,\n get_front_end_url_expression,\n get_full_name_expression,\n)\nfrom datahub.metadata.query_utils import get_sector_name_subquery\nfrom datahub.oauth.scopes import Scope\nfrom datahub.omis.order.models import Order as DBOrder\nfrom datahub.omis.payment.constants import RefundStatus\nfrom datahub.omis.payment.models import Refund\nfrom datahub.search.omis.models import Order\nfrom datahub.search.omis.serializers import SearchOrderSerializer\nfrom datahub.search.views import SearchAPIView, SearchExportAPIView\n\n\nclass SearchOrderParams:\n \"\"\"Search order params.\"\"\"\n\n required_scopes = (Scope.internal_front_end,)\n entity = Order\n serializer_class = SearchOrderSerializer\n\n FILTER_FIELDS = [\n 'primary_market',\n 'sector_descends',\n 'uk_region',\n 'created_on_before',\n 'created_on_after',\n 'assigned_to_adviser',\n 'assigned_to_team',\n 'status',\n 'reference',\n 'total_cost',\n 'subtotal_cost',\n 'contact_name',\n 'company_name',\n 'company',\n ]\n\n REMAP_FIELDS = {\n 'primary_market': 'primary_market.id',\n 'uk_region': 'uk_region.id',\n 'assigned_to_adviser': 'assignees.id',\n 'assigned_to_team': 'assignees.dit_team.id',\n 'company': 'company.id',\n 'reference': 'reference_trigram',\n }\n\n COMPOSITE_FILTERS = {\n 'contact_name': [\n 'contact.name',\n 'contact.name_trigram',\n ],\n 'company_name': [\n 'company.name',\n 'company.name_trigram',\n 'company.trading_name',\n 'company.trading_name_trigram',\n ],\n 'sector_descends': [\n 'sector.id',\n 'sector.ancestors.id',\n ],\n }\n\n\nclass SearchOrderAPIView(SearchOrderParams, SearchAPIView):\n \"\"\"Filtered order search view.\"\"\"\n\n\nclass SearchOrderExportAPIView(SearchOrderParams, SearchExportAPIView):\n \"\"\"Order search export view.\"\"\"\n\n queryset = DBOrder.objects.annotate(\n subtotal_in_pounds=Cast(\n 'subtotal_cost',\n DecimalField(max_digits=19, decimal_places=2),\n ) / 100,\n # This follows the example from\n # https://docs.djangoproject.com/en/2.1/ref/models/expressions/#using-aggregates-within-a-subquery-expression\n net_refund_in_pounds=Subquery(\n Refund.objects.filter(\n order=OuterRef('pk'),\n status=RefundStatus.approved,\n ).order_by(\n ).values(\n 'order',\n ).annotate(\n total_refund=Cast(\n Sum('net_amount'),\n DecimalField(max_digits=19, decimal_places=2),\n ) / 100,\n ).values(\n 'total_refund',\n ),\n output_field=DecimalField(max_digits=19, decimal_places=2),\n ),\n status_name=get_choices_as_case_expression(DBOrder, 'status'),\n link=get_front_end_url_expression('order', 'pk'),\n sector_name=get_sector_name_subquery('sector'),\n company_link=get_front_end_url_expression('company', 'company__pk'),\n contact_name=get_full_name_expression('contact'),\n contact_link=get_front_end_url_expression('contact', 'contact__pk'),\n )\n field_titles = {\n 'reference': 'Order reference',\n 'subtotal_in_pounds': 'Net price',\n 'net_refund_in_pounds': 'Net refund',\n 'status_name': 'Status',\n 'link': 'Link',\n 'sector_name': 'Sector',\n 'primary_market__name': 'Market',\n 'uk_region__name': 'UK region',\n 'company__name': 'Company',\n 'company__registered_address_country__name': 'Company country',\n 'company__uk_region__name': 'Company UK region',\n 'company_link': 'Company link',\n 'contact_name': 'Contact',\n 'contact__job_title': 'Contact job title',\n 'contact_link': 'Contact link',\n 'created_by__dit_team__name': 'Created by team',\n 'created_on': 'Date created',\n 'delivery_date': 'Delivery date',\n 'quote__created_on': 'Date quote sent',\n 'quote__accepted_on': 'Date quote accepted',\n 'paid_on': 'Date payment received',\n 'completed_on': 'Date completed',\n }\n", "sub_path": "datahub/search/omis/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "datahub.oauth.scopes.Scope.internal_front_end", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datahub.oauth.scopes.Scope", "line_number": 22, "usage_type": "name"}, {"api_name": "datahub.search.omis.models.Order", "line_number": 23, "usage_type": "name"}, {"api_name": "datahub.search.omis.serializers.SearchOrderSerializer", "line_number": 24, "usage_type": "name"}, {"api_name": "datahub.search.views.SearchAPIView", "line_number": 70, "usage_type": "name"}, {"api_name": "datahub.search.views.SearchExportAPIView", "line_number": 74, "usage_type": "name"}, {"api_name": "datahub.omis.order.models.Order.objects.annotate", "line_number": 77, "usage_type": "call"}, {"api_name": "datahub.omis.order.models.Order.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "datahub.omis.order.models.Order", "line_number": 77, "usage_type": "name"}, {"api_name": "django.db.models.functions.Cast", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.models.Subquery", "line_number": 84, "usage_type": "call"}, {"api_name": "datahub.omis.payment.models.Refund.objects.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "datahub.omis.payment.models.Refund.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datahub.omis.payment.models.Refund", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.OuterRef", "line_number": 86, "usage_type": "call"}, {"api_name": "datahub.omis.payment.constants.RefundStatus.approved", "line_number": 87, "usage_type": "attribute"}, {"api_name": "datahub.omis.payment.constants.RefundStatus", "line_number": 87, "usage_type": "name"}, {"api_name": "django.db.models.functions.Cast", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 99, "usage_type": "call"}, {"api_name": "datahub.core.query_utils.get_choices_as_case_expression", "line_number": 101, "usage_type": "call"}, {"api_name": "datahub.omis.order.models.Order", "line_number": 101, "usage_type": "argument"}, {"api_name": "datahub.core.query_utils.get_front_end_url_expression", "line_number": 102, "usage_type": "call"}, {"api_name": "datahub.metadata.query_utils.get_sector_name_subquery", "line_number": 103, "usage_type": "call"}, {"api_name": "datahub.core.query_utils.get_front_end_url_expression", "line_number": 104, "usage_type": "call"}, {"api_name": "datahub.core.query_utils.get_full_name_expression", "line_number": 105, "usage_type": "call"}, {"api_name": "datahub.core.query_utils.get_front_end_url_expression", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "647007996", "text": "import pandas as pd\n#import unidecode\nimport re\nimport requests\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n#import soup as soup\nfrom bs4 import BeautifulSoup\n\nimport ssl\nimport csv\n\nurls = [\"https://www.skiresort.info/best-ski-resorts/poland/\", \"https://www.skiresort.info/best-ski-resorts/germany/\", \"https://www.skiresort.info/best-ski-resorts/austria/\" ,\"https://www.skiresort.info/best-ski-resorts/italy/\"]\n\nclass makeDfOfAreas:\n def __init__(self, urls):\n self.urls = urls\n\n def __makeDFWithAreas(self, url):\n getpage = requests.get(url)\n getpage_soup = BeautifulSoup(getpage.text, 'html.parser')\n resort_urls = getpage_soup.findAll('a', {'class':'h3'})\n #print(type(resort_urls))\n resort_urls = [i.text for i in resort_urls]\n df = pd.Series(resort_urls)\n df.to_frame()\n #print(df.at[49])\n #df.columns = ['area']\n df = df.drop(df[df.index > 49].index)\n df = df.str.lower()\n df = df.str.replace('\\d+.\\s', '')\n df = df.str.replace('\\s$', '')\n df = df.str.replace(' ', '')\n df = df.str.replace('/', '')\n df = df.str.replace(' ', '-')\n df = df.str.replace('–-', '')\n df = df.str.replace('(', '')\n df = df.str.replace(')', '')\n df = df.str.replace('.', '')\n df = df.str.replace('’', '')\n df = df.str.replace('ö', 'o')\n df = df.str.replace('ä', 'a')\n df = df.str.replace('ü', 'u')\n df = df.str.replace('ä', 'a')\n df = df.str.replace('ß', 's')\n df = df.str.replace('ą', 'a')\n df = df.str.replace('ę', 'e')\n df = df.str.replace('ć', 'c')\n df = df.str.replace('ł', 'l')\n df = df.str.replace('ń', 'n')\n df = df.str.replace('ó', 'o')\n df = df.str.replace('ś', 's')\n df = df.str.replace('ż', 'z')\n df = df.str.replace('ź', 'z')\n df = df.str.replace('é', 'e')\n df = df.str.replace('è', 'e')\n df = df.str.replace('à', 'a')\n df = df.str.replace('ù', 'u')\n df = df.str.replace('-&-', '-')\n df = df.str.replace(r'(\\.*)', '')\n return df\n\n def appendingAreasNames(self):\n df = pd.DataFrame()\n for site in urls:\n df = pd.concat([df , self.__makeDFWithAreas(site)])\n df = df.rename(columns={0: 'areas' })\n return df\n\n\ndfOfAreas = makeDfOfAreas(urls)\n#print(dfOfAreas)\n\n\n\n\nclass makeUrls:\n def __init__(self, df):\n self.df = df\n\n def makeAreasSizeList(self,df):\n newList = df['areas'].values.tolist()\n areasSizeList = []\n for x in range(0, len(newList)):\n newList[x] = newList[x].replace('\\u200b', '')\n areasSizeList.append('https://www.skiresort.info/ski-resort/' + newList[x] + '/test-result/size/')\n return areasSizeList\n\n def makeAreasLiftList(self,df):\n newList = df['areas'].values.tolist()\n areasLiftList = []\n for x in range(0, len(newList)):\n newList[x] = newList[x].replace('\\u200b', '')\n areasLiftList.append('https://www.skiresort.info/ski-resort/' + newList[x] + '/test-result/lifts-cable-cars/')\n return areasLiftList\n\n\nlists = makeUrls(dfOfAreas.appendingAreasNames())\nareasSizeList = lists.makeAreasSizeList(dfOfAreas.appendingAreasNames())\nareasLiftList = lists.makeAreasLiftList(dfOfAreas.appendingAreasNames())\n\n\n#print(areasSizeList)\n\n\n\n\ndef validateLinks(list):\n\n validatedLinksTable = []\n for x in range(0, len(list)):\n url = requests.get(list[x])\n soup = BeautifulSoup(url.text, 'html.parser')\n lista = soup.findAll('div', {'class': 'description'})\n lista = [d.text for d in lista]\n for y in range(0, len(lista)):\n if len(lista) > 3:\n continue\n if len(lista) < 4:\n validatedLinksTable.append(x)\n return validatedLinksTable\n\n\nclass makeData:\n def __init__(self, list, validateList):\n self.list =list\n self.validateList = validateList\n\n\n def downloadData(self, list, validateList):\n global data\n data = []\n global listOfAreasTemp\n listOfAreasTemp = []\n\n for x in range(0, len(validateList)):\n url = requests.get(list[validateList[x]])\n soup = BeautifulSoup(url.text, 'html.parser')\n lista = soup.findAll('div', {'class': 'description'})\n lista = [d.text for d in lista]\n for y in range(0, len(lista)):\n if len(lista)>3 :\n continue\n #brokenlinks=list[x]\n else:\n lista[y] = lista[y].split()[0]\n if len(lista)<4:\n data.append(lista)\n #print(data)\n #print(lista)\n #print(brokenlinks)\n return data\n\n def downloadData1(self, list, validateList):\n global data\n data = []\n listOfLiftType = ['Aerial', 'Circulating', 'Chairlift', 'T-bar', 'Rope', 'Sunkid']\n for x in range(0, len(validateList)):\n url = requests.get(list[validateList[x]])\n soup = BeautifulSoup(url.text, 'html.parser')\n lista = soup.findAll('div', {'class': 'lift-head'})\n lista = [i.text for i in lista]\n listaTemp = [0, 0, 0, 0, 0, 0]\n for y in range(0, len(lista)):\n if len(lista)>7 :\n continue\n #brokenlinks=list[x]\n else:\n for z in range(0, len(lista)):\n for i in range(0, len(listOfLiftType)):\n if lista[z].split()[1] == listOfLiftType[i]:\n listaTemp[i] = lista[z].split()[0]\n if len(lista)<7 and len(lista)!= 0:\n data.append(listaTemp)\n #print(data)\n #print(lista)\n #print(brokenlinks)\n return data\n\n\n\nlistOfValidateNumbers = validateLinks(areasSizeList)\n\ndownload = makeData(areasSizeList, listOfValidateNumbers)\n\nprint(download.downloadData(areasSizeList, listOfValidateNumbers))\nprint(download.downloadData1(areasLiftList, listOfValidateNumbers))\n\ndataOfAreasSize = download.downloadData(areasSizeList, listOfValidateNumbers)\ncolumsOfAreasSize =['Routes total', 'Elevation difference', 'Lifts total']\ndataOfAresLifts = download.downloadData1(areasLiftList, listOfValidateNumbers)\ncolumsOAreasLift = ['Aerial', 'Circulating', 'Chairlift', 'T-bar', 'Rope', 'Sunkid']\n\nprint(dataOfAreasSize)\nprint(columsOfAreasSize)\ndef makeDF(data, columns):\n return pd.DataFrame(np.array(data), columns = columns)\n\n\n#print(makeDF(dataOfAreasSize,columsOfAreasSize))\n#print(makeDF(dataOfAresLifts, columsOAreasLift))\n\n\ndef export_csv(tab1, tab2):\n tab1.join(tab2)\n return tab1.to_csv ('exportDataframe.csv', index = None, header=True)\n\n\n\nexport_csv(makeDF(dataOfAreasSize,columsOfAreasSize), makeDF(dataOfAresLifts, columsOAreasLift))\n#print(fTable)", "sub_path": "dataCollecting.py", "file_name": "dataCollecting.py", "file_ext": "py", "file_size_in_byte": 6985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 67, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 113, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 114, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 138, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 139, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 160, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "142173686", "text": "import requests\nimport json\nimport pprint\nimport csv\nimport time\n\ntime_start=time.time()\n\n#获取小区经纬度数据：XiaoquCoords\nfilename = \"./district/total_final.csv\"\n\nrows = []\nCoords = []\nqueries = ['地铁站','幼儿园','小学','初中','银行','ATM','购物中心','超市','百货商场','便利店','美食','综合医院']\nradius = 5000\nwith open(filename, 'r') as file:\n reader = csv.DictReader(file)\n XiaoquAddresses = [row['地址'] for row in reader]\n\nfor j in range(2):\n XiaoquCoords = []\n for i in range(j*50, (j+1)*50-1):\n XiaoquAddress = XiaoquAddresses[i]\n XiaoquUrl = 'http://api.map.baidu.com/geocoder/v2/?address=上海市%s' \\\n '&output=json&ak=HRGlosvNHtwcmmUHonaFyAcVr41UzCQG' % (XiaoquAddress)\n XiaoquRes = requests.get(XiaoquUrl)\n XiaoquJson = json.loads(XiaoquRes.text)\n XiaoquCoordLat = XiaoquJson.get('result').get('location').get('lat')\n XiaoquCoordLng = XiaoquJson.get('result').get('location').get('lng')\n coord = '%s,%s' % (XiaoquCoordLat, XiaoquCoordLng)\n XiaoquCoords.append([XiaoquAddress, coord])\n Coords.append([XiaoquAddress,XiaoquCoordLat,XiaoquCoordLng])\n\n for XiaoquCoord in XiaoquCoords:\n # 对于每个搜索字段求规定radius内的总数和平均距离\n contents = [XiaoquCoord[0]]\n for query in queries:\n SearchUrl = 'http://api.map.baidu.com/place/v2/search?query=%s&location' \\\n '=%s&radius=%d&output=json&scope=2&filter=sort_name:distance|sort_rule:1&page_size=20&' \\\n 'ak=HRGlosvNHtwcmmUHonaFyAcVr41UzCQG' % (query, XiaoquCoord[1], radius)\n SearchRes = requests.get(SearchUrl)\n Details = json.loads(SearchRes.text)\n if Details.get('results') != []:\n name = Details.get('results')[0].get('address')\n distance = Details.get('results')[0].get('detail_info').get('distance')\n else:\n name = '%d米内没有' % (radius)\n distance = 0\n content = [name, distance]\n for j in range(2):\n contents.append(content[j])\n rows.append(contents)\n\nheaders = ['小区地址']\nfor query in queries:\n headers.append('最近的%s'%(query))\n headers.append('到最近%s的距离'%(query))\nwith open('variables.csv','w') as f:\n f_csv = csv.writer(f,)\n f_csv.writerow(headers)\n f_csv.writerows(rows)\n\nheaders2 = ['小区地址','lat','lng']\nwith open('coords.csv','w') as f2:\n f_csv = csv.writer(f2,)\n f_csv.writerow(headers2)\n f_csv.writerows(Coords)\n\ntime_end=time.time()\nprint('time cost',time_end-time_start,'s')\n", "sub_path": "spider/map.py", "file_name": "map.py", "file_ext": "py", "file_size_in_byte": 2700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "time.time", "line_number": 7, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 59, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "231689262", "text": "#!/usr/bin/env python3\n\n# Load UIUC resources information from a source (database) to a destination (warehouse)\nimport os\nimport pwd\nimport re\nimport sys\nimport argparse\nimport logging\nimport logging.handlers\nimport signal\nimport datetime\nfrom datetime import datetime, tzinfo, timedelta\nfrom time import sleep\nimport pytz\nCentral_TZ = pytz.timezone(\"US/Central\")\nUTC_TZ = pytz.utc\n\ntry:\n import http.client as httplib\nexcept ImportError:\n import httplib\nimport psycopg2\nimport json\nimport ssl\nimport shutil\n\nimport django\ndjango.setup()\nfrom django.db import DataError, IntegrityError\nfrom django.utils.dateparse import parse_datetime\nfrom resource_v2.models import *\nfrom processing_status.process import ProcessingActivity\n\nimport pdb\n\nclass UTC(tzinfo):\n def utcoffset(self, dt):\n return timedelta(0)\n def tzname(self, dt):\n return 'UTC'\n def dst(self, dt):\n return timedelta(0)\nutc = UTC()\n\nclass HandleLoad():\n def __init__(self):\n self.args = None\n self.config = {}\n self.src = {}\n self.dest = {}\n self.stats = {}\n for var in ['uri', 'scheme', 'path']: # Where contains :\n self.src[var] = None\n self.dest[var] = None\n \n self.Affiliation = 'uiuc.edu'\n # Field Maps \"fm\" local to global\n self.fm = {\n 'record_status': {\n '4': 'planned',\n '3': 'pre-production',\n '2': 'decommissioned',\n '1': 'production',\n }}\n\n self.have_column = ['resource_id', 'info_resourceid',\n 'resource_descriptive_name', 'resource_description',\n 'project_affiliation', 'provider_level',\n 'resource_status', 'current_statuses', 'updated_at']\n\n default_source = 'postgresql://localhost:5432/uiucTest'\n\n parser = argparse.ArgumentParser(epilog='File SRC|DEST syntax: file: 0:\n (self.src['scheme'], self.src['path']) = (self.args.src[0:idx], self.args.src[idx+1:])\n else:\n (self.src['scheme'], self.src['path']) = (self.args.src, None)\n if self.src['scheme'] not in ['file', 'http', 'https', 'postgresql']:\n self.logger.error('Source not {file, http, https}')\n sys.exit(1)\n if self.src['scheme'] in ['http', 'https', 'postgresql']:\n if self.src['path'][0:2] != '//':\n self.logger.error('Source URL not followed by \"//\"')\n sys.exit(1)\n self.src['path'] = self.src['path'][2:]\n if len(self.src['path']) < 1:\n self.logger.error('Source is missing a database name')\n sys.exit(1)\n self.src['uri'] = self.args.src\n\n if not getattr(self.args, 'dest', None): # Tests for None and empty ''\n if 'DESTINATION' in self.config:\n self.args.dest = self.config['DESTINATION']\n if not getattr(self.args, 'dest', None): # Tests for None and empty ''\n self.args.dest = 'analyze'\n idx = self.args.dest.find(':')\n if idx > 0:\n (self.dest['scheme'], self.dest['path']) = (self.args.dest[0:idx], self.args.dest[idx+1:])\n else:\n self.dest['scheme'] = self.args.dest\n if self.dest['scheme'] not in ['file', 'analyze', 'warehouse']:\n self.logger.error('Destination not {file, analyze, warehouse}')\n sys.exit(1)\n self.dest['uri'] = self.args.dest\n\n if self.src['scheme'] in ['file'] and self.dest['scheme'] in ['file']:\n self.logger.error('Source and Destination can not both be a {file}')\n sys.exit(1)\n\n def Connect_Source(self, url):\n idx = url.find(':')\n if idx <= 0:\n self.logger.error('Retrieve URL is not valid')\n sys.exit(1)\n \n (type, obj) = (url[0:idx], url[idx+1:])\n if type not in ['postgresql']:\n self.logger.error('Retrieve URL is not valid')\n sys.exit(1)\n\n if obj[0:2] != '//':\n self.logger.error('Retrieve URL is not valid')\n sys.exit(1)\n \n obj = obj[2:]\n idx = obj.find('/')\n if idx <= 0:\n self.logger.error('Retrieve URL is not valid')\n sys.exit(1)\n (host, path) = (obj[0:idx], obj[idx+1:])\n idx = host.find(':')\n if idx > 0:\n port = host[idx+1:]\n host = host[:idx]\n elif type == 'postgresql':\n port = '5432'\n else:\n port = '5432'\n \n #Define our connection string\n conn_string = \"host='{}' port='{}' dbname='{}' user='{}' password='{}'\".format(host, port, path, self.config['SOURCE_DBUSER'], self.config['SOURCE_DBPASS'] )\n\n # get a connection, if a connect cannot be made an exception will be raised here\n conn = psycopg2.connect(conn_string)\n\n # conn.cursor will return a cursor object, you can use this cursor to perform queries\n cursor = conn.cursor()\n self.logger.info('Connected to PostgreSQL database {} as {}'.format(path, self.config['SOURCE_DBUSER']))\n return(cursor)\n \n def Disconnect_Source(self, cursor):\n cursor.close()\n\n def Retrieve_Resources(self, cursor):\n try:\n sql = 'SELECT * from resource'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n DATA = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n if rowdict.get('record_status', None) not in [1, 2]:\n continue\n if 'last_updated' in rowdict and isinstance(rowdict['last_updated'], datetime):\n rowdict['last_updated'] = Central_TZ.localize(rowdict['last_updated'])\n if 'start_date_time' in rowdict and isinstance(rowdict['start_date_time'], datetime):\n rowdict['start_date_time'] = Central_TZ.localize(rowdict['start_date_time'])\n if 'end_date_time' in rowdict and isinstance(rowdict['end_date_time'], datetime):\n rowdict['end_date_time'] = Central_TZ.localize(rowdict['end_date_time'])\n GLOBALID = 'urn:glue2:GlobalResource:{}.{}'.format(rowdict.get('id', ''), self.Affiliation)\n DATA[GLOBALID] = rowdict\n return(DATA)\n\n def Retrieve_Providers(self, cursor):\n try:\n sql = 'SELECT * from provider'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n DATA = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n GLOBALID = 'urn:glue2:GlobalResourceProvider:{}.{}'.format(rowdict.get('id', ''), self.Affiliation)\n DATA[GLOBALID] = rowdict\n return(DATA)\n\n def Retrieve_Resource_Tags(self, cursor):\n try:\n sql = 'SELECT * from tag'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n tags = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n tags[rowdict['id']] = rowdict['label']\n \n try:\n sql = 'SELECT * from resources_tags'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n resource_tags = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n GLOBALID = 'urn:glue2:GlobalResource:{}.{}'.format(rowdict.get('resource_id', ''), self.Affiliation)\n if GLOBALID not in resource_tags:\n resource_tags[GLOBALID] = []\n try:\n resource_tags[GLOBALID].append(tags[rowdict['tag_id']])\n except:\n pass\n return(resource_tags)\n\n def Retrieve_Resource_Associations(self, cursor):\n try:\n sql = 'SELECT * from associated_resources'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n DATA = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n GLOBALID = 'urn:glue2:GlobalResource:{}.{}'.format(rowdict.get('resource_id', ''), self.Affiliation)\n if GLOBALID not in DATA:\n DATA[GLOBALID] = []\n DATA[GLOBALID].append(str(rowdict['associated_resource_id']))\n return(DATA)\n\n def Retrieve_Guides(self, cursor):\n try:\n sql = 'SELECT * from curated_guide'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n DATA = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n if 'created_at' in rowdict and isinstance(rowdict['created_at'], datetime):\n rowdict['created_at'] = Central_TZ.localize(rowdict['created_at'])\n if 'updated_at' in rowdict and isinstance(rowdict['updated_at'], datetime):\n rowdict['updated_at'] = Central_TZ.localize(rowdict['updated_at'])\n GLOBALID = 'urn:glue2:GlobalGuide:{}.{}'.format(rowdict.get('id', ''), self.Affiliation)\n DATA[GLOBALID] = rowdict\n return(DATA)\n\n def Retrieve_Guide_Resources(self, cursor):\n try:\n sql = 'SELECT * from curated_guide_resource'\n cursor.execute(sql)\n except psycopg2.Error as e:\n self.logger.error(\"Failed '{}' with {}: {}\".format(sql, e.pgcode, e.pgerror))\n exit(1)\n\n COLS = [desc.name for desc in cursor.description]\n DATA = {}\n for row in cursor.fetchall():\n rowdict = dict(zip(COLS, row))\n GLOBALID = 'urn:glue2:GlobalGuideResource:{0}.{2}:{1}.{2}'.format(rowdict.get('curated_guide_id', ''), rowdict.get('resource_id', ''), self.Affiliation)\n DATA[GLOBALID] = rowdict\n return(DATA)\n \n def Warehouse_Resources(self, new_items, item_tags, item_associations):\n self.cur = {} # Items currently in database\n self.new = {} # New resources in document\n now_utc = datetime.now(utc)\n for item in ResourceV2.objects.filter(Affiliation__exact=self.Affiliation):\n self.cur[item.ID] = item\n \n for GLOBALID in new_items:\n item = new_items[GLOBALID]\n # Convert warehouse last_update JSON string to datetime with timezone\n # Incoming last_update is a datetime with timezone\n # Once they are both datetimes with timezone, compare their strings\n # Can't compare directly because tzinfo have different represenations in Python and Django\n if not self.args.ignore_dates:\n try:\n cur_dtm = parse_datetime(self.cur[GLOBALID].EntityJSON['last_updated'].replace(' ',''))\n except:\n cur_dtm = datetime.utcnow()\n try:\n new_dtm = item['last_updated']\n except:\n new_dtm = None\n if str(cur_dtm) == str(new_dtm):\n self.stats['Resource.Skip'] += 1\n continue\n\n if 'last_updated' in item and isinstance(item['last_updated'], datetime):\n item['last_updated'] = item['last_updated'].strftime('%Y-%m-%dT%H:%M:%S%z')\n if 'start_date_time' in item and isinstance(item['start_date_time'], datetime):\n item['start_date_time'] = item['start_date_time'].strftime('%Y-%m-%dT%H:%M:%S%z')\n if 'end_date_time' in item and isinstance(item['end_date_time'], datetime):\n item['end_date_time'] = item['end_date_time'].strftime('%Y-%m-%dT%H:%M:%S%z')\n\n try:\n ProviderID = 'urn:glue2:GlobalResourceProvider:{}.{}'.format(item['provider'], self.Affiliation)\n except:\n ProviderID = None\n\n try:\n ResourceGroup = item['resource_group']\n except:\n ResourceGroup = None\n\n try:\n Type = item['resource_type']\n except:\n Type = None\n\n try:\n QualityLevel = self.fm['record_status'][str(item['record_status'])]\n except:\n QualityLevel = None\n\n try:\n Keywords = ','.join(item_tags[str(item['id'])])\n except:\n Keywords = None\n\n try:\n Associations = ','.join(item_associations[str(item['id'])])\n except:\n Associations = None\n \n if item['short_description'] and len(item['short_description']) > 1000:\n self.logger.warning('Truncating Resource ShortDescription longer than 1000 ID={}'.format(GLOBALID))\n item['short_description'] = item['short_description'][:1000]\n if item['resource_description'] and len(item['resource_description']) > 24000:\n self.logger.warning('Truncating Resource Description longer than 24000 ID={}'.format(GLOBALID))\n item['resource_description'] = item['resource_description'][:24000]\n if item['topics'] and len(item['topics']) > 1000:\n self.logger.warning('Truncating Resource Topics longer than 1000 ID={}'.format(GLOBALID))\n item['topics'] = item['topics'][:1000]\n if Keywords and len(Keywords) > 1000:\n self.logger.warning('Truncating Resource Keywords longer than 1000 ID={}'.format(GLOBALID))\n Keywords = Keywords[:1000]\n \n try:\n model = ResourceV2(ID=GLOBALID,\n Name = item['resource_name'],\n CreationTime = now_utc,\n Validity = None,\n EntityJSON = item,\n Affiliation = self.Affiliation,\n ProviderID = ProviderID,\n ResourceGroup = ResourceGroup,\n Type = Type,\n ShortDescription = item['short_description'],\n Description = item['resource_description'],\n QualityLevel = QualityLevel,\n LocalID = str(item['id']),\n Topics = item['topics'],\n Keywords = Keywords,\n Associations = Associations,\n )\n model.save()\n self.logger.debug('Resource save ID={}'.format(GLOBALID))\n self.new[GLOBALID]=model\n self.stats['Resource.Update'] += 1\n except (DataError, IntegrityError) as e:\n msg = '{} saving ID={}: {}'.format(type(e).__name__, GLOBALID, e)\n self.logger.error(msg)\n return(False, msg)\n\n for GLOBALID in self.cur:\n if GLOBALID not in new_items:\n try:\n ResourceV2.objects.get(pk=GLOBALID).delete()\n self.stats['Resource.Delete'] += 1\n self.logger.info('Resource delete ID={}'.format(GLOBALID))\n except (DataError, IntegrityError) as e:\n self.logger.error('{} deleting ID={}: {}'.format(type(e).__name__, GLOBALID, e))\n return(True, '')\n\n def Warehouse_Providers(self, new_items):\n self.cur = {} # Items currently in database\n self.new = {} # New resources in document\n now_utc = datetime.now(utc)\n for item in ResourceV2Provider.objects.filter(Affiliation__exact=self.Affiliation):\n self.cur[item.ID] = item\n for GLOBALID in new_items:\n item = new_items[GLOBALID]\n try:\n model = ResourceV2Provider(ID=GLOBALID,\n Name = item['name'],\n CreationTime=now_utc,\n Validity=None,\n EntityJSON=item,\n Affiliation=self.Affiliation,\n LocalID=str(item['id']),\n )\n model.save()\n self.logger.debug('ResourceProvider save ID={}'.format(GLOBALID))\n self.new[GLOBALID]=model\n self.stats['ResourceProvider.Update'] += 1\n except (DataError, IntegrityError) as e:\n msg = '{} saving ID={}: {}'.format(type(e).__name__, GLOBALID, e)\n self.logger.error(msg)\n return(False, msg)\n \n for GLOBALID in self.cur:\n if GLOBALID not in new_items:\n try:\n ResourceV2Provider.objects.get(pk=GLOBALID).delete()\n self.stats['ResourceProvider.Delete'] += 1\n self.logger.info('ResourceProvider delete ID={}'.format(GLOBALID))\n except (DataError, IntegrityError) as e:\n self.logger.error('{} deleting ID={}: {}'.format(type(e).__name__, GLOBALID, e))\n return(True, '')\n\n def Warehouse_Guides(self, new_items):\n self.cur = {} # Items currently in database\n self.new = {} # New resources in document\n now_utc = datetime.now(utc)\n for item in ResourceV2Guide.objects.filter(Affiliation__exact=self.Affiliation):\n self.cur[item.ID] = item\n for GLOBALID in new_items:\n item = new_items[GLOBALID]\n if 'created_at' in item and isinstance(item['created_at'], datetime):\n item['created_at'] = item['created_at'].strftime('%Y-%m-%dT%H:%M:%S%z')\n if 'updated_at' in item and isinstance(item['updated_at'], datetime):\n item['updated_at'] = item['updated_at'].strftime('%Y-%m-%dT%H:%M:%S%z')\n try:\n model = ResourceV2Guide(ID=GLOBALID,\n Name = item['title'],\n CreationTime=now_utc,\n Validity=None,\n EntityJSON=item,\n Affiliation=self.Affiliation,\n LocalID=str(item['id']),\n )\n model.save()\n self.logger.debug('Guide save ID={}'.format(GLOBALID))\n self.new[GLOBALID]=model\n self.stats['Guide.Update'] += 1\n except (DataError, IntegrityError) as e:\n msg = '{} saving ID={}: {}'.format(type(e).__name__, GLOBALID, e)\n self.logger.error(msg)\n return(False, msg)\n\n for GLOBALID in self.cur:\n if GLOBALID not in new_items:\n try:\n ResourceV2Guide.objects.get(pk=GLOBALID).delete()\n self.stats['Guide.Delete'] += 1\n self.logger.info('Guide delete ID={}'.format(GLOBALID))\n except (DataError, IntegrityError) as e:\n self.logger.error('{} deleting ID={}: {}'.format(type(e).__name__, GLOBALID, e))\n return(True, '')\n\n def Warehouse_Guide_Resources(self, new_items):\n self.cur = {} # Items currently in database\n self.new = {} # New resources in document\n now_utc = datetime.now(utc)\n for item in ResourceV2GuideResource.objects.all():\n if item.ID.endswith('.' + self.Affiliation):\n self.cur[item.ID] = item\n for GLOBALID in new_items:\n item = new_items[GLOBALID]\n GUIDE_ID = 'urn:glue2:GlobalGuide:{}.{}'.format(item['curated_guide_id'], self.Affiliation)\n RESOURCE_ID = 'urn:glue2:GlobalResource:{}.{}'.format(item['resource_id'], self.Affiliation)\n try:\n model = ResourceV2GuideResource(ID=GLOBALID,\n CuratedGuideID=GUIDE_ID,\n ResourceID=RESOURCE_ID,\n )\n model.save()\n self.logger.debug('GuideResource save ID={}'.format(GLOBALID))\n self.new[GLOBALID]=model\n self.stats['GuideResource.Update'] += 1\n except (DataError, IntegrityError) as e:\n msg = '{} saving ID={}: {}'.format(type(e).__name__, GLOBALID, e)\n self.logger.error(msg)\n return(False, msg)\n\n for GLOBALID in self.cur:\n if GLOBALID not in new_items:\n try:\n ResourceV2GuideResource.objects.get(pk=GLOBALID).delete()\n self.stats['GuideResource.Delete'] += 1\n self.logger.info('GuideResource delete ID={}'.format(GLOBALID))\n except (DataError, IntegrityError) as e:\n self.logger.error('{} deleting ID={}: {}'.format(type(e).__name__, GLOBALID, e))\n return(True, '')\n \n def SaveDaemonLog(self, path):\n # Save daemon log file using timestamp only if it has anything unexpected in it\n try:\n with open(path, 'r') as file:\n lines=file.read()\n file.close()\n if not re.match(\"^started with pid \\d+$\", lines) and not re.match(\"^$\", lines):\n ts = datetime.strftime(datetime.now(), '%Y-%m-%d_%H:%M:%S')\n newpath = '{}.{}'.format(path, ts)\n shutil.copy(path, newpath)\n print('SaveDaemonLog as {}'.format(newpath))\n except Exception as e:\n print('Exception in SaveDaemonLog({})'.format(path))\n return\n\n def exit_signal(self, signal, frame):\n self.logger.critical('Caught signal={}, exiting...'.format(signal))\n sys.exit(0)\n\n def run(self):\n signal.signal(signal.SIGINT, self.exit_signal)\n signal.signal(signal.SIGTERM, self.exit_signal)\n self.logger.info('Starting program={} pid={}, uid={}({})'.format(os.path.basename(__file__), os.getpid(), os.geteuid(), pwd.getpwuid(os.geteuid()).pw_name))\n\n while True:\n pa_application=os.path.basename(__file__)\n pa_function='Warehouse_UIUC'\n pa_id = 'resources'\n pa_topic = 'resources'\n pa_about = 'uiuc.edu'\n pa = ProcessingActivity(pa_application, pa_function, pa_id , pa_topic, pa_about)\n\n if self.src['scheme'] == 'postgresql':\n CURSOR = self.Connect_Source(self.src['uri'])\n\n self.start = datetime.now(utc)\n self.stats['ResourceProvider.Update'] = 0\n self.stats['ResourceProvider.Delete'] = 0\n self.stats['ResourceProvider.Skip'] = 0\n INPUT = self.Retrieve_Providers(CURSOR)\n (rc, warehouse_msg) = self.Warehouse_Providers(INPUT)\n self.end = datetime.now(utc)\n summary_msg = 'Processed ResourceProvider in {:.3f}/seconds: {}/updates, {}/deletes, {}/skipped'.format((self.end - self.start).total_seconds(), self.stats['ResourceProvider.Update'], self.stats['ResourceProvider.Delete'], self.stats['ResourceProvider.Skip'])\n self.logger.info(summary_msg)\n\n RESTAGS = self.Retrieve_Resource_Tags(CURSOR)\n RESASSC = self.Retrieve_Resource_Associations(CURSOR)\n \n self.start = datetime.now(utc)\n self.stats['Resource.Update'] = 0\n self.stats['Resource.Delete'] = 0\n self.stats['Resource.Skip'] = 0\n INPUT = self.Retrieve_Resources(CURSOR)\n (rc, warehouse_msg) = self.Warehouse_Resources(INPUT, RESTAGS, RESASSC)\n self.end = datetime.now(utc)\n summary_msg = 'Processed Resource in {:.3f}/seconds: {}/updates, {}/deletes, {}/skipped'.format((self.end - self.start).total_seconds(), self.stats['Resource.Update'], self.stats['Resource.Delete'], self.stats['Resource.Skip'])\n self.logger.info(summary_msg)\n\n self.start = datetime.now(utc)\n self.stats['Guide.Update'] = 0\n self.stats['Guide.Delete'] = 0\n self.stats['Guide.Skip'] = 0\n INPUT = self.Retrieve_Guides(CURSOR)\n (rc, warehouse_msg) = self.Warehouse_Guides(INPUT)\n self.end = datetime.now(utc)\n summary_msg = 'Processed Guide in {:.3f}/seconds: {}/updates, {}/deletes, {}/skipped'.format((self.end - self.start).total_seconds(), self.stats['Guide.Update'], self.stats['Guide.Delete'], self.stats['Guide.Skip'])\n self.logger.info(summary_msg)\n\n self.start = datetime.now(utc)\n self.stats['GuideResource.Update'] = 0\n self.stats['GuideResource.Delete'] = 0\n self.stats['GuideResource.Skip'] = 0\n INPUT = self.Retrieve_Guide_Resources(CURSOR)\n (rc, warehouse_msg) = self.Warehouse_Guide_Resources(INPUT)\n self.end = datetime.now(utc)\n summary_msg = 'Processed Guide Resource in {:.3f}/seconds: {}/updates, {}/deletes, {}/skipped'.format((self.end - self.start).total_seconds(), self.stats['GuideResource.Update'], self.stats['GuideResource.Delete'], self.stats['GuideResource.Skip'])\n self.logger.info(summary_msg)\n\n self.Disconnect_Source(CURSOR)\n \n pa.FinishActivity(rc, summary_msg)\n break\n\nif __name__ == '__main__':\n router = HandleLoad()\n myrouter = router.run()\n sys.exit(0)\n", "sub_path": "bin/route_uiuc_v2.py", "file_name": "route_uiuc_v2.py", "file_ext": "py", "file_size_in_byte": 29620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pytz.timezone", "line_number": 16, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.tzinfo", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 43, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 74, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 106, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 144, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 148, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 163, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 168, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 179, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 183, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 189, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 204, "usage_type": "call"}, {"api_name": "psycopg2.Error", "line_number": 218, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 228, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 230, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 232, "usage_type": "argument"}, {"api_name": "psycopg2.Error", "line_number": 242, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 258, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 271, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 292, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 310, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 318, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 320, "usage_type": "argument"}, {"api_name": "psycopg2.Error", "line_number": 330, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 345, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 345, "usage_type": "name"}, {"api_name": "django.utils.dateparse.parse_datetime", "line_number": 357, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 359, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 359, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 368, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 370, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 372, "usage_type": "argument"}, {"api_name": "django.db.DataError", "line_number": 440, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 440, "usage_type": "name"}, {"api_name": "django.db.DataError", "line_number": 451, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 451, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 458, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 458, "usage_type": "name"}, {"api_name": "django.db.DataError", "line_number": 476, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 476, "usage_type": "name"}, {"api_name": "django.db.DataError", "line_number": 487, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 487, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 494, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 494, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 499, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 501, "usage_type": "argument"}, {"api_name": "django.db.DataError", "line_number": 516, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 516, "usage_type": "name"}, {"api_name": "django.db.DataError", "line_number": 527, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 527, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 534, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 534, "usage_type": "name"}, {"api_name": "django.db.DataError", "line_number": 551, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 551, "usage_type": "name"}, {"api_name": "django.db.DataError", "line_number": 562, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 562, "usage_type": "name"}, {"api_name": "re.match", "line_number": 572, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 573, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 573, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 573, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 575, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 583, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 586, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 586, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 587, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 587, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 588, "usage_type": "call"}, {"api_name": "os.path", "line_number": 588, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 588, "usage_type": "call"}, {"api_name": "os.geteuid", "line_number": 588, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 588, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 591, "usage_type": "call"}, {"api_name": "os.path", "line_number": 591, "usage_type": "attribute"}, {"api_name": "processing_status.process.ProcessingActivity", "line_number": 596, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 601, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 601, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 607, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 607, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 614, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 614, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 620, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 620, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 624, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 624, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 630, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 630, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 634, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 634, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 640, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 640, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 652, "usage_type": "call"}]} +{"seq_id": "71857813", "text": "import pygame\nimport random\n\npygame.init()\n\n# configuracion de pantalla\nsize = (800, 600) #\nventana = pygame.display.set_mode(size)\nreloj = pygame.time.Clock()\n\n# configurar los colores\n# rgb , (0,255) Red=rojo, Green=verde blue=azul\nrojo = (240, 50, 50)\nverde = (50, 240, 50)\nceleste = (40, 200, 200)\nnegro = (0, 0, 0)\n\n\nfondo=pygame.image.load(\"img/BG00.png\")\nfondo2=pygame.image.load(\"img/BG2.png\")\nfondo3=pygame.image.load(\"img/BG3.png\")\n\n\nfondo=pygame.transform.scale(fondo,(800,600))\nletra=pygame.font.Font(None,40)\n\nventanaAbierta=True\ncontrolNiveles=1\nvelocidadJuego=5\nrecords = 0\n\npantallaInicio = True\npantallaNiveles=False\npantallaJuego = False\npantallaFinal = False\n\nwhile ventanaAbierta:\n\n\n\n # mostrar Pantalla de Inicio\n while pantallaInicio:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n if event.type ==pygame.KEYDOWN:\n if event.key ==pygame.K_SPACE:\n pantallaInicio=False\n pantallaNiveles=True\n\n # Codigo para poner un fondo de color.\n ventana.fill(verde)\n ventana.blit(fondo,(0,0))\n\n mensaje = letra.render(\"BIENVENIDOS ... \", 1, negro)\n ventana.blit(mensaje, (150, 300))\n\n mensaje=letra.render(\"Presiona Espacio para continuar\",1,negro)\n ventana.blit(mensaje,(100,400))\n\n # pygame.draw.polygon(ventana, verde,[(412,290),(612,490),(712,320)])\n pygame.display.update()\n reloj.tick(30)\n\n while pantallaNiveles:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n pantallaNiveles = False\n pantallaJuego = True\n\n # Codigo para poner un fondo de color.\n ventana.fill(verde)\n ventana.blit(fondo, (0, 0))\n\n mensaje = letra.render(\"NIVEL \" + str(controlNiveles), 1, negro)\n ventana.blit(mensaje, (150, 300))\n\n mensaje = letra.render(\"Presiona Espacio para continuar\", 1, negro)\n ventana.blit(mensaje, (100, 400))\n\n # pygame.draw.polygon(ventana, verde,[(412,290),(612,490),(712,320)])\n pygame.display.update()\n reloj.tick(30)\n\n\n\n\n #######################################################\n ###### CONFIGURACION DE VARIABLES DEL JUEGO ##########\n #######################################################\n # configurar los colores\n # rgb , (0,255) Red=rojo, Green=verde blue=azul\n rojo = (240, 50, 50)\n verde = (50, 240, 50)\n celeste = (40, 200, 200)\n negro = (0, 0, 0)\n blanco = (255, 255, 255)\n ventanaAbierta = True\n\n ##configuracion de Naves\n naves = []\n naveEnemiga = pygame.image.load(\"img/nave2.png\")\n nave = pygame.image.load(\"img/nave.png\")\n fondo = pygame.image.load(\"img/fondoEspacio.jpg\")\n xNave = 350\n yNave = 500\n xNave2 = []\n yNave2 = []\n velocidad = []\n velocidadY = []\n fuente = pygame.font.Font(None, 40)\n\n ####OBTENER EL RECORD MAS ALTO#######\n archivo = open(\"records.txt\", \"r\")\n recordAnterior = int(archivo.readline())\n archivo.close()\n ####################################\n\n ######VARIABLES PARA CONTROL DEL DISPARO DE NOSOTROS######\n laser = pygame.image.load(\"img/laser.png\")\n disparoActivo = False\n xDisparo = 0\n yDisparo = 0\n laserVelocidad = 30\n naveEliminada = None\n tiempoFuego = 0\n contadorEliminadas = 0\n ############################################\n ######VARIABLES PARA CONTROL DE LOS ENEMIGOS######\n ############################################\n velocidadJuego=velocidadJuego+5\n for i in range(0, 5):\n naves.append(naveEnemiga)\n xNave2.append(100 * i)\n velocidad.append(velocidadJuego)\n yNave2.append(i * 20)\n velocidadY.append(velocidadJuego)\n\n ###### PARA CARGAR AUDIO#####\n musicaFondo = pygame.mixer_music.load(\"Centroid.mp3\")\n pygame.mixer_music.play(-1) # PARA REPRODUCIR\n\n ############################################\n ######VARIABLES PARA DIPARO DE LOS ENEMIGOS######\n ############################################\n xDisparoE=[]\n yDisparoE=[]\n balaActiva=[]\n velDisparoE=5 +velocidadJuego\n for i in range(0, len(naves)):\n xDisparoE.append(xNave2[i])\n yDisparoE.append(yNave2[i])\n rand=random.randrange(0,30)\n if rand==1:\n balaActiva.append(True)\n else:\n balaActiva.append(False)\n\n #######################################################\n ############### PANTALLA DEL JUEGO ####################\n #######################################################\n while pantallaJuego:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n\n ##eventos del teclado- presiona una tecla\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT: # si la tecla es izquierda.\n xNave = xNave - 20\n if event.key == pygame.K_RIGHT: # si la tela es Derecha\n xNave = xNave + 20\n if event.key == pygame.K_SPACE:\n ##aqui vamos a programar el disparo\n if disparoActivo == False:\n disparoActivo = True\n efectoDisparo = pygame.mixer_music.load(\"disparo.mp3\")\n pygame.mixer_music.play()\n xDisparo = xNave + 45 ##colocar el disparo al centro de la nave\n yDisparo = yNave\n elif event.key==pygame.K_r:\n pantallaJuego=False\n pantallaNiveles=True\n\n ############### DESPLEGAR EL CONTENIDO###############\n ##################################################\n\n # Codigo para poner un fondo de color.\n ventana.blit(fondo, (0, 0))\n ventana.blit(nave, (xNave, yNave))\n\n if naveEliminada != None:\n if tiempoFuego >= 10:\n naves.pop(naveEliminada)\n xNave2.pop(naveEliminada)\n yNave2.pop(naveEliminada)\n naveEliminada = None\n tiempoFuego = 0\n #########PARA TERMINAR EL JUEGO####\n if contadorEliminadas == 5:\n pantallaJuego = False\n pantallaNiveles=True\n controlNiveles=controlNiveles+1\n else:\n tiempoFuego = tiempoFuego + 1\n\n # imprimir las naves enemigas\n for i in range(0, len(naves)):\n ventana.blit(naves[i], (xNave2[i], yNave2[i]))\n\n ###IMPRIMIR LOS RECORDS\n mensaje = fuente.render(\"Records: \" + str(records) + \" pts. \", 1, verde)\n ventana.blit(mensaje, (0, 0))\n\n mensaje = fuente.render(\"Records Anterior: \" + str(recordAnterior) + \" pts. \", 1, blanco)\n ventana.blit(mensaje, (450, 0))\n\n ####mostrar el disparo\n if disparoActivo == True:\n ventana.blit(laser, (xDisparo, yDisparo))\n yDisparo = yDisparo - laserVelocidad\n if yDisparo <= 0:\n disparoActivo = False\n\n ############COLISION CON OTRAS NAVES##########\n for i in range(0, len(naves)):\n if yDisparo <= yNave2[i] and yDisparo >= yNave2[i] - 100 and xDisparo >= xNave2[i] and xDisparo <= \\\n xNave2[i] + 100:\n naveExplosion = pygame.image.load(\"img/nave2Fuego.png\")\n naveEliminada = i\n naves[i] = naveExplosion\n contadorEliminadas = contadorEliminadas + 1\n disparoActivo = False\n records = records + 10\n\n ##################################################\n ############ DISPARO DE LOS ENEMIGOS##############\n ##################################################\n for i in range(0,len(naves)):\n rand=random.randrange(0,20)\n if balaActiva[i]:\n yDisparoE[i]=yDisparoE[i]+velDisparoE\n pygame.draw.circle(ventana, rojo, (xDisparoE[i], yDisparoE[i]), 5)\n\n if yDisparoE[i]>=yNave and yDisparoE[i]=xNave and xDisparoE[i]<=xNave+100:\n pantallaFinal=True\n pantallaJuego=False\n\n if yDisparoE[i]>=600:\n balaActiva[i]=False\n elif rand==1:\n xDisparoE[i]= xNave2[i]\n yDisparoE[i]=yNave2[i]\n balaActiva[i]=True\n\n\n\n\n #####CONTROL DE LAS POSICIONES Y VELOCIDADES######\n for i in range(0, len(naves)):\n xNave2[i] = xNave2[i] + velocidad[i]\n yNave2[i] = yNave2[i] + velocidadY[i]\n if xNave2[i] <= 0:\n velocidad[i] = velocidad[i] * -1\n if xNave2[i] >= 750:\n velocidad[i] = velocidad[i] * -1\n if yNave2[i] <= 0:\n velocidadY[i] = velocidadY[i] * -1\n if yNave2[i] >= 400:\n velocidadY[i] = velocidadY[i] * -1\n\n # pygame.draw.polygon(ventana, verde,[(412,290),(612,490),(712,320)])\n pygame.display.update()\n reloj.tick(20)\n\n\n if pantallaFinal:\n if records > recordAnterior:\n # cambiar el Record\n archivo = open(\"records.txt\", \"w\")\n archivo.write(str(records))\n archivo.close()\n\n # mostrar Pantalla de RECORDS\n while pantallaFinal:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n if event.type ==pygame.KEYDOWN:\n if event.key ==pygame.K_SPACE:\n pantallaFinal=False\n pantallaInicio=True\n velocidadJuego=5\n records=0\n controlNiveles=1\n velDisparo=5\n\n # Codigo para poner un fondo de color.\n ventana.fill(verde)\n ventana.blit(fondo3,(0,0))\n mensaje=letra.render(\"RECORDS\",1,negro)\n ventana.blit(mensaje,(350,50))\n mensaje = letra.render(\"PUNTAJE MÁS ALTO: \"+str(recordAnterior), 1, blanco)\n ventana.blit(mensaje, (100, 150))\n mensaje = letra.render(\"PUNTAJE OBTENIDO: \"+ str(records), 1, verde)\n ventana.blit(mensaje, (100, 300))\n\n\n\n # pygame.draw.polygon(ventana, verde,[(412,290),(612,490),(712,320)])\n pygame.display.update()\n reloj.tick(30)", "sub_path": "FundamentosProgramacion/Juego/MiPrimerJuego_.py", "file_name": "MiPrimerJuego_.py", "file_ext": "py", "file_size_in_byte": 10639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 105, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.load", "line_number": 143, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.play", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 144, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 166, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 168, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 172, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 174, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.load", "line_number": 180, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.play", "line_number": 181, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pygame.K_r", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 232, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 232, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 243, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 246, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 276, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 276, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 289, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 289, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 290, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 291, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 292, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 314, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 314, "usage_type": "attribute"}]} +{"seq_id": "139377790", "text": "# -*- coding: utf-8 -*-\nimport copy\nimport logging\nimport operator\n\nfrom six import iteritems\n\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.core.urlresolvers import reverse\nfrom django.db import transaction\nfrom django.db.models import Q\nfrom django.http import Http404\nfrom django.utils.translation import ugettext as _\nfrom wagtail.wagtailadmin.edit_handlers import FieldPanel, \\\n MultiFieldPanel, FieldRowPanel\nfrom wagtail.wagtailadmin.edit_handlers import StreamFieldPanel\nfrom wagtail.wagtailcore.models import Page, Site\nfrom wagtail.wagtailcore.url_routing import RouteResult\nfrom wagtail.wagtailimages.edit_handlers import ImageChooserPanel\nfrom wagtail.wagtailsearch.index import SearchField\nfrom wagtail.wagtailsnippets.views.snippets import get_snippet_edit_handler, \\\n SNIPPET_EDIT_HANDLERS\nfrom wagtail_modeltranslation.translator import translator, NotRegistered\nfrom .utils import build_localized_fieldname\n\ntry:\n from wagtail.wagtailadmin.views.pages import get_page_edit_handler, \\\n PAGE_EDIT_HANDLERS\nexcept ImportError:\n pass\n\ntry:\n from functools import reduce\nexcept ImportError:\n pass\n\nlogger = logging.getLogger('wagtail.core')\n\n\nclass WagtailTranslator(object):\n _patched_models = []\n\n def __init__(self, model):\n\n # Check if this class was already patched\n if model in WagtailTranslator._patched_models:\n return\n\n WagtailTranslator._base_model = model\n WagtailTranslator._required_fields = {}\n\n # CONSTRUCT TEMPORARY EDIT HANDLER\n if issubclass(model, Page):\n if hasattr(model, 'get_edit_handler'):\n edit_handler_class = model.get_edit_handler()\n else:\n edit_handler_class = get_page_edit_handler(model)\n else:\n edit_handler_class = get_snippet_edit_handler(model)\n WagtailTranslator._base_model_form = edit_handler_class.get_form_class(model)\n\n defined_tabs = WagtailTranslator._fetch_defined_tabs(model)\n\n for tab_name, tab in defined_tabs:\n patched_tab = []\n\n for panel in tab:\n trtab = WagtailTranslator._patch_panel(model, panel)\n\n if trtab:\n for x in trtab:\n patched_tab.append(x)\n\n setattr(model, tab_name, patched_tab)\n\n # DELETE TEMPORARY EDIT HANDLER IN ORDER TO LET WAGTAIL RECONSTRUCT\n # NEW EDIT HANDLER BASED ON NEW TRANSLATION PANELS\n if issubclass(model, Page):\n if hasattr(model, 'get_edit_handler'):\n model.get_edit_handler.cache_clear()\n edit_handler_class = model.get_edit_handler()\n else:\n if model in PAGE_EDIT_HANDLERS:\n del PAGE_EDIT_HANDLERS[model]\n edit_handler_class = get_page_edit_handler(model)\n else:\n if model in SNIPPET_EDIT_HANDLERS:\n del SNIPPET_EDIT_HANDLERS[model]\n edit_handler_class = get_snippet_edit_handler(model)\n\n # Set the required of the translated fields that were required on the original field\n form = edit_handler_class.get_form_class(model)\n for fname, f in form.base_fields.items():\n if fname in WagtailTranslator._required_fields[model]:\n f.required = True\n\n # Do the same to the formsets\n for related_name, formset in iteritems(form.formsets):\n if (formset.model in WagtailTranslator._required_fields and\n WagtailTranslator._required_fields[formset.model]):\n for fname, f in formset.form.base_fields.items():\n if fname in WagtailTranslator._required_fields[formset.model]:\n f.required = True\n\n # Overide page methods\n if issubclass(model, Page):\n model.move = _new_move\n model.set_url_path = _new_set_url_path\n model.route = _new_route\n model.get_site_root_paths = _new_get_site_root_paths\n model.relative_url = _new_relative_url\n model.url = _new_url\n _patch_clean(model)\n _patch_elasticsearch_fields(model)\n\n WagtailTranslator._patched_models.append(model)\n\n @staticmethod\n def _fetch_defined_tabs(defined_class):\n \"\"\"\n Fetch tabs defined by user in models.py\n \"\"\"\n tabs = ()\n\n # If user has defined panels dict on models.py\n if hasattr(defined_class, 'panels'):\n # TEST !!!\n tabs += (('panels', copy.deepcopy(defined_class.panels)),)\n # Check for common tabs\n else:\n if hasattr(defined_class, 'content_panels'):\n tabs += (('content_panels', copy.deepcopy(defined_class.content_panels)),)\n if hasattr(defined_class, 'promote_panels'):\n tabs += (('promote_panels', copy.deepcopy(defined_class.promote_panels)),)\n if hasattr(defined_class, 'settings_panels'):\n tabs += (('settings_panels', copy.deepcopy(defined_class.settings_panels)),)\n\n return tabs\n\n @staticmethod\n def _patch_panel(model, panel):\n \"\"\"\n Generic panel patching function\n \"\"\"\n\n WagtailTranslator._current_model = model\n WagtailTranslator._translation_options = translator.get_options_for_model(model)\n if model not in WagtailTranslator._required_fields:\n WagtailTranslator._required_fields[model] = []\n\n if panel.__class__.__name__ == 'FieldPanel':\n trpanels = WagtailTranslator._patch_fieldpanel(panel)\n elif panel.__class__.__name__ == 'ImageChooserPanel':\n trpanels = WagtailTranslator._patch_imagechooser(panel)\n elif panel.__class__.__name__ == 'MultiFieldPanel':\n trpanels = [WagtailTranslator._patch_multifieldpanel(panel)]\n elif panel.__class__.__name__ == 'InlinePanel':\n WagtailTranslator._patch_inlinepanel(model, panel)\n trpanels = [panel]\n elif panel.__class__.__name__ == 'StreamFieldPanel':\n trpanels = WagtailTranslator._patch_streamfieldpanel(panel)\n elif panel.__class__.__name__ == 'FieldRowPanel':\n trpanels = [WagtailTranslator._patch_fieldrowpanel(panel)]\n else:\n trpanels = [panel]\n\n return trpanels\n\n @classmethod\n def _is_orig_required(cls, field_name):\n \"\"\"\n check if original field is required\n \"\"\"\n if cls._base_model == cls._current_model:\n for fname, f in cls._base_model_form.base_fields.items():\n if fname == field_name:\n return f.required\n else:\n for related_name, formset in iteritems(cls._base_model_form.formsets):\n if formset.model == cls._current_model:\n for fname, f in formset.form.base_fields.items():\n if fname == field_name:\n return f.required\n break\n\n return False\n\n # FieldPanel\n ####################################\n @classmethod\n def _patch_fieldpanel(cls, fieldpanel):\n \"\"\"\n Patch FieldPanels and return one per available language\n \"\"\"\n\n tr_fields = cls._translation_options.fields\n\n translated_fieldpanels = []\n if fieldpanel.field_name in tr_fields:\n for lang in settings.LANGUAGES:\n classes = fieldpanel.classname\n\n if cls._is_orig_required(fieldpanel.field_name) and (lang[0] == settings.LANGUAGE_CODE):\n if (build_localized_fieldname(fieldpanel.field_name, lang[0]) not in\n cls._required_fields[cls._current_model]):\n cls._required_fields[cls._current_model].append(\n build_localized_fieldname(fieldpanel.field_name, lang[0]))\n\n translated_field_name = build_localized_fieldname(fieldpanel.field_name, lang[0])\n translated_fieldpanels.append(\n FieldPanel(translated_field_name, classname=classes, widget=fieldpanel.widget)\n )\n\n else:\n return [fieldpanel]\n\n return translated_fieldpanels\n\n # ImageChooserPanel\n ####################################\n @classmethod\n def _patch_imagechooser(cls, imagechooser):\n \"\"\"\n Patch ImageChooserPanels and return one per available language\n \"\"\"\n tr_fields = cls._translation_options.fields\n\n translated_imagechoosers = []\n if imagechooser.field_name in tr_fields:\n for lang in settings.LANGUAGES:\n\n if cls._is_orig_required(imagechooser.field_name) and (lang[0] == settings.LANGUAGE_CODE):\n if (build_localized_fieldname(imagechooser.field_name, lang[0]) not in\n cls._required_fields[cls._current_model]):\n cls._required_fields[cls._current_model].append(\n build_localized_fieldname(imagechooser.field_name, lang[0])\n )\n\n translated_field_name = build_localized_fieldname(imagechooser.field_name, lang[0])\n translated_imagechoosers.append(ImageChooserPanel(translated_field_name))\n else:\n return [imagechooser]\n\n return translated_imagechoosers\n\n # StreamFieldPanel\n ####################################\n @classmethod\n def _patch_streamfieldpanel(cls, fieldpanel):\n \"\"\"\n Patch StreamFieldPanels and return one per available language\n \"\"\"\n tr_fields = cls._translation_options.fields\n\n translated_fieldpanels = []\n if fieldpanel.field_name in tr_fields:\n for lang in settings.LANGUAGES:\n if cls._is_orig_required(fieldpanel.field_name) and (lang[0] == settings.LANGUAGE_CODE):\n if (build_localized_fieldname(fieldpanel.field_name, lang[0]) not in\n cls._required_fields[cls._current_model]):\n cls._required_fields[cls._current_model].append(\n build_localized_fieldname(fieldpanel.field_name, lang[0])\n )\n\n translated_field_name = build_localized_fieldname(fieldpanel.field_name, lang[0])\n translated_fieldpanels.append(StreamFieldPanel(translated_field_name))\n else:\n return [fieldpanel]\n\n return translated_fieldpanels\n\n @classmethod\n def _patch_multifieldpanel(cls, mfpanel):\n \"\"\"\n Patch MultiFieldPanel\n \"\"\"\n patched_fields = []\n\n for panel in mfpanel.children:\n if panel.__class__.__name__ == 'FieldPanel':\n for item in cls._patch_fieldpanel(panel):\n patched_fields.append(item)\n elif panel.__class__.__name__ == 'ImageChooserPanel':\n for item in cls._patch_imagechooser(panel):\n patched_fields.append(item)\n elif panel.__class__.__name__ == 'FieldRowPanel':\n patched_fields.append(cls._patch_fieldrowpanel(panel))\n else:\n patched_fields.append(panel)\n\n return MultiFieldPanel(patched_fields, classname=mfpanel.classname, heading=mfpanel.heading)\n\n @classmethod\n def _patch_fieldrowpanel(cls, frpanel):\n \"\"\"\n Patch FieldRowPanel\n \"\"\"\n patched_fields = []\n\n for panel in frpanel.children:\n if panel.__class__.__name__ == 'FieldPanel':\n for item in cls._patch_fieldpanel(panel):\n patched_fields.append(item)\n else:\n patched_fields.append(panel)\n\n return FieldRowPanel(\n patched_fields,\n classname=frpanel.classname)\n\n @classmethod\n def _patch_inlinepanel(cls, model, panel):\n relation = getattr(model, panel.relation_name)\n\n related_fieldname = 'related'\n\n try:\n inline_panels = getattr(getattr(relation, related_fieldname).related_model, 'panels', [])\n except AttributeError:\n related_fieldname = 'rel'\n inline_panels = getattr(getattr(relation, related_fieldname).related_model, 'panels', [])\n\n try:\n related_model = getattr(getattr(model, panel.relation_name), related_fieldname).related_model\n WagtailTranslator._translation_options = translator.get_options_for_model(related_model)\n except NotRegistered:\n return None\n translated_inline = []\n for inline_panel in inline_panels:\n for item in cls._patch_panel(related_model, inline_panel):\n translated_inline.append(item)\n\n related_model.panels = translated_inline\n\n\n# Overridden Page methods adapted to the translated fields\n\n@transaction.atomic # only commit when all descendants are properly updated\ndef _new_move(self, target, pos=None):\n \"\"\"\n Extension to the treebeard 'move' method to ensure that url_path is updated too.\n \"\"\"\n old_url_path = Page.objects.get(id=self.id).url_path\n super(Page, self).move(target, pos=pos)\n # treebeard's move method doesn't actually update the in-memory instance, so we need to work\n # with a freshly loaded one now\n # added .specific to use the most specific class so that url_paths are updated to all languages\n new_self = Page.objects.get(id=self.id).specific\n new_url_path = new_self.set_url_path(new_self.get_parent())\n new_self.save()\n new_self._update_descendant_url_paths(old_url_path, new_url_path)\n\n # Log\n logger.info(\"Page moved: \\\"%s\\\" id=%d path=%s\", self.title, self.id, new_url_path)\n\n\ndef _new_set_url_path(self, parent):\n \"\"\"\n This method override populates url_path for each specified language.\n This way we can get different urls for each language, defined\n by page slug.\n \"\"\"\n\n for lang in settings.LANGUAGES:\n if parent:\n parent = parent.specific\n tr_slug = getattr(self, 'slug_' + lang[0]) if hasattr(\n self, 'slug_' + lang[0]) else getattr(self, 'slug')\n\n if not tr_slug:\n tr_slug = getattr(self, 'slug_' + settings.LANGUAGE_CODE) if \\\n hasattr(self, 'slug_' + settings.LANGUAGE_CODE) else \\\n getattr(self, 'slug')\n\n if hasattr(parent, 'url_path_' + lang[0]) and getattr(parent, 'url_path_' + lang[0]) is not None:\n parent_url_path = getattr(parent, 'url_path_' + lang[0])\n else:\n parent_url_path = getattr(parent, 'url_path')\n\n if hasattr(self, 'url_path_' + lang[0]):\n setattr(self, 'url_path_' + lang[0], parent_url_path + tr_slug + '/')\n else:\n setattr(self, 'url_path', parent_url_path + tr_slug + '/')\n else:\n # a page without a parent is the tree root,\n # which always has a url_path of '/'\n if hasattr(self, 'url_path_' + lang[0]):\n setattr(self, 'url_path_' + lang[0], '/')\n else:\n setattr(self, 'url_path', '/')\n\n # update url_path for children pages\n for child in self.get_children():\n child.set_url_path(self.specific)\n\n return self.url_path\n\n\ndef _new_route(self, request, path_components):\n \"\"\"\n Rewrite route method in order to handle languages fallbacks\n \"\"\"\n if path_components:\n # request is for a child of this page\n child_slug = path_components[0]\n remaining_components = path_components[1:]\n\n # try:\n # q = Q()\n # for lang in settings.LANGUAGES:\n # tr_field_name = 'slug_%s' % (lang[0])\n # condition = {tr_field_name: child_slug}\n # q |= Q(**condition)\n # subpage = self.get_children().get(q)\n # except Page.DoesNotExist:\n # raise Http404\n\n # return subpage.specific.route(request, remaining_components)\n\n subpages = self.get_children()\n for page in subpages:\n if page.specific.slug == child_slug:\n return page.specific.route(request, remaining_components)\n raise Http404\n\n else:\n # request is for this very page\n if self.live:\n return RouteResult(self)\n else:\n raise Http404\n\n\n@staticmethod\ndef _new_get_site_root_paths():\n \"\"\"\n Return a list of (root_path, root_url) tuples, most specific path first -\n used to translate url_paths into actual URLs with hostnames\n\n Same method as Site.get_site_root_paths() but without cache\n\n TODO: remake this method with cache and think of his integration in\n Site.get_site_root_paths()\n \"\"\"\n result = [\n (site.id, site.root_page.specific.url_path, site.root_url)\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\n ]\n\n return result\n\n\ndef _new_relative_url(self, current_site):\n \"\"\"\n Return the 'most appropriate' URL for this page taking into account the site we're currently on;\n a local URL if the site matches, or a fully qualified one otherwise.\n Return None if the page is not routable.\n\n Override for using custom get_site_root_paths() instead of\n Site.get_site_root_paths()\n \"\"\"\n for (id, root_path, root_url) in self.get_site_root_paths():\n if self.url_path.startswith(root_path):\n return ('' if current_site.id == id else root_url) + reverse('wagtail_serve',\n args=(self.url_path[len(root_path):],))\n\n\n@property\ndef _new_url(self):\n \"\"\"\n Return the 'most appropriate' URL for referring to this page from the pages we serve,\n within the Wagtail backend and actual website templates;\n this is the local URL (starting with '/') if we're only running a single site\n (i.e. we know that whatever the current page is being served from, this link will be on the\n same domain), and the full URL (with domain) if not.\n Return None if the page is not routable.\n\n Override for using custom get_site_root_paths() instead of\n Site.get_site_root_paths()\n \"\"\"\n root_paths = self.get_site_root_paths()\n\n for (id, root_path, root_url) in root_paths:\n if self.url_path.startswith(root_path):\n return ('' if len(root_paths) == 1 else root_url) + reverse(\n 'wagtail_serve', args=(self.url_path[len(root_path):],))\n\n\ndef _validate_slugs(page):\n \"\"\"\n Determine whether the given slug is available for use on a child page of\n parent_page.\n \"\"\"\n parent_page = page.get_parent()\n\n if parent_page is None:\n # the root page's slug can be whatever it likes...\n return {}\n\n allowed_sibblings = parent_page.specific.allowed_subpage_models()\n siblings = parent_page.get_children().exclude(pk=page.pk)\n\n errors = {}\n\n for lang in settings.LANGUAGES:\n current_slug = 'slug_' + lang[0]\n query_list = []\n\n for model in allowed_sibblings:\n slug = getattr(page, current_slug, '') or ''\n if len(slug) and model is not Page:\n kwargs = {'{0}__{1}'.format(model._meta.model_name, current_slug): slug}\n query_list.append(Q(**kwargs))\n\n if query_list and siblings.filter(reduce(operator.or_, query_list)).exists():\n errors[current_slug] = _(u'Slug already in use')\n\n return errors\n\n\ndef _patch_clean(model):\n old_clean = model.clean\n\n # Call the original clean method to avoid losing validations\n def clean(self):\n old_clean(self)\n errors = _validate_slugs(self)\n\n if errors:\n raise ValidationError(errors)\n\n model.clean = clean\n\n\ndef _patch_elasticsearch_fields(model):\n for field in model.search_fields:\n # Check if the field is a SearchField and if it is one of the fields registered for translation\n if field.__class__ is SearchField and field.field_name in WagtailTranslator._translation_options.fields:\n # If it is we create a clone of the original SearchField to keep all the defined options\n # and replace its name by the translated one\n for lang in settings.LANGUAGES:\n translated_field = copy.deepcopy(field)\n translated_field.field_name = build_localized_fieldname(field.field_name, lang[0])\n model.search_fields = model.search_fields + (translated_field,)\n", "sub_path": "wagtail_modeltranslation/patch_wagtailadmin.py", "file_name": "patch_wagtailadmin.py", "file_ext": "py", "file_size_in_byte": 20610, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 54, "usage_type": "argument"}, {"api_name": "wagtail.wagtailadmin.views.pages.get_page_edit_handler", "line_number": 58, "usage_type": "call"}, {"api_name": "wagtail.wagtailsnippets.views.snippets.get_snippet_edit_handler", "line_number": 60, "usage_type": "call"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 79, "usage_type": "argument"}, {"api_name": "wagtail.wagtailadmin.views.pages.PAGE_EDIT_HANDLERS", "line_number": 84, "usage_type": "name"}, {"api_name": "wagtail.wagtailadmin.views.pages.PAGE_EDIT_HANDLERS", "line_number": 85, "usage_type": "name"}, {"api_name": "wagtail.wagtailadmin.views.pages.get_page_edit_handler", "line_number": 86, "usage_type": "call"}, {"api_name": "wagtail.wagtailsnippets.views.snippets.SNIPPET_EDIT_HANDLERS", "line_number": 88, "usage_type": "name"}, {"api_name": "wagtail.wagtailsnippets.views.snippets.SNIPPET_EDIT_HANDLERS", "line_number": 89, "usage_type": "name"}, {"api_name": "wagtail.wagtailsnippets.views.snippets.get_snippet_edit_handler", "line_number": 90, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 99, "usage_type": "call"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 107, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 129, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 133, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 135, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 137, "usage_type": "call"}, {"api_name": "wagtail_modeltranslation.translator.translator.get_options_for_model", "line_number": 148, "usage_type": "call"}, {"api_name": "wagtail_modeltranslation.translator.translator", "line_number": 148, "usage_type": "name"}, {"api_name": "six.iteritems", "line_number": 180, "usage_type": "call"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 201, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 201, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 204, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 204, "usage_type": "name"}, {"api_name": "utils.build_localized_fieldname", "line_number": 205, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 208, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 210, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 212, "usage_type": "call"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 231, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 231, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 233, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 233, "usage_type": "name"}, {"api_name": "utils.build_localized_fieldname", "line_number": 234, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 237, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 240, "usage_type": "call"}, {"api_name": "wagtail.wagtailimages.edit_handlers.ImageChooserPanel", "line_number": 241, "usage_type": "call"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 258, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 258, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 259, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 259, "usage_type": "name"}, {"api_name": "utils.build_localized_fieldname", "line_number": 260, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 263, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 266, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.StreamFieldPanel", "line_number": 267, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.MultiFieldPanel", "line_number": 292, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldRowPanel", "line_number": 308, "usage_type": "call"}, {"api_name": "wagtail_modeltranslation.translator.translator.get_options_for_model", "line_number": 326, "usage_type": "call"}, {"api_name": "wagtail_modeltranslation.translator.translator", "line_number": 326, "usage_type": "name"}, {"api_name": "wagtail_modeltranslation.translator.NotRegistered", "line_number": 327, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.models.Page.objects.get", "line_number": 344, "usage_type": "call"}, {"api_name": "wagtail.wagtailcore.models.Page.objects", "line_number": 344, "usage_type": "attribute"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 344, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 345, "usage_type": "argument"}, {"api_name": "wagtail.wagtailcore.models.Page.objects.get", "line_number": 349, "usage_type": "call"}, {"api_name": "wagtail.wagtailcore.models.Page.objects", "line_number": 349, "usage_type": "attribute"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 349, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 339, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 339, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 365, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 365, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 373, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 373, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 372, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 372, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 425, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.url_routing.RouteResult", "line_number": 430, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 432, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.models.Site.objects.select_related", "line_number": 448, "usage_type": "call"}, {"api_name": "wagtail.wagtailcore.models.Site.objects", "line_number": 448, "usage_type": "attribute"}, {"api_name": "wagtail.wagtailcore.models.Site", "line_number": 448, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 465, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 486, "usage_type": "call"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 506, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 506, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 512, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 514, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 516, "usage_type": "call"}, {"api_name": "operator.or_", "line_number": 516, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext", "line_number": 517, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 531, "usage_type": "call"}, {"api_name": "wagtail.wagtailsearch.index.SearchField", "line_number": 539, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 542, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 542, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 543, "usage_type": "call"}, {"api_name": "utils.build_localized_fieldname", "line_number": 544, "usage_type": "call"}]} +{"seq_id": "277397496", "text": "import glob\r\nimport openpyxl\r\n#将所有人的周报xlsx放入固定文件夹，xls格式不可用\r\n#获得所有周报的一个名字列表\r\nlistOfReport = glob.glob(r'D:\\documents\\testop\\*.xlsx')\r\n#打开汇总excel\r\nwbAll = openpyxl.load_workbook(r'D:\\documents\\huizong.xlsx')\r\n#当前汇总表的活动工作簿---sheetAll\r\nsheetAll = wbAll.get_active_sheet()\r\n\r\n#获得所有人员名字的列表\r\nNameList =[]\r\nfor cellName in sheetAll['E6': 'E18']:\r\n for rowOfCellObj in cellName:\r\n NameList.append(rowOfCellObj.value)\r\n\r\n#迭代打开文件夹的每个xlsx\t\r\nfor reportname in listOfReport:\r\n print(reportname)\r\n wbPerson = openpyxl.load_workbook(reportname)\r\n sheetPerson = wbPerson.get_active_sheet()\r\n\t\r\n\t#获得每个xlsx的个人名字（名字在D6单元格）\r\n singleName = sheetPerson['D6'].value\r\n for rowNum in range(6,19):\r\n\t\t#sheetAll汇总表的第一个名字在E6（第6行，第5列）\r\n\t\t#对比singleName和汇总表中名字\r\n everyName = sheetAll.cell(row = rowNum, column = 5).value\r\n if singleName == everyName:\r\n\t\t\t#汇总表第6列到第9列的单元格4个\r\n for colNum in range(5,9):\r\n\t\t\t\t#从第6行开始到第19行\r\n sheetAll.cell(row = rowNum , column = colNum +1).value = sheetPerson.cell(row = 6, column = colNum).value\r\nwbAll.save('D:\\\\documents\\\\huizong.xlsx')\r\n\r\n \r\n\r\n\r\n\r\n\r\n", "sub_path": "weeklyReportGather.py", "file_name": "weeklyReportGather.py", "file_ext": "py", "file_size_in_byte": 1386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "glob.glob", "line_number": 5, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 7, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "494205217", "text": "#%%\r\n# Warning\r\nimport warnings\r\n# Ignore warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n#%%\r\n# Matplotlib\r\nimport matplotlib.pyplot as plt\r\n\r\n# Set matplotlib sizes\r\nplt.rc('font', size=20)\r\nplt.rc('axes', titlesize=20)\r\nplt.rc('axes', labelsize=20)\r\nplt.rc('xtick', labelsize=20)\r\nplt.rc('ytick', labelsize=20)\r\nplt.rc('legend', fontsize=20)\r\nplt.rc('figure', titlesize=20)\r\n\r\n#%%p\r\n# TensorFlow\r\n# The magic below allows us to use tensorflow version 2.x\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\n\r\n#%%\r\n# Data Preprocessing\r\n# Create data directory\r\nimport os\r\n\r\nabspath = \"/home/ubuntu/DogBreed\"\r\n\r\n# Make directory\r\ndirectory = os.path.dirname(abspath + 'data/')\r\nif not os.path.exists(directory):\r\n os.makedirs(directory)\r\n\r\n#%%\r\nimport tensorflow_datasets as tfds\r\n\r\n# Get the name of the data\r\ndata_name = 'stanford_dogs'\r\n\r\n# Load data\r\ndata, info = tfds.load(name=data_name, data_dir=abspath + 'data/', as_supervised=True, with_info=True)\r\n\r\n# Get the name of the target\r\ntarget = 'label'\r\n\r\n#%%\r\n# Get the classes\r\nclasses = info.features['label'].names\r\n\r\n# Print the classes\r\nprint(classes)\r\n\r\n# Get the number of classes\r\nn_classes = info.features['label'].num_classes\r\n\r\n# Print the number of classes\r\nprint(info.features['label'].num_classes)\r\n\r\n# Get the training, validation and testing data\r\n\r\n#%%\r\n# Set the training, validation and testing split\r\nsplit_train, split_valid, split_test = 'train[:70%]', 'train[70%:]', 'test'\r\n\r\n# Get the training data\r\ndata_train = tfds.load(name=data_name, split=split_train, data_dir=abspath + 'data/', as_supervised=True)\r\n\r\n# Get the validation data\r\ndata_valid = tfds.load(name=data_name, split=split_valid, data_dir=abspath + 'data/', as_supervised=True)\r\n\r\n# Get the testing data\r\ndata_test = tfds.load(name=data_name, split=split_test, data_dir=abspath + 'data/', as_supervised=True)\r\n\r\n#%%\r\n# Resize the data for the pretrained model\r\n# Set the default input size for the pretrained model\r\ninput_size = [299, 299]\r\n\r\n#%%\r\ndef resize(data, label):\r\n \"\"\"\r\n Resize the data into the default input size for the pretrained model\r\n Parameters\r\n ----------\r\n data: the data\r\n label: the label\r\n \r\n Returns\r\n ----------\r\n The resized data\r\n \"\"\"\r\n\r\n # Resize the data into the default input size for the pretrained model\r\n data_resized = tf.image.resize(data, input_size)\r\n\r\n return data_resized, label\r\n\r\n#%%\r\n# Resize the training data\r\ndata_train = data_train.map(resize)\r\n\r\n# Resize the validation data\r\ndata_valid = data_valid.map(resize)\r\n\r\n# Resize the testing data\r\ndata_test = data_test.map(resize)\r\n\r\n#%%\r\n# Preprocess the data using the pretrained model\r\n\r\n# Set the preprocess_input of the pretrained model\r\npreprocess_input = keras.applications.xception.preprocess_input\r\n\r\n#%%\r\ndef preprocess(data, label):\r\n \"\"\"\r\n Preprocess the data using the pretrained model\r\n Parameters\r\n ----------\r\n data: the data\r\n label: the label\r\n \r\n Returns\r\n ----------\r\n The preprocessed data\r\n \"\"\"\r\n\r\n # Preprocess the data using the pretrained model\r\n data_preprocessed = preprocess_input(data)\r\n\r\n return data_preprocessed, label\r\n\r\n#%%\r\n# Preprocess the training data\r\ndata_train = data_train.map(preprocess)\r\n\r\n# Preprocess the validation data\r\ndata_valid = data_valid.map(preprocess)\r\n\r\n# Preprocess the testing data\r\ndata_test = data_test.map(preprocess)\r\n\r\n#%%\r\n# Shuffle, batch, and prefetch\r\n\r\n# Shuffling the training data\r\ndata_train = data_train.shuffle(buffer_size=1000, seed=42)\r\n\r\n# Set the batch size\r\nbatch_size = 16\r\n\r\n# Batch and prefetch the training data\r\ndata_train = data_train.batch(batch_size).prefetch(1)\r\n\r\n# Batch and prefetch the validation data\r\ndata_valid = data_valid.batch(batch_size).prefetch(1)\r\n\r\n# Batch and prefetch the testing data\r\ndata_test = data_test.batch(batch_size).prefetch(1)\r\n\r\n#%%\r\n# Training\r\n# Create the directory for the model\r\n\r\n# Make directory\r\ndirectory = os.path.dirname(abspath + 'model/')\r\nif not os.path.exists(directory):\r\n os.makedirs(directory)\r\n\r\n#%%\r\n# Build the architecture of the model\r\n\r\n# Add the pretrained layers\r\npretrained_model = keras.applications.xception.Xception(include_top=False, weights='imagenet')\r\n\r\n# Add GlobalAveragePooling2D layer\r\naverage_pooling = keras.layers.GlobalAveragePooling2D()(pretrained_model.output)\r\n\r\n# Add the output layer\r\noutput = keras.layers.Dense(n_classes, activation='softmax')(average_pooling)\r\n\r\n# Get the model\r\nmodel = keras.Model(inputs=pretrained_model.input, outputs=output)\r\n\r\n#%%\r\n# Freeze each of the pretrained layers\r\n\r\nfor layer in pretrained_model.layers:\r\n # Freeze the layer\r\n layer.trainable = False\r\n\r\n#%%\r\n# Set callbacks\r\n\r\nmodel.save(\"model.h5\")\r\n\r\n\r\n# Checkpoint callback\r\ncheckpoint_cb = keras.callbacks.ModelCheckpoint(abspath + '/model/model.h5', save_best_only=True)\r\n\r\n# Early stopping callback\r\nearly_stopping_cb = keras.callbacks.EarlyStopping(patience=2, restore_best_weights=True)\r\n\r\n#%%\r\n# Compile the model\r\n# Use the default learning rate of Adam optimizer\r\n\r\nmodel.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\r\n\r\n#%%\r\n# Train the model\r\n\r\nhistory = model.fit(data_train, epochs=5, validation_data=data_valid, callbacks=[checkpoint_cb, early_stopping_cb])\r\n\r\n#%%\r\n# Create the directory for the plot\r\n\r\n# Make directory\r\ndirectory = os.path.dirname(abspath + 'figure/')\r\nif not os.path.exists(directory):\r\n os.makedirs(directory)\r\n\r\n#%%\r\nimport pandas as pd\r\n\r\n# Create a figure\r\npd.DataFrame(history.history).plot(figsize=(8, 5))\r\n\r\n# Set grid\r\nplt.grid(True)\r\n\r\n# Save and show the figure\r\nplt.tight_layout()\r\nplt.savefig(abspath + 'figure/learning_curve_before_unfreezing.pdf')\r\nplt.show()\r\n\r\n#%%\r\n# Unfreeze the pretrained layers\r\n\r\n# For each layer in the pretrained model\r\nfor layer in pretrained_model.layers:\r\n # Unfreeze the layer\r\n layer.trainable = True\r\n\r\n# %%\r\n# Compile the model\r\n# Use a lower learning rate (factor of 10) of Adam optimizer so that it's less likely to compromise the pretrained weights\r\n\r\nfrom tensorflow.keras.models import load_model\r\nmodel = load_model('model.h5')\r\n\r\nmodel.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\r\n\r\n#%%\r\n# Train the model\r\nhistory = model.fit(data_train, epochs=5, validation_data=data_valid, callbacks=[checkpoint_cb, early_stopping_cb])\r\n\r\n#%%\r\n# Plot the learning curve\r\n# Create a figure\r\npd.DataFrame(history.history).plot(figsize=(8, 5))\r\n\r\n# Set grid\r\nplt.grid(True)\r\n\r\n# Save and show the figure\r\nplt.tight_layout()\r\nplt.savefig(abspath + 'figure/learning_curve_after_unfreezing.pdf')\r\nplt.show()\r\n\r\n#\r\n# # save the model to disk\r\n# model.save(\"model.h5\")\r\n\r\n#%%\r\n# Testing\r\n# Loading the saved model\r\nmodel = keras.models.load_model(abspath + '/model.h5')\r\n\r\n#%%\r\n# Evaluating the model\r\nloss, accuracy = model.evaluate(data_test)", "sub_path": "dog_breed.py", "file_name": "dog_breed.py", "file_ext": "py", "file_size_in_byte": 6968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "warnings.filterwarnings", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow_datasets.load", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow_datasets.load", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow_datasets.load", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow_datasets.load", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.image.resize", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.applications", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 116, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.xception.Xception", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 178, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.GlobalAveragePooling2D", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 181, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 184, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 187, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 203, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 203, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 203, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 206, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 206, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 206, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 212, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 212, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 212, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 225, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 256, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 256, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 256, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 282, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 282, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 282, "usage_type": "name"}]} +{"seq_id": "296239754", "text": "from selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException, TimeoutException\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ec\nimport time\nimport xlsxwriter\n\n\nclass Parser(object):\n \"\"\"\n The class represents WEB Parser, using ChromeDriver.\n So if you have a list of pages, form where you need to grab some data -\n this parser will grab all the data you need automatically\n and save you a lot of your time and nerve cells! =)\n\n Args:\n chromedriver: Path to chromedriver binary.\n deserialize_links_from: Path to file_with_links.txt to be parsed.\n serialize_data_to: Path to file.xlsx where parsed data will be saved.\n \"\"\"\n\n def __init__(self, chromedriver, deserialize_links_from, serialize_data_to):\n self.chromedriver = chromedriver\n self.deserialize_links_from = deserialize_links_from\n self.serialize_data_to = serialize_data_to\n\n def __get_locator_data(self, data_type, locator):\n \"\"\"\n This method gets data from web locators on the page\n \"\"\"\n # If obtained locator exist - this construction returns count of elements it contains\n try:\n elements_number = len(self.driver.find_elements_by_xpath(locator))\n except NoSuchElementException:\n message = f'Element {locator} was not found'\n print(message)\n elements_number = 1\n\n # If the resulting locator contains several elements - read them in turn and append results to the list.\n if elements_number > 1:\n elements_list = []\n for i in range(elements_number):\n # For different data types use different methods to get info from locators\n try:\n if data_type == 'text':\n element = WebDriverWait(self.driver, 20).until(\n ec.presence_of_element_located((By.XPATH, locator + f\"[{i+1}]\"))).text\n elif data_type == 'href':\n element = self.driver.find_element_by_xpath(locator + f\"[{i+1}]\").get_attribute('href')\n elif data_type == 'src':\n element = self.driver.find_element_by_xpath(locator + f\"[{i+1}]\").get_attribute('src')\n # If Timeout or No Such Element Exception reised - show locator and write '' instead of that element\n except (NoSuchElementException, TimeoutException):\n message = f'Element {locator + f\"[{i+1}]\"} was not found'\n print(message)\n element = ''\n\n # Append element's value to the list\n elements_list.append(element)\n\n # Return list of element's values\n return elements_list\n else:\n # If the resulting locator contains one element - read it's value and return it.\n try:\n # For different data types use different methods to get info from locators\n if data_type == 'text':\n element = WebDriverWait(self.driver, 20).until(\n ec.presence_of_element_located((By.XPATH, locator))).text\n elif data_type == 'href':\n element = self.driver.find_element_by_xpath(locator).get_attribute('href')\n elif data_type == 'src':\n element = self.driver.find_element_by_xpath(locator).get_attribute('src')\n # If Timeout or No Such Element Exception reised - show locator and write '' instead of that element\n except (NoSuchElementException, TimeoutException):\n message = f'Element {locator} was not found'\n print(message)\n element = ''\n\n # Return element's value\n return element\n\n @property\n def deserialize_links_from_txt(self):\n \"\"\"\n This method deserializes all links from file.txt\n \"\"\"\n # Parse .txt file for URL links\n with open(self.deserialize_links_from) as f:\n links_list = f.readlines()\n\n # Remove whitespace characters like `\\n` at the end of each line and return them as a list\n return [x.strip() for x in links_list]\n\n def parse_data(self, links_list):\n \"\"\"\n This method:\n 1. Opens each link from the file.txt in Google Chrome using ChromeDriver.\n 2. Then parses (collects) the necessary data from the page.\n 3. Closes opened page.\n 4. Prepares data for storage.\n\n Once all the data is received, this data is written to the file.xslx,\n the file saves and the program terminates.\n \"\"\"\n # Make Google Chrome not wait till the page is fully loaded,\n # and proceed execution if all the required elements are already present and located on the page\n self.capa = DesiredCapabilities.CHROME\n self.capa[\"pageLoadStrategy\"] = \"none\"\n\n # Open XLSX file to write data into it,\n # set worksheet name,\n # enable text wrapping and\n # set text align parameters\n self.workbook = xlsxwriter.Workbook(self.serialize_data_to)\n self.worksheet = self.workbook.add_worksheet('Films')\n self.data_format = self.workbook.add_format({'text_wrap': True})\n self.data_format.set_align('top')\n\n # Parse data\n count = 0\n for page_link in links_list:\n # ReInitialize new ChromeDriver session for every loop,\n # because we kill previouse ChromeDriver session at the end of every loop\n self.driver = webdriver.Chrome(self.chromedriver, desired_capabilities=self.capa)\n\n # Open page\n self.driver.get(page_link)\n\n # Get all the data from the page, that interests us\n film_name_ua = Parser.__get_locator_data(self, 'text', '//*[@id=\"dle-content\"]/div/div/div/h1/span')\n film = Parser.__get_locator_data(\n self, 'src', '/html/body/div[1]/div[1]/div/div/div[2]/div[2]/div/article/div[2]/div[1]/div[5]/iframe')\n about = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-right\"]/div[4]')\n quality = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[1]/div[2]')\n year = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[2]/div[2]/a')\n country = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[3]/div[2]/a')\n genre = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[4]/div[2]/a')\n director = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[6]/div[2]/a')\n actors = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[7]/div[2]/a')\n duration = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[8]/div[2]')\n sound_language = Parser.__get_locator_data(self, 'text', '//*[@id=\"movie-left\"]/div[4]/div[9]/div[2]')\n screenshots = Parser.__get_locator_data(self, 'href', '//*[@id=\"movie-right\"]/div[1]/div[8]/a')\n\n # List of column titles\n titles = [\n \"Film name: \",\n \"Page link: \",\n \"Film: \",\n \"About: \",\n \"Quality: \",\n \"Year: \",\n \"Country: \",\n \"Genre: \",\n \"Director: \",\n \"Actors: \",\n \"Duration: \",\n \"Sound language: \",\n \"Screenshots: \"]\n\n # Write bold column titles to the XML file if it is 1st loop\n if count == 0:\n print(\"Writing bold column titles to file.xlsx\")\n # Write bold column titles to the file.xlsx\n for col_num, data in enumerate(titles):\n self.worksheet.write(0, col_num, str(data), self.workbook.add_format({'bold': True}))\n\n # Print parsed data to the CLI\n print(f\"Writing data to file.xlsx about film #{count+1}:\")\n print(titles[0], film_name_ua)\n print(titles[1], page_link)\n print(titles[2], film)\n print(titles[3], about)\n print(titles[4], quality)\n print(titles[5], year)\n print(titles[6], country)\n print(titles[7], genre)\n print(titles[8], director)\n print(titles[9], actors)\n print(titles[10], duration)\n print(titles[11], sound_language)\n print(titles[12], screenshots)\n print(\" ---------- ---------- ----------\")\n\n # List of all data from the film\n all_film_data = [\n film_name_ua,\n page_link,\n film,\n about,\n quality,\n year,\n country,\n genre,\n director,\n actors,\n duration,\n sound_language,\n screenshots]\n\n # Write data to the file.xlsx row-by-row\n for col_num, data in enumerate(all_film_data):\n if col_num == 3:\n self.worksheet.set_column(col_num, col_num, 100)\n elif col_num == 12:\n self.worksheet.set_column(col_num, col_num, 70)\n else:\n self.worksheet.set_column(col_num, col_num, 25)\n\n # Write formatted data to file.xlsx row-by-row\n self.worksheet.write(count+1, col_num, str(data), self.data_format)\n\n # Loops increment\n count+=1\n\n # Close ChromeDriver window at the end of every loop\n self.driver.close()\n\n # Close XLSX file after all parsed data are added\n self.workbook.close()\n\n # Quit Chrome Driver and finish CLI Parser's execution\n self.driver.quit()\n", "sub_path": "Parser.py", "file_name": "Parser.py", "file_ext": "py", "file_size_in_byte": 10342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 48, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 49, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 55, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 55, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 70, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 71, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 71, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 71, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 71, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 77, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 77, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.CHROME", "line_number": 110, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 110, "usage_type": "name"}, {"api_name": "xlsxwriter.Workbook", "line_number": 117, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 127, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 127, "usage_type": "name"}]} +{"seq_id": "644495776", "text": "import json\nimport os\n\nimport boto3\n\n\nclass RequestException(Exception):\n\n def __init__(self, message):\n self.message = message\n\n\ndef lambda_handler(event, context):\n try:\n dynamodb = boto3.resource('dynamodb')\n table = dynamodb.Table(os.getenv('ORDER_TABLE', 'order'))\n\n body = json.loads(event['body']) if 'body' in event and event['body'] else {}\n\n id_ = body.get('id')\n if not id_:\n raise RequestException('ID não informado!')\n\n response = table.update_item(\n Key={\n 'id': id_,\n },\n UpdateExpression=\"set #status=:r\",\n ExpressionAttributeValues={\n ':r': 'FINISHED',\n },\n ExpressionAttributeNames={\n \"#status\": \"status\"\n },\n ReturnValues=\"UPDATED_NEW\"\n )\n\n return {\n 'statusCode': 200,\n 'body': json.dumps({\n 'ok': 'true',\n **response\n }),\n 'headers': {\n 'Access-Control-Allow-Origin': '*'\n }\n }\n except RequestException as e:\n return {\n 'statusCode': 400,\n 'body': json.dumps({\n 'ok': False,\n 'message': e.message\n }),\n 'headers': {\n 'Access-Control-Allow-Origin': '*'\n }\n }\n", "sub_path": "finish-order/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "boto3.resource", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "58055883", "text": "import pandas as pd\nfrom scipy import stats\n\ndata = pd.read_csv(\"Preprodusingdust.csv\", encoding=\"ANSI\")\ntmp=data.groupby('fog')\n\nfog0=tmp.get_group(0)['PM10']\nfog1=tmp.get_group(1)['PM10']\n\ntTestResult = stats.ttest_ind(fog1, fog0)\ntTestResultDiffVar = stats.ttest_ind(fog1,fog0, equal_var=False)\n\nprint(tTestResult)", "sub_path": "final/TtestFog.py", "file_name": "TtestFog.py", "file_ext": "py", "file_size_in_byte": 317, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_ind", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 10, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_ind", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "191155046", "text": "from django.conf.urls import url\nfrom controls.views import controls, search, controls2_data, controls2_allot, \\\n controls1_submit, sync_db, search3, controls3_submit\nfrom django.contrib.auth import views as auth_views\n\nurlpatterns = [\n url(r'sync_db/$', sync_db, name='sync_db'),\n url(r'controls/$', controls, name='controls'),\n url(r'controls1_submit/$', controls1_submit, name='controls1_submit'),\n url(r'search/$', search, name='search'),\n url(r'controls2_data/$', controls2_data, name='controls2_data'),\n url(r'controls2_allot/$', controls2_allot, name='controls2_allot'),\n url(r'controls3_submit/$', controls3_submit, name='controls3_submit'),\n\n url(r'search3/$', search3, name='search3'),\n # url(r'controls3_allot/$', controls3_allot, name='controls3_allot'),\n url(r'^logout/$',\n auth_views.LogoutView.as_view(), name='logout'),\n url(r'$',\n auth_views.LoginView.as_view(\n template_name='controls/index.html',\n redirect_authenticated_user=True), name='login'),\n ]\n", "sub_path": "controls/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "controls.views.sync_db", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "controls.views.controls", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "controls.views.controls1_submit", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "controls.views.search", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "controls.views.controls2_data", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "controls.views.controls2_allot", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "controls.views.controls3_submit", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "controls.views.search3", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "14524939", "text": "import argparse\nfrom src.io import load_events, export_averages\nfrom src.processing import moving_averages\nimport logging\n\nlogging.getLogger().setLevel(logging.INFO)\n\n\ndef create_arg_parser() -> argparse.ArgumentParser:\n \"\"\"Parse the command line arguments for the unbabel_cli script.\"\"\"\n\n parser = argparse.ArgumentParser(\n prog=\"unbabel_cli\", formatter_class=argparse.ArgumentDefaultsHelpFormatter\n )\n\n parser.add_argument(\n \"-i\", \"--input_file\", required=True, help=\"Path to the events JSON file\"\n )\n\n parser.add_argument(\n \"-w\",\n \"--window_size\",\n required=True,\n type=int,\n help=\"Size of the window to compute the moving average\",\n )\n\n return parser\n\n\ndef main():\n parser = create_arg_parser()\n args = parser.parse_args()\n\n events = load_events(args.input_file)\n averages = moving_averages(events, args.window_size)\n export_averages(averages, \"output.json\")\n", "sub_path": "src/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "attribute"}, {"api_name": "src.io.load_events", "line_number": 35, "usage_type": "call"}, {"api_name": "src.processing.moving_averages", "line_number": 36, "usage_type": "call"}, {"api_name": "src.io.export_averages", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "612644455", "text": "# -*- coding: utf-8 -*-\n\nfrom jsonutil import jsonutil as json\nfrom DateTime import DateTime\nfrom plone.autoform.interfaces import IFormFieldProvider\nfrom plone.supermodel import directives, model\nfrom zope.interface import implementer, provider\nfrom zope.pagetemplate.pagetemplatefile import PageTemplateFile\nfrom zope import schema\nfrom zope.schema.vocabulary import SimpleVocabulary\n\nfrom .. import _\nfrom base import BaseField\nfrom ..document import TemporaryDocument\nfrom ..utils import DateToString, PlominoTranslate\n\n\n@provider(IFormFieldProvider)\nclass IDatagridField(model.Schema):\n \"\"\" Datagrid field schema\n \"\"\"\n\n directives.fieldset(\n 'settings',\n label=_(u'Settings'),\n fields=(\n 'widget',\n 'associated_form',\n 'associated_form_rendering',\n 'field_mapping',\n 'jssettings',\n ),\n )\n\n widget = schema.Choice(\n vocabulary=SimpleVocabulary.fromItems([\n (\"Always dynamic\", \"REGULAR\"),\n (\"Static in read mode\", \"READ_STATIC\"),\n ]),\n title=u'Widget',\n description=u'Field rendering',\n default=\"REGULAR\",\n required=True)\n\n associated_form = schema.Choice(\n vocabulary='Products.CMFPlomino.fields.vocabularies.get_forms',\n title=u'Associated form',\n description=u'Form to use to create/edit rows',\n required=False)\n\n associated_form_rendering = schema.Choice(\n vocabulary=SimpleVocabulary.fromItems([\n (\"Modal\", \"MODAL\"),\n (\"Inline editing\", \"INLINE\"),\n ]),\n title=u'Associate form rendering',\n description=u'Associate form rendering',\n default=\"MODAL\",\n required=True)\n\n field_mapping = schema.TextLine(\n title=u'Columns/fields mapping',\n description=u'Field ids from the associated form, '\n 'ordered as the columns, separated by commas',\n required=False)\n\n jssettings = schema.Text(\n title=u'Javascript settings',\n description=u'jQuery datatable parameters',\n default=u\"\"\"\n\"aoColumns\": [\n { \"sTitle\": \"Column 1\" },\n { \"sTitle\": \"Column 2\", \"sClass\": \"center\" }\n],\n\"bPaginate\": false,\n\"bLengthChange\": false,\n\"bFilter\": false,\n\"bSort\": false,\n\"bInfo\": false,\n\"bAutoWidth\": false,\n\"plominoDialogOptions\": {\n \"width\": 400,\n \"height\": 300\n }\n\"\"\",\n required=False)\n\n\n@implementer(IDatagridField)\nclass DatagridField(BaseField):\n \"\"\"\n \"\"\"\n\n read_template = PageTemplateFile('datagrid_read.pt')\n edit_template = PageTemplateFile('datagrid_edit.pt')\n\n def getParameters(self):\n \"\"\"\n \"\"\"\n return self.context.jssettings\n\n def processInput(self, submittedValue):\n \"\"\"\n \"\"\"\n try:\n return json.loads(submittedValue)\n except:\n return []\n\n def rows(self, value, rendered=False):\n \"\"\"\n \"\"\"\n if value in [None, '']:\n value = []\n if isinstance(value, basestring):\n return value\n elif isinstance(value, DateTime):\n db = self.context.getParentDatabase()\n value = DateToString(value, db=db)\n # TODO does anything require that format here?\n # value = DateToString(value, '%Y-%m-%d')\n elif isinstance(value, dict):\n if rendered:\n value = value['rendered']\n else:\n value = value['rawdata']\n return value\n\n def tojson(self, value, rendered=False):\n \"\"\"\n \"\"\"\n rows = self.rows(value, rendered)\n\n return json.dumps(rows)\n\n def request_items_aoData(self, request):\n \"\"\" Return a string representing REQUEST.items as aoData.push calls.\n \"\"\"\n aoData_templ = \"aoData.push(%s); \"\n aoDatas = []\n for k, v in request.form.items():\n j = json.dumps({'name': k, 'value': v})\n aoDatas.append(aoData_templ % j)\n return '\\n'.join(aoDatas)\n\n def getActionLabel(self, action_id):\n \"\"\"\n \"\"\"\n db = self.context.getParentDatabase()\n if action_id == \"add\":\n label = PlominoTranslate(\n _(\"datagrid_add_button_label\", default=\"Add\"), db)\n child_form_id = self.context.associated_form\n if child_form_id:\n child_form = db.getForm(child_form_id)\n if child_form:\n label += \" \" + child_form.Title()\n return label\n elif action_id == \"delete\":\n return PlominoTranslate(\n _(\"datagrid_delete_button_label\", default=\"Delete\"), db)\n elif action_id == \"edit\":\n return PlominoTranslate(\n _(\"datagrid_edit_button_label\", default=\"Edit\"), db)\n return \"\"\n\n def getColumnLabels(self):\n \"\"\"\n \"\"\"\n if not self.context.field_mapping:\n return []\n\n mapped_fields = [f.strip() for f in self.context.field_mapping.split(',')]\n\n child_form_id = self.context.associated_form\n if not child_form_id:\n return mapped_fields\n\n db = self.context.getParentDatabase()\n\n # get child form\n child_form = db.getForm(child_form_id)\n if not child_form:\n return mapped_fields\n\n # return title for each mapped field if this one exists in the\n # child form\n return [f.Title() for f in [child_form.getFormField(f)\n for f in mapped_fields] if f]\n\n def getRenderedFields(self, editmode=True, creation=False, request={}):\n \"\"\" Return an array of rows rendered using the associated form fields\n \"\"\"\n if not self.context.field_mapping:\n return []\n\n db = self.context.getParentDatabase()\n\n mapped_fields = [f.strip() for f in self.context.field_mapping.split(',')]\n\n # get associated form id\n child_form_id = self.context.associated_form\n if not child_form_id:\n return mapped_fields\n\n # get associated form object\n child_form = db.getForm(child_form_id)\n if not child_form:\n return mapped_fields\n\n target = TemporaryDocument(\n db,\n child_form,\n request,\n validation_mode=False).__of__(db)\n\n # return rendered field for each mapped field if this one exists in the\n # child form\n child_form_fields = [f.getFieldRender(\n child_form,\n target,\n editmode=editmode,\n creation=creation,\n request=request\n ) for f in [child_form.getFormField(f) for f in mapped_fields] if f]\n return json.dumps(child_form_fields)\n\n def getAssociateForm(self):\n child_form_id = self.context.associated_form\n if child_form_id:\n db = self.context.getParentDatabase()\n return db.getForm(child_form_id)\n\n def getFieldValue(self, form, doc=None, editmode_obsolete=False,\n creation=False, request=None):\n \"\"\"\n \"\"\"\n fieldValue = BaseField.getFieldValue(\n self, form, doc, editmode_obsolete, creation, request)\n if not fieldValue:\n return fieldValue\n\n # if doc is not a PlominoDocument, no processing needed\n if not doc or doc.isNewDocument():\n return fieldValue\n\n rawValue = fieldValue\n\n mapped_fields = []\n if self.context.field_mapping:\n mapped_fields = [\n f.strip() for f in self.context.field_mapping.split(',')]\n # item names is set by `PlominoForm.createDocument`\n item_names = doc.getItem(self.context.id + '_itemnames')\n\n if mapped_fields:\n if not item_names:\n item_names = mapped_fields\n\n # fieldValue is a array, where we must replace raw values with\n # rendered values\n child_form_id = self.context.associated_form\n if child_form_id:\n db = self.context.getParentDatabase()\n child_form = db.getForm(child_form_id)\n # zip is procrustean: we get the longest of mapped_fields or\n # fieldValue\n mapped = []\n for row in fieldValue:\n if len(row) < len(item_names):\n row = (row + [''] * (len(item_names) - len(row)))\n row = dict(zip(item_names, row))\n mapped.append(row)\n fieldValue = mapped\n fields = {}\n for f in mapped_fields + item_names:\n fields[f] = None\n fields = fields.keys()\n field_objs = [child_form.getFormField(f) for f in fields]\n # avoid bad field ids\n field_objs = [f for f in field_objs if f is not None]\n fields_to_render = [f.id for f in field_objs\n if f.field_mode in [\"DISPLAY\", ] or\n f.field_type not in [\"TEXT\", \"RICHTEXT\"]]\n\n if fields_to_render:\n rendered_values = []\n for row in fieldValue:\n row['Form'] = child_form_id\n row['Plomino_Parent_Document'] = doc.id\n # We want a new TemporaryDocument for every row\n tmp = TemporaryDocument(\n db, child_form, row, real_doc=doc)\n tmp = tmp.__of__(db)\n for f in fields:\n if f in fields_to_render:\n row[f] = tmp.getRenderedItem(f)\n rendered_values.append(row)\n fieldValue = rendered_values\n\n if mapped_fields and child_form_id:\n mapped = []\n for row in fieldValue:\n mapped.append([row[c] for c in mapped_fields])\n fieldValue = mapped\n\n return {'rawdata': rawValue, 'rendered': fieldValue}\n\n\nclass EditFieldsAsJson(object):\n \"\"\"\n \"\"\"\n def __call__(self):\n context = self.context\n if (hasattr(context, 'getParentDatabase')\n and context.field_type == u'DATAGRID'):\n self.request.RESPONSE.setHeader(\n 'content-type',\n 'application/json; charset=utf-8')\n\n self.field = context.getSettings()\n self.request.set(\"Plomino_Parent_Form\", context.getForm().id)\n self.request.set(\"Plomino_Parent_Field\", context.id)\n return self.field.getRenderedFields(request=self.request)\n\n return \"\"\n", "sub_path": "src/Products/CMFPlomino/fields/datagrid.py", "file_name": "datagrid.py", "file_ext": "py", "file_size_in_byte": 10667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "plone.supermodel.model.Schema", "line_number": 19, "usage_type": "attribute"}, {"api_name": "plone.supermodel.model", "line_number": 19, "usage_type": "name"}, {"api_name": "plone.supermodel.directives.fieldset", "line_number": 23, "usage_type": "call"}, {"api_name": "plone.supermodel.directives", "line_number": 23, "usage_type": "name"}, {"api_name": "zope.schema.Choice", "line_number": 35, "usage_type": "call"}, {"api_name": "zope.schema", "line_number": 35, "usage_type": "name"}, {"api_name": "zope.schema.vocabulary.SimpleVocabulary.fromItems", "line_number": 36, "usage_type": "call"}, {"api_name": "zope.schema.vocabulary.SimpleVocabulary", "line_number": 36, "usage_type": "name"}, {"api_name": "zope.schema.Choice", "line_number": 45, "usage_type": "call"}, {"api_name": "zope.schema", "line_number": 45, "usage_type": "name"}, {"api_name": "zope.schema.Choice", "line_number": 51, "usage_type": "call"}, {"api_name": "zope.schema", "line_number": 51, "usage_type": "name"}, {"api_name": "zope.schema.vocabulary.SimpleVocabulary.fromItems", "line_number": 52, "usage_type": "call"}, {"api_name": "zope.schema.vocabulary.SimpleVocabulary", "line_number": 52, "usage_type": "name"}, {"api_name": "zope.schema.TextLine", "line_number": 61, "usage_type": "call"}, {"api_name": "zope.schema", "line_number": 61, "usage_type": "name"}, {"api_name": "zope.schema.Text", "line_number": 67, "usage_type": "call"}, {"api_name": "zope.schema", "line_number": 67, "usage_type": "name"}, {"api_name": "zope.interface.provider", "line_number": 18, "usage_type": "call"}, {"api_name": "plone.autoform.interfaces.IFormFieldProvider", "line_number": 18, "usage_type": "argument"}, {"api_name": "base.BaseField", "line_number": 90, "usage_type": "name"}, {"api_name": "zope.pagetemplate.pagetemplatefile.PageTemplateFile", "line_number": 94, "usage_type": "call"}, {"api_name": "zope.pagetemplate.pagetemplatefile.PageTemplateFile", "line_number": 95, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil.loads", "line_number": 106, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil", "line_number": 106, "usage_type": "name"}, {"api_name": "DateTime.DateTime", "line_number": 117, "usage_type": "argument"}, {"api_name": "utils.DateToString", "line_number": 119, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil", "line_number": 134, "usage_type": "name"}, {"api_name": "jsonutil.jsonutil.dumps", "line_number": 142, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil", "line_number": 142, "usage_type": "name"}, {"api_name": "utils.PlominoTranslate", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.PlominoTranslate", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.PlominoTranslate", "line_number": 163, "usage_type": "call"}, {"api_name": "document.TemporaryDocument", "line_number": 211, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil.dumps", "line_number": 226, "usage_type": "call"}, {"api_name": "jsonutil.jsonutil", "line_number": 226, "usage_type": "name"}, {"api_name": "base.BaseField.getFieldValue", "line_number": 238, "usage_type": "call"}, {"api_name": "base.BaseField", "line_number": 238, "usage_type": "name"}, {"api_name": "document.TemporaryDocument", "line_number": 292, "usage_type": "call"}, {"api_name": "zope.interface.implementer", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "218542888", "text": "import numpy as np\nimport pandas as pd\nimport progressbar\nimport cPickle as pickle\n\ndef evening_locations(data, prefix, lower_bound = 7, upper_bound = 18):\n\n #load in data\n data = np.asarray(data)\n length, width = data.shape\n print ('there are {} calls recorded in this dataset'.format(length))\n \n #extract callerIDs\n callerID = data[:,0]\n callerID = callerID.astype(str)\n \n #extract timestamp\n timestamp = pd.to_datetime(data[0:length,1], format=\"%d-%m-%Y %H:%M\")\n date = np.asarray(timestamp.date)\n\n #extract districtIDs\n districtID = data[:,2]\n \n #extract cityIDs\n cityID = data[:,3]\n\n #extract prefixes\n prefixes = []\n for i in callerID:\n prefixes.append(i[0])\n\n prefixes = np.asarray(prefixes)\n prefixes = prefixes.astype(int)\n \n unique, counts = np.unique(prefixes, return_counts=True)\n print ('there are {} calls made by callers with prefix {} in this dataset'.format(counts[1], prefix))\n \n #identify callerIDs with the correct prefix\n to_analyse = np.unique(callerID[prefixes == prefix])\n\n #apply time constraints\n h1 = callerID[timestamp.hour >= 18]\n h2 = callerID[timestamp.hour <= 7]\n h = np.append(h1,h2)\n overlap = np.intersect1d(h,to_analyse)\n print ('there are {} callers who match the time constrains with prefix {}'.format(len(overlap), prefix))\n\n #create dictionary\n evening_location = {}\n for i in overlap:\n evening_location[str(i)] = {\n 'cities':[],\n 'dates':[]\n }\n\n #bottleneck - could parallelise better\n print ('writing information to dictionary:')\n bar = progressbar.ProgressBar(maxval=len(overlap), widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])\n bar.start()\n \n counter = 0\n for i in overlap:\n location = np.where(callerID == i)\n cities = cityID[location[0]]\n evening_location[i]['cities'] = cities\n dates = date[location[0]]\n evening_location[i]['dates'] = dates\n bar.update(counter)\n counter +=1\n bar.finish()\n\n \n return evening_location\n\n\n\ndef mobility(dictionary, low_threshold = 2, medium_threshold = 5):\n\n #find the refugees who spend their time in more that one city in the evening over the month\n mobile = []\n for i in dictionary:\n unique, counts = np.unique(dictionary[i]['cities'], return_counts=True)\n if len(unique) > 1:\n mobile.append(i)\n \n print ('there are {} callers in this dataset who moved cities in this dataset'.format(len(mobile)))\n\n #assign an entry to each callerID which gives the cities 'moved' to\n for i in mobile:\n moved = []\n moved.append(dictionary[i]['cities'][0])\n counter = 0\n for j in range(1,len(dictionary[i]['cities'])):\n if dictionary[i]['cities'][j] == dictionary[i]['cities'][j-1]:\n counter += 1\n else:\n counter = 0\n if counter == 4 and dictionary[i]['cities'][j] != moved[-1]:\n moved.append(dictionary[i]['cities'][j])\n dictionary[i]['moved'] = moved\n\n #class callers as low, medium and high mobility\n low_mobility = []\n medium_mobility = []\n high_mobility = []\n for i in mobile:\n if len(dictionary[i]['moved']) <= low_threshold:\n low_mobility.append(i)\n elif low_threshold < len(dictionary[i]['moved']) <= medium_threshold:\n medium_mobility.append(i)\n else:\n high_mobility.append(i)\n\n print ('there are {} callers classed as low mobility in this dataset'.format(len(low_mobility)))\n print ('there are {} callers classed as medium mobility in this dataset'.format(len(medium_mobility)))\n print ('there are {} callers classed as high mobility in this dataset'.format(len(high_mobility)))\n\n return low_mobility, medium_mobility, high_mobility, dictionary\n\nif __name__ == '__main__':\n\n dictionary_main = {}\n \n with open('dataset_3.txt', 'r') as dataset_3:\n k = 0\n for line in dataset_3:\n print ('working on month {}'.format(k))\n if k == 24:\n break\n data = pd.read_csv(line[:-1])\n\n dictionary_sub = evening_locations(data,2)\n\n for i in dictionary_sub:\n if i in dictionary_main:\n dictionary_main[i]['cities'] = np.append(dictionary_main[i]['cities'], dictionary_sub[i]['cities'])\n dictionary_main[i]['dates'] = np.append(dictionary_main[i]['dates'], dictionary_sub[i]['dates'])\n else:\n dictionary_main[i] = {\n 'cities': dictionary_sub[i]['cities'],\n 'dates': dictionary_sub[i]['dates']\n }\n #with open('evening_location_{}.pkl'.format(k)) as sub:\n # pickle.dump(dictionary_sub, sub)\n \n print (\"main dictionary is of length {}\".format(len(dictionary_main)))\n k += 1\n \n print ('writing pickling information')\n \n with open(\"evening_location_main_unordered.pkl\", \"wb\") as fp: #Pickling\n pickle.dump(dictionary_main, fp,1)\n\n \n", "sub_path": "coarse_analysis.py", "file_name": "coarse_analysis.py", "file_ext": "py", "file_size_in_byte": 5232, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.asarray", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 45, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 58, "usage_type": "call"}, {"api_name": "progressbar.Bar", "line_number": 58, "usage_type": "call"}, {"api_name": "progressbar.Percentage", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 137, "usage_type": "call"}, {"api_name": "cPickle.dump", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "454536730", "text": "#!/usr/bin/env python3\n\"\"\"\nThe PyMKM example app.\n\"\"\"\n\n__author__ = \"Andreas Ehrlund\"\n__version__ = \"1.0.2\"\n__license__ = \"MIT\"\n\nimport csv\nimport json\nimport logging\nimport math\nimport os.path\nimport pprint\nimport sys\n\nimport progressbar\nimport tabulate as tb\n\nfrom pymkm_helper import PyMkmHelper\nfrom pymkmapi import PyMkmApi, api_wrapper\n\nPRICE_CHANGES_FILE = 'price_changes.json'\n\n\nclass PyMkmApp:\n logging.basicConfig(stream=sys.stderr, level=logging.WARN)\n\n def __init__(self, config=None):\n self.api = PyMkmApi(config=config)\n\n def start(self):\n while True:\n menu_items = [\n \"Update stock prices\",\n \"Update price for a card\",\n \"List competition for a card\",\n \"Show top 20 expensive items in stock\",\n \"Show account info\",\n \"Clear entire stock (WARNING)\",\n \"Import stock from .\\list.csv\"\n ]\n self.print_menu(menu_items, f\"PyMKM {__version__}\")\n\n choice = input(\"Action number: \")\n\n try:\n if choice == \"1\":\n self.update_stock_prices_to_trend(api=self.api)\n\n elif choice == \"2\":\n search_string = PyMkmHelper.prompt_string(\n 'Search card name')\n self.update_product_to_trend(search_string, api=self.api)\n\n elif choice == \"3\":\n is_foil = False\n search_string = PyMkmHelper.prompt_string(\n 'Search card name')\n if PyMkmHelper.prompt_bool(\"Foil?\") == True:\n is_foil = True\n\n self.list_competition_for_product(\n search_string, is_foil, api=self.api)\n\n elif choice == \"4\":\n self.show_top_expensive_articles_in_stock(20, api=self.api)\n\n elif choice == \"5\":\n self.show_account_info(api=self.api)\n\n elif choice == \"6\":\n self.clear_entire_stock(api=self.api)\n\n elif choice == \"7\":\n self.import_from_csv(api=self.api)\n\n elif choice == \"0\":\n break\n else:\n print(\"Not a valid choice, try again.\")\n except ConnectionError as err:\n print(err)\n\n def print_menu(self, menu_items, title):\n padding = 6\n menu_width = padding + max(len(item) for item in menu_items)\n menu_top_left = 3 * \"─\"\n menu_top_right = (menu_width - len(title) - 1) * \"─\" + \"╮\"\n menu_top = f\"╭{menu_top_left} {title} {menu_top_right}\"\n print(menu_top)\n index = 1\n for item in menu_items:\n print(\"│ {}: {}{}│\".format(\n str(index),\n menu_items[index - 1],\n (menu_width - len(menu_items[index - 1])) * \" \"\n ))\n index += 1\n print(\"│ 0: Exit\" + (len(menu_top) - 10) * \" \" + \"│\")\n print(\"╰\" + (len(menu_top) - 2) * \"─\" + \"╯\")\n\n @api_wrapper\n def update_stock_prices_to_trend(self, api):\n ''' This function updates all prices in the user's stock to TREND. '''\n uploadable_json = []\n if os.path.isfile(PRICE_CHANGES_FILE):\n if PyMkmHelper.prompt_bool(\"Found existing changes. Upload [y] or discard [n]?\") == True:\n with open(PRICE_CHANGES_FILE, 'r') as changes:\n uploadable_json = json.load(changes)\n else:\n os.remove(PRICE_CHANGES_FILE)\n self.update_stock_prices_to_trend(api=self.api)\n\n else:\n uploadable_json = self.calculate_new_prices_for_stock(api=self.api)\n\n if len(uploadable_json) > 0:\n\n self.display_price_changes_table(uploadable_json)\n\n if PyMkmHelper.prompt_bool(\"Do you want to update these prices?\") == True:\n # Update articles on MKM\n api.set_stock(uploadable_json)\n print('Prices updated.')\n else:\n with open(PRICE_CHANGES_FILE, 'w') as outfile:\n json.dump(uploadable_json, outfile)\n print('Prices not updated. Changes saved.')\n else:\n print('No prices to update.')\n\n @api_wrapper\n def update_product_to_trend(self, search_string, api):\n ''' This function updates one product in the user's stock to TREND. '''\n\n try:\n articles = api.find_stock_article(search_string, 1)\n except Exception as err:\n print(err)\n\n if len(articles) > 1:\n article = self.select_from_list_of_articles(articles)\n else:\n article = articles[0]\n print('Found: {} [{}].'.format(article['product']\n ['enName'], article['product']['expansion']))\n r = self.get_price_for_single_article(article, api=self.api)\n\n if r:\n self.draw_price_changes_table([r])\n\n print('\\nTotal price difference: {}.'.format(\n str(round(sum(item['price_diff'] * item['count']\n for item in [r]), 2))\n ))\n\n if PyMkmHelper.prompt_bool(\"Do you want to update these prices?\") == True:\n # Update articles on MKM\n api.set_stock([r])\n print('Price updated.')\n else:\n print('Prices not updated.')\n else:\n print('No prices to update.')\n\n @api_wrapper\n def list_competition_for_product(self, search_string, is_foil, api):\n\n result = api.find_product(search_string, **{\n # 'exact ': 'true',\n 'idGame': 1,\n 'idLanguage': 1,\n # TODO: Add Partial Content support\n # TODO: Add language support\n })\n\n if (result):\n products = result['product']\n\n stock_list_products = [x['idProduct']\n for x in self.get_stock_as_array(api=self.api)]\n products = [x for x in products if x['idProduct']\n in stock_list_products]\n\n if len(products) == 0:\n print('No matching cards in stock.')\n else:\n if len(products) > 1:\n product = self.select_from_list_of_products(\n [i for i in products if i['categoryName'] == 'Magic Single'])\n elif len(products) == 1:\n product = products[0]\n\n self.show_competition_for_product(\n product['idProduct'], product['enName'], is_foil, api=self.api)\n else:\n print('No results found.')\n\n @api_wrapper\n def show_top_expensive_articles_in_stock(self, num_articles, api):\n stock_list = self.get_stock_as_array(api=self.api)\n table_data = []\n total_price = 0\n\n for article in stock_list:\n name = article['product']['enName']\n expansion = article.get('product').get('expansion')\n foil = article.get('isFoil')\n language_code = article.get('language')\n language_name = language_code.get('languageName')\n price = article.get('price')\n table_data.append(\n [name, expansion, u'\\u2713' if foil else '', language_name if language_code != 1 else '', price])\n total_price += price\n if len(stock_list) > 0:\n print('Top {} most expensive articles in stock:\\n'.format(\n str(num_articles)))\n print(tb.tabulate(sorted(table_data, key=lambda x: x[4], reverse=True)[:num_articles],\n headers=['Name', 'Expansion',\n 'Foil?', 'Language', 'Price'],\n tablefmt=\"simple\")\n )\n print('\\nTotal stock value: {}'.format(str(total_price)))\n return None\n\n @api_wrapper\n def show_account_info(self, api):\n pp = pprint.PrettyPrinter()\n pp.pprint(api.get_account())\n\n @api_wrapper\n def clear_entire_stock(self, api):\n stock_list = self.get_stock_as_array(api=self.api)\n if PyMkmHelper.prompt_bool(\"Do you REALLY want to clear your entire stock ({} items)?\".format(len(stock_list))) == True:\n\n # for article in stock_list:\n # article['count'] = 0\n delete_list = [{'count': x['count'], 'idArticle': x['idArticle']}\n for x in stock_list]\n\n api.delete_stock(delete_list)\n print('Stock cleared.')\n else:\n print('Aborted.')\n\n @api_wrapper\n def import_from_csv(self, api):\n print(\"Note the required format: Card, Set name, Quantity, Foil, Language (with header row).\")\n print(\"Cards are added in condition NM.\")\n problem_cards = []\n with open('list.csv', newline='') as csvfile:\n csv_reader = csvfile.readlines()\n index = 0\n bar = progressbar.ProgressBar(\n max_value=(sum(1 for row in csv_reader)) - 1)\n csvfile.seek(0)\n for row in csv_reader:\n if index > 0:\n (name, set_name, count, foil, language, *other) = row.split(',')\n if (all(v is not '' for v in [name, set_name, count])):\n possible_products = api.find_product(name)['product']\n product_match = [x for x in possible_products if x['expansionName']\n == set_name and x['categoryName'] == \"Magic Single\"]\n if len(product_match) == 0:\n problem_cards.append(row)\n elif len(product_match) == 1:\n foil = (True if foil == 'Foil' else False)\n language_id = (\n 1 if language == '' else api.languages.index(language) + 1)\n price = self.get_price_for_product(\n product_match[0]['idProduct'], foil, language_id=language_id, api=self.api)\n card = {\n 'idProduct': product_match[0]['idProduct'],\n 'idLanguage': language_id,\n 'count': count,\n 'price': str(price),\n 'condition': 'NM',\n 'isFoil': ('true' if foil else 'false')\n }\n api.add_stock([card])\n else:\n problem_cards.append(row)\n\n bar.update(index)\n index += 1\n bar.finish()\n if len(problem_cards) > 0:\n try:\n with open('failed_imports.csv', 'w', newline='', encoding='utf-8') as csvfile:\n csv_writer = csv.writer(csvfile)\n csv_writer.writerows(problem_cards)\n print('Wrote failed imports to failed_imports.csv')\n print(\n 'Most failures are due to mismatching set names or multiple versions of cards.')\n except Exception as err:\n print(err.value)\n\n# End of menu item functions ============================================\n\n def select_from_list_of_products(self, products):\n index = 1\n for product in products:\n print('{}: {} [{}] {}'.format(index, product['enName'],\n product['expansionName'], product['rarity']))\n index += 1\n choice = int(input(\"Choose card: \"))\n return products[choice - 1]\n\n def select_from_list_of_articles(self, articles):\n index = 1\n for article in articles:\n product = article['product']\n print('{}: {} [{}] {}'.format(index, product['enName'],\n product['expansion'], product['rarity']))\n index += 1\n choice = int(input(\"Choose card: \"))\n return articles[choice - 1]\n\n def show_competition_for_product(self, product_id, product_name, is_foil, api):\n print(\"Found product: {}\".format(product_name))\n account = api.get_account()['account']\n country_code = account['country']\n articles = api.get_articles(product_id, **{\n 'isFoil': str(is_foil).lower(),\n 'isAltered': 'false',\n 'isSigned': 'false',\n 'minCondition': 'EX',\n 'country': country_code,\n 'idLanguage': 1\n })\n table_data = []\n table_data_local = []\n for article in articles:\n username = article['seller']['username']\n if article['seller']['username'] == account['username']:\n username = '-> ' + username\n item = [\n username,\n article['seller']['address']['country'],\n article['condition'],\n article['count'],\n article['price']\n ]\n if article['seller']['address']['country'] == country_code:\n table_data_local.append(item)\n table_data.append(item)\n if table_data_local:\n self.print_product_top_list(\n \"Local competition:\", table_data_local, 4, 20)\n if table_data:\n self.print_product_top_list(\"Top 20 cheapest:\", table_data, 4, 20)\n else:\n print('No prices found.')\n\n def print_product_top_list(self, title_string, table_data, sort_column, rows):\n print(70*'-')\n print('{} \\n'.format(title_string))\n print(tb.tabulate(sorted(table_data, key=lambda x: x[sort_column], reverse=False)[:rows],\n headers=['Username', 'Country',\n 'Condition', 'Count', 'Price'],\n tablefmt=\"simple\"))\n print(70*'-')\n print('Total average price: {}, Total median price: {}, Total # of articles: {}\\n'.format(\n str(PyMkmHelper.calculate_average(table_data, 3, 4)),\n str(PyMkmHelper.calculate_median(table_data, 3, 4)),\n str(len(table_data))\n )\n )\n\n def calculate_new_prices_for_stock(self, api):\n stock_list = self.get_stock_as_array(api=self.api)\n # HACK: filter out a foil product\n # stock_list = [x for x in stock_list if x['isFoil']]\n\n result_json = []\n total_price = 0\n index = 0\n\n bar = progressbar.ProgressBar(max_value=len(stock_list))\n for article in stock_list:\n updated_article = self.get_price_for_single_article(\n article, api=self.api)\n if updated_article:\n result_json.append(updated_article)\n total_price += updated_article.get('price')\n else:\n total_price += article.get('price')\n index += 1\n bar.update(index)\n bar.finish()\n\n print('Total stock value: {}'.format(str(total_price)))\n return result_json\n\n def get_price_for_single_article(self, article, api):\n # TODO: compare prices also for signed cards, like foils\n if not article.get('isSigned') or True: # keep prices for signed cards fixed\n new_price = self.get_price_for_product(\n article['idProduct'], article['isFoil'], language_id=article['language']['idLanguage'], api=self.api)\n price_diff = new_price - article['price']\n if price_diff != 0:\n return {\n \"name\": article['product']['enName'],\n \"foil\": article['isFoil'],\n \"old_price\": article['price'],\n \"price\": new_price,\n \"price_diff\": price_diff,\n \"idArticle\": article['idArticle'],\n \"count\": article['count']\n }\n\n def get_price_for_product(self, product_id, is_foil, language_id=1, api=None):\n if not is_foil:\n r = api.get_product(product_id)\n found_price = math.ceil(\n r['product']['priceGuide']['TREND'] * 4) / 4\n else:\n found_price = self.get_foil_price(api, product_id, language_id)\n\n if found_price == None:\n raise ValueError('No price found!')\n else:\n return found_price\n\n def display_price_changes_table(self, changes_json):\n # table breaks because of progress bar rendering\n print('\\nBest diffs:\\n')\n sorted_best = sorted(\n changes_json, key=lambda x: x['price_diff'], reverse=True)[:10]\n self.draw_price_changes_table(\n i for i in sorted_best if i['price_diff'] > 0)\n print('\\nWorst diffs:\\n')\n sorted_worst = sorted(changes_json, key=lambda x: x['price_diff'])[:10]\n self.draw_price_changes_table(\n i for i in sorted_worst if i['price_diff'] < 0)\n\n print('\\nTotal price difference: {}.'.format(\n str(round(sum(item['price_diff'] * item['count']\n for item in sorted_best), 2))\n ))\n\n def draw_price_changes_table(self, sorted_best):\n print(tb.tabulate(\n [\n [item['count'],\n item['name'],\n u'\\u2713' if item['foil'] else '',\n item['old_price'],\n item['price'],\n item['price_diff']] for item in sorted_best],\n headers=['Count', 'Name', 'Foil?',\n 'Old price', 'New price', 'Diff'],\n tablefmt=\"simple\"\n ))\n\n def get_foil_price(self, api, product_id, language_id):\n # NOTE: This is a rough algorithm, designed to be safe and not to sell aggressively.\n # 1) See filter parameters below.\n # 2) Set price to lowest + (median - lowest / 4), rounded to closest quarter Euro.\n # 3) Undercut price in own country if not contradicting 2)\n # 4) Never go below 0.25 for foils\n\n account = api.get_account()['account']\n articles = api.get_articles(product_id, **{\n 'isFoil': 'true',\n 'isAltered': 'false',\n 'isSigned': 'false',\n 'minCondition': 'GD',\n 'idLanguage': language_id\n })\n\n keys = ['idArticle', 'count', 'price', 'condition', 'seller']\n foil_articles = [{x: y for x, y in art.items() if x in keys}\n for art in articles]\n local_articles = []\n for article in foil_articles:\n if article['seller']['address']['country'] == account['country'] and article['seller']['username'] != account['username']:\n local_articles.append(article)\n\n local_table_data = []\n for article in local_articles:\n if article:\n local_table_data.append([\n article['seller']['username'],\n article['seller']['address']['country'],\n article['condition'],\n article['count'],\n article['price']\n ])\n\n table_data = []\n for article in foil_articles:\n if article:\n table_data.append([\n article['seller']['username'],\n article['seller']['address']['country'],\n article['condition'],\n article['count'],\n article['price']\n ])\n\n median_price = PyMkmHelper.calculate_median(table_data, 3, 4)\n lowest_price = PyMkmHelper.calculate_lowest(table_data, 4)\n median_guided = PyMkmHelper.round_up_to_quarter(\n lowest_price + (median_price - lowest_price) / 4)\n\n if len(local_table_data) > 0:\n # Undercut if there is local competition\n lowest_in_country = PyMkmHelper.round_down_to_quarter(\n PyMkmHelper.calculate_lowest(local_table_data, 4))\n return max(0.25, min(median_guided, lowest_in_country - 0.25))\n else:\n # No competition in our country, set price a bit higher.\n return PyMkmHelper.round_up_to_quarter(median_guided * 1.2)\n\n def get_stock_as_array(self, api):\n d = api.get_stock()\n\n keys = ['idArticle', 'idProduct', 'product', 'count',\n 'price', 'isFoil', 'isSigned', 'language'] # TODO: [language][languageId]\n stock_list = [{x: y for x, y in article.items() if x in keys}\n for article in d]\n return stock_list\n", "sub_path": "pymkm_app.py", "file_name": "pymkm_app.py", "file_ext": "py", "file_size_in_byte": 20790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.WARN", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pymkmapi.PyMkmApi", "line_number": 31, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_string", "line_number": 53, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 53, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_string", "line_number": 59, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 59, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_bool", "line_number": 61, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 108, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_bool", "line_number": 109, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 109, "usage_type": "name"}, {"api_name": "json.load", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.remove", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_bool", "line_number": 123, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 123, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 129, "usage_type": "call"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 104, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_bool", "line_number": 159, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 159, "usage_type": "name"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 134, "usage_type": "name"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 168, "usage_type": "name"}, {"api_name": "tabulate.tabulate", "line_number": 220, "usage_type": "call"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 201, "usage_type": "name"}, {"api_name": "pprint.PrettyPrinter", "line_number": 230, "usage_type": "call"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 228, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.prompt_bool", "line_number": 236, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 236, "usage_type": "name"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 233, "usage_type": "name"}, {"api_name": "progressbar.ProgressBar", "line_number": 256, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 292, "usage_type": "call"}, {"api_name": "pymkmapi.api_wrapper", "line_number": 248, "usage_type": "name"}, {"api_name": "tabulate.tabulate", "line_number": 360, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper.calculate_average", "line_number": 366, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 366, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.calculate_median", "line_number": 367, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 367, "usage_type": "name"}, {"api_name": "progressbar.ProgressBar", "line_number": 381, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 417, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 445, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper.calculate_median", "line_number": 504, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 504, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.calculate_lowest", "line_number": 505, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 505, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.round_up_to_quarter", "line_number": 506, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 506, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.round_down_to_quarter", "line_number": 511, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 511, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.calculate_lowest", "line_number": 512, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 512, "usage_type": "name"}, {"api_name": "pymkm_helper.PyMkmHelper.round_up_to_quarter", "line_number": 516, "usage_type": "call"}, {"api_name": "pymkm_helper.PyMkmHelper", "line_number": 516, "usage_type": "name"}]} +{"seq_id": "155753119", "text": "# Self Driving Car\n\n# Importing the libraries\nimport numpy as np\nfrom random import random, randint, choice\nimport matplotlib.pyplot as plt\nimport time\nimport cv2\nimport math\nimport torchvision\nimport torch\nimport os\nimport random\nfrom collections import deque\n\n# Importing the Kivy packages\nfrom kivy.app import App\nfrom kivy.uix.widget import Widget\nfrom kivy.uix.button import Button\nfrom kivy.graphics import Color, Ellipse, Line\nfrom kivy.config import Config\nfrom kivy.properties import NumericProperty, ReferenceListProperty, ObjectProperty\nfrom kivy.vector import Vector\nfrom kivy.clock import Clock\nfrom kivy.core.image import Image as CoreImage\nfrom PIL import Image as PILImage\nfrom kivy.graphics.texture import Texture\n\n# Import TD3\nfrom ai import ReplayBuffer, TD3\n\n\n# Adding this line if we don't want the right click to put a red point\nConfig.set('input', 'mouse', 'mouse,multitouch_on_demand')\nConfig.set('graphics', 'resizable', False)\nConfig.set('graphics', 'width', '1429')\nConfig.set('graphics', 'height', '660')\n\n# Introducing last_x and last_y, used to keep the last point in memory when we draw the sand on the map\nlast_x = 0\nlast_y = 0\nn_points = 0\nlength = 0\n# action2rotation = [0,5,-5]\n\n\nim = CoreImage(\"./images/MASK1.png\")\n\n\n\ndef crop(img, x, y , angle = 0, crop_size = 100, scale_size = 32):\n img = np.asarray(img)\n def pad_with(vector, pad_width, iaxis, kwargs):\n pad_value = kwargs.get('padder', 10)\n vector[:pad_width[0]] = pad_value\n vector[-pad_width[1]:] = pad_value \n img = np.pad(img, crop_size // 2, pad_with, padder=255.)\n img_x, img_y = img.shape\n \n x += crop_size // 2\n y += crop_size // 2\n center_x = x + 10\n center_y = y + 5 \n\n cropped_image = img[center_x - crop_size//2:center_x + crop_size//2,center_y - crop_size//2:center_y + crop_size//2]\n\n res = cv2.resize(cropped_image, dsize=(scale_size,scale_size), interpolation=cv2.INTER_CUBIC)\n res = np.expand_dims(res, axis=0)\n res = torch.from_numpy(res)\n return res\n\nfirst_update = True\n\n# Initializing the map\ndef init():\n global sand\n global goal_x\n global goal_y\n global first_update\n global env_name\n global seed\n global start_timesteps\n global eval_freq\n global max_timesteps\n global save_models\n global expl_noise\n global batch_size\n global discount\n global tau\n global policy_noise\n global noise_clip\n global policy_freq\n sand = np.zeros((longueur,largeur))\n img = PILImage.open(\"./images/mask.png\").convert('L')\n sand = np.asarray(img)/255\n goal, _ = SelectRandomLocationTarget()\n goal_x, goal_y = goal[0], goal[1]\n first_update = False\n env_name = \"SelfDrivingCar-v0\" # Name of a environment (set it to any Continous environment you want)\n seed = 0\n start_timesteps = 1e3 # Number of iterations/timesteps before which the model randomly chooses an action, and after which it starts to use the policy network\n eval_freq = 5e2 # How often the evaluation step is performed (after how many timesteps)\n max_timesteps = 4e4 # Total number of iterations/timesteps\n save_models = True # Boolean checker whether or not to save the pre-trained model\n expl_noise = 0.1 # Exploration noise - STD value of exploration Gaussian noise\n batch_size = 32 # Size of the batch\n discount = 0.99 # Discount factor gamma, used in the calculation of the total discounted reward\n tau = 0.005 # Target network update rate\n policy_noise = 0.2 # STD of Gaussian noise added to the actions for the exploration purposes\n noise_clip = 0.5 # Maximum value of the Gaussian noise added to the actions (policy)\n policy_freq = 2 # Number of iterations to wait before the policy network (Actor model) is updated\n\n\n\ndef SelectRandomLocationTarget():\n TargetDict = {'A': [115, 215], 'B': [170, 547], 'C': [500, 50], 'D': [743,607], 'E': [650, 310], 'F': [800, 102], 'G': [697, 546], 'H': [1103, 351],\\\n 'I': [1177, 390], 'J': [1197, 150], 'K': [1326, 27], 'L': [1375, 247]}\n \n LocationDict = {'A': [127,297], 'B': [246,105], 'C': [658,248], 'D': [638,77], 'E': [731,386], 'F': [275,608], 'G': [595, 454], 'H': [864,171],\\\n 'I': [1013,32], 'J': [1297, 304], 'K': [900,88], 'L': [37,571]}\n\n TargetLocationName = ['A','B','C','D','E','F','G','H','I','J','K','L']\n\n # Selecting random target and Location\n Target = choice(TargetLocationName)\n Location = choice(TargetLocationName)\n\n return TargetDict[Target], LocationDict[Location]\n\n\n# Creating the car class\n\nclass Car(Widget):\n \n angle = NumericProperty(0)\n rotation = NumericProperty(0)\n velocity_x = NumericProperty(0)\n velocity_y = NumericProperty(0)\n velocity = ReferenceListProperty(velocity_x, velocity_y)\n\n def move(self, rotation):\n self.pos = Vector(*self.velocity) + self.pos\n self.rotation = rotation\n self.angle = self.angle + self.rotation\n reward = 0\n distance = np.sqrt((self.car.x - goal_x)**2 + (self.car.y - goal_y)**2)\n next_state = crop(sand, self.car.x, self.car.y, self.car.angle)\n if sand[int(self.car.x),int(self.car.y)] > 0:\n self.car.velocity = Vector(0.5, 0).rotate(self.car.angle)\n # print(1, goal_x, goal_y, distance, int(self.car.x),int(self.car.y), im.read_pixel(int(self.car.x),int(self.car.y))) \n reward += -5\n\n else: # otherwise\n self.car.velocity = Vector(2, 0).rotate(self.car.angle)\n reward += -0.5\n # print(0, goal_x, goal_y, distance, int(self.car.x),int(self.car.y), im.read_pixel(int(self.car.x),int(self.car.y)))\n \n if distance < last_distance and distance!=0:\n reward += 2\n elif distance == 0:\n reward += 100\n done = True\n else:\n reward += -0.2\n\n if self.car.x < 5:\n self.car.x = 5\n reward += -50\n done = True\n if self.car.x > self.width - 5:\n self.car.x = self.width - 5\n reward += -50\n done = True\n if self.car.y < 5:\n self.car.y = 5\n reward += -50\n done = True\n if self.car.y > self.height - 5:\n self.car.y = self.height - 5\n reward += -50 \n done = True \n\n return next_state, reward, done\n\n\n\nclass Game(Widget):\n\n car = ObjectProperty(None)\n\n def serve_car(self):\n _, self.car.pos = SelectRandomLocationTarget()\n self.car.velocity = Vector(6, 0)\n\n def update(self, dt):\n\n global longueur\n global largeur \n\n longueur = self.width\n largeur = self.height\n if first_update:\n init()\n def evaluate_policy(policy, eval_episodes=10):\n avg_reward = 0.\n for _ in range(eval_episodes):\n obs = reset(self)\n done = False\n while not done:\n action = policy.select_action(obs)\n obs, reward, done, _ = Car.move(action)\n avg_reward += reward\n avg_reward /= eval_episodes\n print (\"---------------------------------------\")\n print (\"Average Reward over the Evaluation Step: %f\" % (avg_reward))\n print (\"---------------------------------------\")\n return avg_reward \n file_name = \"%s_%s_%s\" % (\"TD3\", env_name, str(seed))\n print (\"---------------------------------------\")\n print (\"Settings: %s\" % (file_name))\n print (\"-------------------------------------- -\")\n\n if not os.path.exists(\"./results\"):\n os.makedirs(\"./results\")\n if save_models and not os.path.exists(\"./pytorch_models\"):\n os.makedirs(\"./pytorch_models\")\n \n torch.manual_seed(seed)\n np.random.seed(seed)\n\n state_dim = [32,32,1]\n action_dim = 1\n max_action = 5\n\n policy = TD3(state_dim, action_dim, max_action)\n\n replay_buffer = ReplayBuffer()\n\n evaluations = [evaluate_policy(policy)]\n\n def mkdir(base, name):\n path = os.path.join(base, name)\n if not os.path.exists(path):\n os.makedirs(path)\n return path\n work_dir = mkdir('exp', 'brs')\n monitor_dir = mkdir(work_dir, 'monitor')\n max_episode_steps = 400 \n\n total_timesteps = 0\n timesteps_since_eval = 0\n episode_num = 0\n done = True\n t0 = time.time() \n\n# We start the main loop over 40,000 timesteps\n while total_timesteps < max_timesteps:\n \n # If the episode is done\n if done:\n\n # If we are not at the very beginning, we start the training process of the model\n if (total_timesteps != 0 and total_timesteps > (batch_size)):\n print(\"Total Timesteps: {} Episode Num: {} Reward: {}\".format(total_timesteps, episode_num, episode_reward))\n policy.train(replay_buffer, episode_timesteps, batch_size, discount, tau, policy_noise, noise_clip, policy_freq)\n\n # We evaluate the episode and we save the policy\n if timesteps_since_eval >= eval_freq:\n timesteps_since_eval %= eval_freq\n evaluations.append(evaluate_policy(policy))\n policy.save(file_name, directory=\"./pytorch_models\")\n np.save(\"./results/%s\" % (file_name), evaluations)\n \n # When the training step is done, we reset the state of the environment\n obs = reset()\n \n # Set the Done to False\n done = False\n \n # Set rewards and episode timesteps to zero\n episode_reward = 0\n episode_timesteps = 0\n episode_num += 1\n \n # Before 10000 timesteps, we play random actions\n if total_timesteps < start_timesteps:\n action = np.random.normal(0, 1, size=1).clip(-1, 1).astype(np.float32)\n else: # After 10000 timesteps, we switch to the model\n action = policy.select_action(obs)\n # If the explore_noise parameter is not 0, we add noise to the action and we clip it\n if expl_noise != 0:\n action = (action + np.random.normal(0, expl_noise, size=1)).clip(-1, 1)\n \n # The agent performs the action in the environment, then reaches the next state and receives the reward\n new_obs, reward, done, _ = move(action)\n \n # We check if the episode is done\n # done_bool = 0 if episode_timesteps + 1 == env._max_episode_steps else float(done)\n if episode_timesteps + 1 == max_episode_steps:\n done = True\n done = float(done)\n # We increase the total reward\n episode_reward += reward\n \n # We store the new transition into the Experience Replay memory (ReplayBuffer)\n replay_buffer.add((obs, new_obs, action, reward, done_bool))\n\n # We update the state, the episode timestep, the total timesteps, and the timesteps since the evaluation of the policy\n obs = new_obs\n episode_timesteps += 1\n total_timesteps += 1 \n timesteps_since_eval += 1\n\n t1 = time.time()\n print(\"Total time taken: {}\".format(t1-t0)) \n evaluations.append(evaluate_policy(policy))\n if save_models: policy.save(\"%s\" % (file_name), directory=\"./pytorch_models\")\n np.save(\"./results/%s\" % (file_name), evaluations) \n CarApp().stop() \n\n # rotation = choice(action2rotation)\n # self.car.move(rotation)\n # if self.car.x > self.width - 5:\n # reset(self)\n\n \n\ndef reset(self):\n _, self.car.pos = SelectRandomLocationTarget()\n self.car.velocity = Vector(6, 0)\n self.car.angle = 0\n state = crop(sand, self.car.x, self.car.y, self.car.angle)\n return state\n\n\nclass MyPaintWidget(Widget):\n\n def on_touch_down(self, touch):\n global length, n_points, last_x, last_y\n with self.canvas:\n Color(0.8,0.7,0)\n d = 10.\n touch.ud['line'] = Line(points = (touch.x, touch.y), width = 10)\n last_x = int(touch.x)\n last_y = int(touch.y)\n n_points = 0\n length = 0\n sand[int(touch.x),int(touch.y)] = 1\n img = PILImage.fromarray(sand.astype(\"uint8\")*255)\n img.save(\"./images/sand.jpg\")\n\n def on_touch_move(self, touch):\n global length, n_points, last_x, last_y\n if touch.button == 'left':\n touch.ud['line'].points += [touch.x, touch.y]\n x = int(touch.x)\n y = int(touch.y)\n length += np.sqrt(max((x - last_x)**2 + (y - last_y)**2, 2))\n n_points += 1.\n density = n_points/(length)\n touch.ud['line'].width = int(20 * density + 1)\n sand[int(touch.x) - 10 : int(touch.x) + 10, int(touch.y) - 10 : int(touch.y) + 10] = 1\n\n \n last_x = x\n last_y = y\n\n# Adding the API Buttons (clear, save and load)\n\nclass CarApp(App):\n\n def build(self):\n parent = Game()\n parent.serve_car()\n Clock.schedule_interval(parent.update, 1.0/60.0)\n self.painter = MyPaintWidget()\n clearbtn = Button(text = 'clear')\n savebtn = Button(text = 'save', pos = (parent.width, 0))\n loadbtn = Button(text = 'load', pos = (2 * parent.width, 0))\n clearbtn.bind(on_release = self.clear_canvas)\n savebtn.bind(on_release = self.save)\n loadbtn.bind(on_release = self.load)\n parent.add_widget(self.painter)\n parent.add_widget(clearbtn)\n parent.add_widget(savebtn)\n parent.add_widget(loadbtn)\n return parent\n\n def clear_canvas(self, obj):\n global sand\n self.painter.canvas.clear()\n sand = np.zeros((longueur,largeur))\n\n def save(self, obj):\n print(\"saving brain...\")\n # brain.save()\n plt.plot(scores)\n plt.show()\n\n def load(self, obj):\n print(\"loading last saved brain...\")\n # brain.load()\n\n# # # Running the whole thing\nif __name__ == '__main__':\n CarApp().run()\n\n\n\n", "sub_path": "Phase2/Session10/AgentMap.py", "file_name": "AgentMap.py", "file_ext": "py", "file_size_in_byte": 14447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "kivy.config.Config.set", "line_number": 34, "usage_type": "call"}, {"api_name": "kivy.config.Config", "line_number": 34, "usage_type": "name"}, {"api_name": "kivy.config.Config.set", "line_number": 35, "usage_type": "call"}, {"api_name": "kivy.config.Config", "line_number": 35, "usage_type": "name"}, {"api_name": "kivy.config.Config.set", "line_number": 36, "usage_type": "call"}, {"api_name": "kivy.config.Config", "line_number": 36, "usage_type": "name"}, {"api_name": "kivy.config.Config.set", "line_number": 37, "usage_type": "call"}, {"api_name": "kivy.config.Config", "line_number": 37, "usage_type": "name"}, {"api_name": "kivy.core.image.Image", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 94, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 94, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 95, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 125, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 126, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 133, "usage_type": "name"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 135, "usage_type": "call"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 136, "usage_type": "call"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 137, "usage_type": "call"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 138, "usage_type": "call"}, {"api_name": "kivy.properties.ReferenceListProperty", "line_number": 139, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 146, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 149, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 154, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 187, "usage_type": "name"}, {"api_name": "kivy.properties.ObjectProperty", "line_number": 189, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.exists", "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": "torch.manual_seed", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 229, "usage_type": "attribute"}, {"api_name": "ai.TD3", "line_number": 235, "usage_type": "call"}, {"api_name": "ai.ReplayBuffer", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 244, "usage_type": "call"}, {"api_name": "time.time", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 272, "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.float32", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 292, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 318, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 330, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 336, "usage_type": "name"}, {"api_name": "kivy.graphics.Color", "line_number": 341, "usage_type": "call"}, {"api_name": "kivy.graphics.Line", "line_number": 343, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 349, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 349, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 358, "usage_type": "call"}, {"api_name": "kivy.app.App", "line_number": 370, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_interval", "line_number": 375, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 375, "usage_type": "name"}, {"api_name": "kivy.uix.button.Button", "line_number": 377, "usage_type": "call"}, {"api_name": "kivy.uix.button.Button", "line_number": 378, "usage_type": "call"}, {"api_name": "kivy.uix.button.Button", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 392, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 397, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 397, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 398, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 398, "usage_type": "name"}]} +{"seq_id": "238265594", "text": "from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport sys, argparse, subprocess\n\nfrom .version import __version__\n\n# jq arguments that consume positionals must be listed here to avoid our parser mistaking them for our positionals\njq_arg_spec = {\"--indent\": 1, \"-f\": 1, \"--from-file\": 1, \"-L\": 1, \"--arg\": 2, \"--argjson\": 2, \"--slurpfile\": 2,\n \"--argfile\": 2, \"--rawfile\": 2, \"--args\": argparse.REMAINDER, \"--jsonargs\": argparse.REMAINDER}\n\nclass Parser(argparse.ArgumentParser):\n def print_help(self):\n yq_help = argparse.ArgumentParser.format_help(self).splitlines()\n print(\"\\n\".join([\"usage: yq [options] [YAML file...]\"] + yq_help[1:] + [\"\"]))\n sys.stdout.flush()\n try:\n subprocess.check_call([\"jq\", \"--help\"])\n except Exception:\n pass\n\ndef get_parser(program_name, description):\n # By default suppress these help strings and only enable them in the specific programs.\n yaml_output_help, yaml_roundtrip_help, width_help, indentless_help = (argparse.SUPPRESS, argparse.SUPPRESS,\n argparse.SUPPRESS, argparse.SUPPRESS)\n xml_output_help, xml_dtd_help, xml_root_help, xml_force_list_help = (argparse.SUPPRESS, argparse.SUPPRESS,\n argparse.SUPPRESS, argparse.SUPPRESS)\n toml_output_help = argparse.SUPPRESS\n\n if program_name == \"yq\":\n current_language = \"YAML\"\n yaml_output_help = \"Transcode jq JSON output back into YAML and emit it\"\n yaml_roundtrip_help = (\"Transcode jq JSON output back into YAML and emit it. \"\n \"Preserve YAML tags and styles by representing them as extra items \"\n \"in their enclosing mappings and sequences while in JSON. This option \"\n \"is incompatible with jq filters that do not expect these extra items.\")\n width_help = \"When using --yaml-output, specify string wrap width\"\n indentless_help = (\"When using --yaml-output, indent block style lists (sequences) \"\n \"with 0 spaces instead of 2\")\n elif program_name == \"xq\":\n current_language = \"XML\"\n xml_output_help = \"Transcode jq JSON output back into XML and emit it\"\n xml_dtd_help = \"Preserve XML Document Type Definition (disables streaming of multiple docs)\"\n xml_root_help = \"When transcoding back to XML, envelope the output in an element with this name\"\n xml_force_list_help = (\"Tag name to pass to force_list parameter of xmltodict.parse(). \"\n \"Can be used multiple times.\")\n elif program_name == \"tq\":\n current_language = \"TOML\"\n toml_output_help = \"Transcode jq JSON output back into TOML and emit it\"\n else:\n raise Exception(\"Unknown program name\")\n\n description = description.replace(\"yq\", program_name).replace(\"YAML\", current_language)\n parser_args = dict(prog=program_name, description=description, formatter_class=argparse.RawTextHelpFormatter)\n if sys.version_info >= (3, 5):\n parser_args.update(allow_abbrev=False) # required to disambiguate options listed in jq_arg_spec\n parser = Parser(**parser_args)\n parser.add_argument(\"--output-format\", default=\"json\", help=argparse.SUPPRESS)\n parser.add_argument(\"--yaml-output\", \"--yml-output\", \"-y\", dest=\"output_format\", action=\"store_const\", const=\"yaml\",\n help=yaml_output_help)\n parser.add_argument(\"--yaml-roundtrip\", \"--yml-roundtrip\", \"-Y\", dest=\"output_format\", action=\"store_const\",\n const=\"annotated_yaml\", help=yaml_roundtrip_help)\n parser.add_argument(\"--width\", \"-w\", type=int, help=width_help)\n parser.add_argument(\"--indentless-lists\", \"--indentless\", action=\"store_true\", help=indentless_help)\n parser.add_argument(\"--explicit-start\", action=\"store_true\", help=argparse.SUPPRESS)\n parser.add_argument(\"--explicit-end\", action=\"store_true\", help=argparse.SUPPRESS)\n parser.add_argument(\"--xml-output\", \"-x\", dest=\"output_format\", action=\"store_const\", const=\"xml\",\n help=xml_output_help)\n parser.add_argument(\"--xml-dtd\", action=\"store_true\", help=xml_dtd_help)\n parser.add_argument(\"--xml-root\", help=xml_root_help)\n parser.add_argument(\"--xml-force-list\", action=\"append\", help=xml_force_list_help)\n parser.add_argument(\"--toml-output\", \"-t\", dest=\"output_format\", action=\"store_const\", const=\"toml\",\n help=toml_output_help)\n parser.add_argument(\"--in-place\", \"-i\", action=\"store_true\", help=\"Edit files in place (no backup - use caution)\")\n parser.add_argument(\"--version\", action=\"version\", version=\"%(prog)s {version}\".format(version=__version__))\n\n for arg in jq_arg_spec:\n parser.add_argument(arg, nargs=jq_arg_spec[arg], dest=arg, action=\"append\", help=argparse.SUPPRESS)\n\n parser.add_argument(\"jq_filter\")\n parser.add_argument(\"input_streams\", nargs=\"*\", type=argparse.FileType(), metavar=\"files\", default=[sys.stdin])\n return parser\n", "sub_path": "yq/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 5194, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.REMAINDER", "line_number": 9, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser.format_help", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 15, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 17, "usage_type": "call"}, {"api_name": "argparse.SUPPRESS", "line_number": 23, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 25, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 26, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "argparse.RawTextHelpFormatter", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 54, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 57, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 64, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 65, "usage_type": "attribute"}, {"api_name": "version.__version__", "line_number": 74, "usage_type": "name"}, {"api_name": "argparse.SUPPRESS", "line_number": 77, "usage_type": "attribute"}, {"api_name": "argparse.FileType", "line_number": 80, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "483361746", "text": "\n# coding: utf-8\n\n# In[2]:\n\n\nimport matplotlib\nimport loadnotebook\nfrom helper import * \nimport os\nimport os.path\n\n# In[3]:\n\n\n#Check the priority and set first\n#And modify whitelist in helper\n#lock_pci and pci_locker are for single pci map\n\nimport sys\nset_value = sys.argv[1]\npci_locker = int(sys.argv[2])\ntime_interval = int(sys.argv[3])\nif pci_locker == 0:\n lock_pci = False\nelse:\n lock_pci = True\n \npriority = 6\n'''\nset_value = 33\n\n#Set lock_pci = True, if you want to show the map for one specific pci\n#And the pci_locker is which pci you want \n\nlock_pci = False\npci_locker = 13\n'''\n#to check is there a missing point we need to regather\n\nsource = get_source(priority, set_value)\n\n#make sure there is the correct path for file to put in\noutput_csv = \"../results/demo_priority_\" + str(priority) + \"/set\" + str(set_value) + \".csv\"\n \ndef get_output_image(prefix=\"\") :\n if lock_pci and pci_locker in whitelist_PCI:\n return \"../results/demo_priority_\" + str(priority) + \"/images/set\" + str(set_value) +\"/\"+str(pci_locker)+ \"_\" + prefix + \".png\"\n else:\n return \"../results/demo_priority_\" + str(priority) + \"/images/set\" + str(set_value) + \"_\" + prefix + \".png\"\n\ndef get_output_image_movie(prefix=\"\") :\n if lock_pci and pci_locker in whitelist_PCI:\n return \"../results/demo_priority_\" + str(priority) + \"/movie_element/set\" + str(set_value) +\"/\"+str(pci_locker)+ \"_\" + prefix + \".png\"\n else:\n return \"../results/demo_priority_\" + str(priority) + \"/movie_element/set\" + str(set_value) + \"_\" + prefix + \".png\"\n\ndef delete_images(): \n if lock_pci:\n folder_path = \"../results/demo_priority_\" + str(priority) + \"/movie_element/set\" + str(set_value) +\"/\"\n else:\n folder_path = \"../results/demo_priority_\" + str(priority) + \"/movie_element/\"\n \n filelist = [ f for f in os.listdir(folder_path) if f.endswith(\".png\") ]\n for file_name in filelist:\n #print(os.path.join(folder_path, file_name))\n os.remove(os.path.join(folder_path, file_name))\n\nresult = pd.read_csv(output_csv) #read csv as df\n#LOCK THE PCI\n\nif lock_pci and pci_locker in whitelist_PCI:\n filter = result[\"PCI\"] == pci_locker\n result=result[filter]\n \n#TIME DEPEND PCI RESULT \n\ndf = result.dropna(subset=[\"RSRQ\"])\ndf=df.reset_index()\nlon_list = df[\"location_x\"].astype('int32')\nlat_list = df[\"location_y\"].astype('int32')\ntime_list = df[\"timestamp\"].apply(str)\nrsrq_list = df[\"RSRQ\"].astype('int32')\n\n#RSRQ Location Map_mean\nrsrq_list=rsrq_list.values\n\nnormalize_rsrq = matplotlib.colors.Normalize(vmin=-30, vmax=-0.4)\n\ncolors_rsrq = [cmap(normalize_rsrq(value))[:3] for value in rsrq_list]\ncolors_rsrq = [[int(x*255) for x in value] for value in colors_rsrq]\n\nend_f=False\ntime_already_dict={}\nrsrq_already_dict={}\ncount = 0\n#time_interval=5\ndelete_images()\nwhile (not end_f and count < 120/time_interval) :\n new_backtorgb = get_map_image()\n time_already_dict, rsrq_already_dict, new_backtorgb,end_f = visualize_time_cmap(new_backtorgb, lon_list,\n lat_list, colors_rsrq,\n time_list, time_already_dict,\n rsrq_already_dict,\n cmap,\n normalize_rsrq, \n get_output_image_movie(\"rsrq_\"+str(count)),\n time_interval=time_interval)\n count=count+1\n \nprint(\"----DONE!!!----\")\n\n", "sub_path": "web/itri/itriheatmap-master/src/web_rsrq_through_time.py", "file_name": "web_rsrq_through_time.py", "file_ext": "py", "file_size_in_byte": 3605, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 88, "usage_type": "attribute"}]} +{"seq_id": "9101945", "text": "import collections\n\n\ndef parse_content_type(c: str) -> tuple[str, str, dict[str, str]] | None:\n \"\"\"\n A simple parser for content-type values. Returns a (type, subtype,\n parameters) tuple, where type and subtype are strings, and parameters\n is a dict. If the string could not be parsed, return None.\n\n E.g. the following string:\n\n text/html; charset=UTF-8\n\n Returns:\n\n (\"text\", \"html\", {\"charset\": \"UTF-8\"})\n \"\"\"\n parts = c.split(\";\", 1)\n ts = parts[0].split(\"/\", 1)\n if len(ts) != 2:\n return None\n d = collections.OrderedDict()\n if len(parts) == 2:\n for i in parts[1].split(\";\"):\n clause = i.split(\"=\", 1)\n if len(clause) == 2:\n d[clause[0].strip()] = clause[1].strip()\n return ts[0].lower(), ts[1].lower(), d\n\n\ndef assemble_content_type(type, subtype, parameters):\n if not parameters:\n return f\"{type}/{subtype}\"\n params = \"; \".join(f\"{k}={v}\" for k, v in parameters.items())\n return f\"{type}/{subtype}; {params}\"\n", "sub_path": "mitmproxy/net/http/headers.py", "file_name": "headers.py", "file_ext": "py", "file_size_in_byte": 1035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.OrderedDict", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "599554967", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport urllib.request, urllib.parse, urllib.error\nfrom bs4 import BeautifulSoup\n\n# 获取当前页新闻链接\ndef page_url(url_n):\n page_n = urllib.request.urlopen(url_n).read()\n soup_n = BeautifulSoup(page_n, 'html.parser')\n title = soup_n.find_all(\"a\", class_=\"news-title\")\n href_n = []\n for i in title:\n href_n.append(i.get(\"href\"))\n return href_n\n\n# 获取每一页的新闻链接\ndef news_url(href):\n href_n = href\n href_1 = page_url(href_n)\n # print(href_1)\n news_u = href_1\n while True:\n page_f = urllib.request.urlopen(href_n).read()\n soup_f = BeautifulSoup(page_f, 'html.parser')\n link_f = soup_f.find_all(\"a\", class_=\"i-pager-next\")\n if link_f:\n url_f = link_f[0].get(\"href\") # 获取下一页网址\n # print(page_url(url_f))\n news_u.append(page_url(url_f))\n href_n = url_f # 修改当前页网址\n continue\n else:\n print(\"OK I got all the news_url!\")\n # print(news_u)\n return news_u\n break\n\nnews_1 = news_url('http://www.poly.com.cn/1091.html')\nfor i in news_1:\n # print(type(i).string)\n if type(i) == \"\":\n print(i)\n elif type(i) == \"\":\n for h in i:\n print(h)\n else:\n print(\"wrong\")", "sub_path": "poly_spider/get_news.py", "file_name": "get_news.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 9, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 9, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 24, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 24, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 24, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "98730186", "text": "# -*- coding: utf-8 -*-\n\n# Canal privado para acceso a GDrive\n\n# Requiere haber añadido manualmente estas claves en settings.xml (copiarlas del settings.xml del addon gdrive habiendo hecho allí el enroll)\n# ...\n# ...\n# ...\n# ...\n# ...\n# ...\n\n# Requerimientos de los contenidos en GDrive:\n\n# - Los vídeos de películas deberían tener esta nomenclatura: Título de la peli (año) [géneros] [calidad]\n# (título y año requeridos para acceder a TMDB. géneros y calidad opcionales)\n# - Los vídeos de episodios deberían tener esta nomenclatura: Título de la serie - SnnEnn (o TnnEnn o nnXnn) - título del episodio\n# (título de serie y Nº de temporada y episodio requeridos para acceder a TMDB. título del episodio opcional)\n# Los vídeos de episodios se identifican por tener SnnEnn o TnnEnn o nnXnn (S,E,T,X pueden ser en mayúsculas o minúsculas, nn números con 1 o 2 cifras)\n# - Las series deberían estar dentro de ciertas carpetas (ID_FOLDERS_TVSHOWS) y tener subcarpetas tipo Season NN (o Temporada NN o Temp NN)\n# (preferiblemente indicar (año) en las carpetas con el nombre de la serie para detectar mejor en TMDB)\n# Ej: Series/The Big Bang Theory (2007)/Temporada 12/\n# - ...\n\n# Ejemplos:\n# Cartas a Roxanne (2019) [Comedia, Drama].mp4\n# Cartas a Roxanne (2019) [Comedia, Drama] [4K].mp4\n# The Big Bang Theory - S12E02 - Penny y Sheldon.mp4\n# The Big Bang Theory - S12E03.avi\n# The Big Bang Theory - s12e04.mkv\n# The Big Bang Theory - T12E05.mkv\n# The Big Bang Theory (2007) - T12E06.mkv\n# The Big Bang Theory - 12x05.mkv\n\n# Notas:\n# - La búsqueda en GD tiene en cuenta los acentos. Ej: Si una peli se titula 101 dálmatas, no se encuentra buscando dalmatas, hay que buscar dálmatas.\n\n# --------------------------------------------------------------\n\n# Parámetros configurables:\n\n# - Indicar en ID_FOLDERS_TVSHOWS las carpetas que contienen series con la estructura de carpetas requerida. Asignar [] si no se usa.\n# (ver los ids de las carpetas en Google Drive, e indicar parejas de ('id', 'nombre') para cada carpeta)\n\n# - Indicar en ID_FOLDERS_SHORTCUTS las carpetas a las que se quiere crear un acceso directo. Asignar [] si no se usa.\n# (ver los ids de las carpetas en Google Drive, e indicar parejas de ('id', 'nombre') para cada carpeta)\n\n# - Indicar en TAGS_GENEROS los tags de géneros por los que buscar en los vídeos. Asignar [] si no se usa.\n# (requiere que los nombres de los ficheros tengan esos géneros escritos)\n\n# - Indicar en TAGS_CALIDADES los tags de calidades por los que buscar en los vídeos. Asignar [] si no se usa.\n# (requiere que los nombres de los ficheros tengan esas calidades escritas)\n\n# - Indicar en SORT_VIDEOS_BY si se prefiere ordenar por fecha de creación o modificación.\n# (cuando se lista el contenido de una carpeta se hace por orden alfabético, pero sinó por el orden indicado)\n\nID_FOLDERS_TVSHOWS = [\n\t('1lDuMkMtwFOXMu1SuGsxf_W0pLT_46bvR', 'ANTERIORES 2020'),\n\t('1wvawPjb7f_ClS20UEOygU1FMm8H_q8_d', 'NUEVAS 2020'),\n\t('1tXnIcLcN70XIAld42wxeJ3xq4FMbIAP-', 'Series Documentales'),\n\t('1pww1Oq3ERcfuDhcK61BFuGb0CT9JEei7', 'Series Infantiles'),\n]\n\nID_FOLDERS_SHORTCUTS = [\n ('1rQQSpZo-WXY9UU1IIoxBqUcGBEbVTPxt', 'Anime'),\n ('1rkVSwrdAowfin-8bGREcDqfTPcKPhrRZ', 'Documentales'),\n ('1UvmEGTVeXQXZkk0biANZy_1lIXE2BFDY', 'Infantil'),\n ('1sVT3qPAmY58O2OoIEIcjzNa3PwWn1tun', 'Peliculas'),\n ('1HEbOPEZjTvZ1N_QmYiHaMcokeQzqd23m', '3D'),\n ('1Bms4hlLmEdAQilUw8RY42HY43EiDvtB4', '4K'),\n]\n\nTAGS_GENEROS = ['Anime', 'Acción', 'Animación', 'Aventura', 'Bélica', 'Ciencia ficción', 'Comedia', 'Crimen', 'Documental', 'Drama', 'Familia', 'Fantasía', 'Guerra', 'Historia', 'Infantil', 'Intriga', 'Misterio', 'Música', 'Romance', 'Suspense', 'Terror', 'Thriller', 'Western']\n\nTAGS_CALIDADES = ['4K', '1080p', '720p', '480p', '360p']\n\nSORT_VIDEOS_BY = 'modifiedTime+desc' # Opciones: 'createdTime+desc' o 'modifiedTime+desc'\n\nCOLOR_FOLDERS_TVSHOWS = 'yellow'\nCOLOR_FOLDERS_SHORTCUTS = 'cyan'\n\n# Parámetros configurables en los settings del addon:\nPERPAGE_VIDEOS = 10 # películas/series por página (No pasar de 30 por la limitación de llamadas a tmdb)\nPERPAGE_FOLDERS = 50 # carpetas por página\n\n# --------------------------------------------------------------\n\nimport sys, re, os, time\n\nif sys.version_info[0] < 3: import urllib\nelse: import urllib.parse as urllib\n\nfrom platformcode import config, logger, platformtools\nfrom core.item import Item\nfrom core import httptools, scrapertools, tmdb\n\nfrom lib.gdrivetools import gdrive\n\n\ndef comprobar_settings_piphav():\n global PERPAGE_VIDEOS, PERPAGE_FOLDERS\n \n tmdb_disabled = config.get_setting(\"tmdb_disabled\", default=False)\n piphav_tmdb_disabled = config.get_setting(\"piphav_tmdb_disabled\", default=False)\n piphav_perpage_with_tmdb = config.get_setting(\"piphav_perpage_with_tmdb\", default=10)\n piphav_perpage_without_tmdb = config.get_setting(\"piphav_perpage_without_tmdb\", default=50)\n piphav_perpage_folders = config.get_setting(\"piphav_perpage_folders\", default=50)\n \n if piphav_tmdb_disabled:\n PERPAGE_VIDEOS = piphav_perpage_without_tmdb\n else:\n PERPAGE_VIDEOS = piphav_perpage_with_tmdb\n\n PERPAGE_FOLDERS = piphav_perpage_folders\n\n if tmdb_disabled != piphav_tmdb_disabled:\n config.set_setting(\"tmdb_disabled\", piphav_tmdb_disabled)\n\n\n# --------------------------------------------------------------\n\ndef mainlist(item):\n return mainlist_drives(item)\n\ndef mainlist_pelis(item):\n return mainlist_drives(item)\n\ndef mainlist_series(item):\n return mainlist_drives(item)\n\n\ndef mainlist_drives(item):\n logger.info()\n itemlist = []\n \n gd = gdrive('gdrive1')\n drives = gd.getDrives()\n if drives is None:\n platformtools.dialog_notification('Acceso a GDrive', 'Falla el login!')\n return itemlist\n\n plot = 'Acceso a todo lo que el usuario tiene permiso (propio, compartido y drives), y acceso directo a carpetas predefinidas.'\n itemlist.append(item.clone( title = 'Acceso Global', action = 'list_drive', drive_id = None, drive_name = 'global', plot=plot ))\n\n plot = 'Acceso sólo a los ficheros/carpetas del propio usuario o que le hayan compartido.'\n itemlist.append(item.clone( title = 'Acceso Usuario', action = 'list_drive', drive_id = 'root', drive_name = 'usuario', plot=plot ))\n\n plot = 'Acceso sólo a los ficheros/carpetas que hay en este drive.'\n for drive_id, drive_name in drives:\n itemlist.append(item.clone( title = 'Acceso Drive: ' + drive_name, action = 'list_drive', drive_id = drive_id, drive_name = drive_name, plot=plot ))\n\n return itemlist\n\n\ndef list_drive(item):\n logger.info()\n itemlist = []\n\n item.category = 'GD_' + item.drive_name\n\n itemlist.append(item.clone( title = 'Últimos vídeos', action = 'list_all', drive_q = \"\", nextPageToken=None ))\n\n itemlist.append(item.clone( title = 'Por años', action = 'anios' ))\n\n if len(TAGS_GENEROS) > 0:\n itemlist.append(item.clone( title = 'Por géneros', action = 'generos' ))\n\n if len(TAGS_CALIDADES) > 0:\n itemlist.append(item.clone( title = 'Por calidades', action = 'calidades' ))\n\n itemlist.append(item.clone( title = 'Buscar vídeos ...', action = 'search', search_type = 'all' ))\n itemlist.append(item.clone( title = 'Buscar vídeos (sin series) ...', action = 'search', search_type = 'movie' ))\n\n if not item.drive_id:\n for fid, fname in ID_FOLDERS_SHORTCUTS:\n itemlist.append(item.clone( title = '[COLOR %s]%s[/COLOR]' % (COLOR_FOLDERS_SHORTCUTS, fname), action = 'carpetas', drive_parent = fid ))\n\n for fid, fname in ID_FOLDERS_TVSHOWS:\n itemlist.append(item.clone( title = '[COLOR %s]%s[/COLOR]' % (COLOR_FOLDERS_TVSHOWS, fname), action = 'carpeta_series', drive_parent = fid ))\n\n if len(ID_FOLDERS_TVSHOWS) > 0:\n itemlist.append(item.clone( title = 'Buscar series ...', action = 'search', search_type = 'tvshow' ))\n\n elif item.drive_id == 'root':\n itemlist.append(item.clone( title = 'Carpetas propias del usuario', action = 'carpetas', drive_parent = 'root' ))\n itemlist.append(item.clone( title = 'Carpetas compartidas con el usuario', action = 'carpetas', drive_parent = '' ))\n\n else:\n itemlist.append(item.clone( title = 'Carpetas del drive ' + item.drive_name, action = 'carpetas', drive_parent = '' ))\n\n return itemlist\n\n\n\ndef anios(item):\n logger.info()\n itemlist = []\n\n from datetime import datetime\n current_year = int(datetime.today().year)\n\n for x in range(current_year, 1950, -1):\n itemlist.append(item.clone( title=str(x), action='list_all', drive_q = \"name+contains+'(%s)'\" % x, nextPageToken=None ))\n\n return itemlist\n\ndef generos(item):\n logger.info()\n itemlist = []\n\n for x in TAGS_GENEROS:\n itemlist.append(item.clone( title=x, action='list_all', drive_q = \"name+contains+'(%s)'\" % urllib.quote_plus(x) ))\n\n return itemlist\n\ndef calidades(item):\n logger.info()\n itemlist = []\n\n for x in TAGS_CALIDADES:\n itemlist.append(item.clone( title=x, action='list_all', drive_q = \"name+contains+'(%s)'\" % urllib.quote_plus(x) ))\n\n return itemlist\n\n\n\ndef carpetas(item):\n logger.info()\n itemlist = []\n comprobar_settings_piphav()\n\n q = \"mimeType+=+'application/vnd.google-apps.folder'+and+trashed%3Dfalse\"\n\n if item.drive_parent: q += \"+and+'\"+str(item.drive_parent)+\"'+in+parents\"\n elif item.drive_id == 'root': q += \"+and+sharedWithMe%3Dtrue\"\n elif item.drive_id: q += \"+and+'\"+str(item.drive_id)+\"'+in+parents\"\n\n gd = gdrive('gdrive1')\n files = gd.getFiles(item.drive_id, q=q, nextPageToken=item.nextPageToken, perpage=PERPAGE_FOLDERS, orden='name')\n if not files or 'files' not in files: return itemlist\n \n for f in files['files']:\n itemlist.append(item.clone( title=f['name'], action='carpetas', drive_parent = f['id'], nextPageToken=None ))\n logger.info('%s %s' % (f['id'], f['name']))\n\n if 'nextPageToken' in files and files['nextPageToken']:\n itemlist.append(item.clone( title=\">> Siguientes carpetas\", nextPageToken=files['nextPageToken'], action='carpetas' ))\n\n if item.drive_parent:\n itemlist.extend(list_all(item)) # añadir vídeos de la carpeta\n\n return itemlist\n\n\ndef carpeta_series(item):\n logger.info()\n itemlist = []\n if not item.drive_parent: return itemlist\n comprobar_settings_piphav()\n \n q = \"mimeType+=+'application/vnd.google-apps.folder'+and+trashed%3Dfalse\"\n q += \"+and+'\"+str(item.drive_parent)+\"'+in+parents\"\n\n gd = gdrive('gdrive1')\n files = gd.getFiles(item.drive_id, q=q, nextPageToken=item.nextPageToken, perpage=PERPAGE_VIDEOS, orden='name')\n if not files or 'files' not in files or len(files['files']) == 0: return itemlist\n \n for f in files['files']:\n title = f['name']\n year = scrapertools.find_single_match(title, '\\((\\d{4})\\)')\n if year:\n title = title.replace('(%s)' % year, '').strip()\n else:\n year = '-'\n\n itemlist.append(item.clone( action='carpeta_temporadas', title=title, drive_parent=f['id'], nextPageToken=None,\n thumbnail=config.get_thumb('dev'),\n contentType='tvshow', contentSerieName=title, infoLabels={'year': year, 'tvshowtitle': title} ))\n\n tmdb.set_infoLabels(itemlist)\n\n if 'nextPageToken' in files and files['nextPageToken']:\n itemlist.append(item.clone( title=\">> Página siguiente\", nextPageToken=files['nextPageToken'], action='carpeta_series' ))\n\n return itemlist\n\ndef carpeta_temporadas(item):\n logger.info()\n itemlist = []\n if not item.drive_parent: return itemlist\n comprobar_settings_piphav()\n\n q = \"mimeType+=+'application/vnd.google-apps.folder'+and+trashed%3Dfalse\"\n q += \"+and+'\"+str(item.drive_parent)+\"'+in+parents\"\n\n gd = gdrive('gdrive1')\n files = gd.getFiles(item.drive_id, q=q, perpage=30, orden='name')\n # ~ if not files or 'files' not in files or len(files['files']) == 0: return itemlist\n if not files or 'files' not in files or len(files['files']) == 0: return list_all(item) # Si no se detectan temporadas listar si hay vídeos\n \n for f in files['files']:\n season = scrapertools.find_single_match(f['name'], '(\\d+)')\n if not season: continue\n\n itemlist.append(item.clone( action='list_all', title=f['name'], drive_parent=f['id'],\n thumbnail=config.get_thumb('dev'),\n contentType='season', contentSeason = season ))\n \n tmdb.set_infoLabels(itemlist)\n\n return itemlist\n\n\ndef list_all(item):\n logger.info()\n itemlist = []\n comprobar_settings_piphav()\n \n # El q = básico sería \"mimeType+contains+'video'+and+trashed%3Dfalse\"\n # Pero para mostrar vídeos que gd no reconoce como tal y trata como 'binarios', ampliamos la búsqueda a 'application/octet-stream' con ciertas extensiones (.mkv, .avi, ...)\n\n # ~ q = \"mimeType+contains+'video'+and+trashed%3Dfalse\"\n q = \"(mimeType+contains+'video'+or+(mimeType+contains+'application/octet-stream'+and+(name+contains+'.mkv'+or+name+contains+'.avi'+or+name+contains+'.mp4'+or+name+contains+'.mpg'+or+name+contains+'.ogg')))+and+trashed%3Dfalse\"\n\n if item.drive_q: q += \"+and+\" + item.drive_q\n if item.drive_parent: q += \"+and+\" + \"'\"+str(item.drive_parent)+\"'+in+parents\"\n else: q += \"+and+modifiedTime>'1970-01-01T12:00:00'\" # si no se accede por carpeta, descartar ficheros con fecha de modificación errónea pq quedan al principio\n\n orden = 'name' if item.drive_parent else SORT_VIDEOS_BY # Si se lista el contenido de una carpeta, por orden alfabético, sinó, por fecha de modificación/creación\n\n gd = gdrive('gdrive1')\n files = gd.getFiles(item.drive_id, q=q, nextPageToken=item.nextPageToken, perpage=PERPAGE_VIDEOS, orden=orden)\n \n if not files or 'files' not in files or len(files['files']) == 0: return itemlist\n \n for f in files['files']:\n \n title = f['name']\n title = re.sub('\\.\\w+$', '', title) # quitar extensión\n\n s_e = scrapertools.find_single_match(title, '(?i)((?:S|T)(\\d+)E(\\d+))')\n if not s_e: s_e = scrapertools.find_single_match(title, '(?i)((\\d+)x(\\d+))')\n if s_e:\n tipo = 'tvshow'\n titulo = title\n if ' -' in title: title = title.split(' -')[0].strip()\n elif '- ' in title: title = title.split('- ')[0].strip()\n if s_e[0] in title: title = title.split(s_e[0])[0].strip()\n if not title: title = titulo\n\n season = s_e[1]\n episode = s_e[2]\n year = scrapertools.find_single_match(title, '\\((\\d{4})\\)')\n if year: \n title = title.replace('(%s)' % year, '').strip()\n titulo = titulo.replace('(%s)' % year, '').strip()\n else: \n year = '-'\n else:\n tipo = 'movie'\n year = scrapertools.find_single_match(title, '\\((\\d{4})\\)')\n if year:\n aux = title.split('(%s)' % year)\n title = aux[0].strip()\n info = aux[1].replace('...','').strip()\n titulo = '%s [COLOR red]%s[/COLOR]' % (title, info)\n else:\n year = '-'\n titulo = title\n title = title.replace('Copia de ', '')\n\n if tipo == 'movie':\n itemlist.append(item.clone( action='findvideos', title=titulo, file_id=f['id'],\n thumbnail=config.get_thumb('dev'),\n contentType='movie', contentTitle=title, infoLabels={'year': year} ))\n else:\n if not episode: continue\n if item.contentSerieName: # viene de listado de series y ya está identificada\n itemlist.append(item.clone( action='findvideos', title=titulo, file_id=f['id'],\n thumbnail=config.get_thumb('dev'),\n contentType='episode', contentSeason = season, contentEpisodeNumber = episode ))\n else:\n itemlist.append(item.clone( action='findvideos', title=titulo, file_id=f['id'],\n thumbnail=config.get_thumb('dev'),\n contentType='episode', contentSerieName=title, contentSeason = season, contentEpisodeNumber = episode ))\n\n tmdb.set_infoLabels(itemlist)\n\n if 'nextPageToken' in files and files['nextPageToken']:\n itemlist.append(item.clone( title=\">> Página siguiente\", nextPageToken=files['nextPageToken'], action='list_all' ))\n \n return itemlist\n\n\ndef findvideos(item):\n logger.info()\n itemlist = []\n \n gd = gdrive('gdrive1')\n\n datos = gd.getFileInfo(item.file_id)\n if datos and 'url_directo' in datos:\n try:\n qlty = '%sx%s' % (datos['videoMediaMetadata']['width'], datos['videoMediaMetadata']['height'])\n except:\n qlty = 'Original'\n try:\n othr = ''\n othr = config.format_bytes(int(datos['size']))\n othr += ', %s' % config.format_seconds_to_duration(int(datos['videoMediaMetadata']['durationMillis'])/1000)\n except:\n pass\n itemlist.append(Item( channel = item.channel, action = 'play', server = 'directo', title = '', url = datos['url_directo'], quality = qlty, other=othr ))\n \n if datos['extra']:\n for v in datos['extra']:\n itemlist.append(Item( channel = item.channel, action = 'play', server = 'directo', title = '', url = v[1], quality = v[0] ))\n\n return itemlist\n\n\n# Para pruebas, y detectar ciertos errores, como por ejemplo 403 \"The download quota for this file has been exceeded.\"\n# ~ def play(item):\n # ~ logger.info()\n # ~ itemlist = []\n \n # ~ url = item.url.split('|Authorization=')[0]\n # ~ headers = {'Authorization': item.url.split('|Authorization=')[1]}\n \n # ~ resp = httptools.downloadpage(url, headers=headers, raise_weberror=False)\n # ~ logger.debug(resp.headers)\n # ~ logger.debug(resp.data)\n\n # ~ return itemlist\n\ndef search_series(item):\n logger.info()\n itemlist = []\n comprobar_settings_piphav()\n\n q = \"mimeType+=+'application/vnd.google-apps.folder'+and+trashed%3Dfalse\"\n\n if len(ID_FOLDERS_TVSHOWS) > 0:\n q += \"+and+(\"\n for i, (fid, fname) in enumerate(ID_FOLDERS_TVSHOWS):\n if i > 0: q += '+or+'\n q += \"'%s'+in+parents\" % fid\n q += \")\"\n\n if item.drive_q: \n q += \"+and+\" + item.drive_q\n\n gd = gdrive('gdrive1')\n files = gd.getFiles(item.drive_id, q=q, nextPageToken=item.nextPageToken, perpage=PERPAGE_VIDEOS, orden='name')\n if not files or 'files' not in files or len(files['files']) == 0: return itemlist\n \n for f in files['files']:\n title = f['name']\n year = scrapertools.find_single_match(title, '\\((\\d{4})\\)')\n if year:\n title = title.replace('(%s)' % year, '').strip()\n else:\n year = '-'\n\n itemlist.append(item.clone( action='carpeta_temporadas', title=title, drive_parent=f['id'], nextPageToken=None,\n thumbnail=config.get_thumb('dev'),\n contentType='tvshow', contentSerieName=title, infoLabels={'year': year, 'tvshowtitle': title} ))\n\n tmdb.set_infoLabels(itemlist)\n\n if 'nextPageToken' in files and files['nextPageToken']:\n itemlist.append(item.clone( title=\">> Página siguiente\", nextPageToken=files['nextPageToken'], action='search_series' ))\n\n return itemlist\n\n\ndef search(item, texto):\n logger.info()\n try:\n item.drive_q = \"name+contains+'%s'\" % urllib.quote_plus(texto.replace(\"'\",''))\n item.nextPageToken = None\n if item.search_type == 'tvshow':\n return search_series(item)\n elif item.search_type == 'movie':\n item.drive_q += \"+and+not+name+contains+'%s'+and+not+name+contains+'%s'\" % (urllib.quote_plus(' - S0'), urllib.quote_plus(' - S1'))\n return list_all(item)\n else:\n return list_all(item)\n except:\n import sys\n for line in sys.exc_info():\n logger.error(\"%s\" % line)\n return []\n", "sub_path": "python/main-classic/addons/plugin.video.piphav2/channels/zzgdrive.py", "file_name": "zzgdrive.py", "file_ext": "py", "file_size_in_byte": 20668, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.version_info", "line_number": 90, "usage_type": "attribute"}, {"api_name": "platformcode.config.get_setting", "line_number": 103, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 103, "usage_type": "name"}, {"api_name": "platformcode.config.get_setting", "line_number": 104, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 104, "usage_type": "name"}, {"api_name": "platformcode.config.get_setting", "line_number": 105, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 105, "usage_type": "name"}, {"api_name": "platformcode.config.get_setting", "line_number": 106, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 106, "usage_type": "name"}, {"api_name": "platformcode.config.get_setting", "line_number": 107, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 107, "usage_type": "name"}, {"api_name": "platformcode.config.set_setting", "line_number": 117, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 117, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 133, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 133, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 136, "usage_type": "call"}, {"api_name": "platformcode.platformtools.dialog_notification", "line_number": 139, "usage_type": "call"}, {"api_name": "platformcode.platformtools", "line_number": 139, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 156, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 156, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 196, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 196, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 200, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 200, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 208, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 208, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 212, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 212, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 217, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 217, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 221, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 221, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 228, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 228, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 238, "usage_type": "call"}, {"api_name": "platformcode.logger.info", "line_number": 244, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 244, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 256, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 256, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 264, "usage_type": "call"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 270, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 270, "usage_type": "name"}, {"api_name": "platformcode.config.get_thumb", "line_number": 277, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 277, "usage_type": "name"}, {"api_name": "core.tmdb.set_infoLabels", "line_number": 280, "usage_type": "call"}, {"api_name": "core.tmdb", "line_number": 280, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 288, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 288, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 296, "usage_type": "call"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 302, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 302, "usage_type": "name"}, {"api_name": "platformcode.config.get_thumb", "line_number": 306, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 306, "usage_type": "name"}, {"api_name": "core.tmdb.set_infoLabels", "line_number": 309, "usage_type": "call"}, {"api_name": "core.tmdb", "line_number": 309, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 315, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 315, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 331, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 339, "usage_type": "call"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 341, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 341, "usage_type": "name"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 342, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 342, "usage_type": "name"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 353, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 353, "usage_type": "name"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 361, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 361, "usage_type": "name"}, {"api_name": "platformcode.config.get_thumb", "line_number": 374, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 374, "usage_type": "name"}, {"api_name": "platformcode.config.get_thumb", "line_number": 380, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 380, "usage_type": "name"}, {"api_name": "platformcode.config.get_thumb", "line_number": 384, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 384, "usage_type": "name"}, {"api_name": "core.tmdb.set_infoLabels", "line_number": 387, "usage_type": "call"}, {"api_name": "core.tmdb", "line_number": 387, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 396, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 396, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 399, "usage_type": "call"}, {"api_name": "platformcode.config.format_bytes", "line_number": 409, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 409, "usage_type": "name"}, {"api_name": "platformcode.config.format_seconds_to_duration", "line_number": 410, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 410, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 413, "usage_type": "call"}, {"api_name": "core.item.Item", "line_number": 417, "usage_type": "call"}, {"api_name": "platformcode.logger.info", "line_number": 437, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 437, "usage_type": "name"}, {"api_name": "lib.gdrivetools.gdrive", "line_number": 453, "usage_type": "call"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 459, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 459, "usage_type": "name"}, {"api_name": "platformcode.config.get_thumb", "line_number": 466, "usage_type": "call"}, {"api_name": "platformcode.config", "line_number": 466, "usage_type": "name"}, {"api_name": "core.tmdb.set_infoLabels", "line_number": 469, "usage_type": "call"}, {"api_name": "core.tmdb", "line_number": 469, "usage_type": "name"}, {"api_name": "platformcode.logger.info", "line_number": 478, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 478, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 480, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 480, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 485, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 485, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 491, "usage_type": "call"}, {"api_name": "platformcode.logger.error", "line_number": 492, "usage_type": "call"}, {"api_name": "platformcode.logger", "line_number": 492, "usage_type": "name"}]} +{"seq_id": "7200563", "text": "\n# coding: utf-8\n\n\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport sys\nimport time\nimport configparser\nimport requests\nimport pickle\nfrom TwitterAPI import TwitterAPI,TwitterRestPager\n\n\ndef get_twitter():\n config = configparser.ConfigParser()\n config.read('Twitter_tokens.cfg')\n consumer_key = config.get('twitter','consumer_key')\n consumer_secret = config.get('twitter','consumer_secret')\n access_token = config.get('twitter','access_token')\n access_token_secret = config.get('twitter','access_token_secret')\n twitter = TwitterAPI(consumer_key, consumer_secret, access_token, access_token_secret)\n return twitter\n\n\ndef get_census_names():\n \"\"\" Fetch a list of common male/female names from the census.\n For ambiguous names, we select the more frequent gender.\"\"\"\n males = requests.get('http://www2.census.gov/topics/genealogy/1990surnames/dist.male.first').text.split('\\n')\n females = requests.get('http://www2.census.gov/topics/genealogy/1990surnames/dist.female.first').text.split('\\n')\n males_pct = dict([(m.split()[0].lower(), float(m.split()[1]))\n for m in males if m])\n females_pct = dict([(f.split()[0].lower(), float(f.split()[1]))\n for f in females if f])\n male_names = set([m for m in males_pct if m not in females_pct or\n males_pct[m] > females_pct[m]])\n female_names = set([f for f in females_pct if f not in males_pct or\n females_pct[f] > males_pct[f]]) \n return male_names, female_names\n\ndef get_first_name(tweet):\n if 'user' in tweet and 'name' in tweet['user']:\n parts = tweet['user']['name'].split()\n if len(parts) > 0:\n return parts[0].lower()\n\ndef sample_tweets(twitter, limit, male_names, female_names,tweets,trend_term):\n while True:\n try:\n # Restrict to U.S.\n for response in twitter.request('statuses/filter',\n {'track':trend_term}):\n if 'user' in response:\n name = get_first_name(response)\n if ((name in male_names or name in female_names) and ('RT' not in response[\"text\"])):\n tweets.append(response)\n if len(tweets) % 10 == 0:\n print('found %d tweets' % len(tweets))\n pickle.dump(tweets, open('USTweets.pkl', 'wb'))\n print(\"Press cancel any time to proceed with the next steps with the datat collected so far. \")\n if len(tweets) >= limit:\n return tweets\n except KeyboardInterrupt:\n print(len(tweets),\" Streams Collected\")\n break\n return tweets\n\n\ndef main():\n api= get_twitter()\n print('Established Twitter connection.')\n male_names, female_names = get_census_names()\n print('found %d female and %d male names' % (len(male_names), len(female_names)))\n print('male name sample:', list(male_names)[:5])\n print('female name sample:', list(female_names)[:5])\n tweets=[]\n tweet_limit=5000\n print(\"streaming tweets on a trending tag \",list(api.request('trends/place',{'id': '2379574'}))[0][\"name\"])\n tweets = sample_tweets(api, tweet_limit, male_names, female_names,tweets,list(api.request('trends/place',{'id': '2379574'}))[0][\"name\"])\n if(len(tweets)>0):\n pickle.dump(tweets, open('USTweets.pkl', 'wb'))\n print('sampled %d tweets' % len(tweets))\n print('top names:', Counter(get_first_name(t) for t in tweets).most_common(10))\n print(\"Kindly execute the cluster.py and the classify.py files for network analysis and tweet sentiment analysis for the sample records\")\n \nif __name__ == '__main__':\n main()\n\n\n\n\n", "sub_path": "Projects-and-Assingments/a4/collect.py", "file_name": "collect.py", "file_ext": "py", "file_size_in_byte": 3786, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "configparser.ConfigParser", "line_number": 17, "usage_type": "call"}, {"api_name": "TwitterAPI.TwitterAPI", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 60, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 82, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "489727995", "text": "# from Dataset import Dataset\nfrom torch.utils.data import DataLoader\nfrom funcs import printProgressBar, writeLineToCSV\nimport os, glob\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport time\nfrom torch.autograd import Variable\nimport numbers\nimport copy\nfrom Models import *\nfrom Criterions import *\n\nclass ModelWrapper():\n def __init__(self, savePath=\"./wrapperDumps\", device=\"cpu\", cutToEqualize=True, printLvl=10):\n super(ModelWrapper, self).__init__()\n '''\n Training, logs, analytics, and everything that can be used to easily train, test and anylyse pytorch models.\n '''\n self.savePath = savePath\n if not os.path.exists(self.savePath): os.makedirs(self.savePath)\n self.printLvl = printLvl\n self.device = device\n self.currentEpoch = 1\n self.cutToEqualize = cutToEqualize # cut targets to equalize the outputs and targets sizes\n self.noMoreTrain = False # Flag for early stopping.\n self.modelStates = {} # Different model states in different epochs.\n self.epochDevLosses = [] # Different epoch losses for the develpment set.\n self.epochTimes = [] # Keeping how much time has passed for each training epoch.\n \n self.checkPointPath = os.path.join(self.savePath, \"checkpoint.pth\")\n self.csvLogPath = os.path.join(self.savePath, \"log.csv\")\n\n torch.manual_seed(0)\n if device==\"cuda\":\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n def setModel(self, modelName, inputDim, outputDim, paramsStr): # the model is referenced!\n if paramsStr != \"\":\n params = paramsStr.split(\"-\")\n params = [int(param) for param in params]\n if modelName == \"Transformer\":\n self.model = myTransformer(inputDim, hidden_size=params[0], nhead=params[1], num_layers=params[2], outputSize=outputDim)\n if modelName == \"GRU\":\n self.model = myGRU(inputDim, hidden_size=params[0], num_layers=params[1], outputSize=outputDim)\n if modelName == \"GRULast\":\n self.model = myGRU(inputDim, hidden_size=params[0], num_layers=params[1], outputSize=outputDim, lastOnly=True)\n if modelName == \"GRUFF\":\n self.model = myGRUFF(inputDim, hidden_size=params[0], num_layers=params[1], FF_hidden_size=params[2], outputSize=outputDim)\n if modelName == \"FFGRUFF\":\n self.model = myFFGRUFF(inputDim, hidden_size=params[0], num_layers=params[1], FF_hidden_size=params[2], outputSize=outputDim)\n if modelName == \"SincTransformer\":\n self.model = mySincTransformer(inputDim, hidden_size=params[0], nhead=params[1], num_layers=params[2], fs=params[3], M=params[4], outputSize=outputDim, device=self.device)\n if modelName == \"LinTanhSinc\":\n self.model = myLinTanhSinc(inputDim, fs=params[0], M=params[1], outputSize=outputDim, device=self.device)\n if modelName == \"SincLinTanhSinc\":\n self.model = mySincLinTanhSinc(inputDim, fs=params[0], M=params[1], outputSize=outputDim, device=self.device)\n if modelName == \"LinLinTanhSinc\":\n self.model = myLinLinTanhSinc(inputDim, hidden_size=params[0], fs=params[1], M=params[2], outputSize=outputDim, device=self.device)\n if modelName == \"LinLinTanh\":\n self.model = myLinLinTanh(inputDim, hidden_size=params[0], outputSize=outputDim, device=self.device)\n if modelName == \"LinTanh\":\n self.model = myLinTanh(inputDim, outputSize=outputDim, device=self.device)\n self.model.to(device=self.device)\n\n def setOptimizer(self, optimizerName, learningRate=0.001):\n optimizers = {\n \"Adam\": torch.optim.Adam(self.model.parameters(), lr=learningRate)\n }\n self.optimizer = optimizers[optimizerName]\n\n def setCriterion(self, criterionName):\n criterions = {\n \"CCC\": CCC_Loss(),\n \"MSELoss\": torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')\n }\n self.criterion = criterions[criterionName]\n\n def trainModel(self, datasetTrain, datasetDev, batchSize=1, maxEpoch=200, loadBefore=True, \n tolerance = 15, minForTolerance=15):\n if loadBefore: self.loadCheckpoint()\n trainDataloader = DataLoader(dataset=datasetTrain, batch_size=batchSize, shuffle=True)\n devDataloader = DataLoader(dataset=datasetDev, batch_size=batchSize, shuffle=False)\n while self.currentEpoch <= maxEpoch:\n if self.noMoreTrain: \n if self.printLvl>0: print(\"Early stopping has been achieved!\")\n break\n self.trainEpoch(trainDataloader)\n devLoss = self.evaluateModel(devDataloader)\n self.modelStates[self.currentEpoch] = copy.deepcopy(self.model.state_dict())\n self.epochDevLosses.append(devLoss)\n if self.printLvl>1: \n printProgressBar(self.currentEpoch, maxEpoch, prefix = 'Training model:', suffix = '| epoch loss: '+str(devLoss), length = \"fit\")\n # print(\"loss\", self.currentEpoch, devLoss)\n self.currentEpoch += 1\n # --- Early Stopping ---\n if (self.currentEpoch - self.getBestEpochIdx() >= tolerance) and self.currentEpoch > minForTolerance:\n self.noMoreTrain = True \n self.saveCheckpoint()\n self.saveLogToCSV()\n if self.printLvl>0: print(\"Training the model has been finished!\")\n\n def trainEpoch(self, trainDataloader):\n timeStart = time.time()\n for (inputs, targets) in trainDataloader:\n self.trainStep(inputs, targets)\n timeElapsed = time.time() - timeStart\n self.epochTimes.append(timeElapsed)\n\n def evaluateModel(self, dataloader):\n self.model.eval()\n lossSum = 0\n dataAmount = 0\n for (inputs, targets) in dataloader:\n loss = self.calcLoss(inputs, targets)\n batchSize = inputs.size()[0]\n dataAmount += batchSize\n lossSum += loss.detach().cpu().numpy() * batchSize\n lossMean = lossSum / dataAmount\n return lossMean\n\n def calcLoss(self, theInput, theTarget):\n targets = Variable(theTarget.float())\n targets = targets.to(device=self.device)\n # print(theInput.size(), targets.size())\n outputs = self.forwardModel(theInput)\n # print(\"sizes\", outputs.size(), targets.size())\n if self.cutToEqualize: # equalising sizes -----------------\n if outputs.size()[1] < targets.size()[1]:\n targets = targets[:,:outputs.size()[1],:]\n elif outputs.size()[1] > targets.size()[1]:\n outputs = outputs[:,:targets.size()[1],:]\n # print(targets.size(), outputs.size())\n loss = self.criterion(outputs, targets)\n return loss\n\n def trainStep(self, theInput, theTarget):\n self.model.train()\n self.optimizer.zero_grad()\n loss = self.calcLoss(theInput, theTarget)\n loss.backward(retain_graph=True)\n self.optimizer.step()\n\n def forwardModel(self, theInput):\n inputs = Variable(theInput.float())\n inputs = inputs.to(device=self.device)\n outputs = self.model(inputs)\n return outputs\n\n def getBestEpochIdx(self):\n return self.epochDevLosses.index(min(self.epochDevLosses))+1\n\n def saveLogToCSV(self):\n csvPath = self.csvLogPath\n headers = [\"epoch\", \"evalLossMean\", \"trainTime\"]\n epochs = list(range(len(self.epochDevLosses)))\n data = np.array([epochs, self.epochDevLosses, self.epochTimes])\n data[0] += 1 # so that epochs start at one not zero! :)\n data = np.transpose(data)\n df = pd.DataFrame(data, columns = headers)\n df.to_csv(csvPath, index=False)\n\n def loadBestModel(self):\n checkpoint = torch.load(self.checkPointPath)\n # print(self.epochDevLosses, self.epochDevLosses.index(min(self.epochDevLosses)), len(self.epochDevLosses))\n # print(checkpoint['modelStates'])\n bestEpoch = self.epochDevLosses.index(min(self.epochDevLosses))+1\n self.model.load_state_dict(checkpoint['modelStates'][bestEpoch])\n\n def loadCheckpoint(self):\n try:\n checkpoint = torch.load(self.checkPointPath)\n self.model.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n self.currentEpoch = checkpoint['currentEpoch']\n self.modelStates = checkpoint['modelStates']\n self.epochDevLosses = checkpoint['epochDevLosses']\n self.epochTimes = checkpoint['epochTimes']\n self.noMoreTrain = checkpoint['noMoreTrain']\n # self.model = torch.load(self.saveFilePath, map_location=self.device)\n except:\n if os.path.exists(self.checkPointPath):\n if self.printLvl>0: print(\"Warning: Could not load previous model, if there was any, it will get overwritten!\")\n\n def saveCheckpoint(self):\n # torch.save(self.model, self.saveFilePath)\n checkpoint = {\n 'model_state_dict': self.model.state_dict(), \n 'optimizer_state_dict': self.optimizer.state_dict(),\n 'currentEpoch': self.currentEpoch,\n 'modelStates': self.modelStates, \n 'epochDevLosses': self.epochDevLosses, \n 'epochTimes': self.epochTimes,\n 'noMoreTrain': self.noMoreTrain, # Saving this to avoid training accidentaly later after reaching early stopping!\n }\n torch.save(checkpoint, self.checkPointPath)\n\n\n\n\n\n", "sub_path": "AER/Experiments/modelWrapper.py", "file_name": "modelWrapper.py", "file_ext": "py", "file_size_in_byte": 9677, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"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": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 86, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 93, "usage_type": "call"}, {"api_name": "funcs.printProgressBar", "line_number": 96, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 175, "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": "torch.save", "line_number": 199, "usage_type": "call"}]} +{"seq_id": "566021834", "text": "\"\"\"\nPrint the length of the longest contig for each file in a directory of fasta files.\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nfrom roblib import read_fasta\n\n__author__ = 'Rob Edwards'\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Print the length of the longest contig for each file in a directory of fasta files')\n parser.add_argument('-d', help='Directory of fasta files', required=True)\n args = parser.parse_args()\n\n for f in os.listdir(args.d):\n fa = read_fasta(os.path.join(args.d, f))\n lengths = [len(fa[x]) for x in fa]\n lengths.sort()\n print(\"{}\\t{}\".format(f, lengths[-1]))\n\n\n", "sub_path": "bin/longest_contig.py", "file_name": "longest_contig.py", "file_ext": "py", "file_size_in_byte": 658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "roblib.read_fasta", "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"}]} +{"seq_id": "547865222", "text": "# -*- coding: utf-8 -*-\n# MinIO Python Library for Amazon S3 Compatible Cloud Storage,\n# (C) 2015 MinIO, 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\nfrom unittest import TestCase\n\nfrom nose.tools import eq_\n\nfrom minio.helpers import get_s3_endpoint, is_valid_endpoint\n\n\nclass GetS3Endpoint(TestCase):\n def test_get_s3_endpoint(self):\n eq_('s3.amazonaws.com', get_s3_endpoint('us-east-1'))\n eq_('s3.amazonaws.com', get_s3_endpoint('foo'))\n eq_('s3-eu-west-1.amazonaws.com', get_s3_endpoint('eu-west-1'))\n eq_('s3.cn-north-1.amazonaws.com.cn', get_s3_endpoint('cn-north-1'))\n\n def test_is_valid_endpoint(self):\n eq_(True, is_valid_endpoint('s3.amazonaws.com'))\n eq_(True, is_valid_endpoint('s3.cn-north-1.amazonaws.com.cn'))\n eq_(True, is_valid_endpoint('minio_server:9000'))\n eq_(True, is_valid_endpoint('s3.server_1.amazonaws.com'))\n", "sub_path": "tests/unit/get_s3_endpoint_test.py", "file_name": "get_s3_endpoint_test.py", "file_ext": "py", "file_size_in_byte": 1404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 24, "usage_type": "name"}, {"api_name": "nose.tools.eq_", "line_number": 26, "usage_type": "call"}, {"api_name": "minio.helpers.get_s3_endpoint", "line_number": 26, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 27, "usage_type": "call"}, {"api_name": "minio.helpers.get_s3_endpoint", "line_number": 27, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 28, "usage_type": "call"}, {"api_name": "minio.helpers.get_s3_endpoint", "line_number": 28, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 29, "usage_type": "call"}, {"api_name": "minio.helpers.get_s3_endpoint", "line_number": 29, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 32, "usage_type": "call"}, {"api_name": "minio.helpers.is_valid_endpoint", "line_number": 32, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 33, "usage_type": "call"}, {"api_name": "minio.helpers.is_valid_endpoint", "line_number": 33, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 34, "usage_type": "call"}, {"api_name": "minio.helpers.is_valid_endpoint", "line_number": 34, "usage_type": "call"}, {"api_name": "nose.tools.eq_", "line_number": 35, "usage_type": "call"}, {"api_name": "minio.helpers.is_valid_endpoint", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "423957531", "text": "from typing import Dict, Optional\n\nfrom graph_db.engine.label import Label\nfrom graph_db.engine.node import Node\nfrom graph_db.engine.property import Property\nfrom graph_db.engine.relationship import Relationship\nfrom graph_db.engine.types import *\nfrom graph_db.engine.types import DFS_CONFIG_PATH\n\nfrom .decoder import RecordDecoder\nfrom .encoder import RecordEncoder\n\nfrom multiprocessing import Process\nimport rpyc\nfrom .manager import start_manager_service\nimport time\nimport json\n\n\nclass IOEngine:\n \"\"\"\n Graph Database IO Engine.\n Processes graph database queries on IO level.\n \"\"\"\n def __init__(self, config_path: str = DFS_CONFIG_PATH):\n self.config_path = config_path\n self.manager_address = [(str, int)]\n\n self.manager_pool = {} # {port : manager_process}\n\n self.parse_config(config_path)\n\n # Setup manager node\n self.create_manager_node(self.manager_address[0][1])\n self.con = rpyc.classic.connect(self.manager_address[0][0], self.manager_address[0][1]) # Connect to manager\n self.manager = self.con.root.Manager()\n\n # Initialize managers statistics\n self.manager.update_stats()\n\n def get_stats(self) -> Dict[str, int]:\n \"\"\"\n Returns total number of records in each type of storage.\n :return: dictionary with stats\n \"\"\"\n return self.manager.get_stats()\n\n # Node\n\n def insert_node(self, node: Node):\n \"\"\"\n Updates node record in node storage.\n :param node: node object\n \"\"\"\n return self._insert_node(node, update=False)\n\n def update_node(self, node: Node):\n \"\"\"\n Updates node record in node storage.\n :param node: node object\n \"\"\"\n return self._insert_node(node, update=True)\n\n def _insert_node(self, node: Node, update: bool = False):\n \"\"\"\n Prepares node record and selects appropriate node storage.\n :param node: node object\n \"\"\"\n if node.get_id() == INVALID_ID:\n node.set_id(self.get_stats()['NodeStorage'])\n\n node_record = RecordEncoder.encode_node(node)\n self.manager.write_record(node_record, 'NodeStorage', update=update)\n\n return node\n\n def select_node(self, node_id: int) -> Dict[str, DB_TYPE]:\n \"\"\"\n Selects node with `id` from the appropriate storage.\n Collects all data from other stores.\n :return:\n \"\"\"\n node_record = self.manager.read_record(node_id, 'NodeStorage')\n if node_record:\n return RecordDecoder.decode_node_record(node_record)\n else:\n print(f'Node #{node_id} was not found')\n return dict()\n\n # Relationship\n\n def insert_relationship(self, rel: Relationship):\n \"\"\"\n Updates relationship record in node storage.\n :param rel: relationship object\n \"\"\"\n return self._insert_relationship(rel, update=False)\n\n def update_relationship(self, rel: Relationship):\n \"\"\"\n Updates relationship record in node storage.\n :param rel: relationship object\n \"\"\"\n return self._insert_relationship(rel, update=True)\n\n def _insert_relationship(self, rel: Relationship, update: bool = False):\n \"\"\"\n Prepares relationship records and select appropriate relationship storage.\n :param rel: node object\n \"\"\"\n if rel.get_id() == INVALID_ID:\n rel.set_id(self.get_stats()['RelationshipStorage'])\n\n relationship_record = RecordEncoder.encode_relationship(rel)\n self.manager.write_record(relationship_record, 'RelationshipStorage', update=update)\n\n return rel\n\n def select_relationship(self, rel_id: object) -> object:\n \"\"\"\n Selects relationship with `id` from the appropriate storage.\n Collects all data from other storages.\n :return:\n \"\"\"\n relationship_record = self.manager.read_record(rel_id, 'RelationshipStorage')\n if relationship_record:\n return RecordDecoder.decode_relationship_record(relationship_record)\n else:\n print(f'Relationship #{rel_id} was not found')\n return dict()\n\n # Label\n\n def insert_label(self, label: Label):\n \"\"\"\n Updates label record in node storage.\n :param label: label object\n \"\"\"\n return self._insert_label(label, update=False)\n\n def update_label(self, label: Label):\n \"\"\"\n Updates node record in node storage.\n :param label: label object\n \"\"\"\n return self._insert_label(label, update=True)\n\n def _insert_label(self, label: Label, update: bool = False):\n \"\"\"\n Prepares label record and select appropriate label storage.\n :param label: label object\n \"\"\"\n if label.get_id() == INVALID_ID:\n label.set_id(self.get_stats()['LabelStorage'])\n\n dynamic_id = self.get_stats()['DynamicStorage']\n self._write_dynamic_data(label.get_name(), dynamic_id)\n\n label_record = RecordEncoder.encode_label(label, dynamic_id)\n self.manager.write_record(label_record, 'LabelStorage', update=update)\n\n return label\n\n def select_label(self, label_id: int) -> Dict[str, DB_TYPE]:\n \"\"\"\n Selects label with `id` from the appropriate storage.\n Collects all data from other stores.\n :param label_id:\n :return:\n \"\"\"\n label_record = self.manager.read_record(label_id, 'LabelStorage')\n if label_record is None:\n print(f'Label #{label_id} was not found')\n return dict()\n\n label_data = RecordDecoder.decode_label_record(label_record)\n\n name = self._build_dynamic_data(label_data['dynamic_id'])\n if name:\n label_data['name'] = name\n else:\n label_data = dict()\n return label_data\n\n # Property\n\n def insert_property(self, prop: Property):\n \"\"\"\n Updates property record in node storage.\n :param prop: prop object\n \"\"\"\n return self._insert_property(prop, update=False)\n\n def update_property(self, prop: Property):\n \"\"\"\n Updates property record in node storage.\n :param prop: prop object\n \"\"\"\n return self._insert_property(prop, update=True)\n\n def _insert_property(self, prop: Property, update: bool = False):\n \"\"\"\n Prepares property record and select appropriate property storage.\n :param prop:\n :return:\n \"\"\"\n if prop.get_id() == INVALID_ID:\n prop.set_id(self.get_stats()['PropertyStorage'])\n\n if update:\n old_property_record = self.manager.read_record(prop.get_id(), 'PropertyStorage')\n old_property_data = RecordDecoder.decode_property_record(old_property_record)\n\n key_dynamic_id = old_property_data['key_id']\n value_dynamic_id = old_property_data['value_id']\n\n old_key = self._build_dynamic_data(key_dynamic_id)\n old_value = self._build_dynamic_data(value_dynamic_id)\n\n if old_key != prop.get_key():\n # key has changed\n key_dynamic_id = self.get_stats()['DynamicStorage']\n self._write_dynamic_data(prop.get_key(), key_dynamic_id)\n elif old_value != prop.get_value():\n # value has changed\n value_dynamic_id = self.get_stats()['DynamicStorage']\n self._write_dynamic_data(prop.get_value(), value_dynamic_id)\n else:\n key_dynamic_id = self.get_stats()['DynamicStorage']\n self._write_dynamic_data(prop.get_key(), key_dynamic_id)\n value_dynamic_id = self.get_stats()['DynamicStorage']\n self._write_dynamic_data(prop.get_value(), value_dynamic_id)\n\n next_prop_id = prop.get_next_property().get_id() if prop.get_next_property() else INVALID_ID\n\n property_record = RecordEncoder.encode_property(prop_id=prop.get_id(),\n used=prop.is_used(),\n key_id=key_dynamic_id,\n value_id=value_dynamic_id,\n next_prop_id=next_prop_id)\n self.manager.write_record(property_record, 'PropertyStorage', update=update)\n\n return prop\n\n def select_property(self, prop_id: int) -> Dict[str, DB_TYPE]:\n \"\"\"\n Selects property with `id` from the appropriate storage.\n Collects all data from other stores.\n :param prop_id:\n :return:\n \"\"\"\n property_record = self.manager.read_record(prop_id, 'PropertyStorage')\n if property_record is None:\n print(f'Property #{prop_id} was not found')\n return dict()\n\n property_data = RecordDecoder.decode_property_record(property_record)\n\n # String data now only\n property_data['key'] = self._build_dynamic_data(property_data['key_id'])\n property_data['value'] = self._build_dynamic_data(property_data['value_id'])\n\n return property_data\n\n def _write_dynamic_data(self, data, dynamic_id):\n dynamic_records = RecordEncoder.encode_dynamic_data(data, dynamic_id)\n for record in dynamic_records:\n self.manager.write_record(record, 'DynamicStorage')\n\n def _build_dynamic_data(self, dynamic_id) -> Optional[DB_TYPE]:\n data = ''\n while True:\n # read from dynamic storage until all data is collected\n dynamic_record = self.manager.read_record(dynamic_id, 'DynamicStorage')\n if dynamic_record is None:\n print(f'Dynamic #{dynamic_id} was not found')\n return None\n \n dynamic_data = RecordDecoder.decode_dynamic_data_record(dynamic_record)\n\n data += dynamic_data['data']\n dynamic_id = dynamic_data['next_chunk_id']\n\n if dynamic_id == INVALID_ID:\n break\n\n if data == 'True':\n return True\n elif data == 'False':\n return False\n else:\n try:\n data = int(data)\n except ValueError:\n try:\n data = float(data)\n except ValueError:\n pass\n\n return data\n\n # DFS control methods\n\n def create_manager_node(self, port=None):\n \"\"\"\n Creates process bind to specific port, stores it in memory and starts it\n :param port:\n :return:\n \"\"\"\n if port in self.manager_pool:\n return\n self.manager_pool[port] = Process(target=start_manager_service, args=(port, self.config_path))\n self.manager_pool[port].start()\n time.sleep(0.1)\n print(f'Manager UP at localhost:{port}')\n\n def get_processes(self):\n \"\"\"\n Collect all processes: worker processes from manager and manager processes\n :return:\n \"\"\"\n processes = [self.manager.get_worker_processes()]\n for p in self.manager_pool.keys():\n processes.append(self.manager_pool[p])\n return processes\n\n def close(self):\n \"\"\"\n Closes file connections of all workers, then terminates all worker processes and finally terminates manager\n process\n :return:\n \"\"\"\n self.manager.flush_workers()\n self.manager.close_workers()\n self.manager_pool[self.manager_address[0][1]].terminate()\n\n def parse_config(self, config_path):\n try:\n with open(config_path, \"r\") as f:\n res = json.load(f)\n except FileNotFoundError:\n with open('../../../' + config_path, \"r\") as f:\n res = json.load(f)\n self.manager_address = [(res['manager_config']['ip'], res['manager_config']['port'])]\n\n", "sub_path": "src/graph_db/fs/io_engine.py", "file_name": "io_engine.py", "file_ext": "py", "file_size_in_byte": 11951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "graph_db.engine.types.DFS_CONFIG_PATH", "line_number": 25, "usage_type": "name"}, {"api_name": "rpyc.classic.connect", "line_number": 35, "usage_type": "call"}, {"api_name": "rpyc.classic", "line_number": 35, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 41, "usage_type": "name"}, {"api_name": "graph_db.engine.node.Node", "line_number": 50, "usage_type": "name"}, {"api_name": "graph_db.engine.node.Node", "line_number": 57, "usage_type": "name"}, {"api_name": "graph_db.engine.node.Node", "line_number": 64, "usage_type": "name"}, {"api_name": "encoder.RecordEncoder.encode_node", "line_number": 72, "usage_type": "call"}, {"api_name": "encoder.RecordEncoder", "line_number": 72, "usage_type": "name"}, {"api_name": "decoder.RecordDecoder.decode_node_record", "line_number": 85, "usage_type": "call"}, {"api_name": "decoder.RecordDecoder", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 77, "usage_type": "name"}, {"api_name": "graph_db.engine.relationship.Relationship", "line_number": 92, "usage_type": "name"}, {"api_name": "graph_db.engine.relationship.Relationship", "line_number": 99, "usage_type": "name"}, {"api_name": "graph_db.engine.relationship.Relationship", "line_number": 106, "usage_type": "name"}, {"api_name": "encoder.RecordEncoder.encode_relationship", "line_number": 114, "usage_type": "call"}, {"api_name": "encoder.RecordEncoder", "line_number": 114, "usage_type": "name"}, {"api_name": "decoder.RecordDecoder.decode_relationship_record", "line_number": 127, "usage_type": "call"}, {"api_name": "decoder.RecordDecoder", "line_number": 127, "usage_type": "name"}, {"api_name": "graph_db.engine.label.Label", "line_number": 134, "usage_type": "name"}, {"api_name": "graph_db.engine.label.Label", "line_number": 141, "usage_type": "name"}, {"api_name": "graph_db.engine.label.Label", "line_number": 148, "usage_type": "name"}, {"api_name": "encoder.RecordEncoder.encode_label", "line_number": 159, "usage_type": "call"}, {"api_name": "encoder.RecordEncoder", "line_number": 159, "usage_type": "name"}, {"api_name": "decoder.RecordDecoder.decode_label_record", "line_number": 176, "usage_type": "call"}, {"api_name": "decoder.RecordDecoder", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 164, "usage_type": "name"}, {"api_name": "graph_db.engine.property.Property", "line_number": 187, "usage_type": "name"}, {"api_name": "graph_db.engine.property.Property", "line_number": 194, "usage_type": "name"}, {"api_name": "graph_db.engine.property.Property", "line_number": 201, "usage_type": "name"}, {"api_name": "decoder.RecordDecoder.decode_property_record", "line_number": 212, "usage_type": "call"}, {"api_name": "decoder.RecordDecoder", "line_number": 212, "usage_type": "name"}, {"api_name": "encoder.RecordEncoder.encode_property", "line_number": 236, "usage_type": "call"}, {"api_name": "encoder.RecordEncoder", "line_number": 236, "usage_type": "name"}, {"api_name": "decoder.RecordDecoder.decode_property_record", "line_number": 257, "usage_type": "call"}, {"api_name": "decoder.RecordDecoder", "line_number": 257, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 245, "usage_type": "name"}, {"api_name": "encoder.RecordEncoder.encode_dynamic_data", "line_number": 266, "usage_type": "call"}, {"api_name": "encoder.RecordEncoder", "line_number": 266, "usage_type": "name"}, {"api_name": "decoder.RecordDecoder.decode_dynamic_data_record", "line_number": 279, "usage_type": "call"}, {"api_name": "decoder.RecordDecoder", "line_number": 279, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 270, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 312, "usage_type": "call"}, {"api_name": "manager.start_manager_service", "line_number": 312, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 314, "usage_type": "call"}, {"api_name": "json.load", "line_number": 340, "usage_type": "call"}, {"api_name": "json.load", "line_number": 343, "usage_type": "call"}]} +{"seq_id": "220966129", "text": "# coding: utf-8\n\nfrom wtforms import StringField\n\nfrom wtforms.validators import Required\n\nfrom lib.widgets import EsForm as Form\nfrom lib.widgets import TwoColsMultiChoiceField\n\nfrom .. import Report\n\n\ndef domain_label(report):\n label = u\"%s | %s\" % (report.domain, report.created.strftime('%d-%m-%Y %H:%M'))\n return label\n\n\nclass DomainForm(Form):\n domain = StringField(u\"Dominio\", [Required(u\"Campo obligatorio\")], description=\"Pj: http://www.google.com\")\n\n\ndef comparative_form_factory(customer=None):\n class ComparativeForm(Form):\n reports = TwoColsMultiChoiceField(\n u\"Dominios\",\n query_factory=lambda: Report.query(customer=customer),\n get_pk=lambda item: item.id,\n get_label=lambda item: domain_label(item))\n\n return ComparativeForm\n", "sub_path": "src/adm/reports/forms/report.py", "file_name": "report.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "lib.widgets.EsForm", "line_number": 18, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 19, "usage_type": "call"}, {"api_name": "wtforms.validators.Required", "line_number": 19, "usage_type": "call"}, {"api_name": "lib.widgets.EsForm", "line_number": 23, "usage_type": "name"}, {"api_name": "lib.widgets.TwoColsMultiChoiceField", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "227144978", "text": "import os\nimport itertools as itr\n\nns = [24, 48, 96, 192]\nks = [1]\nbs = [1, 2, 3]\n\np = 25\ns = .25\nndags = 50\nstrategies = {'random', 'entropy-multiple-mec'}\n\n# os.system(f'python3 make_dataset.py -p {p} -s {.25} -d {ndags} -t erdos-bounded --folder erdos_renyi_multiple_mec')\nfor n, b, k, strat in itr.product(ns, bs, ks, strategies):\n if 'entropy-multiple-mec' == strat:\n if k is None:\n cmd = f'python3 run_experiments.py -n {n} -b {b} -m 2 -s .1 -i gauss --folder erdos_renyi_multiple_mec --strategy entropy-dag-collection-multiple-mec'\n else:\n cmd = f'python3 run_experiments.py -n {n} -b {b} -k {k} -m 2 -s .1 -i gauss --folder erdos_renyi_multiple_mec --strategy entropy-dag-collection-multiple-mec'\n if 'random' == strat:\n if k is None:\n cmd = f'python3 run_experiments.py -n {n} -b {b} -m 2 -s .1 -i gauss --folder erdos_renyi_multiple_mec --strategy random'\n else:\n cmd = f'python3 run_experiments.py -n {n} -b {b} -k {k} -m 2 -s .1 -i gauss --folder erdos_renyi_multiple_mec --strategy random'\n os.system(f'echo \"{cmd}\" > tmp.sh')\n os.system('cat slurm_template.sh tmp.sh > job.sh')\n os.system('rm tmp.sh')\n os.system('sbatch job.sh')\n", "sub_path": "new/run_fig6.py", "file_name": "run_fig6.py", "file_ext": "py", "file_size_in_byte": 1234, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "itertools.product", "line_number": 14, "usage_type": "call"}, {"api_name": "os.system", "line_number": 25, "usage_type": "call"}, {"api_name": "os.system", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 27, "usage_type": "call"}, {"api_name": "os.system", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "552494380", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import preprocessing\nfrom sklearn.preprocessing import PolynomialFeatures\nsns.set()\n\n\ndef calc4(Area,h):\n if h==1:\n data = pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\Rice_OD.csv')\n if h==2:\n data = pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\rice_WB.csv')\n if h==3:\n data = pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\RICE_ASSAM.csv') \n if h==4:\n data = pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\RICE_UTTARPRADESH.csv') \n data = data[data['Production'] < 90000]\n data.describe()\n\n # f, (ax1, ax2, ax3) = plt.subplots(1,3, sharey= True, figsize=(15,3))\n # ax1.scatter(data['Area'], data['Temp'])\n # ax1.set_title('Area and Temp')\n # ax2.scatter(data['Area'], data['Rainfall(cm)'])\n # ax2.set_title('Area and Rainfall')\n # ax3.scatter(data['Temp'], data['Rainfall(cm)'])\n # ax3.set_title('Temp and Rainfall')\n # plt.show()\n\n\n # from statsmodels.stats.outliers_influence import variance_inflation_factor\n\n # vars = data [['Area', 'Temp', 'Rainfall(cm)']]\n # vif = pd.DataFrame()\n # vif['VIF'] = [variance_inflation_factor(vars.values, i) for i in range (vars.shape[1])]\n # vif[\"features\"] = vars.columns\n\n\n # from numpy import cov\n # covariance = cov(data['Temp'], data['Rainfall(cm)'])\n # # print(covariance)\n\n\n\n y = data['Production']\n\n x = data [['Area', 'Crop_Year', 'Rainfall', 'Temperature']]\n x=np.append(x,[[Area, 2020, 0.22, 23]], axis=0)\n #x.reshape(-1,1)\n x.shape\n\n from sklearn.preprocessing import StandardScaler\n\n scaler = StandardScaler()\n\n\n scaler.fit(x)\n\n x_scaled = scaler.transform(x)\n userinput = x_scaled[108,:]\n x_scaled = np.delete(x_scaled, 108, axis=0)\n\n x_scaled.shape\n\n from sklearn.model_selection import train_test_split\n\n x_train, x_test, y_train, y_test = train_test_split(x_scaled, y, test_size=0.2, random_state=420)\n\n reg = LinearRegression()\n reg.fit(x_train, y_train)\n\n r2 = reg.score(x_train, y_train)\n\n x_train.shape\n n = x.shape[0]\n p = x.shape[1]\n adj_r2 = 1 - (1-r2)*(n-1)/ (n-p-1)\n\n # adj_r2\n # r2\n y_train.shape\n\n from sklearn.metrics import r2_score\n productionpred = reg.predict(userinput.reshape(1,-1))\n\n # data = pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\Rice_MP.csv')\n x=data['Production'].values\n y=data['Cost Price'].values\n \n x=np.append(x,productionpred,axis=0)\n \n x_std = preprocessing.scale(x)\n \n userinput=x_std[95]\n x_std=np.delete(x_std,95,axis=0)\n \n linear2=LinearRegression()\n linear2.fit(x_std.reshape(-1,1),y.reshape(-1,1))\n \n cppred=linear2.predict(userinput.reshape(1,-1))\n \n data2=pd.read_excel('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\costvssp.xlsx')\n\n x=data2[['Year','CPI','Consumption Rate','Cost Price']].values \n y=data2['SP'].values\n # x=data2[['Crop_Year','CPI','Consumption','Cost Price']].values \n # y=data2['SELLING P'].values\n #print(r2_score(test_y,prediction_val)) #0.92\n x=np.append(x,[[2020,7.59,20000,cppred]],axis=0)\n \n x_std = preprocessing.scale(x)\n \n userinput=x_std[6,:]\n x_std=np.delete(x_std,6,axis=0)\n \n train_x,test_x,train_y,test_y=train_test_split(x_std,y,test_size=0.1,random_state=0)\n \n poly=PolynomialFeatures(degree = 1)\n x_poly=poly.fit_transform(train_x)\n \n poly.fit(x_poly,train_y)\n linear2=LinearRegression()\n linear2.fit(x_poly,train_y)\n \n sppred=linear2.predict(poly.fit_transform(userinput.reshape(1,-1)))\n \n if h==1: \n graphdata=pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\Rice_OD.csv',encoding='cp1252')\n if h == 2:\n graphdata=pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\rice_WB.csv',encoding='cp1252') \n if h == 3:\n graphdata=pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\RICE_ASSAM.csv',encoding='cp1252') \n if h == 4:\n graphdata=pd.read_csv('C:\\\\Users\\\\aayus\\\\project\\\\cotton_prediction\\\\RICE_UTTARPRADESH.csv',encoding='cp1252') \n mean2011=graphdata[graphdata['Crop_Year']==2011]\n mean2011=mean2011['Cost Price'].mean()\n \n mean2012=graphdata[graphdata['Crop_Year']==2012 ]\n mean2012=mean2012['Cost Price'].mean()\n\n \n mean2013=graphdata[graphdata['Crop_Year']==2013 ]\n mean2013=mean2013['Cost Price'].mean()\n\n mean2010=graphdata[graphdata['Crop_Year']==2010 ]\n mean2010=mean2010['Cost Price'].mean()\n\n mean2014=graphdata[graphdata['Crop_Year']==2014 ]\n mean2014=mean2014['Cost Price'].mean()\n meanlist=[mean2010,mean2011,mean2012,mean2013,mean2014]\n# production\n\n mean2011=graphdata[graphdata['Crop_Year']==2011]\n pmean2011=mean2011['Production'].mean()\n\n mean2012=graphdata[graphdata['Crop_Year']==2012 ]\n pmean2012=mean2012['Production'].mean()\n\n \n\n mean2013=graphdata[graphdata['Crop_Year']==2013 ]\n pmean2013=mean2013['Production'].mean()\n\n mean2010=graphdata[graphdata['Crop_Year']==2010 ]\n pmean2010=mean2010['Production'].mean()\n\n mean2014=graphdata[graphdata['Crop_Year']==2014 ]\n pmean2014=mean2014['Production'].mean()\n \n\n pmeanlist=[pmean2010,pmean2011,pmean2012,pmean2013,pmean2014]\n yearlist=graphdata['Crop_Year'].unique() \n return(cppred,sppred,productionpred,meanlist,yearlist,pmeanlist)\n ", "sub_path": "production/rice_multiple_odwb.py", "file_name": "rice_multiple_odwb.py", "file_ext": "py", "file_size_in_byte": 5661, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "seaborn.set", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.delete", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.delete", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 138, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "251290474", "text": "# -*- coding:utf-8 -*- \n\"\"\"\n交易数据接口 \nCreated on 2014/07/31\n@author: Jimmy Liu\n@group : waditu\n@contact: jimmysoa@sina.cn\n\"\"\"\n\nimport constants\nimport numpy\nfrom enum import Enum\nfrom io import BytesIO\nimport json\nimport lxml.html\nfrom lxml import etree\nimport pandas\nfrom myshare.util import net\nimport time\nimport re\n\n\n# MinutesKLineType = Enum('KLineType', 'day, week, month, five_minutes, fifteen_minutes, thirty_minutes, sixty_minutes')\n\n\ndef get_history_data(code=None, start=None, end=None, k_line='day', retry_count=3, pause=0.001):\n \"\"\"\n 获取个股历史交易记录\n Parameters\n ------\n code:string\n 股票代码 e.g. 600848\n start:string\n 开始日期 format：YYYY-MM-DD 为空时取到API所提供的最早日期数据\n end:string\n 结束日期 format：YYYY-MM-DD 为空时取到最近一个交易日数据\n k_line:string\n 数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D\n retry_count : int, 默认 3\n 如遇网络等问题重复执行的次数 \n pause : int, 默认 0\n 重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题\n return\n -------\n DataFrame\n 属性:日期 ，开盘价， 最高价， 收盘价， 最低价， 成交量， 价格变动 ，涨跌幅，5日均价，10日均价，20日均价，5日均量，10日均量，20日均量，换手率\n \"\"\"\n symbol = _code_to_symbol(code)\n k_line = k_line.lower()\n _is_minutes_k_line = is_minutes_k_line(k_line)\n\n if _is_minutes_k_line:\n url = constants.DAY_PRICE_URL % (constants.K_TYPE[k_line], symbol)\n else:\n url = constants.DAY_PRICE_MIN_URL % (symbol, k_line)\n \n for _ in range(retry_count):\n time.sleep(pause)\n try:\n html = net.request(url, 'utf-8')\n if len(html) < 15: #no data\n return None\n except Exception as e:\n print(e)\n else:\n _json = json.loads(html)\n\n cols = constants.DAY_PRICE_COLUMNS\n\n if len(_json['record'][0]) == 14 | (code in constants.INDEX_LABELS & (not _is_minutes_k_line)):\n # remove column turnover\n cols = cols[:-1]\n\n data_frame = pandas.DataFrame(_json['record'], columns=cols)\n\n if not _is_minutes_k_line:\n data_frame = data_frame.applymap(lambda x: x.replace(u',', u''))\n data_frame[data_frame == ''] = 0\n\n for col in cols[1:]:\n data_frame[col] = data_frame[col].astype(float)\n if start is not None:\n data_frame = data_frame[data_frame.date >= start]\n if end is not None:\n data_frame = data_frame[data_frame.date <= end]\n if (code in constants.INDEX_LABELS) & (k_line in constants.K_MIN_LABELS):\n data_frame = data_frame.drop('turnover', axis=1)\n data_frame = data_frame.set_index('date')\n data_frame = data_frame.sort_index(ascending=False)\n return data_frame\n raise IOError(constants.NETWORK_URL_ERROR_MSG)\n\n\ndef is_minutes_k_line(k_line):\n k_line = k_line.lower()\n if k_line in constants.MINUTE_K_TYPE():\n return True\n if k_line in constants.K_TYPE.keys():\n return False\n raise TypeError('k_line type error.')\n\n\ndef _parsing_dayprice_json(pageNum=1):\n \"\"\"\n 处理当日行情分页数据，格式为json\n Parameters\n ------\n pageNum:页码\n return\n -------\n DataFrame 当日所有股票交易数据(DataFrame)\n \"\"\"\n constants._write_console()\n request = Request(constants.SINA_DAY_PRICE_URL%(constants.P_TYPE['http'], constants.DOMAINS['vsf'],\n constants.PAGES['jv'], pageNum))\n text = urlopen(request, timeout=10).read()\n if text == 'null':\n return None\n reg = re.compile(r'\\,(.*?)\\:') \n text = reg.sub(r',\"\\1\":', text.decode('gbk') if constants.PY3 else text) \n text = text.replace('\"{symbol', '{\"symbol')\n text = text.replace('{symbol', '{\"symbol\"')\n if constants.PY3:\n jstr = json.dumps(text)\n else:\n jstr = json.dumps(text, encoding='GBK')\n js = json.loads(jstr)\n df = pd.DataFrame(pd.read_json(js, dtype={'code':object}),\n columns=constants.DAY_TRADING_COLUMNS)\n df = df.drop('symbol', axis=1)\n# df = df.ix[df.volume > 0]\n return df\n\n\ndef get_tick_data(code=None, date=None, retry_count=3, pause=0.001):\n \"\"\"\n 获取分笔数据\n Parameters\n ------\n code:string\n 股票代码 e.g. 600848\n date:string\n 日期 format：YYYY-MM-DD\n retry_count : int, 默认 3\n 如遇网络等问题重复执行的次数\n pause : int, 默认 0\n 重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题\n return\n -------\n DataFrame 当日所有股票交易数据(DataFrame)\n 属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型\n \"\"\"\n if code is None or len(code)!=6 or date is None:\n return None\n symbol = _code_to_symbol(code)\n for _ in range(retry_count):\n time.sleep(pause)\n try:\n re = Request(constants.TICK_PRICE_URL % (constants.P_TYPE['http'], constants.DOMAINS['sf'], constants.PAGES['dl'],\n date, symbol))\n lines = urlopen(re, timeout=10).read()\n lines = lines.decode('GBK') \n if len(lines) < 20:\n return None\n df = pd.read_table(StringIO(lines), names=constants.TICK_COLUMNS,\n skiprows=[0]) \n except Exception as e:\n print(e)\n else:\n return df\n raise IOError(constants.NETWORK_URL_ERROR_MSG)\n\n\ndef get_sina_dd(code=None, date=None, vol=400, retry_count=3, pause=0.001):\n \"\"\"\n 获取sina大单数据\n Parameters\n ------\n code:string\n 股票代码 e.g. 600848\n date:string\n 日期 format：YYYY-MM-DD\n retry_count : int, 默认 3\n 如遇网络等问题重复执行的次数\n pause : int, 默认 0\n 重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题\n return\n -------\n DataFrame 当日所有股票交易数据(DataFrame)\n 属性:股票代码 股票名称 交易时间 价格 成交量 前一笔价格 类型（买、卖、中性盘）\n \"\"\"\n if code is None or len(code)!=6 or date is None:\n return None\n symbol = _code_to_symbol(code)\n vol = vol*100\n for _ in range(retry_count):\n time.sleep(pause)\n try:\n re = Request(constants.SINA_DD % (constants.P_TYPE['http'], constants.DOMAINS['vsf'], constants.PAGES['sinadd'],\n symbol, vol, date))\n lines = urlopen(re, timeout=10).read()\n lines = lines.decode('GBK') \n if len(lines) < 100:\n return None\n df = pd.read_csv(StringIO(lines), names=constants.SINA_DD_COLS,\n skiprows=[0]) \n if df is not None:\n df['code'] = df['code'].map(lambda x: x[2:])\n except Exception as e:\n print(e)\n else:\n return df\n raise IOError(constants.NETWORK_URL_ERROR_MSG)\n\n\ndef get_today_ticks(code=None, retry_count=3, pause=0.001):\n \"\"\"\n 获取当日分笔明细数据\n Parameters\n ------\n code:string\n 股票代码 e.g. 600848\n retry_count : int, 默认 3\n 如遇网络等问题重复执行的次数\n pause : int, 默认 0\n 重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题\n return\n -------\n DataFrame 当日所有股票交易数据(DataFrame)\n 属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型\n \"\"\"\n if code is None or len(code)!=6 :\n return None\n symbol = _code_to_symbol(code)\n date = du.today()\n for _ in range(retry_count):\n time.sleep(pause)\n try:\n request = Request(constants.TODAY_TICKS_PAGE_URL % (constants.P_TYPE['http'], constants.DOMAINS['vsf'],\n constants.PAGES['jv'], date,\n symbol))\n data_str = urlopen(request, timeout=10).read()\n data_str = data_str.decode('GBK')\n data_str = data_str[1:-1]\n data_str = eval(data_str, type('Dummy', (dict,), \n dict(__getitem__ = lambda s, n:n))())\n data_str = json.dumps(data_str)\n data_str = json.loads(data_str)\n pages = len(data_str['detailPages'])\n data = pd.DataFrame()\n constants._write_head()\n for pNo in range(1, pages+1):\n data = data.append(_today_ticks(symbol, date, pNo,\n retry_count, pause), ignore_index=True)\n except Exception as er:\n print(str(er))\n else:\n return data\n raise IOError(constants.NETWORK_URL_ERROR_MSG)\n\n\ndef _today_ticks(symbol, tdate, pageNo, retry_count, pause):\n constants._write_console()\n for _ in range(retry_count):\n time.sleep(pause)\n try:\n html = lxml.html.parse(constants.TODAY_TICKS_URL % (constants.P_TYPE['http'],\n constants.DOMAINS['vsf'], constants.PAGES['t_ticks'],\n symbol, tdate, pageNo\n )) \n res = html.xpath('//table[@id=\\\"datatbl\\\"]/tbody/tr')\n if constants.PY3:\n sarr = [etree.tostring(node).decode('utf-8') for node in res]\n else:\n sarr = [etree.tostring(node) for node in res]\n sarr = ''.join(sarr)\n sarr = '%s
'%sarr\n sarr = sarr.replace('--', '0')\n df = pd.read_html(StringIO(sarr), parse_dates=False)[0]\n df.columns = constants.TODAY_TICK_COLUMNS\n df['pchange'] = df['pchange'].map(lambda x : x.replace('%', ''))\n except Exception as e:\n print(e)\n else:\n return df\n raise IOError(constants.NETWORK_URL_ERROR_MSG)\n \n \ndef get_today_all():\n \"\"\"\n 一次性获取最近一个日交易日所有股票的交易数据\n return\n -------\n DataFrame\n 属性：代码，名称，涨跌幅，现价，开盘价，最高价，最低价，最日收盘价，成交量，换手率，成交额，市盈率，市净率，总市值，流通市值\n \"\"\"\n constants._write_head()\n df = _parsing_dayprice_json(1)\n if df is not None:\n for i in range(2, constants.PAGE_NUM[0]):\n newdf = _parsing_dayprice_json(i)\n df = df.append(newdf, ignore_index=True)\n return df\n\n\ndef get_realtime_quotes(symbols=None):\n \"\"\"\n 获取实时交易数据 getting real time quotes data\n 用于跟踪交易情况（本次执行的结果-上一次执行的数据）\n Parameters\n ------\n symbols : string, array-like object (list, tuple, Series).\n \n return\n -------\n DataFrame 实时交易数据\n 属性:0：name，股票名字\n 1：open，今日开盘价\n 2：pre_close，昨日收盘价\n 3：price，当前价格\n 4：high，今日最高价\n 5：low，今日最低价\n 6：bid，竞买价，即“买一”报价\n 7：ask，竞卖价，即“卖一”报价\n 8：volumn，成交量 maybe you need do volumn/100\n 9：amount，成交金额（元 CNY）\n 10：b1_v，委买一（笔数 bid volume）\n 11：b1_p，委买一（价格 bid price）\n 12：b2_v，“买二”\n 13：b2_p，“买二”\n 14：b3_v，“买三”\n 15：b3_p，“买三”\n 16：b4_v，“买四”\n 17：b4_p，“买四”\n 18：b5_v，“买五”\n 19：b5_p，“买五”\n 20：a1_v，委卖一（笔数 ask volume）\n 21：a1_p，委卖一（价格 ask price）\n ...\n 30：date，日期；\n 31：time，时间；\n \"\"\"\n symbols_list = ''\n if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(symbols, tuple) or isinstance(symbols, pd.Series):\n for code in symbols:\n symbols_list += _code_to_symbol(code) + ','\n else:\n symbols_list = _code_to_symbol(symbols)\n \n symbols_list = symbols_list[:-1] if len(symbols_list) > 8 else symbols_list \n request = Request(constants.LIVE_DATA_URL%(constants.P_TYPE['http'], constants.DOMAINS['sinahq'],\n _random(), symbols_list))\n text = urlopen(request,timeout=10).read()\n text = text.decode('GBK')\n reg = re.compile(r'\\=\"(.*?)\\\";')\n data = reg.findall(text)\n regSym = re.compile(r'(?:sh|sz)(.*?)\\=')\n syms = regSym.findall(text)\n data_list = []\n syms_list = []\n for index, row in enumerate(data):\n if len(row)>1:\n data_list.append([astr for astr in row.split(',')])\n syms_list.append(syms[index])\n if len(syms_list) == 0:\n return None\n df = pd.DataFrame(data_list, columns=constants.LIVE_DATA_COLS)\n df = df.drop('s', axis=1)\n df['code'] = syms_list\n ls = [cls for cls in df.columns if '_v' in cls]\n for txt in ls:\n df[txt] = df[txt].map(lambda x : x[:-2])\n return df\n\n\ndef get_h_data(code, start=None, end=None, autype='qfq',\n index=False, retry_count=3, pause=0.001, drop_factor=True):\n '''\n 获取历史复权数据\n Parameters\n ------\n code:string\n 股票代码 e.g. 600848\n start:string\n 开始日期 format：YYYY-MM-DD 为空时取当前日期\n end:string\n 结束日期 format：YYYY-MM-DD 为空时取去年今日\n autype:string\n 复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq\n retry_count : int, 默认 3\n 如遇网络等问题重复执行的次数 \n pause : int, 默认 0\n 重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题\n drop_factor : bool, 默认 True\n 是否移除复权因子，在分析过程中可能复权因子意义不大，但是如需要先储存到数据库之后再分析的话，有该项目会更加灵活\n return\n -------\n DataFrame\n date 交易日期 (index)\n open 开盘价\n high 最高价\n close 收盘价\n low 最低价\n volume 成交量\n amount 成交金额\n '''\n \n start = du.today_last_year() if start is None else start\n end = du.today() if end is None else end\n qs = du.get_quarts(start, end)\n qt = qs[0]\n constants._write_head()\n data = _parse_fq_data(_get_index_url(index, code, qt), index,\n retry_count, pause)\n if len(qs)>1:\n for d in range(1, len(qs)):\n qt = qs[d]\n constants._write_console()\n df = _parse_fq_data(_get_index_url(index, code, qt), index,\n retry_count, pause)\n if df is None: # 可能df为空，退出循环\n break\n else:\n data = data.append(df, ignore_index=True)\n if len(data) == 0 or len(data[(data.date>=start)&(data.date<=end)]) == 0:\n return None\n data = data.drop_duplicates('date')\n if index:\n data = data[(data.date>=start) & (data.date<=end)]\n data = data.set_index('date')\n data = data.sort_index(ascending=False)\n return data\n if autype == 'hfq':\n if drop_factor:\n data = data.drop('factor', axis=1)\n data = data[(data.date>=start) & (data.date<=end)]\n for label in ['open', 'high', 'close', 'low']:\n data[label] = data[label].map(constants.FORMAT)\n data[label] = data[label].astype(float)\n data = data.set_index('date')\n data = data.sort_index(ascending = False)\n return data\n else:\n if autype == 'qfq':\n if drop_factor:\n data = data.drop('factor', axis=1)\n df = _parase_fq_factor(code, start, end)\n df = df.drop_duplicates('date')\n df = df.sort('date', ascending=False)\n firstDate = data.head(1)['date']\n frow = df[df.date == firstDate[0]]\n rt = get_realtime_quotes(code)\n if rt is None:\n return None\n if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):\n preClose = float(rt['pre_close'])\n else:\n if du.is_holiday(du.today()):\n preClose = float(rt['price'])\n else:\n if (du.get_hour() > 9) & (du.get_hour() < 18):\n preClose = float(rt['pre_close'])\n else:\n preClose = float(rt['price'])\n \n rate = float(frow['factor']) / preClose\n data = data[(data.date >= start) & (data.date <= end)]\n for label in ['open', 'high', 'low', 'close']:\n data[label] = data[label] / rate\n data[label] = data[label].map(constants.FORMAT)\n data[label] = data[label].astype(float)\n data = data.set_index('date')\n data = data.sort_index(ascending = False)\n return data\n else:\n for label in ['open', 'high', 'close', 'low']:\n data[label] = data[label] / data['factor']\n if drop_factor:\n data = data.drop('factor', axis=1)\n data = data[(data.date>=start) & (data.date<=end)]\n for label in ['open', 'high', 'close', 'low']:\n data[label] = data[label].map(constants.FORMAT)\n data = data.set_index('date')\n data = data.sort_index(ascending = False)\n data = data.astype(float)\n return data\n\n\ndef _parase_fq_factor(code, start, end):\n symbol = _code_to_symbol(code)\n request = Request(constants.HIST_FQ_FACTOR_URL%(constants.P_TYPE['http'],\n constants.DOMAINS['vsf'], symbol))\n text = urlopen(request, timeout=10).read()\n text = text[1:len(text)-1]\n text = text.decode('utf-8') if constants.PY3 else text\n text = text.replace('{_', '{\"')\n text = text.replace('total', '\"total\"')\n text = text.replace('data', '\"data\"')\n text = text.replace(':\"', '\":\"')\n text = text.replace('\",_', '\",\"')\n text = text.replace('_', '-')\n text = json.loads(text)\n df = pd.DataFrame({'date':list(text['data'].keys()), 'factor':list(text['data'].values())})\n df['date'] = df['date'].map(_fun_except) # for null case\n if df['date'].dtypes == np.object:\n df['date'] = df['date'].astype(np.datetime64)\n df = df.drop_duplicates('date')\n df['factor'] = df['factor'].astype(float)\n return df\n\n\ndef _fun_except(x):\n if len(x) > 10:\n return x[-10:]\n else:\n return x\n\n\ndef _parse_fq_data(url, index, retry_count, pause):\n for _ in range(retry_count):\n time.sleep(pause)\n try:\n request = Request(url)\n text = urlopen(request, timeout=10).read()\n text = text.decode('GBK')\n html = lxml.html.parse(StringIO(text))\n res = html.xpath('//table[@id=\\\"FundHoldSharesTable\\\"]')\n if constants.PY3:\n sarr = [etree.tostring(node).decode('utf-8') for node in res]\n else:\n sarr = [etree.tostring(node) for node in res]\n sarr = ''.join(sarr)\n df = pd.read_html(sarr, skiprows = [0, 1])[0]\n if len(df) == 0:\n return pd.DataFrame()\n if index:\n df.columns = constants.HIST_FQ_COLS[0:7]\n else:\n df.columns = constants.HIST_FQ_COLS\n if df['date'].dtypes == np.object:\n df['date'] = df['date'].astype(np.datetime64)\n df = df.drop_duplicates('date')\n except ValueError as e:\n # 时间较早，已经读不到数据\n return None\n except Exception as e:\n print(e)\n else:\n return df\n raise IOError(constants.NETWORK_URL_ERROR_MSG)\n\n\ndef get_index():\n \"\"\"\n 获取大盘指数行情\n return\n -------\n DataFrame\n code:指数代码\n name:指数名称\n change:涨跌幅\n open:开盘价\n preclose:昨日收盘价\n close:收盘价\n high:最高价\n low:最低价\n volume:成交量(手)\n amount:成交金额（亿元）\n \"\"\"\n request = Request(constants.INDEX_HQ_URL%(constants.P_TYPE['http'],\n constants.DOMAINS['sinahq']))\n text = urlopen(request, timeout=10).read()\n text = text.decode('GBK')\n text = text.replace('var hq_str_sh', '').replace('var hq_str_sz', '')\n text = text.replace('\";', '').replace('\"', '').replace('=', ',')\n text = '%s%s'%(constants.INDEX_HEADER, text)\n df = pd.read_csv(StringIO(text), sep=',', thousands=',')\n df['change'] = (df['close'] / df['preclose'] - 1 ) * 100\n df['amount'] = df['amount'] / 100000000\n df['change'] = df['change'].map(constants.FORMAT)\n df['amount'] = df['amount'].map(constants.FORMAT4)\n df = df[constants.INDEX_COLS]\n df['code'] = df['code'].map(lambda x:str(x).zfill(6))\n df['change'] = df['change'].astype(float)\n df['amount'] = df['amount'].astype(float)\n return df\n \n\ndef _get_index_url(index, code, qt):\n if index:\n url = constants.HIST_INDEX_URL%(constants.P_TYPE['http'], constants.DOMAINS['vsf'],\n code, qt[0], qt[1])\n else:\n url = constants.HIST_FQ_URL%(constants.P_TYPE['http'], constants.DOMAINS['vsf'],\n code, qt[0], qt[1])\n return url\n\n\ndef get_hists(symbols, start=None, end=None,\n k_line='D', retry_count=3,\n pause=0.001):\n \"\"\"\n 批量获取历史行情数据，具体参数和返回数据类型请参考get_hist_data接口\n \"\"\"\n df = pd.DataFrame()\n if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(symbols, tuple) or isinstance(symbols, pd.Series):\n for symbol in symbols:\n data = get_hist_data(symbol, start=start, end=end,\n k_line=k_line, retry_count=retry_count,\n pause=pause)\n data['code'] = symbol\n df = df.append(data, ignore_index=True)\n return df\n else:\n return None\n \n \ndef _random(n=13):\n from random import randint\n start = 10**(n-1)\n end = (10**n)-1\n return str(randint(start, end))\n\n\ndef _code_to_symbol(code):\n \"\"\"\n 生成symbol代码标志\n \"\"\"\n if code in constants.INDEX_LABELS:\n return constants.INDEX_LIST[code]\n\n if len(code) == 8 & code[:2].lower() in ['sh', 'sz']:\n return code\n\n if len(code) == 6:\n return 'sh%s' % code if code[:1] in ['5', '6', '9'] else 'sz%s' % code\n\n return ''\n", "sub_path": "myshare/stock/trading.py", "file_name": "trading.py", "file_ext": "py", "file_size_in_byte": 23980, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "constants.DAY_PRICE_URL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "constants.K_TYPE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "constants.DAY_PRICE_MIN_URL", "line_number": 55, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "myshare.util.net.request", "line_number": 60, "usage_type": "call"}, {"api_name": "myshare.util.net", "line_number": 60, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 66, "usage_type": "call"}, {"api_name": "constants.DAY_PRICE_COLUMNS", "line_number": 68, "usage_type": "attribute"}, {"api_name": "constants.INDEX_LABELS", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "call"}, {"api_name": "constants.INDEX_LABELS", "line_number": 86, "usage_type": "attribute"}, {"api_name": "constants.K_MIN_LABELS", "line_number": 86, "usage_type": "attribute"}, {"api_name": "constants.NETWORK_URL_ERROR_MSG", "line_number": 91, "usage_type": "attribute"}, {"api_name": "constants.MINUTE_K_TYPE", "line_number": 96, "usage_type": "call"}, {"api_name": "constants.K_TYPE.keys", "line_number": 98, "usage_type": "call"}, {"api_name": "constants.K_TYPE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "constants._write_console", "line_number": 113, "usage_type": "call"}, {"api_name": "constants.SINA_DAY_PRICE_URL", "line_number": 114, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 114, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 114, "usage_type": "attribute"}, {"api_name": "constants.PAGES", "line_number": 115, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 119, "usage_type": "call"}, {"api_name": "constants.PY3", "line_number": 120, "usage_type": "attribute"}, {"api_name": "constants.PY3", "line_number": 123, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}, {"api_name": "constants.DAY_TRADING_COLUMNS", "line_number": 129, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 157, "usage_type": "call"}, {"api_name": "constants.TICK_PRICE_URL", "line_number": 159, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 159, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 159, "usage_type": "attribute"}, {"api_name": "constants.PAGES", "line_number": 159, "usage_type": "attribute"}, {"api_name": "constants.TICK_COLUMNS", "line_number": 165, "usage_type": "attribute"}, {"api_name": "constants.NETWORK_URL_ERROR_MSG", "line_number": 171, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 197, "usage_type": "call"}, {"api_name": "constants.SINA_DD", "line_number": 199, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 199, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 199, "usage_type": "attribute"}, {"api_name": "constants.PAGES", "line_number": 199, "usage_type": "attribute"}, {"api_name": "constants.SINA_DD_COLS", "line_number": 205, "usage_type": "attribute"}, {"api_name": "constants.NETWORK_URL_ERROR_MSG", "line_number": 213, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 237, "usage_type": "call"}, {"api_name": "constants.TODAY_TICKS_PAGE_URL", "line_number": 239, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 239, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 239, "usage_type": "attribute"}, {"api_name": "constants.PAGES", "line_number": 240, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 247, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 248, "usage_type": "call"}, {"api_name": "constants._write_head", "line_number": 251, "usage_type": "call"}, {"api_name": "constants.NETWORK_URL_ERROR_MSG", "line_number": 259, "usage_type": "attribute"}, {"api_name": "constants._write_console", "line_number": 263, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 265, "usage_type": "call"}, {"api_name": "lxml.html.html.parse", "line_number": 267, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 267, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 267, "usage_type": "name"}, {"api_name": "constants.TODAY_TICKS_URL", "line_number": 267, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 267, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 268, "usage_type": "attribute"}, {"api_name": "constants.PAGES", "line_number": 268, "usage_type": "attribute"}, {"api_name": "constants.PY3", "line_number": 272, "usage_type": "attribute"}, {"api_name": "lxml.etree.tostring", "line_number": 273, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 273, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 275, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 275, "usage_type": "name"}, {"api_name": "constants.TODAY_TICK_COLUMNS", "line_number": 280, "usage_type": "attribute"}, {"api_name": "constants.NETWORK_URL_ERROR_MSG", "line_number": 286, "usage_type": "attribute"}, {"api_name": "constants._write_head", "line_number": 297, "usage_type": "call"}, {"api_name": "constants.PAGE_NUM", "line_number": 300, "usage_type": "attribute"}, {"api_name": "constants.LIVE_DATA_URL", "line_number": 351, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 351, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 351, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 355, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 357, "usage_type": "call"}, {"api_name": "constants.LIVE_DATA_COLS", "line_number": 367, "usage_type": "attribute"}, {"api_name": "constants._write_head", "line_number": 412, "usage_type": "call"}, {"api_name": "constants._write_console", "line_number": 418, "usage_type": "call"}, {"api_name": "constants.FORMAT", "line_number": 438, "usage_type": "attribute"}, {"api_name": "constants.FORMAT", "line_number": 470, "usage_type": "attribute"}, {"api_name": "constants.FORMAT", "line_number": 482, "usage_type": "attribute"}, {"api_name": "constants.HIST_FQ_FACTOR_URL", "line_number": 491, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 491, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 492, "usage_type": "attribute"}, {"api_name": "constants.PY3", "line_number": 495, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 502, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 521, "usage_type": "call"}, {"api_name": "lxml.html.html.parse", "line_number": 526, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 526, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 526, "usage_type": "name"}, {"api_name": "constants.PY3", "line_number": 528, "usage_type": "attribute"}, {"api_name": "lxml.etree.tostring", "line_number": 529, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 529, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 531, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 531, "usage_type": "name"}, {"api_name": "constants.HIST_FQ_COLS", "line_number": 537, "usage_type": "attribute"}, {"api_name": "constants.HIST_FQ_COLS", "line_number": 539, "usage_type": "attribute"}, {"api_name": "constants.NETWORK_URL_ERROR_MSG", "line_number": 550, "usage_type": "attribute"}, {"api_name": "constants.INDEX_HQ_URL", "line_number": 570, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 570, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 571, "usage_type": "attribute"}, {"api_name": "constants.INDEX_HEADER", "line_number": 576, "usage_type": "attribute"}, {"api_name": "constants.FORMAT", "line_number": 580, "usage_type": "attribute"}, {"api_name": "constants.FORMAT4", "line_number": 581, "usage_type": "attribute"}, {"api_name": "constants.INDEX_COLS", "line_number": 582, "usage_type": "attribute"}, {"api_name": "constants.HIST_INDEX_URL", "line_number": 591, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 591, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 591, "usage_type": "attribute"}, {"api_name": "constants.HIST_FQ_URL", "line_number": 594, "usage_type": "attribute"}, {"api_name": "constants.P_TYPE", "line_number": 594, "usage_type": "attribute"}, {"api_name": "constants.DOMAINS", "line_number": 594, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 622, "usage_type": "call"}, {"api_name": "constants.INDEX_LABELS", "line_number": 629, "usage_type": "attribute"}, {"api_name": "constants.INDEX_LIST", "line_number": 630, "usage_type": "attribute"}]} +{"seq_id": "176490482", "text": "\nimport os\nimport re\nimport socket\nimport struct\nimport multiprocessing as mp\nimport sys\n\nimport requests\n\nimport global_var\nfrom gen_ip2as_command import GetCloseDateFile, PreGetSrcFilesInDirs\nfrom download_irrdata import GetOrgAsnFromIRROnLine, GetCountryOrgAsnFromIrrOnline\nfrom ana_inconformity import FindTraceAsInBgpPath, ClassifyAbTrace2, PrintDetourDict\nfrom ana_prefix_traceroute_group_by_prefix_v2 import ChgTrace2ASPath, CompressAsPath, CompressAsPathToMin, CheckTracesByIRRData\nfrom utils_v2 import ClearAsRel, ClearIxpAsSet, ClearSibRel, FindBgpAsInTracePath, GetSibRel, AsnInTracePathList, DropStarsInTraceList, IsSib, PathHasValley_2, GetBgpByPrefix, \\\n GetBgpPathFromBgpPrefixDict_2, ClearBGPByPrefix, GetAsRel, GetIxpAsSet, IsIxpAs, GetSibRelByMultiDataFiles, GetOrgByMultiDataFiles_2, PreLoadAsnInfoFromASNS, \\\n GetAsnInfoFromASNS, ClearAsnInfoFromASNS, ConstrPeerDbInfoDict, IsTwoAsPeerInIXP, GetNeighFromRipe, ClearPeerDbInfoDict, IsAsSet, GetAsRel_2, ClearAsRel_2, \\\n GetNeighOfAs, GetFuncLgDict, GetFuncOfLg, ClearFuncLgDict, GetAsPfxDict, GetRepIpsOfAs, ClearAsPfxDict, GetAsStrOfIpByRv, GetPfx2ASByRv, ClearIp2AsDict, \\\n GetPfx2ASByRvV6, ClearIp2AsDictV6, GetAsStrOfIpByRvV6, GetAsRankDict, ClearAsRankDict, GetAsRankFromDict, Get2AsRel, GetCCNums, GetCCNumsFromDict\n\nall_traces_set = set()\ndef PreGetAllTraces():\n global all_traces_set\n obj_filename = global_var.all_trace_par_path + global_var.all_trace_out_data_dir + global_var.all_trace_out_all_trace_filename\n if not os.path.exists(obj_filename):\n print('Construct all_traces file')\n for year in range(2018,2021):\n for month in range(1,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n os.chdir(global_var.all_trace_par_path + global_var.all_trace_trace_as_res_dir + str(year) + '/' + str(month).zfill(2) + '/')\n for root,dirs,files in os.walk('.'):\n for filename in files: #例：cdg-fr.20180125\n with open(filename, 'r') as rf:\n curline_trace = rf.readline()\n while curline_trace:\n ori_trace_path = curline_trace.strip('\\n')\n trace_path = CompressAsPathToMin(CompressAsPath(ori_trace_path))\n all_traces_set.add(trace_path)\n curline_trace = rf.readline()\n print(len(all_traces_set))\n with open(obj_filename, 'w') as wf:\n wf.write(';'.join(all_traces_set))\n else:\n print('Get all traces from file')\n with open(obj_filename, 'r') as rf:\n data = rf.read()\n all_traces_set = set(data.strip(';').split(';'))\n print(len(all_traces_set))\n\ndef PreGetAllTraces_Mini():\n global all_traces_set\n obj_filename = global_var.par_path + global_var.other_middle_data_dir + 'all_traces'\n if not os.path.exists(obj_filename):\n #if True:\n print('Construct all_traces file')\n for year in range(2018,2021):\n for month in range(1,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n month_str = str(month).zfill(2)\n date = str(year) + month_str + '15'\n for vp in global_var.vps:\n parent_dir = global_var.par_path + global_var.out_my_anatrace_dir + '/' + vp + '_' + date + '/ribs_midar_bdrmapit/'\n filenames = ['final_normal_fin', 'final_unmap_fin', 'final_ab_fin']\n for filename in filenames:\n path = parent_dir + filename\n print(path)\n if not os.path.exists(path):\n continue\n with open(path, 'r') as rf:\n curline_trace = rf.readline()\n while curline_trace:\n #print(curline_trace)\n curline_bgp = rf.readline()\n curline_ip = rf.readline()\n ori_trace_path = curline_trace[curline_trace.index(']') + 1:].strip('\\n').strip(' ')\n trace_path = CompressAsPathToMin(CompressAsPath(ori_trace_path))\n all_traces_set.add(trace_path)\n curline_trace = rf.readline()\n print(len(all_traces_set))\n with open(obj_filename, 'w') as wf:\n #with open(obj_filename, 'a') as wf:\n #wf.write(';')\n wf.write(';'.join(all_traces_set))\n else:\n print('Get all traces from file')\n with open(obj_filename, 'r') as rf:\n data = rf.read()\n all_traces_set = set(data.strip(';').split(';'))\n print(len(all_traces_set))\n\ndef TransOneTraceToUniASTraces(trace):\n #print(trace)\n if not trace.__contains__(' '):\n return set(trace.split('_'))\n res_trace_set = set()\n for elem in trace.split(' ')[0].split('_'):\n sub_trace_set = TransOneTraceToUniASTraces(trace[trace.index(' ') + 1:])\n for sub_trace in sub_trace_set:\n tmp = CompressAsPathToMin(CompressAsPath(elem + ' ' + sub_trace))\n res_trace_set.add(tmp)\n return res_trace_set\n\ndef PreTransAllTraceToUniASTraces():\n global all_traces_set\n res_trace_set = set()\n for trace in all_traces_set:\n #print(trace)\n cur_trace_set = TransOneTraceToUniASTraces(trace)\n res_trace_set |= cur_trace_set\n print(len(res_trace_set))\n #with open(global_var.all_trace_par_path + global_var.all_trace_out_data_dir + global_var.all_trace_out_all_trace_uni_as_filename, 'w') as wf:\n with open(global_var.par_path + global_var.other_middle_data_dir + 'all_traces_uni_as', 'w') as wf:\n wf.write(';'.join(res_trace_set))\n return res_trace_set\n\nall_trace_set_uni_as = set()\ndef PreGetAllTracesWithUniAs():\n global all_trace_set_uni_as\n #with open(global_var.all_trace_par_path + global_var.all_trace_out_data_dir + global_var.all_trace_out_all_trace_uni_as_filename, 'r') as rf:\n with open(global_var.par_path + global_var.other_middle_data_dir + 'all_traces_uni_as', 'r') as rf:\n data = rf.read()\n all_trace_set_uni_as = data.split(';')\n print(len(all_trace_set_uni_as))\n\ndef GetLinkSetFromBgpPath(bgp_path):\n cur_path = CompressAsPathToMin(CompressAsPath(bgp_path))\n link_set = set()\n prev_elem = ''\n for elem in cur_path.split(' '):\n if prev_elem == '' or prev_elem.__contains__(',') or elem.__contains__(','):\n prev_elem = elem\n continue\n link_set.add(prev_elem + ' ' + elem)\n prev_elem = elem\n return link_set\n\ng_bgp_paths_dict = dict()\ng_bgp_link_path_dict = dict() #建立link到path的索引\ndef PreGetAllBgpPaths():\n global g_bgp_paths_dict\n global g_bgp_link_path_dict\n #g_bgp_paths_dict.clear()\n filepath = global_var.par_path + global_var.other_middle_data_dir + 'all_bgp_paths'\n if not os.path.exists(filepath):\n for year in range(2018,2021):\n for month in range(1,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n month_str = str(month).zfill(2)\n date = str(year) + month_str + '15'\n for vp in global_var.vps:\n filename = 'bgp_' + global_var.trace_as_dict[vp] + '_' + date\n print(filename)\n with open(global_var.par_path + global_var.rib_dir + '/bgpdata/' + filename, 'r') as rf:\n curline = rf.readline()\n while curline:\n elems = curline.split('|')\n if len(elems) < 3:\n print(curline)\n curline = rf.readline()\n continue\n cur_path = CompressAsPathToMin(CompressAsPath(elems[2]))\n # if cur_path.__contains__('3491 58593'):\n # print('h')\n if cur_path not in g_bgp_paths_dict.keys():\n g_bgp_paths_dict[cur_path] = dict()\n locate = date + '_' + vp\n if locate not in g_bgp_paths_dict[cur_path].keys():\n g_bgp_paths_dict[cur_path][locate] = 0\n g_bgp_paths_dict[cur_path][locate] += 1\n curline = rf.readline()\n with open(filepath, 'w') as wf:\n #with open(filepath, 'a') as wf: #2021.10.13\n for (bgp_path, info) in g_bgp_paths_dict.items():\n wf.write(bgp_path + ':')\n for (locate, num) in info.items():\n wf.write(locate + ' ' + str(num) + ',')\n wf.write(';')\n else:\n with open(filepath, 'r') as rf:\n data_list = rf.read().strip(';').split(';')\n for elem in data_list:\n items = elem.split(':')\n g_bgp_paths_dict[items[0]] = dict()\n for sub_elem in items[1].strip(',').split(','):\n tmp = sub_elem.split(' ')\n g_bgp_paths_dict[items[0]][tmp[0]] = int(tmp[1])\n #g_bgp_paths_dict[cur_path][locate] += 1\n bgp_path = items[0]\n if bgp_path == '6939 701 5511 39386 41426 48237 35819 15802':\n print('Pre here 1')\n link_set = GetLinkSetFromBgpPath(bgp_path)\n for link in link_set:\n if link not in g_bgp_link_path_dict.keys():\n g_bgp_link_path_dict[link] = set()\n g_bgp_link_path_dict[link].add(bgp_path)\n print(len(g_bgp_paths_dict))\n # with open('test', 'w') as wf:\n # for (bgp_path, info) in g_bgp_paths_dict.items():\n # wf.write(bgp_path + ':')\n # for (locate, num) in info.items():\n # wf.write(locate + ' ' + str(num) + ',')\n # wf.write('\\n')\n # if '7660 2516 3491 58593' in g_bgp_paths_dict.keys():\n # print('in bgp')\n # else:\n # print('not in bgp')\n\n#transient_bgp_date_count = 5\ntransient_bgp_date_count = 1\ntransient_bgp_occur_count = 20\ntransient_bgp_interval = 7200 #7200s, 2 hours\nif_bgp_transient_dict = dict()\ndef BgpSegIsTransient(bgp_seg, wf_one_date_not_transient): #先找出多于一个date的BGP\n global g_bgp_paths_dict\n global if_bgp_transient_dict\n global g_bgp_link_path_dict\n debug_set = g_bgp_link_path_dict['48237 35819']\n if len(debug_set) == 1:\n print('Error occur')\n if bgp_seg in if_bgp_transient_dict.keys():\n return if_bgp_transient_dict[bgp_seg]\n occur_count = 0\n #step 1\n date_set = set()\n locate_set = set()\n if bgp_seg in g_bgp_paths_dict.keys(): #先快速查找\n info = g_bgp_paths_dict[bgp_seg] \n #info: dict(locate: num)\n for locate in info.keys():\n date_set.add(locate[:locate.index('_')])\n locate_set.add(locate)\n if len(date_set) > transient_bgp_date_count:\n if_bgp_transient_dict[bgp_seg] = False\n return False\n # if sum(info.values()) > transient_bgp_occur_count: #2021.9.16 不能用出现的数量衡量，因为路由抖动会在某一瞬间带来大面积的影响\n # if_bgp_transient_dict[bgp_seg] = False\n # return False\n link_set = GetLinkSetFromBgpPath(bgp_seg)\n # if bgp_seg == '6939 701 5511 39386 41426 48237 35819 15802':\n # print('Check here')\n tmp_path_set = set()\n start = True\n for link in link_set:\n if link not in g_bgp_link_path_dict.keys():\n print('This ought not happen. Link: %s, path: %s' %(link, bgp_seg))\n if_bgp_transient_dict[bgp_seg] = True\n return True\n if start:\n tmp_path_set = g_bgp_link_path_dict[link]\n start = False\n else:\n debug_path_set = g_bgp_link_path_dict[link]\n tmp_path_set1 = tmp_path_set & debug_path_set\n if not tmp_path_set1:\n print('This ought not happen. path: %s' %bgp_seg)\n if_bgp_transient_dict[bgp_seg] = True\n return True\n tmp_path_set = tmp_path_set1\n res_path_set = set()\n for tmp_path in tmp_path_set:\n if tmp_path.__contains__(bgp_seg):\n res_path_set.add(tmp_path)\n for res_path in res_path_set:\n if not res_path in g_bgp_paths_dict.keys(): \n print('This ought not happen. res_path: %s' %res_path)\n info = g_bgp_paths_dict[res_path] \n #info: dict(locate: num)\n for locate in info.keys():\n date_set.add(locate[:locate.index('_')])\n locate_set.add(locate)\n if len(date_set) > transient_bgp_date_count:\n if_bgp_transient_dict[bgp_seg] = False\n return False\n # for (bgp_path, info) in g_bgp_paths_dict.items():\n # if bgp_path.__contains__(bgp_seg):\n # for (locate, count) in info.items():\n # date_set.add(locate[:locate.index('_')])\n # locate_set.add(locate)\n # if len(date_set) > transient_bgp_date_count:\n # if_bgp_transient_dict[bgp_seg] = False\n # return False\n # occur_count += count\n # # if occur_count > transient_bgp_occur_count:\n # # if_bgp_transient_dict[bgp_seg] = False\n # # return False\n if len(date_set) > transient_bgp_date_count:# or occur_count > transient_bgp_occur_count:\n if_bgp_transient_dict[bgp_seg] = False\n return False #not transient\n\n #2021.10.13 step 2, 对于只在一个日期里出现过的bgp path，进一步查找出现的时间\n search_path_dir = global_var.par_path + global_var.rib_dir + 'bgpdata/bgp_'\n for locate in locate_set:\n (date, vp) = locate.split('_')\n output = os.popen('grep \\'' + bgp_seg + '\\' ' + search_path_dir + global_var.trace_as_dict[vp] + '_' + date)\n if output:\n data = output.readline()\n min_timestamp = 0\n max_timestamp = 0\n while data:\n timestamp = int(float((data.split('|')[-2])))\n if min_timestamp == 0 or timestamp < min_timestamp:\n min_timestamp = timestamp\n if timestamp > max_timestamp:\n max_timestamp = timestamp\n data = output.readline()\n if (max_timestamp - min_timestamp) > transient_bgp_interval: #不认为是transient\n wf_one_date_not_transient.write(bgp_seg + '\\n')\n if_bgp_transient_dict[bgp_seg] = False\n return False\n\n if_bgp_transient_dict[bgp_seg] = True\n return True\n\ndef GetAllBgpOfAbTrace(filename):\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n wf_one_date_not_transient = open('one_date_not_transient_bgp', 'w') #2021.10.13 这里记录一下一个日期内但超过2小时的bgp path\n for year in range(2018,2021):\n #for year in range(2020,2021):\n for month in range(1,13):\n #for month in range(1,5):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n date = str(year) + str(month).zfill(2) + '15'\n #print(date)\n for vp in global_var.vps:\n #print(vp)\n g_asn = global_var.trace_as_dict[vp] \n bgp_filename = global_var.par_path + global_var.rib_dir + 'bgpdata/bgp_' + g_asn + '_' + date\n GetBgpByPrefix(bgp_filename)\n cur_sub_dir = vp + '_' + date + '/ribs_midar_bdrmapit/' #'nrt-jp.2019030115/'\n if not os.path.exists(cur_sub_dir):\n continue\n wf = open(cur_sub_dir + filename + '_all_bgp', 'w')\n print(date + '_' + vp)\n with open(cur_sub_dir + filename, 'r') as rf:\n curline_trace = rf.readline()\n while curline_trace:\n curline_bgp = rf.readline()\n curline_ip = rf.readline()\n dst_prefix = curline_trace[1:curline_trace.index(' ')]\n #print(dst_prefix)\n bgp_list = GetBgpPathFromBgpPrefixDict_2(dst_prefix)\n wf.write(curline_trace)\n for bgp_path in bgp_list:\n if not BgpSegIsTransient(bgp_path, wf_one_date_not_transient): #只记录出现过多次的bgp path\n wf.write(\"\\t%s\\n\" %CompressAsPath(bgp_path))\n wf.write(curline_ip)\n curline_trace = rf.readline()\n ClearBGPByPrefix()\n wf.close()\n wf_one_date_not_transient.close()\n\ng_all_trace_links_set = set()\ndef PreGetAllTraceLinks(): #ixp hop不算在link里\n global all_trace_set_uni_as\n global g_all_trace_links_set\n PreGetAllTracesWithUniAs()\n #filename = global_var.all_trace_par_path + global_var.all_trace_out_data_dir + global_var.all_trace_out_all_trace_links_filename\n filename = global_var.par_path + global_var.other_middle_data_dir + 'all_trace_links'\n if not os.path.exists(filename):\n for ori_trace in all_trace_set_uni_as:\n trace = CompressAsPathToMin(CompressAsPath(ori_trace))\n trace_list = trace.split(' ')\n pre_elem = None\n for i in range(0, len(trace_list)):\n cur_elem = trace_list[i]\n if (not pre_elem) or (pre_elem == '*') or (pre_elem == '?') or (pre_elem.__contains__('<')) or \\\n (cur_elem == '*') or (cur_elem == '?') or (cur_elem.__contains__('<')) or (pre_elem == cur_elem):\n pre_elem = cur_elem\n continue\n link = pre_elem + ' ' + cur_elem\n if link == '3491 58593':\n print('here')\n if link not in g_all_trace_links_set:\n g_all_trace_links_set.add(link)\n pre_elem = cur_elem\n with open(filename, 'w') as wf:\n wf.write('\\n'.join(list(g_all_trace_links_set)))\n else:\n with open(filename, 'r') as rf:\n data = rf.read()\n g_all_trace_links_set = set(data.split('\\n')) \n\ng_all_possi_trace_links_set = set()\ndef PreGetAllTraceLinks_AllVps(): #ixp hop不算在link里\n global g_all_trace_links_set\n global g_all_possi_trace_links_set\n os.chdir(global_var.par_path + global_var.other_middle_data_dir)\n #filename = global_var.all_trace_par_path + global_var.all_trace_out_data_dir + global_var.all_trace_out_all_trace_links_filename\n w_filename1 = 'all_trace_links_2_from_all_vps'\n w_filename2 = 'all_possi_trace_links_2_from_all_vps'\n if (not os.path.exists(w_filename1)) or (not os.path.exists(w_filename2)):\n for root,dirs,files in os.walk('.'):\n for filename in files: #例：cdg-fr.20180125\n if filename.startswith('all_trace_links_2_from_'):\n with open(filename, 'r') as rf:\n g_all_trace_links_set |= set(rf.read().split(','))\n if filename.startswith('all_possi_trace_links_2_from_'):\n with open(filename, 'r') as rf:\n g_all_possi_trace_links_set |= set(rf.read().split(','))\n with open(w_filename1, 'w') as wf1:\n wf1.write(','.join(list(g_all_trace_links_set)))\n with open(w_filename2, 'w') as wf2:\n wf2.write(','.join(list(g_all_possi_trace_links_set)))\n else:\n with open(w_filename1, 'r') as rf1:\n g_all_trace_links_set = set(rf1.read().split(',')) \n with open(w_filename2, 'r') as rf2:\n g_all_possi_trace_links_set = set(rf2.read().split(',')) \n print(len(g_all_trace_links_set))\n print(len(g_all_possi_trace_links_set))\n # if '3491 58593' in g_all_trace_links_set:\n # print(1)\n # if '3491 58593' in g_all_possi_trace_links_set:\n # print(2)\n # return\n\ndef CheckBgpLinkExistsInTrace(bgp_path, peer_link_set):\n global g_all_trace_links_set \n global g_all_possi_trace_links_set \n prev_elem = ''\n ab_link_set = set()\n for elem in bgp_path.split(' '):\n # if elem == '4635':\n # print('h')\n if prev_elem != '' and (not IsIxpAs(elem)) and (not IsIxpAs(prev_elem)) and \\\n (not IsAsSet(elem)) and (not IsAsSet(prev_elem)):\n link = prev_elem + ' ' + elem\n if link == '7660 4635':\n print('why here')\n if link in peer_link_set or link in ab_link_set:\n prev_elem = elem\n continue\n if (link not in g_all_trace_links_set) and (link not in g_all_possi_trace_links_set) and \\\n (elem not in GetNeighFromRipe(prev_elem)) and (prev_elem not in GetNeighFromRipe(elem)): #abnormal link #2021.8.25 加入ripe的信息\n if IsTwoAsPeerInIXP(prev_elem, elem): #2021.8.25,加入peering db信息\n peer_link_set.add(link)\n else:\n ab_link_set.add(link) \n prev_elem = elem\n return ab_link_set\n\ndef FilterValleyByNewestRel(filename): #发现不同时期的AS rel推断矛盾，用最新的rel再过滤一遍，只要有一次rel推断为non-valley即为non-valley\n PreGetSrcFilesInDirs()\n GetAsRankDict(2020, 4)\n GetAsRel(2020, 4)\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n wf = open(filename + '_filter_by_newest_rel', 'w')\n wf_link = open(filename + '_filter_by_newest_rel_only_link', 'w')\n with open(filename, 'r') as rf:\n bgp_rec = []\n pre_elem = ''\n for elem in rf.read().split('\\n'):\n if not elem.startswith('\\t'): #bgp valley\n if pre_elem:\n valley = pre_elem.split('(')[0]\n if PathHasValley_2(valley): #re-assure valley\n wf.write(pre_elem + '\\n')\n wf.write('\\n'.join(bgp_rec) + '\\n')\n [as1, as2, as3] = valley.split(' ')\n wf_link.write(pre_elem + '\\n')\n wf_link.write('%s(%s)%s(%s)%s\\n' %(GetAsRankFromDict(as1), Get2AsRel(as1, as2), GetAsRankFromDict(as2), Get2AsRel(as2, as3), GetAsRankFromDict(as3)))\n bgp_rec.clear()\n pre_elem = elem\n else:\n bgp_rec.append(elem)\n if pre_elem and PathHasValley_2(pre_elem): #re-assure valley\n wf.write(pre_elem + '\\n')\n wf.write('\\n'.join(bgp_rec) + '\\n')\n wf_link.write(pre_elem + '\\n')\n wf.close()\n wf_link.close()\n ClearAsRel()\n ClearAsRankDict()\n\ndef GetSuspicBgp(filename):\n ab_link_dict = dict()\n valley_dict = dict()\n bgp_path_ab_link_set_dict = dict()\n peer_link_set = set()\n ixp_num = 0\n ripe_num = 0\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n for year in range(2018,2021):\n for month in range(1,13):\n #for month in range(4,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n date = str(year) + str(month).zfill(2) + '15'\n #print(date)\n GetSibRel(year, month)\n GetIxpAsSet()\n GetAsRel(year, month)\n ConstrPeerDbInfoDict(year, month)\n for vp in global_var.vps:\n g_asn = global_var.trace_as_dict[vp] \n bgp_filename = global_var.par_path + global_var.rib_dir + 'bgpdata/bgp_' + g_asn + '_' + date\n GetBgpByPrefix(bgp_filename)\n cur_sub_dir = vp + '_' + date + '/ribs_midar_bdrmapit/' #'nrt-jp.2019030115/'\n if not os.path.exists(cur_sub_dir):\n continue\n locate = date + '_' + vp\n print(locate)\n with open(cur_sub_dir + filename, 'r') as rf:\n curline = rf.readline()\n #dst_key = None\n while curline:\n if not curline.startswith('\\t'):\n #dst_key = curline[:curline.index(']') + 1]\n curline = rf.readline()\n continue\n bgp_path = curline.strip('\\n').strip('\\t')\n # if bgp_path.__contains__('34288 8757 262287'):\n # print('h')\n ab_link_set = None\n if bgp_path not in bgp_path_ab_link_set_dict.keys():\n bgp_path_ab_link_set_dict[bgp_path] = CheckBgpLinkExistsInTrace(bgp_path, peer_link_set)\n ab_link_set = bgp_path_ab_link_set_dict[bgp_path]\n for ab_link in ab_link_set:\n if ab_link not in ab_link_dict.keys():\n ab_link_dict[ab_link] = [dict(), 0]\n if bgp_path not in ab_link_dict[ab_link][0].keys():\n ab_link_dict[ab_link][0][bgp_path] = dict()\n if locate not in ab_link_dict[ab_link][0][bgp_path].keys():\n ab_link_dict[ab_link][0][bgp_path][locate] = 0\n ab_link_dict[ab_link][0][bgp_path][locate] += 1 \n valley_set = PathHasValley_2(bgp_path) #2021.9.7 原来有valley_set的缓存，不应该用，因为不同时期AS关系不一样\n for valley in valley_set:\n if valley not in valley_dict.keys():\n valley_dict[valley] = [dict(), 0]\n if bgp_path not in valley_dict[valley][0].keys():\n valley_dict[valley][0][bgp_path] = dict()\n if locate not in valley_dict[valley][0][bgp_path].keys():\n valley_dict[valley][0][bgp_path][locate] = 0\n valley_dict[valley][0][bgp_path][locate] += 1 \n curline = rf.readline()\n ClearBGPByPrefix()\n ClearAsRel()\n ClearIxpAsSet()\n ClearSibRel()\n ClearPeerDbInfoDict()\n print(len(ab_link_dict.keys()))\n for (ab_link, info) in ab_link_dict.items():\n total_count = 0\n for (bgp_path, sub_info) in info[0].items():\n for (locate, count) in sub_info.items():\n total_count += count\n ab_link_dict[ab_link][1] = total_count\n for (valley, info) in valley_dict.items():\n total_count = 0\n for (bgp_path, sub_info) in info[0].items():\n for (locate, count) in sub_info.items():\n total_count += count\n valley_dict[valley][1] = total_count\n ab_link_sort_list = sorted(ab_link_dict.items(), key=lambda d:d[1][1], reverse = True)\n valley_sort_list = sorted(valley_dict.items(), key=lambda d:d[1][1], reverse = True)\n with open('bgp_ab_link_2', 'w') as wf1:\n print('ab link num: %d' %len(ab_link_dict))\n for elem in ab_link_sort_list:\n wf1.write(\"%s(%d)\\n\" %(elem[0], elem[1][1]))\n for (bgp_path, sub_info) in elem[1][0].items():\n wf1.write(\"\\t%s\\n\" %bgp_path)\n for (locate, count) in sub_info.items():\n wf1.write(\"\\t\\t%s(%d)\\n\" %(locate, count))\n with open('bgp_valley_2', 'w') as wf2:\n for elem in valley_sort_list:\n wf2.write(\"%s(%d)\\n\" %(elem[0], elem[1][1]))\n for (bgp_path, sub_info) in elem[1][0].items():\n wf2.write(\"\\t%s\\n\" %bgp_path)\n for (locate, count) in sub_info.items():\n wf2.write(\"\\t\\t%s(%d)\\n\" %(locate, count))\n\n with open('peer_links', 'w') as wf3:\n print('peer link num: %d' %len(peer_link_set))\n wf3.write('\\n'.join(list(peer_link_set)))\n\ndef IsSibLink(link):\n (asn1, asn2) = link.split(' ')\n org1 = GetOrgByMultiDataFiles_2(asn1)\n org2 = GetOrgByMultiDataFiles_2(asn2)\n return org1 & org2\n\ndef FilterSibLinkInAbBgp(pathname):\n PreGetSrcFilesInDirs()\n GetSibRelByMultiDataFiles(2018, 1)\n #GetSibRel(year, month)\n total_num = 0\n ab_num = 0\n wf = open(pathname + '_1_filtersib', 'w') #filter sibling\n with open(pathname, 'r') as rf:\n group_data = None\n curline = rf.readline()\n while curline:\n if not curline.startswith('\\t'): #link，记录上一组数据\n total_num += 1\n if group_data:\n wf.write(group_data)\n group_data = None\n if not IsSibLink(curline[:curline.index('(')]):\n ab_num += 1\n group_data = curline\n else:\n if group_data:\n group_data += curline\n curline = rf.readline()\n if group_data:\n wf.write(group_data)\n wf.close()\n print(\"Total num: %d, still ab num: %d\" %(total_num, ab_num))\n\ndef PerProcFindNeighInTrace(asn, vp, mode, queue):\n if mode == 'moas':\n output = os.popen('grep _' + asn + ' back_as_' + vp + '*')\n else:\n output = os.popen('grep ' + asn + ' back_as_' + vp + '*')\n data = output.readline()\n res_set = set()\n while data:\n data_list = CompressAsPathToMin(CompressAsPath(data.strip('\\n'))).split(' ')\n if mode == 'moas':\n for elem in data_list:\n if elem.__contains__('_' + asn):\n res_set |= set(elem.split('_'))\n else:\n if asn not in data_list:\n data = output.readline()\n continue\n #index = FindBgpAsInTracePath(asn, data_list)\n index = data_list.index(asn)\n if mode == 'prev_neigh' or mode == 'neigh':\n if index > 1:\n res_set.add(data_list[index - 1])\n if mode == 'next_neigh' or mode == 'neigh':\n if index < len(data_list) - 1:\n res_set.add(data_list[index + 1])\n data = output.readline()\n if res_set:\n #print(res_set)\n queue.put(' '.join(list(res_set)))\n\ndef TmpFindNeighInTrace(asn, mode):\n os.chdir(global_var.all_trace_par_path + global_var.all_trace_download_dir + '2019/01/result/')\n queue = mp.Queue()\n proc_list = []\n for vp in ['ams-nl', 'arn-se', 'bcn-es', 'bjl-gm', 'bwi-us', 'cbg-uk', 'cjj-kr', 'dub-ie', 'eug-us', 'fnl-us', 'hel-fi', 'hkg-cn', 'hlz-nz', 'mty-mx', 'nrt-jp', 'per-au', 'pna-es', 'pry-za', 'sao-br', 'scl-cl', 'sjc2-us', 'syd-au', 'wbu-us', 'yyz-ca', 'zrh2-ch', 'zrh-ch', 'ord-us', 'osl-no', 'rno-us', 'sea-us']:\n proc_list.append(mp.Process(target=PerProcFindNeighInTrace, args=(asn, vp, mode, queue)))\n for elem in proc_list:\n elem.start()\n for elem in proc_list:\n elem.join()\n res_set = set()\n while not queue.empty():\n res_set |= set(queue.get().split(' '))\n # print('trace: ', end='')\n # print(res_set)\n return res_set\n\ndef PerProcFindNeighInBgp(queue, asn, filename, mode):\n output = os.popen('grep ' + asn + ' ' + filename)\n data = output.readline()\n res_set = set()\n while data:\n data_list = CompressAsPath(data.split('|')[2]).split(' ')\n if asn not in data_list:\n data = output.readline()\n continue\n #index = FindBgpAsInTracePath(asn, data_list)\n index = data_list.index(asn)\n if mode == 'prev_neigh' or mode == 'neigh':\n if index > 1:\n res_set.add(data_list[index - 1])\n if mode == 'next_neigh' or mode == 'neigh':\n if index < len(data_list) - 1:\n res_set.add(data_list[index + 1])\n data = output.readline()\n if res_set:\n #print(res_set)\n queue.put(' '.join(list(res_set)))\n\ndef TmpFindNeighInBgp(asn, mode):\n os.chdir(global_var.par_path + global_var.rib_dir + 'all_collectors_one_date_bgpdata/')\n queue = mp.Queue()\n proc_list = []\n for root,dirs,files in os.walk('.'):\n for filename in files: #例：cdg-fr.20180125\n if (not filename.endswith('gz')) and (not filename.endswith('bz2')):\n proc_list.append(mp.Process(target=PerProcFindNeighInBgp, args=(queue, asn, filename, mode)))\n for elem in proc_list:\n elem.start()\n for elem in proc_list:\n elem.join()\n res_set = set()\n while not queue.empty():\n res_set |= set(queue.get().split(' '))\n #print('bgp: ', end='')\n #print(res_set)\n return res_set\n\ndef TmpStat():\n num = 0\n with open('/mountdisk1/ana_c_d_incongruity/out_my_anatrace/bgp_ab_link', 'r') as rf:\n curline = rf.readline()\n while curline:\n if not curline.startswith('\\t'):\n tmp = int(curline[curline.index('(') + 1:curline.index(')')])\n if tmp == 1:\n break\n num += 1\n curline = rf.readline()\n print(num)\n\ndef GetTraceSegOfAbBgpLink(link, all_trace_path_list, as_trace_dict):\n res_set = set()\n (asn1, asn2) = link.split(' ')\n if (not asn1 in as_trace_dict.keys()) or (not asn2 in as_trace_dict.keys()):\n print('asn not in trace')\n return res_set\n join_index = as_trace_dict[asn1] & as_trace_dict[asn2]\n if not join_index:\n print('bgp link not found in trace')\n return res_set\n for index in join_index:\n trace = all_trace_path_list[index]\n trace_list = trace.split(' ')\n index1 = FindBgpAsInTracePath(asn1, trace_list)\n index2 = FindBgpAsInTracePath(asn2, trace_list)\n if index1 != -1 and index2 != -1 and index1 < index2:\n trace_seg = ' '.join(trace_list[index1:index2 + 1])\n res_set.add(trace_seg)\n return res_set\n\ndef AnaAbBgpLink():\n all_trace_path_set = set()\n os.chdir(global_var.par_path + global_var.other_middle_data_dir)\n all_trace_path_filename = 'all_trace_path_2_from_all_vp'\n if not os.path.exists(all_trace_path_filename):\n for root,dirs,files in os.walk('.'):\n for filename in files: #例：cdg-fr.20180125\n if filename.startswith('all_trace_path_2_from_'):\n print(filename)\n with open(filename, 'r') as rf:\n all_trace_path_set |= set(rf.read().strip('\\n').split('\\n'))\n with open(all_trace_path_filename, 'w') as wf:\n wf.write('\\n'.join(list(all_trace_path_set)))\n else:\n with open(all_trace_path_filename, 'r') as rf:\n all_trace_path_set = set(rf.read().strip('\\n').split('\\n'))\n print(len(all_trace_path_set))\n all_trace_path_list = list(all_trace_path_set)\n all_trace_path_set.clear()\n as_trace_dict = dict()\n for i in range(0, len(all_trace_path_list)):\n cur_trace = all_trace_path_list[i]\n for elem in cur_trace.split(' '):\n for asn in elem.split('_'):\n if asn not in as_trace_dict.keys():\n as_trace_dict[asn] = set()\n as_trace_dict[asn].add(i)\n # prev_link_trace_dict = dict()\n # with open(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace', 'r') as rf:\n # prev_link = None\n # trace_set = set()\n # curline = rf.readline()\n # while curline:\n # if not curline.startswith('\\t'): #cur_link, deal prev_link\n # prev_link_trace_dict[prev_link] = trace_set\n # prev_link = curline\n # trace_set = set()\n # else:\n # trace_set.add(curline.strip('\\n').strip('\\t'))\n # curline = rf.readline()\n # prev_link_trace_dict[prev_link] = trace_set\n # last_dealed_link = ''\n # with open(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace', 'r') as rf:\n # elems = rf.read().split('\\n')\n # for i in range(len(elems) - 1, 0, -1):\n # if not elems[i].startswith('\\t'):\n # last_dealed_link = elems[i]\n # break\n # print('last_dealed_link: %s' %last_dealed_link)\n wf = open(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace', 'w')\n begin_flag = False\n with open(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_1_filtersib', 'r') as rf:\n curline = rf.readline()\n #work_flag = False\n while curline:\n # if curline.__contains__('4755 37934'):\n # work_flag = True\n # if not work_flag:\n # curline = rf.readline()\n # continue\n if not curline.startswith('\\t'):\n #if begin_flag:\n if True:\n trace_set = None\n # if curline in prev_link_trace_dict.keys():\n # trace_set = prev_link_trace_dict[curline]\n if False:\n pass\n else:\n print(curline)\n link = curline[:curline.index('(')]\n trace_set = GetTraceSegOfAbBgpLink(link, all_trace_path_list, as_trace_dict)\n wf.write(curline)\n for elem in trace_set:\n wf.write('\\t' + elem + '\\n')\n # if not begin_flag and curline.__contains__(last_dealed_link):\n # begin_flag = True\n curline = rf.readline()\n wf.close()\n\n\ndef GetNoTraceLink():\n stub_threshhold = 5\n PreLoadAsnInfoFromASNS()\n GetCCNums(2021, 4)\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir)\n wf_no_trace = open('bgp_ab_link_2_rel_trace_no_trace', 'w')\n all_num = 0\n stub_link_num = 0\n with open('bgp_ab_link_2_rel_trace', 'r') as rf:\n prev_link = ''\n rel_traces = []\n curline = rf.readline()\n while curline:\n if not curline.startswith('\\t'): #link\n if prev_link: #analysize prev_link\n if len(rel_traces) == 0: #no trace\n (asn1, asn2) = prev_link.split(' ')\n cc_num_1 = GetCCNumsFromDict(asn1)\n cc_num_2 = GetCCNumsFromDict(asn2)\n if cc_num_1 <= stub_threshhold or cc_num_2 <= stub_threshhold:\n stub_link_num += 1\n wf_no_trace.write('%s(%s %s)\\n' %(prev_link, cc_num_1, cc_num_2))\n all_num += 1\n rel_traces = []\n prev_link = curline[:curline.index('(')]\n else: #trace\n rel_traces.append(curline)\n curline = rf.readline()\n wf_no_trace.close()\n print('stub_link_num: %d' %stub_link_num)\n print('all_num: %d' %all_num)\n\ndef GetInfoOfAbLink():\n PreLoadAsnInfoFromASNS()\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir)\n all_trace_path_filename = 'bgp_ab_link_2_rel_trace'\n wf = open('bgp_ab_link_2_rel_trace_with_info', 'w')\n wf_only_link = open('bgp_ab_link_2_rel_trace_with_info_only_link', 'w')\n fst_as_dict = dict()\n snd_as_dict = dict()\n num_has_path_trace = 0\n num_has_no_path_trace = 0\n with open(all_trace_path_filename, 'r') as rf:\n prev_link = ''\n rel_traces = []\n curline = rf.readline()\n while curline:\n if not curline.startswith('\\t'): #link\n if prev_link: #analysize prev_link\n if len(rel_traces) > 0:\n num_has_path_trace += 1\n (asn1, asn2) = prev_link.split(' ')\n (org1, country1, rank1) = GetAsnInfoFromASNS(asn1)\n (org2, country2, rank2) = GetAsnInfoFromASNS(asn2)\n if asn1 not in fst_as_dict.keys():\n fst_as_dict[asn1] = [0, org1, country1, rank1]\n fst_as_dict[asn1][0] += 1\n if asn2 not in snd_as_dict.keys():\n snd_as_dict[asn2] = [0, org2, country2, rank2, []]\n snd_as_dict[asn2][0] += 1\n snd_as_dict[asn2][4].append(asn1)\n wf.write(\"%s:%s,%s,%s\\n\" %(asn1, org1.replace(',', '_'), country1, rank1))\n wf.write(\"%s:%s,%s,%s\\n\" %(asn2, org2.replace(',', '_'), country2, rank2))\n wf_only_link.write(\"%s:%s,%s,%s\\n\" %(asn1, org1.replace(',', '_'), country1, rank1))\n wf_only_link.write(\"%s:%s,%s,%s\\n\\n\" %(asn2, org2.replace(',', '_'), country2, rank2))\n for trace in rel_traces:\n wf.write(trace)\n else:\n num_has_no_path_trace += 1\n rel_traces = []\n prev_link = curline[:curline.index('(')]\n else: #trace\n rel_traces.append(curline)\n curline = rf.readline()\n \n sort_list_1 = sorted(fst_as_dict.items(), key=lambda d:d[1][0], reverse = True) \n with open('bgp_ab_link_2_fst_as_info', 'w') as wf_ab_as:\n for elem in sort_list_1:\n wf_ab_as.write(\"%s(%d):%s,%s,%s\\n\" %(elem[0], elem[1][0], elem[1][1], elem[1][2], elem[1][3]))\n sort_list_2 = sorted(snd_as_dict.items(), key=lambda d:d[1][0], reverse = True)\n with open('bgp_ab_link_2_snd_as_info', 'w') as wf_ab_as:\n for elem in sort_list_2:\n wf_ab_as.write(\"%s(%d):%s,%s,%s\\n\" %(elem[0], elem[1][0], elem[1][1], elem[1][2], elem[1][3]))\n wf_ab_as.write('\\t%s\\n' %(' '.join(elem[1][4])))\n print('num_has_path_trace: %d' %num_has_path_trace)\n print('num_has_no_path_trace: %d' %num_has_no_path_trace)\n ClearAsnInfoFromASNS()\n\ndef GetInfoOfPriorAbLink():\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir)\n fst_as_dict = dict()\n snd_as_dict = dict() \n with open('bgp_ab_link_2_rel_trace_with_info_prior_ab', 'r') as rf:\n curline_ab_fst = rf.readline()\n curline_ab_snd = rf.readline()\n while True:\n wf = None\n curline = rf.readline()\n while curline and curline.startswith('\\t'):\n curline = rf.readline()\n asn1 = curline_ab_fst.split(':')[0]\n (org1, country1, rank1) = curline_ab_fst.strip('\\n').split(':')[1].split(',')\n asn2 = curline_ab_snd.split(':')[0]\n (org2, country2, rank2) = curline_ab_snd.strip('\\n').split(':')[1].split(',')\n if asn1 not in fst_as_dict.keys():\n fst_as_dict[asn1] = [0, org1, country1, rank1]\n fst_as_dict[asn1][0] += 1\n if asn2 not in snd_as_dict.keys():\n snd_as_dict[asn2] = [0, org2, country2, rank2, []]\n snd_as_dict[asn2][0] += 1\n snd_as_dict[asn2][4].append(asn1)\n if curline:\n curline_ab_fst = curline\n curline_ab_snd = rf.readline()\n else:\n break \n sort_list_1 = sorted(fst_as_dict.items(), key=lambda d:d[1][0], reverse = True) \n with open('bgp_ab_link_2_prior_ab_fst_as_info', 'w') as wf_ab_as:\n for elem in sort_list_1:\n wf_ab_as.write(\"%s(%d):%s,%s,%s\\n\" %(elem[0], elem[1][0], elem[1][1], elem[1][2], elem[1][3]))\n sort_list_2 = sorted(snd_as_dict.items(), key=lambda d:d[1][0], reverse = True)\n with open('bgp_ab_link_2_prior_ab_snd_as_info', 'w') as wf_ab_as:\n for elem in sort_list_2:\n wf_ab_as.write(\"%s(%d):%s,%s,%s\\n\" %(elem[0], elem[1][0], elem[1][1], elem[1][2], elem[1][3]))\n wf_ab_as.write('\\t%s\\n' %(' '.join(elem[1][4])))\n\nminimum_trace_num = 3\ndef ClassifyAbLink():\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir)\n r_filename = 'bgp_ab_link_2_rel_trace_with_info'\n wf_one_trace = open(r_filename + '_4_one_trace', 'w')\n wf_one_aster = open(r_filename + '_4_one_aster', 'w')\n wf_all_aster_same_country = open(r_filename + '_3_all_aster_same_country', 'w')\n wf_all_aster_diff_country = open(r_filename + '_2_all_aster_diff_country', 'w')\n wf_one_ab_same_country = open(r_filename + '_3_one_ab_same_country', 'w')\n wf_same_country = open(r_filename + '_2_same_country', 'w')\n wf_other_ab = open(r_filename + '_1_other_ab', 'w')\n count_prior_ab = 0\n with open(r_filename, 'r') as rf:\n curline_ab_fst = rf.readline()\n curline_ab_snd = rf.readline()\n while True:\n wf = None\n curline = rf.readline()\n trace_list = []\n while curline and curline.startswith('\\t'):\n trace_list.append(curline.strip('\\t').strip('\\n'))\n curline = rf.readline()\n has_other_asn = False\n has_only_one_ab = False\n if len(trace_list) < minimum_trace_num:\n wf = wf_one_trace\n else:\n for trace in trace_list:\n elem_list = trace.split(' ')\n #step 1, if trace has only one * or ? in between\n if len(elem_list) == 3:\n if (elem_list[1] == '*' or elem_list[1] == '?'):\n wf = wf_one_aster\n break\n else:\n has_only_one_ab = True\n #step 2, if trace has other asn in between\n if not has_other_asn:\n for elem in elem_list[1:-1]:\n if elem != '*' and elem != '?':\n has_other_asn = True\n break\n fst_country = curline_ab_fst.split(',')[-2]\n snd_country = curline_ab_snd.split(',')[-2]\n #step 2, if trace has only one asn in between and src and dst in the same country\n if not wf:\n if has_only_one_ab and fst_country == snd_country:\n wf = wf_one_ab_same_country\n #step 3, if trace has other asn in between\n if not wf:\n if has_other_asn == False:\n #step 3.1, if in the same country\n if fst_country == snd_country:\n wf = wf_all_aster_same_country\n #step 3.2, if not in the same country\n else:\n wf = wf_all_aster_diff_country\n count_prior_ab += 1\n #step 4, if has other asn in between, but fst_ab and snd_ab are in the same country\n if not wf:\n if fst_country == snd_country:\n wf = wf_same_country\n count_prior_ab += 1\n #step 4, highest possibility of ab\n if not wf:\n wf = wf_other_ab\n count_prior_ab += 1\n\n wf.write('%s%s' %(curline_ab_fst, curline_ab_snd))\n for cur_trace in trace_list:\n wf.write('\\t%s\\n' %cur_trace)\n\n if curline:\n curline_ab_fst = curline\n curline_ab_snd = rf.readline()\n else:\n break\n\n wf_one_trace.close()\n wf_one_aster.close()\n wf_all_aster_same_country.close()\n wf_all_aster_diff_country.close()\n wf_one_ab_same_country.close()\n wf_same_country.close()\n wf_other_ab.close()\n os.system('cat bgp_ab_link_2_rel_trace_with_info_1_other_ab bgp_ab_link_2_rel_trace_with_info_2_same_country bgp_ab_link_2_rel_trace_with_info_2_all_aster_diff_country > bgp_ab_link_2_rel_trace_with_info_prior_ab')\n print('count_prior_ab: %d' %count_prior_ab)\n\ndef ClassifyAbLink_2():\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir)\n r_filename = 'bgp_ab_link_2_rel_trace_with_info'\n wf_one_aster = open(r_filename + '_v2_3_one_aster', 'w')\n wf_all_aster = open(r_filename + '_v2_2_all_aster', 'w')\n wf_same_country = open(r_filename + '_v2_2_same_country', 'w')\n wf_other_ab = open(r_filename + '_v2_1_other_ab', 'w')\n count_one_aster = 0\n count_all_aster = 0\n count_same_country = 0\n count_other_ab = 0\n with open(r_filename, 'r') as rf:\n curline_ab_fst = rf.readline()\n curline_ab_snd = rf.readline()\n while True:\n wf = None\n curline = rf.readline()\n trace_list = []\n while curline and curline.startswith('\\t'):\n trace_list.append(curline.strip('\\t').strip('\\n'))\n curline = rf.readline()\n has_other_asn = False\n for trace in trace_list:\n elem_list = trace.split(' ')\n #step 1, if trace has only one * or ? in between\n if len(elem_list) == 3:\n if (elem_list[1] == '*' or elem_list[1] == '?'):\n wf = wf_one_aster\n count_one_aster += 1\n break\n #step 2, if trace has other asn in between\n if not has_other_asn:\n for elem in elem_list[1:-1]:\n if elem != '*' and elem != '?':\n has_other_asn = True\n break\n fst_country = curline_ab_fst.split(',')[-2]\n snd_country = curline_ab_snd.split(',')[-2]\n #step 2, if trace has only one asn in between and src and dst in the same country\n if not wf:\n if has_other_asn == False:\n count_all_aster += 1\n wf = wf_all_aster\n if fst_country == snd_country:\n wf = wf_same_country\n count_same_country += 1\n #step 4, highest possibility of ab\n if not wf:\n wf = wf_other_ab\n count_other_ab += 1\n\n wf.write('%s%s' %(curline_ab_fst, curline_ab_snd))\n for cur_trace in trace_list:\n wf.write('\\t%s\\n' %cur_trace)\n\n if curline:\n curline_ab_fst = curline\n curline_ab_snd = rf.readline()\n else:\n break\n\n wf_one_aster.close()\n wf_same_country.close()\n wf_all_aster.close()\n wf_other_ab.close()\n print('count_one_aster: %d' %count_one_aster)\n print('count_all_aster: %d' %count_all_aster)\n print('count_same_country: %d' %count_same_country)\n print('count_other_ab: %d' %count_other_ab)\n\ndef CheckAbLinkCheckedByEmails(filename, email_dict, all_content):\n print(filename)\n with open(filename) as rf:\n curline_ab_fst = rf.readline()\n curline_ab_snd = rf.readline()\n while True:\n curline = rf.readline()\n while curline and curline.startswith('\\t'):\n curline = rf.readline()\n ab_snd = curline_ab_snd.split(':')[0]\n if all_content.__contains__(ab_snd):\n for (from_, content) in email_dict.items():\n if content.__contains__(ab_snd):\n print('\\t' + ab_snd + ':' + from_)\n break\n if curline:\n curline_ab_fst = curline\n curline_ab_snd = rf.readline()\n else:\n break\n\ndef CheckEmails():\n email_dict = dict()\n all_content = ''\n with open('email_contents', 'r') as rf:\n data = rf.read()\n for elem in data.strip('').split(''):\n (from_, content) = elem.split('')\n email_dict[from_] = content\n all_content += content\n CheckAbLinkCheckedByEmails(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace_with_info_1_other_ab', email_dict, all_content)\n CheckAbLinkCheckedByEmails(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace_with_info_2_same_country', email_dict, all_content)\n CheckAbLinkCheckedByEmails(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace_with_info_2_all_aster_diff_country', email_dict, all_content)\n\ndef ChgOneIpTrace2ASPath(ip_trace):\n as_trace = ''\n for elem in ip_trace.split(' '):\n if elem == '*':\n as_trace += ' *'\n else:\n asn = ''\n if elem.__contains__('.'): #ipv4 address\n asn = GetAsStrOfIpByRv(elem)\n elif elem.__contains__(':'): #ipv6 address\n asn = GetAsStrOfIpByRvV6(elem)\n else:\n print('False ip form: %s' %asn)\n if not asn:\n as_trace += ' ?'\n else:\n as_trace += ' ' + asn\n\ndef LinkInTrace(link, trace):\n trace_set = TransOneTraceToUniASTraces(trace)\n for elem in trace_set:\n if elem.__contains__(link):\n return True\n return False\n\ndef CheckAbLinkByOnlineTraceroute():\n GetAsRel_2(global_var.par_path + global_var.rel_cc_dir + '20210801.as-rel2.txt')\n os.chdir('/home/slt/code/ana_c_d_incongruity/')\n GetFuncLgDict()\n GetPfx2ASByRv()\n GetPfx2ASByRvV6()\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n wf = open('bgp_ab_link_2_rel_trace_with_info_prior_ab_after_online', 'w')\n with open('bgp_ab_link_2_rel_trace_with_info_prior_ab', 'r') as rf:\n curline_ab_fst = rf.readline()\n curline_ab_snd = rf.readline()\n while True:\n curline = rf.readline()\n existed_trace_list = []\n while curline and curline.startswith('\\t'):\n existed_trace_list.append(curline.strip('\\n').strip('\\t'))\n curline = rf.readline()\n as1 = curline_ab_fst.split(':')[0]\n as2 = curline_ab_snd.split(':')[0]\n dst_ips = GetRepIpsOfAs(as2)\n as1_neighs = GetNeighOfAs(as1)\n as1_neighs.add(as1)\n trace_list = []\n trace_as_list = []\n req = requests.session()\n for asn in as1_neighs:\n funcs = GetFuncOfLg(asn)\n for func in funcs:\n for dst_ip in dst_ips: \n tmp_trace_list = func(dst_ip, req) #调用函数\n for tmp_trace in tmp_trace_list:\n trace_list.append(tmp_trace)\n link = as1 + ' ' + as2\n link_norm = False\n for trace in trace_list:\n as_trace = ChgOneIpTrace2ASPath(trace)\n if LinkInTrace(link, as_trace):\n link_norm = True\n break\n if AsnInTracePathList(as1, as_trace.split(' ')) and AsnInTracePathList(as2, as_trace.split(' ')): #path as1->as2 exists\n existed_trace_list.append(as_trace)\n if link_norm: #find by online traceroute\n pass\n else:\n wf.write(curline_ab_fst)\n wf.write(curline_ab_snd)\n for trace in existed_trace_list:\n wf.write('\\t' + trace + '\\n')\n if curline:\n curline_ab_fst = curline\n curline_ab_snd = rf.readline()\n else:\n break\n ClearAsRel_2()\n ClearFuncLgDict()\n ClearIp2AsDict()\n ClearIp2AsDictV6()\n wf.close()\n\nlink_trace_index_dict = dict()\nbgp_path_in_trace_set_dict = dict()\ndef CheckIfBgpPathInTraces(bgp_path, extra_dict):\n global bgp_path_in_trace_set_dict\n global link_trace_index_dict\n if bgp_path in bgp_path_in_trace_set_dict.keys():\n return bgp_path_in_trace_set_dict[bgp_path]\n elems = bgp_path.split(' ')\n if len(elems) < 2:\n return 0\n link = elems[0] + ' ' + elems[1]\n if link in link_trace_index_dict.keys():\n for trace in link_trace_index_dict[link]:\n if trace.__contains__(bgp_path):\n bgp_path_in_trace_set_dict[bgp_path] = 1\n extra_dict[bgp_path] = 1\n return 1\n bgp_path_in_trace_set_dict[bgp_path] = 0\n extra_dict[bgp_path] = 0\n return 0\n # for elem in bgp_path_in_trace_set_dict.keys():\n # if elem.__contains__(bgp_path):\n # bgp_path_in_trace_set_dict[bgp_path] = bgp_path_in_trace_set_dict[elem]\n # extra_dict[bgp_path] = bgp_path_in_trace_set_dict[elem]\n # return bgp_path_in_trace_set_dict[bgp_path]\n # if bgp_path in all_trace_path_set:\n # bgp_path_in_trace_set_dict[bgp_path] = 1\n # extra_dict[bgp_path] = 1\n # return 1\n # for trace in all_trace_path_set:\n # if trace.__contains__(bgp_path):\n # bgp_path_in_trace_set_dict[bgp_path] = 1\n # extra_dict[bgp_path] = 1\n # return 1\n # bgp_path_in_trace_set_dict[bgp_path] = 0\n # extra_dict[bgp_path] = 0\n # return 0\n\ndef GetLinkTraceIndex():\n link_trace_index_filename = global_var.par_path + global_var.other_middle_data_dir + 'link_trace_index_2_from_all_vp'\n if not os.path.exists(link_trace_index_filename):\n all_trace_path_set = set()\n with open(global_var.par_path + global_var.other_middle_data_dir + 'all_trace_path_2_from_all_vp', 'r') as rf:\n all_trace_path_set = set(rf.read().strip('\\n').split('\\n'))\n for trace_path in all_trace_path_set:\n prev_elem = '*'\n for elem in trace_path.split(' '):\n if prev_elem != '*' and prev_elem != '?' and (not prev_elem.__contains__('<')) and \\\n elem != '*' and elem != '?' and (not elem.__contains__('<')):\n link = prev_elem + ' ' + elem\n if link not in link_trace_index_dict.keys():\n link_trace_index_dict[link] = set()\n link_trace_index_dict[link].add(trace_path)\n prev_elem = elem\n with open(link_trace_index_filename, 'w') as wf:\n for (link, trace_set) in link_trace_index_dict.items():\n wf.write(link + ':' + ','.join(list(trace_set)) + '\\n')\n else:\n with open(link_trace_index_filename, 'r') as rf:\n curline = rf.readline()\n while curline:\n (link, trace_str) = curline.strip('\\n').split(':')\n link_trace_index_dict[link] = set(trace_str.split(','))\n curline = rf.readline()\n\ndef GetAbBgpPath(filename):\n PreGetSrcFilesInDirs()\n global bgp_path_in_trace_set_dict\n global link_trace_index_dict\n GetLinkTraceIndex()\n # all_trace_path_set = set()\n # with open(global_var.par_path + global_var.other_middle_data_dir + 'all_trace_path_2_from_all_vp', 'r') as rf:\n # all_trace_path_set = set(rf.read().strip('\\n').split('\\n'))\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n bgp_path_in_trace_set_dict_filename = 'bgp_path_in_trace_set_dict'\n bgp_path_in_trace_set_dict.clear() \n if os.path.exists(bgp_path_in_trace_set_dict_filename):\n with open(bgp_path_in_trace_set_dict_filename, 'r') as rf:\n for elem in rf.read().split('\\n'):\n if not elem:\n continue\n (bgp_path, val) = elem.split(':')\n bgp_path_in_trace_set_dict[bgp_path] = int(val)\n for year in range(2018,2021):\n for month in range(1,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n date = str(year) + str(month).zfill(2) + '15'\n #print(date)\n # GetSibRel(year, month)\n # GetIxpAsSet()\n # GetAsRel(year, month)\n for vp in global_var.vps:\n g_asn = global_var.trace_as_dict[vp] \n #bgp_filename = global_var.par_path + global_var.rib_dir + 'bgpdata/bgp_' + g_asn + '_' + date\n #GetBgpByPrefix(bgp_filename)\n cur_sub_dir = vp + '_' + date + '/ribs_midar_bdrmapit/' #'nrt-jp.2019030115/'\n if not os.path.exists(cur_sub_dir):\n continue\n locate = date + '_' + vp\n print(locate)\n with open(cur_sub_dir + filename, 'r') as rf:\n extra_dict = dict()\n curline = rf.readline()\n #dst_key = None\n while curline:\n if not curline.startswith('\\t'):\n #dst_key = curline[:curline.index(']') + 1]\n curline = rf.readline()\n continue\n bgp_path = curline.strip('\\n').strip('\\t')\n CheckIfBgpPathInTraces(bgp_path, extra_dict)\n curline = rf.readline()\n print('begin add data')\n with open(bgp_path_in_trace_set_dict_filename, 'a') as wf:\n for (bgp_path, val) in extra_dict.items():\n wf.write(bgp_path + ':' + str(val) + '\\n')\n print('end add data')\n #ClearBGPByPrefix()\n # ClearAsRel()\n # ClearIxpAsSet()\n # ClearSibRel()\n # ClearPeerDbInfoDict()\n ab_count = 0\n total_count = 0\n with open('ab_bgp_path', 'w') as wf:\n for (bgp_path, val) in bgp_path_in_trace_set_dict.items():\n total_count += 1\n if val == 0:\n wf.write(bgp_path + '\\n')\n ab_count += 1\n print('total count: %d' %total_count)\n print('ab count: %d' %ab_count)\n\ndef CountAbPathNum():\n bgp_path_set = set()\n with open(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2', 'r') as rf:\n for elem in rf.read().split('\\n'):\n if elem.startswith('\\t') and not elem.startswith('\\t\\t'):\n bgp_path_set.add(elem.strip('\\t').strip('\\n'))\n print(len(bgp_path_set))\n bgp_path_set.clear()\n with open(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_valley_2', 'r') as rf:\n for elem in rf.read().split('\\n'):\n if elem.startswith('\\t') and not elem.startswith('\\t\\t'):\n bgp_path_set.add(elem.strip('\\t').strip('\\n'))\n print(len(bgp_path_set))\n \ndef CountNonTransientBgpPath(filename):\n bgp_path_set = set()\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n for year in range(2018,2021):\n for month in range(1,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n date = str(year) + str(month).zfill(2) + '15'\n for vp in global_var.vps:\n cur_sub_dir = vp + '_' + date + '/ribs_midar_bdrmapit/' #'nrt-jp.2019030115/'\n if not os.path.exists(cur_sub_dir):\n continue\n with open(cur_sub_dir + filename, 'r') as rf:\n curline = rf.readline()\n while curline:\n if not curline.startswith('\\t'):\n #dst_key = curline[:curline.index(']') + 1]\n curline = rf.readline()\n continue\n bgp_path = curline.strip('\\n').strip('\\t')\n bgp_path_set.add(bgp_path)\n print(len(bgp_path_set))\n\ndef GetCoreASInAbLink(filename):\n wf_core_as = open(filename + '_core_as', 'w')\n wf_other_as = open(filename + '_other_as', 'w')\n num_core_as = 0\n num_other_as = 0\n with open(filename, 'r') as rf:\n curline_ab_fst = rf.readline()\n curline_ab_snd = rf.readline()\n while True:\n curline = rf.readline()\n existed_trace_list = []\n while curline and curline.startswith('\\t'):\n existed_trace_list.append(curline.strip('\\n').strip('\\t'))\n curline = rf.readline()\n as_rank_1_str = curline_ab_fst.split(',')[-1].strip('\\n')\n as_rank_1 = 0xFFFF\n if as_rank_1_str.isdigit():\n as_rank_1 = int(as_rank_1_str)\n wf = wf_other_as\n if as_rank_1 <= 15:\n wf = wf_core_as\n num_core_as += 1\n else:\n num_other_as += 1\n wf.write(curline_ab_fst)\n wf.write(curline_ab_snd)\n for trace in existed_trace_list:\n wf.write('\\t' + trace + '\\n')\n if curline:\n curline_ab_fst = curline\n curline_ab_snd = rf.readline()\n else:\n break\n print('num_core_as:%d' %num_core_as)\n print('num_other_as:%d' %num_other_as)\n wf_core_as.close()\n wf_other_as.close()\n\n\ndef StatLinkNumInSuspicBgp(filename):\n link_set = set()\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n for year in range(2018,2021):\n for month in range(1,13):\n #for month in range(4,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n date = str(year) + str(month).zfill(2) + '15'\n for vp in global_var.vps:\n cur_sub_dir = vp + '_' + date + '/ribs_midar_bdrmapit/' #'nrt-jp.2019030115/'\n if not os.path.exists(cur_sub_dir):\n continue\n with open(cur_sub_dir + filename, 'r') as rf:\n curline = rf.readline()\n #dst_key = None\n while curline:\n if not curline.startswith('\\t'):\n #dst_key = curline[:curline.index(']') + 1]\n curline = rf.readline()\n continue\n bgp_path = curline.strip('\\n').strip('\\t')\n prev_elem = ''\n for elem in bgp_path.split(' '):\n if prev_elem != '':\n link = prev_elem + ' ' + elem\n link_set.add(link)\n prev_elem = elem\n curline = rf.readline()\n print(len(link_set))\n\ndef PathHasLoop(path):\n elems = path.split(' ')\n start = 0\n while elems[start] == '*' or elems[start] == '?' or elems[start] == '<>':\n start += 1\n for i in range(start + 1, len(elems)):\n if elems[i] == '*' or elems[i] == '?' or elems[start] == '<>': #记成前面相同的元素，不处理\n elems[i] = elems[i - 1]\n if elems[i] != elems[i - 1] and elems[i] in elems[:i - 1]:\n return True\n return False\n\ndef TryDealTPAdrress():\n filename = '/mountdisk1/ana_c_d_incongruity/out_my_anatrace/bgp_ab_link_2_rel_trace_with_info_v2_2_same_country'\n wf = open(filename + '_modi_tpadrr', 'w')\n search_path = '/mountdisk1/ana_c_d_incongruity/other_middle_data/compress_trace_to_ori_trace_*'\n with open(filename, 'r') as rf:\n curline = rf.readline()\n while curline:\n if not curline.startswith('\\t'):\n wf.write(curline)\n curline = rf.readline()\n continue\n comp_bgp_path = curline.strip('\\n').strip('\\t')\n elems = comp_bgp_path.split(' ')\n if len(elems) != 3 or elems.__contains__('*') or elems.__contains__('?'):\n wf.write(curline)\n curline = rf.readline()\n continue\n mid_elem = elems[1]\n output = os.popen('grep \\'' + comp_bgp_path + ':\\' ' + search_path)\n if output:\n data = output.readline()\n tp_address_flag = True\n while data:\n path_list = data[data.rindex(':') + 1:].split(',')\n for path in path_list:\n if PathHasLoop(path):\n continue\n count = path.split(' ').count(mid_elem)\n if count != 1: #只要有一个不是，就应该不是\n tp_address_flag = False\n break \n data = output.readline()\n if tp_address_flag: #改写记录\n elems[1] = '?'\n wf.write('\\t' + ' '.join(elems) + '\\n')\n print(curline)\n else:\n wf.write(curline)\n curline = rf.readline()\n wf.close()\n StripMidAllAsterPath('/mountdisk1/ana_c_d_incongruity/out_my_anatrace/bgp_ab_link_2_rel_trace_with_info_v2_2_same_country_modi_tpadrr') \n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n os.system('cat bgp_ab_link_2_rel_trace_with_info_v2_1_other_ab bgp_ab_link_2_rel_trace_with_info_v2_2_same_country_modi_tpadrr_strip_aster > bgp_ab_link_2_rel_trace_with_info_v2_prior_ab')\n\ndef StripMidAllAsterPath(filename):\n wf = open(filename + '_strip_aster', 'w')\n strip_num = 0\n with open(filename, 'r') as rf:\n curline_fst_as = rf.readline() \n while curline_fst_as:\n curline_snd_as = rf.readline()\n trace_list = []\n curline = rf.readline()\n while curline and curline.startswith('\\t'):\n trace_list.append(curline.strip('\\n').strip('\\t'))\n curline = rf.readline()\n mid_all_aster = True\n for trace in trace_list:\n elems = trace.split(' ')\n elem_set = set(elems[1:-1])\n if elem_set and '*' in elem_set:\n elem_set.remove('*')\n if elem_set and '?' in elem_set:\n elem_set.remove('?')\n if elem_set and '<>' in elem_set:\n elem_set.remove('<>')\n if elem_set:\n mid_all_aster = False\n break\n if mid_all_aster:\n #print(trace_list[0])\n strip_num += 1\n else:\n wf.write(curline_fst_as)\n wf.write(curline_snd_as)\n for trace in trace_list:\n wf.write('\\t' + trace + '\\n')\n curline_fst_as = curline\n print('strip_num: %d' %strip_num)\n wf.close()\n\ndef CheckSibInAbTraces():\n os.chdir(global_var.par_path + global_var.out_my_anatrace_dir + '/')\n PreGetSrcFilesInDirs()\n GetSibRelByMultiDataFiles(2018, 1)\n wf = open('bgp_ab_link_2_rel_trace_with_info_v2_prior_ab_filter_sib', 'w')\n num_all_aster = 0\n num_still_ab = 0\n with open('bgp_ab_link_2_rel_trace_with_info_v2_prior_ab', 'r') as rf:\n curline_fst_as = rf.readline() \n while curline_fst_as:\n curline_snd_as = rf.readline()\n trace_list = []\n curline = rf.readline()\n while curline and curline.startswith('\\t'):\n trace_list.append(curline.strip('\\n').strip('\\t'))\n curline = rf.readline()\n fst_as = curline_fst_as[:curline_fst_as.index(':')]\n snd_as = curline_snd_as[:curline_snd_as.index(':')]\n fst_org = GetOrgByMultiDataFiles_2(fst_as)\n snd_org = GetOrgByMultiDataFiles_2(snd_as)\n new_trace_list = []\n link_found = False\n has_other_as = False\n for trace in trace_list:\n elems = trace.split(' ')\n new_elems = []\n for i in range(1, len(elems) - 1):\n sib_as = False\n if elems[i] != '*' and elems[i] != '?' and elems[i] != '<>':\n for asn in elems[i].split('_'):\n tmp_org = GetOrgByMultiDataFiles_2(asn)\n if (tmp_org & fst_org) or (tmp_org & snd_org):\n sib_as = True\n break\n if not sib_as:\n new_elems.append(elems[i])\n if elems[i] != '*' and elems[i] != '?' and elems[i] != '<>':\n has_other_as = True\n if not new_elems:\n link_found = True\n break\n else:\n new_trace_list.append(fst_as + ' ' + ' '.join(new_elems) + ' ' + snd_as)\n if not link_found:\n if has_other_as:\n num_still_ab += 1\n wf.write(curline_fst_as)\n wf.write(curline_snd_as)\n for trace in new_trace_list:\n wf.write('\\t' + trace + '\\n')\n else:\n num_all_aster += 1\n curline_fst_as = curline\n wf.close()\n print('num_all_aster: %d' %num_all_aster)\n print('num_still_ab: %d' %num_still_ab)\n\ndef StatLinkCountInBgp():\n for vp in global_var.vps:\n link_set = set()\n with open(global_var.par_path + global_var.rib_dir + 'bgpdata/bgp_' + global_var.trace_as_dict[vp] + '_20200415', 'r') as rf:\n curline = rf.readline()\n while curline:\n elems = curline.split('|')\n if len(elems) < 3:\n curline = rf.readline()\n continue\n link_set |= GetLinkSetFromBgpPath(elems[2])\n curline = rf.readline()\n print(vp + ':%d' %len(link_set))\n\ndef Tmp():\n bgp_seg = '6939 701 5511 39386 41426 48237 35819 15802'\n link_set1 = GetLinkSetFromBgpPath(bgp_seg)\n tmp_dict = dict()\n for link in link_set1:\n if link not in g_bgp_link_path_dict.keys():\n tmp_dict[link] = set()\n tmp_dict[link].add(bgp_seg)\n link_set2 = GetLinkSetFromBgpPath(bgp_seg)\n tmp_path_set = set()\n start = True\n for link in link_set2:\n if start:\n tmp_path_set = tmp_dict[link]\n start = False\n else:\n debug_path_set = tmp_dict[link]\n tmp_path_set &= debug_path_set\n if not tmp_path_set:\n print('This ought not happen. path: %s' %bgp_seg)\n \n\nif __name__ == '__main__':\n tmp = True\n if tmp:\n Tmp()\n #StatLinkCountInBgp()\n\n #StatLinkNumInSuspicBgp('ana_ab_5_all_bgp')\n #GetCCNums(2021, 4)\n #GetNoTraceLink()\n #ClassifyAbLink_2()\n #GetLinkTraceIndex()\n\n GetCoreASInAbLink(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2_rel_trace_with_info_v2_prior_ab_filter_sib')\n #CountNonTransientBgpPath('ana_ab_5_all_bgp')\n \n # PreGetAllTraceLinks_AllVps()\n # PreGetSrcFilesInDirs()\n # CheckBgpLinkExistsInTrace('34288 8757 262287 30081', set())\n\n # PreGetSrcFilesInDirs()\n # GetSibRelByMultiDataFiles(2018, 1)\n # print(IsSibLink('31133 3450'))\n\n #CountAbPathNum()\n # PreGetSrcFilesInDirs()\n # GetAsRel(2020, 1)\n # PathHasValley_2('7575 11537 20965 21274 21320')\n #CheckEmails()\n print('done')\n while True:\n pass\n #TmpStat()\n #PreGetAllBgpPaths()\n for year in range(2018,2021):\n for month in range(1,13):\n if (year == 2016 and month < 4) or (year == 2020 and month > 4): #2016年4月前peeringdb数据不准，2020年5月后的数据不全\n continue\n ConstrPeerDbInfoDict(year, month)\n ClearPeerDbInfoDict()\n print('done')\n while True:\n pass\n trace_set = TmpFindNeighInTrace(sys.argv[1], 'neigh') #'44244'\n bgp_set = TmpFindNeighInBgp(sys.argv[1], 'neigh')\n tmp_set = trace_set & bgp_set\n print('common (%d): ' %len(tmp_set), end='')\n print(tmp_set)\n tmp_set = trace_set.difference(bgp_set)\n print('trace unique (%d): ' %len(tmp_set), end='')\n print(tmp_set)\n tmp_set = bgp_set.difference(trace_set)\n print('bgp unique (%d): ' %len(tmp_set), end='')\n print(tmp_set)\n #FilterSibLinkInAbBgp(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link')\n else:\n pre_constr_trace_set = False\n if pre_constr_trace_set: #准备工作是建立所有trace集合，最后生成'all_traces_uni_as'文件\n #PreGetAllTraces()\n\n PreGetAllTraces_Mini()\n PreTransAllTraceToUniASTraces()\n #PreGetAllTracesWithUniAs() #以后每次正常工作需要调用这个句子，获取all_trace_set_uni_as\n pass\n else: #正常工作\n PreGetAllTraceLinks() #得到g_all_trace_links_set\n PreGetAllTraceLinks_AllVps() #得到g_all_trace_links_set, 前提条件\n #step 0\n # print('step 0')\n # PreGetAllBgpPaths() #这句和下面一句GetAllBgpOfAbTrace是成对出现的 \n # GetAllBgpOfAbTrace('ana_ab_5')\n #step 1\n # print('step 1')\n # PreGetSrcFilesInDirs()\n # GetSuspicBgp('ana_ab_5_all_bgp')\n # #step 2\n # print('step 2')\n # FilterSibLinkInAbBgp(global_var.par_path + global_var.out_my_anatrace_dir + '/bgp_ab_link_2')\n # AnaAbBgpLink()\n # #step 3\n # print('step 3')\n # GetInfoOfAbLink()\n #step 4, 筛选出prior ab link\n print('step 4')\n # ClassifyAbLink_2()\n # TryDealTPAdrress()\n # GetInfoOfPriorAbLink()\n CheckSibInAbTraces()\n #step 5, 通过在线traceroute筛选ab_link\n #CheckAbLinkByOnlineTraceroute() \n #step 6\n #FilterValleyByNewestRel('bgp_valley_2')\n #step 7 #补\n #GetAbBgpPath('ana_ab_5_all_bgp')\n", "sub_path": "ana_bgp.py", "file_name": "ana_bgp.py", "file_ext": "py", "file_size_in_byte": 78874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "global_var.all_trace_par_path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "global_var.all_trace_out_data_dir", "line_number": 25, "usage_type": "attribute"}, {"api_name": "global_var.all_trace_out_all_trace_filename", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 32, "usage_type": "call"}, {"api_name": "global_var.all_trace_par_path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "global_var.all_trace_trace_as_res_dir", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 33, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 39, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 39, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 54, "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": "global_var.vps", "line_number": 64, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 79, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 79, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 102, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 102, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 115, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 123, "usage_type": "attribute"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 129, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 129, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "global_var.vps", "line_number": 154, "usage_type": "attribute"}, {"api_name": "global_var.trace_as_dict", "line_number": 155, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "global_var.rib_dir", "line_number": 157, "usage_type": "attribute"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 165, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 165, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 295, "usage_type": "attribute"}, {"api_name": "global_var.rib_dir", "line_number": 295, "usage_type": "attribute"}, {"api_name": "os.popen", "line_number": 298, "usage_type": "call"}, {"api_name": "global_var.trace_as_dict", "line_number": 298, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 319, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 319, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 319, "usage_type": "attribute"}, {"api_name": "global_var.vps", "line_number": 329, "usage_type": "attribute"}, {"api_name": "global_var.trace_as_dict", "line_number": 331, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "global_var.rib_dir", "line_number": 332, "usage_type": "attribute"}, {"api_name": "utils_v2.GetBgpByPrefix", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "utils_v2.GetBgpPathFromBgpPrefixDict_2", "line_number": 346, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 350, "usage_type": "call"}, {"api_name": "utils_v2.ClearBGPByPrefix", "line_number": 353, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 363, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 363, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path", "line_number": 364, "usage_type": "attribute"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 366, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 366, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 392, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 392, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 392, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 396, "usage_type": "call"}, {"api_name": "os.path", "line_number": 396, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 397, "usage_type": "call"}, {"api_name": "utils_v2.IsIxpAs", "line_number": 430, "usage_type": "call"}, {"api_name": "utils_v2.IsAsSet", "line_number": 431, "usage_type": "call"}, {"api_name": "utils_v2.GetNeighFromRipe", "line_number": 439, "usage_type": "call"}, {"api_name": "utils_v2.IsTwoAsPeerInIXP", "line_number": 440, "usage_type": "call"}, {"api_name": "gen_ip2as_command.PreGetSrcFilesInDirs", "line_number": 448, "usage_type": "call"}, {"api_name": "utils_v2.GetAsRankDict", "line_number": 449, "usage_type": "call"}, {"api_name": "utils_v2.GetAsRel", "line_number": 450, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 451, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 451, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 451, "usage_type": "attribute"}, {"api_name": "utils_v2.PathHasValley_2", "line_number": 461, "usage_type": "call"}, {"api_name": "utils_v2.GetAsRankFromDict", "line_number": 466, "usage_type": "call"}, {"api_name": "utils_v2.Get2AsRel", "line_number": 466, "usage_type": "call"}, {"api_name": "utils_v2.PathHasValley_2", "line_number": 471, "usage_type": "call"}, {"api_name": "utils_v2.ClearAsRel", "line_number": 477, "usage_type": "call"}, {"api_name": "utils_v2.ClearAsRankDict", "line_number": 478, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 487, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 487, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 487, "usage_type": "attribute"}, {"api_name": "utils_v2.GetSibRel", "line_number": 495, "usage_type": "call"}, {"api_name": "utils_v2.GetIxpAsSet", "line_number": 496, "usage_type": "call"}, {"api_name": "utils_v2.GetAsRel", "line_number": 497, "usage_type": "call"}, {"api_name": "utils_v2.ConstrPeerDbInfoDict", "line_number": 498, "usage_type": "call"}, {"api_name": "global_var.vps", "line_number": 499, "usage_type": "attribute"}, {"api_name": "global_var.trace_as_dict", "line_number": 500, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 501, "usage_type": "attribute"}, {"api_name": "global_var.rib_dir", "line_number": 501, "usage_type": "attribute"}, {"api_name": "utils_v2.GetBgpByPrefix", "line_number": 502, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 504, "usage_type": "call"}, {"api_name": "os.path", "line_number": 504, "usage_type": "attribute"}, {"api_name": "utils_v2.PathHasValley_2", "line_number": 531, "usage_type": "call"}, {"api_name": "utils_v2.ClearBGPByPrefix", "line_number": 541, "usage_type": "call"}, {"api_name": "utils_v2.ClearAsRel", "line_number": 542, "usage_type": "call"}, {"api_name": "utils_v2.ClearIxpAsSet", "line_number": 543, "usage_type": "call"}, {"api_name": "utils_v2.ClearSibRel", "line_number": 544, "usage_type": "call"}, {"api_name": "utils_v2.ClearPeerDbInfoDict", "line_number": 545, "usage_type": "call"}, {"api_name": "utils_v2.GetOrgByMultiDataFiles_2", "line_number": 583, "usage_type": "call"}, {"api_name": "utils_v2.GetOrgByMultiDataFiles_2", "line_number": 584, "usage_type": "call"}, {"api_name": "gen_ip2as_command.PreGetSrcFilesInDirs", "line_number": 588, "usage_type": "call"}, {"api_name": "utils_v2.GetSibRelByMultiDataFiles", "line_number": 589, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 617, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 619, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPathToMin", "line_number": 623, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 623, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 646, "usage_type": "call"}, {"api_name": "global_var.all_trace_par_path", "line_number": 646, "usage_type": "attribute"}, {"api_name": "global_var.all_trace_download_dir", "line_number": 646, "usage_type": "attribute"}, {"api_name": "multiprocessing.Queue", "line_number": 647, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 650, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 663, "usage_type": "call"}, {"api_name": "ana_prefix_traceroute_group_by_prefix_v2.CompressAsPath", "line_number": 667, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 685, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 685, "usage_type": "attribute"}, {"api_name": "global_var.rib_dir", "line_number": 685, "usage_type": "attribute"}, {"api_name": "multiprocessing.Queue", "line_number": 686, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 688, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 691, "usage_type": "call"}, {"api_name": "utils_v2.FindBgpAsInTracePath", "line_number": 729, "usage_type": "call"}, {"api_name": "utils_v2.FindBgpAsInTracePath", "line_number": 730, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 738, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 738, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 738, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 740, "usage_type": "call"}, {"api_name": "os.path", "line_number": 740, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 741, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 785, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 785, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 787, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 787, "usage_type": "attribute"}, {"api_name": "utils_v2.PreLoadAsnInfoFromASNS", "line_number": 819, "usage_type": "call"}, {"api_name": "utils_v2.GetCCNums", "line_number": 820, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 821, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 821, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 821, "usage_type": "attribute"}, {"api_name": "utils_v2.GetCCNumsFromDict", "line_number": 834, "usage_type": "call"}, {"api_name": "utils_v2.GetCCNumsFromDict", "line_number": 835, "usage_type": "call"}, {"api_name": "utils_v2.PreLoadAsnInfoFromASNS", "line_number": 850, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 851, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 851, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 851, "usage_type": "attribute"}, {"api_name": "utils_v2.GetAsnInfoFromASNS", "line_number": 869, "usage_type": "call"}, {"api_name": "utils_v2.GetAsnInfoFromASNS", "line_number": 870, "usage_type": "call"}, {"api_name": "utils_v2.ClearAsnInfoFromASNS", "line_number": 903, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 906, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 906, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 906, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 945, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 945, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 945, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 1028, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 1032, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1032, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1032, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1131, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1131, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1132, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1132, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1133, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1133, "usage_type": "attribute"}, {"api_name": "utils_v2.GetAsStrOfIpByRv", "line_number": 1143, "usage_type": "call"}, {"api_name": "utils_v2.GetAsStrOfIpByRvV6", "line_number": 1145, "usage_type": "call"}, {"api_name": "utils_v2.GetAsRel_2", "line_number": 1161, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1161, "usage_type": "attribute"}, {"api_name": "global_var.rel_cc_dir", "line_number": 1161, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 1162, "usage_type": "call"}, {"api_name": "utils_v2.GetFuncLgDict", "line_number": 1163, "usage_type": "call"}, {"api_name": "utils_v2.GetPfx2ASByRv", "line_number": 1164, "usage_type": "call"}, {"api_name": "utils_v2.GetPfx2ASByRvV6", "line_number": 1165, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 1166, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1166, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1166, "usage_type": "attribute"}, {"api_name": "utils_v2.GetRepIpsOfAs", "line_number": 1179, "usage_type": "call"}, {"api_name": "utils_v2.GetNeighOfAs", "line_number": 1180, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 1184, "usage_type": "call"}, {"api_name": "utils_v2.GetFuncOfLg", "line_number": 1186, "usage_type": "call"}, {"api_name": "utils_v2.AsnInTracePathList", "line_number": 1199, "usage_type": "call"}, {"api_name": "utils_v2.ClearAsRel_2", "line_number": 1213, "usage_type": "call"}, {"api_name": "utils_v2.ClearFuncLgDict", "line_number": 1214, "usage_type": "call"}, {"api_name": "utils_v2.ClearIp2AsDict", "line_number": 1215, "usage_type": "call"}, {"api_name": "utils_v2.ClearIp2AsDictV6", "line_number": 1216, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1258, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 1258, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1259, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1261, "usage_type": "attribute"}, {"api_name": "global_var.other_middle_data_dir", "line_number": 1261, "usage_type": "attribute"}, {"api_name": "gen_ip2as_command.PreGetSrcFilesInDirs", "line_number": 1285, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 1292, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1292, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1292, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1295, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1295, "usage_type": "attribute"}, {"api_name": "global_var.vps", "line_number": 1311, "usage_type": "attribute"}, {"api_name": "global_var.trace_as_dict", "line_number": 1312, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1316, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1316, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1355, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1355, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1361, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1361, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 1369, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1369, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1369, "usage_type": "attribute"}, {"api_name": "global_var.vps", "line_number": 1375, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1377, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 1431, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1431, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1431, "usage_type": "attribute"}, {"api_name": "global_var.vps", "line_number": 1438, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 1440, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1440, "usage_type": "attribute"}, {"api_name": "os.popen", "line_number": 1490, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 1513, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1513, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1513, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 1514, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 1554, "usage_type": "call"}, {"api_name": "global_var.par_path", "line_number": 1554, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1554, "usage_type": "attribute"}, {"api_name": "gen_ip2as_command.PreGetSrcFilesInDirs", "line_number": 1555, "usage_type": "call"}, {"api_name": "utils_v2.GetSibRelByMultiDataFiles", "line_number": 1556, "usage_type": "call"}, {"api_name": "utils_v2.GetOrgByMultiDataFiles_2", "line_number": 1571, "usage_type": "call"}, {"api_name": "utils_v2.GetOrgByMultiDataFiles_2", "line_number": 1572, "usage_type": "call"}, {"api_name": "utils_v2.GetOrgByMultiDataFiles_2", "line_number": 1583, "usage_type": "call"}, {"api_name": "global_var.vps", "line_number": 1611, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1613, "usage_type": "attribute"}, {"api_name": "global_var.rib_dir", "line_number": 1613, "usage_type": "attribute"}, {"api_name": "global_var.trace_as_dict", "line_number": 1613, "usage_type": "attribute"}, {"api_name": "global_var.par_path", "line_number": 1658, "usage_type": "attribute"}, {"api_name": "global_var.out_my_anatrace_dir", "line_number": 1658, "usage_type": "attribute"}, {"api_name": "utils_v2.ConstrPeerDbInfoDict", "line_number": 1683, "usage_type": "call"}, {"api_name": "utils_v2.ClearPeerDbInfoDict", "line_number": 1684, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 1688, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 1689, "usage_type": "attribute"}]} +{"seq_id": "54437508", "text": "from random import choice\n\nimport numpy as np\nimport pygame\n\nfrom player import Player\nfrom road import Road\nfrom room import Room\nfrom utils import *\n\n# constant variables used for the game\nwidth = 75\nheight = 75\nscale = 10\nwhite = (255, 255, 255)\nradius = (width + height) // 20\n\n\ndef draw(window, map, players):\n \"\"\"\n :param window: pygame window used for drawing on it.\n :param map: matrix that represent pixel value on screen.\n :param players: players array, each player represented as circle\n and his ammo and health will be written on screen in the same color.\n \"\"\"\n for i in range(map.shape[0]):\n for j in range(map.shape[1]):\n color = (0, 0, 0) # UNKNOWN\n if map[i, j] == SPACE:\n color = (255, 255, 255)\n elif map[i, j] == AMMO:\n color = (255, 0, 0)\n elif map[i, j] == HEALTH:\n color = (0, 255, 0)\n elif map[i, j] == WALL:\n color = (153, 76, 0)\n pygame.draw.rect(window, color, scale * np.array([i, j, 1, 1]))\n\n position = 10\n font = pygame.font.SysFont('Comic Sans MS', 30)\n for p in players:\n # draw player as circle on screen\n pygame.draw.circle(window, p.color, (int(scale * (p.loc[0] + 0.5)), int(scale * (p.loc[1] + 0.5))), scale // 2)\n\n # write player stats on screen\n text = 'health: ' + str(int(p.health))\n text_surface = font.render(text, False, p.color)\n window.blit(text_surface, (20, position))\n position += 40\n text = 'ammo: ' + str(int(p.ammo))\n text_surface = font.render(text, False, p.color)\n window.blit(text_surface, (20, position))\n position += 40\n pygame.display.update()\n\n\nif __name__ == '__main__':\n # setup windows\n pygame.init()\n win = pygame.display.set_mode((scale * width, scale * height), )\n pygame.display.set_caption('MazeAI')\n pygame.font.init()\n\n # setup elements (rooms and roads)\n rooms = Room.get_rooms(10, width, height, int(width / 7.5), int(height / 7.5))\n roads = Road.get_roads(rooms)\n\n # draw all elements on screen\n arr = np.ones((width, height)) * WALL\n for room in rooms:\n arr[room.x:room.x + room.width, room.y:room.y + room.height] = SPACE\n for i in room.itemList:\n arr[i.loc[0], i.loc[1]] = i.code\n\n for road in roads:\n p1, p2, p3 = road.get_points()\n arr[min(p1[0], p2[0]):max(p1[0], p2[0]) + 1, min(p1[1], p2[1]):max(p1[1], p2[1]) + 1] = SPACE\n arr[min(p2[0], p3[0]):max(p2[0], p3[0]) + 1, min(p2[1], p3[1]):max(p2[1], p3[1]) + 1] = SPACE\n\n # setup player\n players = []\n colors = [(0, 0, 255), (102, 0, 102)]\n for i in range(2):\n player = Player(choice(rooms).get_center(), arr.shape, colors[i])\n player.radius = radius\n players.append(player)\n\n screen = arr\n run = True\n pause = False\n # main loop of the game\n while run:\n pygame.time.delay(10)\n\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n run = False\n elif event.type == pygame.KEYDOWN:\n key = event.key\n if key == pygame.K_1:\n # screen will be the main map of the game\n screen = arr\n elif key == pygame.K_2:\n # screen will be what player1 can see and remember\n screen = players[0].map\n elif key == pygame.K_3:\n # screen will be what player2 can see and remember\n screen = players[1].map\n elif key == pygame.K_SPACE:\n # pause the game\n pause = not pause\n\n if not pause:\n # idle code goes here\n if np.count_nonzero(arr == AMMO) < 2:\n # if there is too little ammo on the screen\n # the game will automatically add ammo boxes\n inds = np.where(arr == SPACE)\n index = np.random.randint(len(inds[0]))\n arr[inds[0][index], inds[1][index]] = AMMO\n if np.count_nonzero(arr == HEALTH) < 2:\n # if there is too little health on the screen\n # the game will automatically add health boxes\n inds = np.where(arr == SPACE)\n index = np.random.randint(len(inds[0]))\n arr[inds[0][index], inds[1][index]] = HEALTH\n\n for player in players:\n if player.health <= 0:\n # player had died, game should be stopped\n pause = True\n break\n for p2 in players:\n if p2 != player:\n if euclidean_distance(player.loc, p2.loc) < 3 * radius:\n # players are close to each other and should know that\n player.enemy = p2\n break\n else:\n player.enemy = None\n\n if arr[player.loc[0], player.loc[1]] != SPACE:\n if arr[player.loc[0], player.loc[1]] == AMMO:\n # if player steps on ammo box it will be removed\n # from map and ammo will be added to the player\n player.ammo = min(100, player.ammo + 10)\n\n elif arr[player.loc[0], player.loc[1]] == HEALTH:\n # if player steps on health box it will be removed\n # from map and health will be added to the player\n player.health = min(100, player.health + 10)\n\n arr[player.loc[0], player.loc[1]] = SPACE\n\n player.update_array(arr)\n player.step()\n\n draw(win, screen, players)\n\n pygame.quit()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.draw.rect", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.font.init", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 62, "usage_type": "attribute"}, {"api_name": "room.Room.get_rooms", "line_number": 65, "usage_type": "call"}, {"api_name": "room.Room", "line_number": 65, "usage_type": "name"}, {"api_name": "road.Road.get_roads", "line_number": 66, "usage_type": "call"}, {"api_name": "road.Road", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 69, "usage_type": "call"}, {"api_name": "room.x", "line_number": 71, "usage_type": "attribute"}, {"api_name": "room.width", "line_number": 71, "usage_type": "attribute"}, {"api_name": "room.y", "line_number": 71, "usage_type": "attribute"}, {"api_name": "room.height", "line_number": 71, "usage_type": "attribute"}, {"api_name": "room.itemList", "line_number": 72, "usage_type": "attribute"}, {"api_name": "road.get_points", "line_number": 76, "usage_type": "call"}, {"api_name": "player.Player", "line_number": 84, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 84, "usage_type": "call"}, {"api_name": "player.radius", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pygame.K_1", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pygame.K_2", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.K_3", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.count_nonzero", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.count_nonzero", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 126, "usage_type": "attribute"}, {"api_name": "player.health", "line_number": 130, "usage_type": "attribute"}, {"api_name": "player.loc", "line_number": 136, "usage_type": "attribute"}, {"api_name": "player.enemy", "line_number": 138, "usage_type": "attribute"}, {"api_name": "player.enemy", "line_number": 141, "usage_type": "attribute"}, {"api_name": "player.loc", "line_number": 143, "usage_type": "attribute"}, {"api_name": "player.loc", "line_number": 144, "usage_type": "attribute"}, {"api_name": "player.ammo", "line_number": 147, "usage_type": "attribute"}, {"api_name": "player.loc", "line_number": 149, "usage_type": "attribute"}, {"api_name": "player.health", "line_number": 152, "usage_type": "attribute"}, {"api_name": "player.loc", "line_number": 154, "usage_type": "attribute"}, {"api_name": "player.update_array", "line_number": 156, "usage_type": "call"}, {"api_name": "player.step", "line_number": 157, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "552975251", "text": "import sys, getopt\nimport gc\nimport matplotlib.pyplot as plt\nimport multiprocessing\nimport numpy as np\n\nfrom functools import partial\nfrom natsort import natsorted\nfrom operator import itemgetter\nfrom os import listdir\nfrom os.path import exists, isfile, join\nfrom sklearn.externals import joblib\nfrom sklearn.metrics import confusion_matrix, classification_report, accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nfrom sklearn.utils.multiclass import unique_labels\nfrom config import DatasetConfig, NetworkParameters, RasterParams\n\nOPERATION_CREATE_ESVM = 30\nOPERATION_PLAY = 99\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndef main(argv):\n \"\"\"\n Main function which shows the usage, retrieves the command line parameters and invokes the required functions to do\n the expected job.\n\n :param argv: (dictionary) options and values specified in the command line\n \"\"\"\n\n print('Entering Ensemble SVM manager')\n\n operation = None\n finish_earlier = False\n\n try:\n opts, args = getopt.getopt(argv, \"hc:n:t:d:fp\", [\"create_esvm=\", \"neighbors=\", \"model_name=\", \"storage_directory=\", \"finish_earlier\", \"play\"])\n except getopt.GetoptError:\n print('esvm_manager.py -h')\n sys.exit(2)\n for opt, arg in opts:\n if opt == \"-h\":\n print('esvm_manager.py -c \\n')\n sys.exit()\n elif opt in [\"-c\", \"--create_esvm\"]:\n dataset_folder = arg\n operation = OPERATION_CREATE_ESVM\n elif opt in [\"-n\", \"--neighbors\"]:\n neighbors = int(arg)\n elif opt in [\"-t\", \"--model_name\"]:\n model_name = arg\n elif opt in [\"-d\", \"--storage_directory\"]:\n store_directory = arg\n elif opt in [\"-f\", \"--finish_earlier\"]:\n finish_earlier = True\n elif opt in [\"-p\", \"--play\"]:\n operation = OPERATION_PLAY\n\n if operation == OPERATION_CREATE_ESVM:\n print('Working with dataset file %s' % dataset_folder)\n print('Using %s neighbors' % neighbors)\n create_esvm(dataset_folder, neighbors, store_directory, model_name, finish_earlier)\n elif operation == OPERATION_PLAY:\n play()\n\n sys.exit()\n\n\ndef prepare_generator_dataset(dataset_folder, padding):\n rasters_folders = [f for f in listdir(dataset_folder) if not isfile(join(dataset_folder, f))]\n\n rasters_folders.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))\n\n print(rasters_folders)\n\n pad_factor = int(padding / 2) * 2\n bigdata = np.zeros(shape=(\n len(rasters_folders), DatasetConfig.DATASET_LST_BANDS_USED, RasterParams.SRTM_MAX_X + pad_factor,\n RasterParams.SRTM_MAX_Y + pad_factor), dtype=np.float32)\n bigdata_gt = np.zeros(shape=(len(rasters_folders), RasterParams.FNF_MAX_X, RasterParams.FNF_MAX_Y),\n dtype=np.uint8)\n\n for i, pck in enumerate(rasters_folders):\n path_to_pck = join(dataset_folder, pck, 'dataset.npz')\n\n print('Loading dataset folder ', pck)\n\n pck_bigdata = None\n item_getter = itemgetter('bigdata')\n with np.load(path_to_pck) as df:\n pck_bigdata = item_getter(df)\n\n half_padding = int(padding / 2)\n pck_bigdata = np.pad(pck_bigdata, [(0, 0), (half_padding, half_padding), (half_padding, half_padding)],\n mode='constant')\n\n bigdata[i] = pck_bigdata\n\n pck_bigdata_gt = None\n item_getter = itemgetter('bigdata_gt')\n with np.load(path_to_pck) as df:\n pck_bigdata_gt = item_getter(df)\n\n bigdata_gt[i] = pck_bigdata_gt\n\n del pck_bigdata\n del pck_bigdata_gt\n\n gc.collect()\n\n bigdata_idx_0 = None\n bigdata_idx_1 = None\n\n path_to_idxs = join(dataset_folder, 'samples_shuffled_factor_idx.npz')\n item_getter = itemgetter('bigdata_idx_0', 'bigdata_idx_1')\n with np.load(path_to_idxs) as df:\n bigdata_idx_0, bigdata_idx_1 = item_getter(df)\n\n bigdata_idx_mix = np.empty((bigdata_idx_0.shape[0] + bigdata_idx_1.shape[0], 3), dtype=bigdata_idx_0.dtype)\n bigdata_idx_mix[0::2] = bigdata_idx_0\n del bigdata_idx_0\n gc.collect()\n bigdata_idx_mix[1::2] = bigdata_idx_1\n del bigdata_idx_1\n gc.collect()\n\n return bigdata, bigdata_gt, bigdata_idx_mix\n\ndef get_item_features(item, feature_groups=None, neighbors=None):\n data_patch = dataset[item[0], :, item[1]: item[1] + neighbors, item[2]: item[2] + neighbors]\n\n np_feat = np.zeros(shape=(data_patch.shape[0] * feature_groups,), dtype=np.float32)\n patch_sliced = data_patch\n for i in range(feature_groups - 1):\n top_row = patch_sliced[:, 0, :]\n bottom_row = patch_sliced[:, -1, :]\n\n patch_sliced = patch_sliced[:, 1:-1, :]\n left_col = patch_sliced[:, :, 0]\n right_col = patch_sliced[:, :, -1]\n\n patch_sliced = patch_sliced[:, :, 1:-1]\n\n border_values = np.concatenate((left_col, right_col, top_row, bottom_row), axis=1)\n np_feat[(i * data_patch.shape[0]):((i + 1) * data_patch.shape[0])] = np.mean(border_values, axis=1)\n\n np_feat[(-1 * data_patch.shape[0]):] = np.reshape(patch_sliced,data_patch.shape[0])\n\n return np_feat\n\ndef get_item_gt(item):\n return dataset_gt[item[0], item[1], item[2]]\n\ndef get_patch_features(data_patch, feature_groups):\n np_feat = np.zeros(shape=(data_patch.shape[0] * feature_groups,), dtype=np.float32)\n patch_sliced = data_patch\n for i in range(feature_groups - 1):\n top_row = patch_sliced[:, 0, :]\n bottom_row = patch_sliced[:, -1, :]\n\n patch_sliced = patch_sliced[:, 1:-1, :]\n left_col = patch_sliced[:, :, 0]\n right_col = patch_sliced[:, :, -1]\n\n patch_sliced = patch_sliced[:, :, 1:-1]\n\n border_values = np.concatenate((left_col, right_col, top_row, bottom_row), axis=1)\n np_feat[(i * data_patch.shape[0]):((i + 1) * data_patch.shape[0])] = np.mean(border_values, axis=1)\n\n np_feat[(-1 * data_patch.shape[0]):] = np.reshape(patch_sliced,data_patch.shape[0])\n\n return np_feat\n\ndef shuffle_train(a):\n assert len(a)\n shuffled_a = np.empty(a.shape, dtype=a.dtype)\n permutation = np.random.permutation(len(a))\n for old_index, new_index in enumerate(permutation):\n shuffled_a[new_index] = a[old_index]\n return shuffled_a\n\ndef process_particular_svm(X_train_process_tuple, feature_groups=None, neighbors=None, store_dir=None, model_n=None, seed=7, process_total=None):\n\n process_number = X_train_process_tuple[1]\n process_X_train = X_train_process_tuple[0]\n print('{0}/{1} - '.format(process_number + 1, process_total), end='')\n\n X_preprocessed_train_bag = np.zeros(shape=(process_X_train.shape[0], dataset.shape[1] * feature_groups), dtype=np.float32)\n Y_preprocessed_train_bag = np.zeros(shape=(process_X_train.shape[0]), dtype=np.uint8)\n\n for pxt_i, pxt_item in enumerate(process_X_train):\n X_preprocessed_train_bag[pxt_i] = get_item_features(pxt_item, feature_groups=feature_groups, neighbors=neighbors)\n Y_preprocessed_train_bag[pxt_i] = get_item_gt(pxt_item)\n\n svm = SVC(kernel='rbf', cache_size=5000, gamma='auto', verbose=0, random_state=seed, max_iter=-1)\n svm.fit(X_preprocessed_train_bag, Y_preprocessed_train_bag)\n\n # print('Storing model...')\n joblib.dump(svm, join(store_dir, model_n + '-{step:03d}'.format(step=process_number) + '.pkl'))\n\n X_preprocessed_train_bag = None\n Y_preprocessed_train_bag = None\n svm = None\n\n gc.collect()\n\n\ndef predict_particular_batch(X_test, feature_groups=None, neighbors=None, svm=None):\n\n X_preprocessed_test_bag = np.zeros(shape=(X_test.shape[0], dataset.shape[1] * feature_groups), dtype=np.float32)\n\n for pxt_i, pxt_item in enumerate(X_test):\n X_preprocessed_test_bag[pxt_i] = get_item_features(pxt_item, feature_groups=feature_groups, neighbors=neighbors)\n\n return svm.predict(X_preprocessed_test_bag)\n\n\ndef create_esvm(dataset_folder, neighbors, store_directory, model_name, finish_earlier):\n print('Starting operation of Ensembled SVM creation')\n\n # fix random seed for reproducibility\n seed = 7\n np.random.seed(seed)\n\n global dataset\n global dataset_gt\n dataset, dataset_gt, dataset_idxs = prepare_generator_dataset(dataset_folder, neighbors)\n\n feature_groups = int(neighbors / 2) + 1\n\n X_train, X_test = train_test_split(dataset_idxs, test_size=0.15, random_state=7)\n\n if store_directory is not '':\n store_dir = store_directory\n else:\n store_dir = 'storage/ersvm/'\n\n if model_name is not '':\n model_n = model_name\n else:\n model_n = 'svm_model'\n\n estimator_step_size = 50\n batch_size = (NetworkParameters.BATCH_SIZE)\n\n steps = int(X_train.shape[0] / batch_size) + 1\n estimator_steps = int(steps / estimator_step_size) + 1\n\n #svm = SVC(C=1.0, kernel='rbf', cache_size=1000, verbose=1, probability=True, random_state=seed)\n\n #sgd_lsvc = linear_model.SGDClassifier(random_state=seed, warm_start=True, verbose=1, n_jobs=-1, alpha=0.00001) # max_features=int(dataset.shape[1]/3)\n epochs=1\n\n if not True:\n estimator_steps_array = np.arange(estimator_steps)\n for e in range(epochs):\n print('\\n==========================')\n print(' EPOCH %s ' % str(e+1))\n print('==========================\\n\\n')\n np.random.shuffle(X_train)\n\n print('Train progress: ', end='')\n for i in range(estimator_steps):\n print('{0}/{1} - '.format(i + 1, estimator_steps), end='')\n start = (i * estimator_step_size) * batch_size\n end = ((i + 1) * estimator_step_size) * batch_size\n end = end if end < X_train.shape[0] else X_train.shape[0]\n\n X_preprocessed_train_bag = np.zeros(shape=(end-start, dataset.shape[1]*feature_groups), dtype=np.float32)\n Y_preprocessed_train_bag = np.zeros(shape=(end-start), dtype=np.uint8)\n pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n X_preprocessed_train_bag = np.array(pool.map(partial(get_item_features, feature_groups=feature_groups, neighbors=neighbors), X_train[start:end,:]))\n Y_preprocessed_train_bag = np.array(pool.map(get_item_gt, X_train[start:end,:]))\n pool.close()\n pool.join()\n\n #svm = SVC(C=1.0, kernel='rbf', cache_size=5000, gamma='auto', verbose=0, probability=True, random_state=seed, max_iter=1000)\n #svm = SVC(C=1.0, kernel='linear', cache_size=5000, gamma='auto', verbose=0, probability=True, random_state=seed, max_iter=1000)\n svm = SVC(kernel='rbf', cache_size=5000, gamma='auto', verbose=0, random_state=seed, max_iter=-1)\n svm.fit(X_preprocessed_train_bag, Y_preprocessed_train_bag)\n\n # print('Storing model...')\n joblib.dump(svm, join(store_dir, model_n + '-{step:03d}'.format(step=i) + '.pkl'))\n\n X_preprocessed_train_bag = None\n Y_preprocessed_train_bag = None\n svm = None\n\n gc.collect()\n if finish_earlier and i == 10:\n break\n\n print('Finished!\\n')\n\n if not True:\n\n Xts = np.split(X_train[:(batch_size * (estimator_steps - 1) * estimator_step_size)], estimator_steps - 1)\n Xts.append(X_train[(batch_size * (estimator_steps - 1) * estimator_step_size):])\n\n n_est_arange = np.arange(estimator_steps)\n\n Xts = zip(Xts, n_est_arange)\n\n pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()-1)\n pool.map(partial(process_particular_svm, feature_groups=feature_groups, neighbors=neighbors, store_dir=store_dir, model_n=model_n, seed=seed, process_total=estimator_steps), Xts)\n pool.close()\n pool.join()\n\n Xts = None\n gc.collect()\n\n print('Finished!\\n')\n\n random_trees_files = [f for f in listdir(store_dir) if not isfile(f) and f.endswith('.pkl')]\n random_trees_files = natsorted(random_trees_files, key=lambda y: y.lower())\n n_estimators = len(random_trees_files)\n\n steps = len(random_trees_files)\n batch_size = int(X_test.shape[0] / steps) + 1\n\n predicted_test = np.zeros(shape=(X_test.shape[0],), dtype=np.uint8)\n expected_test = np.zeros(shape=(X_test.shape[0],), dtype=np.uint8)\n\n print('Starting testing phase...')\n\n values_votes = np.zeros(shape=(X_test.shape[0], ), dtype=np.float32)\n\n print('Test progress: ', end='')\n\n Xtes = np.split(X_test[:(batch_size * (steps - 1))], steps - 1)\n Xtes.append(X_test[(batch_size * (steps - 1)):])\n\n for i in range(steps):\n print('{0}/{1} - '.format(i + 1, steps), end='')\n\n start = i * batch_size\n end = (i + 1) * batch_size\n end = end if end < X_test.shape[0] else X_test.shape[0]\n for file_i, rf_filename in enumerate(random_trees_files):\n\n rf_full_filename = join(store_dir, rf_filename)\n svm = joblib.load(rf_full_filename)\n\n pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n svm_predictions = np.concatenate(np.array(\n pool.map(partial(predict_particular_batch, feature_groups=feature_groups, neighbors=neighbors, svm=svm), Xtes))[:])\n pool.close()\n pool.join()\n\n if i == 0:\n values_votes = svm_predictions\n else:\n values_votes = np.add(values_votes, svm_predictions)\n\n svm = None\n svm_predictions = None\n gc.collect()\n\n pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n expected_test[start:end] = np.array(pool.map(get_item_gt, Xtes[i]))\n pool.close()\n pool.join()\n\n if not True:\n for i in range(steps):\n print('{0}/{1} - '.format(i + 1, steps), end='')\n start = (i) * batch_size\n end = (i + 1) * batch_size\n end = end if end < X_test.shape[0] else X_test.shape[0]\n\n X_partial_preprocessed_test_bag = np.zeros(shape=(end - start, dataset.shape[1] * feature_groups), dtype=np.float32)\n pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n X_partial_preprocessed_test_bag = np.array(pool.map(partial(get_item_features, feature_groups=feature_groups, neighbors=neighbors), X_test[start:end,:]))\n expected_test[start:end] = np.array(pool.map(get_item_gt, X_test[start:end, :]))\n pool.close()\n pool.join()\n\n for file_i, rf_filename in enumerate(random_trees_files):\n rf_full_filename = join(store_dir, rf_filename)\n svm = joblib.load(rf_full_filename)\n\n if file_i == 0:\n batch_votes = svm.predict(X_partial_preprocessed_test_bag)\n else:\n batch_votes = np.add(batch_votes, svm.predict(X_partial_preprocessed_test_bag))\n\n svm = None\n gc.collect()\n\n values_votes[start:end] = batch_votes\n print('Finished!\\n')\n\n predicted_test = np.divide(values_votes, steps)\n predicted_test[predicted_test<0.5] = 0\n predicted_test[predicted_test>=0.5] = 1\n X_partial_preprocessed_test_bag = None\n batch_votes = None\n values_votes = None\n gc.collect()\n #predicted_test = np.argmax(predicted_test, axis=1)\n\n print('Calculating reports and metrics...')\n\n test_acc = accuracy_score(expected_test, predicted_test)\n print('Test score: {test_acc:.4f}'.format(test_acc=test_acc))\n\n print('Storing value accuracy...')\n metrics_filename = join(store_dir, model_n + '-score_{test_acc:.4f}'.format(test_acc=test_acc) + '.txt')\n\n cm = print_confusion_matrix(expected_test, predicted_test, np.array(['No Forest', 'Forest']))\n\n with open(metrics_filename, 'w') as output:\n output.write(str(cm))\n\n class_report = classification_report(expected_test, predicted_test, target_names=np.array(['no forest', 'forest']))\n\n print(class_report)\n\n with open(metrics_filename, 'a') as output:\n output.write('\\n\\n' + str(class_report))\n\n confmat_file = join(store_dir, model_n + '.conf_mat.npz')\n\n print('Storing confusion matrix...')\n if not exists(confmat_file):\n np.savez_compressed(confmat_file, cm=cm)\n\n print('All done!!!')\n\n\ndef play():\n print('Starting operation of play :)')\n pass\n\n\ndef print_confusion_matrix(y_true, y_pred, classes,\n normalize=False,\n title=None):\n \"\"\"\n This function prints the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n\n # Compute confusion matrix\n cm = confusion_matrix(y_true, y_pred)\n # Only use the labels that appear in the data\n classes = classes[unique_labels(y_true, y_pred)]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n return cm\n\nmain(sys.argv[1:])", "sub_path": "src/esvm_manager.py", "file_name": "esvm_manager.py", "file_ext": "py", "file_size_in_byte": 17346, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "warnings.filterwarnings", "line_number": 23, "usage_type": "call"}, {"api_name": "getopt.getopt", "line_number": 39, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "config.DatasetConfig.DATASET_LST_BANDS_USED", "line_number": 80, "usage_type": "attribute"}, {"api_name": "config.DatasetConfig", "line_number": 80, "usage_type": "name"}, {"api_name": "config.RasterParams.SRTM_MAX_X", "line_number": 80, "usage_type": "attribute"}, {"api_name": "config.RasterParams", "line_number": 80, "usage_type": "name"}, {"api_name": "config.RasterParams.SRTM_MAX_Y", "line_number": 81, "usage_type": "attribute"}, {"api_name": "config.RasterParams", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "config.RasterParams.FNF_MAX_X", "line_number": 82, "usage_type": "attribute"}, {"api_name": "config.RasterParams", "line_number": 82, "usage_type": "name"}, {"api_name": "config.RasterParams.FNF_MAX_Y", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 96, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 103, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 121, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 124, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 179, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 191, "usage_type": "attribute"}, {"api_name": "sklearn.svm.SVC", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 201, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 201, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 212, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 225, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 233, "usage_type": "call"}, {"api_name": "config.NetworkParameters.BATCH_SIZE", "line_number": 246, "usage_type": "attribute"}, {"api_name": "config.NetworkParameters", "line_number": 246, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 262, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 272, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 273, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 274, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 275, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 281, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 285, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 285, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 285, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 302, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 306, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 306, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 307, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 312, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 316, "usage_type": "call"}, {"api_name": "natsort.natsorted", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 323, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 324, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 328, "usage_type": "attribute"}, {"api_name": "numpy.split", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 343, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 344, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 344, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 346, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 347, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 355, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 359, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 361, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 373, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 374, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 374, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 375, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 381, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 382, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 382, "usage_type": "name"}, {"api_name": "numpy.add", "line_number": 387, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 395, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 401, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 410, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 412, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.savez_compressed", "line_number": 428, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 452, "usage_type": "call"}, {"api_name": "sklearn.utils.multiclass.unique_labels", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 456, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 464, "usage_type": "attribute"}]} +{"seq_id": "650187155", "text": "from collections import Counter, defaultdict\nfrom itertools import chain\nimport re\nimport pickle\n\nimport nltk\n\nfrom qanta import qlogging\nfrom qanta.datasets.quiz_bowl import QuestionDatabase\nfrom qanta.wikipedia.cached_wikipedia import CachedWikipedia\nfrom ingestion.title_finder import TitleFinder\nfrom ingestion.page_assigner import PageAssigner\n\nlog = qlogging.get(__name__)\n\nsent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\n\ndef add_question(connection, question_id, tournament, category, page,\n content, answer, ans_type=\"\", naqt=-1, protobowl=\"\", fold=\"train\"):\n c = connection.cursor()\n sentences = list(enumerate(sent_detector.tokenize(content)))\n c.executemany('INSERT INTO text VALUES (?, ?, ?)',\n [(question_id, x, y) for x, y in\n sentences])\n c.execute('INSERT INTO questions VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)',\n (question_id, category, page, answer, tournament, ans_type, naqt, protobowl, fold))\n connection.commit()\n\n return sentences\n\nkPRON = re.compile(r\" \\[[^\\]]*\\] \")\n\n# Protobowl category mapping\nkCATS = set([\"Fine_Arts\", \"History\", \"Literature\", \"Other\", \"Science\", \"Social_Science\"])\nkCAT_MAP = dict((x, x) for x in kCATS)\nkCAT_MAP['Mythology'] = \"Social_Science:Mythology\"\nkCAT_MAP['Philosophy'] = \"Social_Science:Philosophy\"\nkCAT_MAP['Religion'] = \"Social_Science:Religion\"\nkCAT_MAP['Geography'] = \"Social_Science:Geography\"\nkCAT_MAP['Trash'] = \"Other:Trash\"\nkCAT_MAP['Current_Events'] = \"Other:CE\"\n\n# Mapping NAQT categories into Protobowl's\nkNAQT_MAP = {}\nkNAQT_MAP[\"TH\"] = \"Social Studies\"\nkNAQT_MAP[\"SS\"] = \"Social Studies\"\nkNAQT_MAP[\"S:A\"] = \"Physics\"\nkNAQT_MAP[\"S:B\"] = \"Biology\"\nkNAQT_MAP[\"S:C\"] = \"Chemistry\"\nkNAQT_MAP[\"S:CS\"] = \"Mathematics\"\nkNAQT_MAP[\"S:ES\"] = \"Earth Science\"\nkNAQT_MAP[\"S:M\"] = \"Mathematics\"\nkNAQT_MAP[\"S:P\"] = \"Physics\"\nkNAQT_MAP[\"PH\"] = \"Social Studies\"\nkNAQT_MAP[\"L\"] = \"Literature\"\nkNAQT_MAP[\"H\"] = \"History\"\nkNAQT_MAP[\"FA:\"] = \"Fine Arts\"\n\nkYEAR = re.compile(\"[0-9]+\")\n\nkANSWER_PATTERN = [\"\\nanswer:\", \"\\nAnswer:\", \"answer:\", \"Answer:\", \"asnwer:\",\n \"answr:\", \"anwer:\", \"\\nanswer\"]\n\n\nclass NaqtQuestion:\n def __init__(self, raw):\n self.metadata = {}\n if raw.startswith(\"\", 1)[1]\n raw = raw.strip()\n header, rest = raw.split(\">\", 1)\n\n header = header.strip()\n assert header.startswith(\"\" in rest:\n raw, rest = rest.split(\"\", 1)\n\n self.text = \"\"\n self.answer = \"\"\n # Not using a regexp because regexp doesn't have an rsplit\n # command and there is a clear precedence for how acceptable\n # answer patterns are\n for ii in kANSWER_PATTERN:\n if ii in raw:\n self.text, self.answer = raw.rsplit(ii, 1)\n self.answer = self.answer.strip().split('\\n')[0]\n self.text = self.text.strip()\n break\n\n if \"\" in rest:\n packets, topics = rest.split(\"\", 1)\n else:\n packets = \"\"\n topics = rest\n\n self.tournaments = \"|\".join(x for x in packets.split(\"\\n\")\n if x.strip())\n\n if \"\" in rest:\n topics = topics.replace(\"\", \"\").strip()\n self.topics = {}\n for ii in topics.split(\"\\n\"):\n if ii.startswith(\"\") or ii.strip()==\"\":\n continue\n first, rest = ii.split('ID=\"', 1)\n id, rest = rest.split('\" TITLE=\"', 1)\n title, rest = rest.split('\"', 1)\n self.topics[int(id)] = title\n\n def year(self):\n years = []\n\n for ii in [int(x) for x in kYEAR.findall(self.tournaments)]:\n if ii < 40:\n ii += 2000\n\n if ii > 70 and ii < 100:\n ii += 1900\n\n years.append(ii)\n\n\n\n if len(years) == 0:\n log.info(\"Bad year from %s\" % self.tournaments)\n return 0\n elif \"invitational series\" in self.tournaments.lower():\n return years[0] / 10 + 1997\n\n elif len(years) > 2:\n years = years[:2]\n\n\n val = max(years)\n if val > 2016 and \"invitational series\" not in self.tournaments.lower():\n log.info(\"Crazy year %i\" % val)\n return val\n\n @staticmethod\n def map_naqt(old_category):\n val = \"Other\"\n for ii in kNAQT_MAP:\n if old_category == ii or old_category.startswith(\"%s:\" % ii):\n val = kNAQT_MAP[ii]\n if old_category.startswith(\"L:R:\"):\n val = \"Social Studies\"\n if old_category == \"\":\n val = \"\"\n return val\n\n @staticmethod\n def naqt_reader(path):\n import os\n from glob import glob\n if os.path.isdir(path):\n files = glob(\"%s/*\" % path)\n else:\n files = [path]\n\n for ii in files:\n with open(ii, encoding='utf-8') as infile:\n for jj in infile.read().split(\"\"):\n if not 'KIND=\"TOSSUP\"' in jj:\n continue\n\n if \"\", 1)[1].strip()\n\n\n q = NaqtQuestion(jj)\n\n # Exclude computational questions\n if \"SUBJECT\" in q.metadata and q.metadata[\"SUBJECT\"].startswith(\"S:CO:\"):\n continue\n yield q\n\n # TODO: handle NAQT special characters better\n @staticmethod\n def clean_naqt(text):\n if \"\", 1)[1].strip()\n text = kPRON.sub(\" \", text)\n text = text.replace(\"{\", \"\").replace(\"}\", \"\").replace(\" (*) \", \" \")\n text = text.replace(\"~\", \"\")\n return text\n\n\ndef assign_fold(tournament, year):\n # Default assumption is that questions are (guesser) training data\n fold = \"guesstrain\"\n\n # Goal: 70% guess, 20% buzzer, 10% dev/test\n\n tourn = tournament.lower()\n\n # Years get messed up for invitational\n if \"invitational series\" in tourn:\n return \"guesstrain\"\n\n if \"intramurals\" in tourn or \"winter\" in tourn:\n fold = \"guessdev\"\n\n # ACF Fall, PACE, etc. are for training the buzzer\n if \"acf\" in tourn or \"invitational\" in tourn or \"novice\" in tourn:\n fold = \"buzzertrain\"\n\n if \"nasat\" in tourn or \"bowl\" in tourn or \"open\" in tourn:\n fold = \"buzzertrain\"\n\n if \"pace\" in tourn or \"nsc\" in tourn or \"acf fall\" in tourn:\n fold = \"buzzerdev\"\n\n # 2016 hsnct tournaments are dev\n if int(year) >= 2015:\n fold = \"dev\"\n\n if \"high school championship\" in tourn or \"pace\" in tourn or \"nasat\" in tourn:\n fold = \"test\"\n\n return fold\n\n\ndef map_protobowl(category, sub_cat):\n \"\"\"\n Map protobowl categories to our categories\n \"\"\"\n\n category = category.replace(\" \", \"_\")\n if category in kCATS:\n if sub_cat:\n return \"%s:%s\" % (category, sub_cat.replace(\" \", \"_\"))\n else:\n return category\n else:\n return kCAT_MAP[category]\n\n\ndef create_db(location):\n \"\"\"\n Creates an empty QB database in this location, return database pointer\n \"\"\"\n import sqlite3\n conn = sqlite3.connect(location)\n c = conn.cursor()\n\n # Create table\n c.execute('''CREATE TABLE questions\n (id integer PRIMARY KEY, category text, page text,\n answer text, tournament text, type text, naqt integer,\n protobowl text, fold text)''')\n\n c.execute('''CREATE TABLE text\n (question integer, sent integer, raw text,\n foreign key(question) REFERENCES questions(id),\n primary key(question, sent))''')\n\n # Save (commit) the changes\n conn.commit()\n\n return conn\n\n\n\nif __name__ == \"__main__\":\n import argparse\n import datetime\n import os\n from glob import glob\n from nltk.tokenize.treebank import TreebankWordTokenizer\n tk = TreebankWordTokenizer().tokenize\n\n parser = argparse.ArgumentParser(description='Import questions')\n parser.add_argument('--naqt_path', type=str, default='data/questions/naqt/2017')\n parser.add_argument('--protobowl', type=str,\n default='data/questions/protobowl/questions-05-05-2017.json')\n parser.add_argument('--unmapped_report', type=str, default=\"unmapped.txt\")\n parser.add_argument('--ambig_report', type=str, default=\"ambiguous.txt\")\n parser.add_argument('--limit_set', type=str,\n default=\"data/external/wikipedia-titles.pickle\")\n parser.add_argument('--direct_path', type=str,\n default='data/internal/page_assignment/direct/')\n parser.add_argument('--ambiguous_path', type=str,\n default='data/internal/page_assignment/ambiguous/')\n parser.add_argument('--unambiguous_path', type=str,\n default='data/internal/page_assignment/unambiguous/')\n parser.add_argument('--db', type=str, default='data/internal/%s.db' %\n datetime.date.today().strftime(\"%Y_%m_%d\"))\n parser.add_argument('--wiki_title',\n default=\"data/enwiki-latest-all-titles-in-ns0.gz\",\n type=str)\n feature_parser = parser.add_mutually_exclusive_group(required=False)\n feature_parser.add_argument('--guess', dest='guess', action='store_true')\n feature_parser.add_argument('--no-guess', dest='guess', action='store_false')\n parser.set_defaults(guess=True)\n parser.add_argument('--csv_out', default=\"protobowl.csv\", type=str)\n\n flags = parser.parse_args()\n\n if flags.guess:\n log.info(\"Will guess page assignments\")\n else:\n log.info(\"Will not guess page assignments\")\n\n conn = create_db(flags.db)\n\n limit = None\n try:\n limit = pickle.load(open(flags.limit_set, 'rb'))\n except IOError:\n log.info(\"Failed to load limit set from %s\" % flags.limit_set)\n limit = None\n\n # Load page assignment information\n pa = PageAssigner(QuestionDatabase.normalize_answer,\n limit)\n for ii in glob(\"%s/*\" % flags.ambiguous_path):\n pa.load_ambiguous(ii)\n for ii in glob(\"%s/*\" % flags.unambiguous_path):\n pa.load_unambiguous(ii)\n for ii in glob(\"%s/*\" % flags.direct_path):\n pa.load_direct(ii)\n\n ambiguous = defaultdict(dict)\n unmapped = Counter()\n folds = Counter()\n last_id = 0\n num_skipped = 0\n if flags.naqt_path:\n for qq in NaqtQuestion.naqt_reader(flags.naqt_path):\n if not qq.text:\n log.info(\"Bad question %s\" % str(qq.metadata[\"ID\"]))\n num_skipped += 1\n\n page = pa(qq.answer, tk(qq.text), naqt=qq.metadata[\"ID\"])\n fold = assign_fold(qq.tournaments, qq.year())\n if page == \"\":\n norm = QuestionDatabase.normalize_answer(qq.answer)\n folds[fold] += 1\n\n if pa.is_ambiguous(norm):\n ambiguous[norm][int(qq.metadata[\"ID\"])] = qq.text\n else:\n unmapped[norm] += 1\n\n\n add_question(conn, last_id, qq.tournaments,\n NaqtQuestion.map_naqt(qq.metadata[\"SUBJECT\"]),\n page, qq.text, qq.answer, naqt=qq.metadata[\"ID\"],\n fold=fold)\n\n last_id += 1\n\n if last_id % 1000 == 0:\n log.info('{} {} {} {}'.format(last_id,\n qq.answer,\n page,\n qq.text))\n log.info(str(qq.tournaments))\n progress = pa.get_counts()\n for ii in progress:\n log.info(\"MAP %s: %s\" % (ii, progress[ii].most_common(5)))\n for ii in folds:\n log.info(\"NAQT FOLD %s: %i\" % (ii, folds[ii]))\n\n if flags.protobowl:\n with open(flags.protobowl) as infile, open(flags.csv_out, 'w') as outfile:\n import json\n from csv import DictWriter\n o = DictWriter(outfile, [\"id\", \"sent\", \"text\", \"ans\", \"page\", \"fold\"])\n o.writeheader()\n for ii in infile:\n try:\n question = json.loads(ii)\n except ValueError:\n log.info(\"Parse error: %s\" % ii)\n num_skipped += 1\n continue\n\n pid = question[\"_id\"][\"$oid\"]\n ans = question[\"answer\"]\n category = map_protobowl(question['category'],\n question.get('subcategory', ''))\n page = pa(ans, tk(question[\"question\"]), pb=pid)\n fold = assign_fold(question[\"tournament\"],\n question[\"year\"])\n sents = add_question(conn, last_id, question[\"tournament\"], category,\n page, question[\"question\"], ans, protobowl=pid,\n fold=fold)\n\n for ii, ss in sents:\n o.writerow({\"id\": pid,\n \"sent\": ii,\n \"text\": ss,\n \"ans\": ans,\n \"page\": page,\n \"fold\": fold})\n\n if page == \"\":\n norm = QuestionDatabase.normalize_answer(ans)\n if pa.is_ambiguous(norm):\n ambiguous[norm][pid] = question[\"question\"]\n else:\n unmapped[norm] += 1\n else:\n folds[fold] += 1\n last_id += 1\n\n if last_id % 1000 == 0:\n progress = pa.get_counts()\n for ii in progress:\n log.info(\"MAP %s: %s\" % (ii, progress[ii].most_common(5)))\n for ii in folds:\n log.info(\"PB FOLD %s: %i\" % (ii, folds[ii]))\n\n log.info(\"Added %i, skipped %i\" % (last_id, num_skipped))\n\n if flags.guess:\n if not os.path.exists(flags.wiki_title):\n import urllib\n urllib.request.urlretrieve(\"http://dumps.wikimedia.org/enwiki/latest/enwiki-latest-all-titles-in-ns0.gz\",\n flags.wiki_title)\n\n tf = TitleFinder(flags.wiki_title, CachedWikipedia(),\n pa.known_pages(),\n QuestionDatabase.normalize_answer)\n\n guesses = tf.best_guess(unmapped)\n else:\n guesses = dict((x, \"\") for x in unmapped)\n\n wiki_total = Counter()\n wiki_answers = defaultdict(set)\n for ii in guesses:\n page = guesses[ii]\n wiki_total[page] += unmapped[ii]\n wiki_answers[page].add(ii)\n\n for ii in [x for x in unmapped if not x in guesses]:\n wiki_answers[''].add(ii)\n\n with open(flags.unmapped_report, 'w') as outfile:\n for pp, cc in wiki_total.most_common():\n for kk in wiki_answers[pp]:\n if not pa.is_ambiguous(kk):\n # TODO: sort by frequency\n outfile.write(\"%s\\t%s\\t%i\\t%i\\n\" %\n (kk, pp, cc, unmapped[kk]))\n\n with open(flags.ambig_report, 'w') as outfile:\n for aa in sorted(ambiguous, key=lambda x: len(ambiguous[x]), reverse=True):\n for ii in ambiguous[aa]:\n outfile.write(\"%s\\t%s\\t%s\\n\" % (str(ii), aa, ambiguous[aa][ii]))\n", "sub_path": "ingestion/create_db.py", "file_name": "create_db.py", "file_ext": "py", "file_size_in_byte": 16078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "qanta.qlogging.get", "line_number": 14, "usage_type": "call"}, {"api_name": "qanta.qlogging", "line_number": 14, "usage_type": "name"}, {"api_name": "nltk.data.load", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.data", "line_number": 16, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 31, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 166, "usage_type": "call"}, {"api_name": "{'os': 'os', 'glob': 'glob.glob'}", "line_number": 180, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 253, "usage_type": "call"}, {"api_name": "nltk.tokenize.treebank.TreebankWordTokenizer", "line_number": 280, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 282, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 297, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 318, "usage_type": "call"}, {"api_name": "ingestion.page_assigner.PageAssigner", "line_number": 324, "usage_type": "call"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase.normalize_answer", "line_number": 324, "usage_type": "attribute"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase", "line_number": 324, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 326, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 328, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 330, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 333, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 334, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 335, "usage_type": "call"}, {"api_name": "{'os': 'os', 'glob': 'glob.glob'}.naqt_reader", "line_number": 339, "usage_type": "call"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase.normalize_answer", "line_number": 347, "usage_type": "call"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase", "line_number": 347, "usage_type": "name"}, {"api_name": "{'os': 'os', 'glob': 'glob.glob'}.map_naqt", "line_number": 357, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 379, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 383, "usage_type": "call"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase.normalize_answer", "line_number": 409, "usage_type": "call"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase", "line_number": 409, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path", "line_number": 428, "usage_type": "attribute"}, {"api_name": "urllib.request.urlretrieve", "line_number": 430, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 430, "usage_type": "attribute"}, {"api_name": "ingestion.title_finder.TitleFinder", "line_number": 433, "usage_type": "call"}, {"api_name": "qanta.wikipedia.cached_wikipedia.CachedWikipedia", "line_number": 433, "usage_type": "call"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase.normalize_answer", "line_number": 435, "usage_type": "attribute"}, {"api_name": "qanta.datasets.quiz_bowl.QuestionDatabase", "line_number": 435, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 441, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 442, "usage_type": "call"}]} +{"seq_id": "629749439", "text": "import numpy as np\nimport cv2\n\n#BackgroundSubtractorMOG2\n#opencv自带的一个视频\ncamera = cv2.VideoCapture(0)\n#创建一个3*3的椭圆核\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n#创建BackgroundSubtractorMOG2\nfgbg = cv2.createBackgroundSubtractorMOG2()\n\nwhile(1):\n ret, frame = camera.read()\n fgmask = fgbg.apply(frame)\n #形态学开运算去噪点\n fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)\n #寻找视频中的轮廓\n im, contours, hierarchy = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n for c in contours:\n #计算各轮廓的周长\n perimeter = cv2.arcLength(c,True)\n if perimeter > 188:\n #找到一个直矩形（不会旋转）\n x,y,w,h = cv2.boundingRect(c)\n #画出这个矩形\n cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)\n\n cv2.imshow('frame',frame)\n cv2.imshow('fgmask', fgmask)\n k = cv2.waitKey(30) & 0xff\n if k == 27:\n break\n\ncamera.release()\ncv2.destroyAllWindows()", "sub_path": "读书笔记《opencv3 计算机视觉 python语言实现》/第4章深度估计与分割/vedio_test.py", "file_name": "vedio_test.py", "file_ext": "py", "file_size_in_byte": 1059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.MORPH_ELLIPSE", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.createBackgroundSubtractorMOG2", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.arcLength", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "491968722", "text": "import sys,os,logging,time,json\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom logging import handlers\nlogger_draw = logging.getLogger(\"draw_log\")\nlogger_draw.setLevel(logging.DEBUG)\nfile_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nfh = handlers.RotatingFileHandler(filename=r\"%s\\log\\draw.log\" %file_path,\n maxBytes=1000000,backupCount=30,\n encoding=\"utf8\")\nformatter_fh = logging.Formatter('%(asctime)s %(levelname)s %(filename)s %(name)s %(lineno)d %(message)s')\nfh.setFormatter(formatter_fh)\nlogger_draw.addHandler(fh)\n\ndef draw_money(user):\n while True:\n sum_money = input(\"输入提取现金额度：\")\n logger_draw.info(\"开始提现操作。\")\n file_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n with open(r\"%s\\datebase\\user_date\" % file_path, \"r\") as user_date:\n remaining_money = json.load(user_date)\n if sum_money == \"q\" :\n logger_draw.info(\"退出提现服务。\")\n break\n elif int(sum_money) <= remaining_money[user] :\n brokerage = int(sum_money) * 0.05\n sum_money = int(sum_money) - brokerage\n remaining_money = int(remaining_money[user]) - int(sum_money)\n print(\"提现成功!\\n\\033[31;1m提现金额：%s\\n手续费：%s\\n剩余金额：%s\\033[0m\" %(sum_money,brokerage,remaining_money))\n logger_draw.info(\"提现成功。\")\n print(\"任意键继续,或按q退出！\")\n a = input()\n if a.upper() == \"Q\":\n break\n else:\n continue\n elif int(sum_money) > remaining_money[user] :\n print(\"\\033[41;1m余额不足，无法提现，任意键继续,或按q退出！\\033[0m\")\n logger_draw.error(\"余额不足，无法提现。\")\n a = input()\n if a.upper() == \"Q\":\n logger_draw.info(\"退出提现服务。\")\n break\n else:\n continue", "sub_path": "第二模块/作业/ATM/core/draw.py", "file_name": "draw.py", "file_ext": "py", "file_size_in_byte": 2093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 2, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 7, "usage_type": "name"}, {"api_name": "logging.Formatter", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "615994460", "text": "import json\n\nimport pytest\nimport requests\nfrom flexmock import flexmock\n\nfrom eduxator import io\n\n\nclass TestEduxIO():\n\n def good_data(self, filename):\n with open('test/files/' + filename + '.json') as data_file:\n data = json.load(data_file)\n return data\n\n def fake_response(self, filename, url=''):\n with open('test/files/' + filename + '.html') as f:\n text = f.read()\n return flexmock(text=text, cookies=flexmock(get_dict=lambda: {}), url=url)\n\n def test_parsing_form(self):\n url = 'https://edux.fit.cvut.cz/courses/BI-3DT/' + \\\n 'classification/view/fulltime/tutorials/3?do=edit'\n flexmock(io.EduxIO).should_receive('get').with_args(url).once().and_return(\n self.fake_response('form', url=url))\n e = io.EduxIO(cookie_dict={})\n e.course = 'BI-3DT'\n e.classpath = ('fulltime', 'tutorials', '3')\n data = e.parse_form_edit_score()\n for key, value in self.good_data('form').items():\n assert data[key] == value\n assert io.EduxIO.all_usernames(data) == set(self.good_data('usernames'))\n assert io.EduxIO.all_columns(data) == set(self.good_data('columns'))\n\n def test_sending_form(self):\n url = 'https://edux.fit.cvut.cz/courses/BI-3DT/classification/view/fulltime/tutorials/3'\n flexmock(io.EduxIO).should_receive('post').with_args(url, {}).once()\n e = io.EduxIO(cookie_dict={})\n e.course = 'BI-3DT'\n e.classpath = ('fulltime', 'tutorials', '3')\n e.submit_form_edit_score({})\n\n def test_parsing_courses(self):\n flexmock(requests).should_receive('get').once().and_return(self.fake_response('courses'))\n e = io.EduxIO(cookie_dict={})\n assert sorted(self.good_data('courses')) == sorted(e.parse_courses_list())\n\n @pytest.mark.parametrize('course', ('BI-3DT', 'BI-3DT.1'))\n def test_parsing_calssification(self, course):\n url = 'https://edux.fit.cvut.cz/courses/{}/classification/view/start'\n url1 = url.format(course)\n url2 = url.format('BI-3DT')\n flexmock(io.EduxIO).should_receive('get').with_args(url1).once().and_return(\n self.fake_response('classification', url=url2))\n e = io.EduxIO(cookie_dict={})\n e.course = course\n tree = e.parse_classification_tree()\n assert e.course == 'BI-3DT'\n", "sub_path": "test/test_io.py", "file_name": "test_io.py", "file_ext": "py", "file_size_in_byte": 2383, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "flexmock.flexmock", "line_number": 20, "usage_type": "call"}, {"api_name": "flexmock.flexmock", "line_number": 25, "usage_type": "call"}, {"api_name": "eduxator.io.EduxIO", "line_number": 25, "usage_type": "attribute"}, {"api_name": "eduxator.io", "line_number": 25, "usage_type": "name"}, {"api_name": "eduxator.io.EduxIO", "line_number": 27, "usage_type": "call"}, {"api_name": "eduxator.io", "line_number": 27, "usage_type": "name"}, {"api_name": "eduxator.io.EduxIO.all_usernames", "line_number": 33, "usage_type": "call"}, {"api_name": "eduxator.io.EduxIO", "line_number": 33, "usage_type": "attribute"}, {"api_name": "eduxator.io", "line_number": 33, "usage_type": "name"}, {"api_name": "eduxator.io.EduxIO.all_columns", "line_number": 34, "usage_type": "call"}, {"api_name": "eduxator.io.EduxIO", "line_number": 34, "usage_type": "attribute"}, {"api_name": "eduxator.io", "line_number": 34, "usage_type": "name"}, {"api_name": "flexmock.flexmock", "line_number": 38, "usage_type": "call"}, {"api_name": "eduxator.io.EduxIO", "line_number": 38, "usage_type": "attribute"}, {"api_name": "eduxator.io", "line_number": 38, "usage_type": "name"}, {"api_name": "eduxator.io.EduxIO", "line_number": 39, "usage_type": "call"}, {"api_name": "eduxator.io", "line_number": 39, "usage_type": "name"}, {"api_name": "flexmock.flexmock", "line_number": 45, "usage_type": "call"}, {"api_name": "eduxator.io.EduxIO", "line_number": 46, "usage_type": "call"}, {"api_name": "eduxator.io", "line_number": 46, "usage_type": "name"}, {"api_name": "flexmock.flexmock", "line_number": 54, "usage_type": "call"}, {"api_name": "eduxator.io.EduxIO", "line_number": 54, "usage_type": "attribute"}, {"api_name": "eduxator.io", "line_number": 54, "usage_type": "name"}, {"api_name": "eduxator.io.EduxIO", "line_number": 56, "usage_type": "call"}, {"api_name": "eduxator.io", "line_number": 56, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "374776634", "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: /home/jovyan/libpysal/examples/geodadata.py\n# Compiled at: 2019-11-18 22:46:38\n# Size of source mod 2**32: 2239 bytes\nimport requests\nfrom bs4 import BeautifulSoup\n\ndef type_of_script():\n \"\"\"Helper function to determine run context\"\"\"\n try:\n ipy_str = str(type(get_ipython()))\n if 'zmqshell' in ipy_str:\n return 'jupyter'\n if 'terminal' in ipy_str:\n return 'ipython'\n except:\n return 'terminal'\n\n\nclass Dataset:\n\n def __init__(self, name, description, n, k, download_url, explain_url):\n self.name = name\n self.description = description\n self.n = n\n self.k = k\n self.download_url = download_url\n self.explain_url = explain_url\n self.dir = name.replace(' ', '_')\n\n def __repr__(self):\n return '%s' % self.description\n\n def explain(self, width=700, height=350):\n \"\"\"Describe the dataset\n \"\"\"\n page = requests.get(self.explain_url)\n soup = BeautifulSoup(page.text, 'html.parser')\n text = soup.get_text(' ')\n trim_idx = text.index('DOWNLOAD DATA')\n text = text[trim_idx + len('DOWNLOAD DATA'):]\n self.text = text.replace('\\xa0', ' ')\n if type_of_script() == 'jupyter':\n from IPython.display import IFrame\n return IFrame((self.explain_url), width=width, height=height)\n print(self.text)\n\n def json_dict(self):\n d = {}\n d['name'] = self.name\n d['description'] = self.description\n d['download_url'] = self.download_url\n d['explain_url'] = self.explain_url\n d['dir'] = self.dir\n return d\n\n\nurl = 'https://geodacenter.github.io/data-and-lab//'\npage = requests.get(url)\nsoup = BeautifulSoup(page.text, 'html.parser')\nsamples = soup.find(class_='samples')\nrows = samples.find_all('tr')\ndatasets = {}\nfor row in rows[1:]:\n data = row.find_all('td')\n name = data[0].text.strip()\n description = data[1].text\n n = data[2].text\n k = data[3].text\n targets = row.find_all('a')\n download_url = targets[1].attrs['href']\n explain_url = targets[0].attrs['href']\n datasets[name] = Dataset(name, description, n, k, download_url, explain_url)", "sub_path": "pycfiles/libpysal-4.2.2.tar/geodadata.cpython-37.py", "file_name": "geodadata.cpython-37.py", "file_ext": "py", "file_size_in_byte": 2369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 41, "usage_type": "call"}, {"api_name": "IPython.display.IFrame", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 62, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 63, "usage_type": "call"}, {"api_name": "{'IFrame': 'IPython.display.IFrame'}", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "529280814", "text": "import random\nimport sys\nimport numpy as np\nfrom scipy.stats import pearsonr\nfrom sklearn.decomposition import TruncatedSVD \nfrom sklearn.metrics import r2_score\nfrom sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC, LinearSVC, SVR\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_selection import chi2, SelectKBest\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn import preprocessing\nfrom model import Comment, Submission, UserGuess\nfrom groups import calc_target, filter_groups\nimport neurolab as nl\nfrom functools import reduce\n\n\n\nclass NeuralNetWrap:\n\n def fit(self, X, y):\n num_inputs = X.shape[1]\n target = np.array([y]).T\n self.net = nl.net.newff([[-5, 5]]*num_inputs, [20, 1])\n return self.net.train(X, target)\n\n\n def predict(self, X):\n return self.net.sim(X)\n\n\n def score(self, X, target):\n out = self.predict(X)\n return r2_score(target, out)\n\n\ndef create_row(comment):\n text = comment.body\n upvotes = comment.upvotes\n time_diff = (comment.created_utc - comment.submission.created_utc).total_seconds()\n parent_votes = None\n parent_text = None\n if comment.parent != None:\n parent_text = comment.parent.body\n parent_votes = comment.parent.upvotes\n else:\n parent_text = comment.submission.title\n parent_votes = comment.submission.upvotes\n data = [time_diff, parent_votes, upvotes]\n corpus = [text, parent_text]\n return (data, corpus)\n\ndef get_static_data():\n return (np.load(\"raw_data.npy\"), np.load(\"corpuses.npy\"))\n\ndef get_data():\n query = Comment.select()\n num_data = []\n corpuses = []\n for comment in query:\n data, corpus = create_row(comment) \n num_data.append(data)\n corpuses.append(corpus)\n return (np.array(num_data), np.array(corpuses))\n\n\ndef training_comments(test_ids, limit=None):\n test_set = set(test_ids)\n comments = Comment.select().join(Submission, on=(Comment.submission == Submission.s_id))\n return filter(lambda comment: comment.c_id not in test_set, comments)\n\n\ndef data_split(num_train, num_test):\n test_comments = [ug.comment for ug in UserGuess.select().join(Comment, on=(Comment.c_id == UserGuess.comment)).join(Submission, on=(Comment.submission == Submission.s_id))]\n train_comments = training_comments((comment.c_id for comment in test_comments))\n tr_num_data, te_num_data = [], []\n tr_corpuses, te_corpuses = [], []\n comments_read = set()\n\n for comment in train_comments:\n comments_read.add(comment.c_id)\n data, corpus = create_row(comment) \n tr_num_data.append(data)\n tr_corpuses.append(corpus)\n\n tr_num_data = random.sample(tr_num_data, num_train)\n tr_corpuses = random.sample(tr_corpuses, num_train)\n\n for comment in test_comments:\n if comment.c_id not in comments_read:\n comments_read.add(comment.c_id)\n data, corpus = create_row(comment) \n te_num_data.append(data)\n te_corpuses.append(corpus)\n\n te_num_data = random.sample(te_num_data, num_test)\n te_corpuses = random.sample(te_corpuses, num_test)\n res_num_data = np.vstack((tr_num_data, te_num_data))\n res_corpuses = np.vstack((tr_corpuses, te_corpuses))\n \n return (res_num_data, res_corpuses)\n\n\ndef extract_features(rows, corpuses, calc_target=calc_target, vectorizer = CountVectorizer()):\n vectorizer.fit(corpuses[:,0])\n vectorizer.fit(corpuses[:,1])\n #the count matrix for the comment corpus\n c_counts = vectorizer.transform(corpuses[:,0])\n #the count matrix for the corpus of the parent comment/submission\n p_counts = vectorizer.transform(corpuses[:,1])\n #create a row vector of similarities between rows in comment and p_counts\n similarity = np.array( [cosine_similarity(c_counts[i:i+1], p_counts[i:i+1])[0] for i in range(c_counts.shape[0])] )\n rshape = lambda x: np.reshape(x, (x.shape[0], 1))\n r0 = rshape(rows[:,0])\n #r1 = rshape(rows[:,1])\n return( preprocessing.scale(np.hstack( [r0, similarity] )), calc_target(rows[:,2]) )\n\ndef extract_raw_count(corpuses, vectorizer = CountVectorizer()):\n result = vectorizer.fit_transform(corpuses[:,0])\n return (result, vectorizer)\n\ndef extract_features2(rows, corpuses, calc_target=calc_target, vectorizer = CountVectorizer(), n_components=15):\n vectorizer.fit(corpuses[:,0])\n #the count matrix for the comment corpus\n c_counts = vectorizer.transform(corpuses[:,0])\n svd = TruncatedSVD(n_components = n_components)\n return( preprocessing.scale(svd.fit_transform(c_counts)), calc_target(rows[:,2]) )\n\ndef extract_features3(rows, corpuses, calc_target=calc_target, vectorizer = CountVectorizer(), n_components=15):\n vectorizer.fit(corpuses[:,0])\n #the count matrix for the comment corpus\n c_counts = vectorizer.transform(corpuses[:,0])\n ch2 = SelectKBest(chi2, k = n_components)\n target = calc_target(rows[:,2])\n return( ch2.fit_transform(c_counts, target).toarray(), target )\n\n\ndef run_tests(train_data, train_target, test_data, test_target, clfs, num_tr=0, print_res=True):\n result = {}\n for name, clf in clfs.items():\n clf.fit(train_data, train_target)\n result[name] = (clf.score(test_data, test_target), clf.score(train_data, train_target))\n if print_res: print(\"{0}: {1}\".format(name, result[name]))\n return result\n\n\ndef test_clf(raw_data, corpuses, gen_clf, ext_feat, num_train=1000, num_test=100, draw_ds=True, no_y=False):\n xx, yy = np.meshgrid(np.arange(1,100, 3), np.arange(1,100, 3))\n xs = xx.ravel() if not no_y else np.arange(1, 100, 3)\n ys = yy.ravel() if not no_y else np.arange(1, 100, 3)\n zs = []\n clfs = []\n print(len(xs))\n count = 0\n for x, y in zip(xs, ys):\n count+=1\n data, target = ext_feat(x, y)(raw_data, corpuses)\n votes = raw_data[:,2]\n train_data, train_target = data[:num_train], target[:num_train]\n test_data, test_target = data[-num_test:], target[-num_test:]\n train_votes, test_votes = raw_data[:num_train][:,2], raw_data[-num_test:][:,2]\n clf = gen_clf(x, y)\n clf.fit(train_data, train_target)\n clfs.append(clf)\n zs.append(clf.score(test_data, test_target))\n max_i = np.argmax(zs)\n min_i = np.argmin(zs)\n if draw_ds:\n data, target = ext_feat(xs[max_i], ys[max_i])(raw_data, corpuses)\n data, target = ext_feat(xs[min_i], ys[min_i])(raw_data, corpuses)\n plot_dc(clfs[min_i], data[:,[0, 1]], target)\n print(zs[max_i])\n print(zs[min_i])\n return (xs, ys, np.array(zs))\n\n\ndef test_knn_bow(raw_data, corpuses, weights=\"uniform\", extract_f=extract_features2):\n gen_clf = lambda x, y: KNeighborsClassifier(weights=weights, n_neighbors=x)\n def ext_feat(x, y):\n return lambda raw_data, corpuses: extract_f(raw_data, corpuses, n_components=y)\n xs, ys, zs = test_clf(raw_data, corpuses, gen_clf, ext_feat, draw_ds=False)\n return xs, ys, zs\n\ndef test_knn_alt(raw_data, corpuses, weights=\"uniform\"):\n\n gen_clf = lambda x, y: KNeighborsClassifier(weights=weights, n_neighbors=x)\n def ext_feat(x, y):\n return lambda raw_data, corpuses: extract_features(raw_data, corpuses)\n xs, ys, zs = test_clf(raw_data, corpuses, gen_clf, ext_feat, draw_ds=False, no_y=True)\n return xs, zs\n\n\ndef test_svm_bow(raw_data, corpuses, extract_f=extract_features2, is_lin=False):\n gen_clf = lambda x, y: LinearSVC(C=0.1*x) if is_lin else SVC(C=0.1*x)\n def ext_feat(x, y):\n return lambda raw_data, corpuses: extract_f(raw_data, corpuses, n_components=y)\n return test_clf(raw_data, corpuses, gen_clf, ext_feat, draw_ds=False)\n\ndef test_svm_alt(raw_data, corpuses, is_lin=False):\n gen_clf = lambda x, y: LinearSVC(C=0.1*x) if is_lin else SVC(C=0.1*x)\n def ext_feat(x, y):\n return lambda raw_data, corpuses: extract_features(raw_data, corpuses)\n xs, ys, zs = test_clf(raw_data, corpuses, gen_clf, ext_feat, draw_ds=False, no_y=True)\n return xs, zs\n\n\ndef plot_surface(xs, ys, zs, xlabel=\"\", ylabel=\"\", zlabel=\"\"):\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.plot_surface(xs, ys, zs)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n ax.set_zlabel(zlabel)\n plt.show()\n\ndef bar_char_words(data, words):\n #reduces over each column adding 1 to the accumulator if the value for that component > 0. \n num_nonzero = [reduce(lambda acc, val: acc+1 if val > 0 else acc, data[:,i]) for i in range(data.shape[1])]\n\n #a function which returns the nonzero indexes for a column\n nn_func = lambda col: {i for i in range(len(col)) if col[i] > 0}\n nn_cols = (nn_func(data[:,i]) for i in range(data.shape[1]))\n nonzeros_indexes = reduce(lambda acc, inds: acc | inds, nn_cols)\n print(\"num components covered: {0}\".format(len(nonzeros_indexes)))\n \n\ndef tocolspace(Z) :\n return ((Z - 1)*2)**2\n\n\ndef get_dc(clf, data, target):\n target = np.array(target)\n x_min, x_max = data[:, 0].min() - 1, data[:, 0].max() + 1\n y_min, y_max = data[:, -1].min() - 1, data[:, -1].max() + 1\n hx, hy = float(x_max - x_min)/100, float(y_max - y_min)/100\n xx, yy = np.meshgrid(np.arange(x_min, x_max, hx), np.arange(y_min, y_max, hy))\n\n npc = np.c_[xx.ravel(), yy.ravel()]\n Z = clf.predict(npc)\n Z = Z.reshape(xx.shape)\n Z = tocolspace(Z)\n return xx, yy, Z\n\ndef plot_reg(clf, data, votes, xlabel=\"\", ylabel=\"\", zlabel=\"\", zlim=1000):\n xx, yy, Z = get_dc(clf, data, votes)\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.plot_surface(xx, yy, Z)\n ax.scatter(data[:,0], data[:,1], votes, c=\"green\")\n ax.set_zlim([0, zlim])\n ax.set_ylabel(ylabel)\n ax.set_zlabel(zlabel)\n ax.set_xlabel(xlabel)\n plt.show()\n\ndef plot_dc(clf, data, target):\n xx, yy, Z = get_dc(clf, data, target)\n\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n tocol = {1: 0, 2: 4, 3: 16}\n target = tocolspace(target)\n plt.figure(1)\n plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n #red=1(0), lightblue=2(4), green=3(16)\n plt.scatter(data[:,0], data[:,-1], c=target, cmap=plt.cm.Paired)\n plt.figure(2)\n plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n #red=1(0), lightblue=2(4), green=3(16)\n plt.show()\n\n\ndef plot_accuracy(test_result):\n import matplotlib\n matplotlib.use(\"Agg\")\n\n import matplotlib.pyplot as plt\n colors = \"bgrcmykw\"\n for name, val in test_result.items():\n plt.plot(range(10, 0), test_result[name], color=colors.pop())\n handles, labels = plt.get_legend_handles_labels()\n plt.legend(handles, labels)\n plt.savefig(\"out.svg\")\n\n\n\ndef genclfs():\n return {\n \"knn\" : KNeighborsClassifier(), \n \"knn weighted\": KNeighborsClassifier(weights=\"distance\"),\n \"Gaussian Naive Bayes\": GaussianNB(), \n \"SVM\": SVC(), \n #\"Neural Net\": NeuralNetWrap(),\n \"Linear SVM\": LinearSVC()\n }\n\n\ndef genregs():\n return {\n \"least squares\": LinearRegression(),\n \"Knearest regression\": KNeighborsRegressor(),\n \"SVM regression\": SVR()\n }\n\n\ndef tostdform(num):\n n, exp = str(num).split('e')\n return \"${0} \\\\times 10^{{{1}}}$\".format(n[:4], exp)\n\ndef test_pearsonr(data_col, votes):\n pm, pval = pearsonr(data_col, votes)\n\ndef test_chi2(raw_counts, target, vectorizer):\n chi2_stat, pval = chi2(raw_counts, target)\n chi_indexes = sorted(range(len(pval)), key=lambda i: pval[i])\n word_map = np.array(vectorizer.get_feature_names())\n\n\ndef main2():\n raw_data, corpuses = get_static_data()\n test_knn_bow(raw_data, corpuses)\n\n\ndef main():\n num_train = 1000\n num_test = 100\n raw_data, corpuses = get_static_data()\n data, target = extract_features(raw_data, corpuses)\n raw_counts, vectorizer = extract_raw_count(corpuses)\n votes = raw_data[:,2]\n train_data, train_target = data[:num_train], target[:num_train]\n test_data, test_target = data[-num_test:], target[-num_test:]\n train_votes, test_votes = raw_data[:num_train][:,2], raw_data[-num_test:][:,2]\n\n total_neg = float( len([x for x in test_target if x == 1]) )\n prior_prob = total_neg/float( len(test_target) )\n print(\"prior probability: {0}\".format(prior_prob))\n\n clfs = genclfs()\n regs = genregs()\n nn = {\"neuralnet\": NeuralNetWrap() }\n run_tests(train_data, train_target, test_data, test_target, clfs)\n #run_tests(train_data, train_votes, test_data, test_votes, nn)\n\n\n\n \n \n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "src/quiz/server/classifier2.py", "file_name": "classifier2.py", "file_ext": "py", "file_size_in_byte": 12891, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "neurolab.net.newff", "line_number": 27, "usage_type": "call"}, {"api_name": "neurolab.net", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 57, "usage_type": "call"}, {"api_name": "model.Comment.select", "line_number": 60, "usage_type": "call"}, {"api_name": "model.Comment", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "model.Submission", "line_number": 72, "usage_type": "argument"}, {"api_name": "model.Comment.select", "line_number": 72, "usage_type": "call"}, {"api_name": "model.Comment", "line_number": 72, "usage_type": "name"}, {"api_name": "model.Comment.submission", "line_number": 72, "usage_type": "attribute"}, {"api_name": "model.Submission.s_id", "line_number": 72, "usage_type": "attribute"}, {"api_name": "model.Submission", "line_number": 77, "usage_type": "argument"}, {"api_name": "model.Comment", "line_number": 77, "usage_type": "argument"}, {"api_name": "model.UserGuess.select", "line_number": 77, "usage_type": "call"}, {"api_name": "model.UserGuess", "line_number": 77, "usage_type": "name"}, {"api_name": "model.Comment.c_id", "line_number": 77, "usage_type": "attribute"}, {"api_name": "model.UserGuess.comment", "line_number": 77, "usage_type": "attribute"}, {"api_name": "model.Comment.submission", "line_number": 77, "usage_type": "attribute"}, {"api_name": "model.Submission.s_id", "line_number": 77, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 89, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 90, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 99, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 102, "usage_type": "call"}, {"api_name": "groups.calc_target", "line_number": 107, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 119, "usage_type": "name"}, {"api_name": "numpy.hstack", "line_number": 119, "usage_type": "call"}, {"api_name": "groups.calc_target", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 121, "usage_type": "call"}, {"api_name": "groups.calc_target", "line_number": 125, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 125, "usage_type": "call"}, {"api_name": "sklearn.decomposition.TruncatedSVD", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 130, "usage_type": "name"}, {"api_name": "groups.calc_target", "line_number": 130, "usage_type": "call"}, {"api_name": "groups.calc_target", "line_number": 132, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectKBest", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.chi2", "line_number": 136, "usage_type": "argument"}, {"api_name": "groups.calc_target", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 181, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 189, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 203, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 223, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 243, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 271, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 273, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 275, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.use", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_legend_handles_labels", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 296, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 297, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 298, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 299, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 301, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 307, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsRegressor", "line_number": 308, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 309, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 318, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.chi2", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 323, "usage_type": "call"}]} +{"seq_id": "474362698", "text": "import os\nfrom os.path import join as pjoin, abspath, dirname\nfrom django.conf import settings\n\n\nJSTOOLS_CLOSURE_COMPILER = abspath(\n pjoin(dirname(__file__),\n os.pardir,\n 'compiler',\n 'compiler.jar'\n )\n)\n\nJSTOOLS_NAMESPACE = 'JSTOOLS'\nJSTOOLS_TMPDIR = '/tmp/jstools/'\n\nif hasattr(settings, 'STATIC_URL'):\n STATIC_URL = settings.STATIC_URL\nelse:\n STATIC_URL = settings.MEDIA_URL\n\nif hasattr(settings, 'STATIC_ROOT'):\n STATIC_ROOT = settings.STATIC_ROOT\nelse:\n STATIC_ROOT = settings.MEDIA_ROOT\n\n", "sub_path": "jstools/conf/defaults.py", "file_name": "defaults.py", "file_ext": "py", "file_size_in_byte": 522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "196810716", "text": "import Tkinter\nimport time\nimport struct\nimport serial\nimport Queue\nimport threading\nimport os.path\nimport haptic_graphing_functions\nimport read_and_write_data_threads\nfrom enum import Enum\n\nser = serial.Serial ('COM13', 115200)\ncurrently_reading = False\n\nclass graph_type(Enum):\n accelerations = 0\n rotations = 1\n temperature = 2\n pitch_roll = 3\n ard_pitch_roll = 4\n \n\nclass gui(Tkinter.Tk):\n \n def __init__(self,parent):\n \n Tkinter.Tk.__init__(self,parent)\n self.parent = parent\n\n self.initialize()\n \n def initialize(self):\n self.minsize(width=300, height=300)\n self.grid()\n\n row_number = 0\n # Initial message\n Label = Tkinter.Label(self, text='Read data from Arduino')\n Label.grid(column=1, row=row_number)\n row_number+= 1\n \n Label = Tkinter.Label(self, text='Time to read(minutes):')\n Label.grid(column=0, row=row_number)\n read_time = Tkinter.Entry(self)\n read_time.focus_set()\n read_time.grid(column=1, row=row_number)\n \n file_name = Tkinter.Entry(self)\n file_name.focus_set()\n file_name.grid(column=1, row=row_number+1)\n \n \n # Start/Stop Buttons\n button = Tkinter.Button(self, text=\"Start Reading!\",\n command=lambda : self.time_reading_button_click(read_time.get(), file_name))\n button.grid(column=2, row=row_number)\n row_number+= 1\n \n self.bind('', lambda event : self.time_reading_button_click(read_time.get(), file_name))\n \n Label = Tkinter.Label(self, text='Filename:')\n Label.grid(column=0, row=row_number) \n row_number+= 1\n \n \n button = Tkinter.Button(self, text=\"Graph Accelerations\",\n command=lambda : self.graph_data(0, file_name))\n button.grid(column=0, row=row_number)\n \n button = Tkinter.Button(self, text=\"Graph Angular Accelerations\",\n command=lambda : self.graph_data(1, file_name))\n button.grid(column=1, row=row_number)\n\n button = Tkinter.Button(self, text=\"Graph Temperature\",\n command=lambda : self.graph_data(2, file_name))\n button.grid(column=2, row=row_number)\n row_number+= 1\n \n button = Tkinter.Button(self, text=\"Graph Pitch/Roll\",\n command=lambda : self.graph_data(3, file_name))\n button.grid(column=0, row=row_number)\n \n button = Tkinter.Button(self, text=\"Graph Arduino Pitch/Roll\",\n command=lambda : self.graph_data(4, file_name))\n button.grid(column=1, row=row_number)\n \n button = Tkinter.Button(self, text=\"Live Graphing\",\n command=lambda : self.start_reading_button_click(read_time.get(), file_name))\n button.grid(column=2, row=row_number)\n row_number+= 1\n \n Label = Tkinter.Label(self, text=' ')\n Label.grid(column=1, row=row_number)\n row_number+= 1\n \n Label = Tkinter.Label(self, text='Write data to Arduino')\n Label.grid(column=1, row=row_number)\n row_number+= 1\n # Variables to be sent to arduino\n Label = Tkinter.Label(self, text='Variable 1:')\n Label.grid(column=0, row=row_number)\n var1Text = Tkinter.Entry(self)\n var1Text.focus_set()\n var1Text.grid(column=1, row=row_number)\n row_number+= 1\n \n Label = Tkinter.Label(self, text='Variable 2:')\n Label.grid(column=0, row=row_number)\n var2Text = Tkinter.Entry(self)\n var2Text.focus_set()\n var2Text.grid(column=1, row=row_number)\n row_number+= 1\n \n Label = Tkinter.Label(self, text='Variable 3:')\n Label.grid(column=0, row=row_number)\n var3Text = Tkinter.Entry(self)\n var3Text.focus_set()\n var3Text.grid(column=1, row=row_number)\n row_number+= 1\n \n # Write Variables Button\n button = Tkinter.Button(self, text=\"Write Values\",\n command=lambda : self.write_values(var1Text, var2Text, var3Text))\n button.grid(column=1, row=row_number)\n row_number+= 1\n \n # Offsets of sensors to be sent to Arduino\n Label = Tkinter.Label(self, text='Write Sensor ID to update offsets')\n Label.grid(column=1, row=row_number)\n row_number+= 1\n \n Label = Tkinter.Label(self, text='Right Shoulder:')\n Label.grid(column=0, row=row_number)\n right_shoulder_offset = Tkinter.Entry(self)\n right_shoulder_offset.focus_set()\n right_shoulder_offset.grid(column=1, row=row_number)\n row_number+= 1\n\n Label = Tkinter.Label(self, text='Left Shoulder:')\n Label.grid(column=0, row=row_number)\n left_shoulder_offset = Tkinter.Entry(self)\n left_shoulder_offset.focus_set()\n left_shoulder_offset.grid(column=1, row=row_number)\n row_number+= 1\n\n Label = Tkinter.Label(self, text='Belt Buckle:')\n Label.grid(column=0, row=row_number)\n belt_buckle_offset = Tkinter.Entry(self)\n belt_buckle_offset.focus_set()\n belt_buckle_offset.grid(column=1, row=row_number)\n row_number+= 1\n\n # Write Offsets Button\n button = Tkinter.Button(self, text=\"Write Offsets\",\n command=lambda : self.write_offsets(right_shoulder_offset, left_shoulder_offset, belt_buckle_offset))\n button.grid(column=1, row=row_number)\n \n \n # Start Reading Python\n def start_reading_button_click(self, read_time, file_name):\n print(\"Reading Data....\")\n if(read_time == ''):\n read_time = 5\n\n file_name = str(file_name.get())\n \n if(file_name == ''):\n file_name = 'haptic_data'\n read_time = float(read_time)\n read_time = int(read_time * 60)\n\n write_header = 0x8005\n write_header = struct.pack('>H', write_header)\n ser.write(write_header)\n \n read_time_minutes = read_time / 60\n read_time_seconds = read_time % 60\n print(\"MINUTES: \" + str(read_time_minutes))\n ser.write(struct.pack('>B', read_time_minutes)) \n ser.write(struct.pack('>B', read_time_seconds)) \n \n \n serial_queue = Queue.Queue()\n read_thread = threading.Thread(target=read_and_write_data_threads.read_data_thread, args=(serial_queue, ser))\n write_thread = threading.Thread(target=read_and_write_data_threads.live_graph_thread, args=(serial_queue, read_thread))\n\n # Start the threads\n read_thread.start()\n #time.sleep(1)\n write_thread.start()\n read_thread.join()\n write_thread.join()\n #currently_reading = False if currently_reading else currently_reading = True\n \n def time_reading_button_click(self, read_time, file_name):\n print(\"Reading Data....\")\n if(read_time == ''):\n read_time = 0.1\n\n file_name = str(file_name.get())\n \n if(file_name == ''):\n file_name = 'haptic_data'\n read_time = float(read_time)\n read_time = int(read_time * 60)\n\n write_header = 0x8005\n write_header = struct.pack('>H', write_header)\n ser.write(write_header)\n \n read_time_minutes = read_time / 60\n read_time_seconds = read_time % 60\n print(\"MINUTES: \" + str(read_time_minutes))\n print(\"SECONDS: \" + str(read_time_seconds))\n ser.write(struct.pack('>B', read_time_minutes)) \n ser.write(struct.pack('>B', read_time_seconds))\n\n serial_queue = Queue.Queue()\n read_thread = threading.Thread(target=read_and_write_data_threads.read_data_thread, args=(serial_queue, ser))\n write_thread = threading.Thread(target=read_and_write_data_threads.write_data_thread, args=(serial_queue, read_thread, file_name))\n\n # Start the threads\n read_thread.start()\n #time.sleep(1)\n write_thread.start()\n\n # Wait until threads are done before ending, Might Wanna take this Out\n # Add failsafes checking to see if those threads are \n read_thread.join()\n write_thread.join()\n \n # Graphs either rotations or accelerations,\n # 0 is accelerations, 1 is rotations\n def graph_data(self, what_to_graph, file_name):\n print(\"Graphing Data!\")\n file_name = str(file_name.get())\n if(file_name == ''):\n file_name = 'haptic_data'\n \n haptic_graphing_functions.graph_data(what_to_graph, file_name)\n\n # Write the variables to Arduino\n def write_values(self, var1Text, var2Text, var3Text):\n\n var_1 = var1Text.get()\n var_2 = var2Text.get()\n var_3 = var3Text.get()\n\n if var_1 == \"\":\n var_1 = 0\n\n if var_2 == \"\":\n var_2 = 0\n\n if var_3 == \"\":\n var_3 = 0 \n\n print(\"Variable 1: \" + str(var_1))\n print(\"Variable 2: \" + str(var_2))\n print(\"Variable 3: \" + str(var_3))\n\n\n write_header = 0x8003\n write_header = struct.pack('>H', write_header)\n #print(write_header)\n #Write the three variables as unsigned shorts with a header\n ser.write(write_header)\n \n \n\n #ser.write(struct.pack('>hhh', int(var_1), int(var_2), int(var_3)))\n\n \n # Write the offsets to Arduino\n def write_offsets(self, right_shoulder_offset, left_shoulder_offset, belt_buckle_offset):\n\n \n right_shoulder_values = right_shoulder_offset.get() \n left_shoulder_values = left_shoulder_offset.get()\n belt_buckle_values = belt_buckle_offset.get()\n empty_form = 0\n\n \n if right_shoulder_values == \"\" or left_shoulder_values == \"\" or belt_buckle_values == \"\":\n empty_form = -1\n\n\n\n print(\"right_shoulder_offset: \" + str(right_shoulder_values))\n print (\"left_shoulder_offset: \" + str(left_shoulder_values))\n print (\"belt_buckle_offset: \" + str(belt_buckle_values))\n\n #write_header = 0x8000\n # Write the three variables as unsigned shorts with a header\n #ser.write(struct.pack('>H', write_header))\n # HARD CODED FOR SENSOR 1\n \n ser.write(struct.pack('>B', 0x80))\n ser.write(struct.pack('>B', 0x00))\n \"\"\"\n ser.write(struct.pack('>B', 0xEF))\n ser.write(struct.pack('>B', 0x33))\n ser.write(struct.pack('>B', 0x01))\n ser.write(struct.pack('>B', 0x55))\n ser.write(struct.pack('>B', 0x03))\n ser.write(struct.pack('>B', 0x9E))\n \n ser.write(struct.pack('>B', 0xEF))\n ser.write(struct.pack('>B', 0x3B))\n ser.write(struct.pack('>B', 0xF4))\n ser.write(struct.pack('>B', 0x51))\n \n ser.write(struct.pack('>B', 0x03))\n ser.write(struct.pack('>B', 0xB6))\n ser.write(struct.pack('>B', 0xFE))\n ser.write(struct.pack('>B', 0x22))\n \n ser.write(struct.pack('>B', 0xF4))\n ser.write(struct.pack('>B', 0x9B))\n ser.write(struct.pack('>B', 0xFD))\n ser.write(struct.pack('>B', 0x8A))\n \"\"\"\n if(empty_form != -1):\n right_shoulder_array = get_offset(int(right_shoulder_values))\n left_shoulder_array = get_offset(int(left_shoulder_values))\n belt_buckle_array = get_offset(int(belt_buckle_values))\n \n print(\"right_shoulder_values: \" + str(right_shoulder_array))\n print (\"left_shoulder_values: \" + str(left_shoulder_array))\n print (\"belt_buckle_values: \" + str(belt_buckle_array))\n \n \n if(not os.path.isfile(\"sensor_\" + str(right_shoulder_values))):\n print(\"Sensor file for right shoulder not found.\")\n elif(not os.path.isfile(\"sensor_\" + str(left_shoulder_values))):\n print(\"Sensor file for left shoulder not found.\")\n elif(not os.path.isfile(\"sensor_\" + str(belt_buckle_values))):\n print(\"Sensor file for the belt buckle not found.\")\n \n #write_header = 0x8000\n # Write the three variables as unsigned shorts with a header\n #ser.write(struct.pack('>H', write_header))\n #ser.write(struct.pack('>B', 0x80))\n #ser.write(struct.pack('>B', 0x00))\n \n \n print(\"SIZE: \" + str(len(right_shoulder_array)))\n for value in right_shoulder_array:\n print(\"Value 1: \" + str(value))\n \n ser.write(struct.pack('>f', value))\n time.sleep(2)\n print(\"BYTES:\" + str(ser.inWaiting()))\n while(ser.inWaiting() > 0):\n byte_1 = struct.unpack('b', ser.read())[0]\n byte_2 = struct.unpack('B', ser.read())[0]\n queue_data = (byte_1 << 8) | (byte_2 & 0xFF)\n print('Arduino_Values:' + str(queue_data))\n \"\"\"\n for value in left_shoulder_array:\n print(\"Value 2: \" + str(value))\n ser.write(struct.pack('>f', value))\n \n for value in belt_buckle_array:\n print(\"Value 3: \" + str(value))\n ser.write(struct.pack('>f', value))\n \"\"\"\n\n# Return a list of values to write to Arduino\ndef get_offset(offset_number):\n\n values_to_write = []\n\n # Open the correct file\n file = open('sensor_' + str(offset_number) + '.txt', 'r')\n\n # For however many values\n for x in range(0, 9):\n values_to_write.append(float(file.readline()))\n \n file.close()\n #print(str(values_to_write))\n return values_to_write\n\n\n\ndef main():\n\n # Sleep to let Arduino initialize\n time.sleep(2)\n\n # Flush buffer\n ser.flushInput()\n\n # Setup GUI\n app = gui(None)\n app.title('Change Variables on Haptic Device')\n app.mainloop()\n ser.close()\n\n# Main \nif __name__ == \"__main__\":\n main()", "sub_path": "Beta/Old - Python/Python BETA/Live Graphing/pyGUI.py", "file_name": "pyGUI.py", "file_ext": "py", "file_size_in_byte": 16642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "serial.Serial", "line_number": 12, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 15, "usage_type": "name"}, {"api_name": "Tkinter.Tk", "line_number": 23, "usage_type": "attribute"}, {"api_name": "Tkinter.Tk.__init__", "line_number": 27, "usage_type": "call"}, {"api_name": "Tkinter.Tk", "line_number": 27, "usage_type": "attribute"}, {"api_name": "Tkinter.Label", "line_number": 38, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 42, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 44, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 48, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 54, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 61, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 66, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 70, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 74, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 79, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 83, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 87, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 92, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 96, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 100, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 102, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 107, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 109, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 114, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 116, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 122, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 128, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 132, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 134, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 139, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 141, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 146, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 148, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 154, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 173, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 179, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 180, "usage_type": "call"}, {"api_name": "Queue.Queue", "line_number": 183, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 184, "usage_type": "call"}, {"api_name": "read_and_write_data_threads.read_data_thread", "line_number": 184, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 185, "usage_type": "call"}, {"api_name": "read_and_write_data_threads.live_graph_thread", "line_number": 185, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 208, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 215, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 216, "usage_type": "call"}, {"api_name": "Queue.Queue", "line_number": 218, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 219, "usage_type": "call"}, {"api_name": "read_and_write_data_threads.read_data_thread", "line_number": 219, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 220, "usage_type": "call"}, {"api_name": "read_and_write_data_threads.write_data_thread", "line_number": 220, "usage_type": "attribute"}, {"api_name": "haptic_graphing_functions.graph_data", "line_number": 240, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 264, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 298, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 333, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 335, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 337, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 337, "usage_type": "name"}, {"api_name": "struct.pack", "line_number": 351, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 352, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 355, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 356, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 390, "usage_type": "call"}]} +{"seq_id": "344122451", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.utils.timezone\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('events', '0003_auto_20150302_1538'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='event',\n name='date_created',\n field=models.DateTimeField(default=django.utils.timezone.now, verbose_name='\\u0414\\u0430\\u0442\\u0430 \\u043f\\u0440\\u0438\\u0441\\u043e\\u0435\\u0434\\u0438\\u043d\\u0435\\u043d\\u0438\\u044f'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='event',\n name='position',\n field=models.IntegerField(null=True, verbose_name='\\u041f\\u0440\\u0438\\u043e\\u0440\\u0438\\u0442\\u0435\\u0442', blank=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='event',\n name='user',\n field=models.ForeignKey(default=1, verbose_name='\\u041f\\u043e\\u043b\\u044c\\u0437\\u043e\\u0432\\u0430\\u0442\\u0435\\u043b\\u044c', to=settings.AUTH_USER_MODEL),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='eventtype',\n name='seo_event',\n field=models.CharField(max_length=255, null=True, verbose_name='\\u0421\\u0415\\u041e \\u041c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f', blank=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='event',\n name='event_type',\n field=models.ForeignKey(related_name='events', verbose_name='\\u0422\\u0438\\u043f', to='events.EventType'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='event',\n name='image',\n field=models.ImageField(max_length=255, upload_to=b'uploads/events', null=True, verbose_name='\\u0418\\u0437\\u043e\\u0431\\u0440\\u0430\\u0436\\u0435\\u043d\\u0438\\u0435', blank=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='event',\n name='place',\n field=models.ForeignKey(verbose_name='\\u0410\\u0434\\u0440\\u0435\\u0441', blank=True, to='places.Place', null=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='event',\n name='price',\n field=models.IntegerField(default=0, help_text='\\u0415\\u0441\\u043b\\u0438 \\u0431\\u0435\\u0441\\u043f\\u043b\\u0430\\u0442\\u043d\\u043e - \\u043e\\u0441\\u0442\\u0430\\u0432\\u044c\\u0442\\u0435 \\u043f\\u043e\\u043b\\u0435 \\u043f\\u0443\\u0441\\u0442\\u044b\\u043c', max_length=255, verbose_name='\\u0426\\u0435\\u043d\\u0430'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='event',\n name='text',\n field=models.TextField(null=True, verbose_name='\\u0410\\u043d\\u043e\\u043d\\u0441', blank=True),\n preserve_default=True,\n ),\n ]\n", "sub_path": "apps/events/migrations/0004_auto_20150316_0702.py", "file_name": "0004_auto_20150316_0702.py", "file_ext": "py", "file_size_in_byte": 3128, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.migrations.swappable_dependency", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.utils", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 41, "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.migrations.AlterField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "573674831", "text": "from prediction_flow.pytorch.nn import MaxPooling\n\nimport torch\n\n\ndef test_max_pooling():\n x = torch.tensor(\n [[[1, 2, 1, 1],\n [1, 1, 3, 1]],\n [[10, 1, 1, 1],\n [1, 1, 4, 1]],\n [[2, 8, 9, 0],\n [1, 1, 1, 1]]])\n\n max_pooling = MaxPooling(dim=1)\n\n actual = max_pooling(x)\n\n assert actual.numpy().tolist() == [\n [1, 2, 3, 1], [10, 1, 4, 1], [2, 8, 9, 1]]\n", "sub_path": "prediction_flow/pytorch/nn/tests/test_pooling.py", "file_name": "test_pooling.py", "file_ext": "py", "file_size_in_byte": 419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.tensor", "line_number": 7, "usage_type": "call"}, {"api_name": "prediction_flow.pytorch.nn.MaxPooling", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "573700314", "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 ]\n\n operations = [\n migrations.CreateModel(\n name='Commit',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('sha', models.CharField(unique=True, max_length=40, verbose_name='sha')),\n ('author_name', models.CharField(max_length=256, verbose_name='author name')),\n ('author_email', models.EmailField(max_length=254, verbose_name='author email')),\n ('date', models.DateTimeField(unique=True, verbose_name='commit date')),\n ('message', models.TextField(verbose_name='message')),\n ],\n options={\n 'verbose_name': 'commit',\n 'verbose_name_plural': 'commits',\n },\n ),\n migrations.CreateModel(\n name='Repository',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('owner', models.CharField(max_length=39, verbose_name='owner')),\n ('repo', models.CharField(max_length=100, verbose_name='repo')),\n ('branch', models.CharField(max_length=256, verbose_name='branch')),\n ],\n options={\n 'verbose_name': 'repository',\n 'verbose_name_plural': 'repositories',\n },\n ),\n migrations.AlterUniqueTogether(\n name='repository',\n unique_together=set([('owner', 'repo', 'branch')]),\n ),\n migrations.AddField(\n model_name='commit',\n name='repository',\n field=models.ForeignKey(verbose_name='repository', to='app.Repository'),\n ),\n ]\n", "sub_path": "turnip/app/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterUniqueTogether", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "459527683", "text": "import json\nimport time\nimport traceback\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.remote.webdriver import WebDriver\n\nfrom config import config, features\nfrom extension.extension import get_extension_path\nfrom .browser import Browser\n\n\nclass Chrome(Browser):\n def get_driver(self):\n chrome_options = Options()\n if self.browser_config[\"type\"] == \"parent\":\n chrome_options.add_argument(f\"load-extension={get_extension_path()}\")\n if \"remote\" in self.browser_config:\n return webdriver.Remote(command_executor=self.browser_config[\"remote\"][\"url\"],\n desired_capabilities=self.browser_config[\"remote\"][\"capabilities\"])\n return webdriver.Chrome(chrome_options=chrome_options)\n\n def run(self):\n self.driver: WebDriver = self.get_driver()\n self.set_size(config[\"width\"], config[\"height\"])\n self.driver.get(self.browser_config[\"url\"])\n if self.browser_config[\"type\"] == \"parent\":\n if features[\"image_difference\"] or features[\"dom_difference\"]:\n self.parent_loop()\n else:\n self.child_loop()\n\n def parent_loop(self):\n while True:\n try:\n if self.actions:\n action = self.actions[0]\n self.actions.pop(0)\n action = json.loads(action)\n if action[\"target\"] != \"parent\":\n continue\n if action[\"action\"] == \"screenshot\":\n self.screenshot = action[\"params\"][\"base64\"]\n else:\n print(f\"Not supported {action}\")\n else:\n time.sleep(.01)\n except:\n traceback.print_exc()\n", "sub_path": "browsers/chrome.py", "file_name": "chrome.py", "file_ext": "py", "file_size_in_byte": 1852, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "browser.Browser", "line_number": 14, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 16, "usage_type": "call"}, {"api_name": "extension.extension.get_extension_path", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.Remote", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 22, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 25, "usage_type": "name"}, {"api_name": "config.config", "line_number": 26, "usage_type": "name"}, {"api_name": "config.features", "line_number": 29, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "220901494", "text": "from django.core.mail import mail_admins\nfrom django.db import models\n\n\nclass Company(models.Model):\n name = models.CharField(max_length=255, unique=True)\n remarks = models.TextField(blank=True)\n url = models.URLField(null=True, blank=True)\n enabled = models.BooleanField(default=True)\n\n def __str__(self):\n return self.name\n\n class Meta:\n verbose_name_plural = 'Companies'\n\n\nclass App(models.Model):\n from .key_generator import Generator\n\n name = models.CharField(max_length=255, unique=True)\n remarks = models.TextField(blank=True)\n url = models.URLField(null=True, blank=True)\n enabled = models.BooleanField(default=True)\n default_validity_type = models.CharField(choices=Generator.VALIDITY_TYPES, max_length=255,\n default=Generator.VALIDITY_TYPES[0])\n default_validity_units = models.FloatField(default=1)\n\n def __str__(self):\n return self.name\n\n\nclass Key(models.Model):\n from .key_generator import Generator\n\n company = models.ForeignKey(Company, null=True, blank=True)\n app = models.ForeignKey(App)\n user = models.TextField()\n key = models.TextField(null=True, blank=True)\n created = models.DateTimeField(auto_now_add=True)\n modified = models.DateTimeField(auto_now=True)\n valid_till = models.DateTimeField(blank=True, null=True)\n validity_type = models.CharField(choices=Generator.VALIDITY_TYPES, max_length=255, blank=True, null=True)\n validity_units = models.FloatField(blank=True, null=True)\n\n def get_app(self):\n if not self.app:\n if self.company.app:\n return self.company.app\n\n def get_validity_type(self):\n if not self.validity_type:\n return self.app.default_validity_type\n return self.validity_type\n\n def get_validity_units(self):\n if not self.validity_units:\n return self.app.default_validity_units\n return self.validity_units\n\n def get_valid_till(self):\n from datetime import date\n\n if self.valid_till:\n return self.valid_till\n if self.get_validity_type() == 'nepali_fy':\n d = date.today()\n return date(d.year + int(self.get_validity_units()), 7, 30)\n elif self.get_validity_type() == 'year':\n d = date.today()\n years = int(self.get_validity_units())\n try:\n return d.replace(year=d.year + years)\n except ValueError:\n return d + (date(d.year + years, 1, 1) - date(d.year, 1, 1))\n elif self.get_validity_type() == 'forever':\n return date(2200, 7, 1)\n\n def get_key(self):\n from .key_generator import Generator\n\n return Generator.generate_from_model(self)\n\n def get_email_subject(self):\n return self.app.name + ' - ' + self.user\n\n def get_email_message(self):\n return \"\"\"\n Key: \"\"\" + self.get_key() + \"\"\"\n App: \"\"\" + self.app.name + \"\"\"\n User: \"\"\" + self.user + \"\"\"\n Valid Till: \"\"\" + str(self.get_valid_till())\n\n def save(self, *args, **kwargs):\n if not self.validity_type:\n self.validity_type = self.app.default_validity_type\n if not self.validity_units:\n self.validity_units = self.app.default_validity_units\n mail_admins(self.get_email_subject(), self.get_email_message())\n super(Key, self).save(*args, **kwargs)\n\n def __str__(self):\n return self.user + ' - ' + str(self.app) + ' - ' + str(self.valid_till)\n\n class Meta:\n unique_together = ('app', 'user')", "sub_path": "generator/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 18, "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.TextField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "key_generator.Generator.VALIDITY_TYPES", "line_number": 25, "usage_type": "attribute"}, {"api_name": "key_generator.Generator", "line_number": 25, "usage_type": "name"}, {"api_name": "key_generator.Generator.VALIDITY_TYPES", "line_number": 26, "usage_type": "attribute"}, {"api_name": "key_generator.Generator", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "key_generator.Generator.VALIDITY_TYPES", "line_number": 43, "usage_type": "attribute"}, {"api_name": "key_generator.Generator", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 67, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 70, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 77, "usage_type": "call"}, {"api_name": "key_generator.Generator.generate_from_model", "line_number": 82, "usage_type": "call"}, {"api_name": "key_generator.Generator", "line_number": 82, "usage_type": "name"}, {"api_name": "django.core.mail.mail_admins", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "463468625", "text": "from ftw.table.basesource import BaseTableSource\nfrom ftw.table.interfaces import ITableSource\nfrom opengever.oneoffixx.browser.form import get_oneoffixx_templates\nfrom opengever.tabbedview.interfaces import IOneoffixxTableSourceConfig\nfrom zope.component import adapter\nfrom zope.interface import implementer\nfrom zope.interface import Interface\n\n\n@implementer(ITableSource)\n@adapter(IOneoffixxTableSourceConfig, Interface)\nclass OneoffixxTableSource(BaseTableSource):\n \"\"\"Base table source adapter for the OneOffixx.\"\"\"\n\n searchable_columns = []\n\n def extend_query_with_textfilter(self, query, text):\n if text:\n if isinstance(text, str):\n text = text.decode('utf-8')\n query['filters'] = text.strip().split(' ')\n return query\n\n def search_results(self, query):\n filters = query.get('filters')\n\n templates = [\n {\n 'title': template.title,\n 'groupname': template.groupname,\n 'content_type': template.content_type,\n }\n for template in get_oneoffixx_templates()\n if not filters or any(\n filter.lower() in template.title.lower()\n or filter.lower() in template.groupname.lower()\n for filter in filters\n )\n ]\n return templates\n", "sub_path": "opengever/tabbedview/oneoffixxsource.py", "file_name": "oneoffixxsource.py", "file_ext": "py", "file_size_in_byte": 1361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "ftw.table.basesource.BaseTableSource", "line_number": 12, "usage_type": "name"}, {"api_name": "opengever.oneoffixx.browser.form.get_oneoffixx_templates", "line_number": 33, "usage_type": "call"}, {"api_name": "zope.interface.implementer", "line_number": 10, "usage_type": "call"}, {"api_name": "ftw.table.interfaces.ITableSource", "line_number": 10, "usage_type": "argument"}, {"api_name": "zope.component.adapter", "line_number": 11, "usage_type": "call"}, {"api_name": "opengever.tabbedview.interfaces.IOneoffixxTableSourceConfig", "line_number": 11, "usage_type": "argument"}, {"api_name": "zope.interface.Interface", "line_number": 11, "usage_type": "argument"}]} +{"seq_id": "621246133", "text": "# -*- coding: utf-8 -*-\n'''\nCreated on 2 août 2013\n\n@author: dmarteau\n'''\nfrom __future__ import print_function\n\nimport sys\nimport nose\n\nfrom processing.process import Worker\nfrom processing.pool import Executor\nfrom nose.tools import ok_\n\nfrom zmq.eventloop import ioloop\nioloop.install()\n\nfrom tornado.ioloop import PeriodicCallback\n\n\nclass TestWorker(Worker):\n \"\"\" Simple class that echo its input\n \"\"\"\n def run(self, message):\n self.write(message)\n\n_ipc_address = 'ipc:///var/tmp/pysched.processing.test.socket'\n\nlb = Executor(_ipc_address, receivehook=None)\nworkers = [TestWorker(_ipc_address) for i in range(3)]\n\n\ndef setup():\n print(\"Starting workers\", file=sys.stderr)\n io_loop = ioloop.IOLoop.instance()\n workers = [TestWorker(_ipc_address) for i in range(3)]\n for w in workers:\n w.start(io_loop=io_loop)\n lb.start(io_loop=io_loop)\n\n\ndef start_ioloop(io_loop, timeout=5000.0):\n def timeout_exc():\n io_loop.stop()\n raise Exception(\"Timeout !\")\n timer = PeriodicCallback(timeout_exc, timeout, io_loop=io_loop)\n timer.start()\n io_loop.start()\n\n\ndef test_worker_connection():\n \"\"\" Testing non blocking worker \"\"\"\n\n io_loop = ioloop.IOLoop.instance()\n\n class ConnectFun():\n def __init__(self):\n self.connection = 0\n\n def __call__(self, address):\n self.connection += 1\n if self.connection >= 3:\n io_loop.stop()\n\n on_connect = ConnectFun()\n lb._connect_callback = on_connect\n\n start_ioloop(io_loop)\n lb._connect_callback = None\n\n expected = len(workers)\n ok_(on_connect.connection == expected, \"{} expected, found {}\".format(expected, on_connect.connection))\n\n\ndef test_worker_pass_messages():\n \"\"\" Testing passing messages \"\"\"\n\n io_loop = ioloop.IOLoop.instance()\n\n class Callback():\n def __init__(self):\n self.responses = []\n\n def __call__(self, response):\n self.responses.append(response)\n if len(self.responses) >= 4:\n io_loop.stop()\n\n messages = ['hello1', 'hello2', 'hello3', 'hello4']\n\n callback = Callback()\n for m in messages:\n lb.send(m, callback)\n\n start_ioloop(io_loop)\n\n n_responses = len(callback.responses)\n expected = len(messages)\n ok_(n_responses == expected, \"{} expected responses, found {}\".format(expected, n_responses))\n\n\nif __name__ == '__main__':\n setup()\n [eval(run)() for run in dir() if 'test_' in run]\n print(\"Done\")\n", "sub_path": "src/processing/tests/test_executor.py", "file_name": "test_executor.py", "file_ext": "py", "file_size_in_byte": 2510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "zmq.eventloop.ioloop.install", "line_number": 17, "usage_type": "call"}, {"api_name": "zmq.eventloop.ioloop", "line_number": 17, "usage_type": "name"}, {"api_name": "processing.process.Worker", "line_number": 22, "usage_type": "name"}, {"api_name": "processing.pool.Executor", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 35, "usage_type": "attribute"}, {"api_name": "zmq.eventloop.ioloop.IOLoop.instance", "line_number": 36, "usage_type": "call"}, {"api_name": "zmq.eventloop.ioloop.IOLoop", "line_number": 36, "usage_type": "attribute"}, {"api_name": "zmq.eventloop.ioloop", "line_number": 36, "usage_type": "name"}, {"api_name": "tornado.ioloop.PeriodicCallback", "line_number": 47, "usage_type": "call"}, {"api_name": "zmq.eventloop.ioloop.IOLoop.instance", "line_number": 55, "usage_type": "call"}, {"api_name": "zmq.eventloop.ioloop.IOLoop", "line_number": 55, "usage_type": "attribute"}, {"api_name": "zmq.eventloop.ioloop", "line_number": 55, "usage_type": "name"}, {"api_name": "nose.tools.ok_", "line_number": 73, "usage_type": "call"}, {"api_name": "zmq.eventloop.ioloop.IOLoop.instance", "line_number": 79, "usage_type": "call"}, {"api_name": "zmq.eventloop.ioloop.IOLoop", "line_number": 79, "usage_type": "attribute"}, {"api_name": "zmq.eventloop.ioloop", "line_number": 79, "usage_type": "name"}, {"api_name": "nose.tools.ok_", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "550220486", "text": "from code.datasets import ChartImageDataset, DFDataset\nfrom code.data_processing import add_ti, candles_to_inputs_and_labels\nfrom code.models import CNN, RNN, load_model, save_model\n\nfrom torch.utils.data import DataLoader, Dataset\n\nimport warnings\nimport torch\nimport os\nimport shutil\nimport pandas as pd\nimport numpy as np\nimport time\n\n\ntorch.backends.cudnn.benchmark = True\n\n# Parameters\nparams = {'batch_size': 1,\n 'shuffle': True,\n 'num_workers': 1}\n\ndef _train(train_dl, model, optim, error_func, debug=False):\n losses = []\n for batch, labels in train_dl:\n if torch.cuda.is_available(): \n batch, labels = batch.cuda().float(), labels.cuda().float()\n else:\n batch, labels = batch.float(), labels.float()\n \n if debug: print(\"batch[0] __str__: {} labels[0] __str__: {}\".format(batch[0], labels[0]))\n # set model to train mode\n model.train()\n \n # clear gradients\n model.zero_grad()\n \n output = model(batch)\n if debug: print(\"OUTPUT: shape: {} __str__ {}\".format(output.shape, output))\n\n loss = error_func(output, labels)\n if debug: print(\"LOSS: {}\".format(loss.item()))\n\n loss.backward()\n optim.step()\n \n losses.append(loss)\n\n return round(float(sum(losses))/len(losses), 6)\n\ndef _valid(valid_dl, model, optim, error_func):\n with torch.set_grad_enabled(False):\n losses = []\n\n for batch, labels in valid_dl:\n if torch.cuda.is_available(): \n batch, labels = batch.cuda().float(), labels.cuda().float()\n else:\n batch, labels = batch.float(), labels.float()\n \n # set to eval mode\n model.eval()\n \n # clear gradients\n model.zero_grad()\n\n output = model(batch)\n loss = error_func(output, labels)\n\n losses.append(loss)\n \n return round(float(sum(losses) / len(losses)), 6)\n\ndef _test(test_dl, model, optim, error_func):\n with torch.set_grad_enabled(False):\n losses = []\n\n for batch, labels in test_dl:\n if torch.cuda.is_available(): \n batch, labels = batch.cuda().float(), labels.cuda().float()\n else:\n batch, labels = batch.float(), labels.float()\n \n # set to eval mode\n model.eval()\n \n # clear gradients\n model.zero_grad()\n\n output = model(batch)\n loss = error_func(output, labels)\n\n losses.append(loss)\n \n return round(float(sum(losses) / len(losses)), 6)\n\ndef RMSE(x, y):\n \n #TODO automate this without model_name\n # have to squish x into a rank 1 tensor with batch_size length with the outputs we want\n if len(list(x.size())) == 2:\n # torch.Size([64, 1])\n x = x.squeeze(1)\n elif len(list(x.size())) == 3:\n # torch.Size([64, 30, 1])\n x = x[:, 29, :] # take only the last prediction from the 30 time periods in our matrix\n x = x.squeeze(1)\n \n mse = torch.nn.MSELoss()\n return torch.sqrt(mse(x, y))\n\ndef train(model, optim, error_func, num_epochs, train_dl, valid_dl, test_dl=None, debug=False):\n \"\"\"Train a PyTorch model with optim as optimizer strategy\"\"\"\n \n for epoch_i in range(num_epochs): \n # forward and backward passes of all batches inside train_gen\n train_loss = _train(train_dl, model, optim, error_func, debug)\n valid_loss = _valid(valid_dl, model, optim, error_func)\n \n # run on test set if provided\n if test_dl is not None: test_output = _test(test_dl, model, optim, error_func)\n else: test_output = \"no test selected\"\n print(\"train loss: {}, valid loss: {}, test output: {}\".format(train_loss, valid_loss, test_output))\n\ndef train_on_df(model, candles_df, lr, num_epochs, model_type, debug):\n torch.backends.cudnn.benchmark = True\n \n candles_df = add_ti(candles_df)\n \n inputs, labels = candles_to_inputs_and_labels(candles_df)\n\n # calculate s - index of train/valid split\n s = int(len(inputs) * 0.7)\n\n if model_type == 'CNN':\n train_ds = ChartImageDataset(inputs[:s], labels[:s])\n valid_ds = ChartImageDataset(inputs[s:], labels[s:])\n elif model_type =='RNN':\n train_ds = DFDataset(inputs[:s], labels[:s])\n valid_ds = DFDataset(inputs[s:], labels[s:])\n \n train_dl = DataLoader(train_ds, drop_last=True, **params)\n valid_dl = DataLoader(valid_ds, drop_last=True, **params)\n\n optim = torch.optim.Adam(model.parameters(), lr)\n \n train(model=model, optim=optim, error_func=RMSE, num_epochs=num_epochs, train_dl=train_dl, valid_dl=valid_dl, debug=debug)\n\ndef train_rnn(candles, file_name, lr, num_epochs, debug):\n if torch.cuda.is_available():\n model = RNN(11, 30, params['batch_size'], 100, 3).cuda()\n else:\n model = RNN(11, 30, params['batch_size'], 100, 3)\n load_model(model, file_name)\n train_on_df(model, candles, lr, num_epochs, 'RNN', debug=debug)\n save_model(model, file_name)\n\ndef train_cnn(candles, file_name, lr, num_epochs, debug):\n model = (CNN().cuda() if torch.cuda.is_available() else CNN())\n load_model(model, file_name)\n train_on_df(model, candles, lr, num_epochs, 'CNN', debug=debug)\n save_model(model, file_name)\n\ndef split_df(dfm, chunk_size):\n \"\"\"Split a dataframe into chunk_size smaller chunks\"\"\"\n def index_marks(nrows, chunk_size):\n return range(1 * chunk_size, (nrows // chunk_size) * chunk_size, chunk_size)\n indices = index_marks(dfm.shape[0], chunk_size)\n return np.split(dfm, indices)\n\nif __name__ == '__main__':\n models = ['RNN', 'CNN']\n model = input(\"Select model to train from {} (default: CNN): \".format(models)) or 'CNN'\n datapath = input(\"Please input path to OCHLV .csv file (default: tests/600_candles.csv): \") or 'tests/600_candles.csv'\n num_chunks = int(input(\"Chunk size for training. Max is num_rows(dataframe) (default: 120): \") or 120) \n outputpath = input(\"Please input path to save and/or load models into/from (default: ./output): \") or './output'\n lr = float(input(\"Learning rate (default: 0.001): \") or 0.001)\n epochs = int(input(\"Epochs (default: 5): \") or 5) \n debug = False\n \n candles_big = pd.read_csv(datapath)\n chunks = split_df(candles_big, num_chunks)\n start_chunk = int(input(\"Select chunk to start training from (starting from 0 to {}) (default: 0): \".format(len(chunks)-1)) or 0)\n \n for i, candles_chunk in enumerate(chunks):\n if i < start_chunk: \n continue\n print(\"{}/{}\".format(i, len(chunks)-1))\n if model == 'RNN':\n train_rnn(candles_chunk, outputpath, lr, epochs, debug)\n elif model == 'CNN':\n train_cnn(candles_chunk, outputpath, lr, epochs, debug)\n", "sub_path": "train_models.py", "file_name": "train_models.py", "file_ext": "py", "file_size_in_byte": 7016, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.backends", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.set_grad_enabled", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.set_grad_enabled", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.sqrt", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 126, "usage_type": "attribute"}, {"api_name": "code.data_processing.add_ti", "line_number": 128, "usage_type": "call"}, {"api_name": "code.data_processing.candles_to_inputs_and_labels", "line_number": 130, "usage_type": "call"}, {"api_name": "code.datasets.ChartImageDataset", "line_number": 136, "usage_type": "call"}, {"api_name": "code.datasets.ChartImageDataset", "line_number": 137, "usage_type": "call"}, {"api_name": "code.datasets.DFDataset", "line_number": 139, "usage_type": "call"}, {"api_name": "code.datasets.DFDataset", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 150, "usage_type": "attribute"}, {"api_name": "code.models.RNN", "line_number": 151, "usage_type": "call"}, {"api_name": "code.models.RNN", "line_number": 153, "usage_type": "call"}, {"api_name": "code.models.load_model", "line_number": 154, "usage_type": "call"}, {"api_name": "code.models.save_model", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 159, "usage_type": "attribute"}, {"api_name": "code.models.CNN", "line_number": 159, "usage_type": "call"}, {"api_name": "code.models.load_model", "line_number": 160, "usage_type": "call"}, {"api_name": "code.models.save_model", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 169, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "569005546", "text": "from typing import List\n\nimport pytest\n\nfrom Common.BaseTestCase import BaseTestCase\nfrom Common.JSONData import load_params_from_json_files\nfrom Common.Tool import Tool\n\n\ndef get_tool_mark(tool: Tool) -> pytest.mark:\n result = None\n \n if tool == Tool.ASC:\n result = pytest.mark.ASC\n elif tool == Tool.CLX000:\n result = pytest.mark.CLX000\n elif tool == Tool.CSV:\n result = pytest.mark.CSV\n elif tool == Tool.PCAP:\n result = pytest.mark.PCAP\n elif tool == Tool.SocketCAN:\n result = pytest.mark.SocketCAN\n \n return result\n\n\ndef pytest_generate_tests(metafunc):\n # Determine if it contains json data.\n if issubclass(metafunc.cls, BaseTestCase):\n parameter_files = metafunc.cls.parameter_files\n \n if not isinstance(parameter_files, List):\n return\n \n if not \"data\" in metafunc.fixturenames:\n return\n \n argvalues = []\n ids = []\n \n for parameter_file in parameter_files:\n # Load the data.\n cases_in_parameter_file = load_params_from_json_files(parameter_file)\n \n # Parametrize using each record.\n for case in cases_in_parameter_file:\n tool = Tool(case.tool)\n mark = get_tool_mark(tool)\n \n if mark is not None:\n argvalues.append(pytest.param(case, marks=mark))\n else:\n argvalues.append(pytest.param(case))\n \n # Use the name if present, else increment.\n if case.name is not None:\n ids.append(case.name)\n else:\n ids.append(str(len(ids)))\n \n # Mark with the tool name and parametrize.\n metafunc.parametrize(argnames=(\"data\",), argvalues=argvalues, ids=ids)\n \n return\n", "sub_path": "Tools/SystemTests/CLX000/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 1917, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "Common.Tool.Tool", "line_number": 10, "usage_type": "name"}, {"api_name": "Common.Tool.Tool.ASC", "line_number": 13, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool", "line_number": 13, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 14, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool.CLX000", "line_number": 15, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool", "line_number": 15, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 16, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool.CSV", "line_number": 17, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool", "line_number": 17, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 18, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool.PCAP", "line_number": 19, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool", "line_number": 19, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool.SocketCAN", "line_number": 21, "usage_type": "attribute"}, {"api_name": "Common.Tool.Tool", "line_number": 21, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Common.BaseTestCase.BaseTestCase", "line_number": 29, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "argument"}, {"api_name": "Common.JSONData.load_params_from_json_files", "line_number": 43, "usage_type": "call"}, {"api_name": "Common.Tool.Tool", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "564782877", "text": "# encodeing=utf-8\n'''\n# Created on 2018年9月17日\n\n@author: TuringEmmy\n'''\nfrom matplotlib import pyplot as plt\n\n\nx =range(2,26,2)\ny =[15,13,14.5,17,20,25,26,26,27,22,18,15]\n\n# 设置图片的大小\nplt.figure( figsize=(20,8), dpi=80)\n# figsize图片大小\n# dpi每英寸上的点个数\n# 绘图\nplt.plot(x,y)\n\n# # 设置x轴的刻度\n_xtick_labels = [i/2 for i in range(4, 49)]\n# plt.xticks(range(25,50))\nplt.xticks(_xtick_labels[::3])\n\nplt.yticks(range(min(y),max(y)+1))\n\n# 保存\n# plt.savefig(\"./image/page15.png\")\n# 展示图形\nplt.show()\n\n\n", "sub_path": "matplotlib_study/page15.py", "file_name": "page15.py", "file_ext": "py", "file_size_in_byte": 549, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "563330901", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('userinfo', '0003_userinfo_avatar_url'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='userinfo',\n name='avatar_url',\n field=models.URLField(blank=True, default='http://res.cloudinary.com/dusvendql/image/upload/v1455886667/blrym6ryw2w3n9m6qveg.png'),\n ),\n ]\n", "sub_path": "userinfo/migrations/0004_auto_20160220_0618.py", "file_name": "0004_auto_20160220_0618.py", "file_ext": "py", "file_size_in_byte": 503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "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.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "368688814", "text": "#!/usr/bin/env python3\n#\n# Copyright (c) 2004-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree. An additional grant\n# of patent rights can be found in the PATENTS file in the same directory.\n\nfrom typing import Dict, List, Optional\n\n\ndef tabulate(\n headers: List[str],\n rows: List[Dict[str, str]],\n header_labels: Optional[Dict[str, str]] = None,\n) -> str:\n \"\"\" Tabulate some data so that it renders reasonably.\n rows - is a list of data that is to be rendered\n headers - is a list of the dictionary keys of the row data to\n be rendered and specifies the order of the fields.\n header_labels - an optional mapping from dictionary key to a\n more human friendly label for that key.\n A missing mapping is defaulted to the uppercased\n version of the key\n Returns a string holding the tabulated result\n \"\"\"\n col_widths = {}\n\n def label(name) -> str:\n label = (header_labels or {}).get(name, \"\")\n if label:\n return label\n return str(name.upper())\n\n def field(obj, name) -> str:\n return str(obj.get(name, \"\"))\n\n for name in headers:\n col_widths[name] = len(label(name))\n for row in rows:\n for name in headers:\n col_widths[name] = max(len(field(row, name)), col_widths[name])\n\n format_string = \"\"\n for col_width in col_widths.values():\n if format_string:\n format_string += \" \"\n format_string += \"{:<%d}\" % col_width\n\n output = format_string.format(*[label(name) for name in headers])\n for row in rows:\n output += \"\\n\"\n output += format_string.format(*[field(row, name) for name in headers])\n return output\n", "sub_path": "eden/cli/tabulate.py", "file_name": "tabulate.py", "file_ext": "py", "file_size_in_byte": 1856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "634302724", "text": "import gym\nimport pickle\n\n\nclass MujocoSaver(gym.core.Wrapper):\n def __init__(self, env, savepath):\n self.savepath = savepath\n super().__init__(env)\n\n self.list_variables = []\n self.nb_traj = 0\n\n def _save_variables(self):\n variables = self.unwrapped.get_save_variables()\n self.list_variables.append(variables)\n\n def reset(self, **kwargs):\n self.list_variables = []\n obs = self.env.reset(**kwargs)\n self._save_variables()\n return obs\n\n def step(self, action):\n obs, reward, done, info = self.env.step(action)\n self._save_variables()\n if done:\n self._dump()\n return obs, reward, done, info\n\n def _dump(self):\n self.nb_traj += 1\n with open(self.savepath, \"ab+\") as f:\n pickle.dump(self.list_variables, f)\n if self.nb_traj % 100 == 0 or self.nb_traj == 1:\n print('MuJoCoSaver save {} trajectory into :{}'.format(self.nb_traj, self.savepath))\n", "sub_path": "baselines/bench/saver.py", "file_name": "saver.py", "file_ext": "py", "file_size_in_byte": 1014, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "gym.core", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "445261671", "text": "from awips.dataaccess import DataAccessLayer\r\nimport matplotlib.tri as mtri\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\r\nfrom math import exp, log\r\nimport numpy as np\r\nfrom metpy.calc import wind_components, lcl, lfc, el, dry_lapse, parcel_profile, dewpoint, wet_bulb_temperature, mean_pressure_weighted\r\nfrom metpy.calc import most_unstable_parcel, parcel_profile_with_lcl, bulk_shear, storm_relative_helicity, lifted_index\r\nfrom metpy.calc import wind_speed, wind_direction, thermo, vapor_pressure, bunkers_storm_motion, pressure_to_height_std\r\nfrom metpy.plots import SkewT, Hodograph\r\nfrom metpy.units import units, concatenate\r\nimport metpy.calc as mpcalc\r\nfrom base64 import b64encode, b64decode\r\nimport codecs\r\nimport sys\r\nfrom datetime import datetime\r\nimport datetime as dt\r\nfrom metpy.units import units\r\nimport metpy.calc as mpcalc\r\nfrom matplotlib.gridspec import GridSpec\r\nfrom matplotlib import gridspec\r\nimport sharppy.sharptab.profile as profile\r\nfrom sharppy.sharptab import utils, winds, params, interp, thermo, watch_type, fire\r\nimport sharppy.sharptab as tab\r\nfrom sharppy.sharptab.constants import *\r\nfrom sharppy.sharptab.constants import MISSING, TOL\r\n\r\n# make unique directory to store output\r\ndef mkdir_p(mypath):\r\n '''Creates a directory. equivalent to using mkdir -p on the command line'''\r\n\r\n from errno import EEXIST\r\n from os import makedirs,path\r\n\r\n try:\r\n makedirs(mypath)\r\n except OSError as exc: # Python >2.5\r\n if exc.errno == EEXIST and path.isdir(mypath):\r\n pass\r\n else: raise\r\n\r\n#grabbing data from NOMADS\r\nstartTime=datetime.now()\r\n\r\nyear = startTime.year\r\n\r\nif startTime.month <10:\r\n month = '0'+str(startTime.month)\r\nelse:\r\n month = str(startTime.month)\r\n\r\nif startTime.day <10:\r\n day = '0'+str(startTime.day)\r\nelse:\r\n day = str(startTime.day)\r\n\r\nif startTime.hour <10:\r\n hour = '0'+str(startTime.hour)\r\nelse:\r\n hour = str(startTime.hour)\r\n \r\n\r\ndef get_init_hr(hour):\r\n if int(hour) <6:\r\n init_hour = '00'\r\n elif int(hour) <12:\r\n init_hour = '06'\r\n elif int(hour) <17:\r\n init_hour = '12'\r\n elif int(hour) <22:\r\n init_hour = '18'\r\n else:\r\n init_hour = '00'\r\n return(init_hour)\r\n\r\nmdate = str(year)+str(month)+str(day)\r\n# Create new directory to store output\r\noutput_dir = str(year)+str(month)+str(day) #this string names the output directory\r\nmkdir_p(output_dir)\r\nmkdir_p(output_dir+'/Soundings/NAM/CHA') #create subdirectory to store GFS output like this\r\n\r\n\r\nDataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\r\nrequest = DataAccessLayer.newDataRequest(\"modelsounding\")\r\nforecastModel = \"ETA\"\r\nrequest.addIdentifier(\"reportType\", forecastModel)\r\nrequest.setParameters(\"pressure\",\"temperature\",\"specHum\",\"uComp\",\"vComp\",\"omega\",\"cldCvr\")\r\n\r\nlocations = DataAccessLayer.getAvailableLocationNames(request)\r\nlocations.sort()\r\nlist(locations)\r\n\r\n\r\n\r\nfor i in range(0,84):\r\n request.setLocationNames(\"KCHA\")\r\n cycles = DataAccessLayer.getAvailableTimes(request, True)\r\n times = DataAccessLayer.getAvailableTimes(request)\r\n\r\n try:\r\n fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)\r\n list(fcstRun)\r\n response = DataAccessLayer.getGeometryData(request,[fcstRun[i]])\r\n except:\r\n print('No times available')\r\n exit\r\n\r\n tmp,prs,sh = np.array([]),np.array([]),np.array([])\r\n uc,vc,om,cld = np.array([]),np.array([]),np.array([]),np.array([])\r\n\r\n\r\n for ob in response:\r\n tmp = np.append(tmp,ob.getNumber(\"temperature\"))\r\n prs = np.append(prs,ob.getNumber(\"pressure\"))\r\n sh = np.append(sh,ob.getNumber(\"specHum\"))\r\n uc = np.append(uc,ob.getNumber(\"uComp\"))\r\n vc = np.append(vc,ob.getNumber(\"vComp\"))\r\n om = np.append(om,ob.getNumber(\"omega\"))\r\n cld = np.append(cld,ob.getNumber(\"cldCvr\"))\r\n\r\n\r\n print(\"parms = \" + str(ob.getParameters()))\r\n print(\"site = \" + str(ob.getLocationName()))\r\n print(\"geom = \" + str(ob.getGeometry()))\r\n print(\"datetime = \" + str(ob.getDataTime()))\r\n print(\"reftime = \" + str(ob.getDataTime().getRefTime()))\r\n print(\"fcstHour = \" + str(ob.getDataTime().getFcstTime()))\r\n print(\"period = \" + str(ob.getDataTime().getValidPeriod()))\r\n fcstHour = str(ob.getDataTime().getFcstTime())\r\n t = (tmp-273.15) * units.degC\r\n p = prs/100 * units.mbar\r\n z = mpcalc.pressure_to_height_std(p)\r\n print(t)\r\n print(p)\r\n print(z)\r\n sfc_hgt = z[0]\r\n print(sfc_hgt)\r\n\r\n u,v = uc*1.94384,vc*1.94384 # m/s to knots\r\n spd = wind_speed(u*units.knots, v*units.knots)\r\n dir = wind_direction(u*units.knots, v*units.knots) * units.deg\r\n\r\n rmix = (sh/(1-sh)) *1000 * units('g/kg')\r\n e = vapor_pressure(p, rmix)\r\n td = dewpoint(e)\r\n \r\n #td = metpy.calc.dewpoint_from_relative_humidity(t, rh)\r\n\r\n td2 = dewpoint(vapor_pressure(p, sh))\r\n \r\n # Calculate Wetbulb Temperature - need to figure out what to do with nan values\r\n #wetbulb = wet_bulb_temperature(p, t, td)\r\n # Sets up the prof object - not sure if this really will work\r\n '''\r\n p_sounding = np.sort(np.append(lev, sfc[0,ilat[0][0], ilon[1][0]]))\r\n ind = np.where(p_sounding >= sfcp[0,ilat[0][0], ilon[1][0]])[0][0]\r\n hgt_sounding = np.insert(z[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, sfc_hgt[0,ilat[0][0], ilon[1][0]].magnitude) * hgt.units\r\n T_sounding = (np.insert(t[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, t[0,0,ilat[0][0], ilon[1][0]].magnitude) * t.units).to(tdc.units)\r\n Td_sounding = np.insert(tdc_up.magnitude, ind, td[0,0,ilat[0][0], ilon[1][0]].magnitude) * tdc_up.units\r\n u_sounding = np.insert(uc[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, u10[0,0,ilat[0][0], ilon[1][0]].magnitude) * usfc.units\r\n v_sounding = np.insert(vc[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, v10[0,0,ilat[0][0], ilon[1][0]].magnitude) * usfc.units\r\n\r\n p_skewt = p_sounding[p_sounding <= sfcp[0,ilat[0][0], ilon[1][0]]]\r\n hgt_skewt = hgt_sounding[p_sounding <= sfcp[0,ilat[0][0], ilon[1][0]]]\r\n T_skewt = T_sounding[p_sounding <= sfcp[0,ilat[0][0], ilon[1][0]]]\r\n Td_skewt = Td_sounding[p_sounding <= sfcp[0,ilat[0][0], ilon[1][0]]]\r\n u_skewt = u_sounding[p_sounding <= sfcp[0,ilat[0][0], ilon[1][0]]].to('kt')\r\n v_skewt = v_sounding[p_sounding <= sfcp[0,ilat[0][0], ilon[1][0]]].to('kt')\r\n \r\n prof = profile.create_profile(profile='default', pres=p, hght=z, tmpc=t, dwpc=td2, wspd=spd, wdir=dir, missing=-9999, strictQC=False)\r\n prof = profile.create_profile(profile='default', pres=p_skewt[::-1], hght=hgt_skewt[::-1], tmpc=T_skewt[::-1], dwpc=Td_skewt[::-1], wspd=wind_spd[::-1], wdir=wind_dir[::-1], missing=-9999, strictQC=False) \r\n\r\n mupcl = params.parcelx(prof,flag=3)\r\n '''\r\n # Ends this block of code\r\n def __mu(self, prof, **kwargs):\r\n '''\r\n Create the most unstable parcel within the lowest XXX hPa, where\r\n XXX is supplied. Default XXX is 400 hPa.\r\n \r\n '''\r\n self.desc = 'Most Unstable Parcel in Lowest %.2f hPa' % self.presval\r\n pbot = prof.pres[prof.sfc]\r\n ptop = pbot - self.presval\r\n self.pres = most_unstable_level(prof, pbot=pbot, ptop=ptop)\r\n self.tmpc = interp.temp(prof, self.pres)\r\n self.dwpc = interp.dwpt(prof, self.pres)\r\n \r\n \r\n def __ml(self, prof, **kwargs):\r\n '''\r\n Create a mixed-layer parcel with mixing within the lowest XXX hPa,\r\n where XXX is supplied. Default is 100 hPa.\r\n If\r\n \r\n '''\r\n self.desc = '%.2f hPa Mixed Layer Parcel' % self.presval\r\n pbot = kwargs.get('pbot', prof.pres[prof.sfc])\r\n ptop = pbot - self.presval\r\n self.pres = pbot\r\n mtheta = mean_theta(prof, pbot, ptop, exact=True)\r\n self.tmpc = thermo.theta(1000., mtheta, self.pres)\r\n mmr = mean_mixratio(prof, pbot, ptop, exact=True)\r\n self.dwpc = thermo.temp_at_mixrat(mmr, self.pres)\r\n \r\n \r\n def __user(self, prof, **kwargs):\r\n '''\r\n Create a user defined parcel.\r\n \r\n '''\r\n self.desc = '%.2f hPa Parcel' % self.presval\r\n self.pres = self.presval\r\n self.tmpc = kwargs.get('tmpc', interp.temp(prof, self.pres))\r\n self.dwpc = kwargs.get('dwpc', interp.dwpt(prof, self.pres))\r\n \r\n \r\n def __effective(self, prof, **kwargs):\r\n '''\r\n Create the mean-effective layer parcel.\r\n \r\n '''\r\n ecape = kwargs.get('ecape', 100)\r\n ecinh = kwargs.get('ecinh', -250)\r\n pbot, ptop = effective_inflow_layer(prof, ecape, ecinh)\r\n if utils.QC(pbot) and pbot > 0:\r\n self.desc = '%.2f hPa Mean Effective Layer Centered at %.2f' % ( pbot-ptop, (pbot+ptop)/2.)\r\n mtha = mean_theta(prof, pbot, ptop)\r\n mmr = mean_mixratio(prof, pbot, ptop)\r\n self.pres = (pbot+ptop)/2.\r\n self.tmpc = thermo.theta(1000., mtha, self.pres)\r\n self.dwpc = thermo.temp_at_mixrat(mmr, self.pres)\r\n else:\r\n self.desc = 'Defaulting to Surface Layer'\r\n self.pres = prof.pres[prof.sfc]\r\n self.tmpc = prof.tmpc[prof.sfc]\r\n self.dwpc = prof.dwpc[prof.sfc]\r\n if utils.QC(pbot): self.pbot = pbot\r\n else: self.pbot = ma.masked\r\n if utils.QC(ptop): self.ptop = ptop\r\n else: self.pbot = ma.masked\r\n\r\n\r\n def helicity(prof, lower, upper, stu=0, stv=0, dp=-1, exact=True):\r\n '''\r\n Calculates the relative helicity (m2/s2) of a layer from lower to upper.\r\n If storm-motion vector is supplied, storm-relative helicity, both\r\n positve and negative, is returned.\r\n Parameters\r\n ----------\r\n prof : profile object\r\n Profile Object\r\n lower : number\r\n Bottom level of layer (m, AGL)\r\n upper : number\r\n Top level of layer (m, AGL)\r\n stu : number (optional; default = 0)\r\n U-component of storm-motion (kts)\r\n stv : number (optional; default = 0)\r\n V-component of storm-motion (kts)\r\n dp : negative integer (optional; default -1)\r\n The pressure increment for the interpolated sounding (mb)\r\n exact : bool (optional; default = True)\r\n Switch to choose between using the exact data (slower) or using\r\n interpolated sounding at 'dp' pressure levels (faster)\r\n Returns\r\n -------\r\n phel+nhel : number\r\n Combined Helicity (m2/s2)\r\n phel : number\r\n Positive Helicity (m2/s2)\r\n nhel : number\r\n Negative Helicity (m2/s2)\r\n '''\r\n if prof.wdir.count() == 0 or not utils.QC(lower) or not utils.QC(upper) or not utils.QC(stu) or not utils.QC(stv):\r\n return ma.masked, ma.masked, ma.masked\r\n\r\n if lower != upper:\r\n lower = interp.to_msl(prof, lower)\r\n upper = interp.to_msl(prof, upper)\r\n plower = interp.pres(prof, lower)\r\n pupper = interp.pres(prof, upper)\r\n if np.isnan(plower) or np.isnan(pupper) or \\\r\n type(plower) == type(ma.masked) or type(pupper) == type(ma.masked):\r\n return np.ma.masked, np.ma.masked, np.ma.masked\r\n if exact:\r\n ind1 = np.where(plower >= prof.pres)[0].min()\r\n ind2 = np.where(pupper <= prof.pres)[0].max()\r\n u1, v1 = interp.components(prof, plower)\r\n u2, v2 = interp.components(prof, pupper)\r\n u = np.concatenate([[u1], prof.u[ind1:ind2+1].compressed(), [u2]])\r\n v = np.concatenate([[v1], prof.v[ind1:ind2+1].compressed(), [v2]])\r\n else:\r\n ps = np.arange(plower, pupper+dp, dp)\r\n u, v = interp.components(prof, ps)\r\n sru = utils.KTS2MS(u - stu)\r\n srv = utils.KTS2MS(v - stv)\r\n layers = (sru[1:] * srv[:-1]) - (sru[:-1] * srv[1:])\r\n phel = layers[layers > 0].sum()\r\n nhel = layers[layers < 0].sum()\r\n else:\r\n phel = nhel = 0\r\n\r\n return phel+nhel, phel, nhel\r\n \r\n \r\n\r\n def sr_wind(prof, pbot=850, ptop=250, stu=0, stv=0, dp=-1):\r\n '''\r\n Calculates a pressure-weighted mean storm-relative wind through a layer.\r\n The default layer is 850 to 200 hPa. This is a thin wrapper around\r\n mean_wind().\r\n Parameters\r\n ----------\r\n prof: profile object\r\n Profile object\r\n pbot : number (optional; default 850 hPa)\r\n Pressure of the bottom level (hPa)\r\n ptop : number (optional; default 250 hPa)\r\n Pressure of the top level (hPa)\r\n stu : number (optional; default 0)\r\n U-component of storm-motion vector (kts)\r\n stv : number (optional; default 0)\r\n V-component of storm-motion vector (kts)\r\n dp : negative integer (optional; default -1)\r\n The pressure increment for the interpolated sounding (mb)\r\n Returns\r\n -------\r\n mnu : number\r\n U-component (kts)\r\n mnv : number\r\n V-component (kts)\r\n '''\r\n return mean_wind(prof, pbot=pbot, ptop=ptop, dp=dp, stu=stu, stv=stv)\r\n \r\n def get_indices(self):\r\n '''\r\n Function to set any additional indices that are included in the \r\n thermo window.\r\n \r\n Parameters\r\n ----------\r\n None\r\n \r\n Returns\r\n -------\r\n None\r\n '''\r\n self.tei = params.tei(self)\r\n self.esp = params.esp(self)\r\n self.mmp = params.mmp(self)\r\n self.wndg = params.wndg(self)\r\n self.sig_severe = params.sig_severe(self)\r\n self.dcape, self.dpcl_ttrace, self.dpcl_ptrace = params.dcape(self)\r\n self.drush = thermo.ctof(self.dpcl_ttrace[-1])\r\n self.mburst = params.mburst(self)\r\n \r\n # Calculate LCL height and plot as black dot\r\n lcl_pressure, lcl_temperature = lcl(p[0], t[0], td[0])\r\n \r\n # Calculate LFC height and plot as yellow dash\r\n lfc_pressure, lfc_temperature = lfc(p, t, td)\r\n \r\n el_pressure, el_temperature = el(p, t, td)\r\n \r\n lcl_hgt = np.round(mpcalc.pressure_to_height_std(lcl_pressure), decimals=1).to(units.meter)/1000\r\n lfc_hgt = np.round(mpcalc.pressure_to_height_std(lfc_pressure), decimals=1).to(units.meter)/1000\r\n el_hgt = np.round(mpcalc.pressure_to_height_std(el_pressure), decimals=1).to(units.meter)/1000\r\n \r\n sb_cape, sb_cin = mpcalc.surface_based_cape_cin(p, t, td)\r\n ml_cape, ml_cin = mpcalc.mixed_layer_cape_cin(p, t, td)\r\n mu_cape, mu_cin = mpcalc.most_unstable_cape_cin(p, t, td)\r\n \r\n #muparc = mpcalc.most_unstable_parcel(p, t, td)\r\n #print(muparc)\r\n #muparc_pressure = np.round(mpcalc.height_to_pressure_std(muparc), decimals=3).to(units.meter)\r\n #print(muparc_pressure)\r\n \r\n mixed_0_3 = mpcalc.mixed_parcel(p, t, td, depth=3000 * units.meter)\r\n print(mixed_0_3) \r\n\r\n \r\n sbcape = np.round(sb_cape, 1)\r\n sbcin = np.round(sb_cin, 1)\r\n mlcape = np.round(ml_cape, 1)\r\n mlcin = np.round(ml_cin, 1)\r\n mucape = np.round(mu_cape, 1)\r\n pw = mpcalc.precipitable_water(p, td)\r\n pw = pw.to(units.inch)\r\n pw = round(pw, 2)\r\n \r\n u_shear01, v_shear01 = mpcalc.bulk_shear(p, u * units('m/s'), v * units('m/s'), depth = 1000 * units.meter)\r\n shear01 = np.round((np.sqrt(u_shear01**2 + v_shear01**2)), 1)\r\n shear01 = shear01.to(units.knots)\r\n shear01 = np.round(shear01)\r\n u_shear005, v_shear005 = mpcalc.bulk_shear(p, u * units('m/s'), v * units('m/s'), depth = 500 * units.meter)\r\n shear005 = np.round((np.sqrt(u_shear005**2 + v_shear005**2)),1)\r\n shear005 = shear005.to(units.knots)\r\n shear005 = np.round(shear005)\r\n u_shear015, v_shear015 = mpcalc.bulk_shear(p, u * units('m/s'), v * units('m/s'), depth = 1500 * units.meter)\r\n shear015 = np.round((np.sqrt(u_shear015**2 + v_shear015**2)),1)\r\n shear015 = shear015.to(units.knots)\r\n shear015 = np.round(shear015)\r\n u_shear02, v_shear02 = mpcalc.bulk_shear(p, u * units('m/s'), v * units('m/s'), depth = 2000 * units.meter)\r\n shear02 = np.round((np.sqrt(u_shear02**2 + v_shear02**2)), 1)\r\n shear02 = shear02.to(units.knots)\r\n shear02 = np.round(shear02)\r\n u_shear03, v_shear03 = mpcalc.bulk_shear(p, u * units('m/s'), v * units('m/s'), depth = 3000 * units.meter)\r\n shear03 = np.round((np.sqrt(u_shear03**2 + v_shear03**2)), 1)\r\n shear03 = shear03.to(units.knots)\r\n shear03 = np.round(shear03)\r\n u_shear06, v_shear06 = mpcalc.bulk_shear(p, u * units('m/s'), v * units('m/s'), depth = 6000 * units.meter)\r\n shear06 = np.round((np.sqrt(u_shear06**2 + v_shear06**2)), 1)\r\n shear06 = shear06.to(units.knots)\r\n shear06 = np.round(shear06)\r\n rmover, lmover, mean = mpcalc.bunkers_storm_motion(p, u * units('m/s'), v * units('m/s'), z)\r\n #srh_01_pos, srh_01_neg, srh_01_tot = mpcalc.storm_relative_helicity( u * units('m/s'), v * units('m/s'), z * units('m'), depth = 1000 * units.meter, bottom = z[0], storm_u = lmover[0], storm_v = lmover[1])\r\n #srh_01 = np.round(srh_01_neg, 1)\r\n #srh_03_pos, srh_03_neg, srh_03_tot = mpcalc.storm_relative_helicity( u * units('m/s'), v * units('m/s'), z * units('m'), depth = 3000 * units.meter, bottom = z[0], storm_u = lmover[0], storm_v = lmover[1])\r\n #srh_03 = np.round(srh_03_neg, 1)\r\n '''\r\n # Compute the low-level (SFC-1500m) mean wind\r\n p_1p5km = interp.pres(prof, interp.to_msl(prof, 1500.))\r\n mnu2, mnv2 = mean_wind_npw(prof, prof.pres[prof.sfc], p_1p5km)\r\n\r\n # Compute the upshear vector\r\n upu = mnu1 - mnu2\r\n upv = mnv1 - mnv2\r\n\r\n # Compute the downshear vector\r\n dnu = mnu1 + upu\r\n dnv = mnv1 + upv\r\n\r\n #return upu, upv, dnu, dnv\r\n \r\n \r\n ## K Index\r\n self.k_idx = tab.utils.INT2STR( prof.k_idx )\r\n ## precipitable water\r\n self.pwat = prof.pwat\r\n ## 0-3km agl lapse rate\r\n self.lapserate_3km = tab.utils.FLOAT2STR( prof.lapserate_3km, 1 )\r\n ## 3-6km agl lapse rate\r\n self.lapserate_3_6km = tab.utils.FLOAT2STR( prof.lapserate_3_6km, 1 )\r\n ## 850-500mb lapse rate\r\n self.lapserate_850_500 = tab.utils.FLOAT2STR( prof.lapserate_850_500, 1 )\r\n ## 700-500mb lapse rate\r\n self.lapserate_700_500 = tab.utils.FLOAT2STR( prof.lapserate_700_500, 1 )\r\n ## convective temperature\r\n self.convT = prof.convT\r\n ## sounding forecast surface temperature\r\n self.maxT = prof.maxT\r\n #fzl = str(int(self.sfcparcel.hght0c))\r\n ## 100mb mean mixing ratio\r\n self.mean_mixr = tab.utils.FLOAT2STR( prof.mean_mixr, 1 )\r\n ## 150mb mean rh\r\n self.low_rh = tab.utils.INT2STR( prof.low_rh )\r\n self.mid_rh = tab.utils.INT2STR( prof.mid_rh )\r\n ## calculate the totals totals index\r\n self.totals_totals = tab.utils.INT2STR( prof.totals_totals )\r\n self.dcape = tab.utils.INT2STR( prof.dcape )\r\n self.drush = prof.drush\r\n self.sigsevere = tab.utils.INT2STR( prof.sig_severe )\r\n self.mmp = tab.utils.FLOAT2STR( prof.mmp, 2 )\r\n self.esp = tab.utils.FLOAT2STR( prof.esp, 1 )\r\n self.wndg = tab.utils.FLOAT2STR( prof.wndg, 1 )\r\n self.tei = tab.utils.INT2STR( prof.tei )\r\n '''\r\n \r\n right_mover,left_mover,wind_mean = mpcalc.bunkers_storm_motion(p, u * units('m/s'), v * units('m/s'), z)\r\n wind_mean = np.round(wind_mean)\r\n wind_mean = wind_mean.to(units.kt)\r\n wind_mean = np.round(wind_mean)\r\n\r\n pos_SRH,neg_SRH,total_SRH = mpcalc.storm_relative_helicity(z, u * units('m/s'), v * units('m/s'), depth = 3000 * units('m'), bottom = z[0], storm_u = lmover[0], storm_v = lmover[1])\r\n pos1_SRH,neg1_SRH,total1_SRH = mpcalc.storm_relative_helicity(z, u * units('m/s'), v * units('m/s'), depth = 1000 * units('m'), bottom = z[0], storm_u = lmover[0], storm_v = lmover[1])\r\n pos2_SRH,neg2_SRH,total2_SRH = mpcalc.storm_relative_helicity(z, u * units('m/s'), v * units('m/s'), depth = 2000 * units('m'), bottom = z[0], storm_u = lmover[0], storm_v = lmover[1])\r\n pos05_SRH,neg05_SRH,total05_SRH = mpcalc.storm_relative_helicity(z, u * units('m/s'), v * units('m/s'), depth = 500 * units('m'), bottom = z[0], storm_u = lmover[0], storm_v = lmover[1])\r\n # Not sure if this is really starting from 1km for the bottom - need to investigate this more\r\n pos13_SRH,neg13_SRH,total13_SRH = mpcalc.storm_relative_helicity(z, u * units('m/s'), v * units('m/s'), depth = 3000 * units('m'), bottom = z[10], storm_u = lmover[0], storm_v = lmover[1])\r\n #print(pos_SRH)\r\n #print(neg_SRH)\r\n #print(total_SRH)\r\n # new arrays\r\n # Need to round these numbers to make the string look pretty\r\n tot_SRH = np.round(total_SRH)\r\n tot1_SRH = np.round(total1_SRH)\r\n tot05_SRH = np.round(total05_SRH)\r\n tot13_SRH = np.round(total13_SRH)\r\n tot2_SRH = np.round(total2_SRH)\r\n \r\n # Layer Lapse Rates\r\n lr_05 = concatenate((mean_pressure_weighted(p, t, height=z, depth=500 * units('meter'))))\r\n lr_13 = concatenate((mean_pressure_weighted(p, t, height=z, depth=2000 * units('meter'), bottom=z[0] + 1000 * units('meter'))))\r\n lr_36 = concatenate((mean_pressure_weighted(p, t, height=z, depth=3000 * units('meter'), bottom=z[0] + 3000 * units('meter'))))\r\n lr_05 = np.round(lr_05)\r\n lr_13 = np.round(lr_13)\r\n lr_36 = np.round(lr_36)\r\n \r\n wind_mean6 = concatenate((mean_pressure_weighted(p, u * units('m/s'), v * units('m/s'), height=z, depth=6000 * units('meter'))))\r\n # mean wind from sfc-500m\r\n wind_500m = concatenate(mean_pressure_weighted(p, u * units('m/s'), v * units('m/s'), height=z,depth=500 * units('meter')))\r\n # mean wind from 5.5-6km\r\n wind_5500m = concatenate(mean_pressure_weighted(p, u * units('m/s'), v * units('m/s'), height=z, depth=500 * units('meter'), bottom=z[0] + 5500 * units('meter')))\r\n # mean wind from sfc-1km\r\n wind_mean1 = concatenate(mean_pressure_weighted(p, u * units('m/s'), v * units('m/s'), height=z,depth=1000 * units('meter')))\r\n # mean wind from 6-9km\r\n wind_6_9m = concatenate(mean_pressure_weighted(p, u * units('m/s'), v * units('m/s'), height=z, depth=3000 * units('meter'), bottom=z[0] + 6000 * units('meter')))\r\n # mean wind from 8.5-9km\r\n wind_8500m = concatenate(mean_pressure_weighted(p, u * units('m/s'), v * units('m/s'), height=z, depth=500 * units('meter'), bottom=z[0] + 8500 * units('meter')))\r\n \r\n shear = wind_mean1 - wind_500m\r\n shear_cross = concatenate([shear[1], -shear[0]])\r\n shear_mag = np.hypot(*(arg.magnitude for arg in shear)) * shear.units\r\n rdev = shear_cross * (7.5 / shear_mag)\r\n \r\n # Add the deviations to the layer average wind to get the RM motion\r\n right_mover1 = wind_mean1 + rdev * units('m/s')\r\n right_mover1 = right_mover1.to(units.knots)\r\n right_mover1 = np.round(right_mover1)\r\n\r\n # Subtract the deviations to get the LM motion\r\n left_mover1 = wind_mean1 - rdev * units('m/s')\r\n left_mover1 = left_mover1.to(units.knots)\r\n left_mover1 = np.round(left_mover1)\r\n \r\n shear69 = wind_8500m - wind_mean6\r\n shear_cross69 = concatenate([shear69[1], -shear69[0]])\r\n shear_mag69 = np.hypot(*(arg.magnitude for arg in shear)) * shear.units\r\n rdev69 = shear_cross69 * (7.5 / shear_mag)\r\n \r\n right_mover69 = wind_mean6 + rdev69 * units('m/s')\r\n right_mover69 = right_mover69.to(units.knots)\r\n right_mover69 = np.round(right_mover69)\r\n \r\n left_mover69 = wind_mean6 - rdev69 * units('m/s')\r\n left_mover69 = left_mover69.to(units.knots)\r\n left_mover69 = np.round(left_mover69)\r\n\r\n # Need to first convert to knots then round to nearest whole number\r\n wind_mean6 = wind_mean6.to(units.knots)\r\n wind_mean6 = np.round(wind_mean6)\r\n wind_500m = wind_500m.to(units.knots)\r\n wind_500m = np.round(wind_500m)\r\n wind_5500m = wind_5500m.to(units.knots)\r\n wind_5500m = np.round(wind_5500m)\r\n wind_mean1 = wind_mean1.to(units.knots)\r\n wind_mean1 = np.round(wind_mean1)\r\n wind_6_9m = wind_6_9m.to(units.knots)\r\n wind_6_9m = np.round(wind_6_9m)\r\n \r\n \r\n bunk_right_dir = np.round(mpcalc.wind_direction(right_mover[0], right_mover[1]))\r\n bunk_left_dir = np.round(mpcalc.wind_direction(left_mover[0], left_mover[1]))\r\n bunk_right_spd = np.round(np.sqrt(right_mover[0]**2 + right_mover[1]**2))\r\n bunk_right_spd = bunk_right_spd.to(units.knots)\r\n bunk_right_spd = np.round(bunk_right_spd)\r\n bunk_left_spd = np.round(np.sqrt(left_mover[0]**2 + left_mover[1]**2))\r\n bunk_left_spd = bunk_left_spd.to(units.knots)\r\n bunk_left_spd = np.round(bunk_left_spd)\r\n bunk_right_dir = np.round(bunk_right_dir)\r\n bunk_left_dir = np.round(bunk_left_dir)\r\n #bunk_right_spd = bunk_right_spd * units.knots\r\n #bunk_left_spd = bunk_left_spd * units.knots\r\n \r\n # Calculate composite parameters\r\n ehi_01 = np.round(np.divide(neg1_SRH * sbcape, 160000 * ((units.m**2 * units.joule)/(units.s**2 * units.kilogram))), 1)\r\n ehi_03 = np.round(np.divide(neg_SRH * sbcape, 160000 * ((units.m**2 * units.joule)/(units.s**2 * units.kilogram))), 1)\r\n scp = np.round(np.divide(sbcape, 1000 * units('J/kg')) * np.divide(shear06, 20 * units('m/s')) * np.divide(neg_SRH, 100 * (units.m**2/units.s**2)), 1)\r\n sig_tor = np.round((mpcalc.significant_tornado(sbcape, lcl_hgt, neg1_SRH, shear06)), 1)\r\n\r\n # Calculate critical angle, then round to nearest whole number\r\n critical_angle = mpcalc.critical_angle(p, u * units('m/s'), v * units('m/s'), z, u_storm = lmover[0], v_storm = lmover[1])\r\n ca = np.round(critical_angle)\r\n \r\n ntmp = tmp\r\n\r\n # where p=pressure(pa), T=temp(C), T0=reference temp(273.16)\r\n rh = 0.263*prs*sh / (np.exp(17.67*ntmp/(ntmp+273.15-29.65)))\r\n vaps = 6.112 * np.exp((17.67 * ntmp) / (ntmp + 243.5))\r\n vapr = rh * vaps / 100\r\n dwpc = np.array(243.5 * (np.log(6.112) - np.log(vapr)) / (np.log(vapr) - np.log(6.112) - 17.67)) * units.degC\r\n\r\n plt.rcParams['figure.figsize'] = (12, 14)\r\n fig = plt.figure(figsize=(24, 14))\r\n\r\n gs = fig.add_gridspec(ncols=2,nrows=1)\r\n\r\n\t# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))\r\n #ax3 = fig.add_subplot(gs[1, 0])\r\n #ax4 = fig.add_subplot(gs[1, 1])\r\n\r\n # Grid for plots\r\n #skew = SkewT(fig, rotation=45, subplot=gs[0, 0])\r\n \r\n # Grid for plots\r\n skew = SkewT(fig, rotation=45, subplot=gs[0, 0])\r\n\r\n\r\n\r\n # Plot the data\r\n skew.plot(p, t, 'r', linewidth=2)\r\n skew.plot(p, td, 'b', linewidth=2)\r\n skew.plot(p, td2, 'y')\r\n skew.plot(p, dwpc, 'g', linewidth=2)\r\n skew.plot_moist_adiabats(color='grey',alpha=0.2)\r\n skew.plot_mixing_lines(color='grey',alpha=0.2)\r\n skew.plot_dry_adiabats(color='grey',alpha=0.2)\r\n #skew.plot(p, wetbulb, 'b', linewidth=1)\r\n skew.plot(lcl_pressure, lcl_temperature, marker=\"_\", color='orange', markersize=30, markeredgewidth=3, label='LCL')\r\n skew.plot(lfc_pressure, lfc_temperature, marker=\"_\", color='brown', markersize=30, markeredgewidth=2, label='LFC')\r\n skew.plot(el_pressure, el_temperature, marker=\"_\", color='darkblue', markersize=30, markeredgewidth=2, label='EL')\r\n skew.ax.text(0.90, lfc_pressure, '- LFC', verticalalignment='center', color='orange', alpha=0.9)\r\n skew.ax.text(0.90, lcl_pressure, '- LCL', verticalalignment='center', color='brown', alpha=0.9)\r\n skew.ax.text(0.90, el_pressure, '- EL', verticalalignment='center', color='darkblue', alpha=0.9)\r\n # Calculate full parcel profile and add to plot as black line\r\n prof = parcel_profile(p, t[0], td[0]).to('degC')\r\n prof_mu = parcel_profile(p, t[0], td[0]).to('degC')\r\n prof_lcl = parcel_profile_with_lcl(p, t, td)\r\n prof_li = lifted_index(p, t, prof)\r\n #li = mpcalc.lifted_index(p, t[0], prof)\r\n print(prof_li)\r\n \r\n lr_700_500 = np.round(-1 * np.divide(t[24]-t[14], (z[24]-z[14])),2)\r\n lr_850_500 = np.round(-1 * np.divide(t[24]-t[12], (z[24]-z[12])),2)\r\n lr_sfc_3 = np.round(-1 * np.divide(t[18]-t[0], (z[18]-z[0])),2)\r\n \r\n # Kinematic Calculations\r\n bulkshear = bulk_shear(p, u*units.knots, v*units.knots)\r\n \r\n skew.plot(p, prof, 'k', linewidth=2)\r\n #skew.plot(p, prof_mu, 'red', linewidth=2)\r\n #skew.plot(p, prof_lcl, 'red', linewidth=2)\r\n \r\n # Shade areas of CAPE and CIN\r\n #skew.shade_cin(p, t, prof, td)\r\n skew.shade_cape(p, t, prof, alpha=0.3)\r\n skew.shade_cape(p, t, prof_mu, alpha=0.5)\r\n\r\n skew.plot_barbs(p, u, v)\r\n skew.ax.set_ylim(1000, 100)\r\n plt.xlabel(\"Temperature [C]\")\r\n skew.ax.set_xlim(-40, 60)\r\n plt.ylabel(\"Height [m above MSL]\")\r\n \r\n plt.figtext( 0.15, 0.36, 'Levels (km):', fontsize=12)\r\n plt.figtext( 0.15, 0.35, 'LCL:', fontsize=12)\r\n plt.figtext( 0.17, 0.35, f'{lcl_hgt}', fontsize=12)\r\n plt.figtext( 0.15, 0.34, 'LFC:', fontsize=12)\r\n plt.figtext( 0.17, 0.34, f'{lfc_hgt:~P}', fontsize=12)\r\n plt.figtext( 0.15, 0.33, 'EL:', fontsize=12)\r\n plt.figtext( 0.17, 0.33, f'{el_hgt:~P}', fontsize=12)\r\n plt.figtext( 0.28, 0.20, 'MLLR:', fontsize=14)\r\n plt.figtext( 0.31, 0.20, f'{lr_700_500:~P}', fontsize=14)\r\n plt.figtext( 0.28, 0.18, 'LLLR:', fontsize=14)\r\n plt.figtext( 0.31, 0.18, f'{lr_sfc_3:~P}', fontsize=14)\r\n plt.figtext( 0.28, 0.16, '0-0.5:', fontsize=14)\r\n plt.figtext( 0.33, 0.16, f'{lr_05[0]:~P}', fontsize=14)\r\n plt.figtext( 0.28, 0.14, '1-3:', fontsize=14)\r\n plt.figtext( 0.33, 0.14, f'{lr_13[0]:~P}', fontsize=14)\r\n plt.figtext( 0.28, 0.12, '3-6:', fontsize=14)\r\n plt.figtext( 0.33, 0.12, f'{lr_36[0]:~P}', fontsize=14)\r\n plt.figtext( 0.37, 0.20, 'SBCAPE:', fontsize=14)\r\n plt.figtext( 0.41, 0.20, f'{sbcape:~P}', fontsize=14)\r\n plt.figtext( 0.37, 0.18, 'SBCIN:', fontsize=14)\r\n \r\n plt.figtext( 0.41, 0.18, f'{sbcin:~P}', fontsize=14)\r\n plt.figtext( 0.37, 0.16, 'MLCAPE:', fontsize=14)\r\n plt.figtext( 0.41, 0.16, f'{mlcape:~P}', fontsize=14)\r\n plt.figtext( 0.37, 0.14, 'MLCIN:', fontsize=14)\r\n plt.figtext( 0.41, 0.14, f'{mlcin:~P}', fontsize=14)\r\n plt.figtext( 0.37, 0.12, 'MUCAPE:', fontsize=14)\r\n plt.figtext( 0.41, 0.12, f'{mucape:~P}', fontsize=14)\r\n plt.figtext( 0.45, 0.20, '0-1km EHI:', fontsize=14)\r\n plt.figtext( 0.50, 0.20, f'{ehi_01:~P}', fontsize=14)\r\n plt.figtext( 0.45, 0.18, '0-3km EHI:', fontsize=14)\r\n plt.figtext( 0.50, 0.18, f'{ehi_03:~P}', fontsize=14)\r\n plt.figtext( 0.45, 0.16, 'SCP:', fontsize=14)\r\n plt.figtext( 0.49, 0.16, f'{scp:~P}', fontsize=14)\r\n plt.figtext( 0.45, 0.14, 'STP:', fontsize=14)\r\n plt.figtext( 0.49, 0.14, f'{sig_tor[0]:~P}', fontsize=14)\r\n plt.figtext( 0.45, 0.12, 'EL T:', fontsize=14)\r\n plt.figtext( 0.49, 0.12, f'{el_temperature:.2~P}', fontsize=14)\r\n #plt.figtext( 0.18, 0.25, f'{el_pressure:.2~P}')\r\n #plt.figtext( 0.80, 0.42, 'BulkShear:')\r\n #plt.figtext( 0.80, 0.42, '{0} knots'.format(bulkshear))\r\n #plt.figtext( 0.25, 0.32, 'LI:')\r\n #plt.figtext( 0.27, 0.32, f'{li:~P}')\r\n #plt.figtext( 0.25, 0.31, 'H7-H5 LR:')\r\n #plt.figtext( 0.32, 0.31, f'{lr_700_500:~P}')\r\n #plt.figtext( 0.25, 0.30, 'H8-H5 LR:')\r\n #plt.figtext( 0.32, 0.30, f'{lr_850_500:~P}')\r\n #plt.figtext( 0.25, 0.29, 'Sfc-3km LR:')\r\n #plt.figtext( 0.32, 0.29, f'{lr_sfc_3:~P}')\r\n \r\n plt.figtext( 0.79, 0.78, 'Bulk Shear', fontsize=14)\r\n plt.figtext( 0.79, 0.76, '0-0.5km:' ,fontsize=14)\r\n plt.figtext( 0.84, 0.76, f'{shear005:~P}', fontsize=14, color='purple')\r\n plt.figtext( 0.79, 0.74, '0-1 km:', fontsize=14)\r\n plt.figtext( 0.84, 0.74, f'{shear01:~P}', fontsize=14, color='purple')\r\n plt.figtext( 0.79, 0.72, '0-1.5km:',fontsize=14)\r\n plt.figtext( 0.84, 0.72, f'{shear015:~P}', fontsize=14, color='purple')\r\n plt.figtext( 0.79, 0.70, '0-2 km:', fontsize=14)\r\n plt.figtext( 0.84, 0.70, f'{shear02:~P}', fontsize=14)\r\n plt.figtext( 0.79, 0.68, '0-3 km:', fontsize=14)\r\n plt.figtext( 0.84, 0.68, f'{shear03:~P}', fontsize=14)\r\n plt.figtext( 0.79, 0.66, '0-6 km:', fontsize=14)\r\n plt.figtext( 0.84, 0.66, f'{shear06:~P}', fontsize=14)\r\n plt.figtext( 0.79, 0.64, 'SR Wind:', fontsize=14)\r\n #plt.figtext( 0.84, 0.59, '{}'.format(sr_wind))\r\n #plt.figtext( 0.65, 0.30, 'SRH 0-1 km:')\r\n #plt.figtext( 0.8, 0.30, f'{srh_01:~P}')\r\n plt.figtext ( 0.55, 0.78, 'Critical SRH', fontsize=14)\r\n plt.figtext ( 0.55, 0.76, 'SRH 0-0.5:', fontsize=14)\r\n plt.figtext ( 0.60, 0.76, f'{tot05_SRH:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.74, 'SRH 0-1:', fontsize=14)\r\n plt.figtext ( 0.59, 0.74, f'{tot1_SRH:~P}', fontsize=14)\r\n plt.figtext (0.55, 0.72, 'SRH 1-3:', fontsize=14)\r\n plt.figtext (0.59, 0.72, f'{tot13_SRH:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.70, 'SRH 0-2:', fontsize=14)\r\n plt.figtext ( 0.59, 0.70, f'{tot2_SRH:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.68, 'SRH 0-3:', fontsize=14)\r\n plt.figtext ( 0.59, 0.68, f'{tot_SRH:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.38, 'Bunkers Rgt:', fontsize=14)\r\n plt.figtext ( 0.61, 0.38, f'{bunk_right_spd:~P} at {bunk_right_dir:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.36, 'Bunkers Lft:', fontsize=14)\r\n plt.figtext ( 0.61, 0.36, f'{bunk_left_spd:~P} at {bunk_left_dir:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.34, '0-6km Mean:', fontsize=14)\r\n plt.figtext ( 0.61, 0.34, f'{wind_mean[0]:~P}', fontsize=14)\r\n plt.figtext ( 0.79, 0.15, 'Critical Angle:', fontsize=14)\r\n plt.figtext ( 0.85, 0.15, f'{ca:~P}', fontsize=14)\r\n plt.figtext ( 0.55, 0.15, 'SR Mean Wind', fontsize=14)\r\n plt.figtext ( 0.59, 0.13, 'LM', fontsize=12)\r\n plt.figtext ( 0.63, 0.13, 'MW', fontsize=12)\r\n plt.figtext ( 0.67, 0.13, 'RM', fontsize=12)\r\n plt.figtext ( 0.55, 0.12, '0-1km:', fontsize=12)\r\n plt.figtext ( 0.58, 0.12, f'{left_mover1[0]:~P}', fontsize=12, color='blue')\r\n plt.figtext ( 0.62, 0.12, f'{wind_mean1[0]:~P}', fontsize=12, color='grey')\r\n plt.figtext ( 0.66, 0.12, f'{right_mover1[0]:~P}', fontsize=12, color='red')\r\n plt.figtext ( 0.55, 0.11, '6-9km:', fontsize=12)\r\n plt.figtext ( 0.58, 0.11, f'{left_mover69[0]:~P}', fontsize=12, color='blue')\r\n plt.figtext ( 0.66, 0.11, f'{right_mover69[0]:~P}', fontsize=12, color='red')\r\n plt.figtext ( 0.62, 0.11, f'{wind_6_9m[0]:~P}', fontsize=12, color='grey')\r\n plt.figtext ( 0.81, 0.23, '0-0.5km Vec', fontsize=10, color='darkblue')\r\n plt.figtext ( 0.81, 0.22, '0-3km Vec', fontsize=10, color='magenta')\r\n plt.figtext ( 0.81, 0.21, '0-6km Vec', fontsize=10, color='gold')\r\n plt.figtext ( 0.67, 0.78, 'SR Mean Wind', fontsize=14)\r\n plt.figtext ( 0.67, 0.76, '0-1km:', fontsize=14)\r\n plt.figtext ( 0.70, 0.76, f'{wind_mean1[0]:~P}', fontsize=14)\r\n plt.figtext ( 0.67, 0.74, '6-9km:', fontsize=14)\r\n plt.figtext ( 0.70, 0.74, f'{wind_6_9m[0]:~P}', fontsize=14)\r\n \r\n\r\n \r\n plt.title( forecastModel + \" \" \\\r\n + ob.getLocationName() \\\r\n# + \"(\"+ str(ob.getGeometry()) + \")\" \\\r\n + \", \" + str(ob.getDataTime()) \\\r\n + \", \" + \"Hour\" + \" \" + str(int(fcstHour)/60/60)\r\n )\r\n\r\n # An example of a slanted line at constant T -- in this case the 0 isotherm\r\n l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\r\n m20 = skew.ax.axvline(-20, color='darkblue', linestyle='--', linewidth=2)\r\n\r\n # Draw hodograph\r\n ax_hod = fig.add_subplot(gs[0, 1])\r\n h = Hodograph(ax_hod, component_range=spd.max()/units.knots)\r\n h.add_grid(increment=20, alpha=0.2)\r\n h.plot_colormapped(u, v, spd)\r\n origin = np.array([[0, 0, 0],[0, 0, 0]])\r\n #plt.quiver(*origin, wind_mean, color='grey', scale=21)\r\n #plt.quiver(*origin, bunk_left_dir, bunk_left_spd, color='grey', scale=21)\r\n #plt.quiver(*origin, u_storm, v_storm, color='grey', scale=21)\r\n \r\n # Draw vector hodograph\r\n ax_hod = inset_axes(ax_hod, '25%', '25%', loc=4)\r\n h = Hodograph(ax_hod, component_range=spd.max()/units.knots)\r\n h.add_grid(increment=20, alpha=0.4)\r\n h.plot(u, v, color='grey', alpha=0.0)\r\n #h.plot(right_mover, 'ko', color='red')\r\n origin = np.array([[0, 0, 0],[0, 0, 0]]) # origin point\r\n plt.quiver(*origin, u_shear005, v_shear005, color='darkblue', scale=21)\r\n plt.quiver(*origin, u_shear03, v_shear03, color='magenta', scale=21)\r\n plt.quiver(*origin, u_shear06, v_shear06, color='gold', scale=21)\r\n \r\n ######## Save the plot\r\n \r\n plt.savefig((output_dir+'/Soundings/NAM/CHA/'+str(int(fcstHour)/60/60)+'.png'),bbox_inches='tight',pad_inches=0.1)\r\n fcst_hr = str(0)\r\n plt.clf()", "sub_path": "sounding_nam_stl.py", "file_name": "sounding_nam_stl.py", "file_ext": "py", "file_size_in_byte": 36545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.changeEDEXHost", "line_number": 83, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 83, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.newDataRequest", "line_number": 84, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 84, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.getAvailableLocationNames", "line_number": 89, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 89, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.getAvailableTimes", "line_number": 97, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 97, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.getAvailableTimes", "line_number": 98, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 98, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.getForecastRun", "line_number": 101, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 101, "usage_type": "name"}, {"api_name": "awips.dataaccess.DataAccessLayer.getGeometryData", "line_number": 103, "usage_type": "call"}, {"api_name": "awips.dataaccess.DataAccessLayer", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 119, "usage_type": "call"}, {"api_name": "metpy.units.units.degC", "line_number": 130, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 130, "usage_type": "name"}, {"api_name": "metpy.units.units.mbar", "line_number": 131, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 131, "usage_type": "name"}, {"api_name": "metpy.calc.pressure_to_height_std", "line_number": 132, "usage_type": "call"}, {"api_name": "metpy.calc", "line_number": 132, "usage_type": "name"}, {"api_name": "metpy.calc.wind_speed", "line_number": 140, "usage_type": "call"}, {"api_name": "metpy.units.units.knots", "line_number": 140, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 140, "usage_type": "name"}, {"api_name": "metpy.calc.wind_direction", "line_number": 141, "usage_type": "call"}, {"api_name": "metpy.units.units.knots", "line_number": 141, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 141, "usage_type": "name"}, {"api_name": "metpy.units.units.deg", "line_number": 141, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 143, "usage_type": "call"}, {"api_name": "metpy.calc.vapor_pressure", "line_number": 144, "usage_type": "call"}, {"api_name": "metpy.calc.dewpoint", "line_number": 145, "usage_type": "call"}, {"api_name": "metpy.calc.dewpoint", "line_number": 149, "usage_type": "call"}, {"api_name": "metpy.calc.vapor_pressure", "line_number": 149, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp.temp", "line_number": 186, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 186, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.dwpt", "line_number": 187, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 187, "usage_type": "name"}, {"api_name": "sharppy.sharptab.thermo.theta", "line_number": 202, "usage_type": "call"}, {"api_name": "sharppy.sharptab.thermo", "line_number": 202, "usage_type": "name"}, {"api_name": "sharppy.sharptab.thermo.temp_at_mixrat", "line_number": 204, "usage_type": "call"}, {"api_name": "sharppy.sharptab.thermo", "line_number": 204, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.temp", "line_number": 214, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 214, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.dwpt", "line_number": 215, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 215, "usage_type": "name"}, {"api_name": "sharppy.sharptab.utils.QC", "line_number": 226, "usage_type": "call"}, {"api_name": "sharppy.sharptab.utils", "line_number": 226, "usage_type": "name"}, {"api_name": "sharppy.sharptab.thermo.theta", "line_number": 231, "usage_type": "call"}, {"api_name": "sharppy.sharptab.thermo", "line_number": 231, "usage_type": "name"}, {"api_name": "sharppy.sharptab.thermo.temp_at_mixrat", "line_number": 232, "usage_type": "call"}, {"api_name": "sharppy.sharptab.thermo", "line_number": 232, "usage_type": "name"}, {"api_name": "sharppy.sharptab.utils.QC", "line_number": 238, "usage_type": "call"}, {"api_name": "sharppy.sharptab.utils", "line_number": 238, "usage_type": "name"}, {"api_name": "sharppy.sharptab.utils.QC", "line_number": 240, "usage_type": "call"}, {"api_name": "sharppy.sharptab.utils", "line_number": 240, "usage_type": "name"}, {"api_name": "sharppy.sharptab.utils.QC", "line_number": 275, "usage_type": "call"}, {"api_name": "sharppy.sharptab.utils", "line_number": 275, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.to_msl", "line_number": 279, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 279, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.to_msl", "line_number": 280, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 280, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.pres", "line_number": 281, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 281, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.pres", "line_number": 282, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 282, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 285, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 288, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp.components", "line_number": 289, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 289, "usage_type": "name"}, {"api_name": "sharppy.sharptab.interp.components", "line_number": 290, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 290, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 294, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp.components", "line_number": 295, "usage_type": "call"}, {"api_name": "sharppy.sharptab.interp", "line_number": 295, "usage_type": "name"}, {"api_name": "sharppy.sharptab.utils.KTS2MS", "line_number": 296, "usage_type": "call"}, {"api_name": "sharppy.sharptab.utils", "line_number": 296, "usage_type": "name"}, {"api_name": "sharppy.sharptab.utils.KTS2MS", "line_number": 297, "usage_type": "call"}, {"api_name": "sharppy.sharptab.utils", "line_number": 297, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.tei", "line_number": 349, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 349, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.esp", "line_number": 350, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 350, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.mmp", "line_number": 351, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 351, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.wndg", "line_number": 352, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 352, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.sig_severe", "line_number": 353, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 353, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.dcape", "line_number": 354, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 354, "usage_type": "name"}, {"api_name": "sharppy.sharptab.thermo.ctof", "line_number": 355, "usage_type": "call"}, {"api_name": "sharppy.sharptab.thermo", "line_number": 355, "usage_type": "name"}, {"api_name": "sharppy.sharptab.params.mburst", "line_number": 356, "usage_type": "call"}, {"api_name": "sharppy.sharptab.params", "line_number": 356, "usage_type": "name"}, {"api_name": "metpy.calc.lcl", "line_number": 359, "usage_type": "call"}, {"api_name": "metpy.calc.lfc", "line_number": 362, "usage_type": "call"}, {"api_name": "metpy.calc.el", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 366, "usage_type": "call"}, {"api_name": "metpy.calc.pressure_to_height_std", "line_number": 366, "usage_type": "call"}, {"api_name": "metpy.calc", "line_number": 366, "usage_type": "name"}, {"api_name": "metpy.units.units.meter", "line_number": 366, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 366, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 367, "usage_type": "call"}, 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"matplotlib.pyplot.figtext", "line_number": 648, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 648, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 649, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 649, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 650, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 650, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 651, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 651, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 652, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 652, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 653, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 653, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 654, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 654, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 655, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 655, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 656, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 656, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 657, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 657, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 658, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 658, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 659, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 659, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 660, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 660, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 661, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 661, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 662, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 662, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 663, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 663, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 664, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 664, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 665, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 665, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 667, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 667, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 668, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 668, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 669, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 669, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 670, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 670, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 671, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 671, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 672, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 672, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 673, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 673, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 674, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 674, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 675, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 675, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 676, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 676, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 677, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 677, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 678, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 678, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 679, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 679, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 680, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 680, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 681, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 681, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 682, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 682, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 683, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 683, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 696, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 696, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 697, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 697, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 698, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 698, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 699, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 699, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 700, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 700, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 701, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 701, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 702, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 702, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 703, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 703, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 704, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 704, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 705, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 705, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 706, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 706, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 707, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 707, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 708, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 708, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 709, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 709, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 713, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 713, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 714, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 714, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 715, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 715, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 716, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 716, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 717, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 717, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 718, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 718, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 719, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 719, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 720, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 720, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 721, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 721, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 722, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 722, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 723, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 723, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 724, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 724, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 725, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 725, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 726, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 726, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 727, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 727, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 728, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 728, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 729, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 729, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 730, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 730, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 731, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 731, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 732, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 732, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 733, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 733, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 734, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 734, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 735, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 735, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 736, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 736, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 737, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 737, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 738, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 738, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 739, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 739, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 740, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 740, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 741, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 741, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 742, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 742, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 743, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 743, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 744, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 744, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 745, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 745, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 746, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 746, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 747, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 747, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 748, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 748, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 749, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 749, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 750, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 750, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 751, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 751, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 755, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 755, "usage_type": "name"}, {"api_name": "metpy.plots.Hodograph", "line_number": 768, "usage_type": "call"}, {"api_name": "metpy.units.units.knots", "line_number": 768, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 768, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 771, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1.inset_locator.inset_axes", "line_number": 777, "usage_type": "call"}, {"api_name": "metpy.plots.Hodograph", "line_number": 778, "usage_type": "call"}, {"api_name": "metpy.units.units.knots", "line_number": 778, "usage_type": "attribute"}, {"api_name": "metpy.units.units", "line_number": 778, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 782, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.quiver", "line_number": 783, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 783, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.quiver", "line_number": 784, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 784, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.quiver", "line_number": 785, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 785, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 789, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 789, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 791, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 791, "usage_type": "name"}]} +{"seq_id": "67647599", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom django.contrib import admin\nfrom django.core.mail import send_mail\nfrom django.conf import settings\nfrom .models import Booking, AssignMechanic\n\n\ndef mark_assigned(modelAdmin, request, queryset):\n queryset.update(status='ASSIGNED')\nmark_assigned.short_description = 'Mark Selected as Assigned'\n\n\ndef mark_delivered(modelAdmin, request, queryset):\n queryset.update(status='DELIVERED')\nmark_delivered.short_description = 'Mark Selected as Delivered'\n\n\n\ndef send_email(modelAdmin, request, queryset): \n for profile in queryset: \n send_mail(\"subject\", \"message\", settings.EMAIL_HOST_USER, [profile.email,], fail_silently=False)\nsend_email.short_description = 'Send Email'\nsend_email.allow_tags = True\n\n\n# class MechanicAdmin(admin.TabularInline):\n# model = AssignMechanic\n# list_display = ['name', \n# 'phone_number',\n# 'email',\n# ]\n \n\n \n \n\n\nclass BookAdmin(admin.ModelAdmin):\n # inlines= [MechanicAdmin]\n list_display = ['name',\n 'email',\n 'phone_number',\n 'schedule_date',\n 'status', ]\n search_fields = ['email', 'brand', 'name', 'oil_change', 'phone_number', 'assigned']\n list_per_page = 25\n list_filter = ['status', 'schedule_date', 'assigned', 'brand', 'service_time']\n list_editable = ['status', ]\n actions = [ mark_assigned, mark_delivered, send_email ]\n\n \n\n \n\n\n \n\nadmin.site.register(Booking, BookAdmin)\n\n# admin.site.register(AssignMechanic)\n\n\n", "sub_path": "services/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.core.mail.send_mail", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_HOST_USER", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Booking", "line_number": 60, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "363086673", "text": "\"\"\"\nUnit tests for resdk/resources/collection.py file.\n\"\"\"\n# pylint: disable=missing-docstring, protected-access\n\nimport unittest\n\nimport six\n\nfrom mock import patch, MagicMock\n\nfrom resdk.resources.collection import BaseCollection, Collection\nfrom resdk.resources.sample import Sample\nfrom resdk.tests.mocks.data import DATA_SAMPLE\n\nDATA1 = MagicMock(\n process_type=\"data:reads:fastq:single:\", **{'files.return_value': ['reads.fq', 'arch.gz']})\n\nDATA2 = MagicMock(\n process_type='data:expression:blah',\n **{'files.return_value': ['output.exp']})\n\n\nclass TestBaseCollection(unittest.TestCase):\n\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_init(self, collection_mock):\n collection_mock.configure_mock(endpoint=\"fake_endpoint\")\n BaseCollection.__init__(collection_mock, id=1, resolwe=MagicMock())\n\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_data_types(self, collection_mock):\n api_mock = MagicMock(**{'data.return_value': MagicMock(**{'get.return_value': DATA_SAMPLE[0]})})\n collection_mock.configure_mock(data=[1, 2], resolwe=MagicMock(api=api_mock))\n\n types = BaseCollection.data_types(collection_mock)\n self.assertEqual(types, [u'data:reads:fastq:single:'])\n\n @patch('resdk.resources.collection.Data')\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_files(self, collection_mock, data_mock):\n collection_mock.configure_mock(data=[1, 2], resolwe=\" \")\n data_mock.side_effect = [DATA1, DATA2]\n\n flist = BaseCollection.files(collection_mock)\n self.assertEqual(set(flist), set(['arch.gz', 'reads.fq', 'output.exp']))\n\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_print_annotation(self, collection_mock):\n with self.assertRaises(NotImplementedError):\n BaseCollection.print_annotation(collection_mock)\n\n\nclass TestBaseCollectionDownload(unittest.TestCase):\n\n @patch('resdk.resources.collection.Data')\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_data_type_short(self, collection_mock, data_mock):\n collection_mock.configure_mock(data=[1, 2], resolwe=MagicMock())\n data_mock.side_effect = [DATA1, DATA2]\n\n BaseCollection.download(collection_mock, data_type='fastq')\n flist = [u'1/reads.fq', u'1/arch.gz']\n collection_mock.resolwe.download_files.assert_called_once_with(flist, None)\n\n @patch('resdk.resources.collection.Data')\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_data_type_tuple(self, collection_mock, data_mock):\n collection_mock.configure_mock(data=[1, 2], resolwe=MagicMock())\n data_mock.side_effect = [DATA1, DATA2]\n\n BaseCollection.download(collection_mock, data_type=('data:expression:', 'output.exp'))\n flist = [u'2/output.exp']\n collection_mock.resolwe.download_files.assert_called_once_with(flist, None)\n\n @patch('resdk.resources.collection.Data')\n @patch('resdk.resources.collection.BaseCollection', spec=True)\n def test_bad_data_type(self, collection_mock, data_mock):\n message = \"Invalid argument value data_type.\"\n with six.assertRaisesRegex(self, ValueError, message):\n BaseCollection.download(collection_mock, data_type=123)\n\n\nclass TestCollection(unittest.TestCase):\n\n @patch('resdk.resources.collection.Collection', spec=True)\n def test_collection_init(self, collection_mock):\n collection_mock.configure_mock(endpoint=\"fake_endpoint\")\n Collection.__init__(collection_mock, id=1, resolwe=MagicMock())\n\n @patch('resdk.resources.collection.Collection', spec=True)\n def test_collection_print_ann(self, collection_mock):\n with self.assertRaises(NotImplementedError):\n Collection.print_annotation(collection_mock)\n\n\nclass TestSample(unittest.TestCase):\n\n @patch('resdk.resources.sample.Sample', spec=True)\n def test_sample_init(self, sample_mock):\n sample_mock.configure_mock(endpoint=\"fake_endpoint\")\n Sample.__init__(sample_mock, id=1, resolwe=MagicMock())\n\n @patch('resdk.resources.sample.Sample', spec=True)\n def test_sample_print_annotation(self, sample_mock):\n with self.assertRaises(NotImplementedError):\n Sample.print_annotation(sample_mock)\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "resdk/tests/unit/test_collections.py", "file_name": "test_collections.py", "file_ext": "py", "file_size_in_byte": 4417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "mock.MagicMock", "line_number": 16, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 19, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 24, "usage_type": "attribute"}, {"api_name": "resdk.resources.collection.BaseCollection.__init__", "line_number": 29, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 29, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 29, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 26, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 33, "usage_type": "call"}, {"api_name": "resdk.tests.mocks.data.DATA_SAMPLE", "line_number": 33, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 34, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection.data_types", "line_number": 36, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 36, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 31, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection.files", "line_number": 45, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 45, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 39, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 40, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection.print_annotation", "line_number": 51, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 51, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 48, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 54, "usage_type": "attribute"}, {"api_name": "mock.MagicMock", "line_number": 59, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection.download", "line_number": 62, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 62, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 56, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 57, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 69, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection.download", "line_number": 72, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 72, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 66, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 67, "usage_type": "call"}, {"api_name": "six.assertRaisesRegex", "line_number": 80, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection.download", "line_number": 81, "usage_type": "call"}, {"api_name": "resdk.resources.collection.BaseCollection", "line_number": 81, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 76, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 77, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 84, "usage_type": "attribute"}, {"api_name": "resdk.resources.collection.Collection.__init__", "line_number": 89, "usage_type": "call"}, {"api_name": "resdk.resources.collection.Collection", "line_number": 89, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 89, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 86, "usage_type": "call"}, {"api_name": "resdk.resources.collection.Collection.print_annotation", "line_number": 94, "usage_type": "call"}, {"api_name": "resdk.resources.collection.Collection", "line_number": 94, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 91, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 97, "usage_type": "attribute"}, {"api_name": "resdk.resources.sample.Sample.__init__", "line_number": 102, "usage_type": "call"}, {"api_name": "resdk.resources.sample.Sample", "line_number": 102, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 102, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 99, "usage_type": "call"}, {"api_name": "resdk.resources.sample.Sample.print_annotation", "line_number": 107, "usage_type": "call"}, {"api_name": "resdk.resources.sample.Sample", "line_number": 107, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 104, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "484160889", "text": "import clusto\nimport llclusto\nimport re\nfrom django.http import HttpResponseBadRequest, HttpResponseNotFound, HttpResponseBadRequest, HttpResponse\nfrom jinx_api.http import HttpResponseInvalidState\nfrom llclusto.drivers import PGIImage, RevertPGIImageError\n\ndef list_servable_pgi_images(request):\n \"\"\"Returns a list of servable PGI images stored on the PGI systemimagers.\n\n Returns:\n A list of pgi image names as strings.\n\n Arguments:\n None\n\n Exceptions Raised:\n None\n \"\"\"\n\n servable_images = []\n \n for image in clusto.get_entities(clusto_types=[llclusto.drivers.PGIImage]):\n if len(image.get_si_servers_stored_on()) > 0:\n servable_images.append(image.name)\n \n return servable_images\n\n\ndef get_hosts_with_image(request, image_name):\n \"\"\"Returns a list of hostnames that are associated with a particular image name.\n\n Returns:\n A list of hostnames as strings.\n \n Arguments:\n image_name -- a string containing the image name.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching image name.\n \"\"\"\n\n hosts = []\n try:\n image = clusto.get_by_name(image_name)\n except LookupError:\n return HttpResponseNotFound(\"Image '%s' not found.\" % image_name)\n \n for host in image.get_hosts_associated_with():\n hosts.append(host.hostname)\n \n return hosts\n\n\ndef list_host_image_associations(request):\n \"\"\"Returns all hostnames and associated PGI image names.\n\n Returns:\n A dictionary keyed on hostnames and the values are the associated image name.\n\n Arguments:\n None\n \n Exceptions Raised:\n None\n \"\"\"\n\n host_images = {}\n for host in clusto.get_entities(attrs=[{'key': 'pgi_image'}]):\n host_images[host.hostname] = host.pgi_image.name\n\n return host_images\n\n\ndef get_current_pgi_image(request, hostname):\n \"\"\"Returns the image assoicated with the hostname passed in.\n\n Returns:\n A string containing the associated image name.\n\n Arguments:\n hostname -- the hostname formatted as a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching hostname.\n JinxInvalidStateError -- more than one host was found matching the hostname.\n \"\"\"\n\n hosts = llclusto.get_by_hostname(hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Hostname '%s' does not exist\" % hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % hostname)\n host = hosts[0]\n \n if host.pgi_image is None:\n return None\n\n return host.pgi_image.name\n\n\ndef get_previous_pgi_image(request, hostname):\n \"\"\"Returns the image that was previously assigned to the hostname.\n\n Returns:\n A string containing the associated image name.\n \n Arguments:\n hostname -- the hostname formatted as a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching hostname.\n JinxInvalidStateError -- more than one host was found matching the hostname.\n \"\"\"\n\n hosts = llclusto.get_by_hostname(hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Hostname '%s' does not exist\" % hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % hostname)\n host = hosts[0]\n\n if host.previous_pgi_image is None:\n return None\n else:\n return host.previous_pgi_image.name\n\n\ndef update_host_image_association(request, hostname, image_name):\n \"\"\"Associate a host with the pgi image.\n \n Returns:\n None\n \n Arguments:\n hostname -- the hostname formatted as a string.\n image_name -- the image name formatted at a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching hostname or image_name.\n JinxInvalidStateError -- more than one host was found matching the hostname.\n \"\"\"\n\n hosts = llclusto.get_by_hostname(hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Hostname '%s' does not exist\" % hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % hostname)\n host = hosts[0]\n\n try:\n image = clusto.get_by_name(image_name)\n except LookupError:\n return HttpResponseNotFound(\"Image '%s' does not exist.\" % image_name)\n \n host.pgi_image = image\n \n return \n\n\ndef rollback_host_image(request, hostname):\n \"\"\"Rollback image association to previous pgi image.\n\n Returns:\n None\n \n Arguments:\n hostname -- the hostname formatted as a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching hostname.\n JinxInvalidStateError -- more than one host was found matching the hostname.\n -- no image was previously associated with this host.\n \"\"\"\n\n hosts = llclusto.get_by_hostname(hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Hostname '%s' does not exist\" % hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % hostname)\n host = hosts[0]\n \n try:\n host.revert_pgi_image()\n except RevertPGIImageError as e:\n return HttpResponseInvalidState(str(e))\n \n return\n\n\ndef get_si_images(request, si_hostname):\n \"\"\"Gets a lists of all images on a systemimager.\n\n Returns:\n A list of image names formatted as strings.\n \n Arguments:\n si_hostname -- the hostname of the systemimager formatted as a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching hostname. \n JinxInvalidStateError -- more than one host was found matching the hostname.\n \"\"\"\n\n hosts = llclusto.get_by_hostname(si_hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Systemimager hostname '%s' does not exist\" % si_hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % si_hostname)\n host = hosts[0]\n \n images = []\n for image in host.get_stored_pgi_images():\n images.append(image.name)\n\n return images\n\n\ndef delete_si_image(request, si_hostname, image_name):\n \"\"\"Deletes the PGI image from systemimager.\n \n Returns:\n None\n \n Arguments:\n si_hostname -- the systemimager hostname formatted as a string.\n image_name -- the image name formatted at a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching systemimager hostname or image_name.\n -- unable to find image on systemimager.\n JinxInvalidStateError -- image is still associated with hosts.\n -- more than one host was found matching the hostname.\n \"\"\"\n\n try:\n image = clusto.get_by_name(image_name)\n except LookupError:\n return HttpResponseNotFound(\"Image '%s' does not exist.\" % image_name)\n \n hosts = llclusto.get_by_hostname(si_hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Systemimager hostname '%s' does not exist\" % si_hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % si_hostname)\n host = hosts[0]\n \n # First, verify no host is using this as a current image\n hosts = image.get_hosts_associated_with()\n if len(hosts) != 0:\n hostnames = [host.hostname for host in hosts]\n return HttpResponseInvalidState(\"Unable to delete image from SI host. Image '%s' currently in use by hosts: '%s'\" % (image_name, \n \",\".join(hostnames)))\n try:\n host.delete_stored_pgi_image(image)\n except LookupError:\n return HttpResponseNotFound(\"Image '%s' is not marked as stored on systemimager '%s'.\" % (image_name, si_hostname))\n\n return\n\n\ndef add_si_image(request, si_hostname, image_name):\n \"\"\"Adds image to systemimager.\n\n Returns:\n None\n\n Arguments:\n si_hostname -- the systemimager hostname formatted as a string.\n image_name -- the image name formatted at a string.\n\n Exceptions Raised:\n JinxDataNotFoundError -- unable to find matching systemimager hostname or image_name.\n JinxInvalidStateError -- more than one host was found matching the hostname.\n \"\"\"\n\n hosts = llclusto.get_by_hostname(si_hostname)\n if len(hosts) < 1:\n return HttpResponseNotFound(\"Systemimager hostname '%s' does not exist\" % si_hostname)\n elif len(hosts) > 1:\n return HttpResponseInvalidState(\"Multiple hosts found matching '%s'\" % si_hostname)\n host = hosts[0]\n \n try:\n image = clusto.get_by_name(image_name)\n except LookupError:\n image = PGIImage(image_name)\n \n host.add_stored_pgi_image(image)\n \n return\n", "sub_path": "src/jinx_api/api/views/pgi.py", "file_name": "pgi.py", "file_ext": "py", "file_size_in_byte": 8874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "clusto.get_entities", "line_number": 23, "usage_type": "call"}, {"api_name": "llclusto.drivers", "line_number": 23, "usage_type": "attribute"}, {"api_name": "clusto.get_by_name", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 47, "usage_type": "call"}, {"api_name": "clusto.get_entities", "line_number": 69, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 89, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 91, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 93, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 116, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 118, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 120, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 144, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 146, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 148, "usage_type": "call"}, {"api_name": "clusto.get_by_name", "line_number": 152, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 154, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 176, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 178, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 180, "usage_type": "call"}, {"api_name": "llclusto.drivers.RevertPGIImageError", "line_number": 185, "usage_type": "name"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 186, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 205, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 207, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 209, "usage_type": "call"}, {"api_name": "clusto.get_by_name", "line_number": 237, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 239, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 241, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 243, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 245, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 252, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 257, "usage_type": "call"}, {"api_name": "llclusto.get_by_hostname", "line_number": 277, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 279, "usage_type": "call"}, {"api_name": "jinx_api.http.HttpResponseInvalidState", "line_number": 281, "usage_type": "call"}, {"api_name": "clusto.get_by_name", "line_number": 285, "usage_type": "call"}, {"api_name": "llclusto.drivers.PGIImage", "line_number": 287, "usage_type": "call"}]} +{"seq_id": "485415070", "text": "from die import Die\nimport pygal\n\ndie_1 =Die()\ndie_2 = Die(10)\nresults =[]\nfor num in range(50000):\n result=die_1.roll() + die_2.roll()\n results.append(result)\n\nfrequencies = []\nmax_result = die_1.num_sides + die_2.num_sides\nfor num in range(2,max_result+1):\n frequency = results.count(num)\n frequencies.append(frequency)\n\nprint(frequencies)\n\nhist = pygal.Bar()\nhist.title=(\"Results of rolling one D6 1000 times.\")\nhist.x_labels = []\nfor i in range(2,max_result+1):\n hist.x_labels.append(str(i))\nhist.x_title =\"Results\"\nhist.y_title =\"Frequency of Result\"\n\nhist.add('D6+D6',frequencies)\nhist.render_to_file('die_visual.svg')\n\n", "sub_path": "数据可视化/die_visual.py", "file_name": "die_visual.py", "file_ext": "py", "file_size_in_byte": 641, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "die.Die", "line_number": 4, "usage_type": "call"}, {"api_name": "die.Die", "line_number": 5, "usage_type": "call"}, {"api_name": "pygal.Bar", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "590412182", "text": "import openpyxl\r\nfrom openpyxl.styles import Border, Side, Font\r\n# from openpyxl import load_workbook\r\n# from openpyxl import *\r\nimport time\r\n# from UI.config.VarConfig import *\r\nimport os\r\n\r\n\r\nclass ParseExcel(object):\r\n\r\n def __init__(self):\r\n self.workbook = None\r\n self.excelFile = None\r\n # 设置字体颜色\r\n self.font = Font(color=None)\r\n # 颜色对应的RGB值\r\n self.RGBDict = {\r\n \"red\":\"FFFF3030\",\r\n \"green\":\"FF008B00\"\r\n }\r\n\r\n def load_work_book(self, excel_path_and_name):\r\n '''\r\n 将Excel文件加载到内存，并获取其workbook对象\r\n '''\r\n try:\r\n # self.workbook = openpyxl.load_workbook(excel_path_and_name, data_only = True)\r\n self.workbook = openpyxl.load_workbook(excel_path_and_name)\r\n except Exception as e:\r\n raise e\r\n self.excelFile = excel_path_and_name\r\n return self.workbook\r\n\r\n def get_sheet_by_name(self, sheetName):\r\n '''\r\n 根据sheet名获取该sheet对象\r\n '''\r\n try:\r\n sheet = self.workbook.get_sheet_by_name(sheetName)\r\n # sheet = self.workbook[sheetName]\r\n return sheet\r\n except Exception as e:\r\n raise e\r\n\r\n def get_sheet_by_index(self, sheetIndex):\r\n '''\r\n 根据sheet的索引获取该sheet对象\r\n '''\r\n try:\r\n sheetname = self.workbook.get_sheet_names()[sheetIndex]\r\n except Exception as e:\r\n raise e\r\n sheet = self.workbook.get_sheet_by_name(sheetname)\r\n return sheet\r\n\r\n def get_rows_number(self, sheet):\r\n '''\r\n 获取sheet中有数据区域的结束行号\r\n '''\r\n return sheet.max_row\r\n\r\n def get_cols_number(self, sheet):\r\n '''\r\n 获取sheet中有数据区域的结束列号\r\n '''\r\n return sheet.max_column\r\n\r\n def get_start_row_number(self, sheet):\r\n '''\r\n 获取sheet中有数据区域的开始行号\r\n '''\r\n return sheet.min_row\r\n\r\n def get_start_col_number(self, sheet):\r\n '''\r\n 获取sheet中有数据区域的开始列号\r\n '''\r\n return sheet.min_colum\r\n\r\n def get_row(self, sheet, rowNo):\r\n '''\r\n 获取sheet中某一行，返回的是这一行所有的数据内容组成的tuple,下标从1开始，sheet.row[1]表示第一行\r\n '''\r\n try:\r\n return sheet.rows[rowNo - 1]\r\n except Exception as e:\r\n raise e\r\n\r\n def get_column(self, sheet, rolNo):\r\n '''\r\n 获取sheet中某一列，返回的是这一列所有的数据内容组成的tuple,下标从1开始，sheet.columns[1]表示第一列\r\n '''\r\n try:\r\n return sheet.columns[rolNo - 1]\r\n # return list(sheet.columns[rolNo - 1])\r\n except Exception as e:\r\n raise e\r\n\r\n\r\n def get_cell_of_value(self, sheet, coordinate = None, rowNo = None, colsNo = None):\r\n '''\r\n 根据单元格所在的位置索引获取该单元格中的值，下标从1开始，sheet.cell(row = 1, column = 1).value，表示excel中第一行第一列的值\r\n '''\r\n if coordinate != None:\r\n try:\r\n return sheet.cell(coordinate = coordinate).value\r\n except Exception as e:\r\n raise e\r\n elif coordinate is None and rowNo is not None and colsNo is not None:\r\n try:\r\n return sheet.cell(row = rowNo, column = colsNo).value\r\n except Exception as e:\r\n raise e\r\n else:\r\n pass\r\n\r\n def get_cell_of_object(self, sheet, coordinate = None, rowNo = None, colsNo = None):\r\n '''\r\n 获取某个单元格的对象，可以根据单元格所在的位置的数字索引，也可以直接根据Excel中单元格的编码及坐标，\r\n 如get_cell_of_object(sheet, coordinate = \"A1\") or get_cell_of_object(sheet, rowNo = 1, colsNo = 2)\r\n '''\r\n if coordinate != None:\r\n try:\r\n return sheet.cell(coordinate = coordinate)\r\n except Exception as e:\r\n raise e\r\n elif coordinate == None and rowNo is not None and colsNo is not None:\r\n try:\r\n return sheet.cell(row = rowNo, column = colsNo)\r\n except Exception as e:\r\n raise e\r\n else:\r\n raise Exception(\"Insufficient Coordinates of cell !\")\r\n\r\n\r\n def write_cell(self, sheet, content, coordinate = None, rowNo = None, colsNo = None, style = None):\r\n '''\r\n 根据单元格在Excel中的编码坐标或者数字索引坐标向单元格中写入数据，\r\n 下标从1开始，参数style表示字体的颜色的名字，比如red，green\r\n '''\r\n if coordinate is not None:\r\n try:\r\n sheet.cell(coordinate = coordinate).value = content\r\n if style is not None:\r\n sheet.cell(coordinate = coordinate).font = Font(color = self.RGBDict[style], size=10)\r\n self.workbook.save(self.excelFile)\r\n except Exception as e:\r\n raise e\r\n elif coordinate == None and rowNo is not None and colsNo is not None:\r\n try:\r\n sheet.cell(row = rowNo, column = colsNo).value = content\r\n if style:\r\n sheet.cell(row = rowNo, column = colsNo).font = Font(color = self.RGBDict[style], size=10)\r\n self.workbook.save(self.excelFile)\r\n except Exception as e:\r\n raise e\r\n else:\r\n raise Exception(\"Insufficient Coordinates of cell !\")\r\n\r\n def write_cell_current_time(self, sheet, coordinate = None, rowNo = None, colsNo = None, style = None):\r\n '''\r\n 写入当前的时间，下标从1开始\r\n '''\r\n now = int(time.time()) # 显示时间戳\r\n time_array = time.localtime(now)\r\n current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time_array)\r\n if coordinate is not None:\r\n try:\r\n sheet.cell(coordinate = coordinate).value = current_time\r\n if style is not None:\r\n sheet.cell(coordinate = coordinate).font = Font(color = self.RGBDict[style], size=10)\r\n self.workbook.save(self.excelFile)\r\n except Exception as e:\r\n raise e\r\n elif coordinate == None and rowNo is not None and colsNo is not None:\r\n try:\r\n sheet.cell(row = rowNo, column = colsNo).value = current_time\r\n if style:\r\n sheet.cell(row = rowNo, column = colsNo).font = Font(color = self.RGBDict[style], size=10)\r\n self.workbook.save(self.excelFile)\r\n except Exception as e:\r\n raise e\r\n else:\r\n raise Exception(\"Insufficient Coordinates of cell !\")\r\n\r\nif __name__ == '__main__':\r\n # 测试代码\r\n pe = ParseExcel()\r\n # 测试所需Excel文件“通用投保验证.xls”，请自行创建\r\n # path = parentDirPath + \"\\\\testData\\\\通用投保验证_S20180290.xlsx\"\r\n path = \"D:\\\\MyDocuments\\\\itw_liuyh01\\桌面\\\\buikdNumber.xlsx\"\r\n # path = \"D:\\\\MyDocuments\\\\itw_liuyh01\\桌面\\\\buikdNumber.xls\"\r\n # print(path)\r\n pe.load_work_book(path)\r\n # print(\"通过名称获取sheet对象的名字：\", pe.get_sheet_by_name(\"200\").title)\r\n # print(\"通过名称获取sheet对象的名字：\", pe.get_sheet_by_name(\"data\"))\r\n # print(\"通过index序号获取sheet对象的名字：\", pe.get_sheet_by_index(1).title)\r\n sheet = pe.get_sheet_by_name(\"200\")\r\n # sheet = pe.get_sheet_by_index(0)\r\n # print(sheet)\r\n # sheet = pe.get_sheet_by_index(0)\r\n # print(\"获取最大行号：\", pe.get_rows_number(sheet)) # 获取最大行号\r\n # print(\"获取最大列号：\", pe.get_cols_number(sheet)) # 获取最大列号\r\n # rows_num = pe.get_rows_number(sheet)\r\n # print(rows_num)\r\n\r\n # rows1 = pe.get_row(sheet, 2)\r\n # print(rows1)\r\n # rows2 = pe.get_row(sheet, 3)\r\n #\r\n\r\n # for i in range(2, rows1):\r\n\r\n # for i in range(2, 502):\r\n\r\n\r\n for i in range(2, 202):\r\n rows1 = pe.get_row(sheet, i)\r\n # print(rows1)\r\n key = []\r\n # print(\"获取第一行：\", rows1)\r\n for x in rows1:\r\n x = x.value\r\n key.append(x)\r\n\r\n # print(key)\r\n\r\n path = \"D:\\MyDocuments\\itw_liuyh01\\桌面\\\\200.txt\"\r\n f = open(path, \"r\")\r\n str = f.read()\r\n\r\n str = str.replace(\"{{personnelName}}\", key[0])\r\n # str = str.replace(\"{{sexCode}}\", key[1])\r\n # str = str.replace(\"{{certificateType}}\", key[2])\r\n str = str.replace(\"{{certificateNo}}\", key[1])\r\n # str = str.replace(\"{{birthday}}\", key[4])\r\n\r\n\r\n print(str)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n #\r\n # value = []\r\n # print(\"获取第二行：\", rows2)\r\n # for y in rows2:\r\n # y = y.value\r\n # value.append(y)\r\n # print(value)\r\n #\r\n # z = dict(zip(key, value))\r\n # print(z)\r\n #\r\n #\r\n #\r\n # cols = pe.get_column(sheet, 1)\r\n # print(\"获取第一列：\", cols)\r\n # for i in cols:\r\n # print(i.value)\r\n #\r\n #\r\n #\r\n # # 获取第n行第n列单元格内容\r\n # print(\"获取第一行第一列单元格内容：\", pe.get_cell_of_value(sheet, rowNo=1, colsNo=1))\r\n # print(\"获取第一行第一列单元格内容：\", pe.get_cell_of_value(sheet, coordinate=\"B3\"))\r\n #\r\n # # 获取第n行第n列单元格对象\r\n # print(\"获取第一行第一列单元格对象：\", pe.get_cell_of_object(sheet, rowNo=1, colsNo=1))\r\n # print(\"获取第一行第一列单元格对象：\", pe.get_cell_of_object(sheet, coordinate=\"B3\"))\r\n # print(\"获取第一行第一列单元格对象：\", pe.get_cell_of_object(sheet, coordinate=\"B3\").value)\r\n #\r\n # # pe.write_cell(sheet, \"TK\", rowNo=2, colsNo=9, style=\"red\")\r\n # # pe.write_cell_current_time(sheet, rowNo=2, colsNo=10)\r\n # #\r\n # # pe.write_cell(sheet, \"TK\", rowNo=3, colsNo=9, style=\"green\")\r\n # # pe.write_cell_current_time(sheet, rowNo=3, colsNo=10)\r\n", "sub_path": "pythoncode/AutomatedTestingFramework/util/ParseExcel.py", "file_name": "ParseExcel.py", "file_ext": "py", "file_size_in_byte": 10228, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "openpyxl.styles.Font", "line_number": 16, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 29, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 146, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 154, "usage_type": "call"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 166, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 167, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 172, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "380175875", "text": "#!/usr/bin/env python\n\nimport requests\nfrom datetime import datetime\nimport time\n\nURL = 'http://httpbin.org/post' # <1>\n\nfor i in range(3):\n response = requests.post( # <2>\n URL,\n data={'date': datetime.now(),\n 'label': 'test_' + str(i)\n }\n\n )\n if response.status_code == requests.codes.OK:\n print(response.content.decode())\n time.sleep(2)\n\n", "sub_path": "EXAMPLES/post_to_rest.py", "file_name": "post_to_rest.py", "file_ext": "py", "file_size_in_byte": 405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.post", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "requests.codes", "line_number": 17, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "251486548", "text": "import urllib.request\nimport sqlite3\nimport time\n\nurl_prefix = \"https://lpo.dt.navy.mil/data/DM/Environmental_Data_Deep_Moor_\"\n\nclass DataManager(object):\n \"\"\"class that will send requests to a web site for data and fill an sqlite3 database with that data\"\"\"\n def __init__(self, db):\n self.url = ''\n self.db = db\n\n def load(self):\n try:\n db_connect = sqlite3.connect(self.db)\n except sqlite3.Error as e:\n print ('Error: {}'.format(e.message))\n try:\n for yr in ('2014', '2015', '2016'):\n if not self.isYearLoaded(db_connect, yr):\n tableData = self.getTable(yr)\n db_connect.executemany('INSERT INTO raw_data(ID, Date, Time, Air_temp, Barometric_press, Dew_point,\\\n Relative_humidity, Wind_dir, Wind_gust, Wind_speed\\\n )\\\n VALUES(NULL, ?, ?, ?, ?, ?, ?, ?, ?, ?)', tableData)\n except sqlite3.Error as e:\n print ('Error: {}'.format(e.message))\n db_connect.close()\n return\n db_connect.commit()\n db_connect.close()\n\n\n def isYearLoaded(self, db_connect, year):\n tableSizeQuery = '''SELECT COUNT(*)\\\n FROM raw_data\\\n WHERE strftime('%Y',Date) = \\'{}\\''''.format(year)\n cursor = db_connect.cursor()\n cursor.execute(tableSizeQuery)\n queryRet = cursor.fetchone()\n ret = int(queryRet[0])\n return True if int(queryRet[0]) > 0 else False\n\n def getTable(self, year):\n try:\n url_connect = urllib.request.urlopen(url_prefix+'{}.txt'.format(year))\n except urllib2.HTTPError as e:\n print ('Error code {0}: {1}'.format(e.code, e.reason))\n url_connect.readline()\n read = url_connect.read().splitlines()\n table = [line.split() for line in read]\n table = self.structureDate4sqlite(table)\n url_connect.close()\n return table\n\n def structureDate4sqlite(self, table):\n # 0 element is date component\n for row in table:\n row[0] = row[0].replace('_','-')\n return table\n\n\n\n\n", "sub_path": "PythonWebStatistics/PythonWebStatistics/DataManager.py", "file_name": "DataManager.py", "file_ext": "py", "file_size_in_byte": 2287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sqlite3.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sqlite3.Error", "line_number": 26, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlopen", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 46, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "142811958", "text": "import requests\nimport json\nimport globalMethods as gm\n\n##################################################\n#############-------KEYWORDS-------###############\n##################################################\ndef findMostRelevants( listOfWords ):\n max = []\n\n while(len(max) < 5 and len(listOfWords)>0):\n index = 0\n maxIndex = -1\n maxScore = -1\n size = len(listOfWords)\n while(index < size):\n if(listOfWords[index]['relevance'] > maxScore):\n maxIndex = index\n maxScore = listOfWords[index]['relevance']\n index +=1\n max.append(listOfWords[maxIndex])\n del listOfWords[maxIndex]\n\n return max\n\ndef findKeywords(text):\n headers = {'Content-Type': 'application/json'}\n data = '{\"text\":\"'+text+'\",\"features\":{\"sentiment\":{},\"categories\":{},\"concepts\":{},\"entities\":{},\"keywords\":{}}}'\n\n analyzeText = requests.post('https://gateway-lon.watsonplatform.net/natural-language-understanding/api/v1/analyze?version=2019-07-12', headers=headers, data=data, auth=('apikey', '7LNEjCMvP6ZcNShjAkjPob7QSCfIHeZMQkn4Ho3dQgte'))\n textResults = analyzeText.json()\n\n keywords = textResults[\"keywords\"]\n newKeywords = gm.capitalizeList(keywords)\n if(len(keywords) < 5): #IF WE COULDN'T GET ENOUGH KEYWORDS, ASK FOR MORE DETAIL\n print(\"Oops! Going to need more detail than that..If you can't be more specific leave this part empty and continue to typing relevant keywords\\n\")\n print(text)\n additionDescription = input(\"Enter more detail. Continue from where you left off..\\n\")\n newInput = text + additionDescription\n\n data = '{\"text\":\"'+newInput+'\",\"features\":{\"sentiment\":{},\"categories\":{},\"concepts\":{},\"entities\":{},\"keywords\":{}}}'\n analyzeText = requests.post('https://gateway-lon.watsonplatform.net/natural-language-understanding/api/v1/analyze?version=2019-07-12', headers=headers, data=data, auth=('apikey', '7LNEjCMvP6ZcNShjAkjPob7QSCfIHeZMQkn4Ho3dQgte'))\n textResults = analyzeText.json()\n keywords = textResults[\"keywords\"]\n keywords = gm.capitalizeList(keywords)\n\n if(len(keywords) < 5): #IF WE STILL HAVEN'T ACQUIRED ENOUGH KEYWORDS, ASK FOR IMPORTANT ASPECTS DIRECTLY FROM THE USER\n importants = len(keywords)\n index = 0\n\n\n prompt = \"Enter an important aspect in your situation, like \"\n while(index < importants - 1):\n prompt = prompt + keywords[index][\"text\"] + \", \"\n index +=1\n prompt += keywords[index][\"text\"] + \" etc. \"\n\n count = 0\n missing = 7 - importants\n\n while(count < missing):\n entry = input(prompt)\n entry = gm.capitalize(entry)\n\n keywordsList = []\n for each in keywords:\n keywordsList.append(each[\"text\"])\n\n\n if(entry not in keywordsList):\n\n keywords.append({\"text\":entry, \"relevance\":0.4})\n synonym = gm.getGoodSynonym(entry)\n if(synonym): #IF IT IS NOT NONE, (SO NOT MORE THAN ONE WORD)\n if(synonym not in keywords):\n print(\"The entry is \"+entry+\" and its synonym is \"+synonym)\n keywords.append({\"text\":gm.capitalize(synonym), \"relevance\":0.4})\n count += 1\n\n\n mostRelevants = findMostRelevants(keywords)\n return mostRelevants\n\n##################################################\n#############-------EMOTIONS-------###############\n##################################################\ndef format(list):\n targets = '['\n size = len(targetsList)\n index = 0\n while(index < size -1):\n targets += '\"' + targetsList[index]+ '\"'\n targets += ','\n index += 1\n targets += '\"' +targetsList[size-1] +'\"'\n targets += ']'\n\n return targets\n\ndef findMax( list ):\n joy = 0\n fear = 0\n disgust = 0\n anger = 0\n\n for entry in emotionWords:\n emotions = entry[\"emotion\"]\n for key in emotions.keys():\n if(key == \"joy\"):\n joy += emotions[\"joy\"]\n elif(key == \"fear\"):\n fear += emotions[\"fear\"]\n elif(key == \"anger\"):\n anger += emotions[\"anger\"]\n elif(key == \"disgust\"):\n disgust += emotions[\"disgust\"]\n\n maximum = max(joy,fear,anger,disgust)\n dominantEmotion = ''\n\n if (maximum == joy):\n dominantEmotion = \"JOY\"\n if (maximum == fear):\n dominantEmotion = \"FEAR\"\n if (maximum == disgust):\n dominantEmotion = \"DISGUST\"\n if (maximum == anger):\n dominantEmotion = \"ANGER\"\n return dominantEmotion\n\ndef findSentiment( text, targets):\n #FORMATTING\n headers = {'Content-Type': 'application/json'}\n params = (('version', '2019-07-12'),)\n\n #INPUTS\n targetsList = [\"apples\",\"oranges\",\"broccoli\"]\n targets = format(targetsList)\n text = '\"I love apples! I do not like oranges.\",\\n'\n\n\n dataEmotions = '{\\n \"text\": '+text+' \"features\": {\\n \"sentiment\": {\\n \"targets\": '+targets+'\\n },\\n \"keywords\": {\\n \"emotion\": true\\n }\\n }\\n}'\n emotions= requests.post('https://gateway-lon.watsonplatform.net/natural-language-understanding/api/v1/analyze?version=2019-07-12', headers=headers, params=params, data=dataEmotions, auth=('apikey', '7LNEjCMvP6ZcNShjAkjPob7QSCfIHeZMQkn4Ho3dQgte'))\n\n emotionsResults = emotions.json()\n emotionsResults[\"sentiment\"]\n emotionWords = emotionsResults[\"keywords\"]\n\n dominant = findMax(emotionWords)\n\n##################################################\n#############-------TINKERING-------##############\n##################################################\n\nfile = open(\"inputs.txt\",\"r\")\nresults = []\n\ndesiredTextIndex = 4 # <<<<<<<<<<<<<-------------------------------------------------- ENTER THE INDEX OF TEXT CORPUS\n\ncount = 0\nfor each in file:\n if(count == desiredTextIndex):\n temp = findKeywords(each)\n results.append(temp)\n count +=1\nprint(results[0])\n", "sub_path": "pythonWatson.py", "file_name": "pythonWatson.py", "file_ext": "py", "file_size_in_byte": 6120, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.post", "line_number": 30, "usage_type": "call"}, {"api_name": "globalMethods.capitalizeList", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 42, "usage_type": "call"}, {"api_name": "globalMethods.capitalizeList", "line_number": 45, "usage_type": "call"}, {"api_name": "globalMethods.capitalize", "line_number": 63, "usage_type": "call"}, {"api_name": "globalMethods.getGoodSynonym", "line_number": 73, "usage_type": "call"}, {"api_name": "globalMethods.capitalize", "line_number": 77, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "286725986", "text": "#-*- coding:utf-8 –*-\nimport sys\nimport pygame\nfrom bullet import Bullet\nfrom alien import Alien\nfrom time import sleep\n\ndef check_keydown_events(event,ai_settings,screen,ship,bullets):\n \"\"\"响应按键\"\"\"\n if event.key == pygame.K_RIGHT:\n ship.moving_right = True\n elif event.key == pygame.K_LEFT:\n ship.moving_left = True\n elif event.key == pygame.K_SPACE:\n #创建一颗子弹，并将其加入到编组bullets中\n fire_bullets(ai_settings, screen, ship, bullets)\n elif event.key == pygame.K_q:\n sys.exit()\n\ndef check_keyup_events(event, ai_settings,screen,ship,bullets):\n \"\"\"响应松开\"\"\"\n if event.key == pygame.K_RIGHT:\n ship.moving_right = False\n elif event.key == pygame.K_LEFT:\n ship.moving_left = False\n\ndef check_events(ai_settings,screen,ship,bullets,stats,play_button,aliens,sb):\n \"\"\"响应按键和鼠标事件\"\"\"\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n elif event.type == pygame.KEYDOWN:\n check_keydown_events(event,ai_settings,screen,ship,bullets)\n elif event.type == pygame.KEYUP:\n check_keyup_events(event, ai_settings,screen,ship,bullets)\n elif event.type == pygame.MOUSEBUTTONDOWN:\n mouse_x, mouse_y = pygame.mouse.get_pos()\n check_play_button(ai_settings, screen, ship, aliens,stats,bullets,sb,play_button,mouse_x,mouse_y)\n\ndef update_screen(ai_settings,screen,ship,aliens,bullets,stats,play_button,sb):\n \"\"\"更新屏幕上的图像，并切换到新屏幕\"\"\"\n # 每次循环时重绘屏幕\n screen.fill(ai_settings.bg_color)\n\n # 在飞船和外星人后面重绘所有子弹\n for bullet in bullets.sprites():\n bullet.draw_bullet()\n ship.blitme()\n aliens.draw(screen)\n\n #显示得分\n sb.show_score()\n\n #如果游戏出非活动状态，就显示Play按钮\n if not stats.game_active:\n play_button.draw_button()\n #else:\n #show_gameover(screen)\n\n # 让最近绘制的屏幕可见\n pygame.display.flip()\n\ndef update_bullets(ai_settings, screen, ship,bullets,aliens,stats,sb):\n \"\"\"更新子弹的位置，并删除已消失的子弹\"\"\"\n #更新子弹的位置\n bullets.update()\n\n # 删除消失的子弹\n for bullet in bullets.copy():\n if bullet.rect.bottom <= 0:\n bullets.remove(bullet)\n\n check_bullet_alien_collisions(ai_settings, screen, ship, bullets, aliens,stats,sb)\n\ndef fire_bullets(ai_settings,screen,ship,bullets):\n if len(bullets) < ai_settings.bullets_allowed:\n new_bullet = Bullet(ai_settings, screen, ship)\n bullets.add(new_bullet)\n\ndef create_fleet(ai_settings,screen,ship,aliens):\n \"\"\"创建外星人群\"\"\"\n alien = Alien(ai_settings, screen)\n\n number_aliens_x = get_number_aliens_x(ai_settings,alien.rect.width)\n number_rows = get_number_rows(ai_settings,ship.rect.height,alien.rect.height)\n #创建第一行外星人\n for row_number in range(number_rows):\n for alien_num in range(number_aliens_x):\n #创建一个外星人，并将其加入当前行\n create_alien(ai_settings, screen, aliens, alien_num,row_number)\n\ndef get_number_aliens_x(ai_settings,alien_width):\n # 计算一行可以容纳多少个外星人\n # 外星人间距为外星人宽度\n\n available_space_x = ai_settings.screen_width - 2 * alien_width\n number_aliens_x = int(available_space_x / (2 * alien_width))\n return number_aliens_x\n\ndef create_alien(ai_settings,screen,aliens,alien_num,row_num):\n alien = Alien(ai_settings, screen)\n alien_width = alien.rect.width\n alien.x = alien_width + 2 * alien_width * alien_num\n alien.rect.x = alien.x\n alien.rect.y = alien.rect.height + 2 * alien.rect.height * row_num\n aliens.add(alien)\n\ndef get_number_rows(ai_settings,ship_height,alien_height):\n \"\"\"计算屏幕可容纳多少行外星人\"\"\"\n available_space_y = ai_settings.screen_height - 3 * alien_height - ship_height\n number_rows = int(available_space_y / (2 * ship_height))\n return number_rows\n\ndef update_aliens(ai_settings,screen, ship, stats, aliens, bullets,sb):\n \"\"\"\n 检查是否有外星人处于屏幕的边缘，并更新整群外星人的位置\n :param aliens:\n :return:\n \"\"\"\n check_aliens_edges(ai_settings,aliens)\n aliens.update()\n\n #检测外星人和飞船之间的碰撞\n if pygame.sprite.spritecollideany(ship,aliens):\n ship_hit(ai_settings, screen, ship, stats, aliens, bullets)\n #检查是否有外星人到达屏幕底端\n check_aliens_bottom(ai_settings, screen, ship, stats, aliens, bullets,sb)\n\ndef check_aliens_edges(ai_settings,aliens):\n \"\"\"所有外星人到达屏幕边缘时采取相应的措施\"\"\"\n for alien in aliens.sprites():\n if alien.check_edges():\n change_aliens_direction(ai_settings,aliens)\n break\n\ndef change_aliens_direction(ai_settings,aliens):\n \"\"\"将整群外星人下移，并改变他们的方向\"\"\"\n for alien in aliens.sprites():\n alien.rect.y += ai_settings.alien_drop_speed\n ai_settings.alien_direction *= -1\n\ndef check_bullet_alien_collisions(ai_settings, screen, ship,bullets, aliens,stats,sb):\n # 检查是否有子弹击中了外星人，如果有就删除相应的子弹和外星人\n collisions = pygame.sprite.groupcollide(bullets, aliens, True, True)\n if collisions:\n for alien in collisions.values():\n stats.score += ai_settings.alien_points * len(alien)\n sb.prep_score()\n check_high_score(stats, sb)\n\n if len(aliens) == 0:\n # 删除现有的子弹\n bullets.empty()\n #提高游戏难度等\n ai_settings.increase_speed()\n # 如果整群外星人都被消灭，就提高一个等级\n stats.level += 1\n sb.prep_level()\n\n #新建一群外星人\n create_fleet(ai_settings, screen, ship, aliens)\n\ndef ship_hit(ai_settings,screen, ship,stats,aliens,bullets,sb):\n \"\"\"相应被外星人撞击到的飞船\"\"\"\n if stats.ships_left > 0:\n #将ship_left减1\n stats.ships_left -= 1\n\n #更新飞船数量剩余显示\n sb.prep_ship()\n\n #清空外星人和子弹列表\n aliens.empty()\n bullets.empty()\n\n #创建一群新的外星人，并将飞船放到屏幕底端中央\n create_fleet(ai_settings, screen, ship, aliens)\n ship.center_ship()\n\n #暂停一会\n sleep(0.5)\n else:\n stats.game_active = False\n pygame.mouse.set_visible(True)\n print(\"Game over!!!\")\n\ndef check_aliens_bottom(ai_settings, screen, ship, stats, aliens, bullets,sb):\n \"\"\"检查是否有外星人到达屏幕底端\"\"\"\n screen_rect = screen.get_rect()\n for alien in aliens.sprites():\n if alien.rect.bottom >= screen_rect.bottom:\n #像飞船被撞倒一样处理\n ship_hit(ai_settings, screen, ship, stats, aliens, bullets,sb)\n break\n\ndef show_gameover(screen):\n #屏幕显示GAME OVER!!!\n text = pygame.font.SysFont(\"宋体\", 50)\n text_fmt = text.render(\"GAME OVER!!!\", 1, (0, 0, 225))\n screen.blit(text_fmt, (screen.get_rect().centerx, screen.get_rect().centery))\n\ndef check_play_button(ai_settings, screen, ship, aliens,stats,bullets,sb,play_button,mouse_x,mouse_y):\n \"\"\"玩家在点击Play按键时开始游戏\"\"\"\n button_clicked = play_button.rect.collidepoint(mouse_x, mouse_y)\n if button_clicked and not stats.game_active:\n #重置游戏设置\n ai_settings.initialize_dynamic_settings()\n #隐藏光标\n pygame.mouse.set_visible(False)\n #重置游戏统计信息\n stats.reset_stats()\n stats.game_active = True\n\n #重置记分牌图像\n sb.prep_score()\n sb.prep_high_score()\n sb.prep_level()\n sb.prep_ship()\n\n # 清空外星人和子弹列表\n aliens.empty()\n bullets.empty()\n\n # 创建一群新的外星人，并将飞船放到屏幕底端中央\n create_fleet(ai_settings, screen, ship, aliens)\n ship.center_ship()\n\n\ndef check_high_score(stats,sb):\n \"\"\"检查是否诞生了最新的最高得分\"\"\"\n if stats.score > stats.high_score:\n stats.high_score = stats.score\n sb.prep_high_score()", "sub_path": "alien_invasion/game_functions.py", "file_name": "game_functions.py", "file_ext": "py", "file_size_in_byte": 8357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.K_RIGHT", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.K_q", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.K_RIGHT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 37, "usage_type": "attribute"}, {"api_name": "bullet.draw_bullet", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 61, "usage_type": "attribute"}, {"api_name": "bullet.rect", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bullet.Bullet", "line_number": 77, "usage_type": "call"}, {"api_name": "alien.Alien", "line_number": 82, "usage_type": "call"}, {"api_name": "alien.rect", "line_number": 84, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 85, "usage_type": "attribute"}, {"api_name": "alien.Alien", "line_number": 101, "usage_type": "call"}, {"api_name": "alien.rect", "line_number": 102, "usage_type": "attribute"}, {"api_name": "alien.x", "line_number": 103, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 104, "usage_type": "attribute"}, {"api_name": "alien.x", "line_number": 104, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollideany", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 124, "usage_type": "attribute"}, {"api_name": "alien.check_edges", "line_number": 132, "usage_type": "call"}, {"api_name": "alien.rect", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.sprite.groupcollide", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 144, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 181, "usage_type": "call"}, {"api_name": "pygame.mouse.set_visible", "line_number": 184, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 184, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pygame.mouse.set_visible", "line_number": 209, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 209, "usage_type": "attribute"}]} +{"seq_id": "528886556", "text": "################################################################################\n#\n# Library: pydstk\n#\n# Copyright 2010 Kitware Inc. 28 Corporate Drive,\n# Clifton Park, NY, 12065, USA.\n#\n# 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################################################################################\n\n\n\"\"\"Contains the core dynamical system implementations.\n\"\"\"\n\n\n__license__ = \"Apache License, Version 2.0\"\n__author__ = \"Roland Kwitt, Kitware Inc., 2013\"\n__email__ = \"E-Mail: roland.kwitt@kitware.com\"\n__status__ = \"Development\"\n\n\nimport copy\nimport time\nimport pickle\nimport numpy as np\nfrom collections import deque\nfrom termcolor import colored\n\nfrom scipy.linalg import eig\nfrom sklearn.manifold import MDS\nfrom sklearn.cluster import KMeans\nfrom sklearn.utils.extmath import randomized_svd\nfrom sklearn.metrics.pairwise import euclidean_distances\n\n\n# import pyds package contents\nimport dsutil.dsinfo as dsinfo\nimport dsutil.dsutil as dsutil\nimport dscore.dsdist as dsdist\n\n# import pyds classes\nfrom dsutil.dsutil import Timer\nfrom dscore.dsexcp import ErrorDS\nfrom dscore.dskpca import kpca, KPCAParam, rbfK, RBFParam\n\n\nclass NonLinearDS(object):\n \"\"\"Non-linear dynamical system class.\n\n Implements parameter estimation for non-linear dynamical systems of the\n form:\n\n x_{t+1} = Ax_{t} + v_{t}\n y_{t} = C(x_{t}) + w_{t}\n\n This code implements the non-linear dynamical systems approach to video\n classification (referred to as \"Kernel Dynamic Textures\") proposed in:\n\n [1] A. Chan and N. Vasconcelos. \"Classifying Video with Kernel Dynamic\n Textures\", In: CVPR (2007)\n \"\"\"\n\n def __init__(self, nStates, kpcaParams, verbose=False):\n \"\"\"Initialize nlds instance.\n\n\n nStates : int\n Number of KDT states.\n\n kpcaParams : KPCAParam instance\n Configured KPCA parameters.\n\n verbose : boolean (default : False)\n Do we want verbose output ?\n \"\"\"\n\n self._Ahat = None\n self._Rhat = None\n self._Qhat = None\n self._Xhat = None\n self._initX0 = None\n self._initM0 = None\n self._initS0 = None\n\n self._kpcaParams = kpcaParams\n self._nStates = nStates\n self._verbose = verbose\n\n self._ready = False\n\n @staticmethod\n def naiveCompare(nlds1, nlds2):\n \"\"\"Compare NLDS parameters (in a naive Frobenius norm manner).\n\n Parameters:\n -----------\n nlds1 : NonLineDS instance\n Target LDS\n\n nlds2: NonLinearDS instance\n Source LDS\n\n Returns:\n --------\n err : float\n Sum of the Frobenius norms of the difference matrices.\n \"\"\"\n\n err = (np.linalg.norm(nlds1._Ahat - nlds2._Ahat, 'fro') +\n np.linalg.norm(nlds1._Qhat - nlds2._Qhat, 'fro') +\n np.linalg.norm(nlds1._Xhat - nlds2._Xhat, 'fro') +\n np.linalg.norm(nlds1._initX0 - nlds2._initX0) +\n np.linalg.norm(nlds1._initM0 - nlds2._initM0) +\n np.linalg.norm(nlds1._initS0 - nlds2._initS0))\n return err\n\n\n def check(self):\n \"\"\"Check validity of LDS parameters.\n\n Currently, this routine only checks if the parameters are set, but not\n if they are actually valid parameters!\n\n Returns:\n --------\n validity : boolean\n True if parameters are valid, False otherwise.\n \"\"\"\n\n for key in self.__dict__:\n if self.__dict__[key] is None:\n return False\n return True\n\n\n def suboptimalSysID(self, Y):\n \"\"\"System identification using KPCA.\n\n Updates the NLDS parameters.\n\n Parameters:\n -----------\n Y : numpy array, shape = (N, D)\n Input data.\n \"\"\"\n\n nStates = self._nStates\n\n # call KPCA to get state estimate\n if self._verbose:\n with Timer('kpca'):\n Xhat = kpca(Y, nStates, self._kpcaParams)\n else:\n Xhat = kpca(Y, nStates, self._kpcaParams)\n\n # estimate rest of parameters\n _, tau = Y.shape\n\n Ahat = Xhat[:,1:tau]*np.linalg.pinv(Xhat[:,0:tau-1])\n Vhat = Xhat[:,1:tau]-Ahat*Xhat[:,0:tau-1]\n Qhat = (Vhat*Vhat.T)/(tau-1)\n initX0 = Xhat[:,0]\n initM0 = np.mean(Xhat, axis=1)\n initS0 = np.diag(np.cov(Xhat))\n\n self._Rhat = 0\n self._Ahat = Ahat\n self._Xhat = Xhat\n self._Vhat = Vhat\n self._Qhat = Qhat\n self._initX0 = initX0\n self._initM0 = initM0\n self._initS0 = initS0\n\n\nclass LinearDS(object):\n \"\"\"Implements a linear dynamical system (LDS) of the form:\n\n x_{t+1} = A*x_{t} + w_{t}\n y_{t} = C*x_{t} + v_{t}\n\n Parameter details (in terms of matrix dimensions):\n\n x_{t} : [k x 1] - State vector at time t\n y_{t} : [N x 1] - Obervation vector at time t\n w_{t} : [k x 1] - State noise at time t\n v_{t} : [N x 1] - Observation noise at time t\n A : [k x k] - State transition matrix\n C : [N x k] - Observation matrix\n \"\"\"\n\n def __init__(self, nStates, approx=False, verbose=False):\n \"\"\"Initialization.\n\n Parameters:\n -----------\n nStates : int\n Number of LDS states.\n\n approx : boolean (default : False)\n Use randomized SVD.\n\n verbose : boolean (default : False)\n Verbose output.\n \"\"\"\n\n self._Ahat = None\n self._Chat = None\n self._Rhat = None\n self._Qhat = None\n self._Xhat = None\n self._Yavg = None\n self._initM0 = None\n self._initS0 = None\n\n self._approx = approx\n self._verbose = verbose\n self._nStates = nStates\n\n if self._nStates < 0:\n raise ErrorDS(\"#states < 0!\")\n\n self._ready = False\n\n\n @staticmethod\n def computeRJF(A):\n \"\"\"Computes real Jordan form (RCF) of matrix.\n\n Parameters:\n -----------\n A : np.array, shape = (N, N)\n Input matrix (e.g., state matrix)\n\n Returns:\n --------\n J : np.ndarray, shape = (N, N)\n A in real Jordan form.\n\n Q : np.ndarray, shape = (N, N)\n Similarity transform, s.t., J = inv(Q)*A*Q\n\n X : np.ndarray, shape = (N, )\n Indicator array for real/imag. parts\n\n Note: This function is based on A. Ravichandran's MATLAB function\n realJordanForm.m (part of the Dynamic Texture Toolbox), available\n from:\n\n http://cis.jhu.edu/~avinash/projects/DTBox\n\n\n (Be carefull with that function, since it's generally advised\n to avoid numerical Jordan form computations in practice due to\n stability reasons.)\n\n Algorithmic strategy:\n ---------------------\n\n The strategy is to first compute the eigenvalues and eigenvectors\n of A; then impose an ordering of the eigenvalues (by sorting the\n imaginary parts in ascending order); next, we find the real eigen-\n values and sort them in descending order; finally, the eigenvalues\n are arranged as Jordan blocks in the matrix J and the eigenvectors\n are arranged in the similarity transform matrix Q.\n \"\"\"\n\n if not A.shape[0] == A.shape[1]:\n raise ErrorDS(\"Input matrix not square!\")\n\n N = A.shape[1]\n eVals, eVecs = eig(A)\n\n # sort by imaginary part\n idx = np.argsort(np.abs(np.imag(eVals)))\n P = eVals[idx]\n V = eVecs[:,idx]\n\n # get real parts and rearrange\n iR = np.isreal(P).astype(int)\n realP = P[np.where(iR==1)[0]]\n realV = V[:, np.where(iR==1)[0]]\n\n idx = np.argsort(realP)[::-1]\n P[0:len(idx)] = realP[idx]\n V[:,0:len(idx)] = realV[:,idx]\n iR[0:len(idx)] = iR[idx]\n\n # build matrices\n J = np.zeros((N, N))\n Q = np.zeros((N, N))\n X = np.zeros((N,))\n\n cnt = 0\n while cnt < N:\n if iR[cnt] == 1:\n Q[:,cnt] = np.real(V[:,cnt])/np.real(V[0,cnt])\n J[cnt,cnt] = np.real(P[cnt])\n X[cnt] = 1\n cnt += 1\n else:\n Q[:,cnt] = np.real(V[:,cnt])\n Q[:,cnt+1] = np.imag(V[:,cnt])\n\n B = np.asarray([[+np.real(P[cnt]), +np.imag(P[cnt])],\n [-np.imag(P[cnt]), +np.real(P[cnt])]])\n J[cnt:cnt+2,cnt:cnt+2] = B\n X[cnt], X[cnt+1] = 1, 0\n cnt += 2\n return (J, np.linalg.inv(Q), X)\n\n\n @staticmethod\n def cluster(D, k=3, verbose=False):\n \"\"\"Cluster LDS's via Multi-Dimensional Scaling and KMeans.\n\n Strategy:\n 1. Build NxN matrix of pairwise similarities\n 2. Run MDS to embed data in R^2\n 3. Run KMeans with k cluster centers\n 4. Find samples closest to the k centers\n\n Paramters:\n ----------\n D: numpy.ndarray, shape = (N, N)\n Precomputed distance matrix.\n\n k: int (default: 3)\n Number of desired cluster centers.\n\n verbose: boolean\n Enable verbose output.\n\n Returns:\n --------\n eData: numpy.ndarray, shape (N, k)\n N d-dimensional samples embedded in R^d.\n\n ids: numpy.ndarray, shape = (k,)\n List of indices identifying the k representatives.\n \"\"\"\n\n assert D.shape[0] == D.shape[1], \"OOps (distance matrix not square)!\"\n\n # build MDS for precomputed similarity matrix\n mds = MDS(metric=True, n_components=2, verbose=True,\n dissimilarity=\"precomputed\")\n\n def __symmetrize(A):\n return A + A.T - np.diag(A.diagonal())\n\n # run MDS on symmetrized similarity matrix\n eData = mds.fit(__symmetrize(D)).embedding_\n\n kmObj = KMeans(k)\n kmObj.fit_predict(eData)\n\n ids = np.zeros((k,), dtype=np.int)\n for i in range(k):\n # sanity check\n cDat = eData[np.where(kmObj.labels_ == i)[0],:]\n assert len(cDat) > 0, \"Oops, empty cluster ...\"\n\n kCen = kmObj.cluster_centers_[i,:]\n x = euclidean_distances(eData, kCen)\n ids[i] = int(np.argsort(x.ravel())[0])\n\n # return distance matrix and ID's of representative LDS's\n return (eData, ids)\n\n\n @staticmethod\n def computeJCFTransform(A,C):\n \"\"\"Compute transform to convert LDS into Jordan Canonical Form (JCF).\n\n Parameters:\n -----------\n A : np.ndarray, shape = (N, N)\n Input state transition matrix.\n\n C : np.ndarray, shape = (D, N)\n Input observation matrix.\n\n Returns:\n --------\n P : np.ndarray, shape = (N, N)\n Transform to convert the LDS, represented by (A, C)\n into JCF (Ac, Cc), s.t.\n\n (Ac, Cc) = (P*A*inv(P), g^T*C*inv(P)),\n\n where g = [1,1,....,1] is C.shape[0] vector of all\n '1', i.e., g^T*C is the column-wise sum of C; this\n is required, since the JCF is defined only for one\n output!\n\n The algorithm is based on A. Ravinchandran's MATLAB function\n convertToCanonicalForm.m, part of the Dynamic Texture Toolbox\n available from:\n\n http://cis.jhu.edu/~avinash/projects/DTBox\n\n Mathematical details on how to obtain the transform to convert\n a LDS into JCF can be found in the PAMI article\n\n A. Ravinchandran and R. Vidal., \"Video Registration Using\n Dynamic Textures\", PAMI 33(1), Jan. 2011\n \"\"\"\n\n if not A.shape[0] == A.shape[1]:\n raise ErrorDS(\"A matrix not square!\")\n\n if not A.shape[0] == C.shape[1]:\n raise ErrorDS(\"(A,C) not compatible!\")\n\n N = A.shape[0]\n I = np.identity(N)\n (J, Q, Cc) = LinearDS.computeRJF(A)\n\n M = np.kron(I, J) + np.kron(-A.T, I)\n T = np.kron(I, Cc)\n a = np.vstack((M, T))\n\n colSumC = np.sum(C, axis=0)\n x = np.zeros(N**2+len(colSumC),)\n x[-N:] = colSumC\n\n P = (np.linalg.pinv(a)*np.asmatrix(x).T).reshape((5,5),order='F')\n return P\n\n\n def convertToJCF(self):\n \"\"\"Converts the LDS to JCF.\n \"\"\"\n\n if not self.check():\n raise ErrorDS(\"System not ready for conversion to JCF!\")\n\n P = self.computeJCFTransform(self._Ahat, self._Chat)\n\n self._ChatJCF = self._Chat*np.linalg.inv(P)\n self._AhatJCF = P*self._Ahat*np.linalg.inv(P)\n self._XhatJCF = P*self._Xhat\n self._initM0JCF = P*self._initM0\n\n #TODO: Transform the remaining parameters (required for synthesis)!\n\n\n def check(self):\n \"\"\"Check validity of LDS parameters.\n\n Currently, this routine only checks if the parameters are set, but not\n if they are actually valid parameters!\n\n Returns:\n --------\n validity : boolean\n True if parameters are valid, False otherwise.\n \"\"\"\n\n for key in self.__dict__:\n if self.__dict__[key] is None:\n return False\n return True\n\n\n def synthesize(self, tau=50, mode=None):\n \"\"\"Synthesize obervations.\n\n Parameters\n ----------\n tau : int (default = 50)\n Synthesize tau frames.\n\n mode : Combination of ['s','q','r']\n 's' - Use the original states\n 'q' - Do NOT add state noise\n 'r' - Add observations noise\n\n In case 's' is specified, 'tau' is ignored and the number of\n frames equals the number of state time points.\n\n Returns\n -------\n I : numpy array, shape = (D, tau)\n Matrix with N D-dimensional column vectors as observations.\n\n X : numpy array, shape = (N, tau)\n Matrix with N tau-dimensional state vectors.\n \"\"\"\n\n if not self._ready:\n raise ErrorDS(\"LDS not ready for synthesis!\")\n\n Bhat = None\n Xhat = self._Xhat\n Qhat = self._Qhat\n Ahat = self._Ahat\n Chat = self._Chat\n Rhat = self._Rhat\n Yavg = self._Yavg\n initM0 = self._initM0\n initS0 = self._initS0\n nStates = self._nStates\n\n if mode is None:\n raise ErrorDS(\"No synthesis mode specified!\")\n\n # use original states -> tau is restricted\n if mode.find('s') >= 0:\n tau = Xhat.shape[1]\n\n # data to be filled and returned\n I = np.zeros((len(Yavg), tau))\n X = np.zeros((nStates, tau))\n\n if mode.find('r') >= 0:\n stdR = np.sqrt(Rhat)\n\n # add state noise, unless user explicitly decides against\n if not mode.find('q') >= 0:\n stdS = np.sqrt(initS0)\n (U, S, V) = np.linalg.svd(Qhat, full_matrices=False)\n Bhat = U*np.diag(np.sqrt(S))\n\n t = 0\n Xt = np.zeros((nStates, 1))\n while (tau<0) or (t= 0:\n Xt1 = Xhat[:,t]\n # first state\n elif t == 0:\n Xt1 = initM0;\n if mode.find('q') < 0:\n Xt1 += stdS*np.rand(nStates)\n # any further states (if mode != 's')\n else:\n Xt1 = Ahat*Xt\n if not mode.find('q') >= 0:\n Xt1 = Xt1 + Bhat*np.rand(nStates)\n\n # synthesizes image\n It = Chat*Xt1 + np.reshape(Yavg,(len(Yavg),1))\n\n # adds observation noise\n if mode.find('r') >= 0:\n It += stdR*np.randn(length(Yavg))\n\n # save ...\n Xt = Xt1;\n I[:,t] = It.reshape(-1)\n X[:,t] = Xt.reshape(-1)\n t += 1\n\n return (I, X)\n\n\n def suboptimalSysID(self, Y):\n \"\"\"Suboptimal system identification using SVD.\n\n Suboptimal system identification based on SVD, as proposed in the\n original work of Doretto et al. [1].\n\n Parameters\n ----------\n Y : numpy array, shape = (N, D)\n Input data with D observations as N-dimensional column vectors.\n \"\"\"\n\n nStates = self._nStates\n\n if self._verbose:\n dsinfo.info(\"using suboptimal SVD-based estimation!\")\n\n (N, tau) = Y.shape\n Yavg = np.mean(Y, axis=1)\n Y = Y - Yavg[:,np.newaxis]\n\n if self._approx:\n if self._verbose:\n with Timer('randomized_svd'):\n (U, S, V) = randomized_svd(Y, nStates)\n else:\n (U, S, V) = randomized_svd(Y, nStates)\n else:\n if self._verbose:\n with Timer('np.linalg.svd'):\n (U, S, V) = np.linalg.svd(Y, full_matrices=0)\n else:\n (U, S, V) = np.linalg.svd(Y, full_matrices=0)\n\n Chat = U[:,0:nStates]\n Xhat = (np.diag(S)[0:nStates,0:nStates] * np.asmatrix(V[0:nStates,:]))\n\n initM0 = np.mean(Xhat[:,0], axis=1)\n initS0 = np.zeros((nStates, 1))\n\n pind = range(tau-1);\n\n phi1 = Xhat[:,pind]\n phi2 = Xhat[:,[i+1 for i in pind]]\n\n Ahat = phi2*np.linalg.pinv(phi1)\n Vhat = phi2-Ahat*phi1;\n Qhat = 1.0/Vhat.shape[1] * Vhat*Vhat.T\n\n errorY = Y - Chat*Xhat\n Rhat = np.var(errorY.ravel())\n\n # save parameters\n self._initS0 = initS0\n self._initM0 = initM0\n self._Yavg = Yavg\n self._Ahat = Ahat\n self._Chat = Chat\n self._Xhat = Xhat\n self._Qhat = Qhat\n self._Rhat = Rhat\n\n if self.check():\n self._ready = True\n\n\n @staticmethod\n def stateSpaceMap(lds1, lds2):\n \"\"\"\n Map parameters from lds1 into space of lds2 (state-space).\n\n Parameters:\n -----------\n lds1 : lds instance\n Target LDS\n\n lds2: lds instance\n Source LDS\n\n Returns:\n --------\n lds : lds instance\n New instance of lds2 (with UPDADED parameters)\n\n err : float\n Absolute difference between the vectorized parameter sets before\n the state-space mapping.\n \"\"\"\n\n # make a shallow copy (no compound object -> no problem)\n lds = copy.copy(lds2)\n\n Chat1 = lds1._Chat\n Chat2 = lds2._Chat\n\n F = np.asmatrix(np.linalg.pinv(Chat2))*Chat1\n\n # compute TRANSFORMED params (rest should be kept the same)\n lds._Chat = lds2._Chat*F\n lds._Ahat = F.T*lds2._Ahat*F\n lds._Qhat = F.T*lds2._Qhat*F\n lds._Rhat = lds2._Rhat\n lds._initM0 = F.T*lds2._initM0\n lds._initS0 = np.diag(F.T*np.diag(lds._initS0.ravel())*F)\n\n err = 0\n err += np.sum(np.abs(lds2._Chat.ravel() - lds1._Chat.ravel()))\n err += np.sum(np.abs(lds2._Ahat.ravel() - lds1._Ahat.ravel()))\n err += np.sum(np.abs(lds2._Qhat.ravel() - lds1._Qhat.ravel()))\n err += np.sum(np.abs(lds2._Rhat.ravel() - lds1._Rhat.ravel()))\n err += np.sum(np.abs(lds2._initM0.ravel() - lds1._initM0.ravel()))\n err += np.sum(np.abs(lds2._initS0.ravel() - lds1._initS0.ravel()))\n err += np.sum(np.abs(lds2._Yavg.ravel() - lds1._Yavg.ravel()))\n return (lds, err)\n\n\nclass OnlineNonLinearDS(NonLinearDS):\n \"\"\"Online version of non-linear DS (for real-time use).\n \"\"\"\n\n def __init__(self, nStates, kpcaParam, bufLen, nShift=1, verbose=False):\n \"\"\" Initialization.\n\n Parameters:\n -----------\n nStates : int\n Number of NLDS states.\n\n kpcaParam: instance of KPCAParam\n KPCA parameters.\n\n bufLen : int\n Length of circular buffer to hold data vectors.\n\n nShift : int (default : 1)\n Shift window by N vectors forward.\n\n verbose : boolean (default : False)\n Verbose output.\n \"\"\"\n\n if nShift == 0:\n raise ErrorDS('nShift == 0!')\n NonLinearDS.__init__(self, nStates, kpcaParam, verbose)\n\n self._buf = deque(maxlen = bufLen)\n [self._buf.append(None) for i in range(bufLen)]\n\n self._nShift = nShift\n self._cnt = nShift - 1\n\n\n def hasChanged(self):\n \"\"\"Did the DS change ?\n \"\"\"\n return self._cnt == self._nShift\n\n\n def update(self, x):\n \"\"\"Update NLDS model (i.e., re-estimate if required)\n\n Parameters:\n -----------\n x : numpy.array, shape = (N, )\n New data vector.\n \"\"\"\n\n self._buf.append(x)\n\n if self._buf.count(None) > 0:\n return\n self._cnt -= 1\n\n if self._cnt == 0 or self._nShift == 1:\n self.suboptimalSysID(np.asarray(self._buf).T)\n self._cnt = self._nShift\n\n\nclass OnlineLinearDS(LinearDS):\n \"\"\"Online version of a linear DS (for real-time use).\n \"\"\"\n\n def __init__(self, nStates, bufLen, nShift=1, approx=False, verbose=False):\n \"\"\" Initialization.\n\n Parameters:\n -----------\n nStates : int\n Number of LDS states.\n\n bufLen : int\n Length of circular buffer to hold data vectors.\n\n nShift : int (default : 1)\n Shift window by N vectors forward.\n\n approx : boolean (default : False)\n Use randomized SVD.\n\n verbose : boolean (default : False)\n Verbose output.\n \"\"\"\n\n if nShift == 0:\n raise ErrorDS('nShift == 0!')\n\n # call base class init\n LinearDS.__init__(self, nStates, approx, verbose)\n\n # initialize buffer and fill with None's\n self._buf = deque(maxlen = bufLen)\n [self._buf.append(None) for i in range(bufLen)]\n\n self._nShift = nShift\n self._cnt = nShift - 1\n\n\n def hasChanged(self):\n \"\"\"Did the DS change ?\n \"\"\"\n return self._cnt == self._nShift\n\n\n def update(self, x):\n \"\"\"Update LDS model (i.e., re-estimate if required)\n\n Parameters:\n -----------\n x : numpy.array, shape = (N, )\n New data vector.\n \"\"\"\n\n self._buf.append(x)\n\n # rampup time ... do nothin\n if self._buf.count(None) > 0:\n return\n\n self._cnt -= 1\n\n if self._cnt == 0 or self._nShift == 1:\n self.suboptimalSysID(np.asarray(self._buf).T)\n self._cnt = self._nShift\n", "sub_path": "dscore/system.py", "file_name": "system.py", "file_ext": "py", "file_size_in_byte": 22956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.linalg.norm", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 127, "usage_type": "attribute"}, {"api_name": "dsutil.dsutil.Timer", "line_number": 164, "usage_type": "call"}, {"api_name": "dscore.dskpca.kpca", "line_number": 165, "usage_type": "call"}, {"api_name": "dscore.dskpca.kpca", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 177, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 234, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 282, "usage_type": "call"}, {"api_name": "scipy.linalg.eig", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.isreal", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 323, "usage_type": "attribute"}, {"api_name": "sklearn.manifold.MDS", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 363, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 371, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 374, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.euclidean_distances", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 379, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 424, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 430, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 434, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 441, "usage_type": "attribute"}, {"api_name": "numpy.asmatrix", "line_number": 441, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 454, "usage_type": "attribute"}, {"api_name": "numpy.linalg.inv", "line_number": 455, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 455, "usage_type": "attribute"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 506, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 531, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 536, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 536, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 540, "usage_type": "call"}, {"api_name": "numpy.rand", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.rand", "line_number": 554, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 557, "usage_type": "call"}, {"api_name": "numpy.randn", "line_number": 561, "usage_type": "call"}, {"api_name": "dsutil.dsinfo.info", "line_number": 587, "usage_type": "call"}, {"api_name": "dsutil.dsinfo", "line_number": 587, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 591, "usage_type": "attribute"}, {"api_name": "dsutil.dsutil.Timer", "line_number": 595, "usage_type": "call"}, {"api_name": "sklearn.utils.extmath.randomized_svd", "line_number": 596, "usage_type": "call"}, {"api_name": "sklearn.utils.extmath.randomized_svd", "line_number": 598, "usage_type": "call"}, {"api_name": "dsutil.dsutil.Timer", "line_number": 601, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 602, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 602, "usage_type": "attribute"}, {"api_name": "numpy.linalg.svd", "line_number": 604, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 604, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 607, "usage_type": "call"}, {"api_name": "numpy.asmatrix", "line_number": 607, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 609, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 610, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 617, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 617, "usage_type": "attribute"}, {"api_name": "numpy.var", "line_number": 622, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 662, "usage_type": "call"}, {"api_name": "numpy.asmatrix", "line_number": 667, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 667, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 667, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 675, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 678, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 678, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 679, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 679, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 680, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 680, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 681, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 681, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 682, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 682, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 683, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 683, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 684, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 684, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 714, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 717, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 746, "usage_type": "call"}, {"api_name": "dscore.dsexcp.ErrorDS", "line_number": 776, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 782, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 813, "usage_type": "call"}]} +{"seq_id": "72061784", "text": "# ----------------------------------------------------------------------------\n# Copyright (c) 2015--, micronota development team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file COPYING.txt, distributed with this software.\n# ----------------------------------------------------------------------------\n\nfrom tempfile import mkdtemp\nfrom shutil import rmtree\nfrom os import listdir\nfrom os.path import join\nfrom unittest import TestCase, main\nfrom filecmp import cmp\nfrom collections import namedtuple\nfrom subprocess import CalledProcessError\n\nfrom skbio.util import get_data_path\nfrom skbio.metadata import Feature\n\nfrom micronota.util import _get_named_data_path\nfrom micronota.bfillings.prodigal import run, _parse_faa\n\n\nclass RunTests(TestCase):\n def setUp(self):\n self.tmp_dir = mkdtemp()\n self.negative_fps = [get_data_path(i) for i in\n ['empty', 'whitespace_only']]\n Case = namedtuple('Case', ['query', 'kwargs', 'outdir'])\n self.cases = [Case(i, j, k) for i, j, k in\n zip([_get_named_data_path(i) for i in\n # modified from NC_018498.gbk\n ['NC_018498_partial_1.gbk',\n 'NC_018498_partial_1.gbk',\n 'NC_018498_partial_2.gbk']],\n [{'-p': 'meta', '-f': 'gbk'},\n {'-p': 'meta'},\n {'-p': 'single'}],\n ['test_1',\n 'test_2',\n 'test_3'])]\n\n def test_wrong_input_fp(self):\n for fp in self.negative_fps:\n with self.assertRaisesRegex(\n CalledProcessError,\n 'returned non-zero exit status'):\n run(fp, self.tmp_dir)\n\n def test_run(self):\n for case in self.cases:\n exp_d = _get_named_data_path(case.outdir)\n obs_d = join(self.tmp_dir, case.outdir)\n run(case.query, obs_d, **case.kwargs)\n for f in listdir(exp_d):\n self.assertTrue(\n cmp(join(obs_d, f), join(exp_d, f), shallow=False))\n\n def tearDown(self):\n # remove the tempdir and contents\n rmtree(self.tmp_dir)\n\n\nclass ParseTests(TestCase):\n def setUp(self):\n self.parse_fp = _get_named_data_path('parse_test.faa')\n self.parse_exp = [\n {Feature(type_='CDS',\n id='1_1',\n right_partial_=False,\n left_partial_=False,\n location='686..1828',\n translation='MKILINKSELNKILKKMNNVIISNNKIKPHHSYFLIEAKEKEINFYANNEYFSVKCNLNKYFLITSKSEPELKQILVPSR*',\n note='\"start_type=ATG;rbs_motif=None;rbs_spacer=None;gc_cont=0.236\"',\n rc_=False): [(685, 1828)],\n Feature(type_='CDS',\n id='1_2',\n location='1828..>2757',\n translation='MNLYDLLELPTTASIKEIKIAYKRLAKRYHPDVNKLGSQTFVEINNAYSILSDPNQKEKYFNYKTQHFID',\n right_partial_=True,\n left_partial_=False,\n note='\"start_type=ATG;rbs_motif=None;rbs_spacer=None;gc_cont=0.271\"',\n rc_=False): [(1827, 2757)]},\n\n {Feature(type_='CDS',\n id='2_1',\n location='21577..22128',\n right_partial_=False,\n left_partial_=False,\n translation='MKKTSPFILRRTKNKVLKELPKKIITDIYVELSEEHQKLYDKQKTDGLKEIKESDAKNALFDV*',\n note='\"start_type=ATG;rbs_motif=None;rbs_spacer=None;gc_cont=0.272\"',\n rc_=False): [(21576, 22128)]}]\n\n def test_pred_parse_faa(self):\n obs = _parse_faa(self.parse_fp)\n for e, o in zip(self.parse_exp, obs):\n self.assertEqual(e, o)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "micronota/bfillings/tests/test_prodigal.py", "file_name": "test_prodigal.py", "file_ext": "py", "file_size_in_byte": 3994, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 25, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 27, "usage_type": "call"}, {"api_name": "skbio.util.get_data_path", "line_number": 28, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 30, "usage_type": "call"}, {"api_name": "micronota.util._get_named_data_path", "line_number": 32, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 47, "usage_type": "argument"}, {"api_name": "micronota.bfillings.prodigal.run", "line_number": 49, "usage_type": "call"}, {"api_name": "micronota.util._get_named_data_path", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "micronota.bfillings.prodigal.run", "line_number": 55, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 56, "usage_type": "call"}, {"api_name": "filecmp.cmp", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 62, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 65, "usage_type": "name"}, {"api_name": "micronota.util._get_named_data_path", "line_number": 67, "usage_type": "call"}, {"api_name": "skbio.metadata.Feature", "line_number": 69, "usage_type": "call"}, {"api_name": "skbio.metadata.Feature", "line_number": 77, "usage_type": "call"}, {"api_name": "skbio.metadata.Feature", "line_number": 86, "usage_type": "call"}, {"api_name": "micronota.bfillings.prodigal._parse_faa", "line_number": 96, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "522311045", "text": "\n\nimport json\nfrom record import add_record, Record, delete_record, edit_record, get_record\nfrom misc import print_help\nfrom misc import print_help\nfrom database import import_database, export_database\nfrom admin import Login, Logout, adminChangePassword, addUser, deleteUser, auditLog, listUsers\nFunc_Dict = {\"imd\":import_database, \"exd\":export_database,\n \"lin\":Login, \"lou\":Logout, \"chp\":adminChangePassword, \"adr\":add_record,\n \"adu\":addUser, \"deu\":deleteUser, \"dal\":auditLog,\"lsu\":listUsers, \"hlp\":print_help,\n \"der\":delete_record, \"edr\":edit_record, \"rer\":get_record}\nfrom datetime import date, datetime \nmessages = {1:\"LF\", 2:\"LS\", 3:\"L1\", 4:\"SPC\", 5:\"FPC\",\n 6:\"AU\", 7:\"DU\", 8:\"LO\"}\nclass Audit():\n def __init__(self):\n self.time = None\n self.type = None\n self.id = None\n\n def print_record(self):\n string = self.time + \";\"\n if(type(self.type) == list):\n for item in self.type:\n string += messages[item] + \";\"\n else:\n string += messages[self.type] + \";\"\n string += str(self.id)\n print(string)\n \n def set_items(self, record_type, ID):\n self.time = datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\")\n self.type = record_type\n self.id = ID\n\n\nclass Current_User():\n def __init__(self):\n self.login_status = False\n self.admin_priviliages = False\n self.current_id = 0\n self.user_dict = {\"2\":\"bob\"}\n self.admin_dict = {\"1\":\"passwd\"}\n self.database = {}\n self.audit_log = []\n self.audit = Audit()\n self.update_audit = False\n self.newly_created = []\n \n def initialization(self):\n #load your dict and stuff\n try:\n with open(\"user.txt\", 'r') as f:\n self.user_dict = json.loads(f.readline())\n f.close()\n except FileNotFoundError:pass\n\n try:\n with open(\"admin.txt\", 'r') as f:\n self.user_dict = json.loads(f.readline())\n f.close()\n except FileNotFoundError:pass\n\n def main_loop(self):\n print(\"Address Book Application, version <1>. Type \\\"HLP\\\" for a list of commands.\")\n while(True):\n user_input = input(\"ABA>\")\n user_input = user_input.lower().strip().split(\" \")\n num_args = len(user_input) - 1\n if user_input[0] == \"ext\":\n print(\"Okay\")\n self.exit()\n if user_input[0] in Func_Dict.keys():\n try:\n if user_input[0] == \"adr\" or user_input[0] == \"edr\":\n temp = []\n for item in user_input[2:]:\n temp.append(item)\n del user_input[2:-1]\n num_args = 2\n if num_args == 0:\n Func_Dict.get(user_input[0])(self)\n elif num_args == 1:\n Func_Dict.get(user_input[0])(self, user_input[1])\n elif num_args == 2:\n Func_Dict.get(user_input[0])(self, user_input[1], user_input[2])\n else:\n print(\"Unrecognized command\")\n except TypeError:\n print(\"Incorrect number of parameters, input help for assistance\")\n else:\n print(\"Unrecognized command\")\n if self.update_audit == True:\n if len(self.audit_log) > 512:\n print(\"aduit log exceeding maximum size\")\n self.update_audit = False\n continue\n self.audit_log.append(self.audit)\n self.audit = Audit()\n self.update_audit = False\n\n def exit(self):\n with open(\"user.txt\", 'w') as f:\n f.write(json.dumps(self.user_dict))\n f.close()\n with open(\"admin.txt\", 'w') as f:\n f.write(json.dumps(self.admin_dict))\n f.close()\n exit(0)\n\nguy = Current_User()\nguy.main_loop()\n", "sub_path": "ABA.py", "file_name": "ABA.py", "file_ext": "py", "file_size_in_byte": 4124, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "database.import_database", "line_number": 9, "usage_type": "name"}, {"api_name": "database.export_database", "line_number": 9, "usage_type": "name"}, {"api_name": "admin.Login", "line_number": 10, "usage_type": "name"}, {"api_name": "admin.Logout", "line_number": 10, "usage_type": "name"}, {"api_name": "admin.adminChangePassword", "line_number": 10, "usage_type": "name"}, {"api_name": "record.add_record", "line_number": 10, "usage_type": "name"}, {"api_name": "admin.addUser", "line_number": 11, "usage_type": "name"}, {"api_name": "admin.deleteUser", "line_number": 11, "usage_type": "name"}, {"api_name": "admin.auditLog", "line_number": 11, "usage_type": "name"}, {"api_name": "admin.listUsers", "line_number": 11, "usage_type": "name"}, {"api_name": "misc.print_help", "line_number": 11, "usage_type": "name"}, {"api_name": "record.delete_record", "line_number": 12, "usage_type": "name"}, {"api_name": "record.edit_record", "line_number": 12, "usage_type": "name"}, {"api_name": "record.get_record", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "455022247", "text": "import urllib.request\nimport urllib\nfrom bs4 import BeautifulSoup\nimport re\nimport csv\nimport datetime\nimport threading\nfrom multiprocessing import Process\n\n\npid = r' 3:\n the.join()\n n = 0\n n = n + 1\n the.join()\n\n\nif __name__== '__main__':\n # now = datetime.datetime.now()\n # format = \"%Y-%m-%d-%H-%M-%S\"\n # file=now.strftime(format)\n\n basereq = Pqcontent()\n basereq.baseaction()\n", "sub_path": "uangrupiah-op.py", "file_name": "uangrupiah-op.py", "file_ext": "py", "file_size_in_byte": 5242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "urllib.request.Request", "line_number": 55, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 55, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 56, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 56, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 57, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 58, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 65, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 95, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "736404", "text": "import cachecontrol\nimport colorsys\nimport github3\nimport math\nimport operator\nimport os\nimport pytest\nimport subprocess\nimport threading\nimport time\nimport yaml\n\nfrom ..prci_github.internals import get_pull_priority\nfrom ..prci_github.internals import PullQueue, NoTaskAvailable\nfrom ..prci_github import TaskQueue, TaskAlreadyTaken, AbstractJob, JobResult\n\npath = os.path.dirname(os.path.realpath(__file__))\nACCOUNT_CONFIG = os.path.join(path, 'test_github.yaml')\nTASKS_CONFIG = os.path.join(path, 'test_tasks.yaml')\n\nwith open(ACCOUNT_CONFIG) as f:\n gh_config = yaml.load(f)\nGH_REPO = gh_config['repo']\nGH_TOKEN = gh_config['token']\nPULL_COUNT = 10\nPRIORITY_COUNT = 3\nbranch_name_template = 'test_branch_{{:0{}d}}'.format(int(math.ceil(math.log(PULL_COUNT, 10))))\npull_title_template = 'PR #{{:0{}d}}'.format(int(math.ceil(math.log(PULL_COUNT, 10))))\nlabel_priority_template = 'priority:{{:0{}d}}'.format(int(math.ceil(math.log(PRIORITY_COUNT, 10))))\n\ndef __colours(num):\n res = []\n for h in range(0, 80, 80/num):\n rgb_floats = colorsys.hsv_to_rgb(h/100., 0.9, 0.9)\n rgb_ints = tuple(int(comp*255) for comp in rgb_floats)\n rgb = (rgb_ints[0] << 16) + (rgb_ints[1] << 8) + rgb_ints[2]\n res.append('#{:06x}'.format(rgb))\n\n return res\n\n\ndef ismonotonic(seq, op):\n items = iter(seq)\n\n try:\n prev = next(items)\n except StopIteration:\n return True\n\n for item in items:\n if not op(prev, item):\n return False\n prev = item\n return True\n\n\nclass J(AbstractJob):\n def __call__(self, depends_results={}):\n dep_results = {}\n for task_name, result in depends_results.items():\n dep_results['{}_description'.format(task_name)] = result.description\n dep_results['{}_url'.format(task_name)] = result.url\n\n cmd = self.cmd.format(\n repo_url=self.target[0],\n pull_ref=self.target[1],\n **dep_results\n )\n\n try:\n url = subprocess.check_output(cmd, shell=True)\n except subprocess.CalledProcessError as e:\n if e.returncode == 1:\n state = 'failure'\n description = 'Test failed: {}'.format(e)\n url = ''\n else:\n state = 'error'\n description = 'An unexpected error occured: {}'.format(e)\n url = ''\n else:\n state = 'success'\n description = 'Test passed'\n\n return JobResult(state, description, url)\n\n\n@pytest.fixture(scope='module')\ndef repo(request):\n gh = github3.login(token=GH_TOKEN)\n cachecontrol.CacheControl(gh.session)\n repo = gh.create_repository(\n GH_REPO,\n has_issues=False,\n has_wiki=False,\n auto_init=True\n )\n\n labels_colours = __colours(PRIORITY_COUNT)\n for p in range(PRIORITY_COUNT):\n repo.create_label(\n label_priority_template.format(p),\n labels_colours[p],\n )\n while True:\n try:\n master_branch = repo.ref('heads/master')\n except github3.exceptions.ClientError as e:\n time.sleep(1)\n else:\n if isinstance(master_branch, github3.git.Reference):\n time.sleep(1)\n break\n\n for num in range(PULL_COUNT):\n branch_name = branch_name_template.format(num+1)\n pull_title = pull_title_template.format(num+1)\n\n # create a tree object containing the change\n tree = repo.create_tree(\n tree=[{\n 'path': 'new file',\n 'mode': '100644',\n 'type': 'blob',\n 'content': \"content of new file\",\n }],\n base_tree=repo.tree(master_branch.object.sha).sha,\n )\n\n # commit the change\n commit = repo.create_commit(\n message='Add new file',\n tree=tree.sha,\n parents=[master_branch.object.sha],\n )\n # create new branch pointing to the new commit\n repo.create_ref(\n 'refs/heads/{}'.format(branch_name),\n commit.sha,\n )\n\n # create pull request from branch\n pull = repo.create_pull(\n title=pull_title,\n head=branch_name,\n base='master',\n )\n\n pull.issue().add_labels(label_priority_template.format(num % PRIORITY_COUNT))\n\n request.addfinalizer(repo.delete)\n\n return repo\n\nclass TestPRCI(object):\n def test_repo_creation(self, repo):\n assert len(list(repo.pull_requests())) == PULL_COUNT\n\n def test_iter_pulls_by_priority(self, repo):\n priorities = [get_pull_priority(p) for p in PullQueue(repo)]\n\n # assert that the sequence is nonincreasing\n assert ismonotonic(priorities, operator.ge), (\n \"Priorities are not nonincreasing: {}\".format(priorities))\n\n def test_task_queue_ordering(self, repo):\n tq = TaskQueue(repo, TASKS_CONFIG, J)\n tq.create_tasks_for_pulls()\n\n tasks_done = []\n\n for task in tq:\n try:\n task.take('R#0')\n except TaskAlreadyTaken:\n continue\n else:\n task.execute()\n\n tasks_done.append({\n 'pull_num': task.pull.number,\n 'pull_prio': get_pull_priority(task.pull), \n 'task_name': task.name,\n 'task_prio': task.priority,\n })\n\n pull_prios = [t['pull_prio'] for t in tasks_done]\n task_prios = {\n p: [t['task_prio'] for t in tasks_done if t['pull_num'] == p]\n for p in set([n['pull_num'] for n in tasks_done])\n }\n\n # pull request priority is nonincreasing\n assert ismonotonic(pull_prios, operator.ge)\n\n # within one pull request tasks priorities are nonincreasing\n assert all([ismonotonic(task_prios[p], operator.ge) for p in task_prios])\n", "sub_path": "github/tests/test_github.py", "file_name": "test_github.py", "file_ext": "py", "file_size_in_byte": 5920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"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": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 22, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 27, "usage_type": "call"}, {"api_name": "math.log", "line_number": 27, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 28, "usage_type": "call"}, {"api_name": "math.log", "line_number": 28, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 29, "usage_type": "call"}, {"api_name": "math.log", "line_number": 29, "usage_type": "call"}, {"api_name": "colorsys.hsv_to_rgb", "line_number": 34, "usage_type": "call"}, {"api_name": "prci_github.AbstractJob", "line_number": 57, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 71, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 72, "usage_type": "attribute"}, {"api_name": "prci_github.JobResult", "line_number": 85, "usage_type": "call"}, {"api_name": "github3.login", "line_number": 90, "usage_type": "call"}, {"api_name": "cachecontrol.CacheControl", "line_number": 91, "usage_type": "call"}, {"api_name": "github3.exceptions", "line_number": 108, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "github3.git", "line_number": 111, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 88, "usage_type": "call"}, {"api_name": "prci_github.internals.get_pull_priority", "line_number": 160, "usage_type": "call"}, {"api_name": "prci_github.internals.PullQueue", "line_number": 160, "usage_type": "call"}, {"api_name": "operator.ge", "line_number": 163, "usage_type": "attribute"}, {"api_name": "prci_github.TaskQueue", "line_number": 167, "usage_type": "call"}, {"api_name": "prci_github.TaskAlreadyTaken", "line_number": 175, "usage_type": "name"}, {"api_name": "prci_github.internals.get_pull_priority", "line_number": 182, "usage_type": "call"}, {"api_name": "operator.ge", "line_number": 194, "usage_type": "attribute"}, {"api_name": "operator.ge", "line_number": 197, "usage_type": "attribute"}]} +{"seq_id": "483568298", "text": "\"\"\"\nCopyright 2021 Nirlep_5252_\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\nimport discord\nimport datetime\nfrom discord.ext import commands\nfrom config import MAIN_COLOR, EMOJIS_FOR_COGS, EMOJIS, EMPTY_CHARACTER, WEBSITE_LINK, SUPPORT_SERVER_LINK\nfrom utils.embed import error_embed\n\n\nasync def get_cog_help(cog, context):\n cog = context.bot.get_cog(cog)\n if cog.qualified_name == 'nsfw' and not context.channel.is_nsfw():\n return error_embed(\n f\"{EMOJIS['tick_no']} Go away horny!\",\n \"Please go to a **NSFW** channel to see the commands.\"\n )\n\n embed = discord.Embed(\n title=f\"{cog.qualified_name.title()} Category\",\n color=MAIN_COLOR\n ).set_thumbnail(url=context.bot.user.avatar.url\n ).add_field(name=EMPTY_CHARACTER, value=f\"[Invite EpicBot]({WEBSITE_LINK}/invite) | [Vote EpicBot]({WEBSITE_LINK}/vote) | [Support Server]({SUPPORT_SERVER_LINK})\", inline=False)\n\n nice = \"\"\n cmds = cog.get_commands()\n\n for e in cmds:\n nice += f\"`{e.name}` - {e.help}\\n\"\n\n embed.description = f\"To get detailed help, please use `{context.clean_prefix}help `\\n\\n**Commands:**\\n{nice}\"\n\n return embed\n\n\nclass EpicBotHelpSelect(discord.ui.Select):\n def __init__(self, placeholder, options, ctx):\n super().__init__(\n placeholder=placeholder,\n options=options\n )\n self.ctx = ctx\n\n async def callback(self, i):\n await i.response.send_message(embed=await get_cog_help(\n self.values[0], self.ctx\n ), ephemeral=True)\n\n\nclass EpicBotHelp(commands.HelpCommand):\n\n async def send_bot_help(self, mapping):\n embed = discord.Embed(\n title=\"Help Command\",\n description=\"Hello, I am a simple, multipurpose Discord bot, built to make your Discord life easier!\\n\\n**Select a category:**\",\n color=MAIN_COLOR,\n timestamp=datetime.datetime.utcnow()\n ).set_thumbnail(url=self.context.bot.user.avatar.url\n ).set_author(name=self.context.bot.user.name, icon_url=self.context.bot.user.avatar.url\n ).set_footer(text=f\"Requested by {self.context.author}\", icon_url=self.context.author.avatar.url)\n\n view_ui = discord.ui.View(timeout=None)\n options = []\n for cog, cmds in mapping.items():\n if cog is not None and cog.qualified_name.lower() == cog.qualified_name:\n embed.add_field(\n name=f\"{EMOJIS_FOR_COGS[cog.qualified_name]} {cog.qualified_name.title()} [ `{len(cmds)}` ]\",\n value=cog.description,\n inline=False\n )\n options.append(discord.SelectOption(\n label=cog.qualified_name.title(),\n description=cog.description,\n value=cog.qualified_name,\n emoji=EMOJIS_FOR_COGS[cog.qualified_name]\n ))\n select = EpicBotHelpSelect(\n placeholder=\"Select a category.\",\n options=options,\n ctx=self.context\n )\n view_ui.add_item(select)\n view_ui.add_item(discord.ui.Button(\n style=discord.ButtonStyle.url,\n url=WEBSITE_LINK,\n label=\"Dashboard\",\n ))\n view_ui.add_item(discord.ui.Button(\n style=discord.ButtonStyle.url,\n url=SUPPORT_SERVER_LINK,\n label=\"Support Server\",\n ))\n view_ui.add_item(discord.ui.Button(\n style=discord.ButtonStyle.url,\n url=f\"{WEBSITE_LINK}/vote\",\n label=\"Vote\",\n ))\n\n await self.context.reply(embed=embed, view=view_ui)\n\n async def send_command_help(self, command):\n if command.cog_name == 'nsfw' and not self.context.channel.is_nsfw():\n return await self.context.reply(embed=error_embed(\n f\"{EMOJIS['tick_no']} Go away horny!\",\n \"Please go to a **NSFW** channel to see the command.\"\n ))\n uwu = \"\"\n aliases = \"\"\n for cancer in command.clean_params:\n uwu += f\"<{cancer}> \"\n for alias in command.aliases:\n aliases += f\"`{alias}` \"\n embed = discord.Embed(\n title=f\"{command.name.title()} Help\",\n description=f\"\"\"\n{command.help}\n\n**Usage:**\n```\n{self.context.clean_prefix}{command.name} {uwu}\n```\n**Aliases:** {aliases if len(aliases) > 0 else \"None\"}\n**Cooldown:** {0 if command._buckets._cooldown == None else command._buckets._cooldown.per} seconds\n \"\"\",\n color=MAIN_COLOR,\n timestamp=datetime.datetime.utcnow()\n ).set_footer(text=f\"Requested by {self.context.author}\", icon_url=self.context.author.avatar.url\n ).set_author(name=self.context.bot.user.name, icon_url=self.context.bot.user.avatar.url\n ).set_thumbnail(url=self.context.bot.user.avatar.url\n ).add_field(name=EMPTY_CHARACTER, value=f\"[Invite EpicBot]({WEBSITE_LINK}/invite) | [Vote EpicBot]({WEBSITE_LINK}/vote) | [Support Server]({SUPPORT_SERVER_LINK})\", inline=False)\n await self.context.reply(embed=embed)\n\n async def send_cog_help(self, cog):\n await self.context.reply(embed=await get_cog_help(cog.qualified_name, self.context))\n\n async def send_group_help(self, group):\n pass\n\n async def send_error_message(self, error):\n await self.context.reply(embed=error_embed(f\"{EMOJIS['tick_no']} Error!\", error))\n", "sub_path": "utils/help.py", "file_name": "help.py", "file_ext": "py", "file_size_in_byte": 5957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "utils.embed.error_embed", "line_number": 27, "usage_type": "call"}, {"api_name": "config.EMOJIS", "line_number": 28, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 32, "usage_type": "call"}, {"api_name": "config.MAIN_COLOR", "line_number": 34, "usage_type": "name"}, {"api_name": "config.EMPTY_CHARACTER", "line_number": 36, "usage_type": "name"}, {"api_name": "config.WEBSITE_LINK", "line_number": 36, "usage_type": "name"}, {"api_name": "config.SUPPORT_SERVER_LINK", "line_number": 36, "usage_type": "name"}, {"api_name": "discord.ui", "line_number": 49, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.HelpCommand", "line_number": 63, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 63, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 66, "usage_type": "call"}, {"api_name": "config.MAIN_COLOR", "line_number": 69, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "discord.ui.View", "line_number": 75, "usage_type": "call"}, {"api_name": "discord.ui", "line_number": 75, "usage_type": "attribute"}, {"api_name": "config.EMOJIS_FOR_COGS", "line_number": 80, "usage_type": "name"}, {"api_name": "discord.SelectOption", "line_number": 84, "usage_type": "call"}, {"api_name": "config.EMOJIS_FOR_COGS", "line_number": 88, "usage_type": "name"}, {"api_name": "discord.ui.Button", "line_number": 96, "usage_type": "call"}, {"api_name": "discord.ui", "line_number": 96, "usage_type": "attribute"}, {"api_name": "discord.ButtonStyle", "line_number": 97, "usage_type": "attribute"}, {"api_name": "config.WEBSITE_LINK", "line_number": 98, "usage_type": "name"}, {"api_name": "discord.ui.Button", "line_number": 101, "usage_type": "call"}, {"api_name": "discord.ui", "line_number": 101, "usage_type": "attribute"}, {"api_name": "discord.ButtonStyle", "line_number": 102, "usage_type": "attribute"}, {"api_name": "config.SUPPORT_SERVER_LINK", "line_number": 103, "usage_type": "name"}, {"api_name": "discord.ui.Button", "line_number": 106, "usage_type": "call"}, {"api_name": "discord.ui", "line_number": 106, "usage_type": "attribute"}, {"api_name": "discord.ButtonStyle", "line_number": 107, "usage_type": "attribute"}, {"api_name": "config.WEBSITE_LINK", "line_number": 108, "usage_type": "name"}, {"api_name": "utils.embed.error_embed", "line_number": 116, "usage_type": "call"}, {"api_name": "config.EMOJIS", "line_number": 117, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 126, "usage_type": "call"}, {"api_name": "config.MAIN_COLOR", "line_number": 138, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "attribute"}, {"api_name": "config.EMPTY_CHARACTER", "line_number": 143, "usage_type": "name"}, {"api_name": "config.WEBSITE_LINK", "line_number": 143, "usage_type": "name"}, {"api_name": "config.SUPPORT_SERVER_LINK", "line_number": 143, "usage_type": "name"}, {"api_name": "utils.embed.error_embed", "line_number": 153, "usage_type": "call"}, {"api_name": "config.EMOJIS", "line_number": 153, "usage_type": "name"}]} +{"seq_id": "188304172", "text": "# Copyright 2019 Atalaya Tech, 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\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport click\nimport logging\n\nfrom google.protobuf.json_format import MessageToJson\nfrom bentoml.deployment.serverless import ServerlessDeployment\nfrom bentoml.deployment.sagemaker import SagemakerDeployment\nfrom bentoml.cli.click_utils import (\n _echo,\n CLI_COLOR_ERROR,\n CLI_COLOR_SUCCESS,\n parse_bento_tag_callback,\n)\nfrom bentoml.yatai import get_yatai_service\nfrom bentoml.proto.deployment_pb2 import (\n ApplyDeploymentRequest,\n DeleteDeploymentRequest,\n GetDeploymentRequest,\n DescribeDeploymentRequest,\n ListDeploymentsRequest,\n Deployment,\n DeploymentSpec,\n DeploymentOperator,\n)\nfrom bentoml.proto.status_pb2 import Status\nfrom bentoml.utils import pb_to_yaml\nfrom bentoml.utils.usage_stats import track_cli\nfrom bentoml.exceptions import BentoMLDeploymentException\nfrom bentoml.deployment.store import ALL_NAMESPACE_TAG\n\nSERVERLESS_PLATFORMS = ['aws-lambda', 'aws-lambda-py2', 'gcp-function']\n\n# pylint: disable=unused-variable\n\nlogger = logging.getLogger(__name__)\n\n\ndef add_legacy_deployment_commands(cli):\n\n # Example usage: bentoml deploy /ARCHIVE_PATH --platform=aws-lambda\n @cli.command(\n help='Deploy BentoML archive as REST endpoint to cloud services',\n short_help='Deploy Bento archive',\n )\n @click.argument('archive-path', type=click.STRING)\n @click.option(\n '--platform',\n type=click.Choice(\n [\n 'aws-lambda',\n 'aws-lambda-py2',\n 'gcp-function',\n 'aws-sagemaker',\n 'azure-ml',\n 'algorithmia',\n ]\n ),\n required=True,\n help='Target platform that Bento archive is going to deployed to',\n )\n @click.option(\n '--region',\n type=click.STRING,\n help='Target region inside the cloud provider that will be deployed to',\n )\n @click.option('--stage', type=click.STRING)\n @click.option(\n '--api-name', type=click.STRING, help='The name of API will be deployed'\n )\n @click.option(\n '--instance-type',\n type=click.STRING,\n help='SageMaker deployment ONLY. The instance type to use for deployment',\n )\n @click.option(\n '--instance-count',\n type=click.INT,\n help='Sagemaker deployment ONLY. Number of instances to use for deployment',\n )\n def deploy(\n archive_path, platform, region, stage, api_name, instance_type, instance_count\n ):\n track_cli('deploy', platform)\n if platform in SERVERLESS_PLATFORMS:\n deployment = ServerlessDeployment(archive_path, platform, region, stage)\n elif platform == 'aws-sagemaker':\n deployment = SagemakerDeployment(\n archive_path, api_name, region, instance_count, instance_type\n )\n else:\n _echo(\n 'Deploying with --platform=%s is not supported in current version of '\n 'BentoML' % platform,\n CLI_COLOR_ERROR,\n )\n return\n\n try:\n output_path = deployment.deploy()\n\n _echo(\n 'Successfully deployed to {platform}!'.format(platform=platform),\n CLI_COLOR_SUCCESS,\n )\n _echo('Deployment archive is saved at: %s' % output_path)\n except Exception as e: # pylint:disable=broad-except\n _echo(\n 'Encounter error when deploying to {platform}\\nError: '\n '{error_message}'.format(\n platform=platform, error_message=str(e)\n ),\n CLI_COLOR_ERROR,\n )\n\n # Example usage: bentoml delete-deployment ARCHIVE_PATH --platform=aws-lambda\n @cli.command(\n help='Delete active BentoML deployment from cloud services',\n short_help='Delete active BentoML deployment',\n )\n @click.argument('archive-path', type=click.STRING)\n @click.option(\n '--platform',\n type=click.Choice(\n [\n 'aws-lambda',\n 'aws-lambda-py2',\n 'gcp-function',\n 'aws-sagemaker',\n 'azure-ml',\n 'algorithmia',\n ]\n ),\n required=True,\n help='The platform bento archive is deployed to',\n )\n @click.option(\n '--region',\n type=click.STRING,\n required=True,\n help='The region deployment belongs to',\n )\n @click.option(\n '--api-name',\n type=click.STRING,\n help='Name of the API function that is deployed',\n )\n @click.option('--stage', type=click.STRING)\n def delete_deployment(archive_path, platform, region, stage, api_name):\n track_cli('delete-deploy', platform)\n if platform in SERVERLESS_PLATFORMS:\n deployment = ServerlessDeployment(archive_path, platform, region, stage)\n elif platform == 'aws-sagemaker':\n deployment = SagemakerDeployment(archive_path, api_name, region)\n else:\n _echo(\n 'Remove deployment with --platform=%s is not supported in current '\n 'version of BentoML' % platform,\n CLI_COLOR_ERROR,\n )\n return\n\n if deployment.delete():\n _echo(\n 'Successfully delete {platform} deployment'.format(platform=platform),\n CLI_COLOR_SUCCESS,\n )\n else:\n _echo(\n 'Delete {platform} deployment unsuccessful'.format(platform=platform),\n CLI_COLOR_ERROR,\n )\n\n # Example usage: bentoml check-deployment-status ARCHIVE_PATH --platform=aws-lambda\n @cli.command(\n help='Check deployment status of BentoML archive',\n short_help='check deployment status',\n )\n @click.argument('archive-path', type=click.STRING)\n @click.option(\n '--platform',\n type=click.Choice(\n [\n 'aws-lambda',\n 'aws-lambda-py2',\n 'gcp-function',\n 'aws-sagemaker',\n 'azure-ml',\n 'algorithmia',\n ]\n ),\n required=True,\n help='Target platform that Bento archive will be deployed to as a REST api \\\n service',\n )\n @click.option(\n '--region',\n type=click.STRING,\n required=True,\n help='Deployment region in cloud provider',\n )\n @click.option('--stage', type=click.STRING)\n @click.option(\n '--api-name',\n type=click.STRING,\n help='The name of API that is deployed as a service.',\n )\n def check_deployment_status(archive_path, platform, region, stage, api_name):\n track_cli('check-deployment-status', platform)\n if platform in SERVERLESS_PLATFORMS:\n deployment = ServerlessDeployment(archive_path, platform, region, stage)\n elif platform == 'aws-sagemaker':\n deployment = SagemakerDeployment(archive_path, api_name, region)\n else:\n _echo(\n 'check deployment status with --platform=%s is not supported in the '\n 'current version of BentoML' % platform,\n CLI_COLOR_ERROR,\n )\n return\n\n deployment.check_status()\n\n return cli\n\n\ndef parse_key_value_pairs(key_value_pairs_str):\n result = {}\n if key_value_pairs_str:\n for key_value_pair in key_value_pairs_str.split(','):\n key, value = key_value_pair.split('=')\n key = key.strip()\n value = value.strip()\n if key in result:\n logger.warning(\"duplicated key '%s' found string map parameter\", key)\n result[key] = value\n return result\n\n\ndef get_deployment_operator_type(platform):\n return DeploymentOperator.Value(platform.upper())\n\n\ndef display_deployment_info(deployment, output):\n if output == 'yaml':\n result = pb_to_yaml(deployment)\n else:\n result = MessageToJson(deployment)\n _echo(result)\n\n\ndef get_deployment_sub_command():\n @click.group()\n def deploy():\n pass\n\n @deploy.command(\n short_help='Create or update a model serving deployment',\n context_settings=dict(ignore_unknown_options=True, allow_extra_args=True),\n )\n @click.argument('--deployment-name', type=click.STRING, required=True)\n @click.option(\n '--bento',\n type=click.STRING,\n required=True,\n callback=parse_bento_tag_callback,\n help='Deployed bento archive, in format of name:version. For example, '\n 'iris_classifier:v1.2.0',\n )\n @click.option(\n '--platform',\n type=click.Choice(\n ['aws_lambda', 'gcp_function', 'aws_sagemaker', 'kubernetes', 'custom']\n ),\n required=True,\n help='Target platform that Bento archive is going to deployed to',\n )\n @click.option('--namespace', type=click.STRING, help='Deployment namespace')\n @click.option(\n '--labels',\n type=click.STRING,\n help='Key:value pairs that attached to deployment.',\n )\n @click.option('--annotations', type=click.STRING)\n @click.option(\n '--region',\n help='Name of the deployed region. For platforms: AWS_Lambda, AWS_SageMaker, '\n 'GCP_Function',\n )\n @click.option(\n '--stage', help='Stage is to identify. For platform: AWS_Lambda, GCP_Function'\n )\n @click.option(\n '--instance-type',\n help='Type of instance will be used for inference. For platform: AWS_SageMaker',\n )\n @click.option(\n '--instance-count',\n help='Number of instance will be used. For platform: AWS_SageMaker',\n )\n @click.option(\n '--api-name',\n help='User defined API function will be used for inference. For platform: '\n 'AWS_SageMaker',\n )\n @click.option(\n '--kube-namespace',\n help='Namespace for kubernetes deployment. For platform: Kubernetes',\n )\n @click.option('--replicas', help='Number of replicas. For platform: Kubernetes')\n @click.option('--service-name', help='Name for service. For platform: Kubernetes')\n @click.option('--service-type', help='Service Type. For platform: Kubernetes')\n @click.option('--output', type=click.Choice(['json', 'yaml']), default='json')\n def apply(\n bento,\n deployment_name,\n platform,\n output,\n namespace,\n labels,\n annotations,\n region,\n stage,\n instance_type,\n instance_count,\n api_name,\n kube_namespace,\n replicas,\n service_name,\n service_type,\n ):\n track_cli('deploy-apply', platform)\n\n bento_name, bento_verison = bento.split(':')\n spec = DeploymentSpec(\n bento_name=bento_name,\n bento_verison=bento_verison,\n operator=get_deployment_operator_type(platform),\n )\n if platform == 'aws_sagemaker':\n spec.sagemaker_operator_config = DeploymentSpec.SageMakerOperatorConfig(\n region=region,\n instance_count=instance_count,\n instance_type=instance_type,\n api_name=api_name,\n )\n elif platform == 'aws_lambda':\n spec.aws_lambda_operator_config = DeploymentSpec.AwsLambdaOperatorConfig(\n region=region, stage=stage\n )\n elif platform == 'gcp_function':\n spec.gcp_function_operator_config = \\\n DeploymentSpec.GcpFunctionOperatorConfig(\n region=region, stage=stage\n )\n elif platform == 'kubernetes':\n spec.kubernetes_operator_config = DeploymentSpec.KubernetesOperatorConfig(\n kube_namespace=kube_namespace,\n replicas=replicas,\n service_name=service_name,\n service_type=service_type,\n )\n else:\n raise BentoMLDeploymentException(\n 'Custom deployment is not supported in current version of BentoML'\n )\n\n result = get_yatai_service().ApplyDeployment(\n ApplyDeploymentRequest(\n deployment=Deployment(\n namespace=namespace,\n name=deployment_name,\n annotations=parse_key_value_pairs(annotations),\n labels=parse_key_value_pairs(labels),\n spec=spec,\n )\n )\n )\n if result.status.status_code != Status.OK:\n _echo(\n 'Failed to apply deployment {name}. code: {error_code}, message: '\n '{error_message}'.format(\n name=deployment_name,\n error_code=Status.Code.Name(result.status.status_code),\n error_message=result.status.error_message,\n ),\n CLI_COLOR_ERROR,\n )\n else:\n _echo(\n 'Successfully apply deployment {}'.format(deployment_name),\n CLI_COLOR_SUCCESS,\n )\n display_deployment_info(result.deployment, output)\n\n @deploy.command()\n @click.option('--name', type=click.STRING, help='Deployment name', required=True)\n @click.option('--namespace', type=click.STRING, help='Deployment namespace')\n def delete(name, namespace):\n track_cli('deploy-delete')\n\n result = get_yatai_service().DeleteDeployment(\n DeleteDeploymentRequest(deployment_name=name, namespace=namespace)\n )\n if result.status.status_code != Status.OK:\n _echo(\n 'Failed to delete deployment {name}. code: {error_code}, message: '\n '{error_message}'.format(\n name=name,\n error_code=Status.Code.Name(result.status.status_code),\n error_message=result.status.error_message,\n ),\n CLI_COLOR_ERROR,\n )\n else:\n _echo('Successfully delete deployment {}'.format(name), CLI_COLOR_SUCCESS)\n\n @deploy.command()\n @click.option('--name', type=click.STRING, help='Deployment name', required=True)\n @click.option('--namespace', type=click.STRING, help='Deployment namespace')\n @click.option('--output', type=click.Choice(['json', 'yaml']), default='json')\n def get(name, output, namespace):\n track_cli('deploy-get')\n\n result = get_yatai_service().GetDeployment(\n GetDeploymentRequest(deployment_name=name, namespace=namespace)\n )\n if result.status.status_code != Status.OK:\n _echo(\n 'Failed to get deployment {name}. code: {error_code}, message: '\n '{error_message}'.format(\n name=name,\n error_code=Status.Code.Name(result.status.status_code),\n error_message=result.status.error_message,\n ),\n CLI_COLOR_ERROR,\n )\n else:\n display_deployment_info(result.deployment, output)\n\n @deploy.command()\n @click.option('--name', type=click.STRING, help='Deployment name', required=True)\n @click.option('--namespace', type=click.STRING, help='Deployment namespace')\n @click.option('--output', type=click.Choice(['json', 'yaml']), default='json')\n def describe(name, output, namespace):\n track_cli('deploy-describe')\n\n result = get_yatai_service().DescribeDeployment(\n DescribeDeploymentRequest(deployment_name=name, namespace=namespace)\n )\n if result.status.status_code != Status.OK:\n _echo(\n 'Failed to describe deployment {name}. code: {error_code}, message: '\n '{error_message}'.format(\n name=name,\n error_code=Status.Code.Name(result.status.status_code),\n error_message=result.status.error_message,\n ),\n CLI_COLOR_ERROR,\n )\n else:\n display_deployment_info(result.deployment, output)\n\n @deploy.command()\n @click.option('--namespace', type=click.STRING)\n @click.option('--all-namespace', type=click.BOOL, default=False)\n @click.option(\n '--limit', type=click.INT, help='Limit how many deployments will be retrieved'\n )\n @click.option(\n '--filter', type=click.STRING, help='Filter retrieved deployments with keywords'\n )\n @click.option(\n '--labels', type=click.STRING, help='List deployments with the giving labels'\n )\n @click.option('--output', type=click.Choice(['json', 'yaml']), default='json')\n def list(output, limit, filter, labels, namespace, all_namespace):\n track_cli('deploy-list')\n\n if all_namespace:\n if namespace is not None:\n logger.warning(\n 'Ignoring `namespace=%s` due to the --all-namespace flag presented',\n namespace,\n )\n namespace = ALL_NAMESPACE_TAG\n\n result = get_yatai_service().ListDeployments(\n ListDeploymentsRequest(\n limit=limit,\n filter=filter,\n labels=parse_key_value_pairs(labels),\n namespace=namespace,\n )\n )\n if result.status.status_code != Status.OK:\n _echo(\n 'Failed to list deployments. code: {error_code}, message: '\n '{error_message}'.format(\n error_code=Status.Code.Name(result.status.status_code),\n error_message=result.status.error_message,\n ),\n CLI_COLOR_ERROR,\n )\n else:\n for deployment_pb in result.deployments:\n display_deployment_info(deployment_pb, output)\n\n return deploy\n", "sub_path": "bentoml/cli/deployment.py", "file_name": "deployment.py", "file_ext": "py", "file_size_in_byte": 18545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 52, "usage_type": "call"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 100, "usage_type": "call"}, {"api_name": "bentoml.deployment.serverless.ServerlessDeployment", "line_number": 102, "usage_type": "call"}, {"api_name": "bentoml.deployment.sagemaker.SagemakerDeployment", "line_number": 104, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 108, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 111, "usage_type": "argument"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 118, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_SUCCESS", "line_number": 120, "usage_type": "argument"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 122, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 124, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 129, "usage_type": "argument"}, {"api_name": "click.argument", "line_number": 62, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 62, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 63, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 65, "usage_type": "call"}, {"api_name": "click.option", "line_number": 78, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 80, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 83, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 83, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 84, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 85, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 87, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 89, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 92, "usage_type": "call"}, {"api_name": "click.INT", "line_number": 94, "usage_type": "attribute"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 166, "usage_type": "call"}, {"api_name": "bentoml.deployment.serverless.ServerlessDeployment", "line_number": 168, "usage_type": "call"}, {"api_name": "bentoml.deployment.sagemaker.SagemakerDeployment", "line_number": 170, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 172, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 175, "usage_type": "argument"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 180, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_SUCCESS", "line_number": 182, "usage_type": "argument"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 185, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 187, "usage_type": "argument"}, {"api_name": "click.argument", "line_number": 137, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 137, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 138, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 140, "usage_type": "call"}, {"api_name": "click.option", "line_number": 153, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 155, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 159, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 161, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 164, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 164, "usage_type": "attribute"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 225, "usage_type": "call"}, {"api_name": "bentoml.deployment.serverless.ServerlessDeployment", "line_number": 227, "usage_type": "call"}, {"api_name": "bentoml.deployment.sagemaker.SagemakerDeployment", "line_number": 229, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 231, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 234, "usage_type": "argument"}, {"api_name": "click.argument", "line_number": 195, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 195, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 196, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 198, "usage_type": "call"}, {"api_name": "click.option", "line_number": 212, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 214, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 218, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 218, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 219, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 221, "usage_type": "attribute"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentOperator.Value", "line_number": 257, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentOperator", "line_number": 257, "usage_type": "name"}, {"api_name": "bentoml.utils.pb_to_yaml", "line_number": 262, "usage_type": "call"}, {"api_name": "google.protobuf.json_format.MessageToJson", "line_number": 264, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 265, "usage_type": "call"}, {"api_name": "click.group", "line_number": 269, "usage_type": "call"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 348, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec", "line_number": 351, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec.SageMakerOperatorConfig", "line_number": 357, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec", "line_number": 357, "usage_type": "name"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec.AwsLambdaOperatorConfig", "line_number": 364, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec", "line_number": 364, "usage_type": "name"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec.GcpFunctionOperatorConfig", "line_number": 369, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec", "line_number": 369, "usage_type": "name"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec.KubernetesOperatorConfig", "line_number": 373, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeploymentSpec", "line_number": 373, "usage_type": "name"}, {"api_name": "bentoml.exceptions.BentoMLDeploymentException", "line_number": 380, "usage_type": "call"}, {"api_name": "bentoml.yatai.get_yatai_service", "line_number": 384, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.ApplyDeploymentRequest", "line_number": 385, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.Deployment", "line_number": 386, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.OK", "line_number": 395, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 395, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 396, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 403, "usage_type": "argument"}, {"api_name": "bentoml.proto.status_pb2.Status.Code.Name", "line_number": 400, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.Code", "line_number": 400, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 400, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 406, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_SUCCESS", "line_number": 408, "usage_type": "argument"}, {"api_name": "click.argument", "line_number": 277, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 277, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 278, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 280, "usage_type": "attribute"}, {"api_name": "bentoml.cli.click_utils.parse_bento_tag_callback", "line_number": 282, "usage_type": "name"}, {"api_name": "click.option", "line_number": 286, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 288, "usage_type": "call"}, {"api_name": "click.option", "line_number": 294, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 294, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 295, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 297, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 300, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 300, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 301, "usage_type": "call"}, {"api_name": "click.option", "line_number": 306, "usage_type": "call"}, {"api_name": "click.option", "line_number": 309, "usage_type": "call"}, {"api_name": "click.option", "line_number": 313, "usage_type": "call"}, {"api_name": "click.option", "line_number": 317, "usage_type": "call"}, {"api_name": "click.option", "line_number": 322, "usage_type": "call"}, {"api_name": "click.option", "line_number": 326, "usage_type": "call"}, {"api_name": "click.option", "line_number": 327, "usage_type": "call"}, {"api_name": "click.option", "line_number": 328, "usage_type": "call"}, {"api_name": "click.option", "line_number": 329, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 329, "usage_type": "call"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 416, "usage_type": "call"}, {"api_name": "bentoml.yatai.get_yatai_service", "line_number": 418, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DeleteDeploymentRequest", "line_number": 419, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.OK", "line_number": 421, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 421, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 422, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 429, "usage_type": "argument"}, {"api_name": "bentoml.proto.status_pb2.Status.Code.Name", "line_number": 426, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.Code", "line_number": 426, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 426, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 432, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_SUCCESS", "line_number": 432, "usage_type": "argument"}, {"api_name": "click.option", "line_number": 413, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 413, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 414, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 414, "usage_type": "attribute"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 439, "usage_type": "call"}, {"api_name": "bentoml.yatai.get_yatai_service", "line_number": 441, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.GetDeploymentRequest", "line_number": 442, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.OK", "line_number": 444, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 444, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 445, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 452, "usage_type": "argument"}, {"api_name": "bentoml.proto.status_pb2.Status.Code.Name", "line_number": 449, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.Code", "line_number": 449, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 449, "usage_type": "name"}, {"api_name": "click.option", "line_number": 435, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 435, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 436, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 436, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 437, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 437, "usage_type": "call"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 462, "usage_type": "call"}, {"api_name": "bentoml.yatai.get_yatai_service", "line_number": 464, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.DescribeDeploymentRequest", "line_number": 465, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.OK", "line_number": 467, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 467, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 468, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 475, "usage_type": "argument"}, {"api_name": "bentoml.proto.status_pb2.Status.Code.Name", "line_number": 472, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.Code", "line_number": 472, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 472, "usage_type": "name"}, {"api_name": "click.option", "line_number": 458, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 458, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 459, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 459, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 460, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 460, "usage_type": "call"}, {"api_name": "bentoml.utils.usage_stats.track_cli", "line_number": 494, "usage_type": "call"}, {"api_name": "bentoml.deployment.store.ALL_NAMESPACE_TAG", "line_number": 502, "usage_type": "name"}, {"api_name": "bentoml.yatai.get_yatai_service", "line_number": 504, "usage_type": "call"}, {"api_name": "bentoml.proto.deployment_pb2.ListDeploymentsRequest", "line_number": 505, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.OK", "line_number": 512, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 512, "usage_type": "name"}, {"api_name": "bentoml.cli.click_utils._echo", "line_number": 513, "usage_type": "call"}, {"api_name": "bentoml.cli.click_utils.CLI_COLOR_ERROR", "line_number": 519, "usage_type": "argument"}, {"api_name": "bentoml.proto.status_pb2.Status.Code.Name", "line_number": 516, "usage_type": "call"}, {"api_name": "bentoml.proto.status_pb2.Status.Code", "line_number": 516, "usage_type": "attribute"}, {"api_name": "bentoml.proto.status_pb2.Status", "line_number": 516, "usage_type": "name"}, {"api_name": "click.option", "line_number": 481, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 481, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 482, "usage_type": "call"}, {"api_name": "click.BOOL", "line_number": 482, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 483, "usage_type": "call"}, {"api_name": "click.INT", "line_number": 484, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 486, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 487, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 489, "usage_type": "call"}, {"api_name": "click.STRING", "line_number": 490, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 492, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 492, "usage_type": "call"}]} +{"seq_id": "344069013", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\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.macosx-10.3-i386/egg/easyshop/catalog/content/product_property.py\n# Compiled at: 2008-09-03 11:14:29\nfrom zope.interface import implements\nfrom Products.Archetypes.atapi import *\nfrom easyshop.core.config import PROJECTNAME\nfrom easyshop.core.interfaces import IProperty\n\nclass ProductProperty(OrderedBaseFolder):\n \"\"\"Product properties hold selectable options.\n \"\"\"\n __module__ = __name__\n implements(IProperty)\n\n def getOptions(self):\n \"\"\"\n \"\"\"\n result = []\n for option in self.objectValues():\n if len(option.getImage()) > 0:\n image_url = option.absolute_url() + '/image_listing'\n else:\n image_url = None\n result.append({'id': option.getId(), 'name': option.Title(), 'url': option.absolute_url(), 'path': ('/').join(option.getPhysicalPath()), 'image_url': image_url, 'price': str(option.getPrice())})\n\n return result\n\n def base_view(self):\n \"\"\"Overwritten to redirect to manage-properties-view of parent product \n or group.\n \"\"\"\n parent = self.aq_inner.aq_parent\n url = parent.absolute_url() + '/' + 'manage-properties-view'\n self.REQUEST.RESPONSE.redirect(url)\n\n\nregisterType(ProductProperty, PROJECTNAME)", "sub_path": "pycfiles/easyshop.catalog-0.1a1-py2.4/product_property.py", "file_name": "product_property.py", "file_ext": "py", "file_size_in_byte": 1437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "zope.interface.implements", "line_number": 16, "usage_type": "call"}, {"api_name": "easyshop.core.interfaces.IProperty", "line_number": 16, "usage_type": "argument"}, {"api_name": "easyshop.core.config.PROJECTNAME", "line_number": 40, "usage_type": "argument"}]} +{"seq_id": "562014609", "text": "import cv2\nimport numpy as np\n\nimage = cv2.imread(\"test_image.png\")\nlane_image = np.copy(image)\n\ngray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)\ncanny_image = cv2.Canny(gray, 120, 420)\n\nmask = np.zeros_like(canny_image)\nheight = image.shape[0]\npolygons = np.array(np.array([\n [(250, height), (600, 600), (1250, 600), (1500, height)]\n]))\ncv2.fillPoly(mask, polygons, 255)\nmasked_image = cv2.bitwise_and(canny_image, mask)\n\n\nlines = cv2.HoughLinesP(masked_image, 2, np.pi/180, 100, np.array([]), minLineLength=40, maxLineGap=5)\nline_image = np.zeros_like(image)\nfor line in lines:\n x1, y1, x2, y2 = line.reshape(4)\n cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10)\n\ncombo_image = cv2.addWeighted(image, 0.8, line_image, 1, 1)\n\ncv2.imshow(\"result\", combo_image)\ncv2.waitKey(0)", "sub_path": "lanes/lanes-07.py", "file_name": "lanes-07.py", "file_ext": "py", "file_size_in_byte": 795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.HoughLinesP", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "474628085", "text": "\"\"\"\nShuffle an Array\nGiven an integer array nums, design an algorithm to randomly shuffle the array. All permutations of the array should be equally likely as a result of the shuffling.\n\nImplement the Solution class:\n\nSolution(int[] nums) Initializes the object with the integer array nums.\nint[] reset() Resets the array to its original configuration and returns it.\nint[] shuffle() Returns a random shuffling of the array.\n\n\nExample 1:\n\nInput\n[\"Solution\", \"shuffle\", \"reset\", \"shuffle\"]\n[[[1, 2, 3]], [], [], []]\nOutput\n[null, [3, 1, 2], [1, 2, 3], [1, 3, 2]]\n\nExplanation\nSolution solution = new Solution([1, 2, 3]);\nsolution.shuffle(); // Shuffle the array [1,2,3] and return its result.\n // Any permutation of [1,2,3] must be equally likely to be returned.\n // Example: return [3, 1, 2]\nsolution.reset(); // Resets the array back to its original configuration [1,2,3]. Return [1, 2, 3]\nsolution.shuffle(); // Returns the random shuffling of array [1,2,3]. Example: return [1, 3, 2]\n\n\n\nConstraints:\n\n1 <= nums.length <= 200\n-106 <= nums[i] <= 106\nAll the elements of nums are unique.\nAt most 5 * 104 calls in total will be made to reset and shuffle.\n Hide Hint #1\nThe solution expects that we always use the original array to shuffle() else some of the test cases fail. (Credits; @snehasingh31)\n\"\"\"\nfrom itertools import permutations\nfrom random import randint\nfrom typing import List\n\n\nclass Solution:\n # Solution 1 - 336 ms\n \"\"\"\n def __init__(self, nums: List[int]):\n self.nums = nums[:]\n self.copy = nums[:]\n\n def reset(self) -> List[int]:\n\n # Resets the array to its original configuration and return it.\n\n self.nums = self.copy[:]\n return self.nums\n\n def shuffle(self) -> List[int]:\n # Returns a random shuffling of the array.\n n = len(self.nums)\n for i in range(n):\n j = randint(i, n - 1)\n self.nums[i], self.nums[j] = self.nums[j], self.nums[i]\n return self.nums\n \"\"\"\n # Solution 2 - 208 ms\n def __init__(self, nums: List[int]):\n self.original = nums\n self.shuffled = None\n\n def reset(self) -> List[int]:\n \"\"\"\n Resets the array to its original configuration and return it.\n \"\"\"\n return self.original\n\n def shuffle(self) -> List[int]:\n \"\"\"\n Returns a random shuffling of the array.\n \"\"\"\n try:\n return list(next(self.shuffled))\n except (TypeError, StopIteration):\n self.shuffled = permutations(self.original)\n return list(next(self.shuffled))\n\n\n# Your Solution object will be instantiated and called as such:\nnums = [[[1, 2, 3]], [], [], []]\nsolution = Solution(nums)\nprint(solution.shuffle())\nprint(solution.shuffle())\nprint(solution.reset())\nprint(solution.shuffle())\n", "sub_path": "src/arrays/shuffle.py", "file_name": "shuffle.py", "file_ext": "py", "file_size_in_byte": 2861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "typing.List", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 71, "usage_type": "name"}, {"api_name": "itertools.permutations", "line_number": 84, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "490042675", "text": "import axelrod as axl\nimport os\nimport utils\n\nfrom players import players\n\nturns = 200\nrepetitions = 100\n\nprocesses = 20\nseed = 1\nfilename = \"data/strategies_std_interactions.csv\"\n\ndef main(players=players):\n # Deleting the file if it exists\n try:\n os.remove(filename)\n except OSError:\n pass\n\n tournament = axl.Tournament(players, turns=turns, repetitions=repetitions, seed=seed)\n\n results = tournament.play(filename=filename, processes=processes)\n utils.obtain_assets(results, \"strategies\", \"std\")\n results.write_summary('assets/std_summary.csv')\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "run_std.py", "file_name": "run_std.py", "file_ext": "py", "file_size_in_byte": 622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "players.players", "line_number": 14, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 17, "usage_type": "call"}, {"api_name": "axelrod.Tournament", "line_number": 21, "usage_type": "call"}, {"api_name": "players.players", "line_number": 21, "usage_type": "argument"}, {"api_name": "utils.obtain_assets", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "93168263", "text": "#coding=utf-8\nfrom pylab import *\nimport random\nimport numpy as np\nfrom PIL import Image\nimport scipy.misc\nfrom scipy.misc import imread\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties\n\ndef convert_message_to_bit(string_text):\n '''convert the secret message from string to bytearray'''\n bit_string=[]\n for char in string_text:\n bit_string.extend([int(d) for d in bin(char)[2:].zfill(8)])\n return np.array(bit_string)\n\nsecret_text=np.random.randint(256, size=48*6)\nsecret_img=reshape(convert_message_to_bit(secret_text),(48,48))\n\nmpl.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus']=False\nrcParams[\"pdf.fonttype\"] = 42\nim=imread('F:\\\\ucid_gray\\\\00003.png')\nh,w=shape(im)\nx=np.random.randint(h-48, size=6)\ny=np.random.randint(w-48, size=6)\n\nfig = plt.figure()\n\nax1 = fig.add_subplot(331)\nax1.imshow(im,cmap='gray')\nax1.set_title(u'原图像（载体）')\n\nax2 = fig.add_subplot(333)\nax2.imshow(im&1,cmap='binary')\nax2.set_title(u'原图像的LSB平面')\n\nsubimg1=im[x[0]:x[0]+48,y[0]:y[0]+48]\nsubimg2=im[x[1]:x[1]+48,y[1]:y[1]+48]\nsubimg3=im[x[2]:x[2]+48,y[2]:y[2]+48]\nsubimg4=im[x[3]:x[3]+48,y[3]:y[3]+48]\nsubimg5=im[x[4]:x[4]+48,y[4]:y[4]+48]\nsubimg6=im[x[5]:x[5]+48,y[5]:y[5]+48]\n\nax3 = fig.add_subplot(334)\nax3.imshow(subimg1,cmap='gray')\nax3.set_title(u'位置1')\n\nax4 = fig.add_subplot(335)\nax4.imshow(subimg2,cmap='gray')\nax4.set_title(u'位置2')\n\nax5 = fig.add_subplot(336)\nax5.imshow(subimg3,cmap='gray')\nax5.set_title(u'位置3')\n\nax6 = fig.add_subplot(337)\nax6.imshow(subimg4,cmap='gray')\nax6.set_title(u'位置4')\n\nax7 = fig.add_subplot(338)\nax7.imshow(subimg5,cmap='gray')\nax7.set_title(u'位置5')\n\nax8 = fig.add_subplot(339)\nax8.imshow(subimg6,cmap='gray')\nax8.set_title(u'位置6')\n\nplt.show()\n", "sub_path": "codes/plots/6pic.py", "file_name": "6pic.py", "file_ext": "py", "file_size_in_byte": 1790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 22, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.misc.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "75801426", "text": "from core.pricing.exceptions import *\nimport core.util.debug as debug\n\nimport config\nimport core.storage as storage\n\nclass DataEngine:\n\n\tdef __init__(self, prices):\n\t\ttry:\n\t\t\tself.data = self.validate(prices)\n\t\texcept DataEngineException as e:\n\t\t\tdebug.error('%s'%e)\n\t\t\tself.data = {}\n\t\texcept Exception as e:\n\t\t\tself.data = {}\n\n\tdef getPrices(self, progress):\n\t\tdebug.error('DataEngine function not implemented')\n\n\t@staticmethod\n\tdef validate(prices):\n\t\tdebug.error('DataEngine function not implemented')\n\n\n\t@staticmethod\n\tdef getData(prices):\n\t\tif prices['data'] and len(prices['data'])>1:\n\t\t\treturn prices['data']\n\n\t\tstore = storage.getStorage(config.PricingStorage)\n\n\t\treturn store.load(prices['path'])", "sub_path": "core/pricing/engine.py", "file_name": "engine.py", "file_ext": "py", "file_size_in_byte": 706, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "core.util.debug.error", "line_number": 13, "usage_type": "call"}, {"api_name": "core.util.debug", "line_number": 13, "usage_type": "name"}, {"api_name": "core.util.debug.error", "line_number": 19, "usage_type": "call"}, {"api_name": "core.util.debug", "line_number": 19, "usage_type": "name"}, {"api_name": "core.util.debug.error", "line_number": 23, "usage_type": "call"}, {"api_name": "core.util.debug", "line_number": 23, "usage_type": "name"}, {"api_name": "core.storage.getStorage", "line_number": 31, "usage_type": "call"}, {"api_name": "core.storage", "line_number": 31, "usage_type": "name"}, {"api_name": "config.PricingStorage", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "472334949", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nfrom flask import Flask, render_template,request,redirect,url_for,jsonify\nfrom script.changeconf import changeconf\nimport os\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/nowstat/',methods=['GET'])\ndef nowstat():\n tlsstat = os.popen('./script/changeconf.sh 3').read().strip('\\n')\n almstat = os.popen('./script/changeconf.sh 4').read().strip('\\n')\n return jsonify({'tlsstat':tlsstat,'almstat':almstat})\n\n@app.route('/tls/', methods=['POST'])\ndef tls():\n version = request.form.get('version')\n tlsstr = \"\"\n if(version=='auto'):\n tlsstr = '\"TLSv1 TLSv1.1 TLSv1.2 TLSv1.3\"'\n elif(version=='v0'):\n tlsstr = \"TLSv1\"\n elif(version=='v1'):\n tlsstr = \"TLSv1.1\"\n elif(version=='v2'):\n tlsstr = \"TLSv1.2\"\n elif(version=='v3'):\n tlsstr = \"TLSv1.3\"\n cmd = './script/changeconf.sh 0 '+tlsstr\n os.system(cmd)\n return redirect(url_for('index'))\n\n@app.route('/algorithm/', methods=['POST'])\ndef algorithm():\n alm1 = request.form.get('alm1')\n alm2 = request.form.get('alm2')\n alm3 = request.form.get('alm3')\n alm4 = request.form.get('alm4')\n alm5 = request.form.get('alm5')\n alm6 = request.form.get('alm6')\n alm7 = request.form.get('alm7')\n almlist = []\n if(alm1 == 'true'):\n almlist.append(\"3DES\") \n if(alm2 == 'true'):\n almlist.append(\"AES\")\n if(alm3 == 'true'):\n almlist.append(\"AESGCM\")\n if(alm4 == 'true'):\n almlist.append(\"Camellia\")\n if(alm5 == 'true'):\n almlist.append(\"IDEA\")\n if(alm6 == 'true'):\n almlist.append(\"RC4\")\n if(alm7 == 'true'):\n almlist.append(\"SEED\")\n almstr = \":\".join(almlist)\n cmd = './script/changeconf.sh 1 '+almstr\n os.system(cmd)\n return redirect(url_for('index'))\n\n\n@app.route('/maxcache/', methods=['POST'])\ndef maxcache():\n value = request.form.get('value')\n if value:\n print(value)\n return redirect(url_for('index'))\n\n@app.route('/activetime/', methods=['POST'])\ndef activetime():\n value = request.form.get('value')\n if value:\n print(value)\n return redirect(url_for('index'))\n\n@app.route('/listenport/', methods=['POST'])\ndef listenport():\n port = request.form.get('port')\n if port:\n print(port)\n return redirect(url_for('index'))\n\n@app.route('/clienturl/', methods=['POST'])\ndef clienturl():\n url = request.form.get('url')\n if url:\n print(url)\n return redirect(url_for('index'))\n\nif __name__ == '__main__':\n app.run(debug=True)", "sub_path": "view.py", "file_name": "view.py", "file_ext": "py", "file_size_in_byte": 2595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 15, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "os.system", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "os.system", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.request.form.get", "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.redirect", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "109272126", "text": "__all__ = ['GameModel']\n\nimport pyglet\nimport os.path\nimport sys\nfrom os.path import join, isdir, basename\nfrom random import choice, randint\nfrom glob import glob\nfrom cocos.sprite import Sprite\nfrom cocos import *\nfrom status import status\n\nCELL_WIDTH, CELL_HEIGHT = 100, 100\nROWS_COUNT, COLS_COUNT = 6, 8\n\n#游戏状态值\nWAITING_PLAYER_MOVEMENT = 1\nPLAYER_DOING_MOVEMENT = 2\nSWAPPING_TILES = 3\nIMPLODING_TILES = 4\nDROPPING_TILES = 5\nGAME_OVER = 6\n\n\nclass GameModel(pyglet.event.EventDispatcher):\n def __init__(self):\n super(GameModel, self).__init__()\n self.tile_grid = {} # Dict仿真稀疏矩阵 key: tuple(x,y), value : tile_type\n self.imploding_tiles = [] # 用于爆破精灵列表 IMPLODING_TILES\n self.dropping_tiles = [] # 在DROPPING_TILES中使用的正在删除的砖精灵列表\n self.swap_start_pos = None # 点击第一个精灵准备交换位置\n self.swap_end_pos = None # 单击的第二个的位置以进行交换\n # 替换是Windows兼容性。\n script_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n os.chdir(script_dir)\n if isdir('images'):\n image_base_path = join(script_dir, 'images')\n else:\n image_base_path = join(sys.prefix, 'share', 'match3cocos2d', 'images')\n pyglet.resource.path = [image_base_path]\n pyglet.resource.reindex()\n self.available_tiles = [basename(s) for s in glob(join(image_base_path, '*.png'))]\n self.game_state = WAITING_PLAYER_MOVEMENT\n self.objectives = []\n self.on_game_over_pause = 0\n\n def start(self):\n self.set_next_level()\n\n def set_next_level(self):\n self.play_time = self.max_play_time = 60\n for elem in self.imploding_tiles + self.dropping_tiles:\n self.view.remove(elem)\n self.on_game_over_pause = 0\n self.fill_with_random_tiles()\n self.set_objectives()\n pyglet.clock.unschedule(self.time_tick)\n pyglet.clock.schedule_interval(self.time_tick, 1)\n\n def time_tick(self, delta):\n self.play_time -= 1\n self.dispatch_event(\"on_update_time\", self.play_time / float(self.max_play_time))\n if self.play_time == 0:\n pyglet.clock.unschedule(self.time_tick)\n self.game_state = GAME_OVER\n self.dispatch_event(\"on_game_over\")\n\n def set_objectives(self):\n objectives = []\n while len(objectives) < 3:\n tile_type = choice(self.available_tiles)\n sprite = self.tile_sprite(tile_type, (0, 0))\n count = randint(1, 20)\n if tile_type not in [x[0] for x in objectives]:\n objectives.append([tile_type, sprite, count])\n\n self.objectives = objectives\n\n def fill_with_random_tiles(self):\n \"\"\"\n 用随机tiles填充tile_grid\n \"\"\"\n for elem in [x[1] for x in self.tile_grid.values()]:\n self.view.remove(elem)\n tile_grid = {}\n # 用随机tile类型填充数据矩阵\n while True: # 循环，直到我们有一个有效的表（没有内爆线）\n for x in range(COLS_COUNT):\n for y in range(ROWS_COUNT):\n tile_type, sprite = choice(self.available_tiles), None\n tile_grid[x, y] = tile_type, sprite\n if len(self.get_same_type_lines(tile_grid)) == 0:\n break\n tile_grid = {}\n\n # 基于指定的tile类型构建精灵\n for key, value in tile_grid.items():\n tile_type, sprite = value\n sprite = self.tile_sprite(tile_type, self.to_display(key))\n tile_grid[key] = tile_type, sprite\n self.view.add(sprite)\n\n self.tile_grid = tile_grid\n\n def swap_elements(self, elem1_pos, elem2_pos):\n tile_type, sprite = self.tile_grid[elem1_pos]\n self.tile_grid[elem1_pos] = self.tile_grid[elem2_pos]\n self.tile_grid[elem2_pos] = tile_type, sprite\n\n def implode_lines(self):\n \"\"\"\n :return: 用多于3个相同类型的元素进行处理\n \"\"\"\n implode_count = {}\n for x, y in self.get_same_type_lines(self.tile_grid):\n tile_type, sprite = self.tile_grid[x, y]\n self.tile_grid[x, y] = None\n self.imploding_tiles.append(sprite) # tiles内爆炸销毁\n # 内嵌爆炸动画\n sprite.do(ScaleTo(0, 0.5) | RotateTo(180, 0.5) + CallFuncS(self.on_tile_remove))\n implode_count[tile_type] = implode_count.get(tile_type, 0) + 1\n # Decrease counter for tiles matching objectives\n for elem in self.objectives:\n if elem[0] in implode_count:\n Scale = ScaleBy(1.5, 0.2)\n elem[2] = max(0, elem[2] - implode_count[elem[0]])\n elem[1].do((Scale + Reverse(Scale)) * 3)\n # 删除已完成的目标\n self.objectives = [elem for elem in self.objectives if elem[2] > 0]\n if len(self.imploding_tiles) > 0:\n self.game_state = IMPLODING_TILES # 等待爆炸动画完成\n pyglet.clock.unschedule(self.time_tick)\n else:\n self.game_state = WAITING_PLAYER_MOVEMENT\n pyglet.clock.schedule_interval(self.time_tick, 1)\n return self.imploding_tiles\n\n def drop_groundless_tiles(self):\n \"\"\"\n 在所有栏上，从下到上：\n a）计算间隙或向下移动已经计算的间隙\n b）顶部落下的瓷砖与间隙一样多\n :return:\n \"\"\"\n tile_grid = self.tile_grid\n\n for x in range(COLS_COUNT):\n gap_count = 0\n for y in range(ROWS_COUNT):\n if tile_grid[x, y] is None:\n gap_count += 1\n elif gap_count > 0: #从Y移动到y-gap_count\n tile_type, sprite = tile_grid[x, y]\n if gap_count > 0:\n sprite.do(MoveTo(self.to_display((x, y - gap_count)), 0.3 * gap_count))\n tile_grid[x, y - gap_count] = tile_type, sprite\n for n in range(gap_count): # 下降多少tiles作为间隙计数\n tile_type = choice(self.available_tiles)\n sprite = self.tile_sprite(tile_type, self.to_display((x, y + n + 1)))\n tile_grid[x, y - gap_count + n + 1] = tile_type, sprite\n sprite.do(\n MoveTo(self.to_display((x, y - gap_count + n + 1)), 0.3 * gap_count) +\n CallFuncS(self.on_drop_completed))\n self.view.add(sprite)\n self.dropping_tiles.append(sprite)\n\n def on_drop_completed(self, sprite):\n self.dropping_tiles.remove(sprite)\n if len(self.dropping_tiles) == 0: # 全部落下的\n self.implode_lines() # 检查新的碰撞\n\n def on_tile_remove(self, sprite):\n status.score += 1\n self.imploding_tiles.remove(sprite)\n self.view.remove(sprite)\n if len(self.imploding_tiles) == 0: #碰撞爆炸完成，跌落tile填补缺口\n self.dispatch_event(\"on_update_objectives\")\n self.drop_groundless_tiles()\n if len(self.objectives) == 0:\n pyglet.clock.unschedule(self.time_tick)\n self.dispatch_event(\"on_level_completed\")\n\n def set_controller(self, controller):\n self.controller = controller\n\n def set_view(self, view):\n self.view = view\n\n def tile_sprite(self, tile_type, pos):\n \"\"\"\n :param tile_type: 数字ID必须在可用图像的范围内\n :param pos:精灵的位置\n :return: 根据tile_type编译精灵\n \"\"\"\n sprite = Sprite(tile_type)\n sprite.position = pos\n sprite.scale = 1\n return sprite\n\n def on_tiles_swap_completed(self):\n self.game_state = DROPPING_TILES\n if len(self.implode_lines()) == 0:\n # 没有碰撞爆炸，回滚游戏\n\n # 开始为两个对象交换动画\n tile_type, sprite = self.tile_grid[self.swap_start_pos]\n sprite.do(MoveTo(self.to_display(self.swap_end_pos), 0.4))\n tile_type, sprite = self.tile_grid[self.swap_end_pos]\n sprite.do(MoveTo(self.to_display(self.swap_start_pos), 0.4) +\n CallFunc(self.on_tiles_swap_back_completed))\n\n # 恢复网格\n self.swap_elements(self.swap_start_pos, self.swap_end_pos)\n self.game_state = SWAPPING_TILES\n\n def on_tiles_swap_back_completed(self):\n self.game_state = WAITING_PLAYER_MOVEMENT\n\n def to_display(self, row_col):\n \"\"\"\n :param row:\n :param col:\n :return: (x, y) 从来自二维( row, col) 阵列位置的显示坐标\n \"\"\"\n row, col = row_col\n return CELL_WIDTH / 2 + row * CELL_WIDTH, CELL_HEIGHT / 2 + col * CELL_HEIGHT\n\n def to_model_pos(self, view_x_y):\n view_x, view_y = view_x_y\n return int(view_x / CELL_WIDTH), int(view_y / CELL_HEIGHT)\n\n def get_same_type_lines(self, tile_grid, min_count=3):\n \"\"\"\n 识别由微元连续元素组成的垂直和水平线\n :param min_count: 识别直线中的最小连续元素\n \"\"\"\n all_line_members = []\n\n # 检查垂直线\n for x in range(COLS_COUNT):\n same_type_list = []\n last_tile_type = None\n for y in range(ROWS_COUNT):\n tile_type, sprite = tile_grid[x, y]\n if last_tile_type == tile_type:\n same_type_list.append((x, y))\n # 结束行，因为类型改变或到达边缘\n if tile_type != last_tile_type or y == ROWS_COUNT - 1:\n if len(same_type_list) >= min_count:\n all_line_members.extend(same_type_list)\n last_tile_type = tile_type\n same_type_list = [(x, y)]\n\n # 检查水平线\n for y in range(ROWS_COUNT):\n same_type_list = []\n last_tile_type = None\n for x in range(COLS_COUNT):\n tile_type, sprite = tile_grid[x, y]\n if last_tile_type == tile_type:\n same_type_list.append((x, y))\n # 行结束，因为类型改变或到达边缘\n if tile_type != last_tile_type or x == COLS_COUNT - 1:\n if len(same_type_list) >= min_count:\n all_line_members.extend(same_type_list)\n last_tile_type = tile_type\n same_type_list = [(x, y)]\n\n # 删除重复\n all_line_members = list(set(all_line_members))\n return all_line_members\n\n def on_mouse_press(self, x, y):\n if self.game_state == WAITING_PLAYER_MOVEMENT:\n self.swap_start_pos = self.to_model_pos((x, y))\n self.game_state = PLAYER_DOING_MOVEMENT\n\n def on_mouse_drag(self, x, y):\n if self.game_state != PLAYER_DOING_MOVEMENT:\n return\n\n start_x, start_y = self.swap_start_pos\n self.swap_end_pos = new_x, new_y = self.to_model_pos((x, y))\n\n distance = abs(new_x - start_x) + abs(new_y - start_y) # 水平+垂直网格步长\n\n # 忽略移动，如果不在第1步离开初始位置\n if new_x < 0 or new_y < 0 or distance != 1:\n return\n\n # 为两个对象启动交换动画\n tile_type, sprite = self.tile_grid[self.swap_start_pos]\n sprite.do(MoveTo(self.to_display(self.swap_end_pos), 0.4))\n tile_type, sprite = self.tile_grid[self.swap_end_pos]\n sprite.do(MoveTo(self.to_display(self.swap_start_pos), 0.4) +\n CallFunc(self.on_tiles_swap_completed))\n\n # 在数据网格中交换元素\n self.swap_elements(self.swap_start_pos, self.swap_end_pos)\n self.game_state = SWAPPING_TILES\n\n def dump_table(self):\n \"\"\"\n :return: 打印播放表，进行调试\n \"\"\"\n for y in range(ROWS_COUNT - 1, -1, -1):\n line_str = ''\n for x in range(COLS_COUNT):\n line_str += str(self.tile_grid[x, y][0])\n print(line_str)\n\n\nGameModel.register_event_type('on_update_objectives')\nGameModel.register_event_type('on_update_time')\nGameModel.register_event_type('on_game_over')\nGameModel.register_event_type('on_level_completed')\n", "sub_path": "Python算法详解/第13章/13-3/match3cocos2d/GameModel.py", "file_name": "GameModel.py", "file_ext": "py", "file_size_in_byte": 12373, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pyglet.event", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.path.realpath", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.chdir", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.prefix", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pyglet.resource", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pyglet.resource.reindex", "line_number": 41, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 42, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "pyglet.clock.unschedule", "line_number": 57, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 58, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pyglet.clock.unschedule", "line_number": 64, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 64, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 71, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 90, "usage_type": "call"}, {"api_name": "pyglet.clock.unschedule", "line_number": 132, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 135, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 135, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 158, "usage_type": "call"}, {"api_name": "status.status.score", "line_number": 173, "usage_type": "attribute"}, {"api_name": "status.status", "line_number": 173, "usage_type": "name"}, {"api_name": "pyglet.clock.unschedule", "line_number": 180, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 180, "usage_type": "attribute"}, {"api_name": "cocos.sprite.Sprite", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "341047477", "text": "from __future__ import division, print_function, absolute_import\n\nimport tensorflow as tf\nimport os\nimport math as mt\nfrom pathlib import Path\nimport keras\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom tflearn.data_utils import to_categorical, pad_sequences, load_csv\nfrom keras.layers import Embedding\nimport numpy as np\nfrom layer import *\n\n#Constants\nGLOVE_DIR = \"/home/abhishek/tensorflow/Projects/glove.6B\"\nMAX_SEQUENCE_LENGTH = 20\nbatch_size = 1024\nBUCKETS = [(20, 20)]\nNUM_LAYERS = 3\nSTATE_SIZE = 256\nembedding_size = 100\nnum_classes = 2\nvocab_size = 95430\nnum_steps = 20\nlearning_rate = 1e-3\ntotal_epochs = 10\n\n#prepare data\n#Load from csv\nquestion_file = Path(\"quo.npz\")\nif question_file.is_file():\n print(\"Loading data from quo.npz\")\n d1 = np.load(question_file)\n question_pair = d1['question_pair']\n label = d1['label']\nelse:\n question_pair,label = load_csv ('/home/abhishek/tensorflow/Projects/Quora/train.csv/train.csv',\\\n target_column=-1, columns_to_ignore=[0,1,2,3], has_header=True,\\\n categorical_labels=False, n_classes=2)\n np.savez('./quo.npz', question_pair=question_pair, label=label)\n\n\n#get questions vocabulary\ndata_file = Path(\"123.npz\")\nif data_file.is_file():\n print(\"Loading data from 123.npz\")\n d2 = np.load(data_file)\n x = d2['x']\n y = d2['y']\nelse:\n vocab_size = 0\n vocab_all = []\n sums = [0, 0]\n for i in range(len(question_pair)):\n for q in range(2):\n ## This following line deletes the ? symbol\n question_pair[i][q] = question_pair[i][q].replace(\"?\", \"\")\n question_pair[i][q] = question_pair[i][q].replace(\",\", \"\")\n question_pair[i][q] = question_pair[i][q].replace(\".\", \"\")\n question_pair[i][q] = question_pair[i][q].replace(\"(\", \"\")\n question_pair[i][q] = question_pair[i][q].replace(\")\", \"\")\n question_pair[i][q] = question_pair[i][q].replace('\"', \"\")\n question_pair[i][q] = question_pair[i][q].replace(\"'\", \"\")\n sums[q] = sums[q] + len(question_pair[i][q].split(\" \"))\n for words in question_pair[i][q].split(\" \"):\n vocab_all.append(words)\n vocab_set = set(vocab_all)\n vocab_unique = list(vocab_set)\n vocab_size = len(vocab_unique)\n print(\"The size of the vocabulary is\", vocab_size)\n\n # Tokenize data\n sequences = []\n seq_data = []\n add_end_of_sentence = []\n tokenizer = Tokenizer(nb_words=vocab_size)\n tokenizer.fit_on_texts(vocab_all)\n x = []\n y = []\n for p in range(len(question_pair)):\n x.append(tokenizer.texts_to_sequences([question_pair[p][0]])[0])\n y.append(tokenizer.texts_to_sequences([question_pair[p][1]])[0])\n if p % 10000 == 0:\n print(\"I am on the question_pair{}\".format(p))\n x = pad_sequences(x, maxlen=MAX_SEQUENCE_LENGTH, padding='post').tolist()\n y = pad_sequences(y, maxlen=MAX_SEQUENCE_LENGTH, padding='post').tolist()\n word_index = tokenizer.word_index\n print('Found %s unique tokens.' % len(word_index))\n x = np.asarray(x)\n y = np.asarray(y)\n print('Shape of sentence 1 tensor:', x.shape)\n print('Shape of sentence 2 tensor:', y.shape)\n print(len(x))\n np.savez('./123.npz', x=x, y=y)\n\n######Creation of batches\ndef batch_iter(x, y, label, batch_size, num_epochs, shuffle=True):\n \"\"\"\n Generates a batch iterator for a dataset.\n \"\"\"\n x_size = len(x)\n y_size = len(y)\n num_batches_per_epoch = int((len(x)-1)/batch_size) + 1\n for epoch in range(num_epochs):\n # Shuffle the data at each epoch\n if shuffle:\n shuffle_indices = np.random.permutation(np.arange(x_size))\n shuffled_x = x[shuffle_indices]\n shuffled_y = y[shuffle_indices]\n shuffled_label = label[shuffle_indices]\n else:\n shuffled_x = x\n shuffled_y = y\n shuffled_label = label\n for batch_num in range(num_batches_per_epoch-1):\n start_index = batch_num * batch_size\n end_index = (batch_num + 1) * batch_size\n yield (shuffled_x[start_index:end_index], shuffled_y[start_index:end_index],shuffled_label[start_index:end_index],epoch)\n\ndef reset_graph():\n if 'sess' in globals() and sess:\n sess.close()\n tf.reset_default_graph()\n\nreset_graph()\n#Placeholders\n\nprint('Creating placeholders')\nsentence_1 = tf.placeholder(tf.int32, shape=[batch_size, num_steps], name='sentence_1')\nsentence_2 = tf.placeholder(tf.int32, shape=[batch_size, num_steps], name='sentence_2')\nlabels = tf.placeholder(tf.int32, shape=[None], name='labels')\n\nglobal_step = tf.Variable(0, dtype = tf.int32, trainable = False, name=\"Global_step\")\n\nembedding = tf.get_variable('embedding_matrix', [vocab_size, STATE_SIZE])\n#labels = tf.get_variable('labels', [num_classes])\nrnn_input1 = tf.nn.embedding_lookup(embedding,sentence_1)\nrnn_input2 = tf.nn.embedding_lookup(embedding,sentence_2)\nprint(rnn_input2.shape)\n\n#Model\n\ncell = tf.nn.rnn_cell.LSTMCell(STATE_SIZE, state_is_tuple=True)\ncell = tf.nn.rnn_cell.MultiRNNCell([cell]*NUM_LAYERS, state_is_tuple=True)\ninit_state = cell.zero_state(batch_size, tf.float32)\nrnn_outputs_1, final_state_1 = tf.nn.dynamic_rnn(cell, rnn_input1, initial_state=init_state)\nrnn_outputs_2, final_state_2 = tf.nn.dynamic_rnn(cell, rnn_input2, initial_state=init_state)\nprint(rnn_outputs_1.shape)\nprint(final_state_1[2][1])\n\n#final_state = final_state_1[2][1] - final_state_2[2][1]\n\nfinal_state1 = tf.stack([final_state_1[2][1],final_state_2[2][1]], axis = 2)\n#final_state1 = tf.transpose(final_state1)\n\n#Final state Manipulation\nwith tf.variable_scope('softmax'):\n input_features = STATE_SIZE * 2\n final_state1 = tf.reshape(final_state1, shape=[-1, input_features])\n W = tf.get_variable('W', [input_features, num_classes], initializer=tf.random_normal_initializer(stddev=0.01))\n b = tf.get_variable('b', [num_classes], initializer=tf.constant_initializer(0.0))\n\n#rnn_outputs = tf.reshape(final_state, [-1, STATE_SIZE])\ny_reshaped = tf.reshape(labels, [-1])\n\nlogits = tf.matmul(final_state1, W) + b\nprint(\"Logits are \",logits.shape)\nprint(\"Labels are \",labels.shape)\ntotal_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits, labels = y_reshaped))\ntrain_step = tf.train.AdamOptimizer(learning_rate).minimize(total_loss,global_step=global_step)\n\nwith tf.name_scope('summaries'):\n tf.summary.scalar(\"loss\",total_loss)\n tf.summary.histogram(\"histogram loss\", total_loss)\n summary_op = tf.summary.merge_all()\n\ncurrent_epoch = 0\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n total_size = len(question_pair)\n# writer = tf.summary.FileWriter('./graphs', sess.graph)\n saver = tf.train.Saver()\n ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/checkpoint'))\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(sess, ckpt.model_checkpoint_path)\n writer = tf.summary.FileWriter('./graphs', sess.graph)\n initial_step = global_step.eval()\n training_losses = []\n iterator = batch_iter(x,y,label,batch_size,total_epochs,True)\n while total_epochs>current_epoch:\n training_loss = 0\n steps = 0\n while steps id, title, link, author, date, answers, visits\n for thread in threads:\n aux = ['Título: ' + thread[1] + '\\n', 'Autor: : ' + thread[3] + '\\n', 'Fecha: ' + thread[4] + '\\n', '\\n']\n res.append(aux)\n\n v = Toplevel()\n sc = Scrollbar(v)\n sc.pack(side=RIGHT, fill=Y)\n lb = Listbox(v, width=150, yscrollcommand=sc.set)\n for row in res:\n lb.insert(END, row[0])\n lb.insert(END, row[1])\n lb.insert(END, row[2])\n lb.insert(END, '')\n lb.pack(side=LEFT, fill=BOTH)\n sc.config(command=lb.yview)\n\n # Crea una listbox con todas las noticias de la listbox\n def list(self):\n self.print_with_scroll(self.find.find_db(None, None))\n\n # Guarda el conjunto de datos pasados como parámetro\n def save(self, objects):\n con = None\n try:\n # aux = [title, link, author, date_parse, content]\n con = lite.connect('test.db')\n with con:\n cur = con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS news\")\n cur.execute(\"CREATE TABLE news(ID INT, TITLE TEXT, LINK TEXT, AUTHOR TEXT, DATE TEXT, CONTENT TEXT)\")\n i = 0\n for obj in objects:\n cur.execute(\"INSERT INTO news VALUES (?, ?, ?, ?, ?, ?)\", (i, obj[0], obj[1], obj[2], obj[3], obj[4]))\n i = i + 1\n messagebox.showinfo(message=\"BD creada correctamente con \" + str(len(objects)) + \" respuestas\", title=\"Aviso\")\n except lite.Error as e:\n print(\"Error {}:\".format(e.args[0]))\n messagebox.showinfo(message=\"Se ha producido un error\", title=\"Aviso\")\n sys.exit(1)\n finally:\n if con:\n con.close()\n\n # Obtiene el conjunto de elementos a seleccionar en función de la categoría\n def spin_box_aux(self, category):\n res = []\n if category == 'author':\n aux = self.find.find_db('', 'author_elements')\n for e in aux:\n if e not in res:\n res.append(e)\n if category == 'date':\n aux = self.find.find_db('', 'dates_elements')\n for e in aux:\n if e[0] not in res:\n res.append(e[0])\n res.sort()\n return res\n\n # Crea una spin box con los elementos de la categoria dada\n # Para que funcoine hay que añadir en spin_box_aux y en find_db los correspondientes if y elseif para ajustar la configuración\n def spin_box(self, category):\n\n # Búsqueda final de elemnto, cogemos el elemento seleccionado por el usuario\n def search():\n res = self.find.find_db(lb.get(), category)\n self.print_with_scroll(res)\n\n elements = self.spin_box_aux(category)\n if len(elements) > 0:\n master = Tk()\n lb = Spinbox(master, values=elements, width=10)\n lb.pack()\n button = Button(master, text='Buscar', command=search)\n button.pack(side=LEFT)\n master.mainloop()\n else:\n messagebox.showinfo(message='No hay elementos suficientes para realizar la búsqueda', title=\"Aviso\")\n\n # Crea una listbox en caso de encontrar resultados, un aviso en caso de no encontrar nada\n def search_aux(self, message, objects, window):\n if len(objects) > 0:\n self.print_with_scroll(objects)\n window.destroy()\n else:\n messagebox.showinfo(message=message, title=\"Aviso\")\n\n # Crea un cuadro de búsqueda en función del tipo elegido\n def search_box(self, selection):\n def search():\n if selection == 'pages':\n if 0 < int(entry.get()) < 5:\n self.save(self.find.find_url('https://www.meneame.net/', entry.get()))\n window.destroy()\n else:\n messagebox.showinfo(message=\"El número de páginas debe estar entre 1 y 4\", title=\"Aviso\")\n elif selection == 'author':\n authors = self.find.find_db(entry.get(), 'author')\n self.search_aux('Ninguna noticia de ese autor ha sido encontrada', authors, window)\n else:\n news = self.find.find_db(entry.get(), 'date')\n self.search_aux('Ninguna noticia con esa fecha ha sido encontrada', news, window)\n\n window = Tk()\n window.title(\"Configuración\")\n if selection == 'pages':\n question = 'Introduzca el número de páginas que desea buscar'\n elif selection == 'author':\n question = '¿Cuál es el nombre del autor a buscar?'\n else: # selection == 'date'\n question = 'Introduzca la fecha a buscar, siguiendo el formato: yyyy-mm-dd'\n\n button = Button(window, text=\"Buscar\", command=search)\n label = Label(window, text=question)\n label.pack(side=LEFT)\n entry = Entry(window)\n entry.pack(side=LEFT)\n button.pack(side=LEFT)\n window.mainloop()\n\n def start(self):\n def close_window():\n root.destroy()\n\n window = Window()\n root = Tk()\n root.geometry(\"198x0\")\n menubar = Menu(root)\n root.config(menu=menubar)\n\n data_menu = Menu(menubar, tearoff=0)\n menubar.add_cascade(label=\"Datos\", menu=data_menu)\n data_menu.add_command(label=\"Cargar\", command=lambda: self.search_box('pages'))\n data_menu.add_command(label=\"Mostrar\", command=window.list)\n data_menu.add_separator()\n data_menu.add_command(label=\"Salir\", command=close_window)\n\n find_menu = Menu(menubar, tearoff=0)\n menubar.add_cascade(label=\"Buscar\", menu=find_menu)\n find_menu.add_command(label=\"Por nombre de autor\", command=lambda: self.spin_box('author'))\n find_menu.add_command(label=\"Por fecha\", command=lambda: self.search_box('date'))\n\n root.mainloop()\n\n\nif __name__ == \"__main__\":\n Window().start()\n", "sub_path": "Ejercicios Phyton/Boletin 5/boletin5.py", "file_name": "boletin5.py", "file_ext": "py", "file_size_in_byte": 8864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "urllib.request.urlopen", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 13, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 102, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 111, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 111, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 114, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 114, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 115, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 147, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 154, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 154, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 162, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 162, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 172, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 172, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 180, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 202, "usage_type": "call"}]} +{"seq_id": "513960479", "text": "import sys\nsys.path.append('..')\nimport numpy as np\nimport pandas as pd \n\nfrom sklearn.model_selection import train_test_split\n\ndef read_param(path, param=3):\n my_file = pd.read_csv(path, header = None)\n my_param = np.array(my_file)\n if param == 3:\n param1 = np.squeeze(my_param[0].astype(np.int))\n param2 = np.squeeze(my_param[1].astype(np.int))\n param3 = np.squeeze(my_param[2].astype(np.int))\n return param1, param2, param3\n else:\n param1 = np.squeeze(my_param[0].astype(np.int))\n param2 = np.squeeze(my_param[1].astype(np.int))\n return param1, param2\n\ndef read_csv_files(path1, path2):\n train_File = pd.read_csv(path1, header = None) \n test_File = pd.read_csv(path2, header = None)\n return(train_File, test_File) \n\ndef split_train_validation(file_name, split_rate):\n X_train_val = np.array(file_name) ### Assigning ###\n header = X_train_val[0,1:] \n X_train_val = X_train_val[1:,1:].astype(np.float) \n Y_train_val = X_train_val[:,-1] \n X_train_val = X_train_val[:,:-1] \n X_train, X_val, Y_train, Y_val \\\n = train_test_split(X_train_val, Y_train_val, test_size=split_rate, random_state=42)\n return(header, X_train, X_val, Y_train, Y_val)\n\ndef split_features_labels(file_name):\n X_ = np.array(file_name) \n X_ = X_[1:,1:].astype(np.float) \n Y_ = X_[:,-1] \n X_ = X_[:,:-1] \n return(X_, Y_)\n\n\n\n\n \n\n\n\n", "sub_path": "Library/assigning_library.py", "file_name": "assigning_library.py", "file_ext": "py", "file_size_in_byte": 1635, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "358440575", "text": "from lib.db_manager import db_manager, token\nfrom lib.settings import *\nimport telebot\n__URL = \"https://api.covid19api.com/summary\"\ndb_object = db_manager(host, user, passwd, database, __URL)\ncovid_19_data = db_object.get_all_data()\n\n\ndef menu():\n exit = False\n while not exit:\n choice = int(\n input(\"1.Update covid19 database\\n2.Show all cases\\n3.Show cases by country\\n0.Exit \\n===>\"))\n if choice == 1:\n db_object.save_all_data(covid_19_data)\n elif choice == 2:\n db_object.show_cases(covid_19_data)\n elif choice == 3:\n country = input(\"Enter your country : \")\n myresult = db_object.show_cases_by_country(covid_19_data, country)\n for item in myresult:\n print(\"Country = \", item[1])\n print(\"Slug = \", item[2])\n print(\"NewConfirmed == \", item[3])\n print(\"TotalConfirmed == \", item[4])\n print(\"NewDeaths == \", item[5])\n print(\"TotalDeaths == \", item[6])\n print(\"NewRecovered == \", item[7])\n print(\"TotalRecovered == \", item[8])\n print(\"Date == \", item[9])\n elif choice == 0:\n exit = True\n print(\"Bye...\")\n\n\n# menu()\n\n\nbot = telebot.TeleBot(token)\n\n\n@bot.message_handler(content_types=['text'])\ndef get_text_messages(message):\n country = message.text\n myresult = db_object.show_cases_by_country(covid_19_data, country)\n print(myresult)\n if message.text == country:\n for item in myresult:\n bot.send_message(message.from_user.id, \"Information about Coronavirus in the world [𝐂𝐎𝐕𝐈𝐃-19]\\n⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯\"+\"\\nCountry → \" + str(item[1]) +\n \"\\nNumber of diseases → \" + str(item[4]) + \"\\nNumber of diseases in one day → \" + str(item[5]) + \"\\nNumber of deaths → \" + str(item[6]) + \"\\nNumber of deaths in one day → \" + str(item[5]) + \"\\nNumber of cured → \" + str(item[8]) + \"\\nNumber of cured in one day → \" + str(item[7]))\n\n\nbot.polling(none_stop=True)\n", "sub_path": "db_oop_classes/start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 2199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "lib.db_manager.db_manager", "line_number": 5, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 39, "usage_type": "call"}, {"api_name": "lib.db_manager.token", "line_number": 39, "usage_type": "argument"}]} +{"seq_id": "466532172", "text": "# from selenium.webdriver.firefox.webdriver import WebDriver\nfrom selenium import webdriver\nfrom fixture.search import SearchHelper\n\n\nclass Application:\n\n def __init__(self, base_url, is_remote, browser, browser_version):\n\n if is_remote:\n remote_capabilities = {\n \"browserName\": browser,\n \"version\": browser_version,\n \"enableVNC\": True, # use to show video on remote web site\n # \"enableVideo\": True # user to save video on remote_url:4444/video\n }\n\n self.wd = webdriver.Remote(\n command_executor=\"http://sfacts-jenkins.devel.ifx:4444/wd/hub\",\n desired_capabilities=remote_capabilities)\n else:\n if browser == \"firefox\":\n self.wd = webdriver.Firefox(firefox_binary=\"C:/Program Files/Mozilla Firefox/firefox.exe\")\n elif browser == \"chrome\":\n self.wd = webdriver.Chrome()\n elif browser == \"ie\":\n self.wd = webdriver.Ie()\n else:\n raise ValueError(f\"Unrecognized {browser}\")\n\n # capabilities={\"marionette\": False}\n\n # self.wd.set_window_position(0, 0)\n # self.wd.set_window_size(1920, 1080)\n\n self.wd.implicitly_wait(20)\n self.search = SearchHelper(self)\n self.base_url = base_url\n\n def is_valid(self):\n try:\n self.wd.current_url\n return True\n except:\n return False\n\n def open_home_page(self):\n wd = self.wd\n wd.get(self.base_url)\n\n def get_type_results(self, type_person):\n return \\\n type_person + \" \" +\\\n str(len(self.wd.find_elements_by_xpath(\"//loader/div[1]/table/tbody/tr[*]/td[1]/a\")))\n\n\n def destroy(self):\n self.wd.quit() # разрушаем фикстуру (останавливаем браузер)\n\n\n", "sub_path": "fixture/application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 1907, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "selenium.webdriver.Remote", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 23, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 23, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.Ie", "line_number": 27, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 27, "usage_type": "name"}, {"api_name": "fixture.search.SearchHelper", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "384759778", "text": "# -*- coding: utf-8 -*-\nfrom marvinbot.utils import get_message\nfrom marvinbot.plugins import Plugin\nfrom marvinbot.handlers import CommonFilters, MessageHandler\nfrom marvinbot.filters import RegexpFilter\nimport logging\nimport re\n\nlog = logging.getLogger(__name__)\n\n\nclass Substitutions(Plugin):\n def __init__(self):\n super(Substitutions, self).__init__('substitutions')\n\n def get_default_config(self):\n return {\n 'short_name': self.name,\n 'enabled': True,\n 'pattern': r'^s/(?P.+?)(?.*?)/(?P[giI]*)$'\n }\n\n def configure(self, config):\n self.plain_pattern = config.get('pattern')\n self.pattern = re.compile(config.get('pattern'))\n pass\n\n def setup_handlers(self, adapter):\n self.add_handler(MessageHandler([CommonFilters.reply, RegexpFilter(self.plain_pattern)], self.on_match), priority=70)\n\n def setup_schedules(self, adapter):\n pass\n\n def on_match(self, update):\n message = update.effective_message\n\n # No message\n if message is None:\n return\n\n text = message.text\n\n # Quit early if there's no text\n if len(text) == 0:\n return\n\n # Filter messages that are not replies\n if not message.reply_to_message:\n return\n\n # Filter replies to messages without text\n if len(message.reply_to_message.text) == 0:\n return\n\n # Check if we are getting s//\n match = self.pattern.match(text)\n if not match:\n return\n\n user_pattern = match.group('pattern')\n user_replacement = match.group('replacement')\n user_flags = match.group('flags')\n\n # Try compiling user pattern\n try:\n flags = re.IGNORECASE if 'i' in user_flags or 'I' in user_flags else 0\n\n pattern = re.compile(user_pattern, flags)\n if not pattern:\n return\n\n count = 0 if 'g' in user_flags else 1\n response_text = pattern.sub(user_replacement, message.reply_to_message.text, count=count)\n\n self.adapter.bot.sendMessage(chat_id=message.chat_id,\n reply_to_message_id=message.reply_to_message.message_id,\n text=response_text,\n disable_web_page_preview=True)\n except Exception as ex:\n log.info(\"Exception when compiling user pattern: {}\".format(ex))\n", "sub_path": "substitutions_plugin/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 2532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "marvinbot.plugins.Plugin", "line_number": 12, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "marvinbot.handlers.MessageHandler", "line_number": 29, "usage_type": "call"}, {"api_name": "marvinbot.handlers.CommonFilters.reply", "line_number": 29, "usage_type": "attribute"}, {"api_name": "marvinbot.handlers.CommonFilters", "line_number": 29, "usage_type": "name"}, {"api_name": "marvinbot.filters.RegexpFilter", "line_number": 29, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "213747352", "text": "import hashlib\nimport unicodedata\nfrom enum import IntEnum, unique\nfrom functools import wraps\nfrom operator import attrgetter\nfrom os import urandom\nfrom stringprep import (\n in_table_a1, in_table_b1, in_table_c12, in_table_c21_c22, in_table_c3,\n in_table_c4, in_table_c5, in_table_c6, in_table_c7, in_table_c8,\n in_table_c9, in_table_d1, in_table_d2\n)\nfrom uuid import uuid4\n\nfrom asn1crypto.x509 import Certificate\n\nfrom scramp.utils import b64dec, b64enc, h, hi, hmac, uenc, xor\n\n# https://tools.ietf.org/html/rfc5802\n# https://www.rfc-editor.org/rfc/rfc7677.txt\n\n\n@unique\nclass ClientStage(IntEnum):\n get_client_first = 1\n set_server_first = 2\n get_client_final = 3\n set_server_final = 4\n\n\n@unique\nclass ServerStage(IntEnum):\n set_client_first = 1\n get_server_first = 2\n set_client_final = 3\n get_server_final = 4\n\n\ndef _check_stage(Stages, current_stage, next_stage):\n if current_stage is None:\n if next_stage != 1:\n raise ScramException(\n f\"The method {Stages(1).name} must be called first.\")\n elif current_stage == 4:\n raise ScramException(\n \"The authentication sequence has already finished.\")\n elif next_stage != current_stage + 1:\n raise ScramException(\n f\"The next method to be called is \"\n f\"{Stages(current_stage + 1).name}, not this method.\")\n\n\nclass ScramException(Exception):\n def __init__(self, message, server_error=None):\n super().__init__(message)\n self.server_error = server_error\n\n def __str__(self):\n s_str = '' if self.server_error is None else f' {self.server_error}'\n return super().__str__() + s_str\n\n\nMECHANISMS = (\n 'SCRAM-SHA-1',\n 'SCRAM-SHA-1-PLUS',\n 'SCRAM-SHA-256',\n 'SCRAM-SHA-256-PLUS',\n 'SCRAM-SHA-512',\n 'SCRAM-SHA-512-PLUS',\n 'SCRAM-SHA3-512',\n 'SCRAM-SHA3-512-PLUS'\n)\n\n\nCHANNEL_TYPES = (\n 'tls-server-end-point',\n 'tls-unique',\n 'tls-unique-for-telnet',\n)\n\n\ndef make_channel_binding(name, ssl_socket):\n if name == 'tls-unique':\n return (name, ssl_socket.get_channel_binding(name))\n elif name == 'tls-server-end-point':\n cert_bin = ssl_socket.getpeercert(binary_form=True)\n cert = Certificate.load(cert_bin)\n\n # Find the hash algorithm to use according to\n # https://tools.ietf.org/html/rfc5929#section-4\n hash_algo = cert.hash_algo\n if hash_algo in ('md5', 'sha1'):\n hash_algo = 'sha256'\n\n if hash_algo == 'sha256':\n hash_f = hashlib.sha256\n else:\n raise ScramException(f\"Hash algorithm {hash_algo} not recognized.\")\n\n return ('tls-server-end-point', hash_f(cert_bin).digest())\n else:\n raise ScramException(f\"Channel binding name {name} not recognized.\")\n\n\nclass ScramMechanism():\n MECH_LOOKUP = {\n 'SCRAM-SHA-1': (hashlib.sha1, False, 4096, 0),\n 'SCRAM-SHA-1-PLUS': (hashlib.sha1, True, 4096, 1),\n 'SCRAM-SHA-256': (hashlib.sha256, False, 4096, 2),\n 'SCRAM-SHA-256-PLUS': (hashlib.sha256, True, 4096, 3),\n 'SCRAM-SHA-512': (hashlib.sha512, False, 4096, 4),\n 'SCRAM-SHA-512-PLUS': (hashlib.sha512, True, 4096, 5),\n 'SCRAM-SHA3-512': (hashlib.sha3_512, False, 10000, 6),\n 'SCRAM-SHA3-512-PLUS': (hashlib.sha3_512, True, 10000, 7),\n }\n\n def __init__(self, mechanism='SCRAM-SHA-256'):\n if mechanism not in MECHANISMS:\n raise ScramException(\n f\"The mechanism name '{mechanism}' is not supported. The \"\n f\"supported mechanisms are {MECHANISMS}.\")\n self.name = mechanism\n self.hf, self.use_binding, self.iteration_count, self.strength = \\\n self.MECH_LOOKUP[mechanism]\n\n def make_auth_info(self, password, iteration_count=None, salt=None):\n if iteration_count is None:\n iteration_count = self.iteration_count\n salt, stored_key, server_key = _make_auth_info(\n self.hf, password, iteration_count, salt=salt)\n return salt, stored_key, server_key, iteration_count\n\n def make_stored_server_keys(self, salted_password):\n _, stored_key, server_key = _c_key_stored_key_s_key(\n self.hf, salted_password)\n return stored_key, server_key\n\n def make_server(self, auth_fn, channel_binding=None, s_nonce=None):\n return ScramServer(\n self, auth_fn, channel_binding=channel_binding, s_nonce=s_nonce)\n\n\ndef _make_auth_info(hf, password, i, salt=None):\n if salt is None:\n salt = urandom(16)\n\n salted_password = _make_salted_password(hf, password, salt, i)\n _, stored_key, server_key = _c_key_stored_key_s_key(hf, salted_password)\n return salt, stored_key, server_key\n\n\ndef _validate_channel_binding(channel_binding):\n if channel_binding is None:\n return\n\n if not isinstance(channel_binding, tuple):\n raise ScramException(\n \"The channel_binding parameter must either be None or a tuple.\")\n\n if len(channel_binding) != 2:\n raise ScramException(\n \"The channel_binding parameter must either be None or a tuple of \"\n \"two elements (type, data).\")\n\n channel_type, channel_data = channel_binding\n if channel_type not in CHANNEL_TYPES:\n raise ScramException(\n \"The channel_binding parameter must either be None or a tuple \"\n \"with the first element a str specifying one of the channel \"\n \"types {CHANNEL_TYPES}.\")\n\n if not isinstance(channel_data, bytes):\n raise ScramException(\n \"The channel_binding parameter must either be None or a tuple \"\n \"with the second element a bytes object containing the bind data.\")\n\n\nclass ScramClient():\n def __init__(\n self, mechanisms, username, password, channel_binding=None,\n c_nonce=None):\n if not isinstance(mechanisms, (list, tuple)):\n raise ScramException(\n \"The 'mechanisms' parameter must be a list or tuple of \"\n \"mechanism names.\")\n\n _validate_channel_binding(channel_binding)\n\n mechs = [ScramMechanism(m) for m in mechanisms]\n mechs = [\n m for m in mechs if channel_binding is not None or (\n channel_binding is None and not m.use_binding)\n ]\n if len(mechs) == 0:\n raise Exception(\"There are no suitable mechanisms in the list.\")\n\n mech = sorted(mechs, key=attrgetter('strength'))[-1]\n self.hf, self.use_binding = mech.hf, mech.use_binding\n self.mechanism_name = mech.name\n\n if self.use_binding:\n if channel_binding is None:\n raise ScramException(\n \"The channel_binding parameter can't be None if channel \"\n \"binding is required.\")\n else:\n if channel_binding is not None:\n raise ScramException(\n \"The channel_binding parameter must be None if channel \"\n \"binding is not required.\")\n\n self.c_nonce = _make_nonce() if c_nonce is None else c_nonce\n self.username = username\n self.password = password\n self.channel_binding = channel_binding\n self.stage = None\n\n def _set_stage(self, next_stage):\n _check_stage(ClientStage, self.stage, next_stage)\n self.stage = next_stage\n\n def get_client_first(self):\n self._set_stage(ClientStage.get_client_first)\n self.client_first_bare, client_first = _get_client_first(\n self.username, self.c_nonce, self.channel_binding)\n return client_first\n\n def set_server_first(self, message):\n self._set_stage(ClientStage.set_server_first)\n self.server_first = message\n self.auth_message, self.nonce, self.salt, self.iterations = \\\n _set_server_first(\n message, self.c_nonce, self.client_first_bare,\n self.channel_binding)\n\n def get_client_final(self):\n self._set_stage(ClientStage.get_client_final)\n self.server_signature, cfinal = _get_client_final(\n self.hf, self.password, self.salt, self.iterations, self.nonce,\n self.auth_message, self.channel_binding)\n return cfinal\n\n def set_server_final(self, message):\n self._set_stage(ClientStage.set_server_final)\n _set_server_final(message, self.server_signature)\n\n\ndef set_error(f):\n @wraps(f)\n def wrapper(self, *args, **kwds):\n try:\n return f(self, *args, **kwds)\n except ScramException as e:\n if e.server_error is not None:\n self.error = e.server_error\n self.stage = ServerStage.set_client_final\n raise e\n return wrapper\n\n\nclass ScramServer():\n def __init__(self, mechanism, auth_fn, channel_binding=None, s_nonce=None):\n self.m = mechanism\n\n _validate_channel_binding(channel_binding)\n\n if mechanism.use_binding:\n if channel_binding is None:\n raise ScramException(\n \"The mechanism requires channel binding, and so \"\n \"channel_binding can't be None.\")\n else:\n if channel_binding is not None:\n raise ScramException(\n \"The mechanism does not support channel binding, and so \"\n \"channel_binding must be None.\")\n\n self.channel_binding = channel_binding\n\n self.s_nonce = _make_nonce() if s_nonce is None else s_nonce\n self.auth_fn = auth_fn\n self.stage = None\n self.server_signature = None\n self.error = None\n\n def _set_stage(self, next_stage):\n _check_stage(ServerStage, self.stage, next_stage)\n self.stage = next_stage\n\n @set_error\n def set_client_first(self, client_first):\n self._set_stage(ServerStage.set_client_first)\n self.nonce, self.user, self.client_first_bare = _set_client_first(\n client_first, self.s_nonce, self.channel_binding)\n salt, self.stored_key, self.server_key, self.i = self.auth_fn(\n self.user)\n self.salt = b64enc(salt)\n\n @set_error\n def get_server_first(self):\n self._set_stage(ServerStage.get_server_first)\n self.auth_message, server_first = _get_server_first(\n self.nonce, self.salt, self.i, self.client_first_bare,\n self.channel_binding)\n return server_first\n\n @set_error\n def set_client_final(self, client_final):\n self._set_stage(ServerStage.set_client_final)\n self.server_signature = _set_client_final(\n self.m.hf, client_final, self.s_nonce, self.stored_key,\n self.server_key, self.auth_message, self.channel_binding)\n\n @set_error\n def get_server_final(self):\n self._set_stage(ServerStage.get_server_final)\n return _get_server_final(self.server_signature, self.error)\n\n\ndef _make_nonce():\n return str(uuid4()).replace('-', '')\n\n\ndef _make_auth_message(nonce, client_first_bare, server_first, cbind_data):\n cbind_input = b64enc(_make_cbind_input(cbind_data))\n msg = client_first_bare, server_first, 'c=' + cbind_input, 'r=' + nonce\n return ','.join(msg)\n\n\ndef _make_salted_password(hf, password, salt, iterations):\n return hi(hf, uenc(saslprep(password)), salt, iterations)\n\n\ndef _c_key_stored_key_s_key(hf, salted_password):\n client_key = hmac(hf, salted_password, b\"Client Key\")\n stored_key = h(hf, client_key)\n server_key = hmac(hf, salted_password, b\"Server Key\")\n\n return client_key, stored_key, server_key\n\n\ndef _check_client_key(hf, stored_key, auth_msg, proof):\n client_signature = hmac(hf, stored_key, auth_msg)\n client_key = xor(client_signature, b64dec(proof))\n key = h(hf, client_key)\n\n if key != stored_key:\n raise ScramException(\n \"The client keys don't match.\", SERVER_ERROR_INVALID_PROOF)\n\n\ndef _make_gs2_header(channel_binding):\n if channel_binding is None:\n return 'n,,'\n else:\n channel_type, _ = channel_binding\n return f'p={channel_type},,'\n\n\ndef _make_cbind_input(channel_binding):\n gs2_header = _make_gs2_header(channel_binding).encode('ascii')\n if channel_binding is None:\n return gs2_header\n else:\n _, cbind_data = channel_binding\n return gs2_header + cbind_data\n\n\ndef _parse_message(msg):\n return dict((e[0], e[2:]) for e in msg.split(',') if len(e) > 1)\n\n\ndef _get_client_first(username, c_nonce, channel_binding):\n try:\n u = saslprep(username)\n except ScramException as e:\n raise ScramException(\n e.args[0], SERVER_ERROR_INVALID_USERNAME_ENCODING)\n\n bare = ','.join((f'n={u}', f'r={c_nonce}'))\n gs2_header = _make_gs2_header(channel_binding)\n return bare, gs2_header + bare\n\n\ndef _set_client_first(client_first, s_nonce, channel_binding):\n first_comma = client_first.index(',')\n second_comma = client_first.index(',', first_comma + 1)\n gs2_header = client_first[:second_comma].split(',')\n gs2_cbind_flag = gs2_header[0]\n gs2_char = gs2_cbind_flag[0]\n\n if gs2_char == 'y':\n if channel_binding is not None:\n raise ScramException(\n \"Recieved GS2 flag 'y' which indicates that the client \"\n \"doesn't think the server supports channel binding, but in \"\n \"fact it does.\",\n SERVER_ERROR_SERVER_DOES_SUPPORT_CHANNEL_BINDING)\n\n elif gs2_char == 'n':\n if channel_binding is not None:\n raise ScramException(\n \"Received GS2 flag 'n' which indicates that the client \"\n \"doesn't require channel binding, but the server does.\",\n SERVER_ERROR_SERVER_DOES_SUPPORT_CHANNEL_BINDING)\n\n elif gs2_char == 'p':\n if channel_binding is None:\n raise ScramException(\n \"Received GS2 flag 'p' which indicates that the client \"\n \"requires channel binding, but the server does not.\",\n SERVER_ERROR_CHANNEL_BINDING_NOT_SUPPORTED)\n\n channel_type, _ = channel_binding\n cb_name = gs2_cbind_flag.split('=')[-1]\n if cb_name != channel_type:\n raise ScramException(\n f\"Received channel binding name {cb_name} but this server \"\n f\"supports the channel binding name {channel_type}.\",\n SERVER_ERROR_UNSUPPORTED_CHANNEL_BINDING_TYPE)\n\n else:\n raise ScramException(\n f\"Received GS2 flag {gs2_char} which isn't recognized.\",\n SERVER_ERROR_OTHER_ERROR)\n\n client_first_bare = client_first[second_comma + 1:]\n msg = _parse_message(client_first_bare)\n c_nonce = msg['r']\n nonce = c_nonce + s_nonce\n user = msg['n']\n\n return nonce, user, client_first_bare\n\n\ndef _get_server_first(\n nonce, salt, iterations, client_first_bare, channel_binding):\n sfirst = ','.join((f'r={nonce}', f's={salt}', f'i={iterations}'))\n auth_msg = _make_auth_message(\n nonce, client_first_bare, sfirst, channel_binding)\n return auth_msg, sfirst\n\n\ndef _set_server_first(\n server_first, c_nonce, client_first_bare, channel_binding):\n msg = _parse_message(server_first)\n if 'e' in msg:\n raise ScramException(f\"The server returned the error: {msg['e']}\")\n nonce = msg['r']\n salt = msg['s']\n iterations = int(msg['i'])\n\n if not nonce.startswith(c_nonce):\n raise ScramException(\n \"Client nonce doesn't match.\", SERVER_ERROR_OTHER_ERROR)\n\n auth_msg = _make_auth_message(\n nonce, client_first_bare, server_first, channel_binding)\n return auth_msg, nonce, salt, iterations\n\n\ndef _get_client_final(\n hf, password, salt_str, iterations, nonce, auth_msg_str, cbind_data):\n salt = b64dec(salt_str)\n salted_password = _make_salted_password(hf, password, salt, iterations)\n client_key, stored_key, server_key = _c_key_stored_key_s_key(\n hf, salted_password)\n\n auth_msg = uenc(auth_msg_str)\n\n client_signature = hmac(hf, stored_key, auth_msg)\n client_proof = xor(client_key, client_signature)\n server_signature = hmac(hf, server_key, auth_msg)\n cbind_input = _make_cbind_input(cbind_data)\n msg = [\n 'c=' + b64enc(cbind_input), 'r=' + nonce, 'p=' + b64enc(client_proof)]\n return b64enc(server_signature), ','.join(msg)\n\n\nSERVER_ERROR_INVALID_ENCODING = 'invalid-encoding'\nSERVER_ERROR_EXTENSIONS_NOT_SUPPORTED = 'extensions-not-supported'\nSERVER_ERROR_INVALID_PROOF = 'invalid-proof'\nSERVER_ERROR_INVALID_ENCODING = 'invalid-encoding'\nSERVER_ERROR_CHANNEL_BINDINGS_DONT_MATCH = 'channel-bindings-dont-match'\nSERVER_ERROR_SERVER_DOES_SUPPORT_CHANNEL_BINDING = \\\n 'server-does-support-channel-binding'\nSERVER_ERROR_SERVER_DOES_NOT_SUPPORT_CHANNEL_BINDING = \\\n 'server does not support channel binding'\nSERVER_ERROR_CHANNEL_BINDING_NOT_SUPPORTED = 'channel-binding-not-supported'\nSERVER_ERROR_UNSUPPORTED_CHANNEL_BINDING_TYPE = \\\n 'unsupported-channel-binding-type'\nSERVER_ERROR_UNKNOWN_USER = 'unknown-user'\nSERVER_ERROR_INVALID_USERNAME_ENCODING = 'invalid-username-encoding'\nSERVER_ERROR_NO_RESOURCES = 'no-resources'\nSERVER_ERROR_OTHER_ERROR = 'other-error'\n\n\ndef _set_client_final(\n hf, client_final, s_nonce, stored_key, server_key, auth_msg_str,\n cbind_data):\n auth_msg = uenc(auth_msg_str)\n\n msg = _parse_message(client_final)\n nonce = msg['r']\n proof = msg['p']\n channel_binding = msg['c']\n if not b64dec(channel_binding) == _make_cbind_input(cbind_data):\n raise ScramException(\n \"The channel bindings don't match.\",\n SERVER_ERROR_CHANNEL_BINDINGS_DONT_MATCH)\n\n if not nonce.endswith(s_nonce):\n raise ScramException(\n \"Server nonce doesn't match.\", SERVER_ERROR_OTHER_ERROR)\n\n _check_client_key(hf, stored_key, auth_msg, proof)\n\n sig = hmac(hf, server_key, auth_msg)\n return b64enc(sig)\n\n\ndef _get_server_final(server_signature, error):\n return f'v={server_signature}' if error is None else f'e={error}'\n\n\ndef _set_server_final(message, server_signature):\n msg = _parse_message(message)\n if 'e' in msg:\n raise ScramException(f\"The server returned the error: {msg['e']}\")\n\n if server_signature != msg['v']:\n raise ScramException(\n \"The server signature doesn't match.\", SERVER_ERROR_OTHER_ERROR)\n\n\ndef saslprep(source):\n # mapping stage\n # - map non-ascii spaces to U+0020 (stringprep C.1.2)\n # - strip 'commonly mapped to nothing' chars (stringprep B.1)\n data = ''.join(\n ' ' if in_table_c12(c) else c for c in source if not in_table_b1(c))\n\n # normalize to KC form\n data = unicodedata.normalize('NFKC', data)\n if not data:\n return ''\n\n # check for invalid bi-directional strings.\n # stringprep requires the following:\n # - chars in C.8 must be prohibited.\n # - if any R/AL chars in string:\n # - no L chars allowed in string\n # - first and last must be R/AL chars\n # this checks if start/end are R/AL chars. if so, prohibited loop\n # will forbid all L chars. if not, prohibited loop will forbid all\n # R/AL chars instead. in both cases, prohibited loop takes care of C.8.\n is_ral_char = in_table_d1\n if is_ral_char(data[0]):\n if not is_ral_char(data[-1]):\n raise ScramException(\n \"malformed bidi sequence\", SERVER_ERROR_INVALID_ENCODING)\n # forbid L chars within R/AL sequence.\n is_forbidden_bidi_char = in_table_d2\n else:\n # forbid R/AL chars if start not setup correctly; L chars allowed.\n is_forbidden_bidi_char = is_ral_char\n\n # check for prohibited output\n # stringprep tables A.1, B.1, C.1.2, C.2 - C.9\n for c in data:\n # check for chars mapping stage should have removed\n assert not in_table_b1(c), \"failed to strip B.1 in mapping stage\"\n assert not in_table_c12(c), \"failed to replace C.1.2 in mapping stage\"\n\n # check for forbidden chars\n for f, msg in (\n (in_table_a1, \"unassigned code points forbidden\"),\n (in_table_c21_c22, \"control characters forbidden\"),\n (in_table_c3, \"private use characters forbidden\"),\n (in_table_c4, \"non-char code points forbidden\"),\n (in_table_c5, \"surrogate codes forbidden\"),\n (in_table_c6, \"non-plaintext chars forbidden\"),\n (in_table_c7, \"non-canonical chars forbidden\"),\n (in_table_c8, \"display-modifying/deprecated chars forbidden\"),\n (in_table_c9, \"tagged characters forbidden\"),\n (is_forbidden_bidi_char, \"forbidden bidi character\")):\n if f(c):\n raise ScramException(msg, SERVER_ERROR_INVALID_ENCODING)\n\n return data\n", "sub_path": "amplify/backend/function/iamxawswrangler/lib/python/scramp/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 20812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "enum.IntEnum", "line_number": 23, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 22, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 31, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 30, "usage_type": "name"}, {"api_name": "asn1crypto.x509.Certificate.load", "line_number": 86, "usage_type": "call"}, {"api_name": "asn1crypto.x509.Certificate", "line_number": 86, "usage_type": "name"}, {"api_name": "hashlib.sha256", "line_number": 95, "usage_type": "attribute"}, {"api_name": "hashlib.sha1", "line_number": 106, "usage_type": "attribute"}, {"api_name": "hashlib.sha1", "line_number": 107, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 108, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 109, "usage_type": "attribute"}, {"api_name": "hashlib.sha512", "line_number": 110, "usage_type": "attribute"}, {"api_name": "hashlib.sha512", "line_number": 111, "usage_type": "attribute"}, {"api_name": "hashlib.sha3_512", "line_number": 112, "usage_type": "attribute"}, {"api_name": "hashlib.sha3_512", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.urandom", "line_number": 144, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 196, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 248, "usage_type": "call"}, {"api_name": "scramp.utils.b64enc", "line_number": 296, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 320, "usage_type": "call"}, {"api_name": "scramp.utils.b64enc", "line_number": 324, "usage_type": "call"}, {"api_name": "scramp.utils.hi", "line_number": 330, "usage_type": "call"}, {"api_name": "scramp.utils.uenc", "line_number": 330, "usage_type": "call"}, {"api_name": "scramp.utils.hmac", "line_number": 334, "usage_type": "call"}, {"api_name": "scramp.utils.h", "line_number": 335, "usage_type": "call"}, {"api_name": "scramp.utils.hmac", "line_number": 336, "usage_type": "call"}, {"api_name": "scramp.utils.hmac", "line_number": 342, "usage_type": "call"}, {"api_name": "scramp.utils.xor", "line_number": 343, "usage_type": "call"}, {"api_name": "scramp.utils.b64dec", "line_number": 343, "usage_type": "call"}, {"api_name": "scramp.utils.h", "line_number": 344, "usage_type": "call"}, {"api_name": "scramp.utils.b64dec", "line_number": 463, "usage_type": "call"}, {"api_name": "scramp.utils.uenc", "line_number": 468, "usage_type": "call"}, {"api_name": "scramp.utils.hmac", "line_number": 470, "usage_type": "call"}, {"api_name": "scramp.utils.xor", "line_number": 471, "usage_type": "call"}, {"api_name": "scramp.utils.hmac", "line_number": 472, "usage_type": "call"}, {"api_name": "scramp.utils.b64enc", "line_number": 475, "usage_type": "call"}, {"api_name": "scramp.utils.b64enc", "line_number": 476, "usage_type": "call"}, {"api_name": "scramp.utils.uenc", "line_number": 500, "usage_type": "call"}, {"api_name": "scramp.utils.b64dec", "line_number": 506, "usage_type": "call"}, {"api_name": "scramp.utils.hmac", "line_number": 517, "usage_type": "call"}, {"api_name": "scramp.utils.b64enc", "line_number": 518, "usage_type": "call"}, {"api_name": "stringprep.in_table_c12", "line_number": 540, "usage_type": "call"}, {"api_name": "stringprep.in_table_b1", "line_number": 540, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 543, "usage_type": "call"}, {"api_name": "stringprep.in_table_d1", "line_number": 556, "usage_type": "name"}, {"api_name": "stringprep.in_table_d2", "line_number": 562, "usage_type": "name"}, {"api_name": "stringprep.in_table_b1", "line_number": 571, "usage_type": "call"}, {"api_name": "stringprep.in_table_c12", "line_number": 572, "usage_type": "call"}, {"api_name": "stringprep.in_table_a1", "line_number": 576, "usage_type": "name"}, {"api_name": "stringprep.in_table_c21_c22", "line_number": 577, "usage_type": "name"}, {"api_name": "stringprep.in_table_c3", "line_number": 578, "usage_type": "name"}, {"api_name": "stringprep.in_table_c4", "line_number": 579, "usage_type": "name"}, {"api_name": "stringprep.in_table_c5", "line_number": 580, "usage_type": "name"}, {"api_name": "stringprep.in_table_c6", "line_number": 581, "usage_type": "name"}, {"api_name": "stringprep.in_table_c7", "line_number": 582, "usage_type": "name"}, {"api_name": "stringprep.in_table_c8", "line_number": 583, "usage_type": "name"}, {"api_name": "stringprep.in_table_c9", "line_number": 584, "usage_type": "name"}]} +{"seq_id": "126228844", "text": "import ast\nfrom pathlib import Path\n\nconversion_map = {'k': 0,\n 'w': 1,\n 'p': 2,\n 's': 3,\n 'n': 4}\n\n\ndef csv_to_txt(csv_filename, output_filename, step):\n # read mapping from csv format\n label_mapping = {}\n with open(csv_filename, 'r') as f:\n for i, line in enumerate(f):\n if i == 0:\n continue\n\n id = int(line.split(\"\\\"\")[0].split(',')[0])\n if id == 0:\n id = 1\n elif id == 1:\n id = id + step\n else:\n id = step * id + 1\n\n label_mapping[id] = ast.literal_eval(line.split('\\\"')[1])\n\n # write mapping in txt format\n with open(output_filename, 'w') as f:\n for key, value in label_mapping.items():\n labels = ''.join([''.join(str(conversion_map[k]) + ', ') for k, val in value.items() if val == 1])\n f.write('%d, %s\\n' % (key, labels[:-2]))\n\n\nif __name__ == '__main__':\n in_filename = 'labels.csv'\n out_filename = 'ground_truth/Muppets-02-04-04/Muppets-02-04-04.txt'\n Path('ground_truth/Muppets-02-04-04').mkdir(parents=True, exist_ok=True)\n step = 12\n csv_to_txt(in_filename, out_filename, step)\n", "sub_path": "csvToText.py", "file_name": "csvToText.py", "file_ext": "py", "file_size_in_byte": 1250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "ast.literal_eval", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "452340028", "text": "\nimport urllib\nimport hashlib\nimport base64\nimport pathlib\nimport time\nimport sys\nimport urllib.request\nimport uuid\n\ndef md5s(data):\n digest = hashlib.md5()\n #print(data)\n for bytes in data:\n l = len(bytes).to_bytes(4,byteorder='big')\n digest.update(l)\n digest.update(bytes)\n return base64.urlsafe_b64encode(digest.digest())\n\ndef get_file(fn):\n return pathlib.Path(fn).read_bytes()\n\ndef signed_req(salt,responseKey,args,opt):\n until = [str(int((time.time()+3600)*1000)).encode(\"utf-8\")]\n uData = until + [s.encode(\"utf-8\") for s in args]\n hash = [md5s([salt] + uData)]\n header = \"=\".join([urllib.parse.quote_plus(e) for e in hash + uData])\n headers = { \"x-r-signed\": header, \"x-r-response-key\": responseKey }\n postReq = urllib.request.Request(headers=headers, **opt) #method=\"POST\",\n postResp = urllib.request.urlopen(postReq)\n if postResp.status!=200:\n raise Exception(\"req sending failed\")\n resp = None\n while resp is None:\n time.sleep(1)\n print(\".\")\n req = urllib.request.Request(url = host+\"/response/\"+responseKey)\n try:\n resp = urllib.request.urlopen(req)\n except:\n pass\n err = resp.getheader(\"x-r-error-message\")\n if not (err is None or err == \"\"):\n raise Exception(\"post handling failed: \"+err)\n\ncmd, salt_path, body_path, host, url, *args = sys.argv\nsalt = get_file(salt_path)\nresponseKey = str(uuid.uuid4())\ndata = get_file(body_path)\nresp = signed_req(salt,responseKey,args,{ \"url\": host+url, \"data\": data })\n", "sub_path": "req.py", "file_name": "req.py", "file_ext": "py", "file_size_in_byte": 1564, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "hashlib.md5", "line_number": 12, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 27, "usage_type": "attribute"}, {"api_name": "urllib.request.Request", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 30, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 37, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "434589307", "text": "from keras.engine.topology import Layer, InputSpec\nimport keras.backend as K\n\nimport numpy as np\n\n\nclass TCLayer(Layer):\n\n def __init__(self, n_clusters, weights=None, alpha=1.0, dist_metric='eucl', **kwargs):\n if 'input_shape' not in kwargs and 'input_dim' in kwargs:\n kwargs['input_shape'] = (kwargs.pop('input_dim'),)\n super(TCLayer, self).__init__(**kwargs)\n self.n_clusters = n_clusters\n self.alpha = alpha\n self.dist_metric = dist_metric\n self.initial_weights = weights\n self.input_spec = InputSpec(ndim=3)\n self.clusters = None\n self.built = False\n\n def build(self, input_shape):\n assert len(input_shape) == 3\n input_dim = input_shape[2]\n input_steps = input_shape[1]\n self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_steps, input_dim))\n self.clusters = self.add_weight(shape=(self.n_clusters, input_steps, input_dim), initializer='glorot_uniform', name='cluster_centers')\n if self.initial_weights is not None:\n self.set_weights(self.initial_weights)\n del self.initial_weights\n self.built = True\n\n def call(self, inputs, **kwargs):\n \"\"\"\n Student t-distribution kernel, probability of assigning encoded sequence i to cluster k.\n q_{ik} = (1 + dist(z_i, m_k)^2)^{-1} / normalization.\n\n Arguments:\n inputs: encoded input sequences, shape=(n_samples, timesteps, n_features)\n Return:\n q: soft labels for each sample. shape=(n_samples, n_clusters)\n \"\"\"\n if self.dist_metric == 'eucl':\n distance = K.sum(K.sqrt(K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2)), axis=-1)\n elif self.dist_metric == 'cid':\n ce_x = K.sqrt(K.sum(K.square(inputs[:, 1:, :] - inputs[:, :-1, :]), axis=1)) # shape (n_samples, n_features)\n ce_w = K.sqrt(K.sum(K.square(self.clusters[:, 1:, :] - self.clusters[:, :-1, :]), axis=1)) # shape (n_clusters, n_features)\n ce = K.maximum(K.expand_dims(ce_x, axis=1), ce_w) / K.minimum(K.expand_dims(ce_x, axis=1), ce_w) # shape (n_samples, n_clusters, n_features)\n ed = K.sqrt(K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2)) # shape (n_samples, n_clusters, n_features)\n distance = K.sum(ed * ce, axis=-1) # shape (n_samples, n_clusters)\n elif self.dist_metric == 'cor':\n inputs_norm = (inputs - K.expand_dims(K.mean(inputs, axis=1), axis=1)) / K.expand_dims(K.std(inputs, axis=1), axis=1) # shape (n_samples, timesteps, n_features)\n clusters_norm = (self.clusters - K.expand_dims(K.mean(self.clusters, axis=1), axis=1)) / K.expand_dims(K.std(self.clusters, axis=1), axis=1) # shape (n_clusters, timesteps, n_features)\n pcc = K.mean(K.expand_dims(inputs_norm, axis=1) * clusters_norm, axis=2) # Pearson correlation coefficients\n distance = K.sum(K.sqrt(2.0 * (1.0 - pcc)), axis=-1) # correlation-based similarities, shape (n_samples, n_clusters)\n elif self.dist_metric == 'acf':\n raise NotImplementedError\n else:\n raise ValueError('Available distances are eucl, cid, cor and acf!')\n q = 1.0 / (1.0 + K.square(distance) / self.alpha)\n q **= (self.alpha + 1.0) / 2.0\n q = K.transpose(K.transpose(q) / K.sum(q, axis=1))\n return q\n\n def compute_output_shape(self, input_shape):\n assert input_shape and len(input_shape) == 3\n return input_shape[0], self.n_clusters\n\n def get_config(self):\n config = {'n_clusters': self.n_clusters, 'dist_metric': self.dist_metric}\n base_config = super(TCLayer, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))\n\n def add_k_centers(self, centers):\n # assert len(centers) == 3\n old_centers = self.get_weights()[0]\n assert old_centers.shape[1] == centers.shape[1] and old_centers.shape[2] == centers.shape[2]\n\n new_centers = np.concatenate([old_centers, centers])\n del self.trainable_weights[0]\n self.clusters = self.add_weight(shape=(new_centers.shape[0], new_centers.shape[1], new_centers.shape[2]), initializer='glorot_uniform',\n name='cluster_centers')\n self.set_weights([new_centers])\n self.n_clusters = self.n_clusters + centers.shape[0]\n return len(new_centers)\n\n def update_weights(self, new_centers):\n # assert len(new_centers) == 3\n old_centers = self.get_weights()[0]\n assert old_centers.shape[1] == new_centers.shape[1] and old_centers.shape[2] == new_centers.shape[2]\n\n if old_centers.shape[0] == new_centers.shape[0]:\n self.set_weights([new_centers])\n else:\n del self.trainable_weights[0]\n self.n_clusters = new_centers.shape[0]\n self.clusters = self.add_weight(shape=(new_centers.shape[0], new_centers.shape[1], new_centers.shape[2]),\n name='cluster_centers')\n self.set_weights([new_centers])\n", "sub_path": "layer_util/TCLayer.py", "file_name": "TCLayer.py", "file_ext": "py", "file_size_in_byte": 5151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "keras.engine.topology.Layer", "line_number": 7, "usage_type": "name"}, {"api_name": "keras.engine.topology.InputSpec", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.engine.topology.InputSpec", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.backend.floatx", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 25, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 43, "usage_type": "name"}, {"api_name": "keras.backend.sqrt", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.backend.expand_dims", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.backend.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 46, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend.maximum", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 47, "usage_type": "name"}, {"api_name": "keras.backend.expand_dims", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.backend.minimum", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.backend.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 48, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.backend.expand_dims", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.backend.expand_dims", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 51, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend.std", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend.expand_dims", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 52, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.backend.std", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 53, "usage_type": "name"}, {"api_name": "keras.backend.expand_dims", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 54, "usage_type": "name"}, {"api_name": "keras.backend.sqrt", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 59, "usage_type": "name"}, {"api_name": "keras.backend.transpose", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 61, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "171416043", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 2 23:41:06 2018\n\n@author: Akshat\n\"\"\"\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nimport time\nimport xlsxwriter\nfrom pprint import pprint\n\nworkbook = xlsxwriter.Workbook(r'C:/Users/Akshat/Desktop/python/Scrape3.xlsx')\nworksheet = workbook.add_worksheet()\n# Add a bold format to use to highlight cells.\nbold = workbook.add_format({'bold': True})\n\n# Write some data headers.\nworksheet.write('A1', 'First Name', bold)\nworksheet.write('B1', 'Middle Name', bold)\nworksheet.write('C1', 'Last Name', bold)\nworksheet.write('D1', ' Status', bold)\nworksheet.write('E1', 'Licence#', bold)\nworksheet.write('F1', 'Business Street Address', bold)\nworksheet.write('G1', 'Business Zip Code', bold)\nworksheet.write('H1', 'Business Phone Number', bold)\nworksheet.write('I1', ' More Info', bold)\n\n\n# Start from the first cell below the headers.\nrow = 1\ncol = 0\n\ndriver = webdriver.Chrome()\nurl='https://www.apps.insurance.maryland.gov/CompanyProducerInfo/ProducerSearch.aspx?NAV=HOME'\ndriver.get(url)\n\ntime.sleep(30)\nselect = Select(driver.find_element_by_id('status'))\ndropdown=([o.text for o in select.options])\nlist_of_values_dropdown=dropdown[1:] # for testing let it start from 4\nprint(list_of_values_dropdown)\nfor i in list_of_values_dropdown:\n \n \n select = Select(driver.find_element_by_id('status'))\n select.select_by_visible_text(i)\n time.sleep(5)\n driver.find_element_by_id('maintable')\n driver.find_element_by_id('ContentPlaceHolder1_SearchButton').click()\n time.sleep(80)\n# change the entries to 10\n countHundred = driver.find_element_by_name(\"records_table_length\")\n countHundred.click()\n countHundred.find_element(By.XPATH, '//option[text()=\"100\"]').click()\n pages=driver.find_element_by_id('records_table_paginate')\n page=pages.find_element_by_tag_name('span')\n page_tags=page.find_elements_by_tag_name('a')\n final_page=page_tags[-1].text\n jam=int(final_page)\n xyz=0\n \n #driver.find_element_by_id(\"text_naic\").click\n \n while xyz= 30):\n currentHalfHour = currentHalfHour + 1\n return currentHalfHour\n\ndef reset_all_actuators(pump, led, fan):\n pump.publish(False)\n led.publish(220)\n fan.publish(False)\n # print(\"reset\")\n\ndef debugPrints(on, agents, s, h, t, l, hour):\n global level_val\n if on:\n print(\"h:\", hour)\n # print(\"Actions are\", action_set)\n\n\n print(\"Moisture:\", s)\n mBase = agents['raiseMoist'].baseM\n hStartWl = agents['raiseHum'].startWl\n print(\"Hstartwl\", int(hStartWl))\n print(\"Mbase\", int(mBase))\n print(\"Water level delta:\", int(hStartWl - level_val))\n print(\"Humid:\", h)\n print(\"Temp:\", t)\n print(\"Lights:\", l)\n for a in agents:\n print(a + \" | State \" + agents[a].state + \" | On: \" + str(agents[a].on))\n\ndef main():\n # Initialize globals\n global light_val\n global temp_val\n global smoist_val\n global hum_val\n\n # Initialize rospy stuff\n wpump_pub, led_pub, fan_pub, ping_pub, cam_pub = init_pubs()\n init_subscribers()\n\n # Instantiate FSMs\n # moisture\n m_down_agent = m_down.FSM(fan=fan_pub, wpump=wpump_pub, moist=smoist_val)\n m_up_agent = m_up.FSM(fan=fan_pub, wpump=wpump_pub, moist=smoist_val)\n # humidity\n h_down_agent = h_down.FSM(fan=fan_pub, wpump=wpump_pub, hum=hum_val)\n h_up_agent = h_up.FSM(fan=fan_pub, wpump=wpump_pub, hum=hum_val)\n # lights\n l_on_agent = l_on_react.FSM(light_pub=led_pub, light=light_val)\n l_off_agent = l_off.FSM(light_pub=led_pub)\n # temp\n t_down_agent = t_down.FSM(fan=fan_pub, lights=led_pub, t=temp_val)\n t_up_agent = t_up.FSM(fan=fan_pub, lights=led_pub, t=temp_val)\n # camera\n cam_agent = cam.FSM(cam=cam_pub, h=0)\n # waterer\n water_agent = add_water.FSM(wpump=wpump_pub)\n\n last_time = rospy.get_time()\n\n scheduler_actions = {\"camera\":cam_agent,\"lightsOn\":l_on_agent,\"lightsOff\":l_off_agent,\"raiseTemp\":t_up_agent,\"lowerTemp\":t_down_agent,\"raiseHum\":h_up_agent,\"lowerHum\":h_down_agent,\"raiseMoist\":m_up_agent,\"lowerMoist\":m_down_agent,\"water\":water_agent}\n \n scheduler = tb_sched_binary.schedule_core(0, 24, 3)\n \n first = True\n lastH = 0\n\n # rospy.sleep(120.0) \n rospy.sleep(5.0)\n\n m_up_agent.set_baseM(smoist_val, False)\n m_down_agent.set_baseM(smoist_val)\n # print(\"set base\")\n # Start loop\n while not rospy.core.is_shutdown():\n\n #TODO: make a find half hour function\n h = find_half_hour()\n\n if first:\n lastH = h\n first = False\n\n action_set = scheduler[h]\n\n for a in scheduler_actions:\n if a in action_set:\n scheduler_actions[a].toggle(True)\n else:\n scheduler_actions[a].toggle(False)\n\n # Check the states, do the transitions\n now_time = rospy.get_time()\n # Moisture\n m_up_agent.check_state(smoist_val, level_val, now_time)\n m_down_agent.check_state(smoist_val)\n # Humidity\n h_up_agent.check_state(hum_val, level_val, h)\n h_down_agent.check_state(hum_val)\n # Lights\n l_on_agent.check_state(light_val)\n l_off_agent.check_state()\n # Temp\n t_up_agent.check_state(temp_val)\n t_down_agent.check_state(temp_val)\n # Cam\n cam_agent.check_state(h)\n # Watering\n water_agent.check_state(level_val, now_time, h)\n\n debugPrints(False, scheduler_actions, smoist_val, hum_val, temp_val, light_val, h)\n\n # ping if over 300 seconds\n if (now_time - last_time >= 300):\n last_time = now_time\n ping_pub.publish(True)\n\n if lastH is not h:\n reset_all_actuators(wpump_pub, led_pub, fan_pub)\n lastH = h\n \n rospy.sleep(1.0)\n\nif __name__ == '__main__':\n main()\n\n \n\n \n\n\n\n", "sub_path": "grow_b/executor.py", "file_name": "executor.py", "file_ext": "py", "file_size_in_byte": 6024, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "rospy.Subscriber", "line_number": 50, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32MultiArray", "line_number": 50, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 51, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32MultiArray", "line_number": 51, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 52, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32MultiArray", "line_number": 52, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 53, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32MultiArray", "line_number": 53, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 54, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float32", "line_number": 54, "usage_type": "argument"}, {"api_name": "rospy.set_param", "line_number": 58, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 59, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 60, "usage_type": "call"}, {"api_name": "std_msgs.msg.Bool", "line_number": 60, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 61, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32", "line_number": 61, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 62, "usage_type": "call"}, {"api_name": "std_msgs.msg.Bool", "line_number": 62, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 63, "usage_type": "call"}, {"api_name": "std_msgs.msg.Bool", "line_number": 63, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 64, "usage_type": "call"}, {"api_name": "std_msgs.msg.String", "line_number": 64, "usage_type": "argument"}, {"api_name": "rospy.get_rostime", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "m_down.FSM", "line_number": 116, "usage_type": "call"}, {"api_name": "m_up.FSM", "line_number": 117, "usage_type": "call"}, {"api_name": "h_down.FSM", "line_number": 119, "usage_type": "call"}, {"api_name": "h_up.FSM", "line_number": 120, "usage_type": "call"}, {"api_name": "l_on_react.FSM", "line_number": 122, "usage_type": "call"}, {"api_name": "l_off.FSM", "line_number": 123, "usage_type": "call"}, {"api_name": "t_down.FSM", "line_number": 125, "usage_type": "call"}, {"api_name": "t_up.FSM", "line_number": 126, "usage_type": "call"}, {"api_name": "cam.FSM", "line_number": 128, "usage_type": "call"}, {"api_name": "add_water.FSM", "line_number": 130, "usage_type": "call"}, {"api_name": "rospy.get_time", "line_number": 132, "usage_type": "call"}, {"api_name": "tb_sched_binary.schedule_core", "line_number": 136, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 142, "usage_type": "call"}, {"api_name": "rospy.core.is_shutdown", "line_number": 148, "usage_type": "call"}, {"api_name": "rospy.core", "line_number": 148, "usage_type": "attribute"}, {"api_name": "rospy.get_time", "line_number": 166, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "643489827", "text": "#!/usr/bin/python3\nfrom numpy import *\nfrom numpy.random import *\nfrom math import *\nimport matplotlib.pyplot as plt\n\ndef sq(x):\n return x * x\n\ndef f(x, t=0):\n k = 2 * (t - 1)\n c1 = pow(sq(x[0] - 0.25) + sq(x[1] - 0.25), k)\n c2 = pow(sq(x[0] - 0.75) + sq(x[1] - 0.25), k)\n c3 = pow(sq(x[0] - 0.75) + sq(x[1] - 0.75), k)\n c4 = pow(sq(x[0] - 0.25) + sq(x[1] - 0.75), k)\n c5 = pow(sq(x[0] - 0.5 ) + sq(x[1] - 0.5 ), k)\n return c1 + c2 + c3 + c4 + c5\n\ndef mutate(p):\n if random() < 0.5:\n return random(), random()\n else:\n dx = normal(0, 0.0001)\n dy = normal(0, 0.0001)\n x = p[0] + dx\n y = p[1] + dy\n x = x - 1 if x > 1 else x + 1 if x < 0 else x\n y = y - 1 if y > 1 else y + 1 if y < 0 else y\n return x, y\n\ndef mh_step(x, t=0):\n y = mutate(x)\n f1 = f(y, t)\n f2 = f(x, t)\n a = min(1, 0 if f1 == 0 else 1 if f2 == 0 else f1 / f2)\n if random() < a:\n return True, y\n return False, x\n\ndef sim_mh():\n # MH\n x0 = random(), random()\n x = x0\n z = []\n M = 100000\n c = 0\n\n for i in range(0, M):\n accept, x = mh_step(x)\n if accept:\n c += 1\n z.append(x)\n\n print(\"MH Acceptance rate:\", 100 * c / M, \"%\")\n plt.hexbin([x for x, y in z], [y for x, y in z])\n\ndef gen_temps(K):\n return flipud(append(cumprod(repeat(0.9, K - 1)), 0))\n\ndef sim_pt_mh():\n # PTMH\n K = 40 # Number of temperatures (of chains)\n N = 2 # Number of steps before exchange\n M = 100000 # Number of MH steps over all chains\n\n T = gen_temps(K)\n if K < 200:\n print(\"PTMH Temperatures:\")\n print(T)\n\n z = [[] for _ in range(0, K)]\n x = list(zip(random(K), random(K)))\n c = repeat(0, K)\n C = int(M / (K * N))\n\n for i in range(0, C): \n for j in range(0, K):\n # Simulate N steps (could be done in parallel)\n for n in range(0, N):\n accept, x[j] = mh_step(x[j], T[j])\n if accept:\n c[j] += 1\n z[j].append(x[j])\n \n # Exchange\n for j in range(K - 1, 0, -1):\n k1 = f(x[j - 1], T[j]) * f(x[j], T[j - 1])\n k2 = f(x[j - 1], T[j - 1]) * f(x[j], T[j])\n a = min(1, 0 if k1 == 0 else 1 if k2 == 0 else k1 / k2)\n if random() < a:\n x[j], x[j - 1] = x[j - 1], x[j]\n\n print(\"PTMH Target acceptance rate:\", 100 * c[0] / (C * N), \"%\")\n print(\"PTMH Highest Temp. acceptance rate:\", 100 * c[-1] / (C * N), \"%\")\n\n plt.hexbin([x for x, y in z[0]], [y for x, y in z[0]])\n\ndef sim_fopt_mh():\n # FOPTMH\n K = 40 # Number of temperatures (of chains)\n N = 2 # Number of steps before exchange\n C = 10 # Number of PTMH steps before temperature update\n M = 100000 # Number of MH steps over all chains\n\n T = gen_temps(K)\n if K < 200:\n print(\"Initial FOPTMH Temperatures:\")\n print(T)\n\n z = [[] for _ in range(0, K)]\n x = list(zip(random(K), random(K)))\n c = repeat(0, K)\n Nu = repeat(0, K)\n Nd = repeat(0, K)\n S = int(M / (K * N * C))\n\n for s in range(0, S):\n for i in range(0, C):\n # For all K chains, simulate N steps (could be done in parallel)\n for j in range(0, K):\n for n in range(0, N):\n accept, x[j] = mh_step(x[j], T[j])\n if accept:\n c[j] += 1\n z[j].append(x[j])\n\n # Exchange\n for j in range(K - 1, 0, -1):\n k1 = f(x[j - 1], T[j]) * f(x[j], T[j - 1])\n k2 = f(x[j - 1], T[j - 1]) * f(x[j], T[j])\n a = min(1, 0 if k1 == 0 else 1 if k2 == 0 else k1 / k2)\n if random() < a:\n x[j], x[j - 1] = x[j - 1], x[j]\n Nu[j] += 1\n Nd[j - 1] += 1\n\n # Update temperatures\n Ns = Nu + Nd\n F = Nu / where(Ns > 0, Ns, 1)\n P = cumsum(F)\n total_F = P[-1]\n U = copy(T)\n for i in range(1, K - 1):\n p = total_F * i / K\n k1 = searchsorted(P, p, side='left')\n k2 = 0 if k1 <= 0 else k1 - 1\n k1 = k1\n t = 0 if P[k1] == P[k2] else (p - P[k2]) / (P[k1] - P[k2])\n U[i] = t * T[k1] + (1 - t) * T[k2]\n T = U\n\n print(\"FOPTMH Target acceptance rate:\", 100 * c[0] / (C * N * S), \"%\")\n print(\"FOPTMH Highest Temp. acceptance rate:\", 100 * c[-1] / (C * N * S), \"%\")\n\n if K < 200:\n print(\"Final FOPTMH Temperatures:\")\n print(T)\n\n plt.hexbin([x for x, y in z[0]], [y for x, y in z[0]])\n\ndef main():\n fig = plt.figure(figsize=(20,10))\n\n s1 = fig.add_subplot(141)\n sim_mh()\n s2 = fig.add_subplot(142, sharex=s1, sharey=s1)\n sim_pt_mh()\n s3 = fig.add_subplot(143, sharex=s1, sharey=s1)\n sim_fopt_mh()\n plt.axis((0, 1, 0, 1))\n\n s4 = fig.add_subplot(144)\n xx = linspace(0, 1, 100)\n yy = linspace(0, 1, 100)\n Z = zeros((len(xx), len(yy)))\n for i in range(len(xx)):\n for j in range(len(yy)):\n Z[i, j] = f((xx[i],yy[j]), 0.95)\n plt.contour(xx, yy, Z)\n\n plt.show()\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "pt.py", "file_name": "pt.py", "file_ext": "py", "file_size_in_byte": 5296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "matplotlib.pyplot.hexbin", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hexbin", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hexbin", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "42288074", "text": "import logging\nimport pprint\nimport boto3\nimport json\nfrom src.utils import awsutils\nfrom botocore.exceptions import ClientError\n\n#logger = logging.getLogger(__name__)\n#ec2 = boto3.resource('ec2')\n\ndef list_roles():\n instances = main()\n\n for i in instances:\n tags = awsutils.get_instance_tags(i)\n s = i[\"IamInstanceProfile\"][\"Arn\"].rpartition('/')[-1]\n pprint.pprint(s)\n\n return instances\n\n\ndef create_profiles(ec2client, iam_client, instances_roles):\n\n for instance_id, role_name in instances_roles.items():\n # 1: create profile\n try:\n res = iam_client.create_instance_profile(InstanceProfileName=instance_id)\n pprint.pprint(res)\n\n except ClientError as e:\n if e.response['Error']['Code'] == 'EntityAlreadyExists':\n pprint.pprint('WARNING: profile already exists: ' + instance_id)\n else:\n raise e\n\n return instances_roles\n\n\ndef attach_roles_to_profiles(ec2client, iam_client, instances_roles):\n\n for instance_id, role_name in instances_roles.items():\n # 2: add a role\n try:\n res = iam_client.add_role_to_instance_profile(InstanceProfileName=instance_id,\n RoleName=role_name)\n pprint.pprint(res)\n\n except ClientError as e:\n if e.response['Error']['Code'] == 'LimitExceeded':\n pprint.pprint('WARNING: profile with role already exists: ' + instance_id + \" --> \" + role_name)\n else:\n raise e\n\n return instances_roles\n\n\ndef attach_profiles_to_ec2s(ec2client, iam_client, instances_roles):\n\n all = {}\n for instance_id, role_name in instances_roles.items():\n # 3: attach profile to instance\n try:\n res = iam_client.get_instance_profile(InstanceProfileName=instance_id)\n\n res_profile = res['InstanceProfile']\n\n profile = {'Arn': res_profile['Arn'], 'Name': res_profile['InstanceProfileName']}\n res = ec2client.associate_iam_instance_profile(IamInstanceProfile=profile,\n InstanceId=instance_id)\n\n pprint.pprint(res_profile)\n all[instance_id] = res_profile\n\n except ClientError as e:\n if e.response['Error']['Code'] == 'IncorrectState':\n pprint.pprint('WARNING: profile already attached: ' + instance_id + \" --> \" + role_name)\n else:\n raise e\n\n return all\n\n\n# main\ndef main():\n vars = awsutils.read_vars()\n ec2client = awsutils.get_ec2_client('us-east-1')\n iam_client = boto3.client('iam')\n\n with open('input/launched_target_ec2s.txt') as infile:\n lst = json.load(infile)\n\n instance_roles = {}\n for i in lst:\n instance_roles[i[5]] = i[1]['IamRole']\n\n res = create_profiles(ec2client, iam_client, instance_roles)\n res = attach_roles_to_profiles(ec2client, iam_client, instance_roles)\n res = attach_profiles_to_ec2s(ec2client, iam_client, instance_roles)\n\n # pprint.pprint(instances)\n\n return res\n\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "src/ec2/attach_roles_to_ec2s.py", "file_name": "attach_roles_to_ec2s.py", "file_ext": "py", "file_size_in_byte": 3165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "src.utils.awsutils.get_instance_tags", "line_number": 15, "usage_type": "call"}, {"api_name": "src.utils.awsutils", "line_number": 15, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 17, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 28, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 30, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 32, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 46, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 48, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 50, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 71, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 74, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 76, "usage_type": "call"}, {"api_name": "src.utils.awsutils.read_vars", "line_number": 85, "usage_type": "call"}, {"api_name": "src.utils.awsutils", "line_number": 85, "usage_type": "name"}, {"api_name": "src.utils.awsutils.get_ec2_client", "line_number": 86, "usage_type": "call"}, {"api_name": "src.utils.awsutils", "line_number": 86, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 87, "usage_type": "call"}, {"api_name": "json.load", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "123960717", "text": "\"\"\"\n\nselenium中文文档: http://selenium-python-zh.readthedocs.io/en/latest/index.html\n\n阅读延伸---虫师的博客: http://www.cnblogs.com/fnng/\n\n本笔记涉及的内容有:\n1.selenium介绍\n2.selenium快速入门\n3.窗口与框架\n4.等待页面加载\n5.待续\n\n\"\"\"\n\nfrom selenium import webdriver\n\n\n###############\n# 1.selenium介绍\n###############\n\n\"\"\"\n\nSelenium是一个Web的自动化测试工具, 最初是为网站自动化测试而开发的,\n但是由于ajax等动态加载html技术的兴起, selenium也被各位大佬用在了爬取网页数据的用途上,\nSelenium可以直接运行在浏览器上, 可以支持所有主流的浏览器(包括无界面浏览器),\n可以接受指令, 让浏览器自动加载页面, 获取需要的数据, 甚至页面截屏\n\n无界面浏览器: PhantomJS, 会把网站加载到内存并执行页面上的javascript, 因为在内存中加载并无法显示出效果\n\n\"\"\"\n\n\n###################\n# 2.selenium快速入门\n###################\n\n# 创建一个浏览器对象\ndriver = webdriver.Chrome()\n\n# 发起请求\ndriver.get('http://www.baidu.com')\n\n# 保存快照\ndriver.save_screenshot('baidu.png')\n\n# 定位到节点输入--也就是在百度搜海贼王\ndriver.find_element_by_id(\"kw\").send_keys(\"海贼王\") # 能输入���节点才可以使用send_keys,\ndriver.find_element_by_id(\"su\").click()\n\n# 关闭浏览器\ndriver.close() # 退出界面\ndriver.quit() # 退出浏览器\n\n\"\"\"\n\nwebdriver可以创建许多浏览器对象, 但是必须要先下载浏览器驱动\n因为我们写的脚本是去操作驱动, 让驱动去操纵浏览器, 而不是直接操纵浏览器\n\n浏览器对象的方法\n1.查看请求信息\ndriver.page_source # 获取源码\ndriver.get_cookies # 获取浏览器中存储的cookies\ndriver.current_url # 查看当前的url\n\n2.退出\ndriver.close() # 退出当前页面\ndriver.quit() # 退出浏览器\n\n3.页面元素定位\nfind_element_by_id # 使用id值定位一个元素\nfind_element_by_xpath # 使用xpath值定位一个元素\nfind_element_by_link_text # 使用文本定位一个元素\nfind_element_by_partial_link_text # 使用部分文本定位一个元素\nfind_element_by_tag_name # 使用标签名定位一个元素\nfind_element_by_class_name # 使用class属性值定位一个元素\nfind_element_by_css_selector # 使用css选择器定位一个元素\n(同时还有find_elements_by_xxxx)\n\nelement 和 elements 的区别是 返回一个数据, 和返回一列表数据\nby_link_text 和 by_partial_link_text 的区别是 全部文本和包含某个的文本\n如果从定位到的节点中获取属性和文本的方法:\n1.get_attribute() 获取属性值\n2.text 获取标签名\n\n4.cookies\ndriver.get_cookies() 可以获取所有cookies,返回一个列表\nfor cookie in cookies: 能够遍历获取所有cookie, 通过键值对取出 {cookie['name']:cookie['value']}\n\ndriver.delete_cookie('CookieName') 根据cookie名删除单个cookie\ndriver.delete_all_cookies() 删除所有cookies\n\n\"\"\"\n\n\n############\n# 3.窗口与框架\n############\n\n\"\"\"\n\n对于弹出页面的处理:\ndriver.window_handles 获取所有的窗口列表\ndriver.switch_to 切换到某一窗口\n\n有些html源码会放进iframe框架中\nIframe框架的处理:\nel = driver.find_element_by_xpath() 定位到框架中\ndriver.switch_to_frame(el) 进入框架\n\n\"\"\"\n\n\n##############\n# 4.等待页面加载\n##############\n\n\n\"\"\"\n\n如果一个网站使用了动态html技术, 那么页面上的部分元素出现时间不确定\n而为了精确定位到元素, 需要进行页面等待\n\n强制等待: time.sleep()\n\n显式等待: 指定一个条件设置等待时间, 如果没有找到或者等待超过设置的时间会抛出一个异常\nelement = WebDriverWait(driver, 10).until(\n EC.presence_of_element_located((By.ID, \"myDynamicElement\"))\n )\n\n隐式等待: 设置一个等待时间期限, 超出等待时间后再去寻找元素, 单位为秒\ndriver.implicitly_wait(10)\n\n\"\"\"", "sub_path": "python库/_selenium.py", "file_name": "_selenium.py", "file_ext": "py", "file_size_in_byte": 4134, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 40, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "132820137", "text": "import sys\nimport socket\nimport time\nimport multiprocessing as MP\n\nimport ipconn\n\ndef doit(ipSet, i, lock):\n\tfw = open(\"../output/result\" + str(i), \"w\")\n\tfilterSet = set()\n\tfor ip in ipSet:\n\t\tret, score = ipconn.IpConnect(ip)\n\t\tif ret :\n\t\t\tfw.write('{0} | {1}\\n'.format(ip, score))\n\t\telse:\n\t\t\tfilterSet.add(ip)\n\tfw.close()\n\tlock.acquire()\n\tfw = open(\"../data/ip.filter\", \"a\")\n\tfor ip in filterSet:\n\t\tfw.write(ip)\n\t\tfw.write('\\n')\n\tfw.close()\n\tlock.release()\n\t\t\t\n\ndef selectIp(ipSet):\n\tipSetLen = len(ipSet)\n\tprocessNum = 4\n\tipPoll = [ipSet[ipSetLen//processNum*(i-1) : ipSetLen//processNum * i] for i in range(1, processNum)]\n\tipPoll.append(ipSet[ipSetLen//processNum * (processNum-1) : ])\n\tlock = MP.Lock()\n\tprocessSet = []\n\tque = MP.Queue()\n\tfor i in range(processNum) :\n\t\tip = ipPoll[i]\t\n\t\tp = MP.Process(target = doit, args = (ip, i, lock))\n\t\tprocessSet.append(p)\n\tfor p in processSet:\n\t\tp.start()\n\tfor p in processSet:\n\t\tp.join()\n\treturn processNum\n\t\n\t", "sub_path": "src/ipselect.py", "file_name": "ipselect.py", "file_ext": "py", "file_size_in_byte": 957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "ipconn.IpConnect", "line_number": 12, "usage_type": "call"}, {"api_name": "multiprocessing.Lock", "line_number": 32, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 34, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "533468802", "text": "from copy import deepcopy\n\nfrom django import template\nfrom django.utils import simplejson\nfrom django.db.models import Count, F\nfrom django.template import resolve_variable\n\nregister = template.Library()\n\ndef to_dictionary(param_string):\n \"Split an http param string into a dictionary\"\n params = {}\n kv_strings = param_string.split('&')\n for kv_string in kv_strings:\n key, value = kv_string.split('=')\n params[key] = value\n return params\n\ndef to_string(param_dict):\n \"Take a dictionary and convert it to a parameter string\"\n params = \"?\"\n for k, v in param_dict.iteritems():\n params += \"%(key)s=%(value)s&\" % {'key': k, 'value': v}\n return params[:-1]\n \n@register.simple_tag()\ndef param_link(request, title, param_string):\n \"Build a parameter string based on an existing request and new param values\"\n \n default_dict = {\n 'page': 1, 'per_page': 10,\n 'type': 'list'\n }\n old_dict = deepcopy(default_dict)\n new_dict = deepcopy(default_dict)\n\n for k, v in request.GET.iteritems():\n old_dict[k] = v\n new_dict.update(to_dictionary(param_string))\n \n for k in old_dict.keys():\n if k not in new_dict or str(old_dict[k]) != str(new_dict[k]):\n return \"%(title)s\" % {'url': to_string(new_dict), 'title': title}\n return title\n\n@register.simple_tag()\ndef page_link(request, page_number):\n return param_link(request, str(page_number), 'page=' + str(page_number))", "sub_path": "dynamic_collections/templatetags/collections_tags.py", "file_name": "collections_tags.py", "file_ext": "py", "file_size_in_byte": 1514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.template.Library", "line_number": 8, "usage_type": "call"}, {"api_name": "django.template", "line_number": 8, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 34, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "163390620", "text": "\"\"\"\nThis module defines a pandoc filter for manubot cite functionality.\n\nRelated development commands:\n\n```shell\n# export to plain text\npandoc \\\n --to=plain \\\n --standalone \\\n --bibliography=manubot/pandoc/tests/test_cite_filter/bibliography.json \\\n --bibliography=manubot/pandoc/tests/test_cite_filter/bibliography.bib \\\n --filter=pandoc-manubot-cite \\\n --filter=pandoc-citeproc \\\n manubot/pandoc/tests/test_cite_filter/input.md\n\n# call the filter manually using pandoc JSON output\npandoc \\\n --to=json \\\n manubot/pandoc/tests/test_cite_filter/input.md \\\n | python manubot/pandoc/test_cite.py markdown\n```\n\nRelated resources on pandoc filters:\n\n- [python pandocfilters package](https://github.com/jgm/pandocfilters)\n- [python panflute package](https://github.com/sergiocorreia/panflute)\n- [panflute Citation class](http://scorreia.com/software/panflute/code.html#panflute.elements.Citation)\n\"\"\"\nimport argparse\nimport logging\n\nimport panflute as pf\n\nfrom manubot.cite.citations import Citations\n\n\nglobal_variables = {\n \"manuscript_citekeys\": list(),\n}\n\n\ndef parse_args():\n \"\"\"\n Read command line arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n description=\"Pandoc filter for citation by persistent identifier. \"\n \"Filters are command-line programs that read and write a JSON-encoded abstract syntax tree for Pandoc. \"\n \"Unless you are debugging, run this filter as part of a pandoc command by specifying --filter=pandoc-manubot-cite.\"\n )\n parser.add_argument(\n \"target_format\",\n help=\"output format of the pandoc command, as per Pandoc's --to option\",\n )\n parser.add_argument(\n \"--input\",\n nargs=\"?\",\n type=argparse.FileType(\"r\", encoding=\"utf-8\"),\n help=\"path read JSON input (defaults to stdin)\",\n )\n parser.add_argument(\n \"--output\",\n nargs=\"?\",\n type=argparse.FileType(\"w\", encoding=\"utf-8\"),\n help=\"path to write JSON output (defaults to stdout)\",\n )\n args = parser.parse_args()\n return args\n\n\ndef _get_citekeys_action(elem, doc):\n \"\"\"\n Panflute action to extract citationId from all Citations in the AST.\n \"\"\"\n if not isinstance(elem, pf.Citation):\n return None\n manuscript_citekeys = global_variables[\"manuscript_citekeys\"]\n manuscript_citekeys.append(elem.id)\n return None\n\n\ndef _citation_to_id_action(elem, doc):\n \"\"\"\n Panflute action to update the citationId of Citations in the AST\n with their manubot-created keys.\n \"\"\"\n if not isinstance(elem, pf.Citation):\n return None\n mapper = global_variables[\"citekey_shortener\"]\n if elem.id in mapper:\n elem.id = mapper[elem.id]\n return None\n\n\ndef _get_reference_link_citekey_aliases(elem, doc):\n \"\"\"\n Extract citekey aliases from the document that were defined\n using markdown's link reference syntax.\n https://spec.commonmark.org/0.29/#link-reference-definitions\n\n Based on pandoc-url2cite implementation by phiresky at\n https://github.com/phiresky/pandoc-url2cite/blob/b28374a9a037a5ce1747b8567160d8dffd64177e/index.ts#L118-L152\n \"\"\"\n if type(elem) != pf.Para:\n # require link reference definitions to be in their own paragraph\n return\n while (\n len(elem.content) >= 3\n and type(elem.content[0]) == pf.Cite\n and len(elem.content[0].citations) == 1\n and type(elem.content[1]) == pf.Str\n and elem.content[1].text == \":\"\n ):\n # paragraph consists of at least a Cite (with one Citaiton),\n # a Str (equal to \":\"), and additional elements, such as a\n # link destination and possibly more link-reference definitions.\n space_index = 3 if type(elem.content[2]) == pf.Space else 2\n destination = elem.content[space_index]\n if type(destination) == pf.Str:\n # paragraph starts with `[@something]: something`\n # save info to citekeys and remove from paragraph\n citekey = elem.content[0].citations[0].id\n citekey_aliases = global_variables[\"citekey_aliases\"]\n if (\n citekey in citekey_aliases\n and citekey_aliases[citekey] != destination.text\n ):\n logging.warning(f\"multiple aliases defined for @{citekey}\")\n citekey_aliases[citekey] = destination.text\n # found citation, add it to citekeys and remove it from document\n elem.content = elem.content[space_index + 1 :]\n # remove leading SoftBreak, before continuing\n if len(elem.content) > 0 and type(elem.content[0]) == pf.SoftBreak:\n elem.content.pop(0)\n\n\ndef _get_load_manual_references_kwargs(doc) -> dict:\n \"\"\"\n Return keyword arguments for Citations.load_manual_references.\n \"\"\"\n manual_refs = doc.get_metadata(\"references\", default=[])\n bibliography_paths = doc.get_metadata(\"bibliography\", default=[])\n if not isinstance(bibliography_paths, list):\n bibliography_paths = [bibliography_paths]\n return dict(paths=bibliography_paths, extra_csl_items=manual_refs,)\n\n\ndef process_citations(doc):\n \"\"\"\n Apply citation-by-identifier to a Python object representation of\n Pandoc's Abstract Syntax Tree.\n\n The following Pandoc metadata fields are considered:\n\n - bibliography (use to define reference metadata manually)\n - citekey-aliases (use to define tags for cite-by-id citations)\n - manubot-requests-cache-path\n - manubot-clear-requests-cache\n - manubot-output-citekeys: path to write TSV table of citekeys\n - manubot-output-bibliography: path to write generated CSL JSON bibliography\n \"\"\"\n citekey_aliases = doc.get_metadata(\"citekey-aliases\", default={})\n if not isinstance(citekey_aliases, dict):\n logging.warning(\n f\"Expected metadata.citekey-aliases to be a dict, \"\n f\"but received a {citekey_aliases.__class__.__name__}. Disregarding.\"\n )\n citekey_aliases = dict()\n\n global_variables[\"citekey_aliases\"] = citekey_aliases\n doc.walk(_get_reference_link_citekey_aliases)\n doc.walk(_get_citekeys_action)\n manuscript_citekeys = global_variables[\"manuscript_citekeys\"]\n citations = Citations(input_ids=manuscript_citekeys, aliases=citekey_aliases)\n citations.csl_item_failure_log_level = \"ERROR\"\n\n requests_cache_path = doc.get_metadata(\"manubot-requests-cache-path\")\n if requests_cache_path:\n from manubot.process.requests_cache import RequestsCache\n\n req_cache = RequestsCache(requests_cache_path)\n req_cache.mkdir()\n req_cache.install()\n if doc.get_metadata(\"manubot-clear-requests-cache\", default=False):\n req_cache.clear()\n\n citations.filter_pandoc_xnos()\n citations.load_manual_references(**_get_load_manual_references_kwargs(doc))\n citations.inspect(log_level=\"WARNING\")\n citations.get_csl_items()\n global_variables[\"citekey_shortener\"] = citations.input_to_csl_id\n doc.walk(_citation_to_id_action)\n\n if requests_cache_path:\n req_cache.close()\n\n citations.write_citekeys_tsv(path=doc.get_metadata(\"manubot-output-citekeys\"))\n citations.write_csl_json(path=doc.get_metadata(\"manubot-output-bibliography\"))\n # Update pandoc metadata with fields that this filter\n # has either consumed, created, or modified.\n doc.metadata[\"bibliography\"] = []\n doc.metadata[\"references\"] = citations.csl_items\n doc.metadata[\"citekey_aliases\"] = citekey_aliases\n\n\ndef main():\n from manubot.command import setup_logging_and_errors, exit_if_error_handler_fired\n\n diagnostics = setup_logging_and_errors()\n args = parse_args()\n # Let panflute handle io to sys.stdout / sys.stdin to set utf-8 encoding.\n # args.input=None for stdin, args.output=None for stdout\n doc = pf.load(input_stream=args.input)\n log_level = doc.get_metadata(\"manubot-log-level\", \"WARNING\")\n diagnostics[\"logger\"].setLevel(getattr(logging, log_level))\n process_citations(doc)\n pf.dump(doc, output_stream=args.output)\n if doc.get_metadata(\"manubot-fail-on-errors\", False):\n exit_if_error_handler_fired(diagnostics[\"error_handler\"])\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "manubot/pandoc/cite_filter.py", "file_name": "cite_filter.py", "file_ext": "py", "file_size_in_byte": 8189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 47, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 59, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 65, "usage_type": "call"}, {"api_name": "panflute.Citation", "line_number": 76, "usage_type": "attribute"}, {"api_name": "panflute.Citation", "line_number": 88, "usage_type": "attribute"}, {"api_name": "panflute.Para", "line_number": 105, "usage_type": "attribute"}, {"api_name": "panflute.Cite", "line_number": 110, "usage_type": "attribute"}, {"api_name": "panflute.Str", "line_number": 112, "usage_type": "attribute"}, {"api_name": "panflute.Space", "line_number": 118, "usage_type": "attribute"}, {"api_name": "panflute.Str", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 129, "usage_type": "call"}, {"api_name": "panflute.SoftBreak", "line_number": 134, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 165, "usage_type": "call"}, {"api_name": "manubot.cite.citations.Citations", "line_number": 175, "usage_type": "call"}, {"api_name": "manubot.process.requests_cache.RequestsCache", "line_number": 182, "usage_type": "call"}, {"api_name": "manubot.command.setup_logging_and_errors", "line_number": 210, "usage_type": "call"}, {"api_name": "panflute.load", "line_number": 214, "usage_type": "call"}, {"api_name": "panflute.dump", "line_number": 218, "usage_type": "call"}, {"api_name": "manubot.command.exit_if_error_handler_fired", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "197869555", "text": "import json\n# 变量序列化\nDict=dict(name='LiMing',age=20,addres='浙江省温州市泰顺县')\nJson=json.dumps(Dict,ensure_ascii=False)\nprint(Json)\n# 序列化变量写入文件\nwith open(r'D:\\Python\\json.txt','w') as file:\n json.dump(Dict,file,ensure_ascii=False)\n# 变量反序列化\nprint(json.loads(Json))\n# 从文件中读取序列化变量数据\nwith open(r'D:\\Python\\json.txt','r') as file:\n Jsdict=json.load(file)\n print(Jsdict)", "sub_path": "Python基础/数据读写操作/json序列化.py", "file_name": "json序列化.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "json.dumps", "line_number": 4, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "246725005", "text": "import config\nimport sys\n\nfrom reminders.emailer import emailer\nfrom reminders import log\n\ndef send_mail(who,what):\n #ok we need person object but for now ok who cares\n log.info(\"With who: %s %s, doing what: %s\" % (who[0],who[1],what))\n \n log.debug(\"Opening message file\")\n myfile = None\n try :\n myfile = open(config.message, \"r\")\n except IOError:\n log.exception(\"IO error in opening \" % config.message)\n sys.exit(1)\n\n data = myfile.read()\n log.debug(\"Closing message file\")\n myfile.close()\n\n data = data.replace(\"NAME\", who[0] + \" \" + who[1])\n data = data.replace(\"SOMETHING\",what)\n \n to = [who[2]]\n #to = ['vgenty@nevis.columbia.edu'] # for debugging\n cc = config.email_cc\n bcc = config.email_bcc\n \n e = emailer.Email(to,cc,bcc,log) \n e.message(data)\n e.send(what + \" in 30 minutes\")\n log.info(\"Sent out message to %s\" % to )\n", "sub_path": "reminders/emailer/send_mail.py", "file_name": "send_mail.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "reminders.log.info", "line_number": 9, "usage_type": "call"}, {"api_name": "reminders.log", "line_number": 9, "usage_type": "name"}, {"api_name": "reminders.log.debug", "line_number": 11, "usage_type": "call"}, {"api_name": "reminders.log", "line_number": 11, "usage_type": "name"}, {"api_name": "config.message", "line_number": 14, "usage_type": "attribute"}, {"api_name": "reminders.log.exception", "line_number": 16, "usage_type": "call"}, {"api_name": "reminders.log", "line_number": 16, "usage_type": "name"}, {"api_name": "config.message", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 17, "usage_type": "call"}, {"api_name": "reminders.log.debug", "line_number": 20, "usage_type": "call"}, {"api_name": "reminders.log", "line_number": 20, "usage_type": "name"}, {"api_name": "config.email_cc", "line_number": 28, "usage_type": "attribute"}, {"api_name": "config.email_bcc", "line_number": 29, "usage_type": "attribute"}, {"api_name": "reminders.emailer.emailer.Email", "line_number": 31, "usage_type": "call"}, {"api_name": "reminders.log", "line_number": 31, "usage_type": "argument"}, {"api_name": "reminders.emailer.emailer", "line_number": 31, "usage_type": "name"}, {"api_name": "reminders.log.info", "line_number": 34, "usage_type": "call"}, {"api_name": "reminders.log", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "516719644", "text": "from __future__ import absolute_import\n\nfrom datetime import timedelta\n\nimport six\nfrom django.db import transaction\nfrom django.utils import timezone\n\nfrom sentry import analytics\nfrom sentry.api.event_search import get_snuba_query_args\nfrom sentry.incidents.models import (\n Incident,\n IncidentActivity,\n IncidentActivityType,\n IncidentGroup,\n IncidentProject,\n IncidentSeen,\n IncidentStatus,\n IncidentSubscription,\n IncidentType,\n TimeSeriesSnapshot,\n)\nfrom sentry.models import (\n Commit,\n Release,\n)\nfrom sentry.incidents.tasks import (\n calculate_incident_suspects,\n send_subscriber_notifications,\n)\nfrom sentry.utils.committers import get_event_file_committers\nfrom sentry.utils.snuba import (\n raw_query,\n SnubaTSResult,\n)\n\nMAX_INITIAL_INCIDENT_PERIOD = timedelta(days=7)\n\n\nclass StatusAlreadyChangedError(Exception):\n pass\n\n\ndef create_incident(\n organization,\n type,\n title,\n query,\n date_started,\n date_detected=None,\n detection_uuid=None,\n projects=None,\n groups=None,\n user=None,\n):\n if date_detected is None:\n date_detected = date_started\n\n if groups:\n group_projects = [g.project for g in groups]\n if projects is None:\n projects = []\n projects = list(set(projects + group_projects))\n\n with transaction.atomic():\n incident = Incident.objects.create(\n organization=organization,\n detection_uuid=detection_uuid,\n status=IncidentStatus.OPEN.value,\n type=type.value,\n title=title,\n query=query,\n date_started=date_started,\n date_detected=date_detected,\n )\n if projects:\n IncidentProject.objects.bulk_create([\n IncidentProject(incident=incident, project=project) for project in projects\n ])\n if groups:\n IncidentGroup.objects.bulk_create([\n IncidentGroup(incident=incident, group=group) for group in groups\n ])\n\n if type == IncidentType.CREATED:\n activity_status = IncidentActivityType.CREATED\n else:\n activity_status = IncidentActivityType.DETECTED\n\n event_stats_snapshot = create_initial_event_stats_snapshot(incident)\n create_incident_activity(\n incident,\n activity_status,\n event_stats_snapshot=event_stats_snapshot,\n user=user,\n )\n analytics.record(\n 'incident.created',\n incident_id=incident.id,\n organization_id=incident.organization_id,\n incident_type=type.value,\n )\n\n calculate_incident_suspects.apply_async(kwargs={'incident_id': incident.id})\n return incident\n\n\ndef update_incident_status(incident, status, user=None, comment=None):\n \"\"\"\n Updates the status of an Incident and write an IncidentActivity row to log\n the change. When the status is CLOSED we also set the date closed to the\n current time and (todo) take a snapshot of the current incident state.\n \"\"\"\n if incident.status == status.value:\n # If the status isn't actually changing just no-op.\n raise StatusAlreadyChangedError()\n with transaction.atomic():\n create_incident_activity(\n incident,\n IncidentActivityType.STATUS_CHANGE,\n user=user,\n value=status.value,\n previous_value=incident.status,\n comment=comment,\n )\n if user:\n subscribe_to_incident(incident, user)\n\n prev_status = incident.status\n\n kwargs = {\n 'status': status.value,\n }\n if status == IncidentStatus.CLOSED:\n kwargs['date_closed'] = timezone.now()\n # TODO: Take a snapshot of the current state once we implement\n # snapshots\n elif status == IncidentStatus.OPEN:\n # If we're moving back out of closed status then unset the closed\n # date\n kwargs['date_closed'] = None\n # TODO: Delete snapshot? Not sure if needed\n\n incident.update(**kwargs)\n analytics.record(\n 'incident.status_change',\n incident_id=incident.id,\n organization_id=incident.organization_id,\n incident_type=incident.type,\n prev_status=prev_status,\n status=incident.status,\n )\n return incident\n\n\ndef set_incident_seen(incident, user=None):\n \"\"\"\n Updates the incident to be seen\n \"\"\"\n incident_seen, created = IncidentSeen.objects.create_or_update(\n incident=incident,\n user=user,\n values={'last_seen': timezone.now()}\n )\n\n return incident_seen\n\n\ndef create_initial_event_stats_snapshot(incident):\n \"\"\"\n Creates an event snapshot representing the state at the beginning of\n an incident. It's intended to capture the history of the events involved in\n the incident, the spike and a short period of time after that.\n \"\"\"\n initial_period_length = min(\n timezone.now() - incident.date_started,\n MAX_INITIAL_INCIDENT_PERIOD,\n )\n end = incident.date_started + initial_period_length\n start = end - (initial_period_length * 8)\n return create_event_stat_snapshot(incident, start, end)\n\n\n@transaction.atomic\ndef create_incident_activity(\n incident,\n activity_type,\n user=None,\n value=None,\n previous_value=None,\n comment=None,\n event_stats_snapshot=None,\n mentioned_user_ids=None,\n):\n if activity_type == IncidentActivityType.COMMENT and user:\n subscribe_to_incident(incident, user)\n value = six.text_type(value) if value is not None else value\n previous_value = six.text_type(previous_value) if previous_value is not None else previous_value\n activity = IncidentActivity.objects.create(\n incident=incident,\n type=activity_type.value,\n user=user,\n value=value,\n previous_value=previous_value,\n comment=comment,\n event_stats_snapshot=event_stats_snapshot,\n )\n\n if mentioned_user_ids:\n user_ids_to_subscribe = set(mentioned_user_ids) - set(IncidentSubscription.objects.filter(\n incident=incident,\n user_id__in=mentioned_user_ids,\n ).values_list('user_id', flat=True))\n if user_ids_to_subscribe:\n IncidentSubscription.objects.bulk_create([\n IncidentSubscription(incident=incident, user_id=mentioned_user_id)\n for mentioned_user_id in user_ids_to_subscribe\n ])\n send_subscriber_notifications.apply_async(\n kwargs={'activity_id': activity.id},\n countdown=10,\n )\n if activity_type == IncidentActivityType.COMMENT:\n analytics.record(\n 'incident.comment',\n incident_id=incident.id,\n organization_id=incident.organization_id,\n incident_type=incident.type,\n user_id=user.id if user else None,\n activity_id=activity.id,\n )\n\n return activity\n\n\ndef update_comment(activity, comment):\n \"\"\"\n Specifically updates an IncidentActivity with type IncidentActivityType.COMMENT\n \"\"\"\n\n return activity.update(comment=comment)\n\n\ndef delete_comment(activity):\n \"\"\"\n Specifically deletes an IncidentActivity with type IncidentActivityType.COMMENT\n \"\"\"\n\n return activity.delete()\n\n\ndef create_event_stat_snapshot(incident, start, end):\n \"\"\"\n Creates an event stats snapshot for an incident in a given period of time.\n \"\"\"\n event_stats = get_incident_event_stats(incident, start, end)\n return TimeSeriesSnapshot.objects.create(\n start=start,\n end=end,\n values=[[row['time'], row['count']] for row in event_stats.data['data']],\n period=event_stats.rollup,\n )\n\n\ndef build_incident_query_params(incident, start=None, end=None):\n params = {\n 'start': incident.date_started if start is None else start,\n 'end': incident.current_end_date if end is None else end,\n }\n group_ids = list(IncidentGroup.objects.filter(\n incident=incident,\n ).values_list('group_id', flat=True))\n if group_ids:\n params['issue.id'] = group_ids\n project_ids = list(IncidentProject.objects.filter(\n incident=incident,\n ).values_list('project_id', flat=True))\n if project_ids:\n params['project_id'] = project_ids\n\n return get_snuba_query_args(incident.query, params)\n\n\ndef get_incident_event_stats(incident, start=None, end=None, data_points=50):\n \"\"\"\n Gets event stats for an incident. If start/end are provided, uses that time\n period, otherwise uses the incident start/current_end.\n \"\"\"\n kwargs = build_incident_query_params(incident, start=start, end=end)\n rollup = max(int(incident.duration.total_seconds() / data_points), 1)\n return SnubaTSResult(\n raw_query(\n aggregations=[\n ('count()', '', 'count'),\n ],\n orderby='time',\n groupby=['time'],\n rollup=rollup,\n referrer='incidents.get_incident_event_stats',\n limit=10000,\n **kwargs\n ),\n kwargs['start'],\n kwargs['end'],\n rollup,\n )\n\n\ndef get_incident_aggregates(incident):\n \"\"\"\n Calculates aggregate stats across the life of an incident.\n - count: Total count of events\n - unique_users: Total number of unique users\n \"\"\"\n kwargs = build_incident_query_params(incident)\n return raw_query(\n aggregations=[\n ('count()', '', 'count'),\n ('uniq', 'tags[sentry:user]', 'unique_users'),\n ],\n referrer='incidents.get_incident_aggregates',\n limit=10000,\n **kwargs\n )['data'][0]\n\n\ndef subscribe_to_incident(incident, user):\n return IncidentSubscription.objects.get_or_create(incident=incident, user=user)\n\n\ndef unsubscribe_from_incident(incident, user):\n return IncidentSubscription.objects.filter(incident=incident, user=user).delete()\n\n\ndef get_incident_subscribers(incident):\n return IncidentSubscription.objects.filter(incident=incident)\n\n\ndef get_incident_activity(incident):\n return IncidentActivity.objects.filter(\n incident=incident,\n ).select_related('user', 'event_stats_snapshot', 'incident')\n\n\ndef get_incident_suspects(incident, projects):\n return Commit.objects.filter(\n incidentsuspectcommit__incident=incident,\n releasecommit__release__projects__in=projects,\n ).distinct()\n\n\ndef get_incident_suspect_commits(incident):\n groups = list(incident.groups.all())\n # For now, we want to track whether we've seen a commit before to avoid\n # duplicates. We'll probably use a commit being seen across multiple groups\n # as a way to increase score in the future.\n seen = set()\n for group in groups:\n event = group.get_latest_event_for_environments()\n try:\n committers = get_event_file_committers(group.project, event)\n except (Release.DoesNotExist, Commit.DoesNotExist):\n continue\n\n for committer in committers:\n for (commit, _) in committer['commits']:\n if commit.id in seen:\n continue\n seen.add(commit.id)\n yield commit.id\n", "sub_path": "src/sentry/incidents/logic.py", "file_name": "logic.py", "file_ext": "py", "file_size_in_byte": 11299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 65, "usage_type": "name"}, {"api_name": "sentry.incidents.models.Incident.objects.create", "line_number": 66, "usage_type": "call"}, {"api_name": "sentry.incidents.models.Incident.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.Incident", "line_number": 66, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentStatus.OPEN", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentStatus", "line_number": 69, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentProject.objects.bulk_create", "line_number": 77, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentProject.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentProject", "line_number": 77, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentProject", "line_number": 78, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentGroup.objects.bulk_create", "line_number": 81, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentGroup.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentGroup", "line_number": 81, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentGroup", "line_number": 82, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentType.CREATED", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentType", "line_number": 85, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentActivityType.CREATED", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivityType", "line_number": 86, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentActivityType.DETECTED", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivityType", "line_number": 88, "usage_type": "name"}, {"api_name": "sentry.analytics.record", "line_number": 97, "usage_type": "call"}, {"api_name": "sentry.analytics", "line_number": 97, "usage_type": "name"}, {"api_name": "sentry.incidents.tasks.calculate_incident_suspects.apply_async", "line_number": 104, "usage_type": "call"}, {"api_name": "sentry.incidents.tasks.calculate_incident_suspects", "line_number": 104, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 117, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentActivityType.STATUS_CHANGE", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivityType", "line_number": 120, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentStatus.CLOSED", "line_number": 134, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentStatus", "line_number": 134, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 135, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 135, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentStatus.OPEN", "line_number": 138, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentStatus", "line_number": 138, "usage_type": "name"}, {"api_name": "sentry.analytics.record", "line_number": 145, "usage_type": "call"}, {"api_name": "sentry.analytics", "line_number": 145, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentSeen.objects.create_or_update", "line_number": 160, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSeen.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentSeen", "line_number": 160, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 163, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 163, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 176, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 176, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentActivityType.COMMENT", "line_number": 195, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivityType", "line_number": 195, "usage_type": "name"}, {"api_name": "six.text_type", "line_number": 197, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 198, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentActivity.objects.create", "line_number": 199, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentActivity.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivity", "line_number": 199, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects.filter", "line_number": 210, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects", "line_number": 210, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentSubscription", "line_number": 210, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects.bulk_create", "line_number": 215, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects", "line_number": 215, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentSubscription", "line_number": 215, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentSubscription", "line_number": 216, "usage_type": "call"}, {"api_name": "sentry.incidents.tasks.send_subscriber_notifications.apply_async", "line_number": 219, "usage_type": "call"}, {"api_name": "sentry.incidents.tasks.send_subscriber_notifications", "line_number": 219, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentActivityType.COMMENT", "line_number": 223, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivityType", "line_number": 223, "usage_type": "name"}, {"api_name": "sentry.analytics.record", "line_number": 224, "usage_type": "call"}, {"api_name": "sentry.analytics", "line_number": 224, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 184, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 184, "usage_type": "name"}, {"api_name": "sentry.incidents.models.TimeSeriesSnapshot.objects.create", "line_number": 257, "usage_type": "call"}, {"api_name": "sentry.incidents.models.TimeSeriesSnapshot.objects", "line_number": 257, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.TimeSeriesSnapshot", "line_number": 257, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentGroup.objects.filter", "line_number": 270, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentGroup.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentGroup", "line_number": 270, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentProject.objects.filter", "line_number": 275, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentProject.objects", "line_number": 275, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentProject", "line_number": 275, "usage_type": "name"}, {"api_name": "sentry.api.event_search.get_snuba_query_args", "line_number": 281, "usage_type": "call"}, {"api_name": "sentry.utils.snuba.SnubaTSResult", "line_number": 291, "usage_type": "call"}, {"api_name": "sentry.utils.snuba.raw_query", "line_number": 292, "usage_type": "call"}, {"api_name": "sentry.utils.snuba.raw_query", "line_number": 316, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects.get_or_create", "line_number": 328, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects", "line_number": 328, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentSubscription", "line_number": 328, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects.filter", "line_number": 332, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects", "line_number": 332, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentSubscription", "line_number": 332, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects.filter", "line_number": 336, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentSubscription.objects", "line_number": 336, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentSubscription", "line_number": 336, "usage_type": "name"}, {"api_name": "sentry.incidents.models.IncidentActivity.objects.filter", "line_number": 340, "usage_type": "call"}, {"api_name": "sentry.incidents.models.IncidentActivity.objects", "line_number": 340, "usage_type": "attribute"}, {"api_name": "sentry.incidents.models.IncidentActivity", "line_number": 340, "usage_type": "name"}, {"api_name": "sentry.models.Commit.objects.filter", "line_number": 346, "usage_type": "call"}, {"api_name": "sentry.models.Commit.objects", "line_number": 346, "usage_type": "attribute"}, {"api_name": "sentry.models.Commit", "line_number": 346, "usage_type": "name"}, {"api_name": "sentry.utils.committers.get_event_file_committers", "line_number": 361, "usage_type": "call"}, {"api_name": "sentry.models.Release.DoesNotExist", "line_number": 362, "usage_type": "attribute"}, {"api_name": "sentry.models.Release", "line_number": 362, "usage_type": "name"}, {"api_name": "sentry.models.Commit.DoesNotExist", "line_number": 362, "usage_type": "attribute"}, {"api_name": "sentry.models.Commit", "line_number": 362, "usage_type": "name"}]} +{"seq_id": "216799740", "text": "# import system things\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport os\r\nfrom dataset import Dataset\r\n# from datetime import datetime\r\nfrom scipy.spatial import distance\r\nfrom RetrievalEvaluation import RetrievalEvaluation\r\nimport smtplib\r\n# import helpers\r\nimport inference\r\n# import visualize\r\nfrom normData import normData\r\n# for email #####################\r\nserver = smtplib.SMTP('smtp.gmail.com', 587)\r\nserver.starttls()\r\nserver.login('daiguoxian29@gmail.com', 'Dai29->Fool')\r\n\r\nmsg = 'Running is finished'\r\n\r\n# prepare data\r\nsketch_train_list = './sketchTrain.txt'\r\nsketch_test_list = './sketchTest.txt'\r\nshape_list = './shape.txt'\r\ndataset = Dataset(sketch_train_list, sketch_test_list, shape_list)\r\nsketch_test_mean, sketch_test_std, sketch_train_mean, sketch_train_std, shape_mean, shape_std = dataset.normalizeData()\r\nnormLabel = 1\r\n# setup siamese network\r\n# net_type = 'metricAuto' # metricOnly: only use metric learning, metricAuto: using autoencoder with metric learning\r\nnet_type = 'holistic'\r\n# net_type = 'metricOnly'\r\nbatch_train_size = 30\r\nbatch_test_size = 300\r\ntest_interval = 1\r\ndisplay_interval = 1\r\nlearning_rate = 0.1\r\nmomentum = 0.1\r\n# resulTxt = net_type + '_noTanh.txt'\r\niterNum = 200000\r\nresulTxt = net_type + str(iterNum) + '_ss_.txt'\r\npreRecTxt = net_type + str(iterNum) + '_ss_preRec.txt'\r\n# resId = open(preRecTxt, 'w')\r\nlogid = open(resulTxt, 'w')\r\nrunOption = 0\r\n# Construct Network\r\nsiamese = inference.siamese(net_type=net_type)\r\n\r\n# model_path = './models/' + net_type + '_noTanh_bal0001'# The reconstruction loss without tanh for output, the weight for reconstruction loss is 0.001\r\nmodel_path = './models/' + net_type\r\n# train_step = tf.train.GradientDescentOptimizer(0.01).minimize(siamese.loss)\r\nif net_type == 'metricOnly':\r\n train_step = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss)\r\n train_step_2 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss_2)\r\n train_step_1 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss_1)\r\nelif net_type == 'holistic':\r\n train_step = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss)\r\n train_step_2 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss_2)\r\n train_step_1 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss_1)\r\n\r\nelif net_type == 'metricAuto':\r\n train_step = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss)\r\n train_step_3 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.recon_error)\r\n train_step_2 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss_2)\r\n train_step_1 = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum).minimize(siamese.loss_1)\r\n\r\n\r\nsaver = tf.train.Saver(max_to_keep=10000)\r\ninit = tf.initialize_all_variables()\r\nweights_file = os.path.join('./models', net_type, 'weight-' + str(iterNum))\r\nwith tf.Session() as sess:\r\n print('Initializing all variables')\r\n sess.run(init)\r\n if runOption == 1: # starting from random weights\r\n print('Starting training from scratch*******')\r\n elif runOption == 0: # restoring from previous weights\r\n print('Loading pretrained weight ****************')\r\n saver.restore(sess, weights_file)\r\n for step in range(1):\r\n # calculate sketch train fea\r\n if True:\r\n C_depths = dataset.retrievalParamSS().astype(int) # for retrieval evaluation\r\n srcLabel = np.array(dataset.sketch_test_label).astype(float)\r\n dstLabel = np.array(dataset.sketch_train_label).astype(float)\r\n\r\n # saver.save(sess, model_path, global_step=step)\r\n backup_sketch_test_ptr = dataset.sketch_test_ptr # backup for training\r\n dataset.sketch_test_ptr = 0\r\n batch_num = dataset.sketch_test_size / batch_test_size\r\n batch_left = dataset.sketch_test_size % batch_test_size\r\n sketch_test_feaset = np.zeros((dataset.sketch_test_size, 100))\r\n if batch_left == 0:\r\n for i in range(batch_num):\r\n temp_x1s, temp_y1s = dataset.next_batch(batch_test_size, 'sketch_test')\r\n if normLabel:\r\n temp_x1s = normData(temp_x1s, sketch_test_mean, sketch_test_std)\r\n temp_fea = sess.run([siamese.o1], feed_dict={siamese.x1: temp_x1s})\r\n temp_fea = np.array(temp_fea)\r\n sketch_test_feaset[i*batch_test_size: (i+1)*batch_test_size] = temp_fea\r\n else:\r\n for i in range(batch_num):\r\n temp_x1s, temp_y1s = dataset.next_batch(batch_test_size, 'sketch_test')\r\n if normLabel:\r\n temp_x1s = normData(temp_x1s, sketch_test_mean, sketch_test_std)\r\n temp_fea = sess.run([siamese.o1], feed_dict={siamese.x1: temp_x1s})\r\n temp_fea = np.array(temp_fea) # cast list into array\r\n sketch_test_feaset[i*batch_test_size: (i+1)*batch_test_size] = temp_fea\r\n # calculate the left features\r\n remain_num = dataset.sketch_test_size - batch_num * batch_test_size\r\n temp_x1s, temp_y1s = dataset.next_batch(remain_num, 'sketch_test')\r\n if normLabel:\r\n temp_x1s = normData(temp_x1s, sketch_test_mean, sketch_test_std)\r\n temp_fea = sess.run([siamese.o1], feed_dict={siamese.x1: temp_x1s})\r\n temp_fea = np.array(temp_fea)\r\n sketch_test_feaset[batch_num*batch_test_size:] = temp_fea\r\n # calculate shape fea\r\n backup_sketch_ptr = dataset.sketch_train_ptr # backup for training\r\n dataset.sketch_train_ptr = 0\r\n batch_num = dataset.sketch_train_size / batch_test_size\r\n batch_left = dataset.sketch_train_size % batch_test_size\r\n shape_feaset = np.zeros((dataset.sketch_train_size, 100))\r\n if batch_left == 0:\r\n for i in range(batch_num):\r\n temp_x1s, temp_y1s = dataset.next_batch(batch_test_size, 'sketch_train')\r\n if normLabel:\r\n temp_x1s = normData(temp_x1s, sketch_train_mean, sketch_train_std)\r\n temp_fea = sess.run([siamese.o1], feed_dict={siamese.x1: temp_x1s})\r\n temp_fea = np.array(temp_fea)\r\n shape_feaset[i*batch_test_size: (i+1)*batch_test_size] = temp_fea\r\n else:\r\n for i in range(batch_num):\r\n temp_x1s, temp_y1s = dataset.next_batch(batch_test_size, 'sketch_train')\r\n if normLabel:\r\n temp_x1s = normData(temp_x1s, sketch_train_mean, sketch_train_std)\r\n temp_fea = sess.run([siamese.o1], feed_dict={siamese.x1: temp_x1s})\r\n temp_fea = np.array(temp_fea) # cast list into array\r\n shape_feaset[i*batch_test_size: (i+1)*batch_test_size] = temp_fea\r\n # calculate the left features\r\n remain_num = dataset.sketch_train_size - batch_num * batch_test_size\r\n temp_x1s, temp_y1s = dataset.next_batch(remain_num, 'sketch_train')\r\n if normLabel:\r\n temp_x1s = normData(temp_x1s, sketch_train_mean, sketch_train_std)\r\n temp_fea = sess.run([siamese.o1], feed_dict={siamese.x1: temp_x1s})\r\n temp_fea = np.array(temp_fea)\r\n shape_feaset[batch_num*batch_test_size:] = temp_fea\r\n dataset.shape_ptr = backup_sketch_ptr # restore pointer\r\n distM = distance.cdist(sketch_test_feaset, shape_feaset)\r\n nn_av, ft_av, st_av, dcg_av, e_av, map_, p_points, pre, rec = RetrievalEvaluation(C_depths, distM, dstLabel, srcLabel)\r\n pre = np.reshape(pre, (1, 8550))\r\n rec = np.reshape(rec, (1, 8550))\r\n preRec = np.concatenate((rec, pre), axis=0)\r\n np.savetxt(preRecTxt, preRec, fmt='%.5f')\r\n logid.write('The NN is {:.5f}\\nThe FT is {:.5f}\\nThe ST is {:.5f}\\nThe DCG is {:.5f}\\nThe E is {:.5f}\\nThe MAP is {:.5f}'.format(nn_av, ft_av, st_av, dcg_av, e_av, map_))\r\n msg = 'SHREC 2014 Autometric Iteration {} \\nThe NN is {:.5f}\\nThe FT is {:.5f}\\nThe ST is {:.5f}\\nThe DCG is {:.5f}\\nThe E is {:.5f}\\nThe MAP is {:.5f}\\n'.format(step, nn_av, ft_av, st_av, dcg_av, e_av, map_)\r\n print('SHREC 2014 Autometric Iteration {} \\nThe NN is {:.5f}\\nThe FT is {:.5f}\\nThe ST is {:.5f}\\nThe DCG is {:.5f}\\nThe E is {:.5f}\\nThe MAP is {:.5f}\\n'.format(step, nn_av, ft_av, st_av, dcg_av, e_av, map_))\r\n # for email\r\n server = smtplib.SMTP('smtp.gmail.com', 587)\r\n server.starttls()\r\n server.login('daiguoxian29@gmail.com', 'Dai29->Fool')\r\n server.sendmail('daiguoxian29@gmail.com', 'daiguoxian29@gmail.com', msg)\r\n # print 'The NN is %5f' % (nn_av)\r\n # print 'The FT is %5f' % (ft_av)\r\n # print 'The ST is %5f' % (st_av)\r\n # print 'The DCG is %5f' % (dcg_av)\r\n # print 'The E is %5f' % (e_av)\r\n # print 'The MAP is %5f' % (map_)\r\n # email seeting ###########\r\n server = smtplib.SMTP('smtp.gmail.com', 587)\r\n server.starttls()\r\n server.login('daiguoxian29@gmail.com', 'Dai29->Fool')\r\n server.sendmail('daiguoxian29@gmail.com', 'daiguoxian29@gmail.com', 'SHREC 13 Running (10000 iterations) run.py is finished')\r\n server.quit()\r\n logid.close()\r\n\r\n", "sub_path": "testResult_ss.py", "file_name": "testResult_ss.py", "file_ext": "py", "file_size_in_byte": 9939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "smtplib.SMTP", "line_number": 15, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "dataset.normalizeData", "line_number": 26, "usage_type": "call"}, {"api_name": "inference.siamese", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MomentumOptimizer", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.initialize_all_variables", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 70, "usage_type": "call"}, {"api_name": "dataset.retrievalParamSS", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "dataset.sketch_test_label", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "dataset.sketch_train_label", "line_number": 83, "usage_type": "attribute"}, {"api_name": "dataset.sketch_test_ptr", "line_number": 86, "usage_type": "attribute"}, {"api_name": "dataset.sketch_test_ptr", "line_number": 87, "usage_type": "attribute"}, {"api_name": "dataset.sketch_test_size", "line_number": 88, "usage_type": "attribute"}, {"api_name": "dataset.sketch_test_size", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "dataset.sketch_test_size", "line_number": 90, "usage_type": "attribute"}, {"api_name": "dataset.next_batch", "line_number": 93, "usage_type": "call"}, {"api_name": "normData.normData", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "dataset.next_batch", "line_number": 101, "usage_type": "call"}, {"api_name": "normData.normData", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "dataset.sketch_test_size", "line_number": 108, "usage_type": "attribute"}, {"api_name": "dataset.next_batch", "line_number": 109, "usage_type": "call"}, {"api_name": "normData.normData", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "dataset.sketch_train_ptr", "line_number": 116, "usage_type": "attribute"}, {"api_name": "dataset.sketch_train_ptr", "line_number": 117, "usage_type": "attribute"}, {"api_name": "dataset.sketch_train_size", "line_number": 118, "usage_type": "attribute"}, {"api_name": "dataset.sketch_train_size", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "dataset.sketch_train_size", "line_number": 120, "usage_type": "attribute"}, {"api_name": "dataset.next_batch", "line_number": 123, "usage_type": "call"}, {"api_name": "normData.normData", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "dataset.next_batch", "line_number": 131, "usage_type": "call"}, {"api_name": "normData.normData", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "dataset.sketch_train_size", "line_number": 138, "usage_type": "attribute"}, {"api_name": "dataset.next_batch", "line_number": 139, "usage_type": "call"}, {"api_name": "normData.normData", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "dataset.shape_ptr", "line_number": 145, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 146, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 146, "usage_type": "name"}, {"api_name": "RetrievalEvaluation.RetrievalEvaluation", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 151, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 156, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "410502421", "text": "# -*- coding: utf-8 -*-\n\"\"\"Part of speech mapping constants and functions for NLPIR/ICTCLAS.\n\nThis module is used by :mod:`pynlpir` to format segmented words for output.\n\n\"\"\"\nfrom __future__ import unicode_literals\nimport logging\n\n\nlogger = logging.getLogger('pynlpir.pos_map')\n\n#: A dictionary that maps part of speech codes returned by NLPIR to\n#: human-readable names (English and Chinese).\nPOS_MAP = {\n 'n': ('名词', 'noun', {\n 'nr': ('人名', 'personal name', {\n 'nr1': ('汉语姓氏', 'Chinese surname'),\n 'nr2': ('汉语名字', 'Chinese given name'),\n 'nrj': ('日语人名', 'Japanese personal name'),\n 'nrf': ('音译人名', 'transcribed personal name')\n }),\n 'ns': ('地名', 'toponym', {\n 'nsf': ('音译地名', 'transcribed toponym'),\n }),\n 'nt': ('机构团体名', 'organization/group name'),\n 'nz': ('其它专名', 'other proper noun'),\n 'nl': ('名词性惯用语', 'noun phrase'),\n 'ng': ('名词性语素', 'noun morpheme'),\n }),\n 't': ('时间词', 'time word', {\n 'tg': ('时间词性语素', 'time morpheme'),\n }),\n 's': ('处所词', 'locative word'),\n 'f': ('方位词', 'noun of locality'),\n 'v': ('动词', 'verb', {\n 'vd': ('副动词', 'auxiliary verb'),\n 'vn': ('名动词', 'noun-verb'),\n 'vshi': ('动词\"是\"', 'verb 是'),\n 'vyou': ('动词\"有\"', 'verb 有'),\n 'vf': ('趋向动词', 'directional verb'),\n 'vx': ('行事动词', 'performative verb'),\n 'vi': ('不及物动词', 'intransitive verb'),\n 'vl': ('动词性惯用语', 'verb phrase'),\n 'vg': ('动词性语素', 'verb morpheme'),\n }),\n 'a': ('形容词', 'adjective', {\n 'ad': ('副形词', 'auxiliary adjective'),\n 'an': ('名形词', 'noun-adjective'),\n 'ag': ('形容词性语素', 'adjective morpheme'),\n 'al': ('形容词性惯用语', 'adjective phrase'),\n }),\n 'b': ('区别词', 'distinguishing word', {\n 'bl': ('区别词性惯用语', 'distinguishing phrase'),\n }),\n 'z': ('状态词', 'status word'),\n 'r': ('代词', 'pronoun', {\n 'rr': ('人称代词', 'personal pronoun'),\n 'rz': ('指示代词', 'demonstrative pronoun', {\n 'rzt': ('时间指示代词', 'temporal demonstrative pronoun'),\n 'rzs': ('处所指示代词', 'locative demonstrative pronoun'),\n 'rzv': ('谓词性指示代词', 'predicate demonstrative pronoun'),\n }),\n 'ry': ('疑问代词', 'interrogative pronoun', {\n 'ryt': ('时间疑问代词', 'temporal interrogative pronoun'),\n 'rys': ('处所疑问代词', 'locative interrogative pronoun'),\n 'ryv': ('谓词性疑问代词', 'predicate interrogative pronoun'),\n }),\n 'rg': ('代词性语素', 'pronoun morpheme'),\n }),\n 'm': ('数词', 'numeral', {\n 'mq': ('数量词', 'numeral-plus-classifier compound'),\n }),\n 'q': ('量词', 'classifier', {\n 'qv': ('动量词', 'verbal classifier'),\n 'qt': ('时量词', 'temporal classifier'),\n }),\n 'd': ('副词', 'adverb'),\n 'p': ('介词', 'preposition', {\n 'pba': ('介词“把”', 'preposition 把'),\n 'pbei': ('介词“被”', 'preposition 被'),\n }),\n 'c': ('连词', 'conjunction', {\n 'cc': ('并列连词', 'coordinating conjunction'),\n }),\n 'u': ('助词', 'particle', {\n 'uzhe': ('着', 'particle 着'),\n 'ule': ('了／喽', 'particle 了/喽'),\n 'uguo': ('过', 'particle 过'),\n 'ude1': ('的／底', 'particle 的/底'),\n 'ude2': ('地', 'particle 地'),\n 'ude3': ('得', 'particle 得'),\n 'usuo': ('所', 'particle 所'),\n 'udeng': ('等／等等／云云', 'particle 等/等等/云云'),\n 'uyy': ('一样／一般／似的／般', 'particle 一样/一般/似的/般'),\n 'udh': ('的话', 'particle 的话'),\n 'uls': ('来讲／来说／而言／说来', 'particle 来讲/来说/而言/说来'),\n 'uzhi': ('之', 'particle 之'),\n 'ulian': ('连', 'particle 连'),\n }),\n 'e': ('叹词', 'interjection'),\n 'y': ('语气词', 'modal particle'),\n 'o': ('拟声词', 'onomatopoeia'),\n 'h': ('前缀', 'prefix'),\n 'k': ('后缀', 'suffix'),\n 'x': ('字符串', 'string', {\n 'xe': ('Email字符串', 'email address'),\n 'xs': ('微博会话分隔符', 'hashtag'),\n 'xm': ('表情符合', 'emoticon'),\n 'xu': ('网址URL', 'URL'),\n 'xx': ('非语素字', 'non-morpheme character'),\n }),\n 'w': ('标点符号', 'punctuation mark', {\n 'wkz': ('左括号', 'left parenthesis/bracket'),\n 'wky': ('右括号', 'right parenthesis/bracket'),\n 'wyz': ('左引号', 'left quotation mark'),\n 'wyy': ('右引号', 'right quotation mark'),\n 'wj': ('句号', 'period'),\n 'ww': ('问号', 'question mark'),\n 'wt': ('叹号', 'exclamation mark'),\n 'wd': ('逗号', 'comma'),\n 'wf': ('分号', 'semicolon'),\n 'wn': ('顿号', 'enumeration comma'),\n 'wm': ('冒号', 'colon'),\n 'ws': ('省略号', 'ellipsis'),\n 'wp': ('破折号', 'dash'),\n 'wb': ('百分号千分号', 'percent/per mille sign'),\n 'wh': ('单位符号', 'unit of measure sign'),\n }),\n 'g': ('其他', 'others', {\n 'ga': ('通讯社', 'news agency', {\n 'gaas': ('通讯社as', 'news agency as'),\n 'gaau': ('通讯社au', 'news agency au'),\n 'gacb': ('通讯社cb', 'news agency cb'),\n 'gacn': ('通讯社cn', 'news agency cn'),\n 'gaes': ('通讯社es', 'news agency es'),\n 'gafr': ('通讯社fr', 'news agency fr'),\n 'gagm': ('通讯社gm', 'news agency gm'),\n 'gahk': ('通讯社hk', 'news agency hk'),\n 'gaid': ('通讯社id', 'news agency id'),\n 'gain': ('通讯社in', 'news agency in'),\n 'gait': ('通讯社it', 'news agency it'),\n 'gajp': ('通讯社jp', 'news agency jp'),\n 'gakr': ('通讯社kr', 'news agency kr'),\n 'gakrn': ('通讯社krn', 'news agency krn'),\n 'game': ('通讯社me', 'news agency me'),\n 'gars': ('通讯社rs', 'news agency rs'),\n 'gatw': ('通讯社tw', 'news agency tw'),\n 'gauk': ('通讯社uk', 'news agency uk'),\n 'gaus': ('通讯社us', 'news agency us'),\n 'gayr': ('通讯社yr', 'news agency yr'),\n }),\n 'gjtgj': ('车辆', 'vehicle'),\n 'gms': ('食物', 'food'),\n 'gn': ('新闻', 'news', {\n 'gnan': ('新闻an', 'news an'),\n 'gnbj': ('新闻bj', 'news bj'),\n 'gncq': ('新闻cq', 'news cq'),\n 'gndq': ('新闻dq', 'news dq'),\n 'gnfj': ('新闻fj', 'news fj'),\n 'gngd': ('新闻gd', 'news gd'),\n 'gngs': ('新闻gs', 'news gs'),\n 'gngx': ('新闻gx', 'news gx'),\n 'gngz': ('新闻gz', 'news gz'),\n 'gnhan': ('新闻han', 'news han'),\n 'gnheb': ('新闻heb', 'news heb'),\n 'gnhen': ('新闻hen', 'news hen'),\n 'gnhk': ('新闻hk', 'news hk'),\n 'gnhl': ('新闻hl', 'news hl'),\n 'gnhub': ('新闻hub', 'news hub'),\n 'gnhun': ('新闻hun', 'news hun'),\n 'gnjl': ('新闻jl', 'news jl'),\n 'gnjs': ('新闻js', 'news js'),\n 'gnjx': ('新闻jx', 'news jx'),\n 'gnln': ('新闻ln', 'news ln'),\n 'gnnx': ('新闻nx', 'news nx'),\n 'gnqg': ('新闻qg', 'news qg'),\n 'gnsa': ('新闻sa', 'news sa'),\n 'gnsc': ('新闻sc', 'news sc'),\n 'gnsd': ('新闻sd', 'news sd'),\n 'gnsh': ('新闻sh', 'news sh'),\n 'gnsx': ('新闻sx', 'news sx'),\n 'gntj': ('新闻tj', 'news tj'),\n 'gntw': ('新闻tw', 'news tw'),\n 'gnxj': ('新闻xj', 'news xj'),\n 'gnxz': ('新闻xz', 'news xz'),\n 'gnyn': ('新闻yn', 'news yn'),\n 'gnzj': ('新闻zj', 'news zj'),\n 'gnzy': ('新闻zy', 'news zy'),\n }),\n 'gr': ('广播电台', 'radio station', {\n 'grc': ('广播电台c', 'radio station c'),\n 'grjyy': ('广播电台jyy', 'radio station jyy'),\n 'grqg': ('广播电台qg', 'radio station qg'),\n 'grs': ('广播电台s', 'radio station s'),\n }),\n 'gt': ('电视台', 'tv station', {\n 'gtc': ('电视台c', 'tv station c'),\n 'gthk': ('电视台hk', 'tv station hk'),\n 'gtqg': ('电视台qg', 'tv station qg'),\n 'gts': ('电视台s', 'tv station s'),\n 'gtw': ('电视台w', 'tv station w'),\n }),\n 'gw': ('网站', 'website', {\n 'gwah': ('网站ah', 'website ah'),\n 'gwbj': ('网站bj', 'website bj'),\n 'gwcj': ('网站cj', 'website cj'),\n 'gwcq': ('网站cq', 'website cq'),\n 'gwdb': ('网站db', 'website db'),\n 'gwdc': ('网站dc', 'website dc'),\n 'gwfj': ('网站fj', 'website fj'),\n 'gwgd': ('网站gd', 'website gd'),\n 'gwgs': ('网站gs', 'website gs'),\n 'gwgx': ('网站gx', 'website gx'),\n 'gwhan': ('网站han', 'website han'),\n 'gwheb': ('网站heb', 'website heb'),\n 'gwhen': ('网站hen', 'website hen'),\n 'gwhl': ('网站hl', 'website hl'),\n 'gwhub': ('网站hub', 'website hub'),\n 'gwhun': ('网站hun', 'website hun'),\n 'gwit': ('网站it', 'website it'),\n 'gwnm': ('网站nm', 'website nm'),\n 'gwqc': ('网站qc', 'website qc'),\n 'gwqh': ('网站qh', 'website qh'),\n 'gwqz': ('网站qz', 'website qz'),\n 'gwsa': ('网站sa', 'website sa'),\n 'gwsc': ('网站sc', 'website sc'),\n 'gwsd': ('网站sd', 'website sd'),\n 'gwsh': ('网站sh', 'website sh'),\n 'gwss': ('网站ss', 'website ss'),\n 'gwsx': ('网站sx', 'website sx'),\n 'gwsz': ('网站sz', 'website sz'),\n 'gwtj': ('网站tj', 'website tj'),\n 'gwxj': ('网站xj', 'website xj'),\n 'gwyn': ('网站yn', 'website yn'),\n 'gwz': ('网站z', 'website z'),\n 'gwzj': ('网站zj', 'website zj'),\n 'gwot': ('网站ot', 'website ot'),\n }),\n }),\n}\n\nINVALID_POS = ['n_new', 'mg', ]\n\n\ndef _get_pos_name(pos_code, names='parent', english=True, pos_map=POS_MAP):\n \"\"\"Gets the part of speech name for *pos_code*.\"\"\"\n pos_code = pos_code.lower() # Issue #10\n if names not in ('parent', 'child', 'all'):\n raise ValueError(\"names must be one of 'parent', 'child', or \"\n \"'all'; not '%s'\" % names)\n logger.debug(\"Getting %s POS name for '%s' formatted as '%s'.\" %\n ('English' if english else 'Chinese', pos_code, names))\n for i in range(1, len(pos_code) + 1):\n try:\n pos_key = pos_code[0:i]\n pos_entry = pos_map[pos_key]\n break\n except KeyError:\n if i == len(pos_code):\n if pos_code not in INVALID_POS:\n logger.warning(\"part of speech not recognized: '%s'\"\n % pos_code)\n return None # Issue #20\n pos = (pos_entry[1 if english else 0], )\n if names == 'parent':\n logger.debug(\"Part of speech name found: '%s'\" % pos[0])\n return pos[0]\n if len(pos_entry) == 3 and pos_key != pos_code:\n sub_map = pos_entry[2]\n logger.debug(\"Found parent part of speech name '%s'. Descending to \"\n \"look for child name for '%s'\" % (pos_entry[1], pos_code))\n sub_pos = _get_pos_name(pos_code, names, english, sub_map)\n\n if names == 'all':\n # sub_pos can be None sometimes (e.g. for a word '甲')\n pos = pos + sub_pos if sub_pos else pos\n else:\n pos = (sub_pos, )\n\n name = pos if names == 'all' else pos[-1]\n logger.debug(\"Part of speech name found: '%s'\" % repr(name)\n if isinstance(name, tuple) else name)\n return name\n\n\ndef get_pos_name(code, name='parent', english=True):\n \"\"\"Gets the part of speech name for *code*.\n\n :param str code: The part of speech code to lookup, e.g. ``'nsf'``.\n :param str name: Which part of speech name to include in the output. Must\n be one of ``'parent'``, ``'child'``, or ``'all'``. Defaults to\n ``'parent'``. ``'parent'`` indicates that only the most generic name\n should be used, e.g. ``'noun'`` for ``'nsf'``. ``'child'`` indicates\n that the most specific name should be used, e.g.\n ``'transcribed toponym'`` for ``'nsf'``. ``'all'`` indicates that all\n names should be used, e.g. ``('noun', 'toponym',\n 'transcribed toponym')`` for ``'nsf'``.\n :param bool english: Whether to return an English or Chinese name.\n :returns: ``str`` (``unicode`` for Python 2) if *name* is ``'parent'`` or\n ``'child'``. ``tuple`` if *name* is ``'all'``.\n\n \"\"\"\n return _get_pos_name(code, name, english)\n", "sub_path": "pynlpir/pos_map.py", "file_name": "pos_map.py", "file_ext": "py", "file_size_in_byte": 13367, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "543000285", "text": "import os\nimport wx\nimport wx.media\n\nfrom services.recognition_service import *\nfrom services.microphone_service import *\nfrom services.sound_service import *\n\ndirName = os.path.dirname(os.path.abspath(__file__))\nbitmapDir = os.path.join(dirName, '../../bitmaps')\n\n\nclass PatientAutoTestingPanel(wx.Panel):\n\n def __init__(self, parent, testing_model, test_settings, recognition_service_settings):\n wx.Panel.__init__(self, parent=parent)\n\n self.parent = parent\n self.testing_model = testing_model\n self.test_settings = test_settings\n self.recognition_service_settings = recognition_service_settings\n\n self.current_testing_item = 0\n\n self.frame = parent\n self.SetSize((800, 600))\n self.layoutControls()\n sp = wx.StandardPaths.Get()\n self.currentFolder = sp.GetDocumentsDir()\n\n def layoutControls(self):\n wx.InitAllImageHandlers()\n\n self.fioLabel = wx.StaticText(self, label=\"{} {}\".format(\"ФИО: \",\n self.testing_model.firstName + \" \" + self.testing_model.secondName))\n self.birthdayLabel = wx.StaticText(self, label=\"{} {}\".format(\"Год рождения: \", self.testing_model.birthday))\n self.fileLabel = wx.StaticText(self, label=\"\", size=(400, 30))\n self.playRecLabel = wx.StaticText(self, label=\"\", size=(400, 30))\n self.countdownLabel = wx.StaticText(self, label=\"\", size=(400, 30))\n\n self.textLabel = wx.StaticText(self, label=\"Распознанный текст\")\n self.textRes = wx.TextCtrl(self, style=wx.TE_MULTILINE | wx.TE_WORDWRAP,\n value=\"\", name=\"Результаты распознавания\", size=(300, 100))\n self.textRes.Bind(wx.EVT_TEXT, self.onKeyTyped)\n\n self.startBtn = wx.Button(self, style=wx.SL_VERTICAL | wx.SL_INVERSE, label=\"Начать сессию\", size=(120, 30))\n self.startBtn.Bind(wx.EVT_BUTTON, self.startSession)\n\n self.nextBtn = wx.Button(self, style=wx.SL_VERTICAL | wx.SL_INVERSE, label=\"Показать результаты\", size=(120, 30))\n self.nextBtn.Bind(wx.EVT_BUTTON, self.nextPanel)\n\n self.mainSizer = wx.BoxSizer(wx.VERTICAL)\n self.mainSizer.Add(self.fioLabel)\n self.mainSizer.Add(self.birthdayLabel)\n self.mainSizer.Add(self.fileLabel, 0, wx.ALL, 5)\n self.mainSizer.Add(self.playRecLabel, 0, wx.ALL, 5)\n self.mainSizer.Add(self.countdownLabel, 0, wx.ALL, 5)\n self.mainSizer.Add(self.textLabel, 0, wx.ALL, 5)\n self.mainSizer.Add(self.textRes, 0, wx.ALL, 5)\n\n self.hSizerBtn = wx.BoxSizer(wx.HORIZONTAL)\n self.hSizerBtn.Add(self.startBtn, 0, wx.ALL, 5)\n self.hSizerBtn.Add(self.nextBtn, 0, wx.ALL, 5)\n\n self.mainSizer.Add(self.hSizerBtn, 0, wx.ALL, 5)\n\n self.SetSizer(self.mainSizer)\n self.Layout()\n self.nextBtn.Disable()\n\n def update(self):\n wx.Yield()\n\n self.startBtn.Enable()\n self.nextBtn.Disable()\n\n self.fioLabel.SetLabel(\n \"{} {}\".format(\"ФИО: \", self.testing_model.firstName + \" \" + self.testing_model.secondName))\n self.birthdayLabel.SetLabel(\"{} {}\".format(\"Год рождения: \", self.testing_model.birthday))\n if self.current_testing_item < self.test_settings.audioFilesNumber:\n test_item = self.testing_model.testingItems[self.current_testing_item]\n index = str(self.current_testing_item + 1) + \" из \" + str(self.test_settings.audioFilesNumber) + \": \"\n self.fileLabel.SetLabel(index + test_item.initialAudioFilePath)\n else:\n self.fileLabel.SetLabel(\"Все файлы кончились\")\n self.nextBtn.Enable()\n\n def play(self):\n self.update()\n test_item = self.testing_model.testingItems[self.current_testing_item]\n\n self.textRes.Clear()\n self.playRecLabel.SetLabel(\"Воспроизведение\")\n noise_file = self.recognition_service_settings.noises_dir + self.test_settings.noiseFile \\\n if self.test_settings.noiseFile != '' \\\n else None\n play_file( self.recognition_service_settings.words_dir + test_item.initialAudioFilePath,self.test_settings.volumeLevelNoice, noise_file)\n\n def onKeyTyped(self, event):\n test_item = self.testing_model.testingItems[self.current_testing_item]\n test_item.resultAudioFilePath = test_item.initialAudioFilePath\n test_item.resultTest = event.GetString().lower()\n test_item.isCorrect = test_item.initialText == test_item.resultTest\n\n def record(self):\n self.startRecord()\n self.playRecLabel.SetLabel(\"Запись\")\n delay_time = self.test_settings.delay * 1000 # getting delay in milliseconds\n count = 0\n while count < delay_time:\n count += 250\n self.countdownLabel.SetLabel(self.get_countdown_text(delay_time, count))\n wx.MilliSleep(250)\n self.stopRecord()\n wx.Yield()\n wx.MilliSleep(250)\n\n def get_countdown_text(self, total_time, left_time):\n left_ticks = 16\n right_ticks = 16\n mid_ticks = 5\n total_ticks = left_ticks + right_ticks + mid_ticks\n full_sym = '+'\n empty_sym = '~'\n edge_sym = '!'\n ticks = int(left_time / total_time * total_ticks)\n left_line = edge_sym\n right_line = \"\"\n if ticks <= left_ticks:\n left_line += full_sym * ticks + empty_sym * (left_ticks - ticks)\n right_line = empty_sym * right_ticks + edge_sym\n elif ticks > left_ticks + mid_ticks:\n left_line += full_sym * left_ticks\n right_line = (ticks - (left_ticks + mid_ticks)) * full_sym + empty_sym * (total_ticks - ticks) + edge_sym\n else:\n left_line += full_sym * left_ticks\n right_line = empty_sym * right_ticks + edge_sym\n minutes = (total_time - left_time) // 60000\n if minutes < 10:\n minutes = \"0\" + str(minutes)\n else:\n minutes = str(minutes)\n seconds = ((total_time - left_time) // 1000) % 60\n if seconds < 10:\n seconds = \"0\" + str(seconds)\n else:\n seconds = str(seconds)\n return left_line + \" \" + minutes + \":\" + seconds + \" \" + right_line\n\n def startRecord(self):\n self.playRecLabel.SetLabel(\"Запись\")\n self.recording_data = RecordingData()\n start_recording(self.recording_data, self.recognition_service_settings)\n print(\"Start recording\")\n\n def stopRecord(self):\n self.update()\n test_item = self.testing_model.testingItems[self.current_testing_item]\n test_item.resultAudioFilePath = test_item.initialAudioFilePath\n wav_file_with_speech = stop_recording(test_item.resultAudioFilePath, self.recording_data,\n self.recognition_service_settings)\n self.playRecLabel.SetLabel(\"Распознавание\")\n print(\"Stop Recording\")\n\n if self.recognition_service_settings.is_svc_available:\n text = recognize_wav_file(wav_file_with_speech,\n self.recognition_service_settings.recognize_service_url)\n print(\"Result : {}\".format(text))\n\n self.textRes.Clear()\n if text is None:\n self.textRes.write(\"< Произошла ошибка, подробности в консоли >\")\n return\n\n self.textRes.write(text)\n test_item.resultTest = text.lower()\n test_item.isCorrect = test_item.initialText == test_item.resultTest\n else:\n self.textRes.write(\"< Сервис распознавания речи недоступен >\")\n test_item.resultTest = \"автоматически не распознано\"\n test_item.isCorrect = test_item.initialText == test_item.resultTest\n wx.Yield()\n wx.MilliSleep(1000)\n\n def nextPanel(self, event):\n\n print(\"Testing Model content {}\".format(self.testing_model))\n\n self.Hide()\n next_panel = self.parent.patient_result_panel\n next_panel.update()\n next_panel.Show()\n self.Layout()\n\n def startSession(self, event):\n self.startBtn.Disable()\n blank_cursor = wx.Cursor(wx.CURSOR_BLANK)\n self.SetCursor(blank_cursor)\n\n for i in range(self.test_settings.audioFilesNumber):\n self.current_testing_item = i\n self.play()\n self.record()\n self.update()\n\n cursor = wx.Cursor(wx.CURSOR_ARROW)\n self.SetCursor(cursor)\n self.playRecLabel.SetLabel(\"Конец сессии\")\n self.nextBtn.Enable()\n", "sub_path": "libs/scripts/src/main_panels/patient_auto_testing_panel.py", "file_name": "patient_auto_testing_panel.py", "file_ext": "py", "file_size_in_byte": 8829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 13, "usage_type": "attribute"}, {"api_name": "wx.Panel.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "wx.Panel", "line_number": 16, "usage_type": "attribute"}, {"api_name": "wx.StandardPaths.Get", "line_number": 28, "usage_type": "call"}, {"api_name": "wx.StandardPaths", "line_number": 28, "usage_type": "attribute"}, {"api_name": "wx.InitAllImageHandlers", "line_number": 32, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 34, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 36, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 37, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 39, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 41, "usage_type": "call"}, {"api_name": "wx.TextCtrl", "line_number": 42, "usage_type": "call"}, {"api_name": "wx.TE_MULTILINE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "wx.TE_WORDWRAP", "line_number": 42, "usage_type": "attribute"}, {"api_name": "wx.EVT_TEXT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 46, "usage_type": "call"}, {"api_name": "wx.SL_VERTICAL", "line_number": 46, "usage_type": "attribute"}, {"api_name": "wx.SL_INVERSE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 47, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 49, "usage_type": "call"}, {"api_name": "wx.SL_VERTICAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.SL_INVERSE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 52, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 56, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 58, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 61, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 61, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 62, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 63, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "wx.Yield", "line_number": 72, "usage_type": "call"}, {"api_name": "wx.MilliSleep", "line_number": 113, "usage_type": "call"}, {"api_name": "wx.Yield", "line_number": 115, "usage_type": "call"}, {"api_name": "wx.MilliSleep", "line_number": 116, "usage_type": "call"}, {"api_name": "wx.Yield", "line_number": 182, "usage_type": "call"}, {"api_name": "wx.MilliSleep", "line_number": 183, "usage_type": "call"}, {"api_name": "wx.Cursor", "line_number": 197, "usage_type": "call"}, {"api_name": "wx.CURSOR_BLANK", "line_number": 197, "usage_type": "attribute"}, {"api_name": "wx.Cursor", "line_number": 206, "usage_type": "call"}, {"api_name": "wx.CURSOR_ARROW", "line_number": 206, "usage_type": "attribute"}]} +{"seq_id": "18889072", "text": "from __future__ import division\nimport threading\nimport cv2\nimport numpy as np\nfrom data.Player import Player\n\nclass VideoCapture:\n\n def __init__(self, player, player2=None):\n self.player = player\n self.player2 = None\n self.data = dict()\n self.frame = None\n self.VIDEO_SIZE = (400, 300)\n\n self.data[player.player_id] = {\n 'cam_pos': self.player.mallet.pos.state,\n 'pos': self.player.mallet.pos.state,\n 'last_pos': self.player.mallet.pos.state,\n 'vel': [(0, 0)]\n # 'vel': (0, 0)\n }\n self.set_color_mask(self.player)\n if player2:\n self.player2 = player2\n self.data[player2.player_id] = {\n 'cam_pos': self.player2.mallet.pos.state,\n 'pos': self.player2.mallet.pos.state,\n 'last_pos': self.player2.mallet.pos.state,\n # 'vel': (0, 0)\n 'vel': [(0, 0)]\n }\n self.set_color_mask(self.player2)\n\n self._stop_capture = threading.Event()\n self._stop_image_processing = threading.Event()\n\n def set_color_mask(self, player):\n \"\"\"\n Assigns lower/upper and circle color for player\n :param player:\n :return:\n \"\"\"\n if player.playerColor == Player.PLAYER_BLUE:\n self.data[player.playerColor]['lower'] = np.array([90, 80, 80], dtype=np.uint8)\n self.data[player.playerColor]['upper'] = np.array([110, 255, 255], dtype=np.uint8)\n self.data[player.playerColor]['circle_color'] = (255, 0, 0)\n else:\n self.data[player.playerColor]['lower'] = np.array([21, 58, 28], dtype=np.uint8)\n self.data[player.playerColor]['upper'] = np.array([105, 224, 154], dtype=np.uint8)\n self.data[player.playerColor]['circle_color'] = (0, 0, 255)\n\n def convert_position(self, player_id, tup):\n \"\"\"\n Convert coordinates to pygame proper one\n :param player_id:\n :param tup: (x, y)\n :return:\n \"\"\"\n GAME_SIZE = (800, 600)\n # left side\n if player_id == Player.PLAYER_RED:\n x = tup[0] * GAME_SIZE[0]/self.VIDEO_SIZE[0]\n y = tup[1] * GAME_SIZE[1]/self.VIDEO_SIZE[1]\n # right side\n else:\n x = tup[0] * GAME_SIZE[0]/self.VIDEO_SIZE[0] + GAME_SIZE[0]/2\n y = tup[1] * GAME_SIZE[1]/self.VIDEO_SIZE[1]\n\n return int(x), int(y)\n\n def get_image(self):\n \"\"\"\n Loop which retrieve frames from Camera\n :return:\n \"\"\"\n cap = cv2.VideoCapture(0)\n while not self._stop_capture.is_set():\n _, frame = cap.read()\n self.frame = cv2.resize(cv2.flip(frame, 1), self.VIDEO_SIZE)\n for player_id in self.data.keys():\n cv2.circle(self.frame, self.data[player_id]['cam_pos'], 10, self.data[player_id]['circle_color'], 2)\n cv2.imshow('frame', self.frame)\n k = cv2.waitKey(33) & 0xFF\n cv2.destroyAllWindows()\n\n def get_players_data(self, player_id):\n \"\"\"\n Loop which save position of colored objects.\n :param player_id:\n :return:\n \"\"\"\n while not self._stop_image_processing.is_set():\n\n if self.frame is None:\n continue\n if player_id == Player.PLAYER_RED:\n frame = self.frame[:, :self.VIDEO_SIZE[0]//2]\n else:\n frame = self.frame[:, self.VIDEO_SIZE[0]//2:]\n frame = cv2.blur(frame, (3, 3))\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n self.data[player_id]['last_pos'] = self.data[player_id]['pos']\n if len(self.data[player_id]['vel']) > 4:\n self.data[player_id]['vel'].pop(0)\n mask = cv2.inRange(hsv, self.data[player_id]['lower'], self.data[player_id]['upper'])\n kernel = np.ones((5, 5), np.uint8)\n mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n mask = cv2.medianBlur(mask, 5)\n\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n maximumArea = 0\n bestContour = None\n for contour in contours:\n currentArea = cv2.contourArea(contour)\n if currentArea > maximumArea:\n bestContour = contour\n maximumArea = currentArea\n\n if bestContour is not None:\n M = cv2.moments(bestContour)\n x, y = int(M['m10']/M['m00']), int(M['m01']/M['m00'])\n game_x, game_y = self.convert_position(player_id, (x, y))\n if player_id == Player.PLAYER_BLUE:\n x += self.VIDEO_SIZE[0]//2\n self.data[player_id]['cam_pos'] = (x, y)\n self.data[player_id]['pos'] = game_x, game_y\n self.data[player_id]['vel'].append(((game_x - self.data[player_id]['last_pos'][0]), (game_y - self.data[player_id]['last_pos'][1])))\n # self.data[player_id]['vel'] = (game_x - self.data[player_id]['last_pos'][0]), (game_y - self.data[player_id]['last_pos'][1])\n else:\n self.data[player_id]['vel'].append((0, 0))\n # self.data[player_id]['vel'] = (0, 0)\n\n def start_capture(self):\n \"\"\"\n starting new thread - get_image\n :return:\n \"\"\"\n threading.Thread(target=self.get_image).start()\n\n def start_image_processing(self, player):\n \"\"\"\n starting new thread - get_players_data\n :return:\n \"\"\"\n threading.Thread(target=self.get_players_data, args=(player.player_id,)).start()\n\n def stop_capture(self):\n \"\"\"\n stopping threads - get_image\n :return:\n \"\"\"\n self._stop_capture.set()\n\n def stop_image_processing(self):\n \"\"\"\n stopping threads - get_players_data\n :return:\n \"\"\"\n self._stop_image_processing.set()\n\n @property\n def pos(self):\n \"\"\"\n Gives position of object(s)\n :return: (x, y) or (x1, y1), (x2, y2)\n \"\"\"\n if self.player2:\n return self.data[self.player.player_id]['pos'], self.data[self.player2.player_id]['pos']\n return self.data[self.player.player_id]['pos']\n\n @property\n def vel(self):\n \"\"\"\n Gives velocity of object(s)\n :return: (vx, vy) or (vx1, vy1), (vx2, vy2)\n \"\"\"\n p1_vel = (sum([x[0] for x in self.data[self.player.player_id]['vel']])/len(self.data[self.player.player_id]['vel']), sum([x[1] for x in self.data[self.player.player_id]['vel']])/len(self.data[self.player.player_id]['vel']))\n # print self.data[self.player.player_id]['vel'], self.data[self.player2.player_id]['vel']\n if self.player2:\n p2_vel = (sum([x[0] for x in self.data[self.player2.player_id]['vel']])/len(self.data[self.player2.player_id]['vel']), sum([x[1] for x in self.data[self.player2.player_id]['vel']])/len(self.data[self.player2.player_id]['vel']))\n # print p1_vel, p2_vel\n # return self.data[self.player.player_id]['vel'], self.data[self.player2.player_id]['vel']\n return p1_vel, p2_vel\n return p1_vel\n # return self.data[self.player.player_id]['vel']", "sub_path": "data/VideoCapture.py", "file_name": "VideoCapture.py", "file_ext": "py", "file_size_in_byte": 7317, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "threading.Event", "line_number": 35, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 36, "usage_type": "call"}, {"api_name": "data.Player.Player.PLAYER_BLUE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "data.Player.Player", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 50, "usage_type": "attribute"}, {"api_name": "data.Player.Player.PLAYER_RED", "line_number": 62, "usage_type": "attribute"}, {"api_name": "data.Player.Player", "line_number": 62, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 85, "usage_type": "call"}, {"api_name": "data.Player.Player.PLAYER_RED", "line_number": 97, "usage_type": "attribute"}, {"api_name": "data.Player.Player", "line_number": 97, "usage_type": "name"}, {"api_name": "cv2.blur", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 107, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 111, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 111, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 121, "usage_type": "call"}, {"api_name": "data.Player.Player.PLAYER_BLUE", "line_number": 124, "usage_type": "attribute"}, {"api_name": "data.Player.Player", "line_number": 124, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 139, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "472587474", "text": "import sys\r\nsys.path.append('./modules/')\r\n\r\nimport numpy as np\r\nfrom docopt import docopt\r\nfrom scipy.sparse import csc_matrix, coo_matrix, linalg\r\nfrom composes.utils import io_utils\r\nfrom composes.semantic_space.space import Space\r\nfrom composes.utils.py_matrix_utils import nonzero_invert\r\nfrom composes.transformation.scaling.ppmi_weighting import PpmiWeighting\r\nfrom composes.matrix.sparse_matrix import SparseMatrix\r\nfrom dsm import save_pkl_files, load_pkl_files\r\nimport logging\r\nimport time\r\n\r\n\r\ndef main():\r\n \"\"\"\r\n Compute the smoothed and shifted (P)PMI matrix from a co-occurrence matrix. Smoothing is performed as described in\r\n\r\n Omer Levy, Yoav Goldberg, and Ido Dagan. 2015. Improving distributional similarity with lessons learned from word embeddings. Trans. ACL, 3.\r\n\r\n \"\"\"\r\n\r\n # Get the arguments\r\n args = docopt('''Compute the smoothed and shifted (P)PMI matrix from a co-occurrence matrix and save it in pickle format.\r\n\r\n Usage:\r\n ppmi.py [-l] \r\n\r\n = the prefix for the input files (.sm for the matrix, .rows and .cols) and output files (.ppmi)\r\n = shifting parameter\r\n = smoothing parameter\r\n = output path for space\r\n\r\n Options:\r\n -l, --len normalize final vectors to unit length\r\n\r\n ''')\r\n\r\n is_len = args['--len']\r\n dsm_prefix = args['']\r\n k = int(args[''])\r\n alpha = float(args[''])\r\n outPath = args['']\r\n\r\n logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\r\n logging.info(__file__.upper())\r\n start_time = time.time() \r\n\r\n # Get space with sparse matrix\r\n dsm = load_pkl_files(dsm_prefix)\r\n id2row = dsm.get_id2row()\r\n id2column = dsm.get_id2column()\r\n\r\n # Get probabilities\r\n matrix_ = dsm.cooccurrence_matrix\r\n\r\n matrix_.assert_positive()\r\n row_sum = matrix_.sum(axis = 1)\r\n col_sum = matrix_.sum(axis = 0)\r\n\r\n # Compute smoothed P_alpha(c)\r\n smooth_col_sum = np.power(col_sum, alpha)\r\n col_sum = smooth_col_sum/smooth_col_sum.sum()\r\n\r\n # Compute P(w)\r\n row_sum = nonzero_invert(row_sum)\r\n col_sum = nonzero_invert(col_sum)\r\n \r\n # Apply epmi weighting (without log)\r\n matrix_ = matrix_.scale_rows(row_sum)\r\n matrix_ = matrix_.scale_columns(col_sum)\r\n\r\n # Apply log weighting\r\n matrix_.mat.data = np.log(matrix_.mat.data)\r\n\r\n # Shift values\r\n matrix_.mat.data -= np.log(k)\r\n\r\n # Eliminate negative counts\r\n matrix_.mat.data[matrix_.mat.data <= 0] = 0.0\r\n\r\n # Eliminate zero counts\r\n matrix_.mat.eliminate_zeros()\r\n \r\n matrix_ = matrix_.get_mat()\r\n \r\n if is_len:\r\n # L2-normalize vectors\r\n l2norm1 = linalg.norm(matrix_, axis=1, ord=2)\r\n l2norm1[l2norm1==0.0] = 1.0 # Convert 0 values to 1\r\n matrix_ /= l2norm1.reshape(len(l2norm1),1)\r\n\r\n dsm = Space(SparseMatrix(matrix_), id2row, id2column)\r\n \r\n # Save the Space object in pickle format\r\n save_pkl_files(dsm, outPath + \".ppmi.sm\", save_in_one_file=False)\r\n logging.info(\"--- %s seconds ---\" % (time.time() - start_time)) \r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "representations/ppmi.py", "file_name": "ppmi.py", "file_ext": "py", "file_size_in_byte": 3242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "docopt.docopt", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "dsm.load_pkl_files", "line_number": 52, "usage_type": "call"}, {"api_name": "dsm.get_id2row", "line_number": 53, "usage_type": "call"}, {"api_name": "dsm.get_id2column", "line_number": 54, "usage_type": "call"}, {"api_name": "dsm.cooccurrence_matrix", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.power", "line_number": 64, "usage_type": "call"}, {"api_name": "composes.utils.py_matrix_utils.nonzero_invert", "line_number": 68, "usage_type": "call"}, {"api_name": "composes.utils.py_matrix_utils.nonzero_invert", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 79, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.norm", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg", "line_number": 91, "usage_type": "name"}, {"api_name": "composes.semantic_space.space.Space", "line_number": 95, "usage_type": "call"}, {"api_name": "composes.matrix.sparse_matrix.SparseMatrix", "line_number": 95, "usage_type": "call"}, {"api_name": "dsm.save_pkl_files", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "135387510", "text": "import click\nimport pandas as pd\nfrom csv_linter.checks import carriage_returns, unnamed_columns, zero_count_columns\n\n\n#def carriage_returns(df):\n# for index, row in df.iterrows():\n# for column, field in row.iteritems():\n# try:\n# if \"\\r\\n\" in field:\n# return index, column, field\n# except TypeError:\n# continue\n#\n#\n#def unnamed_columns(df):\n# bad_columns = []\n# for key in df.keys():\n# if \"Unnamed\" in key:\n# bad_columns.append(key)\n# return len(bad_columns)\n#\n#\n#def zero_count_columns(df):\n# bad_columns = []\n# for key in df.keys():\n# if df[key].count() == 0:\n# bad_columns.append(key)\n# return bad_columns\n#\n\n@click.command()\n@click.argument('filename', type=click.Path(exists=True))\ndef main(filename):\n df = pd.read_csv(filename)\n for column in zero_count_columns(df):\n click.echo(f\"Warning: Column '{column}' has no items in it\")\n unnamed = unnamed_columns(df)\n if unnamed:\n click.echo(f\"Warning: found {unnamed} columns that are Unnamed\")\n carriage_field = carriage_returns(df)\n if carriage_field:\n index, column, field = carriage_field\n click.echo((\n f\"Warning: found carriage returns at index {index}\"\n f\" of column '{column}':\")\n )\n click.echo(f\" '{field[:50]}'\")\n", "sub_path": "chapter11/linter-modularized/csv_linter/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "csv_linter.checks.zero_count_columns", "line_number": 36, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 37, "usage_type": "call"}, {"api_name": "csv_linter.checks.unnamed_columns", "line_number": 38, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 40, "usage_type": "call"}, {"api_name": "csv_linter.checks.carriage_returns", "line_number": 41, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 44, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 48, "usage_type": "call"}, {"api_name": "click.command", "line_number": 32, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 33, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "648608861", "text": "#dump_index_7dayago\n\nimport os\nimport json\nfrom elasticsearch import Elasticsearch\n\n#\ndef dump_objects(output_directory, response_es):\n dir = os.path.join(output_directory, \"dump\")\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n for doc in response_es['hits']['hits']:\n filepath = os.path.join(dir, doc['_id'] + '.json')\n with open(filepath, 'w') as outfile:\n json.dump(doc['_source'], outfile, indent=2)\n print(\"Written {}\".format(filepath))\n\n\ndef dump_jsom(output_directory, response_json):\n dir = os.path.join(output_directory, \"dump\")\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n filepath = os.path.join(dir, \"json_file\" + str(response_json['total']) + '.json')\n with open(filepath, 'w') as outfile:\n json.dump(response_json['hits'], outfile, indent=2)\n\n## init Elasticsearch API using localhost:9200 endpoint\nes = Elasticsearch()\n\nes_index = raw_input(\"Define index name. Defalt: logstash\") or \"logstash\"\n\nres = es.search(index=es_index, body={\"query\": {\"match_all\": {}}}, size=1000)\nprint(\"Got %d Hits:\" % res['hits']['total'])\nprint(res['hits']['hits'])\n\n#for hit in res['hits']['hits']:\n# print(hit[\"_source\"])\n\n## dump response to json file\n#dump_objects(\"./dump_\"+es_index, res)\ndump_jsom(\"./dump_\"+es_index, res['hits'])\n\n", "sub_path": "admin/dump_index_7daysago.py", "file_name": "dump_index_7daysago.py", "file_ext": "py", "file_size_in_byte": 1319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 16, "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.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.makedirs", "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": "json.dump", "line_number": 27, "usage_type": "call"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "455041853", "text": "from __future__ import absolute_import, print_function\n\nfrom tweepy import Stream\nfrom tweepy import OAuthHandler\nfrom tweepy.streaming import StreamListener\nimport time\nimport json\n\nckey = 'QtjbyJiJ3NLR2nYuU5SVbaEMF' \ncsecret = 'jDiECspRBGt9SdrieY3BBOtIDXFiFox3zZjAY4i7gCzAjSPd8y'\natoken = '721453542519795712-LNBQIFa7V7uhzt2YyUC92yvX7twmbCl'\nasecret = '1iZRfVomihmYf7PWgbyjtJ3YStpfW5BwKKY5gjRxCRawz'\n\ntemp = input('Please enter a restaurant name: ')\n\t\nclass listener(StreamListener):\n\n def on_data(self, data):\n\t\n decoded = json.loads(data)\n\n datastring=('@%s: %s' % (decoded['user']['screen_name'], decoded['text'].encode('ascii', 'ignore')))\n print(datastring)\n print('')\n savefile=open('twitterdata.txt','a')\n savefile.write(datastring)\n savefile.write('\\n')\n savefile.close()\n return True\n\n def on_error(self, status):\n print(status)\n\nif __name__ == '__main__':\n l = listener()\n auth = OAuthHandler(ckey, csecret)\n auth.set_access_token(atoken, asecret)\n\n # GEOBOX for the locations\n\n stream = Stream(auth, l)\n stream.filter(track=[str(temp)+' Restaurant'], locations=[], languages=['en'])\n\n", "sub_path": "tweepy/test_data.py", "file_name": "test_data.py", "file_ext": "py", "file_size_in_byte": 1182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tweepy.streaming.StreamListener", "line_number": 16, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 36, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "110111128", "text": "import pygame\nimport random\nimport os\n\nfrom jogador import Jogador\nfrom inimigo import Inimigo\nfrom meteoro import Meteoro\nfrom atirar import Atirar\nfrom escudo import Escudo\nfrom boost import Boost\nfrom Sprites import *\nfrom vida import Vida\nfrom Som import *\n\n\npygame.init()\n\n\n#definindo que minha janela tera a largura e altura especificada\nWIN = pygame.display.set_mode((largura, altura))\n\n#novo evento criado para aumentar a pontuação conforme passa o tempo\ntempo = pygame.USEREVENT + 1\npygame.time.set_timer(tempo, 5000)\n\n#fontes\nfonte = pygame.font.Font(os.path.join(BASE_DIR, \"assets\", \"levycrayola.TTF\"), 50)\nfonte_fim_de_jogo = pygame.font.Font(os.path.join(BASE_DIR, \"assets\", \"levycrayola.TTF\"), 60)\n\n#cores\nbranco = (255,255,255)\npreto = (0,0,0)\n\n# Lista sons (se novos sprites de som forem criados, adicionar nesta lista =) )\nlista_sons = [MUSICA_FIM, EXPLODIU, COLIDIU, MORTE]\n\n\nclass Main():\n def main(self, volume):\n run = True\n FPS = 60\n nivel = 1\n vidas = 5\n clock = pygame.time.Clock()\n parado = True\n\n # Pontuação para o próximo nível\n pontuacao_limite = 800\n\n #variáveis pro inimigo\n inimigos = []\n onda_de_inimigos = 0\n velocidade_inimigo = 2\n\n #variáveis pro meteoro\n meteoros = []\n velocidade_meteoro = 1\n\n #variáveis pro boost\n boosts = []\n onda_de_boost = 1\n velocidade_boost = 3\n\n #vidas\n vidas_a_captar = []\n onda_de_vida = 1\n velocidade_vida = 5\n\n #escudo\n escudos = []\n velocidade_escudo = 3\n onda_de_escudos = 1\n ativar_escudo = False\n TIMERESCUDO = pygame.USEREVENT\n pygame.time.set_timer(TIMERESCUDO, 0)\n\n # Saude\n height_barra = 10\n saude = 100\n\n # Lasers\n resfriamento_laser = 0\n tempo_resfriamento = 20\n velocidade_laser = 7\n lasers_inimigos = []\n lasers_jogador = []\n\n # Jogador\n movimento_jogador = 8\n jogador = Jogador(int(largura / 2 - WH_JOGADOR / 2), int(altura - 125), altura, largura, JOGADOR, JOGADOR_PARADO, ESCUDO_NO_JOGADOR, ESCUDO_NO_JOGADOR2, LASER_JOGADOR, saude)\n\n fim_de_jogo = False\n contador_fim_de_jogo = 0\n\n # Definindo volume\n for som in lista_sons:\n som.set_volume(volume)\n\n def desenhar_janela(pos_y):\n WIN.blit(PLANO_DE_FUNDO, (0, pos_y))\n WIN.blit(PLANO_DE_FUNDO, (0, pos_y - altura + 1))\n WIN.blit(PORTA, (574, 543))\n\n # mostrando textos na tela\n label_vidas = fonte.render(f\"Vidas: {vidas}\", True, preto) # 1 - suavização de serrilhado\n label_nivel = fonte.render(f\"Nivel: {nivel}\", True, preto)\n label_pontuacao = fonte.render(f\"{jogador.pontuacao}\", True, preto)\n\n WIN.blit(label_vidas, (10, 10))\n WIN.blit(label_nivel, (largura - label_nivel.get_width() - 10, 10))\n WIN.blit(label_pontuacao, (10, 595))\n\n for inimigo in inimigos:\n inimigo.desenhar(WIN)\n\n for meteoro in meteoros:\n meteoro.desenhar(WIN)\n \n for laser_inimigo in lasers_inimigos:\n laser_inimigo.desenhar(WIN)\n\n for laser_jogador in lasers_jogador:\n laser_jogador.desenhar(WIN)\n\n for boost in boosts:\n boost.desenhar(WIN)\n\n for escudo in escudos:\n escudo.desenhar(WIN)\n\n for vida in vidas_a_captar:\n vida.desenhar(WIN)\n\n if ativar_escudo:\n jogador.desenhar_escudo(WIN)\n\n jogador.desenhar(WIN, height_barra, parado)\n\n if fim_de_jogo:\n pygame.mixer.music.stop()\n WIN.blit(EXPLOSAO, (jogador.x - 50, jogador.y-20))\n fim_de_jogo_label = fonte_fim_de_jogo.render(\"Aguarde\", True, preto)\n WIN.blit(fim_de_jogo_label, (largura / 2 - fim_de_jogo_label.get_width() / 2,\n altura / 2 - fim_de_jogo_label.get_height() / 2))\n\n pygame.display.update() # sempre que for desenhar, devemos atualizar a tela colocando a \"nova imagem\" por cima das outras que estavam desenhadas\n pos_y = 0 #posicao inicial da tela de fundo\n \n while run:\n clock.tick(FPS)\n desenhar_janela(pos_y)\n pos_y += velocidade_inimigo/2 #tela de fundo se move sempre a metade da velocidade do inimigo\n \n if pos_y >= altura:\n pos_y = 0 #reseta posicao da tela de fundo\n\n if jogador.saude <= 0:\n if vidas >= 1:\n jogador.saude = saude\n vidas -= 1\n\n if vidas <= 0:\n fim_de_jogo = True\n contador_fim_de_jogo += 1\n\n # A pontuacao é retornada quando o jogador perde\n if fim_de_jogo:\n velocidade_inimigo = 0\n if contador_fim_de_jogo == FPS/60:\n MORTE.play()\n elif contador_fim_de_jogo > FPS * 3:\n MUSICA_FIM.play()\n return True, jogador.pontuacao\n else:\n continue\n\n # Subida de nivel, Velocidade do Inimigo, do Laser e do Meteoro\n if jogador.pontuacao >= pontuacao_limite:\n if nivel < 3:\n if nivel == 1:\n velocidade_meteoro += 1\n pontuacao_limite = 1800\n if nivel == 2:\n pontuacao_limite = 3000\n velocidade_inimigo += 0.5\n velocidade_laser += 0.5\n elif nivel == 3:\n pontuacao_limite = 4000\n velocidade_inimigo += 0.5\n velocidade_laser += 0.5\n velocidade_meteoro += 1\n elif nivel == 4:\n pontuacao_limite = 5000\n velocidade_inimigo += 0.5\n velocidade_laser += 0.5\n elif nivel > 4:\n pontuacao_limite += pontuacao_limite * 0.3\n velocidade_inimigo += 1\n velocidade_laser += 1\n velocidade_meteoro += 1\n nivel += 1\n #print(\"vel inimigo:\", velocidade_inimigo)\n\n # lógica do inimigo\n if len(inimigos) == 0:\n onda_de_inimigos += 3\n\n for i in range(onda_de_inimigos):\n inimigo = Inimigo(random.randrange(50, largura - 128),\n random.randrange(-8000 * (nivel / 5), -128),\n str(random.randrange(1, 4)), altura, largura, saude) # ver depois sobre o -1500\n inimigos.append(inimigo)\n\n for inimigo in inimigos[:]:\n inimigo.movimentar(velocidade_inimigo, inimigos)\n\n if random.randrange(0, 4 * FPS) == 1:\n lasers_inimigos.append(Atirar.atirar(inimigo, altura, largura))\n\n if inimigo.colisao(jogador, inimigos):\n COLIDIU.play()\n if not ativar_escudo:\n jogador.dano(15)\n\n # lógica de criação, remoção e movimento dos meteoros\n if len(meteoros) == 0:\n meteoro = Meteoro(-110, random.randrange(0, 400), altura, largura, METEORO)\n meteoros.append(meteoro)\n if nivel > 1:\n meteoro2 = Meteoro(-510, random.randrange(-300, 250), altura, largura, METEORO)\n meteoros.append(meteoro2)\n if nivel > 2:\n meteoro3 = Meteoro(-910, random.randrange(-600, -50), altura, largura, METEORO)\n meteoros.append(meteoro3)\n if nivel > 3:\n meteoro4 = Meteoro(-1310, random.randrange(-1100, -400), altura, largura, METEORO)\n meteoros.append(meteoro4)\n if nivel > 4:\n meteoro6 = Meteoro(-1710, random.randrange(-1150, -550), altura, largura, METEORO)\n meteoros.append(meteoro6)\n if nivel > 5:\n meteoro5 = Meteoro(-1110, random.randrange(-700, -150), altura, largura, METEORO)\n meteoros.append(meteoro5)\n if nivel > 6:\n meteoro7 = Meteoro(-710, random.randrange(-600, 0), altura, largura, METEORO)\n meteoros.append(meteoro7)\n\n for meteoro in meteoros[:]:\n meteoro.movimentar(velocidade_meteoro, meteoros)\n\n if meteoro.colisao(jogador, meteoros):\n COLIDIU.play()\n if not ativar_escudo:\n jogador.dano(15)\n\n #logica boost\n if len(boosts) == 0:\n if random.randrange(0, 5000) == 9:\n onda_de_boost += 1\n\n for i in range(onda_de_boost):\n if random.randrange(0, 5000) == 9:\n boost = Boost(-110, random.randrange(0, 400), altura, largura, BOOST)\n boosts.append(boost)\n\n for boost in boosts[:]:\n boost.movimentar(velocidade_boost, boosts)\n\n if boost.colisao(jogador, boosts):\n jogador.inc_pontuacao(1000)\n\n #lógica do escudo\n if len(escudos) == 0:\n onda_de_escudos += 1\n\n for i in range(onda_de_escudos):\n if random.randrange(0, 5000) == 9:\n escudo = Escudo(random.randrange(50, largura - 128), random.randrange(-8000 * (nivel / 5), -128), altura, largura, ESCUDO) # ver depois sobre o -1500\n escudos.append(escudo)\n\n for escudo in escudos[:]:\n escudo.movimentar(velocidade_escudo, escudos)\n\n if escudo.colisao(jogador, escudos):\n pygame.time.set_timer(TIMERESCUDO, 4000)\n ativar_escudo = True\n\n #lógica da vida\n if len(vidas_a_captar) == 0:\n onda_de_vida += 1\n\n for _ in range(onda_de_vida):\n if random.randrange(0, 5000) == 9:\n vida = Vida(random.randrange(50, largura - 128), random.randrange(-8000 * (nivel / 5), -128), altura, largura, VIDA) # ver depois sobre o -1500\n vidas_a_captar.append(vida)\n\n for vida in vidas_a_captar[:]:\n vida.movimentar(velocidade_vida, vidas_a_captar)\n\n if vida.colisao(jogador, vidas_a_captar):\n if jogador.saude + 10 <= 90:\n jogador.saude += 10\n elif jogador.saude + 10 > 90 and jogador.saude + 10 < 100:\n jogador.saude = 100\n\n # EVENTOS\n # vai passar por todos os eventos que ocorreram, 60 vezes por segundo\n for event in pygame.event.get():\n if event.type == pygame.QUIT: # se clicar no botão de fechar, o while se encerra, ou seja, o jogo fecha\n quit()\n \n if event.type == pygame.MOUSEBUTTONDOWN:\n if not resfriamento_laser:\n lasers_jogador.append(Atirar.atirar(jogador, altura, largura))\n resfriamento_laser = 1\n \n if event.type == tempo:\n jogador.inc_pontuacao(10)\n\n if event.type == TIMERESCUDO:\n pygame.time.set_timer(TIMERESCUDO, 0)\n ativar_escudo = False\n\n mouse = pygame.mouse.get_pos()\n click = pygame.mouse.get_pressed()\n \n if 632 > mouse[0] > 593 and 629 > mouse[1] > 569:\n if click[0] == 1:\n return True, jogador.pontuacao\n\n teclas = pygame.key.get_pressed() # retorna um dicioonário de todas as teclas e diz se estão pressionadas ou não\n\n # movimentos do jogador de acordo com a tecla pressionada\n parado = True\n if pygame.KEYDOWN:\n if teclas[pygame.K_a] and jogador.x - movimento_jogador > 0: # esquerda\n parado =jogador.movimentar(-movimento_jogador, 0)\n if teclas[pygame.K_d] and jogador.x + movimento_jogador + jogador.get_width() < largura: # direita\n parado =jogador.movimentar(movimento_jogador, 0)\n if teclas[pygame.K_w] and jogador.y - movimento_jogador > 0: # cima\n parado =jogador.movimentar(0, -movimento_jogador)\n if teclas[\n pygame.K_s] and jogador.y + movimento_jogador + jogador.get_height() + 2 * height_barra < altura: # baixo\n parado = jogador.movimentar(0, movimento_jogador)\n\n for laser_inimigo in lasers_inimigos:\n laser_inimigo.movimentar(velocidade_laser, lasers_inimigos)\n if laser_inimigo.colisao(jogador, lasers_inimigos):\n # Definir o som de quando o jogador tomar um dano de laser\n if not ativar_escudo:\n jogador.dano(15)\n\n # Resfriamento Laser\n if resfriamento_laser >= tempo_resfriamento:\n resfriamento_laser = 0\n elif resfriamento_laser > 0:\n resfriamento_laser += 1\n\n for laser_jogador in lasers_jogador:\n laser_jogador.movimentar(-velocidade_laser, lasers_jogador)\n for inimigo in inimigos:\n if laser_jogador.colisao(inimigo, lasers_jogador):\n EXPLODIU.play()\n jogador.inc_pontuacao(100)\n try:\n inimigos.remove(inimigo)\n except ValueError:\n print(\"ValueError\")\n pass\n for meteoro in meteoros:\n laser_jogador.colisao(meteoro, lasers_jogador)", "sub_path": "Jogo/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 14309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.init", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.USEREVENT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.time.set_timer", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.USEREVENT", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.time.set_timer", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 75, "usage_type": "attribute"}, {"api_name": "jogador.Jogador", "line_number": 90, "usage_type": "call"}, {"api_name": "jogador.pontuacao", "line_number": 107, "usage_type": "attribute"}, {"api_name": "inimigo.desenhar", "line_number": 114, "usage_type": "call"}, {"api_name": "meteoro.desenhar", "line_number": 117, "usage_type": "call"}, {"api_name": "boost.desenhar", "line_number": 126, "usage_type": "call"}, {"api_name": "escudo.desenhar", "line_number": 129, "usage_type": "call"}, {"api_name": "vida.desenhar", "line_number": 132, "usage_type": "call"}, {"api_name": "jogador.desenhar_escudo", "line_number": 135, "usage_type": "call"}, {"api_name": "jogador.desenhar", "line_number": 137, "usage_type": "call"}, {"api_name": "pygame.mixer.music.stop", "line_number": 140, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 140, "usage_type": "attribute"}, {"api_name": "jogador.x", "line_number": 141, "usage_type": "attribute"}, {"api_name": "jogador.y", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 146, "usage_type": "attribute"}, {"api_name": "jogador.saude", "line_number": 157, "usage_type": "attribute"}, {"api_name": "jogador.saude", "line_number": 159, "usage_type": "attribute"}, {"api_name": "jogador.pontuacao", "line_number": 173, "usage_type": "attribute"}, {"api_name": "jogador.pontuacao", "line_number": 178, "usage_type": "attribute"}, {"api_name": "inimigo.Inimigo", "line_number": 209, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 209, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 210, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 211, "usage_type": "call"}, {"api_name": "inimigo.movimentar", "line_number": 215, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 217, "usage_type": "call"}, {"api_name": "atirar.Atirar.atirar", "line_number": 218, "usage_type": "call"}, {"api_name": "atirar.Atirar", "line_number": 218, "usage_type": "name"}, {"api_name": "inimigo.colisao", "line_number": 220, "usage_type": "call"}, {"api_name": "jogador.dano", "line_number": 223, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 227, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 227, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 230, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 230, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 233, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 233, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 236, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 236, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 239, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 239, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 242, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 242, "usage_type": "call"}, {"api_name": "meteoro.Meteoro", "line_number": 245, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 245, "usage_type": "call"}, {"api_name": "meteoro.movimentar", "line_number": 249, "usage_type": "call"}, {"api_name": "meteoro.colisao", "line_number": 251, "usage_type": "call"}, {"api_name": "jogador.dano", "line_number": 254, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 258, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 262, "usage_type": "call"}, {"api_name": "boost.Boost", "line_number": 263, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 263, "usage_type": "call"}, {"api_name": "boost.movimentar", "line_number": 267, "usage_type": "call"}, {"api_name": "boost.colisao", "line_number": 269, "usage_type": "call"}, {"api_name": "jogador.inc_pontuacao", "line_number": 270, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 277, "usage_type": "call"}, {"api_name": "escudo.Escudo", "line_number": 278, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 278, "usage_type": "call"}, {"api_name": "escudo.movimentar", "line_number": 282, "usage_type": "call"}, {"api_name": "escudo.colisao", "line_number": 284, "usage_type": "call"}, {"api_name": "pygame.time.set_timer", "line_number": 285, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 285, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 293, "usage_type": "call"}, {"api_name": "vida.Vida", "line_number": 294, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 294, "usage_type": "call"}, {"api_name": "vida.movimentar", "line_number": 298, "usage_type": "call"}, {"api_name": "vida.colisao", "line_number": 300, "usage_type": "call"}, {"api_name": "jogador.saude", "line_number": 301, "usage_type": "attribute"}, {"api_name": "jogador.saude", "line_number": 302, "usage_type": "attribute"}, {"api_name": "jogador.saude", "line_number": 303, "usage_type": "attribute"}, {"api_name": "jogador.saude", "line_number": 304, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 308, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 308, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 312, "usage_type": "attribute"}, {"api_name": "atirar.Atirar.atirar", "line_number": 314, "usage_type": "call"}, {"api_name": "atirar.Atirar", "line_number": 314, "usage_type": "name"}, {"api_name": "jogador.inc_pontuacao", "line_number": 318, "usage_type": "call"}, {"api_name": "pygame.time.set_timer", "line_number": 321, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 321, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 324, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 324, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 325, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 325, "usage_type": "attribute"}, {"api_name": "jogador.pontuacao", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 331, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 331, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 335, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 336, "usage_type": "attribute"}, {"api_name": "jogador.x", "line_number": 336, "usage_type": "attribute"}, {"api_name": "jogador.movimentar", "line_number": 337, "usage_type": "call"}, {"api_name": "pygame.K_d", "line_number": 338, "usage_type": "attribute"}, {"api_name": "jogador.x", "line_number": 338, "usage_type": "attribute"}, {"api_name": "jogador.get_width", "line_number": 338, "usage_type": "call"}, {"api_name": "jogador.movimentar", "line_number": 339, "usage_type": "call"}, {"api_name": "pygame.K_w", "line_number": 340, "usage_type": "attribute"}, {"api_name": "jogador.y", "line_number": 340, "usage_type": "attribute"}, {"api_name": "jogador.movimentar", "line_number": 341, "usage_type": "call"}, {"api_name": "pygame.K_s", "line_number": 343, "usage_type": "attribute"}, {"api_name": "jogador.y", "line_number": 343, "usage_type": "attribute"}, {"api_name": "jogador.get_height", "line_number": 343, "usage_type": "call"}, {"api_name": "jogador.movimentar", "line_number": 344, "usage_type": "call"}, {"api_name": "jogador.dano", "line_number": 351, "usage_type": "call"}, {"api_name": "jogador.inc_pontuacao", "line_number": 364, "usage_type": "call"}]} +{"seq_id": "259637865", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport time\nfrom collections import namedtuple\nimport logging\nimport threading\nfrom pathlib import Path\nimport socket\nimport serial\nfrom bluetooth import *\nimport errno\n\n\"\"\"\nPython Numeric Broadcast Protocol\n\nThis module implements HP Tuners / Track Addict Numeric Broadcast Protocol\n\nWiFI Implementation\n\"\"\"\n\n__version__ = '0.0.24'\nhome = str(Path.home())\n\nNbpKPI = namedtuple('NbpKPI', 'name, unit, value')\nNbpPayload = namedtuple('NbpPayload', 'timestamp, packettype, nbpkpilist')\n\nlogger = logging.getLogger('pynbp')\nfh = logging.FileHandler('{0}/pynbp.log'.format(home))\nfh.setLevel(logging.DEBUG)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s\\n%(message)s')\nfh.setFormatter(formatter)\nlogger.addHandler(fh)\nlogging.basicConfig(level=logging.WARNING)\n\n\nclass BasePyNBP(threading.Thread):\n def __init__(self, nbpqueue, device_name='PyNBP', protocol_version='NBP1', min_update_interval=0.2):\n self.device_name = device_name\n self.protocol_version = protocol_version\n self.last_update_time = 0\n self.packettime = 0\n self.kpis = {}\n self.nbpqueue = nbpqueue\n self.updatelist = []\n self.min_update_interval = min_update_interval\n threading.Thread.__init__(self)\n\n def run(self):\n raise NotImplemented\n\n def metedata(self):\n return str.encode(\"@NAME:{0}\\n\\n\".format(self.device_name))\n\n def _genpacket(self, type='ALL'):\n packet = \"*{0},{1},{2:.6f}\\n\".format(self.protocol_version, type, self.packettime)\n\n if self.updatelist and type != 'ALL':\n kpis = [self.kpis[k] for k in self.updatelist]\n else:\n kpis = self.kpis.values()\n\n for kpi in kpis:\n if kpi.unit:\n packet += '\"{0}\",\"{1}\":{2}\\n'.format(kpi.name, kpi.unit, kpi.value)\n else:\n packet += '\"{0}\":{1}\\n'.format(kpi.name, kpi.value)\n\n packet += \"#\\n\\n\"\n\n return str.encode(packet)\n\n\nclass PyNBP(BasePyNBP):\n def __init__(self, nbpqueue, device='/dev/rfcomm0', device_name='PyNBP', protocol_version='NBP1',\n min_update_interval=0.2):\n super().__init__(nbpqueue, device_name=device_name, protocol_version=protocol_version,\n min_update_interval=min_update_interval)\n self.device = device\n\n def run(self):\n connected = False\n serport = None\n\n while True:\n nbppayload = self.nbpqueue.get()\n\n self.packettime = nbppayload.timestamp\n\n for kpi in nbppayload.nbpkpilist:\n if kpi.name not in self.updatelist:\n self.updatelist.append(kpi.name)\n self.kpis[kpi.name] = kpi\n\n if not connected:\n try:\n serport = serial.serial_for_url(self.device)\n connected = True\n except:\n logging.info('Comm Port conection not open - waiting for connection')\n\n if connected and serport.is_open:\n try:\n if serport.in_waiting > 0:\n logger.info(serport.read(serport.in_waiting).decode())\n\n if time.time() - self.last_update_time > self.min_update_interval:\n if nbppayload.packettype == 'UPDATE':\n nbppacket = self._genpacket(type=nbppayload.packettype)\n elif nbppayload.packettype == 'ALL':\n nbppacket = self._genpacket(type=nbppayload.packettype)\n elif nbppayload.packettype == 'METADATA':\n nbppacket = self.metedata()\n else:\n logging.info('Invalid packet type {0}.'.format(nbppayload.packettype))\n\n logger.warning(nbppacket.decode())\n\n serport.write(nbppacket)\n self.updatelist = []\n self.last_update_time = time.time()\n\n except:\n logging.exception('Serial Write Failed. Closing port.')\n serport.close()\n connected = False\n\n\nclass WifiPyNBP(BasePyNBP):\n def __init__(self, nbpqueue, ip='127.0.0.1', port=35000, device_name='PyNBP', protocol_version='NBP1',\n min_update_interval=0.2):\n super().__init__(nbpqueue, device_name=device_name, protocol_version=protocol_version,\n min_update_interval=min_update_interval)\n self.ip = ip\n self.port = port\n\n def run(self):\n connected = False\n serport = None\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.settimeout(1.0)\n logging.warning('Binding to {0}:{1}'.format(self.ip, self.port))\n sock.bind((self.ip, self.port))\n sock.listen(1)\n\n while True:\n nbppayload = self.nbpqueue.get()\n\n self.packettime = nbppayload.timestamp\n\n for kpi in nbppayload.nbpkpilist:\n if kpi.name not in self.updatelist:\n self.updatelist.append(kpi.name)\n self.kpis[kpi.name] = kpi\n\n if not connected:\n try:\n conn, client_address = sock.accept()\n connected = True\n logging.warning('Connection from {0} open'.format(client_address))\n except:\n logging.info('Socket conection not open - waiting for connection')\n\n if connected:\n try:\n data = conn.recv(1024)\n if data:\n text = data.decode().strip()\n logger.info(text)\n if text == \"!ALL\":\n logging.warning('ALL Packet Requested. Sending')\n conn.sendall(self._genpacket('ALL'))\n\n if time.time() - self.last_update_time > self.min_update_interval:\n if nbppayload.packettype == 'UPDATE':\n nbppacket = self._genpacket(type=nbppayload.packettype)\n elif nbppayload.packettype == 'ALL':\n nbppacket = self._genpacket(type=nbppayload.packettype)\n elif nbppayload.packettype == 'METADATA':\n nbppacket = self.metedata()\n else:\n logging.info('Invalid packet type {0}.'.format(nbppayload.packettype))\n\n logger.warning(nbppacket.decode())\n\n conn.sendall(nbppacket)\n self.updatelist = []\n self.last_update_time = time.time()\n\n except:\n logging.exception('Wifi Write Failed. Closing port.')\n conn.close()\n connected = False\n\nclass BTPyNBP(BasePyNBP):\n def __init__(self, nbpqueue, device_name='PyNBP', protocol_version='NBP1',\n min_update_interval=0.2, loglevel=logging.WARNING):\n logging.basicConfig(level=loglevel)\n super().__init__(nbpqueue, device_name=device_name, protocol_version=protocol_version,\n min_update_interval=min_update_interval)\n\n def run(self):\n connected = False\n serport = None\n sock = BluetoothSocket( RFCOMM )\n sock.settimeout(1.0)\n sock.setblocking(False)\n # logging.warning('Binding to {0}:{1}'.format(self.ip, self.port))\n sock.bind((\"\", PORT_ANY))\n sock.listen(1)\n\n port = sock.getsockname()[1]\n\n uuid = \"94f39d29-7d6d-437d-973b-fba39e49d4ee\"\n\n advertise_service(sock, \"SampleServer\",\n service_id=uuid,\n service_classes=[uuid, SERIAL_PORT_CLASS],\n profiles=[SERIAL_PORT_PROFILE],\n # protocols = [ OBEX_UUID ]\n )\n\n print(\"Waiting for connection on RFCOMM channel %d\" % port)\n\n while True:\n # logging.warning('1')\n nbppayload = self.nbpqueue.get()\n # logging.warning('2')\n self.packettime = nbppayload.timestamp\n\n for kpi in nbppayload.nbpkpilist:\n if kpi.name not in self.updatelist:\n self.updatelist.append(kpi.name)\n self.kpis[kpi.name] = kpi\n\n if not connected:\n # logging.warning('3')\n try:\n conn, client_address = sock.accept()\n conn.setblocking(False)\n connected = True\n logging.warning('Connection from {0} open'.format(client_address))\n except:\n logging.info('Socket conection not open - waiting for connection')\n\n if connected:\n # logging.warning('4')\n try:\n data = conn.recv(1024)\n except BluetoothError as e:\n pass\n # err, msg = e.args[0]\n # if err == errno.EAGAIN or err == errno.EWOULDBLOCK:\n # logging.warning('no data received...')\n # pass\n # else:\n # logging.exception('some bullshit happened')\n # logging.exception('err is {}'.format(err))\n # raise\n else:\n text = data.decode().strip()\n logging.info(text)\n if text == \"!ALL\":\n logging.info('ALL Packet Requested. Sending')\n conn.sendall(self._genpacket('ALL'))\n # logging.warning('5')\n try:\n if time.time() - self.last_update_time > self.min_update_interval:\n if nbppayload.packettype == 'UPDATE':\n nbppacket = self._genpacket(type=nbppayload.packettype)\n elif nbppayload.packettype == 'ALL':\n nbppacket = self._genpacket(type=nbppayload.packettype)\n elif nbppayload.packettype == 'METADATA':\n nbppacket = self.metedata()\n else:\n logging.warning('Invalid packet type {0}.'.format(nbppayload.packettype))\n\n logging.info(nbppacket.decode())\n\n conn.sendall(nbppacket)\n self.updatelist = []\n self.last_update_time = time.time()\n else:\n logging.info('not enough time has passed..')\n\n except:\n logging.warning('Bluetooth Write Failed. Closing port.')\n conn.close()\n connected = False\n", "sub_path": "pynbp/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 11026, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pathlib.Path.home", "line_number": 22, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 24, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 33, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 36, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 46, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 46, "usage_type": "attribute"}, {"api_name": "serial.serial_for_url", "line_number": 96, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 114, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 123, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 139, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 139, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 139, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 141, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 159, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 170, "usage_type": "call"}, {"api_name": "time.time", "line_number": 173, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 181, "usage_type": "call"}, {"api_name": "time.time", "line_number": 187, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 190, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 196, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 197, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 241, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 243, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 261, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 263, "usage_type": "call"}, {"api_name": "time.time", "line_number": 267, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 275, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 277, "usage_type": "call"}, {"api_name": "time.time", "line_number": 281, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 283, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "191673327", "text": "import sys\nimport numpy as np\nfrom imageio import imread, imwrite\nfrom scipy.ndimage.filters import convolve\n\n# tqdm is not strictly necessary, but it gives us a pretty progress bar\n# to visualize progress.\nfrom tqdm import trange\n\ndef calc_energy(img):\n\n filter_du = np.array([\n [1.0, 2.0, 1.0],\n [0.0, 0.0, 0.0],\n [-1.0, -2.0, -1.0],\n ])\n # Dado que jpg tiene tres canales replicamos la matriz 1D por una 3D para cada canal\n filter_du = np.stack([filter_du] * 3, axis=2)\n \n filter_dv = np.array([\n [1.0, 0.0, -1.0],\n [2.0, 0.0, -2.0],\n [1.0, 0.0, -1.0],\n ])\n # Dado que jpg tiene tres canales replicamos la matriz 1D por una 3D para cada canal\n filter_dv = np.stack([filter_dv] * 3, axis=2)\n\n img = img.astype('float32')\n \n convolved = np.absolute(convolve(img, filter_du)) + np.absolute(convolve(img, filter_dv))\n #sumamos las energias de los canales red, green and blue\n energy_map = convolved.sum(axis=2)\n \n return energy_map\n \n \ndef main():\n\n in_filename = sys.argv[1]\n out_filename = sys.argv[2]\n\n img = imread(in_filename)\n out = calc_energy(img)\n print(out)\n print(out.shape)\n imwrite(out_filename, out)\n\nif __name__ == '__main__':\n main()", "sub_path": "proyecto/codigo/seam_carving_jpg/imageColor_energy.py", "file_name": "imageColor_energy.py", "file_ext": "py", "file_size_in_byte": 1268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.convolve", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "imageio.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "imageio.imwrite", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "335467616", "text": "\n\nimport os, math\nfrom PIL import Image\n \n \ndef circle():\n ima = Image.open(\"/Users/luxutao/Downloads/a.jpg\").convert(\"RGBA\")\n # ima = ima.resize((200, 200), Image.ANTIALIAS)\n size = ima.size\n print(size)\n \n # 因为是要圆形，所以需要正方形的图片\n r2 = min(size[0], size[1])\n if size[0] != size[1]:\n ima = ima.resize((r2, r2), Image.ANTIALIAS)\n \n # 最后生成圆的半径\n r3 = int(int(r2)/2)\n imb = Image.new('RGBA', (r3*2, r3*2),(255,255,255,255))\n pima = ima.load() # 像素的访问对象\n pimb = imb.load()\n r = float(r2/2) #圆心横坐标\n \n for i in range(r2):\n for j in range(r2):\n lx = abs(i-r) #到圆心距离的横坐标\n ly = abs(j-r)#到圆心距离的纵坐标\n l = (pow(lx,2) + pow(ly,2))** 0.5 # 三角函数 半径\n \n if l < r3:\n pimb[i-(r-r3),j-(r-r3)] = pima[i,j]\n imb.save(\"/Users/luxutao/Downloads/test_circle.png\")\n \ncircle()\n", "sub_path": "circle.py", "file_name": "circle.py", "file_ext": "py", "file_size_in_byte": 988, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PIL.Image.open", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 8, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "644351709", "text": "import re\n\nimport discord\nfrom discord.ext import commands\n\nfrom cogs.utils import calc_magic\n\n\nclass MagicCog(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command(\n name='magic',\n description='Shows how much magic is needed to one shot a monster with given HP',\n usage='`monster hp` `spell attack` `args: -[alsed]`',\n )\n async def magic_command(self, ctx, hp: int, spell_attack: int, args: str = None):\n modifiers_msg = f'Spell Attack: {spell_attack}\\n'\n modifier = 1.0 * spell_attack\n\n if args:\n if re.search(r'-[^ls]*[ls][^ls]*', args): # loveless or elemental staff\n modifier *= 1.25\n modifiers_msg += f'Staff Multiplier: 1.25x\\n'\n if re.search(r'-[^e]*e[^e]*', args): # elemental advantage\n modifier *= 1.50\n modifiers_msg += f'Elemental Advantage: 1.50x\\n'\n elif re.search(r'-[^d]*d[^d]*', args): # elemental disadvantage\n modifier *= 0.50\n modifiers_msg += f'Elemental Disadvantage: 0.50x\\n'\n\n magic_msg = ''\n\n if args and re.search(r'-[^a]*a[^a]*', args): # elemental amp\n modifiers_msg += f'BW Elemental Amp: 1.30x\\n'\n modifiers_msg += f'FP/IL Elemental Amp: 1.40x\\n\\n'\n\n # F/P and I/L\n fpil_magic = calc_magic(monster_hp=hp, modifier=modifier * 1.4)\n magic_msg += f'Magic for F/P or I/L: {fpil_magic}\\n'\n\n # BW\n bw_magic = calc_magic(monster_hp=hp, modifier=modifier * 1.3)\n magic_msg += f'Magic for BW: {bw_magic}'\n else:\n magic = calc_magic(monster_hp=hp, modifier=modifier)\n magic_msg += f'\\nMagic: {magic}'\n\n embed = discord.Embed(title='Magic Calculator',\n description=f'The magic required to one shot a monster with {hp} HP')\n embed.add_field(name='Magic Required', value=magic_msg, inline=True)\n embed.add_field(name='Modifiers', value=modifiers_msg, inline=True)\n embed.set_author(name=f'{ctx.author}', icon_url=ctx.author.avatar_url)\n\n await ctx.send(embed=embed)\n\n @magic_command.error\n async def magic_error(self, ctx, error):\n if error in (commands.MissingRequiredArgument, commands.ArgumentParsingError, commands.ConversionError,\n commands.TooManyArguments, commands.UserInputError):\n return await ctx.send(\n f'Usage: {self.bot.command_prefix}{ctx.invoked_with} \\n'\n f'Args:\\n'\n f'\\t-a: Elemental Amplification\\n'\n f'\\t-l: Loveless Staff\\n'\n f'\\t-s: Elemental Staff\\n'\n f'\\t-e: Elemental Advantage\\n'\n f'\\t-d: Elemental Disadvantage\\n\\n'\n f'Example Usage: {self.bot.command_prefix}{ctx.invoked_with} 43376970 570 -al'\n )\n\n\ndef setup(bot):\n bot.add_cog(MagicCog(bot))\n", "sub_path": "cogs/magic.py", "file_name": "magic.py", "file_ext": "py", "file_size_in_byte": 3001, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "re.search", "line_number": 23, "usage_type": "call"}, {"api_name": "re.search", "line_number": 26, "usage_type": "call"}, {"api_name": "re.search", "line_number": 29, "usage_type": "call"}, {"api_name": "re.search", "line_number": 35, "usage_type": "call"}, {"api_name": "cogs.utils.calc_magic", "line_number": 40, "usage_type": "call"}, {"api_name": "cogs.utils.calc_magic", "line_number": 44, "usage_type": "call"}, {"api_name": "cogs.utils.calc_magic", "line_number": 47, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 50, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 13, "usage_type": "name"}, {"api_name": "discord.ext.commands.MissingRequiredArgument", "line_number": 60, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 60, "usage_type": "name"}, {"api_name": "discord.ext.commands.ArgumentParsingError", "line_number": 60, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.ConversionError", "line_number": 60, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.TooManyArguments", "line_number": 61, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 61, "usage_type": "name"}, {"api_name": "discord.ext.commands.UserInputError", "line_number": 61, "usage_type": "attribute"}]} +{"seq_id": "223676158", "text": "\r\nimport torch\r\nimport logging\r\nfrom torch.utils.data import DataLoader\r\nimport utils\r\nimport torch.nn.functional as F\r\n# import ctcdecode\r\nfrom itertools import groupby\r\nfrom metrics.wer import get_wer_delsubins\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom src.iterative_generate import IterativeGenerate\r\nimport ctcdecode\r\n\r\nclass Trainer(object):\r\n def __init__(self, opts, model, criterion, vocabulary, vocab_size, blank_id):\r\n self.opts = opts\r\n self.model = model\r\n self.criterion = criterion\r\n self.vocab_size = vocab_size\r\n self.blank_id = blank_id\r\n self.pad = vocabulary.pad()\r\n self.unk = vocabulary.unk()\r\n self.eos = vocabulary.eos()\r\n self.bos = vocabulary.bos()\r\n\r\n\r\n self.cuda = torch.cuda.is_available()\r\n if self.cuda:\r\n self.criterion = self.criterion.cuda()\r\n self.model = self.model.cuda()\r\n\r\n self._num_updates = 0\r\n\r\n # params = []\r\n # for params in self.model.parameters():\r\n # if params not in self.model.decoder.parameters():\r\n # params.append(params)\r\n params = list(filter(lambda p: p.requires_grad, self.model.parameters()))\r\n self.optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate,\r\n weight_decay=self.opts.weight_decay)\r\n\r\n\r\n logging.info('| num. module params: {} (num. trained: {})'.format(\r\n sum(p.numel() for p in params),\r\n sum(p.numel() for p in params if p.requires_grad),\r\n ))\r\n\r\n self.dec_generator = IterativeGenerate(vocabulary, model)\r\n\r\n # self._build_optimizer(params, self.opts.optimizer, lr=self.opts.learning_rate,\r\n # momentum=self.opts.momentum, weight_decay=self.opts.weight_decay)\r\n self.decoder_vocab = [chr(x) for x in range(20000, 20000 + self.vocab_size)]\r\n self.decoder = ctcdecode.CTCBeamDecoder(self.decoder_vocab, beam_width=self.opts.beam_width,\r\n blank_id=self.blank_id, num_processes=10)\r\n\r\n\r\n def train_step(self, samples, update_freq=10):\r\n self._set_seed() # seed is changed with the update_steps\r\n self.model.train()\r\n self.criterion.train()\r\n self.optimizer.zero_grad()\r\n\r\n samples = self._prepare_sample(samples)\r\n loss = self.criterion(self.model, samples)\r\n loss /= update_freq\r\n loss.backward()\r\n \r\n self.set_num_updates(self.get_num_updates() + 1)\r\n \r\n if (self.get_num_updates()+1) % update_freq == 0:\r\n self.optimizer.step()\r\n self.optimizer.zero_grad()\r\n \r\n return loss, self.get_num_updates()\r\n\r\n def train_decoder_step(self, samples):\r\n self._set_seed() # seed is changed with the update_steps\r\n self.model.train()\r\n self.criterion.train()\r\n self.optimizer.zero_grad()\r\n\r\n samples = self._prepare_sample(samples)\r\n\r\n loss, _, _ = self.criterion.forward_decoder(self.model, samples)\r\n\r\n loss.backward()\r\n self.optimizer.step()\r\n self.optimizer.zero_grad()\r\n self.set_num_updates(self.get_num_updates() + 1)\r\n return loss, self.get_num_updates()\r\n\r\n\r\n def valid_step(self, samples, decoded_dict):\r\n with torch.no_grad():\r\n self.model.eval()\r\n self.criterion.eval()\r\n\r\n samples = self._prepare_sample(samples)\r\n video = samples[\"data\"]\r\n len_video = samples[\"len_data\"]\r\n label = samples[\"label\"]\r\n len_label = samples[\"len_label\"]\r\n video_id = samples['id']\r\n\r\n logits, len_video = self.model(video, len_video)\r\n logits = F.softmax(logits, dim=-1)\r\n pred_seq, _, _, out_seq_len = self.decoder.decode(logits, len_video)\r\n\r\n# logits = tf.transpose(tf.constant(logits.cpu().numpy()), [1, 0, 2]) # [len, batch, vocab_size]\r\n# len_video = tf.constant(len_video.cpu().numpy(), dtype=tf.int32)\r\n# decoded, _ = tf.nn.ctc_beam_search_decoder(logits, len_video, beam_width=5, top_paths=1)\r\n# pred_seq = tf.sparse.to_dense(decoded[0]).numpy() # print(pred_seq.shape, decoded[0].dense_shape)\r\n \r\n \r\n err_delsubins = np.zeros([4])\r\n count = 0\r\n correct = 0\r\n start = 0\r\n for i, length in enumerate(len_label):\r\n end = start + length\r\n ref = label[start:end].tolist()\r\n# hyp = [x for x in pred_seq[i] if x != 0]\r\n hyp = [x[0] for x in groupby(pred_seq[i][0][:out_seq_len[i][0]].tolist())]\r\n# if i== 0:\r\n# if len(hyp) == 0:\r\n# logging.info(\"Here hyp is None!!!!\")\r\n# logging.info(\"video id: {}\".format(video_id[i]))\r\n# logging.info(\"ref: {}\".format(\" \".join(str(i) for i in ref)))\r\n# logging.info(\"hyp: {}\".format(\" \".join(str(i) for i in hyp)))\r\n\r\n# logging.info(\"\\n\")\r\n decoded_dict[video_id[i]] = hyp\r\n correct += int(ref == hyp)\r\n err = get_wer_delsubins(ref, hyp)\r\n err_delsubins += np.array(err)\r\n count += 1\r\n start = end\r\n assert end == label.size(0)\r\n return err_delsubins, correct, count\r\n\r\n def valid_step_tf(self, samples, decoded_dict):\r\n with torch.no_grad():\r\n self.model.eval()\r\n self.criterion.eval()\r\n\r\n samples = self._prepare_sample(samples)\r\n video = samples[\"data\"]\r\n len_video = samples[\"len_data\"]\r\n label = samples[\"label\"]\r\n len_label = samples[\"len_label\"]\r\n video_id = samples['id']\r\n\r\n logits, len_video = self.model(video, len_video)\r\n logits = F.softmax(logits, dim=-1)\r\n# pred_seq, _, _, out_seq_len = self.decoder.decode(logits, len_video)\r\n\r\n logits = tf.transpose(tf.constant(logits.cpu().numpy()), [1, 0, 2]) # [len, batch, vocab_size]\r\n len_video = tf.constant(len_video.cpu().numpy(), dtype=tf.int32)\r\n decoded, _ = tf.nn.ctc_beam_search_decoder(logits, len_video, beam_width=5, top_paths=1)\r\n pred_seq = tf.sparse.to_dense(decoded[0]).numpy() # print(pred_seq.shape, decoded[0].dense_shape)\r\n \r\n \r\n err_delsubins = np.zeros([4])\r\n count = 0\r\n correct = 0\r\n start = 0\r\n for i, length in enumerate(len_label):\r\n end = start + length\r\n ref = label[start:end].tolist()\r\n hyp = [x for x in pred_seq[i] if x != 0]\r\n # hyp = [x[0] for x in groupby(pred_seq[i][0][:out_seq_len[i][0]].tolist())]\r\n# if i== 0:\r\n# if len(hyp) == 0:\r\n# logging.info(\"Here hyp is None!!!!\")\r\n# logging.info(\"video id: {}\".format(video_id[i]))\r\n# logging.info(\"ref: {}\".format(\" \".join(str(i) for i in ref)))\r\n# logging.info(\"hyp: {}\".format(\" \".join(str(i) for i in hyp)))\r\n\r\n# logging.info(\"\\n\")\r\n decoded_dict[video_id[i]] = hyp\r\n correct += int(ref == hyp)\r\n err = get_wer_delsubins(ref, hyp)\r\n err_delsubins += np.array(err)\r\n count += 1\r\n start = end\r\n assert end == label.size(0)\r\n return err_delsubins, correct, count\r\n\r\n \r\n \r\n def valid_decoder_step(self, samples, decoded_dict):\r\n with torch.no_grad():\r\n self.model.eval()\r\n self.criterion.eval()\r\n\r\n samples = self._prepare_sample(samples)\r\n video = samples[\"data\"]\r\n len_video = samples[\"len_data\"]\r\n label = samples[\"label\"]\r\n len_label = samples[\"len_label\"]\r\n video_id = samples['id']\r\n\r\n err_delsubins = np.zeros([4])\r\n count = 0\r\n correct = 0\r\n start = 0\r\n hypos = self.dec_generator.generate(video, len_video)\r\n for i, length in enumerate(len_label):\r\n end = start + length\r\n ref = label[start:end].tolist()\r\n # for j, hypo in enumerate(hypos[i][0]): # 这里只有1个，beam_size=1\r\n hyp = self.post_process_prediction(hypos[i][0][\"tokens\"])\r\n\r\n# if i == 0:\r\n# if len(hyp) == 0:\r\n# logging.info(\"Here hyp is None!!!!\")\r\n# logging.info(\"video id: {}\".format(video_id[i]))\r\n# logging.info(\"ref: {}\".format(\" \".join(str(i) for i in ref)))\r\n# logging.info(\"hyp: {}\".format(\" \".join(str(i) for i in hyp)))\r\n\r\n# logging.info(\"\\n\")\r\n decoded_dict[video_id[i]] = hyp\r\n correct += int(ref == hyp)\r\n err = get_wer_delsubins(ref, hyp)\r\n err_delsubins += np.array(err)\r\n count += 1\r\n start = end\r\n assert end == label.size(0)\r\n return err_delsubins, correct, count\r\n\r\n def get_batch_iterator(self, datasets, batch_size, shuffle, num_workers=8, drop_last=False):\r\n return DataLoader(datasets,\r\n batch_size=batch_size,\r\n shuffle=shuffle,\r\n num_workers=num_workers,\r\n collate_fn=datasets.collate_fn_video,\r\n drop_last=drop_last)\r\n\r\n\r\n def save_checkpoint(self, filename, epoch, num_updates, loss):\r\n state_dict = {\r\n 'epoch': epoch,\r\n 'num_updates': num_updates,\r\n 'model_state_dict': self.model.state_dict(),\r\n 'optimizer_state_dict': self.optimizer.state_dict(),\r\n \"loss\": loss,\r\n }\r\n torch.save(state_dict, filename)\r\n\r\n\r\n def load_checkpoint(self, filename):\r\n state_dict = torch.load(filename)\r\n epoch = state_dict[\"epoch\"]\r\n num_updates = state_dict[\"num_updates\"]\r\n loss = state_dict[\"loss\"]\r\n self.model.load_state_dict(state_dict[\"model_state_dict\"])\r\n self.optimizer.load_state_dict(state_dict[\"optimizer_state_dict\"])\r\n self.optimizer.load_state_dict(state_dict[\"optimizer_state_dict\"])\r\n return epoch, num_updates, loss\r\n\r\n\r\n def get_num_updates(self):\r\n \"\"\"Get the number of parameters updates.\"\"\"\r\n return self._num_updates\r\n\r\n def set_num_updates(self, num_updates):\r\n \"\"\"Set the number of parameters updates.\"\"\"\r\n self._num_updates = num_updates\r\n\r\n def _set_seed(self):\r\n # Set seed based on opts.seed and the update number so that we get\r\n # reproducible results when resuming from checkpoints\r\n seed = self.opts.seed + self.get_num_updates()\r\n torch.manual_seed(seed)\r\n if self.cuda:\r\n torch.cuda.manual_seed(seed)\r\n\r\n def _prepare_sample(self, sample):\r\n if sample is None or len(sample) == 0:\r\n return None\r\n\r\n if self.cuda:\r\n sample = utils.move_to_cuda(sample)\r\n \r\n return sample\r\n \r\n def adjust_learning_rate(self, epoch):\r\n \"\"\"Sets the learning rate to the initial LR decayed by 10 every 10 epochs\"\"\"\r\n if epoch <= 10:\r\n lr = self.opts.learning_rate\r\n elif epoch <= 20:\r\n lr = self.opts.learning_rate * 0.1\r\n else:\r\n lr = self.opts.learning_rate * 0.05\r\n for param_group in self.optimizer.param_groups:\r\n param_group['lr'] = lr\r\n \r\n def dynamic_freeze_layers(self, epoch):\r\n \r\n if epoch <= self.opts.stage_epoch:\r\n for p in self.model.decoder.parameters():\r\n p.requires_grad = False\r\n params = list(filter(lambda p: p.requires_grad, self.model.parameters()))\r\n if epoch == 1:\r\n logging.info('| num. ctc module params: {} (num. trained: {})'.format(\r\n sum(p.numel() for p in params),\r\n sum(p.numel() for p in params if p.requires_grad)))\r\n else:\r\n if self.opts.train_cnn_in_decoder and self.opts.ctc_weight > 0.0:\r\n for p in self.model.parameters():\r\n p.requires_grad = True\r\n else:\r\n for p in self.model.parameters():\r\n p.requires_grad = False\r\n for p in self.model.decoder.parameters():\r\n p.requires_grad = True\r\n\r\n params = list(filter(lambda p: p.requires_grad, self.model.parameters()))\r\n # if epoch == self.opts.stage_epoch + 1:\r\n logging.info('| num. module params: {} (num. trained: {})'.format(\r\n sum(p.numel() for p in params),\r\n sum(p.numel() for p in params if p.requires_grad)))\r\n\r\n def post_process_prediction(self, tensor):\r\n return [x.item() for x in tensor if x not in [self.pad, self.unk, self.bos, self.eos]]", "sub_path": "src/trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 13276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.cuda.is_available", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "src.iterative_generate.IterativeGenerate", "line_number": 49, "usage_type": "call"}, {"api_name": "ctcdecode.CTCBeamDecoder", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 116, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 124, "usage_type": "call"}, {"api_name": "metrics.wer.get_wer_delsubins", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 155, "usage_type": "name"}, {"api_name": "tensorflow.transpose", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.ctc_beam_search_decoder", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tensorflow.sparse.to_dense", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.sparse", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 164, "usage_type": "call"}, {"api_name": "metrics.wer.get_wer_delsubins", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 204, "usage_type": "call"}, {"api_name": "metrics.wer.get_wer_delsubins", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 277, "usage_type": "attribute"}, {"api_name": "utils.move_to_cuda", "line_number": 284, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 306, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 321, "usage_type": "call"}]} +{"seq_id": "562962931", "text": "import cv2\nimport time\nimport os\nfrom moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\nimport pympi\nimport re\nfrom fnmatch import fnmatch\nimport argparse\n\n\ndef video_to_frames(input_loc, output_loc):\n \"\"\"\n Function to extract frames from a video.\n Specify: path to video and output path\n \"\"\"\n try:\n os.mkdir(output_loc)\n except OSError:\n pass\n # Log the time\n time_start = time.time()\n # Start capturing the feed\n cap = cv2.VideoCapture(input_loc)\n # Find the number of frames\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) -1\n print(video_length)\n print (\"Number of frames: \", video_length)\n count = 0\n print (\"Converting video..\\n\")\n # Start converting the video\n while cap.isOpened():\n # Extract the frame\n ret, frame = cap.read()\n # Write the results back to output location.\n resized_frame = cv2.resize(frame, (720, 480), interpolation=cv2.INTER_AREA)\n cv2.imwrite(output_loc + \"/%#05d.jpg\" % (count+1), resized_frame)\n count = count + 1\n # If there are no more frames left\n if (count > (video_length-1)):\n # Log the time again\n time_end = time.time()\n # Release the feed\n cap.release()\n # Print stats\n print (\"Done extracting frames.\\n%d frames extracted\" % count)\n print (\"It took %d seconds for conversion.\" % (time_end-time_start) + str(\"\\n\\n\"))\n break\n \n return(video_length)\n\ndef frames_to_video(dir_path, ext, output):\n \"\"\"\n Function to compile frames into a video.\n Specify directory of images, extention of images ex. png, output name followed by codec ex.mp4\n \"\"\"\n# dir_path = './Outputs/final/'\n# ext = 'png'\n# output = 'output_video.mp4'\n\n images = []\n for f in os.listdir(dir_path):\n if f.endswith(ext):\n images.append(f)\n print(len(images))\n # Determine the width and height from the first image\n image_path = os.path.join(dir_path, images[0])\n frame = cv2.imread(image_path)\n cv2.imshow('video',frame)\n height, width, channels = frame.shape\n\n # Define the codec and create VideoWriter object\n fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case\n out = cv2.VideoWriter(output, fourcc, 20.0, (width, height))\n\n for image in images:\n\n image_path = os.path.join(dir_path, image)\n frame = cv2.imread(image_path)\n\n out.write(frame) # Write out frame to video\n\n cv2.imshow('video',frame)\n if (cv2.waitKey(1) & 0xFF) == ord('q'): # Hit `q` to exit\n break\n\n # Release everything if job is finished\n out.release()\n cv2.destroyAllWindows()\n\n print(\"The output video is {}\".format(output))\n \ndef extract_videos_from_annotations(root, gloss_list):\n \"\"\"\n Function to extract videos from eaf annotations.\n Additionally it creates folders with the extracted frames for each video.\n Specify the path of the eaf file and the gloss list.\n ex. (\"./Raw_videos/original_video\",[\"NS\", \"1H\", \"2H\"])\n Make sure the eaf file has the same name as the video\n \"\"\"\n def check_folders(gl_name):\n directory1 = \"./Data/\"+gl_name+\"/Videos/\"\n if not os.path.exists(directory1):\n os.makedirs(directory1)\n\n # Find the eaf file\n cwd = os.getcwd()\n root = str(cwd)\n pattern = \"*.eaf\"\n\n\n for root, dirs, files in os.walk(root, topdown=False):\n for name in files:\n if fnmatch(name, pattern):\n video_name = re.sub('\\.eaf$', '', name)+\".mp4\"\n print(video_name)\n video_name = root+\"/\"+video_name\n file = pympi.Eaf(file_path=root+\"/\"+name)\n tier_names = file.get_tier_names()\n\n\n for tier_name in tier_names: \n annotations = file.get_annotation_data_for_tier(tier_name)\n count = 0\n for annotation in annotations:\n for gloss in gloss_list:\n if annotation[2] == gloss:\n start = annotation[0]\n end = annotation[1]\n print(start/1000,end/1000)\n check_folders(gloss)\n ffmpeg_extract_subclip(video_name, start/1000, end/1000, targetname=\"Data/\"+str(gloss)+\"/Videos/\"+\"%#05d.mp4\" % (count+1))\n # Comment next line if you don't want to extract the frames for each video\n video_to_frames(\"Data/\"+str(gloss)+\"/Videos/\"+\"%#05d.mp4\" % (count+1), \"Data/\"+str(gloss)+\"/\"+\"%#05d\" % (count+1) )\n count = count+1\n if count == 0:\n print(\"No annotation found with this name\")\n\ndef extract_videos_from_annotations_colab(video_name, eaf_file_name, gloss_list):\n \"\"\"\n Function to extract videos from eaf annotations.\n Additionally it creates folders with the extracted frames for each video.\n \"\"\"\n def check_folders(gl_name):\n directory1 = \"openpose/\"+gl_name+\"/\"\n if not os.path.exists(directory1):\n os.makedirs(directory1)\n\n\n file = pympi.Eaf(file_path=eaf_file_name)\n tier_names = file.get_tier_names()\n\n\n for tier_name in tier_names: \n annotations = file.get_annotation_data_for_tier(tier_name)\n count = 0\n for annotation in annotations:\n for gloss in gloss_list:\n if annotation[2] == gloss:\n start = annotation[0]\n end = annotation[1]\n print(start/1000,end/1000)\n check_folders(gloss)\n ffmpeg_extract_subclip(video_name, start/1000, end/1000, targetname=\"openpose/\"+str(gloss)+\"/\"+\"%#05d.mp4\" % (count+1))\n # Comment next line if you don't want to extract the frames for each video\n # video_to_frames(\"Data/\"+str(gloss)+\"/Videos/\"+\"%#05d.mp4\" % (count+1), \"Data/\"+str(gloss)+\"/\"+\"%#05d\" % (count+1) )\n count = count+1\n if count == 0:\n print(\"No annotation found with this name\")", "sub_path": "video_utils.py", "file_name": "video_utils.py", "file_ext": "py", "file_size_in_byte": 6290, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 36, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 73, "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": "cv2.imread", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 103, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 106, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 111, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 113, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 114, "usage_type": "call"}, {"api_name": "pympi.Eaf", "line_number": 117, "usage_type": "call"}, {"api_name": "moviepy.video.io.ffmpeg_tools.ffmpeg_extract_subclip", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 146, "usage_type": "call"}, {"api_name": "pympi.Eaf", "line_number": 149, "usage_type": "call"}, {"api_name": "moviepy.video.io.ffmpeg_tools.ffmpeg_extract_subclip", "line_number": 163, "usage_type": "call"}]} +{"seq_id": "523436530", "text": "# pylint:disable=missing-docstring,line-too-long,redefined-outer-name\nfrom urlparse import urljoin\nfrom datetime import date, datetime, timedelta\nfrom scrapy import Spider, Request\nfrom spiders.others.fed.eia.items import EIAItem\nfrom dateutil import parser\n\n__author__ = \"Talha Ashraf\"\n__modified__ = \"Darren Wickham\"\n\n\nclass WhatsNew(Spider):\n name = \"eia-whats-new\"\n start_urls = [\"http://www.eia.gov/about/new/index.cfm?r=0\"]\n\n download_delay = 0.5\n\n date_until = date(2011, 1, 2)\n total_days = (date.today() - timedelta(days=30)) - date_until\n total_days = total_days.days\n count_days = 0\n\n def parse(self, response):\n yield Request(response.url, callback=parse_latest)\n yield Request(response.url, callback=self.parse_old, dont_filter=True)\n\n def parse_old(self, response):\n urls = response.css('.main_col').xpath(\n \"h2[count(a) < 2]\").css(\"a::attr(href)\").extract()\n titles = [\"\".join(title.css(\"a *::text\").extract())\n for title in response.css('.main_col').xpath(\"h2[count(a) < 2]\")]\n dates = response.css(\n '.main_col > p.dat > i:first-child::text').extract()\n summaries = [\"\".join(dat.xpath(\"text()\").extract())\n for dat in response.css('.main_col > p.dat')]\n\n for url, title, date, summary in zip(urls, titles, dates, summaries):\n if self.date_until <= format_date(date):\n item = EIAItem()\n item[\"url\"] = urljoin(response.url, url)\n item[\"title\"] = title.strip()\n item[\"publishdate\"] = date.strip()\n item[\"summary\"] = summary.strip()\n item[\"type\"] = \"What's New\"\n yield item\n\n if self.count_days < self.total_days:\n self.count_days += 30\n url = urljoin(response.url,\n \"index.cfm?r=%d\" % self.count_days)\n yield Request(url, callback=self.parse_old)\n\ndef parse_latest(response):\n latest_urls = response.css(\"div.feature h2 a::attr(href)\").extract()\n latest_titles = response.css(\"div.feature h2 ::text\").extract()\n latest_dates = response.css(\"div.feature p.dat ::text\").extract()\n latest_summaries = response.css(\n \"div.feature p:not(.dat) ::text\").extract()\n\n for latest_url, latest_title, latest_date, latest_summary in zip(latest_urls, latest_titles, latest_dates, latest_summaries):\n item = EIAItem()\n item[\"url\"] = urljoin(response.url, latest_url)\n item[\"title\"] = latest_title.strip()\n item[\"publishdate\"] = parser.parse(latest_date, fuzzy=True)\n item[\"summary\"] = latest_summary.strip()\n item[\"type\"] = \"What's New\"\n yield item\n\ndef format_date(date):\n format_date = datetime.strptime(date, \"%b %d, %Y\")\n return format_date.date()\n", "sub_path": "spiders/others/fed/eia/WhatsNew.py", "file_name": "WhatsNew.py", "file_ext": "py", "file_size_in_byte": 2844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "scrapy.Spider", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 24, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 38, "usage_type": "argument"}, {"api_name": "spiders.others.fed.eia.items.EIAItem", "line_number": 39, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.date.strip", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 42, "usage_type": "name"}, {"api_name": "urlparse.urljoin", "line_number": 49, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 51, "usage_type": "call"}, {"api_name": "spiders.others.fed.eia.items.EIAItem", "line_number": 61, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 62, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 64, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 64, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 70, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "439806276", "text": "#!/usr/bin/env python\n# -*- coding:UTF-8 -*-\n# AUTHOR: Yunrui Zhou\n# FILE: C:\\Users\\d1286\\Desktop\\机密文件\\PYTHON\\大作业\\爬虫练习\\豆瓣电影基础信息爬取（requests+json+lxml）.py\n# DATE: 2020/05/12 周二\n# TIME: 16:40:17\n\n# DESCRIPTION:爬取豆瓣电影界面的电影简介.打印出来，后续可以写到文件里\n#技术路线就是 requests--json---lxml 当然不止这条路线。\n#这里用面向对象，也可也不用。。。直接用函数也行\nimport requests\nfrom lxml import etree\nimport json\nfrom selenium import webdriver\n\nclass SpiderMoiveSummary():\n '''\n \n '''\n #初始化\n #self是面向对象里的一个形参，必须得有，当然不用面向对象的时候，就不用这个参数\n def __init__(self,num):\n #这个url是得到该网页的json数据 num对应一个页面的电影数,如何获得这个url,就是到原网页,f12--network--里面找,对找,一般容易找到\n self.url = \"https://movie.douban.com/j/search_subjects?type=movie&tag=%E7%83%AD%E9%97%A8&sort=recommend&page_limit={}&page_start=\".format(num) \n #设定headers 简单地反爬虫，如何获取呢，也是在爬取的页面F12--network，随便点一个，然后点击headers就可以看到\n self.headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.129 Safari/537.36',\n 'Referer': 'https://movie.douban.com/explore'\n }\n #每个电影具体网站的通用格式{}内填电影的id\n self.basic_url = 'https://movie.douban.com/subject/{}/'\n #简介所在的路径\n self.summary_path = '//span[@property= \"v:summary\"]/text()'\n #获取请求\n def get_requests(self,url):\n try:\n response = requests.get(url, headers =self.headers)\n response.raise_for_status\n return response\n except:\n return ''\n #得到电影的id号码和电影的名字\n def get_movie(self):\n movie_id_List = []\n movie_name_list = []\n responses = self.get_requests(self.url)\n content = responses.content.decode()\n all_info = json.loads(content)['subjects']#json.loads(json数据)函数是将json数据变成python的字典\n for i in range(len(all_info)):\n movie_id = all_info[i]['id']\n movie_name = all_info[i]['title']\n movie_rate = all_info[i]['rate']\n movie_name_list.append([movie_name,movie_rate])\n movie_id_List.append(movie_id)\n return movie_id_List,movie_name_list\n #获取电影的简介并存储到字典中\n def movie_summary(self):\n movieIdList,movieNameList = self.get_movie()\n summary_list = []\n movieName_Summary_dict = {}\n #Moviecomment ={}\n print(\"START\".center(150,'-'))\n for num in movieIdList:\n responses = self.get_requests(self.basic_url.format(num))\n data = responses.content\n element = etree.HTML(data)#将页面内容作为一个element元素\n summary = element.xpath(self.summary_path)#定位到简介所在的路径,返回一个简介列表\n summary_list.append(summary[0])#因为该列表只有一个值,所以用0索引拿出来\n #driver = webdriver.Firefox()\n #driver.get(self.basic_url.format(num))\n #comment = []\n #element_key = driver.find_elements_by_id(\"hot-comments\")\n #eles = element_key[0].find_elements_by_tag_name('p')\n #for ele in eles:\n # comment.append(ele)\n \n #接下来是整理数据,将电影名字,评分,简介都放到一个字典里,我的方法有点捞,可以想想有米有其他的\n for i in range(len(summary_list)):\n movieName_Summary_dict[movieNameList[i][0]+ ' rates: '+movieNameList[i][1]]=summary_list[i]\n return movieName_Summary_dict\n \n def loadFile(self):\n movie = self.movie_summary()\n f = open(\"movie.txt\", 'w', encoding='utf-8')\n json.dump(movie,f,indent = 4,ensure_ascii=False)\n f.close()\n\n def print_movieAndsummary(self):\n \"\"\"将电影名字和简介打印出来\"\"\"\n movie = self.movie_summary()\n for k,v in movie.items():\n print('movie : {} '.format(k)+'\\n'+'summary: {}'.format(v)+'\\n')\n\nsp =SpiderMoiveSummary(20)\nsp.loadFile()\n#之前的self就是这里的sp，sp是这个对象的实例化。\n", "sub_path": "豆瓣电影基础信息爬取（requests+json+lxml）.py", "file_name": "豆瓣电影基础信息爬取（requests+json+lxml）.py", "file_ext": "py", "file_size_in_byte": 4503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 66, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 66, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "480850706", "text": "from bson import ObjectId\nfrom fastapi import HTTPException\nfrom fastapi import status\nfrom pymongo.collection import Collection\n\nfrom common.consts import LATITUDE, LONGITUDE, COUNTRY, CITY, STREET, LOCATIONS\nfrom routes.utils.locations import reverse_geo_location\n\n\ndef missing_param_exception(key):\n raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=f'the {key} parameter is required')\n\n\nclass DataValidators:\n @staticmethod\n def locations(obj):\n if (\n obj.get('country')\n and obj.get('city')\n and obj.get('street')\n and not obj.get('latitude')\n and not obj.get('longitude')\n ):\n # we got strings repr of the location, let's try and fish its lat/long\n extra = reverse_geo_location(obj, from_latlon=False)\n obj[LATITUDE] = extra[0][LATITUDE]\n obj[LONGITUDE] = extra[0][LONGITUDE]\n elif (\n obj.get(LATITUDE)\n and obj.get(LONGITUDE)\n and not obj.get(COUNTRY)\n and not obj.get(CITY)\n and not obj.get(STREET)\n ):\n extra = reverse_geo_location(obj, from_latlon=True)\n obj[STREET], obj[CITY], obj[COUNTRY] = extra[0][STREET], extra[0][CITY], extra[0][COUNTRY]\n\n return obj\n\n @classmethod\n def get_d_type_validator(cls, d_type):\n d_types_to_funcs = {\n LOCATIONS: DataValidators.locations\n }\n return d_types_to_funcs[d_type]\n\n\nclass BaseUploaderFromAPI:\n \"\"\"\n Describes uploading object with relations\n \"\"\"\n collection: Collection = None\n id_field: str = None\n allowed_data_types: list = None\n\n def __init__(self, data, obj_id=None, new=True):\n self.initial_data = data\n if not obj_id and not new:\n raise ValueError('if you want to update a record, supply its id')\n self.obj_id = obj_id\n self.new = new\n\n async def upload(self):\n main_obj_id = self.serialize_input_for_upsert()\n for data_type in self.allowed_data_types:\n data = self.initial_data.get(data_type)\n await self._create_and_upload_related_data(data, inserted_id=main_obj_id, data_type=data_type)\n return main_obj_id\n\n async def _create_and_upload_related_data(self, data, inserted_id, data_type):\n inserted_id = inserted_id or self.obj_id\n validator_func = DataValidators.get_d_type_validator(d_type=data_type)\n validated_data = []\n for obj in data:\n obj[self.id_field] = ObjectId(inserted_id)\n validated_data.append(validator_func(obj))\n self.collection.insert_many(validated_data)\n\n def serialize_input_for_upsert(self) -> dict:\n raise NotImplemented\n\n def upsert_main_object(self, data):\n query = {'_id': ObjectId(self.obj_id)} if self.obj_id else data\n obj = self.collection.update_one(query, data, upsert=True)\n self.obj_id = obj.inserted_id\n return obj.inserted_id\n\n def log_meta(self, **kwargs):\n base = {\n 'collection': self.collection.name,\n }\n if self.obj_id:\n base['object_id'] = self.obj_id\n base['mode'] = 'create' if self.new else 'update'\n base.update(**kwargs)\n return base\n", "sub_path": "routes/utils/upsert_data.py", "file_name": "upsert_data.py", "file_ext": "py", "file_size_in_byte": 3336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "fastapi.HTTPException", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 11, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 11, "usage_type": "name"}, {"api_name": "routes.utils.locations.reverse_geo_location", "line_number": 25, "usage_type": "call"}, {"api_name": "common.consts.LATITUDE", "line_number": 26, "usage_type": "name"}, {"api_name": "common.consts.LONGITUDE", "line_number": 27, "usage_type": "name"}, {"api_name": "common.consts.LATITUDE", "line_number": 29, "usage_type": "argument"}, {"api_name": "common.consts.LONGITUDE", "line_number": 30, "usage_type": "argument"}, {"api_name": "common.consts.COUNTRY", "line_number": 31, "usage_type": "argument"}, {"api_name": "common.consts.CITY", "line_number": 32, "usage_type": "argument"}, {"api_name": "common.consts.STREET", "line_number": 33, "usage_type": "argument"}, {"api_name": "routes.utils.locations.reverse_geo_location", "line_number": 35, "usage_type": "call"}, {"api_name": "common.consts.STREET", "line_number": 36, "usage_type": "name"}, {"api_name": "common.consts.CITY", "line_number": 36, "usage_type": "name"}, {"api_name": "common.consts.COUNTRY", "line_number": 36, "usage_type": "name"}, {"api_name": "common.consts.LOCATIONS", "line_number": 43, "usage_type": "name"}, {"api_name": "pymongo.collection.Collection", "line_number": 52, "usage_type": "name"}, {"api_name": "bson.ObjectId", "line_number": 75, "usage_type": "call"}, {"api_name": "bson.ObjectId", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "349618778", "text": "import matplotlib.pyplot as plt\nimport skimage\nimport numpy as np\nfrom skimage import io, morphology, measure, color, transform\nfrom skimage.filters import threshold_otsu\nimport glob\nimport multiprocessing\nimport time\n\ndef apply_otsu_segmentation(img):\n thresh = threshold_otsu(img)\n binary = img > thresh\n return binary\n\n\ndef apply_morphology_operation(binary, operator_size=35):\n binary_dil = morphology.binary_dilation(\n binary, morphology.square(operator_size))\n return binary_dil\n\n\ndef locate_max_region(binary):\n label_image = measure.label(binary)\n region_list = measure.regionprops(label_image)\n region_area_list = np.array([region.area for region in region_list])\n region_max_index = np.argmax(region_area_list)\n region_max_box = region_list[region_max_index].bbox\n minr, minc, maxr, maxc = region_max_box\n return (minr, minc, maxr, maxc)\n\n\ndef apply_preprocess(img, rescale=5):\n img_resize = transform.rescale(img, 1/rescale, multichannel=False)\n img_resize_binary = apply_otsu_segmentation(img_resize)\n img_resize_morphology = apply_morphology_operation(img_resize_binary)\n minr, minc, maxr, maxc = locate_max_region(img_resize_morphology)\n img_crop = img[minr*rescale:maxr*rescale, minc*rescale:maxc*rescale]\n mask_resize_crop = img_resize_morphology[minr:maxr, minc:maxc]\n mask_crop = transform.resize(mask_resize_crop, img_crop.shape)\n img_crop_denoise = img_crop * mask_crop\n return img_crop_denoise\n\n\ndef save_preprocess(img_path):\n img_path_preprocess = img_path.replace('images', 'preprocess')\n img = io.imread(img_path)\n img_preprocess = apply_preprocess(img)\n io.imsave(img_path_preprocess, img_preprocess.astype('uint8'))\n return\n\n\nif __name__ == \"__main__\":\n start = time.time()\n img_path_list = glob.glob('/data/zcwang/BE223c/data/images/*.png')\n pool = multiprocessing.Pool(processes=16)\n for img_path in img_path_list:\n pool.apply_async(save_preprocess, (img_path, ))\n pool.close() \n pool.join()\n end = time.time()\n print(\"Sub-processes done. %f s used\"%(end-start))", "sub_path": "Deployment/flask-app/Model_code/utils/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 2107, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "skimage.filters.threshold_otsu", "line_number": 11, "usage_type": "call"}, {"api_name": "skimage.morphology.binary_dilation", "line_number": 17, "usage_type": "call"}, {"api_name": "skimage.morphology", "line_number": 17, "usage_type": "name"}, {"api_name": "skimage.morphology.square", "line_number": 18, "usage_type": "call"}, {"api_name": "skimage.morphology", "line_number": 18, "usage_type": "name"}, {"api_name": "skimage.measure.label", "line_number": 23, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 23, "usage_type": "name"}, {"api_name": "skimage.measure.regionprops", "line_number": 24, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 26, "usage_type": "call"}, {"api_name": "skimage.transform.rescale", "line_number": 33, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 33, "usage_type": "name"}, {"api_name": "skimage.transform.resize", "line_number": 39, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 39, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 46, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 46, "usage_type": "name"}, {"api_name": "skimage.io.imsave", "line_number": 48, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 48, "usage_type": "name"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 54, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "431095674", "text": "import smtplib\nimport datetime\nimport json\nfrom email.message import EmailMessage\n\n#IMPORTANT: gmail has built in feature that blocks emails written with smtp, \n# enable \"Less secure app access\" for sender email that use gmail\n\n\ndef alert_issues(template_issues, action_issues, uuid, config, env):\n '''\n Alert using smtp ssl\n '''\n\n # if len(template_issues) > 0 or len(action_issues) > 0 or len(timing_issues) > 0:\n if len(template_issues) > 0 or len(action_issues) > 0:\n msg = EmailMessage()\n msg['Subject'] = \"SCTE issues \" + str(datetime.date.today())\n msg['From'] = env[\"sender_email\"]\n msg['To'] = config[\"recipient_emails\"]\n\n body = str()\n body += \"\"\n body += \"UUID : \" + uuid + \"

\"\n if len(template_issues) != 0:\n body += \"

Template Issues:

\"\n body += \"

The following pair of trigguers do not comply with the configuration template.

\"\n body += \"
    \"\n for target_list in template_issues:\n #body += \"
  1. \" + \"

    Timestamp:

    \" + \" \" + str(target_list[\"timestamp\"]) + \"
    \" + \"

    Input Trigger:

    \" + \" \" + str(target_list[\"input_trigger\"]) + \"
    \" + \"

    Input Trigger:

    \" + \" \" + str(target_list[\"output_trigger\"]) + \"


    2. \"\n body += \"This template value is missing or wrong template action value: \" + target_list + \"

    \"\n body += \"
\"\n \n if len(action_issues) != 0:\n body += \"

Action Issues:

\"\n body += \"

The following pair of trigguers actions do not comply with the configuration predetermined action.

\"\n body += \"
    \"\n for target_list in action_issues:\n body += \"
  1. \" + \"

    Timestamp:

    \" + \" \" + str(target_list[\"timestamp\"]) + \"

    Action:

    \" + \" \" + str(target_list[\"output_trigger\"].split(\":\")[4]) + \"
    \" + \"

    Input Trigger:

    \" + \" \" + str(target_list[\"input_trigger\"]) + \"
    \" + \"

    Output Trigger:

    \" + \" \" + str(target_list[\"output_trigger\"]) + \"


    2. \"\n body += \"
\"\n\n # if len(timing_issues) != 0:\n # body += \"

Timing Issues:

\"\n # body += \"

The following pair of trigguers deviate from the timing tolerance.

\"\n # body += \"
    \"\n # for target_list in timing_issues:\n # body += \"
  1. \" + \"

    Break Start:

    \" + \" \" + str(target_list[\"break_start_trigger\"][\"input_trigger\"]) + \"
    \" + \" \" + str(target_list[\"break_start_trigger\"][\"output_trigger\"]) + \"
    \" + \"

    Break End:

    \" + \" \" + str(target_list[\"break_end_trigger\"][\"input_trigger\"]) + \"
    \" + \" \" + str(target_list[\"break_end_trigger\"][\"output_trigger\"]) + \"


    2. \" \n # body += \"
\"\n\n if body == \"\":\n body += \"

No issues during log verification.

\"\n\n body += \"\"\n\n msg.set_content(body)\n\n msg.add_alternative(body, subtype='html')\n\n with smtplib.SMTP_SSL(\"smtp.gmail.com\", 465) as smtp:\n # IMPORTANT load email and password\n smtp.login(env[\"sender_email\"], env[\"password\"])\n smtp.send_message(msg)\n smtp.close()\n", "sub_path": "triggeralert.py", "file_name": "triggeralert.py", "file_ext": "py", "file_size_in_byte": 3400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "email.message.EmailMessage", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 18, "usage_type": "attribute"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "561639", "text": "# Import Required Libraries\n\nimport cv2\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport face_recognition\nimport tqdm\n\n\nclass DataAugumentation:\n def __init__(self):\n # Try on a single Image\n self.FILE_PATH = \"without_mask/155.jpg\"\n self.MASK_IMAGE_PATH = \"ImagesWithMask/default_mask.png\"\n\n self.INPUT_DIR = \"Training_Data/without_mask\"\n self.SAVE_DIR = \"Training_Data/augmented\"\n\n # Define Blurring Kernel Size Ranges, a Random Size would be chosen in the Specified Ranges\n # Greater the Size, Higher is the Blurring Effect (Adjustments can be made according to the needs)\n\n self.motion_blur_kernel_range = (6, 10)\n self.average_blur_kernel_range = (3, 9)\n self.gaussian_blur_kernel_range = (3, 10)\n\n # Set Blurring Kernels to Use and their associated Probabilities\n\n self.Blurring_Kernels = [\"none\", \"motion\", \"gaussian\", \"average\"]\n self.Probs = [0.1, 0.4, 0.25, 0.25]\n\n # Mathematical Function to Return Perpendicular Distance of a Point from a Line\n def get_distance_from_point_to_line(self, point, line_point1, line_point2):\n (p, q) = point\n (x1, y1) = line_point1\n (x2, y2) = line_point2\n\n A = (y2 - y1)\n B = (x1 - x2)\n\n distance = np.abs(A * p + B * q - A * x1 - B * y1) / np.sqrt(A ** 2 + B ** 2)\n\n return int(distance)\n\n # Mathematical Function to Return Rotated Co-ordinates of a Point around a Reference Point (Origin)\n def get_point_rotation(self, point,origin,angle):\n\n (p,q) = point\n (x,y) = origin\n\n rotated_p = x + np.cos(angle)*(p-x) - np.sin(angle)*(q-y)\n rotated_q = y + np.sin(angle)*(p-x) + np.cos(angle)*(q-y)\n\n return [int(rotated_p),int(rotated_q)]\n\n # Function to Wear Mask to the Human Faces found in a given Input Image\n def Mask_Faces(self, image_path, mask_image_path):\n masked_faces_image = Image.open(image_path)\n\n image = face_recognition.load_image_file(image_path)\n mask_img = Image.open(mask_image_path)\n\n # Get Face Landmark Co-ordinates\n face_landmarks = face_recognition.face_landmarks(image)\n\n # Return if no Faces found or Required Landmarks not found\n if len(face_landmarks) == 0:\n # print(\"No Faces found in \"+image_path.split('/')[-1]+\" !\")\n return None\n\n for face in face_landmarks:\n\n if 'nose_bridge' not in face or 'chin' not in face:\n continue\n\n # Nose Point (Top of Mask)\n nose_bridge = face['nose_bridge']\n nose_point = ((np.array(nose_bridge[0]) + np.array(nose_bridge[1])) / 2).astype(np.uint64)\n\n chin = face['chin']\n chin_len = len(chin)\n\n # Chin Points (Bottom, Left and Right of Mask)\n chin_bottom_point = np.array(chin[chin_len // 2])\n chin_left_point = np.array(chin[chin_len // 8])\n chin_right_point = np.array(chin[chin_len * 7 // 8])\n\n # Dimensions for the Mask\n width = mask_img.width\n height = mask_img.height\n width_ratio = 1.15\n new_mask_height = int(np.linalg.norm(nose_point - chin_bottom_point))\n\n # Prepare Left Half of the Mask with appropriate Size\n mask_left_img = mask_img.crop((0, 0, width // 2, height))\n mask_left_width = self.get_distance_from_point_to_line(chin_left_point, nose_point, chin_bottom_point)\n mask_left_width = int(mask_left_width * width_ratio)\n mask_left_img = mask_left_img.resize((mask_left_width, new_mask_height))\n\n # Prepare Right Half of the Mask with appropriate Size\n mask_right_img = mask_img.crop((width // 2, 0, width, height))\n mask_right_width = self.get_distance_from_point_to_line(chin_right_point, nose_point, chin_bottom_point)\n mask_right_width = int(mask_right_width * width_ratio)\n mask_right_img = mask_right_img.resize((mask_right_width, new_mask_height))\n\n # Join the 2 Halves to Produce the New Mask Image with the Correct Size\n new_mask_size = (mask_left_img.width + mask_right_img.width, new_mask_height)\n new_mask_img = Image.new('RGBA', new_mask_size)\n new_mask_img.paste(mask_left_img, (0, 0), mask_left_img)\n new_mask_img.paste(mask_right_img, (mask_left_img.width, 0), mask_right_img)\n\n # Calculate Angle of Rotation (Tilted Face) and Rotate the Mask\n angle_radian = np.arctan2(chin_bottom_point[1] - nose_point[1], chin_bottom_point[0] - nose_point[0])\n rotation_angle_radian = (np.pi / 2) - angle_radian\n rotation_angle_degree = (rotation_angle_radian * 180) / np.pi\n rotation_center = (mask_left_width, new_mask_height // 2)\n rotated_mask_img = new_mask_img.rotate(rotation_angle_degree, expand=True, center=rotation_center)\n\n # Calcualate Co-ordinates for Pasting the Mask on the Input Image\n center_x = (nose_point[0] + chin_bottom_point[0]) // 2\n center_y = (nose_point[1] + chin_bottom_point[1]) // 2\n\n mask_corner_points = [[center_x - mask_left_width, center_y - (new_mask_height // 2)],\n [center_x + mask_right_width, center_y - (new_mask_height // 2)],\n [center_x + mask_right_width, center_y + (new_mask_height // 2)],\n [center_x - mask_left_width, center_y + (new_mask_height // 2)]]\n\n # Make Sure Image Dimentions doesn't exceed 99999\n rotated_mask_topleft_corner = np.array([99999, 99999])\n\n for point in mask_corner_points:\n rotated_mask_topleft_corner = np.minimum(rotated_mask_topleft_corner,\n self.get_point_rotation(point, (center_x, center_y),\n -rotation_angle_radian))\n\n # Paste the Mask on Image and Return it\n masked_faces_image.paste(rotated_mask_img,\n (rotated_mask_topleft_corner[0], rotated_mask_topleft_corner[1]),\n rotated_mask_img)\n\n return masked_faces_image\n\n # Function to Mask Faces present in all Images within a directory\n def Generate_Masked_Images (self, images_path,save_path,mask_image_path):\n\n print(\"Augmenting Images, Please Wait !\")\n # Loop through all the Files\n for file_name in tqdm.notebook.tqdm(os.listdir(images_path)):\n\n # Mask and Save in the Specified save_path\n try:\n\n masked_face_image = self.Mask_Faces(os.path.join(images_path,file_name),mask_image_path)\n masked_face_image.save(os.path.join(save_path,file_name.split('.')[0]+\"_masked.\"+\n file_name.split('.')[-1]))\n except:\n continue\n\n print(\"Done !\")\n\n def motion_blur(self, img):\n # Choose a Random Kernel Size\n kernel_size = np.random.randint(self.motion_blur_kernel_range[0], self.motion_blur_kernel_range[1])\n kernel = np.zeros((kernel_size, kernel_size))\n\n # Random Selection of Direction of Motion Blur\n types = [\"vertical\", \"horizontal\", \"main_diagonal\", \"anti_diagonal\"]\n choice = np.random.choice(types)\n\n if choice == \"vertical\":\n kernel[:, int((kernel_size - 1) / 2)] = np.ones(kernel_size) / kernel_size\n\n elif choice == \"horizontal\":\n kernel[int((kernel_size - 1) / 2), :] = np.ones(kernel_size) / kernel_size\n\n elif choice == \"main_diagonal\":\n\n for i in range(kernel_size):\n kernel[i][i] = 1 / kernel_size\n\n elif choice == \"anti_diagonal\":\n\n for i in range(kernel_size):\n kernel[i][kernel_size - i - 1] = 1 / kernel_size\n\n # Convolve and Return the Blurred Image\n return cv2.filter2D(img, -1, kernel)\n\n # Add a Random Blur Effect to an Image with a Random Kernel Size (in the Specified Ranges)\n\n def get_blurred_picture(self, img_path):\n # Randomly choose a Blurring Technique\n choice = np.random.choice(self.Blurring_Kernels, p=self.Probs)\n\n # Load Image\n img = cv2.imread(img_path)\n\n if choice == \"none\":\n\n random_blurred_img = img\n\n elif choice == \"motion\":\n\n random_blurred_img = self.motion_blur(img)\n\n elif choice == \"gaussian\":\n\n kernel_size = np.random.randint(self.gaussian_blur_kernel_range[0], self.gaussian_blur_kernel_range[1])\n\n if kernel_size % 2 == 0:\n kernel_size -= 1\n\n random_blurred_img = cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n\n elif choice == \"average\":\n\n kernel_size = np.random.randint(self.average_blur_kernel_range[0], self.average_blur_kernel_range[1])\n random_blurred_img = cv2.blur(img, (kernel_size, kernel_size))\n\n # Return Blurred Image\n return random_blurred_img\n\n # Function to Randomly Blur all Images within a directory\n\n def Blur_Images(self, images_path, save_path):\n\n print(\"Blurring Images, Please Wait !\")\n\n # Loop through all the Files\n for file_name in tqdm.notebook.tqdm(os.listdir(images_path)):\n\n # Mask and Save in the Specified save_path\n try:\n\n blurred_image = self.get_blurred_picture(os.path.join(images_path, file_name))\n cv2.imwrite(os.path.join(save_path, file_name.split('.')[0] + \"_blurred.\" + \\\n file_name.split('.')[-1]), blurred_image)\n except:\n continue\n\n print(\"Done !\")\n\n def preprocessData(self):\n masked_face_image = self.Mask_Faces(self.FILE_PATH,self.MASK_IMAGE_PATH)\n\n plt.figure(figsize=(10,10))\n plt.subplot(1,2,1)\n plt.title(\"Original\")\n plt.imshow(Image.open(self.FILE_PATH))\n plt.subplot(1,2,2)\n plt.title(\"Masked\")\n plt.imshow(masked_face_image)\n plt.show()\n\n # Call the Function for a Directory\n\n self.Generate_Masked_Images (self.INPUT_DIR,self.SAVE_DIR,self.MASK_IMAGE_PATH)\n\n # Add Motion Blur to an Image in a Random Direction\n\n # Try on a single Image\n\n FILE_PATH = \"Training_Data/without_mask/95.jpg\"\n\n blurred_image = self.get_blurred_picture(FILE_PATH)\n\n plt.figure(figsize=(10,10))\n plt.subplot(1,2,1)\n plt.title(\"Original\")\n plt.imshow(Image.open(FILE_PATH))\n plt.subplot(1,2,2)\n plt.title(\"Blurred\")\n plt.imshow(blurred_image[:,:,::-1])\n plt.show()\n\n # Call the Function for a Directory\n self.Blur_Images (self.INPUT_DIR,self.SAVE_DIR)", "sub_path": "com_in_ineuron_ai_preprocessing/data_preprocessor.py", "file_name": "data_preprocessor.py", "file_ext": "py", "file_size_in_byte": 10934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.abs", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 53, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 59, "usage_type": "name"}, {"api_name": "face_recognition.load_image_file", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 62, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 62, "usage_type": "name"}, {"api_name": "face_recognition.face_landmarks", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.uint64", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 93, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 109, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.arctan2", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 133, "usage_type": "call"}, {"api_name": "tqdm.notebook.tqdm", "line_number": 149, "usage_type": "call"}, {"api_name": "tqdm.notebook", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 149, "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": "numpy.random.randint", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 175, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 194, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 209, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 218, "usage_type": "attribute"}, {"api_name": "cv2.blur", "line_number": 219, "usage_type": "call"}, {"api_name": "tqdm.notebook.tqdm", "line_number": 231, "usage_type": "call"}, {"api_name": "tqdm.notebook", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 250, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 271, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}]} +{"seq_id": "95455614", "text": "import dataset\nimport ranking as rnk\nimport evaluate as evl\nimport numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nfrom torch import nn, optim\nfrom torch.utils.data import Dataset, TensorDataset, DataLoader\nimport itertools\n\nimport math\ndata = dataset.get_dataset().get_data_folds()[0]\ndata.read_data()\n\n\nclass Model():\n def __init__(self, n_feature, n_hidden, learning_rate, sigma):\n self.ranknet = nn.Sequential(\n nn.Linear(n_feature, n_hidden),\n nn.ReLU(),\n nn.Linear(n_hidden, 1)\n )\n self.optimizer = optim.SGD(self.ranknet.parameters(), lr=learning_rate)\n\n\ndef eval_model(model, data_fold):\n x = torch.from_numpy(data_fold.feature_matrix).float()\n y = data_fold.label_vector\n model.ranknet.eval()\n \n output = model.ranknet(x)\n output = output.detach().cpu().numpy().squeeze()\n\n scores = evl.evaluate(data_fold, np.asarray(output)) \n\n return scores \n\n \ndef train_batch(documentfeatures, labels, model, sig):\n \n model.ranknet.train()\n model.optimizer.zero_grad()\n \n output = model.ranknet(documentfeatures)\n \n loss = pairwiseloss(output, labels, sig)\n \n loss.sum().backward()\n\n model.optimizer.step()\n \n return model\n \n \ndef pairwiseloss(preds, labels, sigma):\n preds = preds.squeeze() \n \n pairs = list(itertools.combinations(range(preds.shape[0]), 2))\n idx1, idx2 = [pair[0] for pair in pairs], [pair[1] for pair in pairs]\n \n S = torch.sign(labels[idx1] - labels[idx2])\n s = preds[idx1] - preds[idx2] \n\n lambda_ij = sigma * (0.5 * (1 - S) - (1 / (1 + torch.exp(sigma * s))))\n \n lambda_i = torch.zeros((preds.shape[0], preds.shape[0]))\n lambda_i[np.triu_indices(preds.shape[0], k=1)] = lambda_ij\n lambda_i = (lambda_i - lambda_i.T).sum(1)\n \n return preds * lambda_i.detach()\n \n \n \n \ndef plot_ndcg_arr(losses, ndcgs):\n x = np.arange(len(losses))\n fig, ax = plt.subplots()\n \n ax.plot(x, losses, label='ARR')\n ax.plot(x, ndcgs, label='NDCG')\n ax.set_xlabel(\"Batch % 2000\")\n ax.set_ylabel(\"Score\")\n ax.set_title(\"RankNet SU LTR\")\n legend = ax.legend(loc='upper center')\n \n plt.show() \n \n \n \ndef hyperparam_search():\n\n epochs = 30\n learning_rates = [10**-2, 10**-3]\n n_hiddens = [150, 200, 250, 300, 350]\n sigmas = [10**-2, 10**-3]\n\n best_ndcg = 0\n for learning_rate in learning_rates:\n for n_hidden in n_hiddens:\n for sigma in sigmas:\n\n print(\"\\nTesting learning_rate = {}, n_hidden = {} and sigma = {}\".format(learning_rate, n_hidden, sigma))\n model = Model(data.num_features, n_hidden, learning_rate, sigma)\n\n last_ndcg = 0\n for epoch in range(epochs):\n\n model.ranknet.train()\n for qid in range(0, data.train.num_queries()):\n if data.train.query_size(qid) < 2:\n continue\n s_i, e_i = data.train.query_range(qid)\n\n documentfeatures = torch.tensor(data.train.feature_matrix[s_i:e_i]).float()\n labels = torch.tensor(data.train.label_vector[s_i:e_i])\n\n model = train_batch(documentfeatures, labels, model, sigma) \n \n scores = eval_model(model, data.validation)\n \n ndcg = scores[\"ndcg\"][0]\n print(\"Epoch: {}, ndcg: {}\".format(epoch, ndcg))\n \n if ndcg < last_ndcg:\n break\n last_ndcg = ndcg\n if ndcg > best_ndcg:\n best_ndcg = ndcg\n best_params = {\"learning_rate\": learning_rate, \"n_hidden\": n_hidden, \"epoch\": epoch, \"sigma\": sigma} \n print(\"Best parameters:\", best_params)\n \n return best_params\n\ndef train_best(best_params):\n epochs = best_params[\"epoch\"]\n n_hidden = best_params[\"n_hidden\"]\n learning_rate = best_params[\"learning_rate\"]\n sigma = best_params[\"sigma\"]\n \n model = Model(data.num_features, n_hidden, learning_rate, sigma)\n\n arrs, ndcgs = [], []\n for epoch in range(epochs):\n eval_count = 0\n for qid in range(0, data.train.num_queries()):\n if data.train.query_size(qid) < 2:\n continue\n s_i, e_i = data.train.query_range(qid)\n \n documentfeatures = torch.tensor(data.train.feature_matrix[s_i:e_i]).float()\n labels = torch.tensor(data.train.label_vector[s_i:e_i])\n model = train_batch(documentfeatures, labels, model, sigma) \n eval_count +=1\n if eval_count % 2000 == 0:\n scores = eval_model(model, data.validation)\n ndcgs.append(scores[\"ndcg\"][0])\n arrs.append(scores[\"relevant rank\"][0])\n print(\"Epoch: {}, ndcg: {}\".format(epoch, scores[\"ndcg\"][0]))\n \n return arrs, ndcgs, model\n \n\nif __name__ == \"__main__\":\n #determine best hyper parameters\n best_params = hyperparam_search()\n #train best model\n ndcgs, model = train_best(best_params)\n #performance on test set\n scores = eval_model(model, data.test)\n\n \n \n\n \n\n", "sub_path": "hw3/pairwise_SU.py", "file_name": "pairwise_SU.py", "file_ext": "py", "file_size_in_byte": 5470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "dataset.get_dataset", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 28, "usage_type": "call"}, {"api_name": "evaluate.evaluate", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 35, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.sign", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.triu_indices", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "12265948", "text": "import csv\nimport numpy\nimport matplotlib.pyplot as plt\nimport classify_file as cf\nfrom colorsys import hls_to_rgb\nimport random\nimport matplotlib\nimport numpy as np\nimport matplotlib as mpl\nimport os\n#import skbio.math.diversity.alpha\n\n#run this after analysis scripts 1 and 2!\n#names, indices, all_samples, all_treatments = analysis_script_1.get_meta_treatments('16S_metadata.csv') \nfiles = ['0_percent_grouped.csv', '1_percent_grouped.csv', '2_daily_r_percent_grouped.csv', '3_daily_percent_grouped.csv', '4_LG_percent_grouped.csv', '5_RG_percent_grouped.csv', '6_beg_end_percent_grouped.csv']\nsimper_means_files = ['', '', '2_daily_r_percent_simper_mean.csv', '3_daily_percent_simper_mean.csv', '4_LG_percent_simper_mean.csv', '5_RG_percent_simper_mean.csv', '']\ntaxonomy = 'Taxonomy.csv'\n\ndef get_OTU_taxonomy(level, OTU):\n #levels: Genus = 6, Family = 5, Order = 4, Class = 3, Phylum = 2\n with open(taxonomy, 'rU') as f:\n reader = csv.reader(f)\n rows = []\n for row in reader:\n rows.append(row)\n for a in range(len(rows)):\n if rows[a][0] == OTU:\n returning = rows[a]\n this_level = returning[level]\n return this_level\n \ndef get_distinct_colors(n):\n colors = []\n for i in numpy.arange(0., 360., 360. / n):\n h = i / 360.\n l = (50 + numpy.random.rand() * 10) / 100.\n s = (90 + numpy.random.rand() * 10) / 100.\n colors.append(hls_to_rgb(h, l, s))\n random.shuffle(colors)\n return colors\n \ndef stacked_barchart(fn, level=6):\n new_fn = cf.classify_file(fn, level)\n new_fn = cf.group_file(new_fn, True)\n #new_fn = '0_percent_grouped_Genus_grouped.csv'\n new_fn = fn\n count = 0\n with open(new_fn, 'rU') as f:\n reader = csv.reader(f)\n rows = []\n for row in reader:\n rows.append(row)\n OTU, numbers = [], []\n for a in range(len(rows)):\n if a > 0:\n count += 1\n this_row = []\n for b in range(len(rows[a])):\n if b == 0:\n OTU.append(rows[a][b])\n elif b < 71:\n this_row.append(float(rows[a][b]))\n numbers.append(this_row)\n new_rows = rows[0]\n del new_rows[0]\n gens = []\n for a in range(len(new_rows)):\n if new_rows[a][2] == '_':\n gens.append(int(new_rows[a][1]))\n else:\n gens.append(int(new_rows[a][1]+new_rows[a][2]))\n count = 0\n for a in range(len(numbers)):\n count += numbers[a][5]\n G, R, L, SG, SR = [], [], [], [], []\n for c in range(len(numbers)):\n G.append([])\n R.append([])\n L.append([])\n SG.append([])\n SR.append([])\n begs, ends, mids, daily = [], [], [], []\n shorts, new = [], []\n gens_G, gens_R, gens_L, gens_SG, gens_SR = [], [], [], [], []\n colors = get_distinct_colors(96)\n for z in range(len(new_rows)):\n begs.append(new_rows[z][0])\n ends.append(new_rows[z][-1])\n if len(new_rows[z]) > 5:\n shorts.append(new_rows[z])\n mids.append(int(new_rows[z][4]))\n daily.append(1)\n else:\n mids.append(0)\n daily.append(0)\n for d in range(len(numbers)):\n for e in range(len(new_rows)):\n this_number = numbers[d][e]\n beg, end, gen, mid = begs[e], ends[e], int(gens[e]), mids[e]\n if beg == 'L':\n if end == 'G':\n G[d].append(this_number)\n if d == 0:\n gens_G.append(gen)\n if end == 'R':\n R[d].append(this_number)\n if d == 0:\n gens_R.append(gen)\n if end == 'L':\n L[d].append(this_number)\n if d == 0:\n gens_L.append(gen)\n if beg == 'S':\n if mid == 0 or mid == 2:\n if end == 'G':\n SG[d].append(this_number)\n if d == 0:\n gens_SG.append(gen)\n if end == 'R':\n SR[d].append(this_number)\n if d == 0:\n gens_SR.append(gen)\n new_OTU = []\n for o in range(len(OTU)):\n string = ''\n count1 = 0\n new_line = False\n for p in range(len(OTU[o])):\n count1 += 1\n if OTU[o][p] == '_':\n if not new_line:\n string = r'${'+string+'}$'\n new_line = True\n string+=' \\n '\n else:\n string+=OTU[o][p]\n if p == len(OTU[o])-1 and not new_line:\n string = r'${'+string+'}$'\n new_OTU.append(string)\n \n OTU = new_OTU\n ax1 = plt.subplot2grid((3, 4), (0, 0), colspan=3) \n ax2 = plt.subplot2grid((3, 4), (1, 0), colspan=3, sharex=ax1)\n ax3 = plt.subplot2grid((3, 4), (2, 0), colspan=3, sharex=ax1)\n ax4 = plt.subplot2grid((3, 4), (0, 3), colspan=1)\n ax5 = plt.subplot2grid((3, 4), (1, 3), colspan=1, sharex=ax4)\n ax1.set_title('Long')\n ax4.set_title('Short')\n colors = get_distinct_colors(len(numbers))\n \n data = numpy.array(G)\n bottom = numpy.cumsum(data, axis=0)\n ax1.bar(gens_G, data[0], color=colors[0], label=OTU[0])\n for j in xrange(1, data.shape[0]):\n ax1.bar(gens_G, data[j], color=colors[j], bottom=bottom[j-1], label=OTU[j])\n \n data = numpy.array(R)\n bottom = numpy.cumsum(data, axis=0)\n ax2.bar(gens_R, data[0], color=colors[0], label=OTU[0])\n for j in xrange(1, data.shape[0]):\n ax2.bar(gens_R, data[j], color=colors[j], bottom=bottom[j-1], label=OTU[j])\n \n data = numpy.array(L)\n bottom = numpy.cumsum(data, axis=0)\n ax3.bar(gens_L, data[0], color=colors[0], label=OTU[0])\n for j in xrange(1, data.shape[0]):\n ax3.bar(gens_L, data[j], color=colors[j], bottom=bottom[j-1], label=OTU[j])\n \n data = numpy.array(SG)\n bottom = numpy.cumsum(data, axis=0)\n ax4.bar(gens_SG, data[0], color=colors[0], label=OTU[0])\n for j in xrange(1, data.shape[0]):\n ax4.bar(gens_SG, data[j], color=colors[j], bottom=bottom[j-1], label=OTU[j])\n \n data = numpy.array(SR)\n bottom = numpy.cumsum(data, axis=0)\n ax5.bar(gens_SR, data[0], color=colors[0], label=OTU[0])\n for j in xrange(1, data.shape[0]):\n ax5.bar(gens_SR, data[j], color=colors[j], bottom=bottom[j-1], label=OTU[j])\n \n ax4.set_xlim([15.8, 21])\n ax5.set_xticks([16, 17, 18, 19, 20])\n ax1.set_ylim([0, 100])\n ax2.set_ylim([0, 100])\n ax3.set_ylim([0, 100])\n ax4.set_ylim([0, 100])\n ax5.set_ylim([0, 100])\n ax1.set_xlim([-0.2, 20])\n plt.tight_layout()\n plt.setp(ax1.get_xticklabels(), visible=False)\n plt.setp(ax2.get_xticklabels(), visible=False)\n plt.setp(ax4.get_xticklabels(), visible=False)\n plt.setp(ax4.get_yticklabels(), visible=False)\n plt.setp(ax5.get_yticklabels(), visible=False)\n #ax1.set_ylabel('Relative abundance (%)')\n ax2.set_ylabel('Relative abundance (%)')\n #ax3.set_ylabel('Relavive abundance (%)')\n plt.subplots_adjust(left=0.09,right=0.97,top=0.96,bottom=0.08,wspace=0.1, hspace=0.1)\n ax4.legend(loc='upper left', bbox_to_anchor=(1, 1.1), ncol=3, fontsize=11)\n os.chdir('/Users/u1560915/Documents/GitHub/ChitinActivity/MiSeq/16S/Figures/')\n plt.savefig('Supplementary fig 10.pdf', bbox_inches='tight')\n plt.close()\n return G, R, L, SG, SR, gens_G, gens_R, gens_L, gens_SG, gens_SR\nos.chdir('/Users/u1560915/Documents/GitHub/ChitinActivity/MiSeq/16S/Supplementary 6 diversity 10 abundance/')\nstacked_barchart('0_percent_grouped.csv', 6)\n#levels: Genus = 6, Family = 5, Order = 4, Class = 3, Phylum = 2\n\ndef plot_heatmap():\n os.chdir('/Users/u1560915/Documents/GitHub/ChitinActivity/MiSeq/16S/Supplementary 6 diversity 10 abundance/')\n #plot heatmap with diversity and richness represented as a color for each generation\n #You can run them with Bray-Curtis, Jaccard, weighted or unweighted UniFrac to answer different questions. For example, if your variable is significant for Bray-Curtis/weighted UniFrac but not Jaccard/unweighted UniFrac, this means your groups tend to have the same OTUs (richness) but different abundances of those OTUs (diversity). When variables are signficant for Bray-Curtis/Jaccard but not UniFrac, this indicates that your samples have different specific OTUs but similar taxa.\n #High Bray-Curtis = diversity and abundance\n with open('0_diversity.csv', 'rU') as f:\n reader = csv.reader(f)\n rows = []\n for row in reader:\n rows.append(row) \n coverage = [[], [], [], [], []]\n bergerparker = [[], [], [], [], []]\n chao = [[], [], [], [], []]\n shannon = [[], [], [], [], []]\n simpsons = [[], [], [], [], []]\n for a in range(len(rows[0])):\n if a > 0:\n name = rows[0][a]\n beg, end = name[0], name[-1]\n if beg == 'L' and end == 'G':\n coverage[0].append(rows[1][a])\n bergerparker[0].append(rows[2][a])\n chao[0].append(rows[3][a])\n shannon[0].append(rows[4][a])\n simpsons[0].append(rows[5][a])\n elif beg == 'L' and end == 'R':\n coverage[1].append(rows[1][a])\n bergerparker[1].append(rows[2][a])\n chao[1].append(rows[3][a])\n shannon[1].append(rows[4][a])\n simpsons[1].append(rows[5][a])\n elif beg == 'L' and end == 'L':\n coverage[2].append(rows[1][a])\n bergerparker[2].append(rows[2][a])\n chao[2].append(rows[3][a])\n shannon[2].append(rows[4][a])\n simpsons[2].append(rows[5][a])\n elif beg == 'S' and end == 'G':\n coverage[3].append(rows[1][a])\n bergerparker[3].append(rows[2][a])\n chao[3].append(rows[3][a])\n shannon[3].append(rows[4][a])\n simpsons[3].append(rows[5][a])\n elif beg == 'S' and end == 'R':\n coverage[4].append(rows[1][a])\n bergerparker[4].append(rows[2][a])\n chao[4].append(rows[3][a])\n shannon[4].append(rows[4][a])\n simpsons[4].append(rows[5][a])\n all_diversity = [coverage, bergerparker, chao, shannon, simpsons]\n all_max, all_min = [], []\n for b in range(len(all_diversity)):\n m, s = 0, 1\n maxs, mins = [], []\n for c in range(len(all_diversity[b])):\n maxs.append(max(all_diversity[b][c]))\n mins.append(min(all_diversity[b][c]))\n for d in range(len(all_diversity[b][c])):\n all_diversity[b][c][d] = float(all_diversity[b][c][d])\n if all_diversity[b][c][d] > m:\n m = all_diversity[b][c][d]\n if all_diversity[b][c][d] < s:\n s = all_diversity[b][c][d]\n ma, mi = max(maxs), min(mins)\n all_max.append(float(ma))\n all_min.append(float(mi))\n for e in range(len(all_diversity)):\n m = all_max[e]\n for f in range(len(all_diversity[e])):\n for g in range(len(all_diversity[e][f])):\n num = all_diversity[e][f][g]/all_max[e]\n all_diversity[e][f][g] = num\n all_max, all_min = [], []\n for z in range(len(all_diversity)):\n maxs, mins = [], []\n for y in range(len(all_diversity[z])):\n maxs.append(max(all_diversity[z][y]))\n mins.append(min(all_diversity[z][y]))\n all_max.append(max(maxs))\n all_min.append(min(mins))\n ax1 = plt.subplot2grid((3, 4), (0, 0), colspan=3) \n ax2 = plt.subplot2grid((3, 4), (1, 0), colspan=3, sharex=ax1, sharey=ax1)\n ax3 = plt.subplot2grid((3, 4), (2, 0), colspan=3, sharex=ax1, sharey=ax1)\n ax4 = plt.subplot2grid((3, 4), (0, 3), colspan=1, sharey=ax1)\n ax5 = plt.subplot2grid((3, 4), (1, 3), colspan=1, sharex=ax4, sharey=ax1)\n x1 = [0, 1, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20]\n x2 = [0, 1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20]\n x3 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20]\n x4 = [16, 17, 18, 19, 20]\n x5 = [16, 17, 18, 19, 20]\n for a in range(21):\n if a != x1[a]:\n x1.insert(a, a)\n all_diversity[0][0].insert(a, 0)\n all_diversity[1][0].insert(a, 0)\n all_diversity[2][0].insert(a, 0)\n all_diversity[3][0].insert(a, 0)\n all_diversity[4][0].insert(a, 0)\n for b in range(21):\n if b != x2[b]:\n x2.insert(b, b)\n all_diversity[0][1].insert(b, 0)\n all_diversity[1][1].insert(b, 0)\n all_diversity[2][1].insert(b, 0)\n all_diversity[3][1].insert(b, 0)\n all_diversity[4][1].insert(b, 0)\n for c in range(21):\n if c != x3[c]:\n x3.insert(c, c)\n all_diversity[0][2].insert(c, 0)\n all_diversity[1][2].insert(c, 0)\n all_diversity[2][2].insert(c, 0)\n all_diversity[3][2].insert(c, 0)\n all_diversity[4][2].insert(c, 0)\n G, R, L, GS, RS = [all_diversity[0][0], all_diversity[1][0], all_diversity[2][0], all_diversity[3][0], all_diversity[4][0]], [all_diversity[0][1], all_diversity[1][1], all_diversity[2][1], all_diversity[3][1], all_diversity[4][1]], [all_diversity[0][2], all_diversity[1][2], all_diversity[2][2], all_diversity[3][2], all_diversity[4][2]], [all_diversity[0][3], all_diversity[1][3], all_diversity[2][3], all_diversity[3][3], all_diversity[4][3]], [all_diversity[0][4], all_diversity[1][4], all_diversity[2][4], all_diversity[3][4], all_diversity[4][4]]\n cmap = 'Blues'\n norm = matplotlib.colors.Normalize(vmin=0, vmax=1)\n colormap = matplotlib.cm.get_cmap(cmap, 256)\n m = matplotlib.cm.ScalarMappable(norm=norm, cmap=colormap)\n colors1 = []\n for a in range(len(G)):\n this_row = []\n for b in range(len(G[a])):\n if G[a][b] == 0:\n color = (0, 0, 0, 0)\n else:\n color = m.to_rgba(G[a][b])\n this_row.append(color)\n colors1.append(this_row)\n colors = colors1\n l = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n plot = [l, l, l, l]\n data = numpy.array(plot)\n bottom = numpy.cumsum(data, axis=0)\n ax1.bar(x1, data[0], color=colors[0], width=1.0)\n for j in xrange(1, data.shape[0]):\n ax1.bar(x1, data[j], color=colors[j], bottom=bottom[j-1], width=1.0)\n colors2 = []\n for a in range(len(R)):\n this_row = []\n for b in range(len(R[a])):\n if R[a][b] == 0:\n color = (0, 0, 0, 0)\n else:\n color = m.to_rgba(R[a][b])\n this_row.append(color)\n colors2.append(this_row)\n colors = colors2\n ax2.bar(x1, data[0], color=colors[0], width=1.0)\n for j in xrange(1, data.shape[0]):\n ax2.bar(x1, data[j], color=colors[j], bottom=bottom[j-1], width=1.0)\n colors3 = []\n for a in range(len(L)):\n this_row = []\n for b in range(len(L[a])):\n if L[a][b] == 0:\n color = (0, 0, 0, 0)\n else:\n color = m.to_rgba(L[a][b])\n this_row.append(color)\n colors3.append(this_row)\n colors = colors3\n ax3.bar(x1, data[0], color=colors[0], width=1.0)\n for j in xrange(1, data.shape[0]):\n ax3.bar(x1, data[j], color=colors[j], bottom=bottom[j-1], width=1.0)\n \n s = [1, 1, 1, 1, 1]\n plot = [s, s, s, s]\n data = numpy.array(plot)\n bottom = numpy.cumsum(data, axis=0)\n colors4 = []\n for a in range(len(GS)):\n this_row = []\n for b in range(len(GS[a])):\n color = m.to_rgba(GS[a][b])\n this_row.append(color)\n colors4.append(this_row)\n colors = colors4\n ax4.bar(x4, data[0], color=colors[0], width=1.0)\n for j in xrange(1, data.shape[0]):\n ax4.bar(x4, data[j], color=colors[j], bottom=bottom[j-1], width=1.0)\n \n colors5 = []\n for a in range(len(RS)):\n this_row = []\n for b in range(len(RS[a])):\n color = m.to_rgba(RS[a][b])\n this_row.append(color)\n colors5.append(this_row)\n colors = colors5\n ax5.bar(x4, data[0], color=colors[0], width=1.0)\n for j in xrange(1, data.shape[0]):\n ax5.bar(x4, data[j], color=colors[j], bottom=bottom[j-1], width=1.0)\n ax1.set_xlim([0, 21])\n plt.setp(ax4.get_yticklabels(), visible=False)\n plt.setp(ax5.get_yticklabels(), visible=False)\n plt.setp(ax1.get_xticklabels(), visible=False)\n plt.setp(ax2.get_xticklabels(), visible=False)\n plt.setp(ax4.get_xticklabels(), visible=False)\n ax3.set_xticks(x1)\n os.chdir('/Users/u1560915/Documents/GitHub/ChitinActivity/MiSeq/16S/Figures/')\n #plt.savefig('All_diversity.pdf', bbox_inches='tight')\n plt.close() \n \n names = ['Coverage', 'Berger-Parker', 'Chao', 'Shannon', 'Simpsons']\n x = [x1, x2, x3, x4, x5]\n all_colors = [colors1, colors2, colors3, colors4, colors5]\n y = [l, l, l, s, s]\n for a in range(5):\n diversity = all_diversity[a]\n ax1 = plt.subplot2grid((10, 4), (0, 0), colspan=3) \n ax2 = plt.subplot2grid((10, 4), (1, 0), colspan=3, sharex=ax1, sharey=ax1)\n ax3 = plt.subplot2grid((10, 4), (2, 0), colspan=3, sharex=ax1, sharey=ax1)\n ax4 = plt.subplot2grid((10, 4), (0, 3), colspan=1, sharey=ax1)\n ax5 = plt.subplot2grid((10, 4), (1, 3), colspan=1, sharex=ax4, sharey=ax1)\n ax6 = plt.subplot2grid((10, 4), (2, 3), colspan=1)\n ax6.set_ylim([0.1,1])\n maxs, mins = [], []\n ma, mi = 0, 1\n for c in range(len(diversity)):\n maxs.append(max(diversity[c]))\n mins.append(min(diversity[c]))\n for d in range(len(diversity[c])):\n if diversity[c][d] > ma:\n ma = diversity[c][d]\n if diversity[c][d] < mi:\n mi = diversity[c][d]\n axes = [ax1, ax2, ax3, ax4, ax5]\n for b in range(5):\n ma, mi = all_max[b], all_min[b]\n cmap = mpl.cm.Blues\n norm = matplotlib.colors.Normalize(vmin=mi, vmax=ma)\n colormap = matplotlib.cm.get_cmap(cmap, 256)\n m = matplotlib.cm.ScalarMappable(norm=norm, cmap=colormap)\n colors = []\n for c in range(len(diversity[b])):\n if diversity[b][c] == 0:\n color = (0, 0, 0, 0)\n else:\n color = m.to_rgba(diversity[b][c])\n colors.append(color)\n axes[b].bar(x[b], y[b], color=colors, width=1.0)\n plt.setp(ax1.get_yticklabels(), visible=False)\n plt.setp(ax1.get_xticklabels(), visible=False)\n plt.setp(ax2.get_yticklabels(), visible=False)\n plt.setp(ax3.get_yticklabels(), visible=False)\n plt.setp(ax3.get_xticklabels(), visible=False)\n plt.setp(ax4.get_yticklabels(), visible=False)\n plt.setp(ax5.get_yticklabels(), visible=False)\n plt.setp(ax1.get_xticklabels(), visible=False)\n plt.setp(ax2.get_xticklabels(), visible=False)\n plt.setp(ax4.get_xticklabels(), visible=False)\n plt.setp(ax5.get_xticklabels(), visible=False)\n ax1.tick_params(axis='y',which='both',left='off',right='off')\n ax2.tick_params(axis='y',which='both',left='off',right='off')\n ax3.tick_params(axis='y',which='both',left='off',right='off')\n ax4.tick_params(axis='y',which='both',left='off',right='off')\n ax5.tick_params(axis='y',which='both',left='off',right='off')\n ax1.set_title('Long')\n ax4.set_title('Short')\n ax1.text(-2.5, 0.25, 'Good')\n ax2.text(-3.5, 0.25, 'Random')\n ax3.text(-2.5, 0.25, 'Light')\n ax1.plot([2,3], [0,1], 'k')\n ax1.plot([5,6], [0,1], 'k')\n ax1.plot([7,8], [0,1], 'k')\n ax1.plot([15,16], [0,1], 'k')\n ax2.plot([4,5], [0,1], 'k')\n ax2.plot([7,8], [0,1], 'k')\n ax2.plot([15,16], [0,1], 'k')\n ax2.plot([17,18], [0,1], 'k')\n ax3.plot([15,16], [0,1], 'k')\n cmap = mpl.cm.Blues\n norm = mpl.colors.Normalize(vmin=mi, vmax=ma)\n cb1 = mpl.colorbar.ColorbarBase(ax6, cmap=cmap, norm=norm, orientation='horizontal')\n cb1.set_ticks([])\n #cb1.set_ticklabels(['Low', 'High'])\n #cb1.ax.tick_params(labelsize=8) \n cb1.ax.text(-0.1, -1, 'Low')\n cb1.ax.text(0.9, -1, 'High')\n ax1.set_xlim([0, 21])\n plt.subplots_adjust(left=0.09,right=0.97,top=0.96,bottom=0.08,wspace=0.15, hspace=0.5)\n os.chdir('/Users/u1560915/Documents/GitHub/ChitinActivity/MiSeq/16S/Figures/')\n #plt.savefig('Diversity'+names[a]+'.pdf', bbox_inches='tight')\n if names[a] == 'Simpsons':\n plt.savefig('Supplementary fig 6.pdf', bbox_inches='tight')\n plt.close() \n return\nplot_heatmap()", "sub_path": "MiSeq/16S/Supplementary 6 diversity 10 abundance/0_plotting_all_daily.py", "file_name": "0_plotting_all_daily.py", "file_ext": "py", "file_size_in_byte": 21084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "csv.reader", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "colorsys.hls_to_rgb", "line_number": 38, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 39, "usage_type": "call"}, {"api_name": "classify_file.classify_file", "line_number": 43, "usage_type": "call"}, {"api_name": "classify_file.group_file", "line_number": 44, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 204, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 209, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 324, "usage_type": "attribute"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 325, "usage_type": "attribute"}, {"api_name": "matplotlib.cm.ScalarMappable", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 326, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 402, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 402, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 403, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 403, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 404, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 404, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 405, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 405, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 406, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 406, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 408, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 410, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 410, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 418, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 418, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 419, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 419, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 420, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 420, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 421, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 421, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 422, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 422, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 423, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 423, "usage_type": "name"}, {"api_name": "matplotlib.cm", "line_number": 438, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 439, "usage_type": "attribute"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 440, "usage_type": "attribute"}, {"api_name": "matplotlib.cm.ScalarMappable", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 441, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 450, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 450, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 451, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 451, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 452, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 452, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 453, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 453, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 454, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 454, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 455, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 455, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 457, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 458, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 458, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 459, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 460, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 460, "usage_type": "name"}, {"api_name": "matplotlib.cm", "line_number": 480, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 481, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 481, "usage_type": "attribute"}, {"api_name": "matplotlib.colorbar.ColorbarBase", "line_number": 482, "usage_type": "call"}, {"api_name": "matplotlib.colorbar", "line_number": 482, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 489, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 489, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 490, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 493, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 493, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 494, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 494, "usage_type": "name"}]} +{"seq_id": "406781600", "text": "\"\"\"\n @author : Manouchehr Rasouli\n @data : 19 March 2018\n @since : 19 March 2018\n\"\"\"\nimport logging\n\n\ndef logger(level, message):\n FORMAT = '%(asctime)-15s %(level)s %(data)s %(message)-8s'\n logging.basicConfig(format=FORMAT)\n d = {'level': level, 'data': message}\n logger = logging.getLogger('monitoring_engine')\n logger.warning('%s', ' ', extra=d)\n", "sub_path": "logger/logging.py", "file_name": "logging.py", "file_ext": "py", "file_size_in_byte": 377, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "572909458", "text": "from django.urls import path\n\nfrom analysis.views import check, report, bot_check, dashboard, flag_as_fake\n\nurlpatterns = [\n path('check/', check, name='check'),\n path('report/', report, name='report'),\n path('dashboard/', dashboard, name='dashboard'),\n path('flag-fake/', flag_as_fake, name='flag_as_fake'),\n path('bot-check/', bot_check, name='bot_check'),\n]\n", "sub_path": "analysis/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 392, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "analysis.views.check", "line_number": 6, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "analysis.views.report", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "analysis.views.dashboard", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "analysis.views.flag_as_fake", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "analysis.views.bot_check", "line_number": 10, "usage_type": "argument"}]} +{"seq_id": "246103156", "text": "from __future__ import unicode_literals\nfrom itertools import chain\n\nfrom django.db import migrations\n\n\ndef populate_permissions_lists(apps):\n permission_class = apps.get_model('auth', 'Permission')\n\n instructor_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='instructor')\n\n student_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='student')\n\n period_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='period')\n\n year_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='year')\n\n semester_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='semester')\n\n course_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='course')\n\n section_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='section')\n\n registration_permissions = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='registration')\n\n perm_view_instructor = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='instructor',\n codename='view_instructor')\n\n perm_view_student = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='student',\n codename='view_student')\n\n perm_view_period = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='period',\n codename='view_period')\n\n perm_view_year = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='year',\n codename='view_year')\n\n perm_view_semester = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='semester',\n codename='view_semester')\n\n perm_view_course = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='course',\n codename='view_course')\n\n perm_view_section = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='section',\n codename='view_section')\n\n perm_view_registration = permission_class.objects.filter(content_type__app_label='courseinfo',\n content_type__model='registration',\n codename='view_registration')\n\n ci_user_permissions = chain(perm_view_instructor,\n perm_view_period,\n perm_view_year,\n perm_view_student,\n perm_view_semester,\n perm_view_course,\n perm_view_section,\n perm_view_registration)\n\n ci_scheduler_permissions = chain(instructor_permissions,\n period_permissions,\n year_permissions,\n semester_permissions,\n course_permissions,\n section_permissions,\n perm_view_student,\n perm_view_registration)\n\n ci_registrar_permissions = chain(student_permissions,\n registration_permissions,\n perm_view_instructor,\n perm_view_period,\n perm_view_year,\n perm_view_course,\n perm_view_semester,\n perm_view_section)\n\n my_groups_initialization_list = [\n {\n \"name\": \"ci_user\",\n \"permissions_list\": ci_user_permissions,\n },\n {\n \"name\": \"ci_scheduler\",\n \"permissions_list\": ci_scheduler_permissions,\n },\n {\n \"name\": \"ci_registrar\",\n \"permissions_list\": ci_registrar_permissions,\n },\n ]\n return my_groups_initialization_list\n\n\ndef add_group_permissions_data(apps, schema_editor):\n groups_initialization_list = populate_permissions_lists(apps)\n\n group_model_class = apps.get_model('auth', 'Group')\n for group in groups_initialization_list:\n if group['permissions_list'] is not None:\n group_object = group_model_class.objects.get(\n name=group['name']\n )\n group_object.permissions.set(group['permissions_list'])\n group_object.save()\n\n\ndef remove_group_permissions_data(apps, schema_editor):\n groups_initialization_list = populate_permissions_lists(apps)\n\n group_model_class = apps.get_model('auth', 'Group')\n for group in groups_initialization_list:\n if group['permissions_list'] is not None:\n group_object = group_model_class.objects.get(\n name=group['name']\n )\n list_of_permissions = group['permissions_list']\n for permission in list_of_permissions:\n group_object.permissions.remove(permission)\n group_object.save()\n\n\nclass Migration(migrations.Migration):\n dependencies = [\n ('courseinfo', '0004_create_groups'),\n ]\n\n operations = [\n migrations.RunPython(\n add_group_permissions_data,\n remove_group_permissions_data\n )\n ]\n", "sub_path": "courseinfo/migrations/0005_create_group_permissions.py", "file_name": "0005_create_group_permissions.py", "file_ext": "py", "file_size_in_byte": 6970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "itertools.chain", "line_number": 66, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 75, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.migrations.Migration", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 138, "usage_type": "name"}, {"api_name": "django.db.migrations.RunPython", "line_number": 144, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 144, "usage_type": "name"}]} +{"seq_id": "601314630", "text": "import hashlib\nimport json\nimport os\nimport sys\nimport time\nimport unittest\n\nimport numpy as np\nimport requests\nimport skimage.io\n\nimport slicedimage\nfrom tests.utils import (\n build_skeleton_manifest,\n ContextualChildProcess,\n TemporaryDirectory,\n unused_tcp_port,\n)\n\n\nclass TestCachingBackend(unittest.TestCase):\n def setUp(self, timeout_seconds=5):\n self.contexts = []\n self.tempdir = TemporaryDirectory()\n self.contexts.append(self.tempdir)\n self.port = unused_tcp_port()\n\n if sys.version_info[0] == 2:\n module = \"SimpleHTTPServer\"\n elif sys.version_info[0] == 3:\n module = \"http.server\"\n else:\n raise Exception(\"unknown python version\")\n\n self.contexts.append(ContextualChildProcess(\n [\n \"python\",\n \"-m\",\n module,\n str(self.port),\n ],\n cwd=self.tempdir.name,\n ).__enter__())\n\n end = time.time() + timeout_seconds\n while True:\n try:\n requests.get(\"http://0.0.0.0:{port}\".format(port=self.port))\n break\n except requests.ConnectionError:\n if time.time() > end:\n raise\n\n def tearDown(self):\n for context in self.contexts:\n context.__exit__(*sys.exc_info())\n\n def test_cached_backend(self):\n \"\"\"\n Generate a tileset consisting of a single TIFF tile. Deposit it where the HTTP server can\n find the tileset, and fetch it. Then delete the TIFF file and re-run Reader.parse_doc with\n the same url and manifest to make sure we get the same results pulling the file\n from the cache\n \"\"\"\n # write the tiff file\n data = np.random.randint(0, 65535, size=(100, 100), dtype=np.uint16)\n file_path = os.path.join(self.tempdir.name, \"tile.tiff\")\n skimage.io.imsave(file_path, data, plugin=\"tifffile\")\n with open(file_path, \"rb\") as fh:\n checksum = hashlib.sha256(fh.read()).hexdigest()\n manifest = build_skeleton_manifest()\n manifest['tiles'].append(\n {\n \"coordinates\": {\n \"x\": [\n 0.0,\n 0.0001,\n ],\n \"y\": [\n 0.0,\n 0.0001,\n ]\n },\n \"indices\": {\n \"hyb\": 0,\n \"ch\": 0,\n },\n \"file\": \"tile.tiff\",\n \"format\": \"tiff\",\n \"sha256\": checksum\n },\n )\n with open(os.path.join(self.tempdir.name, \"tileset.json\"), \"w\") as fh:\n fh.write(json.dumps(manifest))\n\n result = slicedimage.Reader.parse_doc(\n \"tileset.json\",\n \"http://localhost:{port}/\".format(port=self.port))\n\n self.assertTrue(np.array_equal(list(result.tiles())[0].numpy_array, data))\n\n os.remove(os.path.join(self.tempdir.name, \"tile.tiff\"))\n result = slicedimage.Reader.parse_doc(\n \"tileset.json\",\n \"http://localhost:{port}/\".format(port=self.port))\n\n self.assertTrue(np.array_equal(list(result.tiles())[0].numpy_array, data))\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "sub_path": "tests/io/v0_0_0/test_caching_backend.py", "file_name": "test_caching_backend.py", "file_ext": "py", "file_size_in_byte": 3374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tests.utils.TemporaryDirectory", "line_number": 24, "usage_type": "call"}, {"api_name": "tests.utils.unused_tcp_port", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tests.utils.ContextualChildProcess", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 50, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "skimage.io.io.imsave", "line_number": 68, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 68, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 68, "usage_type": "name"}, {"api_name": "hashlib.sha256", "line_number": 70, "usage_type": "call"}, {"api_name": "tests.utils.build_skeleton_manifest", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 94, "usage_type": "call"}, {"api_name": "slicedimage.Reader.parse_doc", "line_number": 96, "usage_type": "call"}, {"api_name": "slicedimage.Reader", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 100, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "slicedimage.Reader.parse_doc", "line_number": 103, "usage_type": "call"}, {"api_name": "slicedimage.Reader", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 107, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "268407341", "text": "import os\nimport datetime\nfrom . import file\nfrom .notifications import Common\nfrom .custom_logger import log\ndef determine_action(url, driver, scroll_pause_time, reverse_chronological, file_name, txt, csv, markdown, logging_locations):\n common_message = Common()\n now = datetime.datetime.now\n txt_exists = os.path.isfile(f'{file_name}.txt') if txt else False\n csv_exists = os.path.isfile(f'{file_name}.csv') if csv else False\n md_exists = os.path.isfile(f'{file_name}.md') if markdown else False\n txt_videos = None\n csv_videos = None\n md_videos = None\n current_condition = (txt, txt_exists, csv, csv_exists, markdown, md_exists)\n update_conditions = set(\n (\n (True, True, True, True, True, True),\n (True, True, True, True, False, False),\n (True, True, False, False, True, True),\n (False, False, True, True, True, True),\n (True, True, False, False, False, False),\n (False, False, False, False, True, True),\n (False, False, True, True, False, False),\n )\n )\n if current_condition in update_conditions: videos_list, txt_videos, csv_videos, md_videos = file.scroller.scroll_to_old_videos(url, driver, scroll_pause_time, logging_locations, file_name, txt_exists, csv_exists, md_exists)\n else: videos_list = file.scroller.scroll_to_bottom (url, driver, scroll_pause_time, logging_locations)\n if len(videos_list) == 0:\n log(common_message.no_videos_found, logging_locations)\n return\n if txt:\n if txt_exists: file.write.update_file('txt', videos_list, file_name, reverse_chronological, logging_locations, timestamp=now().isoformat().replace(':', '-').replace('.', '_'), stored_in_file=txt_videos)\n else: file.write.create_file('txt', videos_list, file_name, reverse_chronological, logging_locations, timestamp=now().isoformat().replace(':', '-').replace('.', '_'))\n if csv:\n if csv_exists: file.write.update_file('csv', videos_list, file_name, reverse_chronological, logging_locations, timestamp=now().isoformat().replace(':', '-').replace('.', '_'), stored_in_file=csv_videos)\n else: file.write.create_file('csv', videos_list, file_name, reverse_chronological, logging_locations, timestamp=now().isoformat().replace(':', '-').replace('.', '_'))\n if markdown:\n if md_exists: file.write.update_file('md', videos_list, file_name, reverse_chronological, logging_locations, timestamp=now().isoformat().replace(':', '-').replace('.', '_'), stored_in_file=md_videos)\n else: file.write.create_file('md', videos_list, file_name, reverse_chronological, logging_locations, timestamp=now().isoformat().replace(':', '-').replace('.', '_'))\n", "sub_path": "python/yt_videos_list/program.py", "file_name": "program.py", "file_ext": "py", "file_size_in_byte": 2605, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "notifications.Common", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "custom_logger.log", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "615824348", "text": "import pyrealsense2 as rs\r\nimport numpy as np\r\nimport cv2\r\nimport math\r\nimport datetime\r\nimport socket\r\nimport json\r\nimport os\r\nfrom yolo import YOLO\r\nimport yolo_cam_vR as vR\r\n'''\r\n[深度カメラの表示用プログラム・本番用]\r\nrgbカメラと深度カメラの画角合わせ・深度カメラ画像へのフィルタがかかっている\r\n本番では、このプログラムで取得している画像や情報等を用いること\r\n[参考]\r\nhttps://qiita.com/gatideatui/items/7e76d85149b9b95f3888\r\nhttps://qiita.com/idev_jp/items/3eba792279d836646664\r\n'''\r\n\r\n#ソケット通信の始まり\r\nclass Connect():\r\n def __init__(self):\r\n self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n self.s.connect(('192.168.0.35', 50007))\r\n\r\n def setsenddata(self, data):\r\n strdata = json.dumps(data)\r\n bindata = strdata.encode()\r\n # サーバにメッセージを送る\r\n self.s.sendall(bindata)\r\n\r\n def getrecvdata(self):\r\n # ネットワークのバッファサイズは1024。サーバからの文字列を取得する\r\n data = self.s.recv(1024)\r\n return data\r\n\r\n def close(self):\r\n self.s.close()\r\n\r\n# カメラの設定を指定するクラス\r\nclass AppState:\r\n def __init__(self, *args, **kwargs):\r\n self.WIN_NAME = 'RealSense'\r\n self.pitch, self.yaw = math.radians(-10), math.radians(-15)\r\n self.translation = np.array([0, 0, -1], dtype=np.float32)\r\n self.distance = 2\r\n self.prev_mouse = 0, 0\r\n self.mouse_btns = [False, False, False]\r\n self.paused = False\r\n self.decimate = 1\r\n self.scale = True\r\n self.color = True\r\n\r\n def reset(self):\r\n self.pitch, self.yaw, self.distance = 0, 0, 2\r\n self.translation[:] = 0, 0, -1\r\n\r\n @property\r\n def rotation(self):\r\n Rx, _ = cv2.Rodrigues((self.pitch, 0, 0))\r\n Ry, _ = cv2.Rodrigues((0, self.yaw, 0))\r\n return np.dot(Ry, Rx).astype(np.float32)\r\n\r\n @property\r\n def pivot(self):\r\n return self.translation + np.array((0, 0, self.distance), dtype=np.float32)\r\n\r\n# depthカメラのフィルタ周りの設定\r\nstate = AppState()\r\ndecimate = rs.decimation_filter()\r\ndecimate.set_option(rs.option.filter_magnitude, 1 ** state.decimate)\r\ndepth_to_disparity = rs.disparity_transform(True)\r\ndisparity_to_depth = rs.disparity_transform(False)\r\nspatial = rs.spatial_filter()\r\nspatial.set_option(rs.option.filter_smooth_alpha, 0.6)\r\nspatial.set_option(rs.option.filter_smooth_delta, 8)\r\ntemporal = rs.temporal_filter()\r\ntemporal.set_option(rs.option.filter_smooth_alpha, 0.5)\r\ntemporal.set_option(rs.option.filter_smooth_delta, 20)\r\nhole_filling = rs.hole_filling_filter()\r\n\r\n\r\n# 深度画像とRGB画像を録画\r\ndef realsence():\r\n # rgb画像の画角を取得\r\n align = rs.align(rs.stream.color)\r\n\r\n # 設定の反映\r\n config = rs.config()\r\n\r\n # RGB画像と深度情報の解像度・fpsの指定\r\n config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)\r\n config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)\r\n\r\n # ストリーミング開始\r\n pipeline = rs.pipeline()\r\n profile = pipeline.start(config)\r\n\r\n intr = profile.get_stream(rs.stream.color).as_video_stream_profile().get_intrinsics()\r\n print(intr.width, intr.height, intr.fx, intr.fy, intr.ppx, intr.ppy)\r\n\r\n c=Connect()\r\n\r\n try:\r\n while True:\r\n # フレーム待ち\r\n frames = pipeline.wait_for_frames()\r\n aligned_frames = align.process(frames)\r\n\r\n # RGB画像\r\n RGB_frame = aligned_frames.get_color_frame()\r\n RGB_image = np.asanyarray(RGB_frame.get_data())\r\n\r\n # 深度情報\r\n depth_frame = aligned_frames.get_depth_frame()\r\n\r\n # 深度情報に対するフィルタ処理\r\n depth_frame = decimate.process(depth_frame)\r\n depth_frame = depth_to_disparity.process(depth_frame)\r\n depth_frame = spatial.process(depth_frame)\r\n depth_frame = temporal.process(depth_frame)\r\n depth_frame = disparity_to_depth.process(depth_frame)\r\n depth_frame = hole_filling.process(depth_frame)\r\n\r\n # 深度情報の整形\r\n depth_image = np.asanyarray(depth_frame.get_data()) # 深度情報を読み込み\r\n depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.0255),\r\n cv2.COLORMAP_JET) # 深度情報を疑似カラー画像に変換\r\n \r\n human_num,dis_list,dis_flag= vR.detect_img(YOLO(),RGB_image,depth_image)\r\n send_data(c,human_num,dis_list,dis_flag)\r\n # 表示表示\r\n #images = np.hstack((RGB_image, depth_colormap))\r\n #cv2.imshow('RealSense', images) # 画像表示\r\n\r\n if cv2.waitKey(1) & 0xff == 27: # ESCで終了\r\n cv2.destroyAllWindows()\r\n break\r\n\r\n finally:\r\n # ストリーミング停止\r\n pipeline.stop()\r\n c.close()\r\n\r\n# データ送信（ソケット通信）\r\ndef send_data(c, human_num, dis_list, dis_flag):\r\n #データ送信\r\n data = {\r\n \"id\":3,\r\n \"num\":human_num, #入室人数\r\n \"metre\":dis_list, #各人物の距離推定\r\n \"metre_NG\": dis_flag #距離推定の結果２m以内の有無\r\n }\r\n print(data)\r\n c.setsenddata(data)\r\n send_status = c.getrecvdata()\r\n\r\n\r\nif __name__ == '__main__':\r\n realsence()", "sub_path": "yolov3/realsense_v3.py", "file_name": "realsense_v3.py", "file_ext": "py", "file_size_in_byte": 5563, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "socket.socket", "line_number": 23, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 23, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.Rodrigues", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.Rodrigues", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pyrealsense2.decimation_filter", "line_number": 70, "usage_type": "call"}, {"api_name": "pyrealsense2.option", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pyrealsense2.disparity_transform", "line_number": 72, "usage_type": "call"}, {"api_name": "pyrealsense2.disparity_transform", "line_number": 73, "usage_type": "call"}, {"api_name": "pyrealsense2.spatial_filter", "line_number": 74, "usage_type": "call"}, {"api_name": "pyrealsense2.option", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pyrealsense2.option", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pyrealsense2.temporal_filter", "line_number": 77, "usage_type": "call"}, {"api_name": "pyrealsense2.option", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pyrealsense2.option", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pyrealsense2.hole_filling_filter", "line_number": 80, "usage_type": "call"}, {"api_name": "pyrealsense2.align", "line_number": 86, "usage_type": "call"}, {"api_name": "pyrealsense2.stream", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pyrealsense2.config", "line_number": 89, "usage_type": "call"}, {"api_name": "pyrealsense2.stream", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pyrealsense2.format", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pyrealsense2.stream", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pyrealsense2.format", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pyrealsense2.pipeline", "line_number": 96, "usage_type": "call"}, {"api_name": "pyrealsense2.stream", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.asanyarray", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 128, "usage_type": "attribute"}, {"api_name": "yolo_cam_vR.detect_img", "line_number": 130, "usage_type": "call"}, {"api_name": "yolo.YOLO", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "101060563", "text": "#!/usr/bin/python3\n\"\"\"\nSends a request to the URL and displays the body of the response in utf-8 with\nrequests module.\n\"\"\"\n\nfrom sys import argv\nimport requests\n\n\nif __name__ == \"__main__\":\n url = argv[1]\n req = requests.get(url)\n\n if req.status_code >= 400:\n print('Error code:', req.status_code)\n else:\n print(req.text)\n", "sub_path": "0x11-python-network_1/7-error_code.py", "file_name": "7-error_code.py", "file_ext": "py", "file_size_in_byte": 348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "56006596", "text": "import curses\n\nfrom menu import Menu\nfrom game import chooseLevel, addLevel, solveLevel\n\n\ndef main(stdscr):\n\n stdscr.keypad(True)\n curses.curs_set(False)\n curses.init_pair(True, curses.COLOR_BLACK, curses.COLOR_WHITE)\n\n mainMenu = Menu(stdscr, (('Играть', chooseLevel, (stdscr,)),\n ('Добавить кроссворд', addLevel , (stdscr,)),\n ('Решить кроссворд', solveLevel, (stdscr,)),\n ('Выйти', print, ('exit',))), 'ЯПОНСКИЕ КРОССВОРДЫ')\n\n mainMenu.show()\n\nif __name__ == '__main__':\n curses.wrapper(main)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "curses.curs_set", "line_number": 10, "usage_type": "call"}, {"api_name": "curses.init_pair", "line_number": 11, "usage_type": "call"}, {"api_name": "curses.COLOR_BLACK", "line_number": 11, "usage_type": "attribute"}, {"api_name": "curses.COLOR_WHITE", "line_number": 11, "usage_type": "attribute"}, {"api_name": "menu.Menu", "line_number": 13, "usage_type": "call"}, {"api_name": "game.chooseLevel", "line_number": 13, "usage_type": "name"}, {"api_name": "game.addLevel", "line_number": 14, "usage_type": "name"}, {"api_name": "game.solveLevel", "line_number": 15, "usage_type": "name"}, {"api_name": "curses.wrapper", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "636268953", "text": "from django.conf.urls.i18n import i18n_patterns\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.urls import path, include\n\nfrom app import views, wishlist, shop\nfrom app.administration import products, slides, company, testimonials\nfrom sabadiaz.settings import MEDIA_URL, MEDIA_ROOT, STATIC_URL, STATIC_ROOT\n\nurlpatterns = [\n\n # admin\n path('admin/', admin.site.urls),\n\n # set languages\n path('i18n/', include('django.conf.urls.i18n')),\n\n # Business Card\n path('vcard', views.business_card, name='business_card'),\n\n # Administration - Products\n path('administration/products', products.view, name='admin_products'),\n path('administration/products/create', products.create_product, name='admin_create_product'),\n path('administration/products//edit', products.edit_product, name='admin_edit_product'),\n path('administration/products//delete', products.delete_product, name='admin_delete_product'),\n path('administration/products//images//delete', products.delete_product_image,\n name='admin_delete_product_image'),\n path('administration/products//images//edit', products.edit_product_image,\n name='admin_edit_product_image'),\n\n # Administration\n # Company\n path('administration/website/company/update', company.company_data, name='admin_website_company_data'),\n path('administration/website/company/logo', company.company_logo, name='admin_website_company_logo'),\n\n # Slides\n path('administration/website/slides', slides.view, name='admin_website_slides'),\n path('administration/website/slides//update_image', slides.update_image,\n name='admin_website_slides_update_image'),\n\n # Testimonials\n path('administration/website/testimonials', testimonials.view, name='admin_website_testimonials'),\n path('administration/website/testimonials//update_avatar', testimonials.update_avatar,\n name='admin_website_testimonials_update_avatar'),\n]\n\nurlpatterns += i18n_patterns(\n\n # index\n path('', views.index, name='index'),\n # path('', views.coming_soon, name='index'),\n\n # Login\n path('login', views.login_user, name='login'),\n\n # Logout\n path('logout', views.logout_user, name='logout'),\n\n # Register\n path('register', views.register, name='register'),\n\n # My Account\n path('account', views.account, name='account'),\n\n # About Us\n path('about', views.about, name='about'),\n\n # Contact Us\n path('contact', views.contact, name='contact'),\n\n # Wishlist\n path('wishlist', wishlist.view, name='wishlist'),\n path('wishlist//add', wishlist.add, name='wishlist_add'),\n path('wishlist//delete', wishlist.delete, name='wishlist_delete'),\n\n # Our Products\n path('products', shop.view, name='products'),\n path('products//details', shop.details, name='product_details'),\n path('products//reviews', shop.reviews, name='product_reviews'),\n\n)\n\nurlpatterns += static(STATIC_URL, document_root=STATIC_ROOT)\nurlpatterns += static(MEDIA_URL, document_root=MEDIA_ROOT)\n", "sub_path": "sabadiaz/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3235, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "app.views.business_card", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "app.administration.products.view", "line_number": 22, "usage_type": "attribute"}, {"api_name": "app.administration.products", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "app.administration.products.create_product", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.administration.products", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "app.administration.products.edit_product", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.administration.products", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "app.administration.products.delete_product", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.administration.products", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "app.administration.products.delete_product_image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.administration.products", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "app.administration.products.edit_product_image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app.administration.products", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "app.administration.company.company_data", "line_number": 33, "usage_type": "attribute"}, {"api_name": "app.administration.company", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "app.administration.company.company_logo", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.administration.company", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "app.administration.slides.view", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.administration.slides", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "app.administration.slides.update_image", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.administration.slides", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "app.administration.testimonials.view", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.administration.testimonials", "line_number": 42, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "app.administration.testimonials.update_avatar", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.administration.testimonials", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.urls.i18n.i18n_patterns", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "app.views.index", "line_number": 50, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 50, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "app.views.login_user", "line_number": 54, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 54, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "app.views.logout_user", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 57, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "app.views.register", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 63, "usage_type": "call"}, {"api_name": "app.views.account", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 63, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 66, "usage_type": "call"}, {"api_name": "app.views.about", "line_number": 66, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 66, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 69, "usage_type": "call"}, {"api_name": "app.views.contact", "line_number": 69, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 69, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 72, "usage_type": "call"}, {"api_name": "app.wishlist.view", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.wishlist", "line_number": 72, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 73, "usage_type": "call"}, {"api_name": "app.wishlist.add", "line_number": 73, "usage_type": "attribute"}, {"api_name": "app.wishlist", "line_number": 73, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 74, "usage_type": "call"}, {"api_name": "app.wishlist.delete", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.wishlist", "line_number": 74, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 77, "usage_type": "call"}, {"api_name": "app.shop.view", "line_number": 77, "usage_type": "attribute"}, {"api_name": "app.shop", "line_number": 77, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 78, "usage_type": "call"}, {"api_name": "app.shop.details", "line_number": 78, "usage_type": "attribute"}, {"api_name": "app.shop", "line_number": 78, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 79, "usage_type": "call"}, {"api_name": "app.shop.reviews", "line_number": 79, "usage_type": "attribute"}, {"api_name": "app.shop", "line_number": 79, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 83, "usage_type": "call"}, {"api_name": "sabadiaz.settings.STATIC_URL", "line_number": 83, "usage_type": "argument"}, {"api_name": "sabadiaz.settings.STATIC_ROOT", "line_number": 83, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 84, "usage_type": "call"}, {"api_name": "sabadiaz.settings.MEDIA_URL", "line_number": 84, "usage_type": "argument"}, {"api_name": "sabadiaz.settings.MEDIA_ROOT", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "25176114", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport re, ipaddress\nfrom collections import OrderedDict\n\nfrom cloudshell.devices.autoload.autoload_builder import AutoloadDetailsBuilder\n\nfrom f5.autoload.f5_autoload_structure import F5RealServer\nfrom f5.autoload.snmp_if_table import SnmpIfTable\nfrom f5.standards.load_balancing.autoload_structure import GenericChassis, GenericPort, \\\n GenericPowerPort, GenericRealServer, GenericResource, GenericServerFarm\nfrom f5.utils.name_generator import UniqueNameGenerator\n\n\nclass F5GenericSNMPAutoload(object):\n VENDOR = \"F5\"\n SNMP_ERRORS = [r'No\\s+Such\\s+Object\\s+currently\\s+exists']\n\n def __init__(self, snmp_handler, shell_name, shell_type, resource_name, logger):\n \"\"\"Basic init with injected snmp handler and logger\n\n :param snmp_handler:\n :param logger:\n :return:\n \"\"\"\n\n self.snmp_handler = snmp_handler\n self.shell_name = shell_name\n self.shell_type = shell_type\n self.resource_name = resource_name\n self.logger = logger\n self.elements = {}\n self.snmp_handler.set_snmp_errors(self.SNMP_ERRORS)\n self.resource = GenericResource(shell_name=shell_name,\n shell_type=shell_type,\n name=resource_name,\n unique_id=resource_name)\n\n def load_f5_mib(self):\n \"\"\"\n Loads f5 specific mibs inside snmp handler\n\n \"\"\"\n path = os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"mibs\"))\n self.snmp_handler.update_mib_sources(path)\n\n def discover(self, supported_os):\n \"\"\"General entry point for autoload,\n read device structure and attributes: chassis, modules, submodules, ports, port-channels and power supplies\n\n :return: AutoLoadDetails object\n \"\"\"\n\n if not self._is_valid_device_os(supported_os):\n raise Exception(self.__class__.__name__, 'Unsupported device OS')\n\n self.logger.info(\"*\" * 70)\n self.logger.info(\"Start SNMP discovery process .....\")\n\n self.load_f5_mib()\n self.snmp_handler.load_mib([\"F5-BIGIP-SYSTEM-MIB\"])\n self._get_device_details()\n self._get_server_farms()\n self._get_chassis_attributes()\n self._get_power_ports()\n self._get_ports()\n\n autoload_details = AutoloadDetailsBuilder(self.resource).autoload_details()\n self._log_autoload_details(autoload_details)\n return autoload_details\n\n def _log_autoload_details(self, autoload_details):\n \"\"\"\n Logging autoload details\n :param autoload_details:\n :return:\n \"\"\"\n self.logger.debug(\"-------------------- ----------------------\")\n for resource in autoload_details.resources:\n self.logger.debug(\n \"{0:15}, {1:20}, {2}\".format(resource.relative_address, resource.name, resource.unique_identifier))\n self.logger.debug(\"-------------------- ----------------------\")\n\n self.logger.debug(\"-------------------- ---------------------\")\n for attribute in autoload_details.attributes:\n self.logger.debug(\"-- {0:15}, {1:60}, {2}\".format(attribute.relative_address, attribute.attribute_name,\n attribute.attribute_value))\n self.logger.debug(\"-------------------- ---------------------\")\n\n def _is_valid_device_os(self, supported_os):\n \"\"\"Validate device OS using snmp\n :return: True or False\n \"\"\"\n\n system_description = self.snmp_handler.get_property('SNMPv2-MIB', 'sysDescr', '0')\n self.logger.debug('Detected system description: \\'{0}\\''.format(system_description))\n result = re.search(r\"({0})\".format(\"|\".join(supported_os)),\n system_description,\n flags=re.DOTALL | re.IGNORECASE)\n\n if result:\n return True\n else:\n error_message = 'Incompatible driver! Please use this driver for \\'{0}\\' operation system(s)'. \\\n format(str(tuple(supported_os)))\n self.logger.error(error_message)\n return False\n\n def _get_device_model(self):\n \"\"\"Get device model from the SNMPv2 mib\n\n :return: device model\n :rtype: str\n \"\"\"\n\n return self.snmp_handler.get_property('SNMPv2-MIB', 'sysObjectID', '0')\n\n def _get_device_model_name(self):\n \"\"\"Get device model name from the CSV file map\n\n :return: device model model\n :rtype: str\n \"\"\"\n\n return self.snmp_handler.get_property('F5-BIGIP-SYSTEM-MIB', 'sysPlatformInfoMarketingName', '0')\n\n def _get_device_os_version(self):\n \"\"\"Get device OS Version form snmp SNMPv2 mib\n\n :return: device model\n :rtype: str\n \"\"\"\n\n os_table = self.snmp_handler.get_table(\"F5-BIGIP-SYSTEM-MIB\", \"sysSwStatusActive\")\n active_os_index = [k for k, v in os_table.iteritems() if \"true\" in v.get(\"sysSwStatusActive\", \"\")][-1]\n if active_os_index:\n # For some reason pysnmp cannot convert \"F5-BIGIP-SYSTEM-MIB\", \"sysSwStatusVersion\" , active_os_index\n return self.snmp_handler.get_table_field((\"1.3.6.1.4.1.3375.2.1.9.4.2.1.4.\" + active_os_index)).get(\n \"sysSwStatusVersion\")\n\n def _get_device_details(self):\n \"\"\" Get root element attributes \"\"\"\n\n self.logger.info(\"Building Root\")\n\n self.resource.contact_name = self.snmp_handler.get_property('SNMPv2-MIB', 'sysContact', '0')\n self.resource.system_name = self.snmp_handler.get_property('SNMPv2-MIB', 'sysName', '0')\n self.resource.location = self.snmp_handler.get_property('SNMPv2-MIB', 'sysLocation', '0')\n self.resource.os_version = self._get_device_os_version()\n self.resource.model = self._get_device_model()\n self.resource.model_name = self._get_device_model_name()\n self.resource.vendor = self.VENDOR\n\n def _add_element(self, relative_path, resource, parent_id=\"\"):\n \"\"\"Add object data to resources and attributes lists\n\n :param resource: object which contains all required data for certain resource\n \"\"\"\n\n rel_seq = relative_path.split(\"/\")\n\n if len(rel_seq) == 1: # Chassis connected directly to root\n self.resource.add_sub_resource(relative_path, resource)\n else:\n if parent_id:\n parent_object = self.elements.get(parent_id, self.resource)\n else:\n parent_object = self.elements.get(\"/\".join(rel_seq[:-1]), self.resource)\n\n rel_path = re.search(r\"\\d+\", rel_seq[-1]).group()\n parent_object.add_sub_resource(rel_path, resource)\n # parent_object.add_sub_resource(rel_seq[-1], resource)\n\n self.elements.update({relative_path: resource})\n\n def _get_chassis_attributes(self):\n \"\"\" Get Chassis element attributes \"\"\"\n\n self.logger.info(\"Building Chassis\")\n chassis_table = self.snmp_handler.get_table(\"F5-BIGIP-SYSTEM-MIB\", \"sysChassisSlotTable\")\n if not chassis_table:\n chassis_table[\"0\"] = {}\n\n for chassis in chassis_table:\n chassis_object = GenericChassis(shell_name=self.shell_name,\n name=\"Chassis {}\".format(chassis),\n unique_id=\"{}.{}.{}\".format(self.resource_name, \"chassis\", chassis))\n\n chassis_object.model = self.snmp_handler.get_property(\"F5-BIGIP-SYSTEM-MIB\", \"sysGeneralHwName\", 0)\n chassis_object.serial_number = chassis_table[chassis].get(\n \"sysChassisSlotSerialNumber\") or self.snmp_handler.get_property(\"F5-BIGIP-SYSTEM-MIB\",\n \"sysGeneralChassisSerialNum\", 0)\n\n relative_address = \"{0}\".format(chassis)\n\n self._add_element(relative_path=relative_address, resource=chassis_object)\n\n self.logger.info(\"Added Chassis {}\".format(chassis))\n self.logger.info(\"Building Chassis completed\")\n\n def _get_power_ports(self):\n \"\"\"Get attributes for power ports provided in self.power_supply_list\n\n :return:\n \"\"\"\n\n self.logger.info(\"Building PowerPorts\")\n power_port_dict = self.snmp_handler.get_table(\"F5-BIGIP-SYSTEM-MIB\", \"sysChassisPowerSupplyIndex\")\n for power_port in power_port_dict.keys():\n power_port_id = power_port_dict[power_port].get(\"sysChassisPowerSupplyIndex\")\n if power_port_id:\n chassis_id = \"0\"\n relative_address = \"{0}/PP{1}\".format(chassis_id, power_port_id)\n\n power_port = GenericPowerPort(shell_name=self.shell_name,\n name=\"PP{0}\".format(power_port_id),\n unique_id=\"{0}.{1}.{2}\".format(self.resource_name, \"power_port\",\n power_port_id))\n\n self._add_element(relative_path=relative_address, resource=power_port, parent_id=chassis_id)\n\n self.logger.info(\"Added Power Port {}\".format(power_port_id))\n self.logger.info(\"Building Power Ports completed\")\n\n def _get_server_farms(self):\n server_farms_names = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmVirtualServName\")\n server_farms_addresses = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmVirtualServAddr\")\n server_farms_addresses_type = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmVirtualServAddrType\")\n server_farms_ports = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmVirtualServPort\")\n server_farms_pools = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmVirtualServDefaultPool\")\n servers_pools = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmPoolMemberPoolName\")\n server_pools_algorithm = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmPoolLbMode\")\n server_pools_algorithm_names = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmPoolName\")\n server_pools_members = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmPoolMemberStatNodeName\")\n servers_names = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmNodeAddrName\")\n servers_addresses = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmNodeAddrAddr\")\n servers_addresses_type = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmNodeAddrAddrType\")\n servers_monitors = self.snmp_handler.get_table(\"F5-BIGIP-LOCAL-MIB\", \"ltmNodeAddrAddrType\")\n servers_pools = OrderedDict(servers_pools)\n\n farm_names_generator = UniqueNameGenerator()\n for farm_idx, (farm_id, farm_value) in enumerate(server_farms_names.items()):\n server_farm_name = farm_value.get(\"ltmVirtualServName\")\n if not server_farm_name:\n continue\n server_farm_port = server_farms_ports.get(farm_id, {}).get(\"ltmVirtualServPort\")\n server_farm_pool = server_farms_pools.get(farm_id, {}).get(\"ltmVirtualServDefaultPool\")\n farm_name = server_farm_name.strip(\"/\").replace(\"/\", \"_\")\n farm_name = farm_names_generator.get_unique_name(farm_name)\n\n server_farm = GenericServerFarm(shell_name=self.shell_name,\n name=\"Server Farm {}\".format(farm_name),\n unique_id=\"{}.{}.{}\".format(self.resource_name, \"server_farm\", farm_idx))\n server_farm.virtual_server_address = self._get_ip_address(server_farms_addresses.get(farm_id, {}).get(\n \"ltmVirtualServAddr\"), server_farms_addresses_type.get(farm_id, {}).get(\"ltmVirtualServAddrType\"))\n server_farm.virtual_server_port = server_farm_port\n server_farm.algorithm = \"\"\n self.resource.add_sub_resource(farm_idx, server_farm)\n\n if not server_farm_pool:\n continue\n\n # todo: rework this with one iteration, not for each server farm\n srvr_names_generator = UniqueNameGenerator()\n for server_idx, (servers_pool_id, servers_pool_value) in enumerate(servers_pools.iteritems()):\n if server_farm_pool == servers_pool_value.get(\"ltmPoolMemberPoolName\"):\n algorithm_key = [k for k, v in server_pools_algorithm_names.iteritems() if\n server_farm_pool == v.get(\"ltmPoolName\")]\n server_farm.algorithm = server_pools_algorithm.get(algorithm_key[0]).get(\n \"ltmPoolLbMode\").replace(\n \"'\", \"\")\n server_node = server_pools_members.get(servers_pool_id).get(\"ltmPoolMemberStatNodeName\")\n server_id = [k for k, v in servers_names.iteritems() if server_node == v.get(\"ltmNodeAddrName\")][0]\n if server_id:\n server_name = servers_names.get(server_id, {}).get(\"ltmNodeAddrName\").strip(\"/\").replace(\"/\", \"_\")\n server_name = srvr_names_generator.get_unique_name(server_name)\n\n unique_id = \"{}.{}.{}.{}\".format(self.resource_name, \"real_server\", farm_idx, server_idx)\n real_server = F5RealServer(\n name=\"Real Server {}\".format(server_name),\n shell_name=self.shell_name,\n unique_id=unique_id)\n\n real_server.address = self._get_ip_address(\n servers_addresses.get(server_id, {}).get(\"ltmNodeAddrAddr\"),\n servers_addresses_type.get(server_id, {}).get(\n \"ltmNodeAddrAddrType\"))\n real_server.monitors = servers_monitors.get(server_id, {}).get(\"ltmNodeAddrMonitorRule\")\n server_farm.add_sub_resource(server_idx, real_server)\n\n def _get_ports(self):\n \"\"\"Get resource details and attributes for every port in self.port_list\n\n :return:\n \"\"\"\n\n self.logger.info(\"Load Ports:\")\n duplex_table = self.snmp_handler.get_table(\"F5-BIGIP-SYSTEM-MIB\", \"sysInterfaceMediaMaxDuplex\")\n lldp_table = self.snmp_handler.get_table(\"F5-BIGIP-SYSTEM-MIB\", \"sysLldpNeighborsTableLocalInterface\")\n if_table = SnmpIfTable(snmp_handler=self.snmp_handler, logger=self.logger)\n port_dict = self.snmp_handler.get_table(\"F5-BIGIP-SYSTEM-MIB\", \"sysInterfaceName\")\n for port in port_dict:\n if not port:\n continue\n port_name = port_dict[port].get(\"sysInterfaceName\")\n port_if_entity = if_table.get_if_index_from_port_name(port_name, [\"mgmt\"])\n if not port_if_entity:\n continue\n port_object = GenericPort(shell_name=self.shell_name,\n name=\"Port {}\".format(port_name),\n unique_id=\"{0}.{1}.{2}\".format(self.resource_name, \"port\", port))\n port_object.mac_address = port_if_entity.if_mac\n port_object.l2_protocol_type = port_if_entity.if_type\n port_object.ipv4_address = port_if_entity.ipv4_address\n port_object.ipv6_address = port_if_entity.ipv6_address\n port_object.port_description = port_if_entity.if_port_description\n port_object.bandwidth = port_if_entity.if_speed\n port_object.mtu = port_if_entity.if_mtu\n port_object.duplex = duplex_table.get(port).get(\n \"sysInterfaceMediaMaxDuplex\", \"Half\").strip(\"'\").replace(\"none\", \"Half\").capitalize()\n port_object.auto_negotiation = port_if_entity.auto_negotiation\n if lldp_table:\n lldp_table_key = [k for k, v in lldp_table if v[\"sysLldpNeighborsTableLocalInterface\"] == port][-1]\n if lldp_table_key:\n port_object.adjacent = '{remote_host} through {remote_port}'.format(remote_host=\n self.snmp_handler.get_property(\n \"F5-BIGIP-SYSTEM-MIB\",\n \"sysLldpNeighborsTableSysName\",\n index=lldp_table_key),\n remote_port=\n self.snmp_handler.get_property(\n \"F5-BIGIP-SYSTEM-MIB\",\n \"sysLldpNeighborsTablePortDesc\",\n index=lldp_table_key))\n\n self._add_element(relative_path=\"0/0\", resource=port_object)\n self.logger.info(\"Added Port\" + port_name)\n\n self.logger.info(\"Building Ports completed\")\n\n def _get_ip_address(self, string, stype):\n result = \"\"\n if stype is None:\n stype = \"\"\n try:\n if \"6\" in stype:\n result = ipaddress.IPv6Network(string, strict=False)\n else:\n result = ipaddress.IPv4Network(string, strict=False)\n except ipaddress.AddressValueError as e:\n self.logger.debug(\"Failed to load ip address\", exc_info=1)\n if \"6\" in stype:\n result = ipaddress.IPv6Network(string, strict=False)\n else:\n result = ipaddress.IPv4Network(string[:4], strict=False)\n finally:\n return str(result).replace(\"/32\", \"\")\n", "sub_path": "src/f5/autoload/f5_generic_snmp_autoload.py", "file_name": "f5_generic_snmp_autoload.py", "file_ext": "py", "file_size_in_byte": 17571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "f5.standards.load_balancing.autoload_structure.GenericResource", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 46, "usage_type": "call"}, {"api_name": "cloudshell.devices.autoload.autoload_builder.AutoloadDetailsBuilder", "line_number": 70, "usage_type": "call"}, {"api_name": "re.search", "line_number": 99, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 101, "usage_type": "attribute"}, {"api_name": "re.IGNORECASE", "line_number": 101, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 172, "usage_type": "call"}, {"api_name": "f5.standards.load_balancing.autoload_structure.GenericChassis", "line_number": 187, "usage_type": "call"}, {"api_name": "f5.standards.load_balancing.autoload_structure.GenericPowerPort", "line_number": 217, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 241, "usage_type": "call"}, {"api_name": "f5.utils.name_generator.UniqueNameGenerator", "line_number": 243, "usage_type": "call"}, {"api_name": "f5.standards.load_balancing.autoload_structure.GenericServerFarm", "line_number": 253, "usage_type": "call"}, {"api_name": "f5.utils.name_generator.UniqueNameGenerator", "line_number": 266, "usage_type": "call"}, {"api_name": "f5.autoload.f5_autoload_structure.F5RealServer", "line_number": 281, "usage_type": "call"}, {"api_name": "f5.autoload.snmp_if_table.SnmpIfTable", "line_number": 302, "usage_type": "call"}, {"api_name": "f5.standards.load_balancing.autoload_structure.GenericPort", "line_number": 311, "usage_type": "call"}, {"api_name": "ipaddress.IPv6Network", "line_number": 349, "usage_type": "call"}, {"api_name": "ipaddress.IPv4Network", "line_number": 351, "usage_type": "call"}, {"api_name": "ipaddress.AddressValueError", "line_number": 352, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv6Network", "line_number": 355, "usage_type": "call"}, {"api_name": "ipaddress.IPv4Network", "line_number": 357, "usage_type": "call"}]} +{"seq_id": "25276453", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n\n\"\"\"\n\nimport logging\n\nfrom PIL import Image, ImageDraw, ImageFont\n\nimport Pytorinox.morph as morph\n\n# (\"tiny.ttf\", 6),\n# (\"ProggyTiny.ttf\", 16),\n# (\"creep.bdf\", 16),\n# (\"miscfs_.ttf \", 12),\n# (\"FreePixel.ttf\", 12)]:\n\nMENU_FONT_HEIGHT = 14\n\ndef Offset(t, image_dim, object_dim, speed=4.0):\n deltax = image_dim[0] - object_dim[0]\n deltay = image_dim[1] - object_dim[1]\n offsetx = (t*speed) % (2.0 * deltax)\n if offsetx > deltax:\n offsetx = 2 * deltax - offsetx\n offsety = (t*speed) % (2.0 * deltay)\n if offsety > deltay:\n offsety = 2 * deltay - offsety\n return offsetx, offsety\n\ndef SumWidth(imgs, spacing):\n width = -spacing\n for img in imgs:\n width += img.size[0] + spacing\n return width\n \n\nclass Video:\n\n def __init__(self, device, w, h):\n self._device = device\n self._last_screen = None\n self._font = ImageFont.truetype(\"../Fonts/code2000.ttf\", 30)\n self._menu_font = ImageFont.truetype(\"../Fonts/code2000.ttf\", MENU_FONT_HEIGHT)\n font_dali = morph.LoadMorphFont(\"Pytorinox/DaliFonts/\", \"E.xbm\")\n self._dali_string = morph.DaliString(*font_dali)\n font_clock = morph.LoadMorphFont(\"Pytorinox/DaliFonts/\", \"G.xbm\")\n self._dali_clock = morph.DaliClock(*font_clock, \"%H:%M\")\n self._image = Image.new(\"RGB\", (w, h))\n self._draw = ImageDraw.Draw(self._image)\n\n def Morphed(self, c1, c2, frac):\n bitmaps = self._dali_string.GetBitmapsForStrings(c1, c2, frac)\n assert len(bitmaps) == 1\n self._last_screen = (c1, c2, frac)\n self._Clear()\n self._draw.bitmap((40,0), bitmaps[0], fill=\"blue\")\n self._device.show(self._image)\n\n def ShowTime(self, t, steps):\n self._Clear()\n self._last_screen = None\n bitmaps = self._dali_clock.GetBitmapsForTime(t, steps, 0)\n width = SumWidth(bitmaps, 2)\n height = bitmaps[0].size[1]\n offset = Offset(t, self._image.size, (width, height)) \n x = offset[0]\n y = offset[1]\n for img in bitmaps:\n self._draw.bitmap((x,y), img, fill=\"#111\")\n x += img.size[0] + 2\n self._device.show(self._image) \n \n def _Clear(self):\n self._draw.rectangle(((0,0), self._image.size), fill=\"#000\")\n\n \n def Message(self, msg1, msg2, msg3):\n if self._last_screen == (msg1, msg2, msg3):\n return\n self._last_screen = (msg1, msg2, msg3)\n self._Clear()\n self._draw.text((0, 0), msg1, fill=\"yellow\", font=self._font)\n self._draw.text((0, 35), msg2, fill=\"yellow\")\n self._draw.text((0, 50), msg3, fill=\"yellow\")\n self._device.show(self._image) \n\n def Menu(self, msgs):\n self._last_screen = msgs\n self._Clear()\n for i, msg in enumerate(msgs):\n x = 0\n y = i * MENU_FONT_HEIGHT\n if i >= 5:\n x += 64\n y -= 60 \n self._draw.text((x, y), msg, fill=\"yellow\", font=self._menu_font)\n self._device.show(self._image) \n \nif __name__ == \"__main__\":\n import Pytorinox.framebuffer as framebuffer\n import time\n \n def main():\n logging.basicConfig(level=logging.INFO)\n fb = framebuffer.Framebuffer(1)\n logging.info(\"Size %s %d\", fb.size, fb.bits_per_pixel)\n video = Video(fb, 128, 64)\n video.Message(\"Welcome\", \"0123456789012345\", \"0123456789012345\")\n\n chars = [\" \", \"0\", \" \", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n font, font_dim = morph.LoadMorphFont(\"Pytorinox/DaliFonts/\", \"E.xbm\")\n for n, c1 in enumerate(chars):\n c2 = chars[(n + 1) % len(chars)]\n for step in range(21):\n video.Morphed(c1, c2, step/ 20.0)\n\n \n time.sleep(1.5)\n for i in range(1000):\n video.ShowTime(time.time(), 10)\n time.sleep(0.2)\n\n\n device.off()\n \n\n main()\n", "sub_path": "DialANumber/video.py", "file_name": "video.py", "file_ext": "py", "file_size_in_byte": 4102, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PIL.ImageFont.truetype", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 45, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 46, "usage_type": "name"}, {"api_name": "Pytorinox.morph.LoadMorphFont", "line_number": 47, "usage_type": "call"}, {"api_name": "Pytorinox.morph", "line_number": 47, "usage_type": "name"}, {"api_name": "Pytorinox.morph.DaliString", "line_number": 48, "usage_type": "call"}, {"api_name": "Pytorinox.morph", "line_number": 48, "usage_type": "name"}, {"api_name": "Pytorinox.morph.LoadMorphFont", "line_number": 49, "usage_type": "call"}, {"api_name": "Pytorinox.morph", "line_number": 49, "usage_type": "name"}, {"api_name": "Pytorinox.morph.DaliClock", "line_number": 50, "usage_type": "call"}, {"api_name": "Pytorinox.morph", "line_number": 50, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 52, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 107, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 107, "usage_type": "attribute"}, {"api_name": "Pytorinox.framebuffer.Framebuffer", "line_number": 108, "usage_type": "call"}, {"api_name": "Pytorinox.framebuffer", "line_number": 108, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 109, "usage_type": "call"}, {"api_name": "Pytorinox.morph.LoadMorphFont", "line_number": 114, "usage_type": "call"}, {"api_name": "Pytorinox.morph", "line_number": 114, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "320877337", "text": "from model.contact import Contact\nimport random\nimport string\nimport os.path\nimport jsonpickle\nimport getopt\nimport sys\n\n\ntry:\n opts, args = getopt.getopt(sys.argv[1:], \"n:f:\", [\"number of contacts\", \"file\"])\nexcept getopt.GetoptError as err:\n getopt.usage()\n sys.exit(2)\n\nn = 5\nf = \"data/contacts.json\"\n\nfor o, a in opts:\n if o == \"-n\":\n n = int(a)\n elif o == \"-f\":\n f = a\n\n\ndef random_string(prefix, maxlen):\n symbols = string.ascii_letters + string.digits + \" \"*10\n return prefix + \"\".join([random.choice(symbols) for i in range(random.randrange(maxlen))]).strip()\n\n\ntestdata = [\n Contact(firstname=random_string(\"firstname\", 10), middlename=\"bbbb\", lastname=random_string(\"lastname\", 10),\n nickname=\"dddd\", title=\"eeee\", company=\"ffff\", address=random_string(\"address\", 20), homephone=\"1111111\",\n mobilephone=\"2222222\", workphone=\"3333333\", fax=\"4444444\", email1=\"email1@abc.com\", email2=\"email2@abc.com\",\n email3=\"email3@abc.com\", homepage=\"abrvalh.abc.com\", birthday=\"1\", birthmonth=\"January\", birthyear=\"1999\",\n annivday=\"2\", annivmonth=\"February\", annivyear=\"2000\", sec_address=\"sec address\", sec_home=\"home\",\n sec_notes=\"notes\")\n for i in range(n)\n]\n\nfile = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"..\", f)\n\nwith open(file, \"w\") as out:\n jsonpickle.set_encoder_options(\"json\", indent=2)\n out.write(jsonpickle.encode(testdata))\n\n", "sub_path": "generator/contact.py", "file_name": "contact.py", "file_ext": "py", "file_size_in_byte": 1456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "getopt.getopt", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "getopt.GetoptError", "line_number": 12, "usage_type": "attribute"}, {"api_name": "getopt.usage", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 27, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 27, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 28, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 28, "usage_type": "call"}, {"api_name": "model.contact.Contact", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.path.abspath", "line_number": 41, "usage_type": "call"}, {"api_name": "jsonpickle.set_encoder_options", "line_number": 44, "usage_type": "call"}, {"api_name": "jsonpickle.encode", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "281835200", "text": "from django.shortcuts import render\nfrom django.db.models import Q\nfrom .models import Post, Course\n\n\n\ndef catalog(request):\n template = 'catalog/catalog.html'\n\n if request.method =='GET':\n query = request.GET.get('q')\n submitbutton = request.GET.get('submit')\n\n results = Post.objects.all()\n\n if query is not None:\n lookups = Q(title=query) | Q(description=query)\n results = Post.objects.filter(lookups).distinct()\n\n context ={'results':results,\n 'submitbutton':submitbutton,}\n\n\n\n return render(request,template,context)\n\n\n\n\ndef room(request):\n\n template = \"room/room.html\"\n\n courses = Course.objects.all()\n context = {'courses':courses}\n\n\n return render(request,template,context)\n", "sub_path": "catalog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 777, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "models.Post.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Course.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Course.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Course", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "564175045", "text": "from pytube import YouTube\nfrom pytube import Playlist\nfrom prettytable import PrettyTable\nimport os\nfrom os import path\nimport os\n\nSave_Path = 'VideoDownload'\n\nplaylist = input(\"Please enter link to Playlist: \")\n\nglobal totalsize\ntotalsize = 0\n\ndef progressBar(current, total, bytes_done, bytes_total, barLength=40):\n percent = float(current) * 100 / total\n arrow = '-' * int(percent / 100 * barLength - 1) + '>'\n spaces = ' ' * (barLength - len(arrow))\n\n print('Progress: [%s%s] %d %% %s' % (arrow, spaces, percent, str(bytes_done) + \"/\" + str(bytes_total)),\n end='\\r')\n\npreviousprogress = 0\n\ndef on_progress(stream, chunk, bytes_remaining):\n global previousprogress\n total_size = stream.filesize\n bytes_downloaded = total_size - bytes_remaining\n\n liveprogress = (int)(bytes_downloaded / total_size * 100)\n if liveprogress > previousprogress:\n previousprogress = liveprogress\n progressBar(liveprogress, 100, round(bytes_downloaded / 1024 / 1024, 2), round(total_size / 1024 / 1024, 2))\n\n\ndef findbestvideo(mp4files):\n maxres = None\n maxfps = None\n maxitagnum = None\n maxmb = None\n\n def formatres(res):\n res = res[:-1]\n res = int(res)\n return res\n\n for typ in mp4files:\n if maxres == None:\n maxres = typ.resolution\n maxfps = typ.fps\n maxitagnum = typ.itag\n maxmb = round(typ.filesize/1024/1024, 2)\n elif formatres(typ.resolution) >= formatres(maxres):\n if typ.fps >= maxfps:\n maxres = typ.resolution\n maxfps = typ.fps\n maxitagnum = typ.itag\n maxmb = round(typ.filesize / 1024 / 1024, 2)\n return maxitagnum, maxres, maxfps, maxmb\n\ndef findbestaudio(audiofil):\n maxres = None\n maxitagnum = None\n maxmb = None\n\n def formatres(res):\n res = res[:-4]\n res = int(res)\n return res\n\n for typ in audiofil:\n if maxres == None:\n maxres = typ.abr\n maxitagnum = typ.itag\n maxmb = round(typ.filesize / 1024 / 1024, 2)\n elif formatres(typ.abr) >= formatres(maxres):\n maxres = typ.abr\n maxitagnum = typ.itag\n maxmb = round(typ.filesize / 1024 / 1024, 2)\n return maxitagnum, maxres, maxmb\n\n\nFileName = input(\"Enter Name to start downloading: \")\nContinue = None\n\ntry:\n if path.exists(Save_Path):\n if path.exists(os.path.join(Save_Path, FileName)):\n print()\n print()\n print()\n text = \"There is already a file called\"+FileName+'in'+Save_Path+'Folder. Please type continue to begin download or type exit to stop. This will remove the old file: '\n\n while Continue == None:\n val = input(text)\n if str.lower(val) == \"continue\":\n Continue = True\n os.remove(os.path.join(Save_Path, FileName))\n os.mkdir(os.path.join(Save_Path, FileName))\n elif str.lower(val) == \"exit\":\n Continue = False\n else:\n print(\"Please Input a valid value\")\n else:\n os.mkdir(os.path.join(Save_Path, FileName))\n Continue = True\n else:\n os.mkdir(Save_Path)\n os.mkdir(Save_Path+'/'+FileName)\n Continue = True\nexcept:\n Exception(\"A lot went wrong\")\n\ntypeoffile = None\n\nwhile not typeoffile:\n typeoffile = input('Would you like to download video or audio: ')\n\n if str.lower(typeoffile)=='video':\n typeoffile='video'\n elif str.lower(typeoffile)=='audio':\n typeoffile='audio'\n else:\n typeoffile=None\n\ntotaldownloaded = []\n\nif Continue:\n try:\n pl_response = Playlist(playlist)\n for vid_item in pl_response:\n VideoInfo = YouTube(vid_item)\n VideoInfo.register_on_progress_callback(on_progress)\n VideoFiles = VideoInfo.streams.filter(file_extension=\"mp4\", type=\"video\", progressive=\"True\")\n AudioFiles = VideoInfo.streams.filter(file_extension=\"mp4\", type='audio')\n downloading = None\n if VideoFiles[0] and typeoffile=='video':\n downloading, r, f, mb = findbestvideo(VideoFiles)\n totaldownloaded.append([VideoInfo.title, r,f,mb])\n elif AudioFiles[0] and typeoffile=='audio':\n downloading, r, mb = findbestaudio(AudioFiles)\n totaldownloaded.append([VideoInfo.title, r, mb])\n if downloading:\n Video = VideoInfo.streams.get_by_itag(downloading)\n Video.download(output_path=os.path.join(Save_Path, FileName))\n print()\n print(\"Download Done.\")\n print(\"Video can be found in\", Save_Path+'/'+str(FileName), \"Folder with file name\", '\\\"', VideoInfo.title +\" \\\".\")\n previousprogress = 0\n totalsize += Video.filesize\n else:\n print(\"Something went wrong while downloading a video. Video ID:\", VideoInfo.title)\n\n print()\n\n print()\n print()\n print(\"All Videos Downloaded\")\n num = 1\n\n if typeoffile=='video':\n newtab = PrettyTable()\n newtab.field_names = ['Index', 'Name', 'Resolution', 'FPS', 'FileSize (MB)']\n for x in totaldownloaded:\n newtab.add_row([num, x[0], x[1], x[2], x[3]])\n num +=1\n\n newtab.add_row(['', '', '', '', ''])\n newtab.add_row(['Total', '', '', '', ''])\n newtab.add_row([len(totaldownloaded), '', '', '', round(totalsize / 1024 / 1024, 2)])\n print(newtab)\n else:\n newtab = PrettyTable()\n newtab.field_names = ['Index', 'Name', 'Quality', 'FileSize (MB)']\n for x in totaldownloaded:\n newtab.add_row([num, x[0], x[1], x[2]])\n num += 1\n\n newtab.add_row(['', '', '', ''])\n newtab.add_row(['Total', '', '', ''])\n newtab.add_row([len(totaldownloaded), '', '', round(totalsize / 1024 / 1024, 2)])\n print(newtab)\n except:\n print(\"Unknown Error Happened. Please check the playlist id\")\n", "sub_path": "playlist.py", "file_name": "playlist.py", "file_ext": "py", "file_size_in_byte": 6256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.exists", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 109, "usage_type": "call"}, {"api_name": "pytube.Playlist", "line_number": 130, "usage_type": "call"}, {"api_name": "pytube.YouTube", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "prettytable.PrettyTable", "line_number": 162, "usage_type": "call"}, {"api_name": "prettytable.PrettyTable", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "213364844", "text": "# Imports\nfrom ..common import Error\nfrom ..common import EqualityMixin\nimport json\nimport requests\n\n# Globals\n_clients = {}\nJSON_HEADERS = {\"content-type\": \"application/json\"}\n\n#: The default hostname for a connection to an :class:`~apibuilder.server.APIServer`\nDEFAULT_HOSTNAME = \"localhost\"\n#: The default port for a connection to an :class:`~apibuilder.server.APIServer`\nDEFAULT_PORT = 80\n#: The handle to which a connection is registered if no handle is supplied\nDEFAULT_HANDLE = \"default\"\n\n__all__ = [\"NoSuchConnectionError\", \"AuthenticationError\", \"InternalServerError\"]\n__all__ += [\"APIClient\", \"connect\", \"register_connection\", \"get_client\"]\n__all__ += [\"DEFAULT_HOSTNAME\", \"DEFAULT_PORT\", \"DEFAULT_HANDLE\"]\n\nclass NoSuchConnectionError(Error):\n \"\"\"\n This error should be thrown when a client attempts to retreive a connection\n with a given handle and there is no registered connection for that handle\n\n :param handle: The requested connection handle\n \"\"\"\n def __init__(self, handle):\n message = \"There is no existing connection with the given handle %s\" % \\\n handle\n super(NoSuchConnectionError, self).__init__(message)\nclass AuthenticationError(Error):\n \"\"\"\n This error should be thrown when a request to a server returns a 401 error\n \"\"\"\n def __init__(self):\n super(AuthenticationError, self).__init__(\"Credentials rejected\")\nclass InternalServerError(Error):\n \"\"\"\n This error should be thrown when a request to a server returns a 500 error\n \"\"\"\n def __init__(self):\n super(InternalServerError, self).__init__(\"Internal server error\")\n\nclass MyRequests(object):\n \"\"\"\n Wraps the important function calls from the requests library and provides\n some default error handling for them\n \"\"\"\n def get(self, *args, **kwargs):\n \"\"\"\n Wraps :func:`requests.get` and raises errors on 401s and 500s\n \"\"\"\n return self.__check_result(requests.get(*args, **kwargs))\n def post(self, *args, **kwargs):\n \"\"\"\n Wraps :func:`requests.post` and raises errors on 401s and 500s\n \"\"\"\n return self.__check_result(requests.post(*args, **kwargs))\n def put(self, *args, **kwargs):\n \"\"\"\n Wraps :func:`requests.put` and raises errors on 401s and 500s\n \"\"\"\n return self.__check_result(requests.put(*args, **kwargs))\n def delete(self, *args, **kwargs):\n \"\"\"\n Wraps :func:`requests.delete` and raises errors on 401s and 500s\n \"\"\"\n return self.__check_result(requests.delete(*args, **kwargs))\n def __check_result(self, res):\n if res.status_code == 401:\n raise AuthenticationError()\n elif res.status_code == 500:\n raise InternalServerError()\n return res\nmyRequests = MyRequests()\n\nclass APIClient(EqualityMixin):\n \"\"\"\n Represents a connection to an :class:`~apibuilder.server.APIServer`\n instance.\n\n Calls to an endpoint on the :class:`~apibuilder.server.APIServer`, will be\n formatted ``http://:``, where\n ````, ````, and ```` are replaced with the values of\n their eponymous initialization parameters and ```` is replaced by\n the target endpoint (e.g. \"/sensor\" for a class named ``Sensor`` registered\n to the :class:`~apibuilder.server.APIServer` instance).\n\n :param hostname: The hostname of the machine on which the\n :class:`~apibuilder.server.APIServer` instance is running\n :param port: The port on which the :class:`~apibuilder.server.APIServer`\n instance is running\n :param prefix: A prefix to apply to all URLs being requested\n :param auth: A (username, password) tuple of the credentials that should be\n used to access the server\n \"\"\"\n def __init__(self, hostname=DEFAULT_HOSTNAME, port=DEFAULT_PORT,\n prefix=None, auth=None):\n self.requests = myRequests\n self.hostname = hostname\n self.port = str(port)\n self.prefix = prefix\n self.auth = auth\n def get_object(self, cls_name, _id):\n \"\"\"\n Retreive information on an object of a given type with a given id.\n Returns a dictionary of the relevant information\n\n :param cls_name: The name of the object type that should be queried\n :param _id: The ID string of the desired object\n \"\"\"\n url = self.build_url(cls_name, _id)\n res = self.requests.get(url, auth=self.auth)\n if res.status_code == 200:\n return res.json()\n def update_object(self, cls_name, obj_info):\n \"\"\"\n Update the information on an object of a given type to the given\n information. Returns True if the operation succeeded and False otherwise\n\n :param cls_name: The name of the object type that should be queried\n\n :param obj_info: A dictionary of all of the attributes of the object\n being updated\n \"\"\"\n url = self.build_url(cls_name, obj_info[\"_id\"])\n res = self.requests.put(url, json.dumps(obj_info), headers=JSON_HEADERS,\n auth=self.auth)\n if res.status_code == 204:\n return True\n else:\n return False\n def delete_object(self, cls_name, _id):\n \"\"\"\n Delete the object of the given type with the given id.\n\n :param cls_name: The name of the object type that should be queried\n :param _id: The ID string of the object to be deleted\n \"\"\"\n url = self.build_url(cls_name, _id)\n res = self.requests.delete(url, auth=self.auth)\n if res.status_code == 204:\n return True\n else:\n return False\n def list_object(self, cls_name, **kwargs):\n \"\"\"\n List all of the objects of the given type. Returns an array of\n dictionaries, each of which contains all of the attributes of an object\n on the server.\n\n :param cls_name: The name of the object type that should be queried\n :param kwargs: Additional arguments to be passed in the query. These\n can be used to filter the resulting list by some attribute. For the\n structure of these arguments, see the mongoengine `Querying the\n database `_ page\n \"\"\"\n url = self.build_url(cls_name)\n return self.requests.get(url, auth=self.auth, params=kwargs,\n headers=JSON_HEADERS).json()\n def create_object(self, cls_name, obj_info):\n \"\"\"\n Create a new object of the given type with the given set of attributes.\n Returns a dictionary of all of the attributes of the newly created\n object if the operation succeeded. Returns None if the operation failed.\n\n :param cls_name: The name of the object type that should be queried\n :param obj_info: A dictionary of all of the attributes of the object\n being created\n \"\"\"\n url = self.build_url(cls_name)\n res = self.requests.post(url, data=json.dumps(obj_info),\n headers=JSON_HEADERS, auth=self.auth)\n if res.status_code == 201:\n return res.json()\n def build_url(self, *args):\n \"\"\"\n Builds a url of the form ``http://:``,\n where ````, ````, and ```` are replaced with the\n values of their eponymous initialization parameters and ````\n is replaced with the list of endpoint arguments, separated by ``/``s.\n\n :param args: A list of strings that defines the endpoint to be visited.\n \"\"\"\n url = \"http://\"\n url += self.hostname\n url += \":\"\n url += self.port\n url += self.prefix or \"\"\n for arg in args:\n url += \"/\"\n url += arg\n return url\n\ndef connect(hostname=DEFAULT_HOSTNAME, port=DEFAULT_PORT, prefix=None,\n auth=None, handle=DEFAULT_HANDLE):\n \"\"\"\n Create a connection to an :class:`apibuilder.server.APIServer` instance and\n register it.\n\n :param hostname: The hostname of the machine on which the\n :class:`~apibuilder.server.APIServer` instance is running\n :param port: The port on which the :class:`~apibuilder.server.APIServer`\n instance is running\n :param prefix: A prefix to apply to all URLs being requested\n :param auth: A (username, password) tuple of the credentials that should be\n used to access the server\n :param handle: The handle to associate wih the connection to this\n :class:`~apibuilder.server.APIServer` instance\n \"\"\"\n register_connection(APIClient(hostname, port, prefix, auth), handle)\n\ndef register_connection(client, handle=DEFAULT_HANDLE):\n \"\"\"\n Register an :class:`~apibuilder.client.connection.APIClient` instance\n\n :param client: The :class:`~apibuilder.client.connection.APIClient` instance\n to register\n :param handle: The handle to associate with the\n :class:`~apibuilder.client.connection.APIClient` instance\n \"\"\"\n global _clients\n if not isinstance(client, APIClient):\n raise TypeError(\"client must be an APIClient instance\")\n _clients[handle] = client\n\ndef get_client(handle=DEFAULT_HANDLE):\n \"\"\"\n Retreive the stored :class:`~apibuilder.client.connection.APIClient`\n instance associated with a given handle\n\n :param handle: The handle of the\n :class:`~apibuilder.client.connection.APIClient` instance to retreive.\n \"\"\"\n global _clients\n if not handle in _clients:\n raise NoSuchConnectionError(handle)\n return _clients[handle]\n", "sub_path": "apibuilder/client/connection.py", "file_name": "connection.py", "file_ext": "py", "file_size_in_byte": 9657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "common.Error", "line_number": 22, "usage_type": "name"}, {"api_name": "common.Error", "line_number": 33, "usage_type": "name"}, {"api_name": "common.Error", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 70, "usage_type": "call"}, {"api_name": "common.EqualityMixin", "line_number": 79, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "285298547", "text": "\"\"\"\nCopyright (c) 2017 Eric Shook. All rights reserved.\nUse of this source code is governed by a BSD-style license that can be found in the LICENSE file.\n@author: eshook (Eric Shook, eshook@gmail.edu)\n@contributors: \n\"\"\"\n\nimport rasterio\nimport rasterio.features\nfrom collections import defaultdict\n\n\nfrom .Primitive import *\nfrom ..bobs.Bobs import *\n\n'''\nTODO\n1. Pass in __name__ rather than have it hard coded. More elegant.\n2. Set name properly in super so it doesn't have to be duplicated.\n'''\n\nclass PartialSumPrim(Primitive):\n def __init__(self):\n\n # Call the __init__ for Primitive \n super(PartialSumPrim,self).__init__(\"PartialSum\")\n\n def __call__(self, zone = None, data = None):\n\n # Create the key_value output bob\n out_kv = KeyValue(zone.h,zone.w,zone.y,zone.x)\n\n # Loop over the raster (RLayer)\n for r in range(len(data.data)):\n for c in range(len(data.data[0])):\n key = str(zone.data[r][c])\n if key in out_kv.data:\n out_kv.data[key]['val'] += data.data[r][c]\n out_kv.data[key]['cnt'] += 1\n else:\n out_kv.data[key] = {}\n out_kv.data[key]['val'] = data.data[r][c]\n out_kv.data[key]['cnt'] = 1\n \n return out_kv\n\nPartialSum = PartialSumPrim()\n\nclass AggregateSumPrim(Primitive):\n def __init__(self):\n\n # Call the __init__ for Primitive \n super(AggregateSumPrim,self).__init__(\"AggregateSum\")\n\n def __call__(self, *args):\n \n # Since it is an aggregator/reducer it takes in a list of bobs\n boblist = args\n \n # Set default values for miny,maxy,minx,maxx using first entry\n miny = maxy = boblist[0].y\n minx = maxx = boblist[0].x\n \n # Loop over bobs to find maximum spatial extent\n for bob in boblist:\n # Find miny,maxy,minx,maxx\n miny = min(miny,bob.y)\n maxy = max(maxy,bob.y)\n minx = min(minx,bob.x)\n maxx = max(maxx,bob.x)\n \n # Create the key_value output Bob that (spatially) spans all input bobs\n out_kv = KeyValue(miny, minx, maxy-miny, maxx-minx)\n\n # Set data to be an empty dictionary\n out_kv.data = {}\n \n # Loop over bobs, get keys and sum the values and counts\n for bob in boblist:\n # Loop over keys\n for key in bob.data:\n \n if key in out_kv.data:\n out_kv.data[key]['val']+=bob.data[key]['val']\n out_kv.data[key]['cnt']+=bob.data[key]['cnt']\n else:\n out_kv.data[key] = {} # Create the entry and set val/cnt\n out_kv.data[key]['val']=bob.data[key]['val']\n out_kv.data[key]['cnt']=bob.data[key]['cnt']\n \n return out_kv\n\nAggregateSum = AggregateSumPrim()\n\n\nclass AveragePrim(Primitive):\n def __init__(self):\n\n # Call the __init__ for Primitive \n super(AveragePrim,self).__init__(\"Average\")\n\n def __call__(self, sums = None):\n # Create the key_value output bob for average\n out_kv = KeyValue(sums.y, sums.x, sums.h, sums.w)\n\n for key in sums.data:\n out_kv.data[key] = float(sums.data[key]['val']) / float(sums.data[key]['cnt'])\n\n return out_kv\n\nAverage = AveragePrim()\n \n# FIXME: Still in development.\nclass PartialSumRasterizePrim(Primitive):\n def __init__(self):\n\n # Call the __init__ for Primitive \n super(PartialSumRasterizePrim,self).__init__(\"PartialSumRasterize\")\n\n def __call__(self, zone = None, data = None, properties_name = None):\n\n # Create the transform for rasterio to rasterize the vector zones\n #print(\"bounds\",data.x, data.y, data.x+data.w, data.y+data.h, data.cellsize, data.cellsize)\n transform = rasterio.transform.from_origin(data.x,data.y+data.h,data.cellsize,data.cellsize)\n \n properties_name = 'STATEFP' # or 'geoid'\n \n # Create zoneshapes, which is the geometry + state FP\n zoneshapes = ((f['geometry'],int(f['properties'][properties_name])) for f in zone.data)\n arr = rasterio.features.rasterize(shapes = zoneshapes, out_shape=data.data.shape, transform = transform)\n \n \n # TEMPORARY FOR LOOKING AT THE RESULTS\n if(False):\n with rasterio.open(\"examples/data/glc2000.tif\") as src:\n profile = src.profile\n profile.update(count=1,compress='lzw')\n with rasterio.open('result.tif','w',**profile) as dst:\n dst.write_band(1,arr)\n \n print(\"arr min=\",np.min(arr))\n print(\"arr max=\",np.max(arr))\n #print(\"arr avg=\",np.avg(arr))\n print(\"arr shape\",arr.shape)\n \n \n # Create the key_value output bob\n out_kv = KeyValue(zone.h,zone.w,zone.y,zone.x)\n\n print(\"Processing raster of size\",data.nrows,\"x\",data.ncols)\n \n # Instead of looping over raster we can\n # zip zone[r] and data[r] to get key/value pairs\n # then we can apply for k,v in pairs: d[k] +=v\n # from : https://stackoverflow.com/questions/9285995/python-generator-expression-for-accumulating-dictionary-values\n # look here too : https://bugra.github.io/work/notes/2015-01-03/i-wish-i-knew-these-things-when-i-first-learned-python/\n # Loop over the raster (RLayer)\n '''\n for r in range(len(data.data)):\n for c in range(len(data.data[0])):\n key = str(arr[r][c])\n if key in out_kv.data:\n out_kv.data[key]['val'] += data.data[r][c]\n out_kv.data[key]['cnt'] += 1\n else:\n out_kv.data[key] = {}\n out_kv.data[key]['val'] = data.data[r][c]\n out_kv.data[key]['cnt'] = 1\n '''\n \n #https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.unique.html#numpy.unique\n counts = np.unique(arr,return_counts=True)\n print(\"counts=\",counts)\n \n # Loop over zone IDs\n for z in counts[0]:\n print(\"zoneid\",z)\n \n # Create a dictionary from collections.defaultdict\n d=defaultdict(int)\n # Loop over the data and\n # Zip the zone keys (arr) and the data values into key,value pairs\n # Then add up the values from data and put into dictionary\n for r in range(len(data.data)):\n \n \n if(r%100==0):\n print(\"r=\",r,\"/\",len(data.data))\n #Try 1, too slow \n #kvzip = zip(arr[r],data.data[r])\n #for k,v in kvzip: d[k]+=v\n \n # Try 2, faster than Try 1, but still too slow.\n '''\n zonerow = arr[r]\n datarow = data.data[r]\n # Loop over unique zones\n for z in counts[0]:\n # This should set elements for zone z to 1, all others to 0\n zonemask = zonerow == z\n # Should zero out entries that are not the same as zone\n # So now you have an array of data elements that all belong to zone z\n datamask = datarow * zonemask\n # Add them all up and put them in the array\n d[z]+=np.sum(datamask)\n '''\n \n # Try 3, zonemask entire arrays (memory intensive, but faster)\n for z in counts[0]:\n print(\"z=\",z)\n \n # This should set elements for zone z to 1, all others to 0\n zonemask = arr == z\n # Should zero out entries that are not the same as zone\n # So now you have an array of data elements that all belong to zone z\n datamask = data.data * zonemask\n # Add them all up and put them in the array\n d[z]+=np.sum(datamask)\n \n # Loop over d and counts to create output keyvalue bob \n for i in range(len(counts[0])):\n countskey = counts[0][i]\n countscnt = counts[1][i]\n dsum = d[countskey]\n out_kv.data[countskey] = {}\n out_kv.data[countskey]['val'] = dsum\n out_kv.data[countskey]['cnt'] = countscnt\n \n del arr\n\n return out_kv\n\nPartialSumRasterize = PartialSumRasterizePrim()\n\n", "sub_path": "forest/primitives/Primitives.py", "file_name": "Primitives.py", "file_ext": "py", "file_size_in_byte": 8562, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "rasterio.transform.from_origin", "line_number": 124, "usage_type": "call"}, {"api_name": "rasterio.transform", "line_number": 124, "usage_type": "attribute"}, {"api_name": "rasterio.features.rasterize", "line_number": 130, "usage_type": "call"}, {"api_name": "rasterio.features", "line_number": 130, "usage_type": "attribute"}, {"api_name": "rasterio.open", "line_number": 135, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 138, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "626578872", "text": "# coding: ISO-8859-1\nimport nltk\nimport string\nimport os\nfrom unicodedata import normalize\nnltk.download('stopwords')\nnltk.download('rslp')\n\n__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n\nprint()\nprint(\"LOADING DATA FROM: \")\nprint(__location__+\"/preprocessado.txt AS UTF-8\")\n\n# Open the file with read only permit\nf = open(__location__+\"/preprocessado.txt\", encoding=\"utf-8\")\n# use readlines to read all lines in the file\n# The variable \"lines\" is a list containing all lines in the file\nlines = f.readlines()\n# close the file after reading the lines.\nf.close()\n\nbase=[]\n\n\nfor line in lines:\n pos = line.find(': ')\n if pos < 0:\n continue\n base.append((line[pos+2:],'neutro'))\n\n\n# base = [('O amor é lindo!','alegria'),\n# ('Eu sou admirada por muitos.','alegria'), \n# ('Me sinto completamente amado.','alegria'), \n# ('Amar é maravilhoso!','alegria'), \n# ('Estou me sentindo muito animado novamente.','alegria'), \n# ('Eu estou muito bem hoje.','alegria'), \n# ('Que belo dia para dirigir um carro novo!','alegria'), \n# ('O dia está muito bonito!','alegria'), \n# ('Estou contente com o resultado do teste que fiz no dia de ontem!','alegria'), \n# ('Nossa amizade é amor, vai durar para sempre!', 'alegria'), \n# ('Estou amedrontado.', 'medo'), \n# ('Ele está me ameaçando à dias.', 'medo'), \n# ('Isso me deixa apavorada!', 'medo'), \n# ('Este lugar é apavorante?', 'medo'), \n# ('Se perdermos outro jogo, seremos eliminados, e isso me deixa com pavor.', 'medo'), \n# ('Tome cuidado com o lobisomem!', 'medo'), \n# ('Se eles descobrirem, estamos encrencados.', 'medo'), \n# ('Estou tremendo de medo.', 'medo'), \n# ('Eu tenho muito medo dele.', 'medo'), \n# ('Estou com medo do resultado dos meus testes.', 'medo')]\n\n#print(['Base'] + base)\nprint()\ndef tokeniza(texto):\n \n tokens = []\n for(palavras, emocao) in texto:\n token = [p for p in palavras.split()]\n tokens.append((token, emocao))\n return tokens\n\ntokens = tokeniza(base)\n#print(['Tokens'] + tokens)\nprint()\n\ndef descapitaliza(texto):\n \n descapitalizado = []\n for(palavras, emocao) in texto:\n descap = [p.casefold() for p in palavras]\n descapitalizado.append((descap, emocao))\n return descapitalizado\n\ntextoDescapitalizado = descapitaliza(tokens)\n#print(['Case Folding'] + textoDescapitalizado)\nprint()\n\n\ndef removeAcentos(texto): \n frasesSemAcento = []\n for(palavras, emocao) in texto:\n semAcento = [normalize('NFKD', p).encode('ASCII','ignore').decode('ASCII') for p in palavras]\n frasesSemAcento.append((semAcento, emocao))\n return frasesSemAcento\n\ntextoSemAcentos = removeAcentos(textoDescapitalizado)\n#print(['Sem acentos'] + textoSemAcentos)\nprint()\n\ndef removePontuacao(texto):\n \n frasesSemPonto = []\n for(frase, emocao) in texto:\n palavrasSemPonto = []\n for palavras in frase:\n semPonto=''\n semPonto = [char for char in palavras if char not in string.punctuation]\n palavraSemPonto = ''.join(semPonto)\n palavrasSemPonto.append(palavraSemPonto)\n frasesSemPonto.append((palavrasSemPonto,emocao)) \n return frasesSemPonto\n\ntextoSemPontuacao = removePontuacao(textoSemAcentos)\n#print(['Sem pontuação'] + textoSemPontuacao)\nprint() \n\nstopwordsNLTK = nltk.corpus.stopwords.words('english')\n\ndef removeStopWords(texto):\n \n frases=[]\n for(palavras, emocao) in texto:\n semStop = [p for p in palavras if p not in stopwordsNLTK]\n frases.append((semStop, emocao))\n return frases\n\ntextoSemStopWords = removeStopWords(textoSemPontuacao)\n#print(['Sem Stopwords'] + textoSemStopWords)\nprint()\n\ndef retornaPalavras(texto):\n \n frasesPreProcessadas = []\n for(palavras, emocao) in texto:\n frasesPreProcessadas.extend(palavras)\n return frasesPreProcessadas\n\ndef buscaFrequencia(texto):\n qtd = nltk.FreqDist(texto) #retorna a frequencia de cada palavera\n return qtd\n\nfrequenciaSW = buscaFrequencia(retornaPalavras(textoSemStopWords))\nlista=frequenciaSW.most_common(10000)\nf= open(\"output.csv\",\"w+\")\nf.write('word,freq')\nprint('FREQUENCIA_--------------------------')\nfor x in lista:\n content = '\\n\"'+x[0]+'\",\"'+str(x[1])+'\"'\n print(content)\n f.write(content)\n\n\n\n# def aplicaStemmer(texto):\n \n# stemmer = nltk.stem.RSLPStemmer() #metodo para a utlizacao da lingua portuguesa\n# frasesStemming = []\n# for(palavras, emocao) in texto:\n# comStemming = [str(stemmer.stem(p)) for p in palavras]\n# frasesStemming.append((comStemming, emocao))\n# return frasesStemming\n\n# textoStemming = aplicaStemmer(textoSemStopWords)\n# print(['Stemming'] + textoStemming)\n# print()\n\n# textoPreProcessado = retornaPalavras(textoStemming)\n# frequencia = buscaFrequencia(textoPreProcessado)\n\n# print('FREQUENCIA_--------------------------')\n\n# print(frequencia.most_common(100)) #50 primeiras palavas mais frequentes\n\n# def removePalavrasRepetidas(freq):\n# palavras = freq.keys()\n# return palavras\n\n# textoSemRepeticao = removePalavrasRepetidas(frequencia)\n\n# def extraiPalavras(documento):\n \n# doc = set(documento)\n# carcteristicas = {}\n# for palavras in textoSemRepeticao:\n# carcteristicas['%s' % palavras] = (palavras in doc)\n# return carcteristicas\n\n# caracteristicasFrase = extraiPalavras(['am', 'nov','dia'])\n# print(caracteristicasFrase)\n# print()\n\n# baseCompleta = nltk.classify.apply_features(extraiPalavras, textoStemming) #aplica uma caracteristica (caracteristica,base a ser aplicada). o objetivo aqui eh indicar quais palavras aparecem em cada tipo de frase. essa base que vai ser passada como parametro para o algoritmo de aprendizagem\"\"\"\n\n# print(baseCompleta)", "sub_path": "Contagem_de_palavras/pre_processamento.py", "file_name": "pre_processamento.py", "file_ext": "py", "file_size_in_byte": 5886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "nltk.download", "line_number": 6, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 84, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 99, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 109, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 109, "usage_type": "attribute"}, {"api_name": "nltk.FreqDist", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "95184891", "text": "from flask_cors import CORS, cross_origin\nfrom flask import Flask, jsonify\nfrom flask import request\nfrom services.search_query import search_for_youtube, search_for_google\nfrom services.scoring_pattern import get_sentence_similarity\nfrom services.text_processing import get_summary_from_bert, get_answer_for_question\n\napp = Flask(__name__)\ncors = CORS(app)\napp.config['CORS_HEADERS'] = 'Content-Type'\n\n\n@app.route('/api/v1/article/summary', methods=['POST'])\n@cross_origin()\ndef get_summary():\n input_request = request.json\n summarized_content = get_summary_from_bert(input_request['content'])\n return jsonify({'summary': summarized_content})\n\n\n@app.route('/api/v1/article/answer', methods=['POST'])\n@cross_origin()\ndef get_answer():\n input_request = request.json\n answer = get_answer_for_question(input_request['question'], input_request['content'])\n return jsonify({'answer': answer})\n\n\n@app.route('/api/v1/article/answer/validate', methods=['POST'])\n@cross_origin()\ndef validate_answer():\n input_request = request.json\n answer, score = get_sentence_similarity(input_request['user_answer'], input_request['actual_answer'])\n response = {'is_true': answer, 'similarity_score': score}\n return jsonify(response)\n\n\n@app.route('/api/v1/article/getvideoref', methods=['GET'])\n@cross_origin()\ndef get_video_reference():\n query_token = request.args.get('query')\n results = search_for_youtube(query_token)\n return jsonify({'youtube_results': results})\n\n\n@app.route('/api/v1/article/getwebref', methods=['GET'])\n@cross_origin()\ndef get_web_reference():\n query_token = request.args.get('query')\n results = search_for_google(query_token)\n return jsonify({'web_url_results': results})\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "sub_path": "backend-server/apps/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1775, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "services.text_processing.get_summary_from_bert", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "services.text_processing.get_answer_for_question", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "services.scoring_pattern.get_sentence_similarity", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "services.search_query.search_for_youtube", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "services.search_query.search_for_google", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 51, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "97053191", "text": "#!/usr/bin/env python\n########################################################################\n# $HeadURL$\n# File : dirac-proxy-init.py\n# Author : Adrian Casajus\n########################################################################\n__RCSID__ = \"$Id$\"\n\nimport sys\nimport DIRAC\nfrom DIRAC import gLogger\nfrom DIRAC.Core.Base import Script\n#from DIRAC.FrameworkSystem.Client.ProxyGeneration import CLIParams, generateProxy\nfrom DIRAC.FrameworkSystem.Client import ProxyGeneration, ProxyUpload\nfrom DIRAC.Core.Security import X509Chain\n\nif __name__ == \"__main__\":\n pxParams = ProxyGeneration.CLIParams()\n pxParams.registerCLISwitches()\n\n Script.disableCS()\n Script.parseCommandLine( ignoreErrors = True )\n\n gLogger.notice( \"Generating proxy...\" )\n result = ProxyGeneration.generateProxy( pxParams )\n if not result[ 'OK' ]:\n gLogger.error( result[ 'Message' ] )\n sys.exit( 1 )\n proxyLocation = result[ 'Value' ]\n if pxParams.uploadProxy:\n proxyChain = X509Chain.X509Chain()\n result = proxyChain.loadChainFromFile( proxyLocation )\n if not result[ 'OK' ]:\n gLogger.error( \"Could not load the proxy: %s\" % result[ 'Message' ] )\n sys.exit( 1 )\n result = proxyChain.getIssuerCert()\n if not result[ 'OK' ]:\n gLogger.error( \"Could not load the proxy: %s\" % result[ 'Message' ] )\n sys.exit( 1 )\n userCert = result[ 'Value' ]\n secsLeft = userCert.getRemainingSecs()[ 'Value' ] - 300\n\n gLogger.notice( \"Uploading proxy to ProxyManager...\" )\n upParams = ProxyUpload.CLIParams()\n upParams.onTheFly = True\n upParams.proxyLifeTime = secsLeft\n for k in ( 'diracGroup', 'certLoc', 'keyLoc', 'userPasswd' ):\n setattr( upParams, k , getattr( pxParams, k ) )\n\n result = ProxyUpload.uploadProxy( upParams )\n if not result[ 'OK' ]:\n gLogger.error( result[ 'Message' ] )\n sys.exit( 1 )\n gLogger.notice( \"Proxy uploaded\" )\n\n sys.exit( 0 )\n", "sub_path": "FrameworkSystem/scripts/dirac-proxy-init.py", "file_name": "dirac-proxy-init.py", "file_ext": "py", "file_size_in_byte": 1914, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "DIRAC.FrameworkSystem.Client.ProxyGeneration.CLIParams", "line_number": 18, "usage_type": "call"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyGeneration", "line_number": 18, "usage_type": "name"}, {"api_name": "DIRAC.Core.Base.Script.disableCS", "line_number": 21, "usage_type": "call"}, {"api_name": "DIRAC.Core.Base.Script", "line_number": 21, "usage_type": "name"}, {"api_name": "DIRAC.Core.Base.Script.parseCommandLine", "line_number": 22, "usage_type": "call"}, {"api_name": "DIRAC.Core.Base.Script", "line_number": 22, "usage_type": "name"}, {"api_name": "DIRAC.gLogger.notice", "line_number": 24, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 24, "usage_type": "name"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyGeneration.generateProxy", "line_number": 25, "usage_type": "call"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyGeneration", "line_number": 25, "usage_type": "name"}, {"api_name": "DIRAC.gLogger.error", "line_number": 27, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 27, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 28, "usage_type": "call"}, {"api_name": "DIRAC.Core.Security.X509Chain.X509Chain", "line_number": 31, "usage_type": "call"}, {"api_name": "DIRAC.Core.Security.X509Chain", "line_number": 31, "usage_type": "name"}, {"api_name": "DIRAC.gLogger.error", "line_number": 34, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 34, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "DIRAC.gLogger.error", "line_number": 38, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 38, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "DIRAC.gLogger.notice", "line_number": 43, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 43, "usage_type": "name"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyUpload.CLIParams", "line_number": 44, "usage_type": "call"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyUpload", "line_number": 44, "usage_type": "name"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyUpload.uploadProxy", "line_number": 50, "usage_type": "call"}, {"api_name": "DIRAC.FrameworkSystem.Client.ProxyUpload", "line_number": 50, "usage_type": "name"}, {"api_name": "DIRAC.gLogger.error", "line_number": 52, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 52, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 53, "usage_type": "call"}, {"api_name": "DIRAC.gLogger.notice", "line_number": 54, "usage_type": "call"}, {"api_name": "DIRAC.gLogger", "line_number": 54, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "202609292", "text": "from __future__ import print_function\nfrom PIL import Image \n \n\n#import stuff for image processing\n\nfrom PIL import Image\nimport numpy as np\nimport scipy.misc\n\nimport sys\nimport os\n\nsys.path.insert(0, os.path.abspath('..'))\n\nfrom clint.arguments import Args\nfrom clint.textui import puts, colored, indent\n\nargs = Args()\n#print(str(args.all))\n\nif len(args) > 6:\n print('Too many arguments')\n sys.exit()\nelif len(args) < 1:\n print('Too few arguments, check help with -h')\n sys.exit()\n\n\n#with indent(4, quote='>>>'):\n #puts(colored.blue('Aruments passed in: ') + str(args.all))\n #puts(colored.blue('Flags detected: ') + str(args.flags))\n #puts(colored.blue('Files detected: ') + str(args.files))\n #puts(colored.blue('NOT Files detected: ') + str(args.not_files))\n #puts(colored.blue('Grouped Arguments: ') + str(dict(args.grouped)))\n\n#print()\n\ngrouped_var = dict(args.grouped)\n\nper_value = 0\n\nfor m in list(args.all):\n\n m = str(m)\n #print(m)\n #sys.exit()\n \n #m = m_str[8:-3]\n #print(m)\n if m == '-i':\n infile = str(grouped_var['-i'])\n \n infile_name = infile[8:-3]\n\n\n elif m == '-o':\n outfile = str(grouped_var['-o'])\n \n outfile_name = outfile[8:-3]\n\n elif m == '-p':\n per = str(grouped_var['-p'])\n #print(per)\n per_value = np.int(per[8:-3])\n\n\n elif m == '-h':\n print('Only works with jpg file format')\n print('Usage: \\n compress -i input_file -o output_file')\n print('[-p] : Perecentage of Singular values you want to use')\n print('[-p] : Default is 50%')\n print('[-p] : if given 0, it also takes default')\n sys.exit()\n\nif (per_value > 100) or (per_value < 0):\n print('Unrealistic Value for -p, Please choose values between 1 and 100')\nelif per_value == 0:\n per_value = 50\n\n\ndef cmp(infile_name, outfile_name, per_value):\n img = Image.open(infile_name)\n\n img_g = img\n #img_g = img.convert('LA')\n imgnp = np.array(list(img_g.getdata(band=0)), float)\n imgnp.shape = (img_g.size[1], img_g.size[0])\n\n # singular value decomposition:\n\n U, D, V = np.linalg.svd(imgnp)\n\n\n\n for i in [np.int(np.round(np.size(D)*per_value/100))]:\n #print(np.int(np.round(np.size(D)/2)))\n\n #plt.figure(figsize=(9, 6))\n new_img = np.matrix(U[:, :i]) * np.diag(D[:i]) * np.matrix(V[:i, :])\n #plt.imshow(new_img, cmap='gray')\n #plt.show()\n \n scipy.misc.toimage(new_img).save(outfile_name)\n \n Image.fromarray((image_array*255).astype('uint8'), mode='L').convert('RGB').save(filename)\n\ncmp(infile_name, outfile_name, per_value)\n", "sub_path": "compressjpg/compresssvd.py", "file_name": "compresssvd.py", "file_ext": "py", "file_size_in_byte": 2647, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.insert", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "clint.arguments.Args", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 83, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.linalg.svd", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 100, "usage_type": "call"}, {"api_name": "scipy.misc.misc.toimage", "line_number": 104, "usage_type": "call"}, {"api_name": "scipy.misc.misc", "line_number": 104, "usage_type": "attribute"}, {"api_name": "scipy.misc", "line_number": 104, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 106, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 106, "usage_type": "name"}]} +{"seq_id": "279516603", "text": "# Redes Convergentes - Universidade Federal do ABC\r\n\r\n# Professor: Carlos Alberto Kamienski \r\n\r\n\r\n# **** Projeto Cloud Streaming de Audio codificado em Ogg Opus! ****\r\n\r\n\r\n# Cliente, responsavel por receber e tocar o audio enviado pelo server! \r\n \r\n\r\n# Gabriel Batista da Silva, 11047216\r\n# Henrique Fantato, 21053916\r\n# Mikael Alves Monteiro, 21055813\r\n\r\n\r\nimport socket,os\r\nimport threading, wave, pyaudio, pickle,struct, pydub\r\n\r\nhost_name = socket.gethostname()\r\nhost_ip = socket.gethostbyname(host_name) # '192.168.1.102'\r\nprint(host_ip)\r\nport = 9611\r\n\r\ndef audio_stream():\r\n\t\r\n\tp = pyaudio.PyAudio()\r\n\tCHUNK = 1024\r\n\tstream = p.open(format=p.get_format_from_width(2),\r\n\t\t\t\t\tchannels=2,\r\n\t\t\t\t\trate=48000,\r\n\t\t\t\t\toutput=True,\r\n\t\t\t\t\tframes_per_buffer=CHUNK)\r\n\t\t\t\t\t\r\n\t# create socket\r\n\tclient_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\r\n\tsocket_address = (host_ip,port-1)\r\n\r\n\tprint('server listening at',socket_address)\r\n\tclient_socket.connect(socket_address) \r\n\tprint(\"CLIENT CONNECTED TO\",socket_address)\r\n\r\n\tdata = b\"\"\r\n\tpayload_size = struct.calcsize(\"Q\")\r\n\r\n\twhile True:\r\n\t\ttry:\r\n\t\t\twhile len(data) < payload_size:\r\n\t\t\t\tpacket = client_socket.recv(4*1024) # 4K\r\n\t\t\t\tif not packet: break\r\n\t\t\t\tdata+=packet\r\n\r\n\t\t\tpacked_msg_size = data[:payload_size]\r\n\t\t\tdata = data[payload_size:]\r\n\t\t\tmsg_size = struct.unpack(\"Q\",packed_msg_size)[0]\r\n\r\n\t\t\twhile len(data) < msg_size:\r\n\t\t\t\tdata += client_socket.recv(4*1024)\r\n\r\n\t\t\tframe_data = data[:msg_size]\r\n\t\t\tdata = data[msg_size:]\r\n\t\t\tframe = pickle.loads(frame_data)\r\n\r\n\t\t\tstream.write(frame) \r\n \r\n\t\texcept:\r\n\r\n\t\t\tbreak\r\n\r\n\tclient_socket.close()\r\n\tprint('Audio closed')\r\n\tos._exit(1)\r\n\t\r\nt1 = threading.Thread(target=audio_stream, args=())\r\nt1.start()\r\n", "sub_path": "client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 1746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "socket.gethostname", "line_number": 20, "usage_type": "call"}, {"api_name": "socket.gethostbyname", "line_number": 21, "usage_type": "call"}, {"api_name": "pyaudio.PyAudio", "line_number": 27, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 36, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 36, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 36, "usage_type": "attribute"}, {"api_name": "struct.calcsize", "line_number": 44, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 55, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 62, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 72, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "583315465", "text": "import logging\nfrom argparse import ArgumentParser, Namespace\nfrom pathlib import Path\n\nimport PyLogger\nfrom Organizer import Console, PROCESSING_FOLDER, COLLECTION_FOLDER, list_files, del_folder, extract_archive, \\\n create_archive, PublisherInfo, SeriesInfo, ComicInfo, load_info, save_info, slugify_publisher, slugify_series, \\\n slugify_comic, add_comicvine_info, add_league_info, add_metron_info, COMICVINE_API_KEY, LOCG_API_KEY, \\\n LOCG_CLIENT_ID, METRON_USERNAME, METRON_PASSWORD\n\nLOGGER = logging.getLogger('Organizer')\n\n\ndef main(input_path: str, pull_info: bool, show_variants: bool, manual_info: bool, image_check: bool, debug: bool = False):\n Console.display_heading('Welcome to Comic Organizer')\n input_folder = Path(input_path).resolve()\n if not input_folder.exists():\n LOGGER.error(f\"Invalid Input Folder: `{input_folder}`\")\n return\n for comic_file in list_files(input_folder, ('.cbr', '.cbz',)):\n LOGGER.info(f\"Extracting {comic_file.stem}\")\n Console.display_sub_heading(comic_file.stem)\n comic_folder = extract_archive(src=comic_file, dest=PROCESSING_FOLDER)\n if not comic_folder:\n continue\n info_files = list_files(comic_folder, ('.xml', '.json', '.yaml',))\n if len(info_files) == 1:\n comic_info = load_info(info_files[0])\n elif len(info_files) > 1:\n selected = Console.display_menu(items=[x.stem for x in info_files], exit_text='None', prompt='Select Info File')\n if len(info_files) >= selected >= 1:\n comic_info = load_info(info_files[selected - 1])\n else:\n comic_info = None\n else:\n comic_info = None\n if not comic_info:\n publisher = PublisherInfo(\n title=Console.request_str(prompt='Publisher Title')\n )\n series = SeriesInfo(\n publisher=publisher,\n start_year=Console.request_int(prompt='Series Start Year') or 2020,\n title=Console.request_str(prompt='Series Title'),\n volume=Console.request_int(prompt='Series Volume') or 1\n )\n comic_info = ComicInfo(\n series=series,\n number=Console.request_str(prompt='Issue Number') or '1'\n )\n else:\n if not comic_info.series.publisher.title:\n comic_info.series.publisher.title = Console.request_str(prompt='Publisher Title')\n if not comic_info.series.title:\n comic_info.series.title = Console.request_str(prompt='Series Title')\n if not comic_info.number:\n comic_info.number = Console.request_str(prompt='Issue Number')\n comic_info.reset()\n if pull_info:\n if LOCG_API_KEY and LOCG_CLIENT_ID:\n Console.display_item_value(item='Pulling info from', value='League of Comic Geeks')\n comic_info = add_league_info(comic_info=comic_info, show_variants=show_variants)\n if METRON_USERNAME and METRON_PASSWORD:\n Console.display_item_value(item='Pulling info from', value='Metron')\n comic_info = add_metron_info(comic_info=comic_info, show_variants=show_variants)\n if COMICVINE_API_KEY:\n Console.display_item_value(item='Pulling info from', value='Comicvine')\n comic_info = add_comicvine_info(comic_info=comic_info, show_variants=show_variants)\n if manual_info:\n Console.display_text('Manually adding info')\n # comic_info = add_manual_info(comic_info=comic_info, show_variants=show_variants)\n\n for file in info_files:\n file.unlink(missing_ok=True)\n\n save_info(file=comic_folder.joinpath('ComicInfo.json'), comic_info=comic_info)\n publisher_folder = COLLECTION_FOLDER.joinpath(slugify_publisher(title=comic_info.series.publisher.title))\n series_folder = publisher_folder.joinpath(slugify_series(title=comic_info.series.title, volume=comic_info.series.volume))\n comic_file_path = series_folder.joinpath(slugify_comic(series_slug=series_folder.name, comic_format=comic_info.comic_format, number=comic_info.number))\n if image_check:\n Console.request_str(prompt='Press to continue')\n\n clean_archive = create_archive(src=comic_folder, filename=comic_file_path.name)\n if not clean_archive:\n continue\n\n del_folder(comic_folder)\n comic_file.unlink(missing_ok=True)\n\n publisher_folder.mkdir(parents=True, exist_ok=True)\n series_folder.mkdir(parents=True, exist_ok=True)\n clean_comic = series_folder.joinpath(f\"{comic_file_path.name}.cbz\")\n if not clean_comic.exists():\n clean_archive.rename(clean_comic)\n else:\n LOGGER.error(f\"Unable to move the result as a file with the same name already exists: {clean_comic}\")\n LOGGER.info(f\"Cleaned {clean_comic.stem}\")\n\n\ndef parse_arguments() -> Namespace:\n parser = ArgumentParser(prog='Comic-Organizer')\n parser.add_argument('--input-folder', type=str, required=True)\n parser.add_argument('--pull-info', action='store_true')\n parser.add_argument('--show-variants', action='store_true')\n parser.add_argument('--add-manual-info', action='store_true')\n parser.add_argument('--manual-image-check', action='store_true')\n parser.add_argument('-d', '--debug', action='store_true')\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n try:\n args = parse_arguments()\n PyLogger.init('Comic-Organizer', file_level=logging.DEBUG if args.debug else logging.INFO, console_level=logging.INFO if args.debug else logging.WARNING)\n main(input_path=args.input_folder, pull_info=args.pull_info, show_variants=args.show_variants, manual_info=args.add_manual_info, image_check=args.manual_image_check,\n debug=args.debug)\n except KeyboardInterrupt:\n pass\n", "sub_path": "Organizer/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 5974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "Organizer.Console.display_heading", "line_number": 15, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "Organizer.list_files", "line_number": 20, "usage_type": "call"}, {"api_name": "Organizer.Console.display_sub_heading", "line_number": 22, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 22, "usage_type": "name"}, {"api_name": "Organizer.extract_archive", "line_number": 23, "usage_type": "call"}, {"api_name": "Organizer.PROCESSING_FOLDER", "line_number": 23, "usage_type": "name"}, {"api_name": "Organizer.list_files", "line_number": 26, "usage_type": "call"}, {"api_name": "Organizer.load_info", "line_number": 28, "usage_type": "call"}, {"api_name": "Organizer.Console.display_menu", "line_number": 30, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 30, "usage_type": "name"}, {"api_name": "Organizer.load_info", "line_number": 32, "usage_type": "call"}, {"api_name": "Organizer.PublisherInfo", "line_number": 38, "usage_type": "call"}, {"api_name": "Organizer.Console.request_str", "line_number": 39, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 39, "usage_type": "name"}, {"api_name": "Organizer.SeriesInfo", "line_number": 41, "usage_type": "call"}, {"api_name": "Organizer.Console.request_int", "line_number": 43, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 43, "usage_type": "name"}, {"api_name": "Organizer.Console.request_str", "line_number": 44, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 44, "usage_type": "name"}, {"api_name": "Organizer.Console.request_int", "line_number": 45, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 45, "usage_type": "name"}, {"api_name": "Organizer.ComicInfo", "line_number": 47, "usage_type": "call"}, {"api_name": "Organizer.Console.request_str", "line_number": 49, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 49, "usage_type": "name"}, {"api_name": "Organizer.Console.request_str", "line_number": 53, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 53, "usage_type": "name"}, {"api_name": "Organizer.Console.request_str", "line_number": 55, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 55, "usage_type": "name"}, {"api_name": "Organizer.Console.request_str", "line_number": 57, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 57, "usage_type": "name"}, {"api_name": "Organizer.LOCG_API_KEY", "line_number": 60, "usage_type": "name"}, {"api_name": "Organizer.LOCG_CLIENT_ID", "line_number": 60, "usage_type": "name"}, {"api_name": "Organizer.Console.display_item_value", "line_number": 61, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 61, "usage_type": "name"}, {"api_name": "Organizer.add_league_info", "line_number": 62, "usage_type": "call"}, {"api_name": "Organizer.METRON_USERNAME", "line_number": 63, "usage_type": "name"}, {"api_name": "Organizer.METRON_PASSWORD", "line_number": 63, "usage_type": "name"}, {"api_name": "Organizer.Console.display_item_value", "line_number": 64, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 64, "usage_type": "name"}, {"api_name": "Organizer.add_metron_info", "line_number": 65, "usage_type": "call"}, {"api_name": "Organizer.COMICVINE_API_KEY", "line_number": 66, "usage_type": "name"}, {"api_name": "Organizer.Console.display_item_value", "line_number": 67, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 67, "usage_type": "name"}, {"api_name": "Organizer.add_comicvine_info", "line_number": 68, "usage_type": "call"}, {"api_name": "Organizer.Console.display_text", "line_number": 70, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 70, "usage_type": "name"}, {"api_name": "Organizer.save_info", "line_number": 76, "usage_type": "call"}, {"api_name": "Organizer.COLLECTION_FOLDER.joinpath", "line_number": 77, "usage_type": "call"}, {"api_name": "Organizer.COLLECTION_FOLDER", "line_number": 77, "usage_type": "name"}, {"api_name": "Organizer.slugify_publisher", "line_number": 77, "usage_type": "call"}, {"api_name": "Organizer.slugify_series", "line_number": 78, "usage_type": "call"}, {"api_name": "Organizer.slugify_comic", "line_number": 79, "usage_type": "call"}, {"api_name": "Organizer.Console.request_str", "line_number": 81, "usage_type": "call"}, {"api_name": "Organizer.Console", "line_number": 81, "usage_type": "name"}, {"api_name": "Organizer.create_archive", "line_number": 83, "usage_type": "call"}, {"api_name": "Organizer.del_folder", "line_number": 87, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 101, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 100, "usage_type": "name"}, {"api_name": "PyLogger.init", "line_number": 114, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 114, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 114, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 114, "usage_type": "attribute"}]} +{"seq_id": "285134215", "text": "##############################################################\n# Joint Coordinate System (JCS) as per Grood and Suntay (1983)\n##############################################################\nimport numpy as np\nimport json\nfrom typing import List, Dict, Any, Callable, Union\nfrom abc import ABC, abstractmethod\n\nfrom utility.C3D import C3DServer\n\n\ndef norm(a):\n return a / np.linalg.norm(a, axis=1)[:, None]\n\n\ndef vec_dot(a, b):\n return np.einsum('ij,ij->i', a, b)\n\n\nclass Segment:\n parent_joints: List['JCS']\n\n def __init__(self, lateral, frontal, longitudinal, name=None, normalize=True, update=False):\n self._lateral = Segment.check_input(lateral)\n self._frontal = Segment.check_input(frontal)\n self._longitudinal = Segment.check_input(longitudinal)\n self.name = name\n self.parent_joints = []\n self.update_mode = update\n if normalize:\n self.normalize_axes()\n\n def normalize_axes(self):\n self._lateral = norm(self._lateral)\n self._frontal = norm(self._frontal)\n self._longitudinal = norm(self._longitudinal)\n self.update_parents()\n\n def update_parents(self):\n if not self.update_mode:\n return\n for joint in self.parent_joints:\n joint.update()\n\n # region Axis getters-setters, parent JCS update\n @property\n def lateral(self):\n return self._lateral\n\n @lateral.setter\n def lateral(self, value):\n self._lateral = Segment.check_input(value)\n self.update_parents()\n\n @property\n def frontal(self):\n return self._frontal\n\n @frontal.setter\n def frontal(self, value):\n self._frontal = Segment.check_input(value)\n self.update_parents()\n\n @property\n def longitudinal(self):\n return self._longitudinal\n\n @longitudinal.setter\n def longitudinal(self, value):\n self._longitudinal = Segment.check_input(value)\n self.update_parents()\n\n @property\n def axes(self):\n return [self.lateral, self.frontal, self.longitudinal]\n # endregion\n\n @staticmethod\n def check_input(a: np.ndarray):\n try:\n if 3 not in a.shape:\n raise Exception('JCS only works in 3D')\n if a.ndim > 2:\n raise Exception('Too many dimensions of input array')\n return np.atleast_2d(a) if a.shape[-1] is 3 else a.T\n except (AttributeError, TypeError):\n raise Exception('Use numpy ndarray-like for Segment objects')\n\n\n\nclass JCS:\n _segment_a: Segment\n _segment_b: Segment\n\n def __init__(self, segment_a: Segment, segment_b: Segment,\n body_fixed_axes=(0, 2), reference_axes=(1,1),\n side='R', name=None):\n self._segment_a = segment_a\n self._segment_b = segment_b\n self._segment_a.parent_joints.append(self)\n self._segment_b.parent_joints.append(self)\n self.body_fixed_axes = body_fixed_axes\n self.reference_axes = reference_axes\n self.remainder_axes = self.get_remainder_axes()\n\n self.name = name\n self.side = side\n\n self.floating_axis, self.flexion, self.abduction, self.rotation = self.update()\n\n def get_remainder_axes(self):\n if np.any([self.body_fixed_axes[i] == self.reference_axes[i] for i in range(2)]):\n raise Exception('Duplicate axis used in joint')\n self.remainder_axes = \\\n tuple([tuple({0,1,2}-set(used_axes))[0] for used_axes in zip(self.body_fixed_axes, self.reference_axes)])\n return self.remainder_axes\n\n def update(self):\n self.floating_axis = norm(np.cross(self.e3, self.e1, axis=1))\n sin_alpha = -vec_dot(self.floating_axis, self.e1s)\n cos_alpha = vec_dot(self.e1r, self.floating_axis)\n\n cos_beta = vec_dot(self.e1, self.e3)\n\n sin_gamma = (-1 if self.side is 'R' else 1) * vec_dot(self.floating_axis, self.e3s)\n cos_gamma = vec_dot(self.e1r, self.floating_axis)\n\n self.flexion = np.arctan2(sin_alpha, cos_alpha)\n self.abduction = (1 if self.side is 'R' else -1) * (np.arccos(cos_beta) - np.pi/2)\n self.rotation = np.arctan2(sin_gamma, cos_gamma)\n return self.floating_axis, self.flexion, self.abduction, self.rotation\n\n # region Property setters and getters (and update)\n @property\n def e1(self):\n return self._segment_a.axes[self.body_fixed_axes[0]]\n\n @property\n def e1r(self):\n return self._segment_a.axes[self.reference_axes[0]]\n\n @property # Sign determining axis\n def e1s(self):\n return self._segment_a.axes[self.remainder_axes[0]]\n\n @property\n def e3(self):\n return self._segment_b.axes[self.body_fixed_axes[1]]\n\n @property\n def e3r(self):\n return self._segment_b.axes[self.reference_axes[1]]\n\n @property # Sign determining axis\n def e3s(self):\n return self._segment_b.axes[self.remainder_axes[1]]\n\n @property\n def angle_array(self):\n return np.array([self.flexion, self.abduction, self.rotation]).T\n\n @property\n def segment_a(self):\n # Getter of segment a\n return self._segment_a\n\n @segment_a.setter\n def segment_a(self, value):\n self._segment_a = value\n self._segment_a.parent_joints.append(self)\n self.update()\n\n @property\n def segment_b(self):\n # Getter of segment a\n return self._segment_b\n\n @segment_b.setter\n def segment_b(self, value):\n self._segment_b = value\n self._segment_b.parent_joints.append(self)\n self.update()\n\n # endregion\n\n\nclass MarkerSet(ABC):\n dict_joint: Dict[str, Union[JCS, tuple]]\n dict_segment: Dict[str, Union[Segment, Callable]]\n\n # Does not remove nans!\n @abstractmethod\n def __init__(self, c3d_file, emg_file=None):\n self.c3d_file = c3d_file\n self.emg_file = emg_file\n\n self.list_marker = []\n self.dict_marker = {}\n\n self.list_emg = []\n self.dict_emg = {}\n\n self.dict_segment = {} # Populate with methods for getting segments, will be replaced by actual segments\n self.dict_joint = {} # Populate with 'JCS_name': ('Seg A', 'Seg B'), will be replaced by actual JCSs\n self.c3d_freq = 0\n self.emg_freq = 0\n\n def load_c3d(self, load_emg=True):\n with C3DServer() as c3d:\n c3d.open_c3d(self.c3d_file)\n self.dict_marker = c3d.get_marker_dict(self.list_marker)\n self.c3d_freq = c3d.get_video_frame_rate()\n if load_emg:\n if self.emg_file is None:\n self.dict_emg = c3d.get_analog_dict(self.list_emg)\n self.emg_freq = c3d.get_analog_frame_rate()\n else:\n self.dict_emg, self.emg_freq = self.get_emg_data()\n\n def proc_joints(self):\n if not self.dict_marker:\n self.load_c3d()\n self.marker_preproc()\n\n for key in self.dict_segment.keys():\n self.dict_segment[key] = self.dict_segment[key]()\n for key, (seg_a, seg_b, s) in self.dict_joint.items():\n self.dict_joint[key] = JCS(self.dict_segment[seg_a], self.dict_segment[seg_b], side=s)\n\n def save_json(self, save_path):\n dict_out = {k: i.angle_array.tolist() for k, i in self.dict_joint.items()}\n dict_out['EMG'] = {k: i for k, i in self.dict_emg.items()}\n dict_out['Framerate'] = self.c3d_freq\n dict_out['Sampling Frequency'] = self.emg_freq\n\n with open(save_path, 'w') as fp:\n json.dump(dict_out, fp, indent=4)\n \n return\n\n def marker_preproc(self):\n return\n\n def get_emg_data(self):\n raise NotImplementedError('No default emg data loading!')\n", "sub_path": "jcs.py", "file_name": "jcs.py", "file_ext": "py", "file_size_in_byte": 7732, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.linalg.norm", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.einsum", "line_number": 17, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.atleast_2d", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.arctan2", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 187, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 188, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 188, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 189, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 192, "usage_type": "name"}, {"api_name": "utility.C3D.C3DServer", "line_number": 209, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 237, "usage_type": "call"}]} +{"seq_id": "404575486", "text": "import argparse\nfrom datetime import datetime, timedelta\nimport json\nimport logging\nimport os\nimport sys\n\nimport jinja2\n\n\ndef get_logger():\n \"\"\"\n Create logging handler\n \"\"\"\n ## Create logger\n logger = logging.getLogger('garminvisualisation')\n logger.setLevel(logging.DEBUG)\n\n # create file handler which logs even debug messages\n fh = logging.FileHandler('garminvisualisation.log')\n fh.setLevel(logging.DEBUG)\n # create formatter and add it to the handlers\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n fh.setFormatter(formatter)\n # add the handlers to the logger\n logger.addHandler(fh)\n\n # create stdout logger\n ch = logging.StreamHandler(sys.stdout)\n ch.setLevel(logging.WARNING)\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n ch.setFormatter(formatter)\n logger.addHandler(ch)\n\n return logger\n\n\ndef unix_to_python(timestamp):\n return datetime.utcfromtimestamp(float(timestamp))\n\n\ndef python_to_string(timestamp, dt_format='%Y-%m-%dT%H:%M:%S.0'):\n \"\"\"\n Example: 2017-11-11T03:30:00.0\n \"\"\"\n return datetime.fromtimestamp(int(timestamp)).strftime(dt_format)\n\n\ndef add_totalsteps_to_summary(content):\n result = []\n totalsteps = 0\n for item in content:\n totalsteps = totalsteps + item['steps']\n item['totalsteps'] = totalsteps\n result.append(item)\n return result\n\n\ndef summary_to_graphdata(content):\n \"\"\"Returns a list of datetime tuples with:\n {'datetime': [\"2017-08-12T22:00:00.0\", ..],\n 'totalsteps': [0, 0, 0, 10, 32, 42, 128, ..],\n 'sleeping_steps': [0, 0, None, None, None, 0, None, ..],\n 'active_steps': [None, None, 10, 500, 120242, None, ..],\n 'sedentary_steps': [None\n \"\"\"\n datetimes = []\n totalsteps_list = []\n sleeping_steps_list = []\n active_steps_list = []\n highlyactive_steps_list = []\n sedentary_steps_list = []\n generic_steps_list = []\n totalsteps = 0\n\n for item in content:\n sleeping_steps = 0\n active_steps = 0\n highlyactive_steps = 0\n sedentary_steps = 0\n generic_steps = 0\n datetimes.append(item['startGMT'] + 'Z')\n totalsteps = totalsteps + item['steps']\n totalsteps_list.append(totalsteps)\n if item['primaryActivityLevel'] == 'sedentary':\n sedentary_steps = item['steps']\n elif item['primaryActivityLevel'] == 'active':\n active_steps = item['steps']\n elif item['primaryActivityLevel'] == 'highlyActive':\n highlyactive_steps = item['steps']\n elif item['primaryActivityLevel'] == 'sleeping':\n sleeping_steps = item['steps']\n elif item['primaryActivityLevel'] == 'generic' or item['primaryActivityLevel'] == 'none' or item['primaryActivityLevel'] == 'unmonitored':\n generic_steps = item['steps']\n else:\n #print(item['primaryActivityLevel'])\n print('Unknown activity level found:')\n print(item)\n\n sleeping_steps_list.append(sleeping_steps)\n active_steps_list.append(active_steps)\n highlyactive_steps_list.append(highlyactive_steps)\n sedentary_steps_list.append(sedentary_steps)\n generic_steps_list.append(generic_steps)\n\n return {'datetime': datetimes, 'totalsteps': totalsteps_list,\n 'sleeping_steps': sleeping_steps_list, 'active_steps': active_steps_list,\n 'highlyactive_steps': highlyactive_steps_list, 'sedentary_steps': sedentary_steps_list,\n 'generic_steps': generic_steps_list}\n\n\ndef heartrate_to_graphdata(content):\n values = content['heartRateValues']\n rates = []\n for value in values:\n rates.append([python_to_string(value[0]/1000), value[1]])\n return rates\n\n\ndef stress_to_graphdata(content):\n values = content['stressValuesArray']\n stress = []\n for value in values:\n if value[1] > 0:\n stress.append([python_to_string(value[0]/1000), value[1]])\n return stress\n\n\ndef sleep_to_graphdata(content):\n return {'sleepEndTimestamp': python_to_string(content['dailySleepDTO']['sleepEndTimestampGMT']/1000),\n 'sleepStartTimestamp': python_to_string(content['dailySleepDTO']['sleepStartTimestampGMT']/1000),\n }\n\n\ndef parse_wellness(wellness, content):\n try:\n content['allMetrics']\n except TypeError:\n # Not a correct wellness file\n return wellness\n\n for item in content['allMetrics']['metricsMap']:\n if 'SLEEP_' in item:\n key = item[len('SLEEP_'):].lower()\n else:\n key = item.lstrip('WELLNESS_').lower()\n for value in content['allMetrics']['metricsMap'][item]:\n if key not in wellness:\n wellness[key] = {}\n if value['value']:\n wellness[key][value['calendarDate']] = int(value['value'])\n else:\n wellness[key][value['calendarDate']] = None\n return wellness\n\n\ndef parse_files(logger, directory, target_directory):\n heartrate = {}\n stress = {}\n sleep = {}\n summary = []\n wellness = {}\n for filename in sorted(os.listdir(directory)):\n if filename.endswith(\"_summary.json\"):\n # parse summary, create graph\n with open(os.path.join(directory, filename), 'r') as f:\n content = json.load(f)\n summary.append((filename.split('_')[0], summary_to_graphdata(content)))\n elif filename.endswith(\"_heartrate.json\"):\n # parse heartrate, create graph\n with open(os.path.join(directory, filename), 'r') as f:\n content = json.load(f)\n heartrate[filename.split('_')[0]] = heartrate_to_graphdata(content)\n elif filename.endswith(\"_stress.json\"):\n # parse stress, create graph\n with open(os.path.join(directory, filename), 'r') as f:\n content = json.load(f)\n stress[filename.split('_')[0]] = stress_to_graphdata(content)\n elif filename.endswith(\"_sleep.json\"):\n # parse stress, create graph\n with open(os.path.join(directory, filename), 'r') as f:\n content = json.load(f)\n sleep[filename.split('_')[0]] = sleep_to_graphdata(content)\n elif filename.endswith(\"_wellness.json\"):\n # parse wellness data\n with open(os.path.join(directory, filename), 'r') as f:\n content = json.load(f)\n wellness = parse_wellness(wellness, content)\n else:\n logger.info('Skipping file %s', filename)\n continue\n\n # Reverse list so latest days are on top\n summary = summary[::-1]\n\n return {'summaries': summary, 'wellness': wellness, 'heartrate': heartrate, 'stress': stress, 'sleep': sleep}\n\n\ndef generate_wellnesspage(template_dir, outputfile, alldata):\n \"\"\" Generate graphs for the various measurements\"\"\"\n loader = jinja2.FileSystemLoader(template_dir)\n environment = jinja2.Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)\n\n try:\n template = environment.get_template('wellness.html')\n except jinja2.exceptions.TemplateNotFound as e:\n print('E Template not found: {} in template dir {}'.format(str(e), template_dir))\n sys.exit(2)\n\n output = template.render(alldata)\n with open(outputfile, 'w') as pf:\n pf.write(output)\n\n\ndef generate_dailystats(logger, template_dir, outputdir, alldata):\n \"\"\" Generate graphs for the various measurements \"\"\"\n loader = jinja2.FileSystemLoader(template_dir)\n environment = jinja2.Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)\n\n try:\n template = environment.get_template('dailystats.html')\n except jinja2.exceptions.TemplateNotFound as e:\n logger.error('Template not found: %s in template dir %s', str(e), template_dir)\n sys.exit(2)\n\n nextday = None\n for datestamp, summary in alldata['summaries']:\n outputfile = os.path.join(outputdir, datestamp + '.html')\n thisdate = datetime.strptime(datestamp, '%Y-%m-%d')\n alldata['datedayofweek'] = thisdate.strftime('%A') # Day of week, e.g., Sunday, Monday...\n alldata['datestamp'] = datestamp\n alldata['summary'] = summary\n alldata['previousday'] = (thisdate - timedelta(days=1)).strftime('%Y-%m-%d') # Assume there was no gap\n alldata['nextday'] = nextday\n output = template.render(alldata)\n with open(outputfile, 'w') as pf:\n pf.write(output)\n nextday = datestamp\n\n\ndef run_visualisation():\n logger = get_logger()\n\n parser = argparse.ArgumentParser(description='Garmin Data Visualiser',\n epilog='Because the hell with APIs!', add_help='How to use',\n prog='python visualise.py -i -o ')\n parser.add_argument('-i', '--input', required=False,\n help='Input directory.', default=os.path.join(os.getcwd(), 'Wellness/'))\n parser.add_argument('-o', '--output', required=False,\n help='Output directory.', default=os.path.join(os.getcwd(), 'Graphs/'))\n args = vars(parser.parse_args())\n\n # Sanity check, before we do anything:\n if args['input'] is None and args['output'] is None:\n print(\"Must specify an input (-i) directory for the Wellness data, and an output (-o) directory for graphs.\")\n sys.exit()\n\n # Try to use the user argument from command line\n outputdir = args['output']\n inputdir = args['input']\n alldata = parse_files(logger, inputdir, outputdir)\n template_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'templates')\n #outputfile = os.path.join(outputdir, 'wellness.html')\n\n # Create output directory (if it does not already exist).\n if not os.path.exists(outputdir):\n os.makedirs(outputdir)\n\n #generate_wellnesspage(template_dir, outputfile, alldata)\n generate_dailystats(logger, template_dir, outputdir, alldata)\n\n\nif __name__ == \"__main__\":\n run_visualisation()\n", "sub_path": "visualisation.py", "file_name": "visualisation.py", "file_ext": "py", "file_size_in_byte": 10144, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 167, "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": "json.load", "line_number": 172, "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": "json.load", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 182, "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": "json.load", "line_number": 187, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 201, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 202, "usage_type": "call"}, {"api_name": "jinja2.exceptions", "line_number": 206, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 208, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 217, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 218, "usage_type": "call"}, {"api_name": "jinja2.exceptions", "line_number": 222, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 229, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 229, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 233, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 244, "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": "os.getcwd", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 250, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path", "line_number": 262, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 267, "usage_type": "call"}]} +{"seq_id": "115766561", "text": "import gzip\nfrom json import dump\nfrom logging import getLogger\n\nfrom bidso.utils import replace_extension\nfrom .utils import make_bids_name\nfrom .io.dataglove import parse_dataglove_log\nfrom .io.pulse_and_resp_scanner import parse_scanner_physio\nfrom .io.flip import parse_flip_physio\n\nlg = getLogger(__name__)\n\n\ndef convert_physio(rec, dest_path, name):\n \"\"\"Convert physiological signal to BIDS format.\n\n Parameters\n ----------\n rec : instance of Recording\n recording of type 'physio' (like dataglove or heart rate)\n dest_path : path\n full path to modality folder\n name : dict\n dictionary with parts to make bids name\n\n Notes\n -----\n StartTime in the .json file gives the offset from the start of the recording.\n If the tsv contains a \"time\" column, the \"time\" info is already aligned\n with the recording (so you don't need to add StartTime).\n \"\"\"\n for file in rec.list_files():\n if file.format == 'dataglove':\n name['recording'] = 'recording-dataglove'\n tsv, hdr = parse_dataglove_log(file.path)\n\n elif file.format == 'scanphyslog':\n name['recording'] = 'recording-resp'\n tsv, hdr = parse_scanner_physio(file.path)\n\n elif file.format == 'flip':\n name['recording'] = 'recording-flip'\n tsv, hdr = parse_flip_physio(file.path)\n\n else:\n lg.info(f'There is no function to convert \"{file.format}\" physio')\n return\n\n hdr['StartTime'] = rec.offset\n for time_col in ('time', 'time [s]'):\n if time_col in tsv.columns:\n tsv[time_col] += rec.offset\n\n if name['recording'] is None:\n lg.warning('No file associated with physio recording')\n return\n\n physio_tsv = dest_path / f'{make_bids_name(name, \"physio\")}'\n _write_physio(tsv, physio_tsv)\n\n physio_json = replace_extension(physio_tsv, '.json')\n with physio_json.open('w') as f:\n dump(hdr, f, indent=2)\n\n\ndef _write_physio(physio, physio_tsv):\n\n content = physio.to_csv(sep='\\t', index=False, header=False, float_format='%.3f')\n with gzip.open(physio_tsv, 'wb') as f:\n f.write(content.encode())\n", "sub_path": "xelo2/bids/physio.py", "file_name": "physio.py", "file_ext": "py", "file_size_in_byte": 2201, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "io.dataglove.parse_dataglove_log", "line_number": 35, "usage_type": "call"}, {"api_name": "io.pulse_and_resp_scanner.parse_scanner_physio", "line_number": 39, "usage_type": "call"}, {"api_name": "io.flip.parse_flip_physio", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.make_bids_name", "line_number": 58, "usage_type": "call"}, {"api_name": "bidso.utils.replace_extension", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 63, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "501344435", "text": "#!/cluster/home/lferemen/anaconda2/bin/python\n\n# Import the nearest neighbor class\nfrom sklearn.neighbors import KNeighborsClassifier \nfrom sklearn import cross_validation\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\nfrom sklearn.feature_selection import RFE\n\nimport numpy as np\n\n#X = np.load(\"data_arrays/inputExclH.npy\")\n#y = np.load(\"data_arrays/outputExclH.npy\")\n#names = ['higgsPt', 'Mll', 'MT', 'Ptll', 'DPhill', 'MET', 'lepPt0', 'lepPt1', 'lepEta0', 'lepEta1']\n\nXtmp = np.load(\"data_arrays/inputExclHAll.npy\")\n\n####remove all NaNs and infinities\nfilter = []\ntmpX = np.array(Xtmp.tolist())\nfor i in range(Xtmp.shape[1]):\n\tif np.any(np.isnan(tmpX[:,i:i+1]).any(axis=0)): # if any NaN is in this column\n\t\tpass\n\t\tfilter.append(False)\n\telse :\n\t\tfilter.append(True)\nfilteredNames = np.compress(np.array(filter), np.array(Xtmp.dtype.names), axis=0)\nnames = filteredNames.tolist()\nX = Xtmp[names]\n\n###X = Xtmp[:,~np.isnan(Xtmp).any(axis=0)]\n\ny = np.load(\"data_arrays/outputExclHAll.npy\")\n\n# split X into train and test sets\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y,test_size=0.4, random_state=0)\n\n######## use randomforests to rank each variable, so that we can select which variables to use\n#######rf = RandomForestRegressor(n_estimators=20, max_depth=4)\n#######scores = []\n########looping over columns and scoring each\n#######for i in range(X_train.shape[1]):\n#######\tscore = cross_validation.cross_val_score(rf, X_train[:, i:i+1], y_train, scoring=\"r2\",\n#######\t\t\t\t\t\t cv=cross_validation.ShuffleSplit(len(X_train), 3, .3))\n#######\tscores.append((round(np.mean(score), 3), names[i]))\n#######print sorted(scores, reverse=True)\n#\ntrf = RFE(RandomForestClassifier(),n_features_to_select=50,verbose=1)\nXt_train = trf.fit_transform(X_train,y_train)\n\napprovedIndices = trf.get_support()\napprovedNames = np.compress(approvedIndices, filteredNames, axis=0)\nprint(approvedNames)\n\nprint(\"Shape before transform = \",X_train.shape)\nprint(\"Shape after transform = \",Xt_train.shape)\n\n#from matplotlib import pyplot as plt\n###plotting the accepted features in feature space\n#plt.imshow(trf.get_support().reshape((8, 7)), interpolation=\"nearest\", cmap=plt.cm.Blues)\n#plt.show()\n\n#\n## Set hyper-parameters, for controlling algorithm\n#clf = KNeighborsClassifier(n_neighbors=5)\n#\n## Learn a model from training data\n#clf.fit(X_train, y_train)\n#\n## make some predictions\n#print(clf.predict_proba(X_test[:10])) \n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.load", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.compress", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.cross_validation", "line_number": 35, "usage_type": "name"}, {"api_name": "sklearn.feature_selection.RFE", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.compress", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "532358408", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nfrom cycler import cycler\nfrom os.path import exists\nfrom figura_encontrar import plot_encontrar\n\nsys.path.append(\"..\")\nfrom funciones import Bpara_Bperp, UTC_to_hdec, donde, fechas\nfrom importar_datos import importar_mag_1s, importar_swea, importar_swia\n\n\n\"\"\"\nPermite elegir dos tiempos (límites) + uno central para la MPB y los guarda en un txt\nTiene flag\nya están quasipara grupo 1, 2, 3, 4\nquasiperp grupo 3\n\"\"\"\n\nnp.set_printoptions(precision=4)\n\ngrupo = input(\"grupo\\n\")\n\npath = f\"outs_catalogo_previa/grupo{grupo}/\"\n\ndates = fechas()\ntime = int(input(\"hora (HH)\\n\"))\nd_str = str(dates[0] + \"-\" + dates[1] + \"-\" + dates[2])\n\ncatalogo = np.genfromtxt(f\"../outputs/grupo{grupo}/jacob_dayside.txt\", dtype=\"str\")\n\nfecha = catalogo[:, 0]\nhora_mpb = catalogo[:, 2]\n\nfor ff in range(len(fecha)):\n if d_str == fecha[ff]:\n if time == int(hora_mpb[ff].split(\":\")[0]):\n print(\"yes\")\n num = ff\n\nplt.rcParams[\"axes.prop_cycle\"] = cycler(\n \"color\",\n [\"#003f5c\", \"#ffa600\", \"#de425b\", \"#68abb8\", \"#f3babc\", \"#6cc08b\", \"#cacaca\"],\n)\n\nyear, month, day = fecha[num].split(\"-\")\nt_mpb = UTC_to_hdec(hora_mpb[num])\n\nti = t_mpb - 1\nif ti < 0:\n ti = 0.2\ntf = t_mpb + 1\nif tf > 24:\n tf = 24\n\nmag, t, B, posicion = importar_mag_1s(year, month, day, ti, tf)\nB_norm = np.linalg.norm(B, axis=1)\nB_para, B_perp_norm, tpara = Bpara_Bperp(B, t, ti + 0.2, tf - 0.2)\n\nswia, t_swia, i_density, i_temp, vel_mso = importar_swia(year, month, day, ti, tf)\n\nswea, t_swea, energia, flux_plot = importar_swea(year, month, day, ti, tf)\nenergias = [50 + i * 50 for i in range(3)]\nif type(t_swea) != int:\n JE_pds = np.zeros((len(t_swea), len(energias)))\n\n for i, e in enumerate(energias):\n j = donde(energia, e)\n JE_pds[:, i] = flux_plot[j]\nelse:\n JE_pds = 0\n\nval_MPB = plot_encontrar(\n \"MPB\",\n fecha[num],\n tpara,\n B_para,\n B_perp_norm,\n t,\n B,\n t_mpb,\n B_norm,\n t_swia,\n vel_mso,\n i_density,\n t_swea,\n JE_pds,\n energias,\n)\n\nflag_MPB = None\nwhile flag_MPB == None:\n flag = input(\"MPB confiable? y/n\\n\")\n if flag == \"y\":\n flag_MPB = 1\n elif flag == \"n\":\n flag_MPB = 0\n\nfilepath = f\"../outputs/grupo{grupo}/limites_mpb_jacob_revised.txt\"\n\nif not exists(filepath):\n with open(filepath, \"w\") as file:\n file.write(\"date\\tMPB_min\\tMPB\\tMPB_max\\tflag\\ttheta\\tbeta\\n\")\n\nwith open(filepath, \"a\") as file:\n file.write(\n f\"{fecha[num]}\\t{val_MPB[0]}\\t{val_MPB[1]}\\t{val_MPB[2]}\\t{flag_MPB}\\t{catalogo[num, -2]}\\t{catalogo[num, -1]}\\n\"\n )\n", "sub_path": "bs_mpb/afinar_mpb_jacob_por_fecha.py", "file_name": "afinar_mpb_jacob_por_fecha.py", "file_ext": "py", "file_size_in_byte": 2611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.set_printoptions", "line_number": 20, "usage_type": "call"}, {"api_name": "funciones.fechas", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 41, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "cycler.cycler", "line_number": 41, "usage_type": "call"}, {"api_name": "funciones.UTC_to_hdec", "line_number": 47, "usage_type": "call"}, {"api_name": "importar_datos.importar_mag_1s", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 57, "usage_type": "attribute"}, {"api_name": "funciones.Bpara_Bperp", "line_number": 58, "usage_type": "call"}, {"api_name": "importar_datos.importar_swia", "line_number": 60, "usage_type": "call"}, {"api_name": "importar_datos.importar_swea", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "funciones.donde", "line_number": 68, "usage_type": "call"}, {"api_name": "figura_encontrar.plot_encontrar", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "358030915", "text": "# _*_ coding:utf-8 _*_ \nimport requests\nimport cv2 as cv\nimport numpy as np\n\nimport random\nfrom configparser import ConfigParser\nfrom http import cookiejar\nimport logging\nimport re\nimport os\n\nfrom .image_process import Image_process\n\nlogging.basicConfig(level = logging.INFO)\n__LOGGER = logging.getLogger(__name__)\n\nclass Config_Tools:\n def __init__(self):\n self.__LOGGER = logging.getLogger(__name__)\n self.__cfg = ConfigParser()\n self.__cfg.read('config.ini')\n self.__config = self.cfg_to_dict('Config')\n\n def check_path(self)->None:\n # 找到当前执行主目录的绝对路径并写入配置\n current_dir = os.path.abspath('.')\n if '\\\\' in current_dir:\n current_dir = current_dir.replace('\\\\','/')\n \n if current_dir == self.__config['path']:\n self.__LOGGER.info('主目录路径正确')\n else:\n self.__LOGGER.warning('主目录路径有误，更新配置{}'.format(self.__config['path']))\n self.__cfg.set('DEFAULT','path',current_dir)\n with open('config.ini','w') as configfile:\n self.__cfg.write(configfile)\n self.__LOGGER.info('已重置主目录路径为{}'.format(current_dir))\n \n def cfg_to_dict(self,module:str)->dict:\n # 根据输入模块提取对应配置返还字典\n conf = {}\n for k,v in self.__cfg.items(module):\n conf[k]=v\n\n return conf\n\ndef time_standard(timestr:str)->list:\n \n numb = timestr.split(':')\n start = eval(numb[0])*60+eval(numb[1])\n end = start+180\n result = (str(start),str(end))\n \n return result\n\ndef get_code_images(number:int):\n a = Config_Tools()\n cfg = a.cfg_to_dict('image')\n path = cfg['image_download_path']\n headers = eval(cfg['download_headers'])\n \n try:\n with requests.session() as session:\n session.headers.update(headers)\n for i in range(number):\n\n url = cfg['url']+str(random.random())\n f = session.get(url,stream = True)\n f.raise_for_status()\n\n if f.status_code == 200:\n with open('{}{}{}'.format(path,str(i),'.png'),'wb') as wenjian:\n for chunk in f.iter_content(1024):\n wenjian.write(chunk)\n except Exception as e:\n __LOGGER.warning(e.args)\n else:\n __LOGGER.info('第{}次下载成功'.format(i))\n \ndef make_wait_label(path1,path2):\n tools = Image_process()\n files= os.listdir(path1)\n files.sort(key=lambda x:int(x[:-4]))\n counter = 0\n for name in files:\n image = tools.handle_img(path1+\"/\"+name)\n for i in image:\n cv.imwrite(path2+'/'+'{}.png'.format(counter),i)\n print('第{}图切割完毕'.format(counter))\n counter +=1\n\ndef OneHotEncode(number):\n label = np.zeros((10))\n label[number] = 1\n return label\n\ndef datasoup(path):\n files= os.listdir(path)\n files.sort(key=lambda x:int(eval(x[:-4])))\n data = []\n label = []\n for name in files:\n image = cv.imread(path+'/'+name,0)\n for i in range(image.shape[0]):\n for j in range(image.shape[1]):\n image[i,j] = 255-image[i,j]\n image = image.astype(\"float\")/255.0\n nparray =image.flatten()\n data.append(nparray)\n namelist = name.split('.')\n label.append(OneHotEncode(eval(namelist[1])))\n label = np.array(label)\n data = np.array(data)\n return label,data\n\nif __name__ == '__main__':\n get_code_images(100)\n path1 = \"G:/gitproject/Library/image_code/test\"\n path2 = \"G:/gitproject/Library/image_code/test_cut\"\n make_wait_label(path1,path2)\n\n #label,data = datasoup(path2)\n #np.save(\"test_label.npy\",label)\n #np.save(\"test_data.npy\",data)\n #print(label)\n #print(data)\n pass", "sub_path": "Mypackages/custom_tools.py", "file_name": "custom_tools.py", "file_ext": "py", "file_size_in_byte": 3879, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 64, "usage_type": "call"}, {"api_name": "random.random", "line_number": 68, "usage_type": "call"}, {"api_name": "image_process.Image_process", "line_number": 82, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "48103824", "text": "\nimport hashlib\nfrom Crypto.Cipher import AES\nfrom Crypto import Random\n\ndef gen_iv():\n\n return Random.new().read(16)\n\ndef key_from_string(string,length=None):\n if length:\n if length == 16:\n return hashlib.blake2b(str(string).encode('utf-8')+b\"saltyPP4\",digest_size=16).digest()\n if length <= 128:\n return hashlib.sha512(bytes(str(string)*length*21,'utf-8')+b\"saltyPP4\").digest()[:length]\n else:\n raise Exception('Length should be less than or equal to 128!')\n else:\n return hashlib.sha512(bytes(str(string) * length * 21, 'utf-8')).digest()\n\ndef encrypt(b,keystring,IV=None):\n\n if not keystring:\n return b\n\n if type(b) == int:\n\n b = b.to_bytes(1,'little')\n\n key = key_from_string(str(keystring),16)\n IV = Random.new().read(16) if not IV else IV\n\n encryption_suite = AES.new(key, AES.MODE_CFB, IV)\n encrypted = encryption_suite.encrypt(b)\n\n return encrypted\n\ndef decrypt(b,keystring,IV):\n\n if not keystring:\n return b\n\n if type(b) == int:\n\n b = b.to_bytes(1,'little')\n\n key = key_from_string(str(keystring),16)\n IV = IV\n\n decryption_suite = AES.new(key, AES.MODE_CFB, IV)\n decrypted = decryption_suite.decrypt(b)\n\n return decrypted\n", "sub_path": "PyPakket4/PakketShared/crypto_aes.py", "file_name": "crypto_aes.py", "file_ext": "py", "file_size_in_byte": 1272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "Crypto.Random.new", "line_number": 8, "usage_type": "call"}, {"api_name": "Crypto.Random", "line_number": 8, "usage_type": "name"}, {"api_name": "hashlib.blake2b", "line_number": 13, "usage_type": "call"}, {"api_name": "hashlib.sha512", "line_number": 15, "usage_type": "call"}, {"api_name": "hashlib.sha512", "line_number": 19, "usage_type": "call"}, {"api_name": "Crypto.Random.new", "line_number": 31, "usage_type": "call"}, {"api_name": "Crypto.Random", "line_number": 31, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 33, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 33, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CFB", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 50, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 50, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CFB", "line_number": 50, "usage_type": "attribute"}]} +{"seq_id": "180573212", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sys\nfrom PyQt5.QtGui import QFont\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nimport sql\nimport decimal\n\ninsert_propt = \\\n ['building, room_number, capacity', 'dept_name, building, budget(budget > 0)',\n 'course_id, title, dept_name, credits(credits > 0)', 'ID, name, dept_name, salary(salary > 29000)',\n 'course_id, sec_id, semester(Spring, Summer, Winter, Fall), year, building, room_number,time_slot_id'\n , 'ID, course_id, sec_id, year', 'ID, name, dept_name, tot_cred(tot_cred >= 0)',\n 'ID, course_id, sec_id, semester, year, grade', 's_ID, i_ID',\n 'time_slot_id, day, start_hr[0,24), start_min[0,60), end_hr[0,24), end_min[0,60', 'course_id, prerea_id']\ndelete_propt = \\\n ['building, room_number', 'dept_name', 'course_id', 'ID', 'course_id, sec_id, semester, year',\n 'ID, cpirse_id, sec_id, semester, year'\n , 'ID', 'ID, course_id, sec_id, semester, year', 's_ID', 'time_slot_id, day, start_hr, start_min',\n 'course_id, prereq_id']\n\nask = \\\n ['normal_query1', 'normal_query2', 'join_query1', 'join_query2', 'nested_query1', 'nested_query2', 'group_query1',\n 'group_query2','auto_sql']\n\nquery_info = \\\n ['查找所有教师的名字', '查找所有在Computer Science系中且工资大于70000的教师姓名',\n '对于大学中所有讲授课程的教师，找出他们的姓名以及所讲述的所有课程标识',\n '找出所有教师的姓名，他们工资至少比Biology系某一个教师的工资高',\n '找出所有在2009年秋季开课不在2010年秋季学期开课的所有课程',\n '找出不同的学生总数，他们选修了ID为10101的教师所讲授的课程',\n '平均工资超过42000的系的系名和平均工资', '找出每个系在2010年春季学期坚守一门课程的教师人数','请自行输入合法的sql语句']\n\nquery_sql = \\\n['''select name from instructor;''',\n '''select name from instructor where dept_name='Comp. Sci.' and salary > 70000;''',\n '''select name, course_id from instructor, teaches where instructor.ID = teaches.ID;''',\n '''select distinct T.name from instructor as T , instructor as S \nwhere T.salary > S.salary and S.dept_name = 'Biology'; ''',\n '''select distinct course_id from section where semester = 'Fall' and year = 2009 and \n course_id not in (select course_id from section where semester = 'Spring' and year = 2010);''',\n '''select count(distinct ID) from takes where (course_id, sec_id, semester, year) in \n (select course_id, sec_id, semester, year from teaches where teaches.ID = '10101');''',\n '''select dept_name, avg(salary) as avg_salary from instructor group by dept_name having avg(salary) > 42000;''',\n '''select dept_name, count(distinct ID) as instr_count from instructor natural join teaches\n where semester = 'Spring' and year = 2010 group by dept_name'''\n ]\n\n\nclass MyTable(QTableWidget):\n def __init__(self):\n super().__init__()\n\n def update(self, colname, colinfo):\n self.clear()\n self.setWindowTitle(\"查询结果\")\n self.resize(600,300)\n self.setColumnCount(len(colname))\n self.setRowCount(len(colinfo))\n self.setHorizontalHeaderLabels(colname)\n for i in range(len(colinfo)):\n for j in range(len(colname)):\n\n self.setItem(i, j, QTableWidgetItem(str(colinfo[i][j])))\n self.move(100,100)\n if not self.isVisible():\n self.show()\n\n\nclass INSERT(QWidget):\n def __init__(self, x):\n super().__init__()\n\n self.cb = QComboBox()\n self.db = x\n layout = QVBoxLayout()\n\n self.setGeometry(300, 300, 300, 200)\n self.setWindowTitle('q')\n self.cb.addItems(\n ['classroom', 'department', 'course', 'instructor', 'section', 'teaches', 'student', 'takes', 'advisor'\n , 'time_slot', 'prereq'])\n self.btn = QPushButton('Done')\n self.text = QTextEdit()\n self.text.setToolTip('building, room_number, capacity')\n self.cb.currentIndexChanged.connect(self.selectionchange)\n self.btn.clicked.connect(self.insert)\n self.label = QLabel()\n layout.addWidget(self.label)\n layout.addWidget(self.btn)\n layout.addWidget(self.cb)\n layout.addWidget(self.text)\n\n self.setLayout(layout)\n\n def selectionchange(self):\n self.text.setToolTip(insert_propt[self.cb.currentIndex()])\n\n def insert(self):\n text_string = self.text.toPlainText()\n text_string = text_string.strip('\\n')\n x = text_string.split(',')\n sql_string = 'insert into ' + self.cb.currentText() + ' values' + ' ('\n le = len(x)\n sql_string = sql_string + '\\'' + x[0] + '\\''\n for id in range(1, le):\n sql_string = sql_string + ', \\'' + x[id] + '\\''\n sql_string = sql_string + ');'\n print(sql_string)\n try:\n self.db.insert(sql_string)\n except Exception as msg:\n self.label.setText(str(msg))\n\n def handle_click(self):\n if not self.isVisible():\n self.show()\n\n def handle_close(self):\n self.close()\n\n\nclass DELETE(QWidget):\n\n def __init__(self, x):\n super().__init__()\n self.db = x\n self.cb = QComboBox()\n layout = QVBoxLayout()\n\n self.setGeometry(300, 300, 300, 200)\n self.setWindowTitle('q')\n self.cb.addItems(\n ['classroom', 'department', 'course', 'instructor', 'section', 'teaches', 'student', 'takes', 'advisor'\n , 'time_slot', 'prereq'])\n self.btn = QPushButton('Done')\n self.text = QTextEdit()\n self.cb.currentIndexChanged.connect(self.selectionchange)\n self.btn.clicked.connect(self.delete)\n self.label = QLabel()\n layout.addWidget(self.label)\n layout.addWidget(self.btn)\n layout.addWidget(self.cb)\n layout.addWidget(self.text)\n self.setLayout(layout)\n\n def selectionchange(self):\n self.text.setToolTip(delete_propt[self.cb.currentIndex()])\n\n def delete(self):\n text_string = self.text.toPlainText()\n text_string = text_string.strip('\\n')\n sql_string = 'delete from ' + self.cb.currentText() + 'where' + text_string\n try:\n self.db.delete(sql_string)\n except Exception as msg:\n self.label.setText(str(msg))\n\n def handle_click(self):\n if not self.isVisible():\n self.show()\n\n def handle_close(self):\n self.close()\n\n\nclass ASK(QWidget):\n def __init__(self, x, ta):\n super().__init__()\n self.table = ta\n self.db = x\n self.setGeometry(300, 300, 300, 200)\n self.setWindowTitle('query')\n self.cb = QComboBox()\n self.btn = QPushButton('Done')\n self.text = QTextEdit()\n layout = QVBoxLayout()\n self.label = QLabel()\n self.label.setText(query_info[0])\n self.cb.addItems(ask)\n self.cb.currentIndexChanged.connect(self.selectchange)\n self.btn.clicked.connect(self.query)\n layout.addWidget(self.btn)\n layout.addWidget(self.cb)\n layout.addWidget(self.label)\n layout.addWidget(self.text)\n self.setLayout(layout)\n\n def selectchange(self):\n self.label.setText(query_info[self.cb.currentIndex()])\n\n def query(self):\n colname = []\n colinfo = []\n if self.cb.currentIndex() < 8:\n sql_string = \"select * from db\" + str(self.cb.currentIndex()) + \";\"\n try:\n colname, colinfo = self.db.query(sql_string)\n except Exception as msg:\n self.label.setText(str(msg))\n else:\n sql_string = self.text.toPlainText()\n try:\n colname, colinfo = self.db.query(sql_string)\n except Exception as msg:\n self.label.setText(str(msg))\n self.table.update(colname, colinfo)\n self.table.show()\n\n def handle_click(self):\n if not self.isVisible():\n self.show()\n\n def handle_close(self):\n self.close()\n\n\nclass Example(QWidget):\n\n close_signal = pyqtSignal()\n def __init__(self, insert, ask, delete, x):\n super().__init__()\n\n QToolTip.setFont(QFont('SansSerif', 10))\n\n self.setToolTip('This is a QWidget widget')\n self.sql = x\n self.btn = QPushButton('插入', self)\n self.btn.resize(self.btn.sizeHint())\n self.btn1 = QPushButton('删除', self)\n self.btn1.resize(self.btn1.sizeHint())\n self.btn2 = QPushButton('提交',self)\n self.btn2.resize(self.btn2.sizeHint())\n self.btn3 = QPushButton('查询', self)\n self.btn3.resize(self.btn3.sizeHint())\n self.btn4 = QPushButton('回滚',self)\n self.btn4.resize(self.btn4.sizeHint())\n self.btn2.move(60,0)\n self.btn4.move(60,60)\n self.btn.move(0, 60)\n self.btn1.move(0, 120)\n self.setGeometry(300, 300, 300, 200)\n self.setWindowTitle('Tooltips')\n self.btn.setToolTip(\"插入操作\")\n self.btn1.setToolTip(\"删除操作\")\n self.btn3.setToolTip(\"查询操作\")\n self.btn.clicked.connect(insert.handle_click)\n self.btn1.clicked.connect(delete.handle_click)\n self.btn3.clicked.connect(ask.handle_click)\n self.btn2.clicked.connect(self.sql.commit)\n self.btn4.clicked.connect(self.sql.rollback)\n\n\nif __name__ == '__main__':\n mysql = sql.Mysql()\n for i in range(8):\n sql_string = \"drop view db\" + str(i) + \";\"\n mysql.create_view(sql_string)\n for i in range(8):\n sql_string = \"create view \" + \"db\" + str(i) + \" as \" + query_sql[i]\n mysql.create_view(sql_string)\n app = QApplication(sys.argv)\n table = MyTable()\n ask = ASK(mysql, table)\n insert = INSERT(mysql)\n delete = DELETE(mysql)\n\n ex = Example(insert, ask, delete, mysql)\n ex.show()\n sys.exit(app.exec_())\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 9984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "PyQt5.QtGui.QFont", "line_number": 226, "usage_type": "call"}, {"api_name": "sql.Mysql", "line_number": 257, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 264, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 272, "usage_type": "call"}]} +{"seq_id": "7522210", "text": "import ibis\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom pytest import param\n\npytestmark = pytest.mark.mapd\npytest.importorskip('pymapd')\n\n\n@pytest.mark.parametrize(('result_fn', 'expected'), [\n param(\n lambda t: t[t, ibis.literal(1).degrees().name('n')].limit(1)['n'],\n 57.2957795130823,\n id='literal_degree'\n ),\n param(\n lambda t: t[t, ibis.literal(1).radians().name('n')].limit(1)['n'],\n 0.0174532925199433,\n id='literal_radians'\n ),\n param(\n lambda t: t.double_col.corr(t.float_col),\n 1.000000000000113,\n id='double_float_correlation'\n ),\n param(\n lambda t: t.double_col.cov(t.float_col),\n 91.67005567565313,\n id='double_float_covariance'\n )\n])\ndef test_operations_scalar(alltypes, result_fn, expected):\n result = result_fn(alltypes).execute()\n np.testing.assert_allclose(result, expected)\n\n\n@pytest.mark.parametrize(('result_fn', 'check_result'), [\n param(\n lambda t: (\n t[t.date_string_col][t.date_string_col.ilike('10/%')].limit(1)\n ),\n lambda v: v.startswith('10/'),\n id='string_ilike'\n )\n])\ndef test_string_operations(alltypes, result_fn, check_result):\n result = result_fn(alltypes).execute()\n\n if isinstance(result, pd.DataFrame):\n result = result.values[0][0]\n assert check_result(result)\n", "sub_path": "ibis/mapd/tests/test_operations.py", "file_name": "test_operations.py", "file_ext": "py", "file_size_in_byte": 1390, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pytest.importorskip", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.param", "line_number": 13, "usage_type": "call"}, {"api_name": "ibis.literal", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 18, "usage_type": "call"}, {"api_name": "ibis.literal", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.param", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pytest.param", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "111719674", "text": "from django.contrib import admin\nfrom .models import Profile\n# Register your models here.\n\n# admin 계정을 통해 db 데이터 입출력\n\n# admin 페이지에 Profile 데이터를 저장할 수 있도록 구성\n@admin.register(Profile)\nclass ProfileAdmin(admin.ModelAdmin) :\n list_display = ['id','nickname','user']\n list_display_links = ['nickname','user']\n search_fields = ['nickname']\n", "sub_path": "LeeDongu/merge_login/accounts/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Profile", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "542151992", "text": "import csv\nfrom datetime import date\nimport os\nfrom PIL import Image, ImageTk\nimport random\nfrom random import shuffle\n\nROOT = os.path.dirname(os.path.realpath(__file__))\nCOMMON = os.path.join(ROOT, 'media', 'Common')\nIMG_PKMN_DIR = os.path.join(ROOT, 'media', 'pokemon')\nPLAYER_DIR = os.path.join(ROOT, 'players')\nDATA = os.path.join(ROOT, 'data')\nTRAINERS = os.path.join(ROOT, 'media', 'trainers')\n\nTypeChart = {\n ('Bug', 'Bug') : 0,\n ('Bug', 'Dark') : 1,\n ('Bug', 'Dragon') : 0,\n ('Bug', 'Electric') : 0,\n ('Bug', 'Fairy') : -1,\n ('Bug', 'Fighting') : -1,\n ('Bug', 'Fire') : -1,\n ('Bug', 'Flying') : -1,\n ('Bug', 'Ghost') : -1,\n ('Bug', 'Grass') : 1,\n ('Bug', 'Ground') : 0,\n ('Bug', 'Ice') : 0,\n ('Bug', 'Normal') : 0,\n ('Bug', 'Poison') : -1,\n ('Bug', 'Psychic') : 1,\n ('Bug', 'Rock') : 0,\n ('Bug', 'Steel') : -1,\n ('Bug', 'Water') : 0,\n\n ('Dark', 'Bug') : 0,\n ('Dark', 'Dark') : -1,\n ('Dark', 'Dragon') : 0,\n ('Dark', 'Electric') : 0,\n ('Dark', 'Fairy') : -1,\n ('Dark', 'Fighting') : -1,\n ('Dark', 'Fire') : 0,\n ('Dark', 'Flying') : 0,\n ('Dark', 'Ghost') : 1,\n ('Dark', 'Grass') : 0,\n ('Dark', 'Ground') : 0,\n ('Dark', 'Ice') : 0,\n ('Dark', 'Normal') : 0,\n ('Dark', 'Poison') : 0,\n ('Dark', 'Psychic') : 1,\n ('Dark', 'Rock') : 0,\n ('Dark', 'Steel') : 0,\n ('Dark', 'Water') : 0,\n\n ('Dragon', 'Bug') : 0,\n ('Dragon', 'Dark') : 0,\n ('Dragon', 'Dragon') : 1,\n ('Dragon', 'Electric') : 0,\n ('Dragon', 'Fairy') : -1,\n ('Dragon', 'Fighting') : 0,\n ('Dragon', 'Fire') : 0,\n ('Dragon', 'Flying') : 0,\n ('Dragon', 'Ghost') : 0,\n ('Dragon', 'Grass') : 0,\n ('Dragon', 'Ground') : 0,\n ('Dragon', 'Ice') : 0,\n ('Dragon', 'Normal') : 0,\n ('Dragon', 'Poison') : 0,\n ('Dragon', 'Psychic') : 0,\n ('Dragon', 'Rock') : 0,\n ('Dragon', 'Steel') : -1,\n ('Dragon', 'Water') : 0,\n\n ('Electric', 'Bug') : 0,\n ('Electric', 'Dark') : 0,\n ('Electric', 'Dragon') : -1,\n ('Electric', 'Electric') : -1,\n ('Electric', 'Fairy') : 0,\n ('Electric', 'Fighting') : 0,\n ('Electric', 'Fire') : 0,\n ('Electric', 'Flying') : 1,\n ('Electric', 'Ghost') : 0,\n ('Electric', 'Grass') : -1,\n ('Electric', 'Ground') : -1,\n ('Electric', 'Ice') : 0,\n ('Electric', 'Normal') : 0,\n ('Electric', 'Poison') : 0,\n ('Electric', 'Psychic') : 0,\n ('Electric', 'Rock') : 0,\n ('Electric', 'Steel') : 0,\n ('Electric', 'Water') : 1,\n\n ('Fairy', 'Bug') : 0,\n ('Fairy', 'Dark') : 1,\n ('Fairy', 'Dragon') : 1,\n ('Fairy', 'Electric') : 0,\n ('Fairy', 'Fairy') : 0,\n ('Fairy', 'Fighting') : 1,\n ('Fairy', 'Fire') : -1,\n ('Fairy', 'Flying') : 0,\n ('Fairy', 'Ghost') : 0,\n ('Fairy', 'Grass') : 0,\n ('Fairy', 'Ground') : 0,\n ('Fairy', 'Ice') : 0,\n ('Fairy', 'Normal') : 0,\n ('Fairy', 'Poison') : -1,\n ('Fairy', 'Psychic') : 0,\n ('Fairy', 'Rock') : 0,\n ('Fairy', 'Steel') : -1,\n ('Fairy', 'Water') : 0,\n\n ('Fighting', 'Bug') : -1,\n ('Fighting', 'Dark') : 1,\n ('Fighting', 'Dragon') : 0,\n ('Fighting', 'Electric') : 0,\n ('Fighting', 'Fairy') : 1,\n ('Fighting', 'Fighting') : 0,\n ('Fighting', 'Fire') : 0,\n ('Fighting', 'Flying') : -1,\n ('Fighting', 'Ghost') : -1,\n ('Fighting', 'Grass') : 0,\n ('Fighting', 'Ground') : 0,\n ('Fighting', 'Ice') : 1,\n ('Fighting', 'Normal') : 1,\n ('Fighting', 'Poison') : -1,\n ('Fighting', 'Psychic') : -1,\n ('Fighting', 'Rock') : 1,\n ('Fighting', 'Steel') : 1,\n ('Fighting', 'Water') : 0,\n\n ('Fire', 'Bug') : 1,\n ('Fire', 'Dark') : 0,\n ('Fire', 'Dragon') : -1,\n ('Fire', 'Electric') : 0,\n ('Fire', 'Fairy') : 0,\n ('Fire', 'Fighting') : 0,\n ('Fire', 'Fire') : -1,\n ('Fire', 'Flying') : 0,\n ('Fire', 'Ghost') : 0,\n ('Fire', 'Grass') : 1,\n ('Fire', 'Ground') : 0,\n ('Fire', 'Ice') : 1,\n ('Fire', 'Normal') : 0,\n ('Fire', 'Poison') : 0,\n ('Fire', 'Psychic') : 0,\n ('Fire', 'Rock') : -1,\n ('Fire', 'Steel') : 1,\n ('Fire', 'Water') : -1,\n\n ('Flying', 'Bug') : 1,\n ('Flying', 'Dark') : 0,\n ('Flying', 'Dragon') : 0,\n ('Flying', 'Electric') : -1,\n ('Flying', 'Fairy') : 0,\n ('Flying', 'Fighting') : 1,\n ('Flying', 'Fire') : 0,\n ('Flying', 'Flying') : 0,\n ('Flying', 'Ghost') : 0,\n ('Flying', 'Grass') : 1,\n ('Flying', 'Ground') : 0,\n ('Flying', 'Ice') : 0,\n ('Flying', 'Normal') : 0,\n ('Flying', 'Poison') : 0,\n ('Flying', 'Psychic') : 0,\n ('Flying', 'Rock') : -1,\n ('Flying', 'Steel') : -1,\n ('Flying', 'Water') : 0,\n\n ('Ghost', 'Bug') : 0,\n ('Ghost', 'Dark') : -1,\n ('Ghost', 'Dragon') : 0,\n ('Ghost', 'Electric') : 0,\n ('Ghost', 'Fairy') : 0,\n ('Ghost', 'Fighting') : 0,\n ('Ghost', 'Fire') : 0,\n ('Ghost', 'Flying') : 0,\n ('Ghost', 'Ghost') : 1,\n ('Ghost', 'Grass') : 0,\n ('Ghost', 'Ground') : 0,\n ('Ghost', 'Ice') : 0,\n ('Ghost', 'Normal') : -1,\n ('Ghost', 'Poison') : 0,\n ('Ghost', 'Psychic') : 1,\n ('Ghost', 'Rock') : 0,\n ('Ghost', 'Steel') : 0,\n ('Ghost', 'Water') : 0,\n\n ('Grass', 'Bug') : -1,\n ('Grass', 'Dark') : 0,\n ('Grass', 'Dragon') : -1,\n ('Grass', 'Electric') : 0,\n ('Grass', 'Fairy') : 0,\n ('Grass', 'Fighting') : 0,\n ('Grass', 'Fire') : -1,\n ('Grass', 'Flying') : -1,\n ('Grass', 'Ghost') : 0,\n ('Grass', 'Grass') : -1,\n ('Grass', 'Ground') : 1,\n ('Grass', 'Ice') : 0,\n ('Grass', 'Normal') : 0,\n ('Grass', 'Poison') : -1,\n ('Grass', 'Psychic') : 0,\n ('Grass', 'Rock') : 1,\n ('Grass', 'Steel') : -1,\n ('Grass', 'Water') : 1,\n\n ('Ground', 'Bug') : -1,\n ('Ground', 'Dark') : 0,\n ('Ground', 'Dragon') : 0,\n ('Ground', 'Electric') : 1,\n ('Ground', 'Fairy') : 0,\n ('Ground', 'Fighting') : 0,\n ('Ground', 'Fire') : 1,\n ('Ground', 'Flying') : -1,\n ('Ground', 'Ghost') : 0,\n ('Ground', 'Grass') : -1,\n ('Ground', 'Ground') : 0,\n ('Ground', 'Ice') : 0,\n ('Ground', 'Normal') : 0,\n ('Ground', 'Poison') : 1,\n ('Ground', 'Psychic') : 0,\n ('Ground', 'Rock') : 1,\n ('Ground', 'Steel') : 1,\n ('Ground', 'Water') : 0,\n\n ('Ice', 'Bug') : 0,\n ('Ice', 'Dark') : 0,\n ('Ice', 'Dragon') : 1,\n ('Ice', 'Electric') : 0,\n ('Ice', 'Fairy') : 0,\n ('Ice', 'Fighting') : 0,\n ('Ice', 'Fire') : -1,\n ('Ice', 'Flying') : 1,\n ('Ice', 'Ghost') : 0,\n ('Ice', 'Grass') : 1,\n ('Ice', 'Ground') : 1,\n ('Ice', 'Ice') : -1,\n ('Ice', 'Normal') : 0,\n ('Ice', 'Poison') : 0,\n ('Ice', 'Psychic') : 0,\n ('Ice', 'Rock') : 0,\n ('Ice', 'Steel') : -1,\n ('Ice', 'Water') : -1,\n\n ('Normal', 'Bug') : 0,\n ('Normal', 'Dark') : 0,\n ('Normal', 'Dragon') : 0,\n ('Normal', 'Electric') : 0,\n ('Normal', 'Fairy') : 0,\n ('Normal', 'Fighting') : 0,\n ('Normal', 'Fire') : 0,\n ('Normal', 'Flying') : 0,\n ('Normal', 'Ghost') : -1,\n ('Normal', 'Grass') : 0,\n ('Normal', 'Ground') : 0,\n ('Normal', 'Ice') : 0,\n ('Normal', 'Normal') : 0,\n ('Normal', 'Poison') : 0,\n ('Normal', 'Psychic') : 0,\n ('Normal', 'Rock') : -1,\n ('Normal', 'Steel') : -1,\n ('Normal', 'Water') : 0,\n\n ('Poison', 'Bug') : 0,\n ('Poison', 'Dark') : 0,\n ('Poison', 'Dragon') : 0,\n ('Poison', 'Electric') : 0,\n ('Poison', 'Fairy') : 1,\n ('Poison', 'Fighting') : 0,\n ('Poison', 'Fire') : 0,\n ('Poison', 'Flying') : 0,\n ('Poison', 'Ghost') : -1,\n ('Poison', 'Grass') : 1,\n ('Poison', 'Ground') : -1,\n ('Poison', 'Ice') : 0,\n ('Poison', 'Normal') : 0,\n ('Poison', 'Poison') : -1,\n ('Poison', 'Psychic') : 0,\n ('Poison', 'Rock') : -1,\n ('Poison', 'Steel') : -1,\n ('Poison', 'Water') : 0,\n\n ('Psychic', 'Bug') : 0,\n ('Psychic', 'Dark') : -1,\n ('Psychic', 'Dragon') : 0,\n ('Psychic', 'Electric') : 0,\n ('Psychic', 'Fairy') : 0,\n ('Psychic', 'Fighting') : 1,\n ('Psychic', 'Fire') : 0,\n ('Psychic', 'Flying') : 0,\n ('Psychic', 'Ghost') : 0,\n ('Psychic', 'Grass') : 0,\n ('Psychic', 'Ground') : 0,\n ('Psychic', 'Ice') : 0,\n ('Psychic', 'Normal') : 0,\n ('Psychic', 'Poison') : 1,\n ('Psychic', 'Psychic') : -1,\n ('Psychic', 'Rock') : 0,\n ('Psychic', 'Steel') : -1,\n ('Psychic', 'Water') : 0,\n\n ('Rock', 'Bug') : 1,\n ('Rock', 'Dark') : 0,\n ('Rock', 'Dragon') : 0,\n ('Rock', 'Electric') : 0,\n ('Rock', 'Fairy') : 0,\n ('Rock', 'Fighting') : -1,\n ('Rock', 'Fire') : 1,\n ('Rock', 'Flying') : 1,\n ('Rock', 'Ghost') : 0,\n ('Rock', 'Grass') : 0,\n ('Rock', 'Ground') : -1,\n ('Rock', 'Ice') : 1,\n ('Rock', 'Normal') : 0,\n ('Rock', 'Poison') : 0,\n ('Rock', 'Psychic') : 0,\n ('Rock', 'Rock') : 0,\n ('Rock', 'Steel') : -1,\n ('Rock', 'Water') : 0,\n\n ('Steel', 'Bug') : 0,\n ('Steel', 'Dark') : 0,\n ('Steel', 'Dragon') : 0,\n ('Steel', 'Electric') : -1,\n ('Steel', 'Fairy') : 1,\n ('Steel', 'Fighting') : 0,\n ('Steel', 'Fire') : -1,\n ('Steel', 'Flying') : 0,\n ('Steel', 'Ghost') : 0,\n ('Steel', 'Grass') : 0,\n ('Steel', 'Ground') : 0,\n ('Steel', 'Ice') : 1,\n ('Steel', 'Normal') : 0,\n ('Steel', 'Poison') : 0,\n ('Steel', 'Psychic') : 0,\n ('Steel', 'Rock') : 1,\n ('Steel', 'Steel') : -1,\n ('Steel', 'Water') : -1,\n\n ('Water', 'Bug') : 0,\n ('Water', 'Dark') : 0,\n ('Water', 'Dragon') : -1,\n ('Water', 'Electric') : 0,\n ('Water', 'Fairy') : 0,\n ('Water', 'Fighting') : 0,\n ('Water', 'Fire') : 1,\n ('Water', 'Flying') : 0,\n ('Water', 'Ghost') : 0,\n ('Water', 'Grass') : -1,\n ('Water', 'Ground') : 1,\n ('Water', 'Ice') : 0,\n ('Water', 'Normal') : 0,\n ('Water', 'Poison') : 0,\n ('Water', 'Psychic') : 0,\n ('Water', 'Rock') : 1,\n ('Water', 'Steel') : 0,\n ('Water', 'Water') : -1\n}\n\ndef type_logic(attackingPokemon, defendingPokemon):\n matchup = 0\n if ('Delta Stream' in attackingPokemon.ability):\n return True\n if ('Multitype' in attackingPokemon.ability or\n 'Multitype' in defendingPokemon.ability or\n 'RKS System' in attackingPokemon.ability or\n 'RKS System' in defendingPokemon.ability):\n return False\n for x in attackingPokemon.type:\n if x:\n for y in defendingPokemon.type:\n if y:\n if (('Levitate' in defendingPokemon.ability and 'Ground' in x) or\n ('Flash Fire' in defendingPokemon.ability and 'Fire' in x) or\n ('Water Bubble' in defendingPokemon.ability and 'Fire' in x) or\n ('Water Absorb' in defendingPokemon.ability and 'Water' in x) or\n ('Storm Drain' in defendingPokemon.ability and 'Water' in x) or\n ('Dry Skin' in defendingPokemon.ability and 'Water' in x) or\n ('Lightningrod' in defendingPokemon.ability and 'Electric' in x) or\n ('Volt Absorb' in defendingPokemon.ability and 'Electric' in x) or\n ('Motor Drive' in defendingPokemon.ability and 'Electric' in x) or\n ('Sap Sipper' in defendingPokemon.ability and 'Grass' in x) or\n ('Desolate Land' in defendingPokemon.ability and 'Water' in x) or\n ('Primordial Sea' in defendingPokemon.ability and 'Fire' in x) or\n ('Prankster' in attackingPokemon.ability and 'Dark' in y)):\n matchup -= 1\n elif (('Fluffy' in defendingPokemon.ability and 'Fire' in x) or\n ('Dry Skin' in defendingPokemon.ability and 'Fire' in x) or\n ('Steelworker' in attackingPokemon.ability and 'Rock' in y) or\n ('Steelworker' in attackingPokemon.ability and 'Fairy' in y) or\n ('Steelworker' in attackingPokemon.ability and 'Ice' in y)):\n matchup += 1\n elif ('Scrappy' in attackingPokemon.ability and 'Ghost' in y):\n matchup += 0\n elif (('Tinted Lens' in attackingPokemon.ability and 'Bug' in x) and\n ('Fairy' in y or 'Fighting' in y or 'Fire' in y or\n 'Flying' in y or 'Ghost' in y or 'Poison' in y or\n 'Steel' in y)):\n matchup += 0\n elif (('Tinted Lens' in attackingPokemon.ability and 'Flying' in x) and\n ('Electric' in y or 'Rock' in y or 'Steel' in y)):\n matchup += 0\n elif (('Tinted Lens' in attackingPokemon.ability and 'Normal' in x) and\n ('Rock' in y or 'Steel' in y)):\n matchup += 0\n elif (('Tinted Lens' in attackingPokemon.ability and 'Poison' in x) and\n ('Ghost' in y or 'Ground' in y or 'Poison' in y or\n 'Rock' in y)):\n matchup += 0\n elif (('Tinted Lens' in attackingPokemon.ability and 'Psychic' in x) and\n ('Psychic' in y or 'Steel' in y)):\n matchup += 0\n else:\n matchup += TypeChart[(x, y)]\n if matchup > 0:\n return True\n else:\n return False\n\nALL_POKEMON_S = []\nALL_POKEMON_D = []\nABILITIES = {}\nMEGA_STONES = []\nZ_CRYSTALS = []\nBERRIES = []\nPLAYERS = []\nplayerNames = []\nTIERS_SINGLES = ['LC', 'LC Uber', 'Untiered', 'NFE', 'PU', 'NU', 'RU', 'UU', 'OU', 'Uber']\nTIERS_DOUBLES = ['LC', 'Untiered', 'DUU', 'DOU', 'DUber']\nGENERATIONS = ['Kanto', 'Johto', 'Hoenn', 'Sinnoh', 'Unova', 'Kalos', 'Alola']\nTYPES = ['Bug', 'Dark', 'Dragon', 'Electric', 'Fairy', 'Fighting', 'Fire', 'Flying', 'Ghost', 'Grass', 'Ground', 'Ice', 'Normal', 'Poison', 'Psychic', 'Rock', 'Steel', 'Water']\nITEMS = ['Mega Stones', 'Z-Crystals', 'Berries', 'Choice Band', 'Choice Scarf', 'Choice Specs', 'Leftovers', 'Life Orb']\nGIMMICKS = ['Sun', 'Rain', 'Sand', 'Hail', 'Trick Room', 'Baton Pass', 'E-Terrain', 'G-Terrain', 'M-Terrain', 'P-Terrain']\nALL_BANNERS = []\nmonth = int(date.today().strftime('%m'))\nday = int(date.today().strftime('%d'))\n\nclass Pokemon:\n def __init__(self, row):\n self.name = row[0]\n self.dex = row[1]\n self.type = [row[2], row[3]]\n self.tier = row[4]\n self.rarity = row[5]\n self.tag = row[6]\n self.item = row[7]\n self.ability = row[8]\n self.evSpread = row[9]\n self.nature = row[10]\n self.ivSpread = row[11]\n self.moves = [row[12], row[13], row[14], row[15]]\n # statistics\n self.generated_draft = int(row[16])\n self.generated_nemesis = int(row[17])\n self.generated_random = int(row[18])\n self.picked_draft = int(row[19])\n self.picked_nemesis = int(row[20])\n self.banned = int(row[21])\n\nwith open(os.path.join(DATA, 'Singles.csv'), 'r', encoding='utf-8') as fileName:\n reader = csv.reader(fileName)\n next(reader, None)\n for row in reader:\n ALL_POKEMON_S.append(Pokemon(row))\n\nwith open(os.path.join(DATA, 'Abilities.csv'), 'r', encoding='utf-8') as fileName:\n reader = csv.reader(fileName)\n for row in reader:\n ABILITIES[row[0]] = [row[x] for x in range(1,4) if row[x] != '']\n\nwith open(os.path.join(DATA, 'Items.csv'), 'r', encoding='utf-8') as fileName:\n reader = csv.reader(fileName)\n for row in reader:\n if row[0].endswith('ite') and row[0] != 'Eviolite':\n MEGA_STONES.append(row[0])\n if row[0].endswith('ium Z'):\n Z_CRYSTALS.append(row[0])\n if row[0].endswith('Berry'):\n BERRIES.append(row[0])\n\nwith open(os.path.join(DATA, 'Banners.csv'), 'r', encoding='utf-8') as file:\n reader = csv.reader(file)\n for row in reader:\n ALL_BANNERS.append(row)\n", "sub_path": "Gen7/Pokemon.py", "file_name": "Pokemon.py", "file_ext": "py", "file_size_in_byte": 16760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "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.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 434, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 434, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 435, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 435, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 459, "usage_type": "call"}, {"api_name": "os.path", "line_number": 459, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 460, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 466, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 470, "usage_type": "call"}, {"api_name": "os.path", "line_number": 470, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 471, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 480, "usage_type": "call"}, {"api_name": "os.path", "line_number": 480, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 481, "usage_type": "call"}]} +{"seq_id": "560075747", "text": "import asyncio\r\nfrom unittest import TestCase\r\n\r\nfrom alibabacloud_tea_rpc.client import Client\r\nfrom alibabacloud_tea_rpc.models import Config\r\nfrom alibabacloud_tea_util.models import RuntimeOptions\r\nfrom Tea.exceptions import TeaException, UnretryableException\r\n\r\nimport threading\r\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\r\n\r\n\r\nclass Request(BaseHTTPRequestHandler):\r\n def do_GET(self):\r\n self.send_response(200)\r\n self.send_header('Content-type', 'application/json')\r\n self.end_headers()\r\n self.wfile.write(b'{\"result\": \"server test\"}')\r\n\r\n def do_POST(self):\r\n self.send_response(400)\r\n self.send_header('Content-type', 'application/json')\r\n self.end_headers()\r\n self.wfile.write(b'{\"result\": \"server error\"}')\r\n\r\n\r\ndef run_server():\r\n server = HTTPServer(('localhost', 8888), Request)\r\n server.serve_forever()\r\n\r\n\r\nclass TestClient(TestCase):\r\n @classmethod\r\n def setUpClass(cls):\r\n server = threading.Thread(target=run_server)\r\n server.setDaemon(True)\r\n server.start()\r\n\r\n def test_init(self):\r\n conf = Config()\r\n try:\r\n Client(conf)\r\n except Exception as e:\r\n self.assertIsInstance(e, TeaException)\r\n try:\r\n Client(None)\r\n except Exception as e:\r\n self.assertIsInstance(e, TeaException)\r\n self.assertEqual(\r\n \"'config' can not be unset\",\r\n e.message\r\n )\r\n\r\n conf = Config(\r\n access_key_id='access_key_id',\r\n security_token='security_token',\r\n protocol='protocol',\r\n region_id='region_id',\r\n read_timeout=1000,\r\n connect_timeout=5000,\r\n http_proxy='http_proxy',\r\n https_proxy='https_proxy',\r\n endpoint='endpoint',\r\n no_proxy='no_proxy',\r\n max_idle_conns=1,\r\n network='network',\r\n user_agent='user_agent',\r\n suffix='suffix',\r\n endpoint_type='endpoint_type',\r\n open_platform_endpoint='open_platform_endpoint',\r\n type='type',\r\n )\r\n conf.access_key_secret = 'access_key_secret'\r\n client = Client(conf)\r\n self.assertIsNotNone(client)\r\n\r\n def test_do_request(self):\r\n conf = Config(\r\n access_key_id='access_key_id',\r\n security_token='security_token',\r\n protocol='http',\r\n region_id='region_id',\r\n read_timeout=1000,\r\n connect_timeout=5000,\r\n endpoint='127.0.0.1:8888',\r\n max_idle_conns=1\r\n )\r\n conf.access_key_secret = 'access_key_secret'\r\n runtime = RuntimeOptions(\r\n autoretry=False,\r\n max_attempts=2\r\n )\r\n client = Client(conf)\r\n res = client.do_request(\r\n action='action',\r\n protocol='http',\r\n method='GET',\r\n version='version',\r\n auth_type='auth_type',\r\n query={},\r\n body={},\r\n runtime=runtime\r\n )\r\n self.assertEqual({'result': 'server test'}, res)\r\n try:\r\n client.do_request(\r\n action='action',\r\n protocol='http',\r\n method='POST',\r\n version='version',\r\n auth_type='auth_type',\r\n query={},\r\n body={},\r\n runtime=runtime\r\n )\r\n assert False\r\n except Exception as e:\r\n self.assertIsInstance(e, UnretryableException)\r\n\r\n def test_model(self):\r\n conf = Config(\r\n access_key_id='access_key_id',\r\n access_key_secret='access_key_secret',\r\n protocol='http',\r\n endpoint='127.0.0.1:8888'\r\n )\r\n conf.validate()\r\n conf = Config(\r\n access_key_id='access_key_id',\r\n access_key_secret='access_key_secret',\r\n protocol='http',\r\n region_id=None,\r\n endpoint='127.0.0.1:8888',\r\n network=None,\r\n suffix=None\r\n )\r\n self.assertIsInstance(conf, Config)\r\n conf.validate()\r\n conf.to_map()\r\n conf.from_map({})\r\n\r\n def test_do_request_async(self):\r\n async def request(method):\r\n conf = Config(\r\n access_key_id='access_key_id',\r\n security_token='security_token',\r\n protocol='http',\r\n region_id='region_id',\r\n read_timeout=1000,\r\n connect_timeout=5000,\r\n endpoint='127.0.0.1:8888',\r\n max_idle_conns=1\r\n )\r\n conf.access_key_secret = 'access_key_secret'\r\n runtime = RuntimeOptions(\r\n autoretry=False,\r\n max_attempts=2\r\n )\r\n client = Client(conf)\r\n return await client.do_request_async(\r\n action='action',\r\n protocol='http',\r\n method=method,\r\n version='version',\r\n auth_type='auth_type',\r\n query={},\r\n body={},\r\n runtime=runtime\r\n )\r\n\r\n loop = asyncio.get_event_loop()\r\n task1 = asyncio.ensure_future(request('GET'))\r\n loop.run_until_complete(task1)\r\n self.assertEqual({'result': 'server test'}, task1.result())\r\n task2 = request('POST')\r\n try:\r\n loop.run_until_complete(task2)\r\n assert False\r\n except Exception as e:\r\n self.assertIsInstance(e, UnretryableException)\r\n\r\n def test_default_any(self):\r\n self.assertEqual('test', Client.default_any('test', 'test1'))\r\n self.assertEqual('test1', Client.default_any(None, 'test1'))\r\n", "sub_path": "python/tests/test_client.py", "file_name": "test_client.py", "file_ext": "py", "file_size_in_byte": 5863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 13, "usage_type": "name"}, {"api_name": "http.server.HTTPServer", "line_number": 28, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 32, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 35, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 40, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 42, "usage_type": "call"}, {"api_name": "Tea.exceptions.TeaException", "line_number": 44, "usage_type": "argument"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 46, "usage_type": "call"}, {"api_name": "Tea.exceptions.TeaException", "line_number": 48, "usage_type": "argument"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 54, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 74, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 78, "usage_type": "call"}, {"api_name": "alibabacloud_tea_util.models.RuntimeOptions", "line_number": 89, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 93, "usage_type": "call"}, {"api_name": "Tea.exceptions.UnretryableException", "line_number": 118, "usage_type": "argument"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 121, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 128, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 137, "usage_type": "argument"}, {"api_name": "alibabacloud_tea_rpc.models.Config", "line_number": 144, "usage_type": "call"}, {"api_name": "alibabacloud_tea_util.models.RuntimeOptions", "line_number": 155, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 159, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 171, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 172, "usage_type": "call"}, {"api_name": "Tea.exceptions.UnretryableException", "line_number": 180, "usage_type": "argument"}, {"api_name": "alibabacloud_tea_rpc.client.Client.default_any", "line_number": 183, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 183, "usage_type": "name"}, {"api_name": "alibabacloud_tea_rpc.client.Client.default_any", "line_number": 184, "usage_type": "call"}, {"api_name": "alibabacloud_tea_rpc.client.Client", "line_number": 184, "usage_type": "name"}]} +{"seq_id": "296507285", "text": "from rlp.sedes import (\n CountableList,\n)\n\nfrom eth.vm.forks.byzantium.blocks import (\n ByzantiumBlock,\n)\n\n\nfrom eth.vm.forks.byzantium.transactions import (\n ByzantiumTransaction,\n)\n\nfrom eth.vm.forks.stretch.headers import (\n StretchBlockHeader\n)\n\nfrom eth.vm.forks.stretch.xmessage import (\n StretchXMessage,\n StretchXMessageReceived\n)\n\nclass StretchBlock(ByzantiumBlock):\n transaction_class = ByzantiumTransaction\n xmessage_sent_class = StretchXMessage\n xmessage_received_class = StretchXMessageReceived\n fields = [\n ('header', StretchBlockHeader),\n ('transactions', CountableList(transaction_class)),\n ('xmessages_sent', CountableList(xmessage_sent_class)),\n ('xmessages_received', CountableList(xmessage_received_class)),\n ('uncles', CountableList(StretchBlockHeader))\n ]\n", "sub_path": "eth/vm/forks/stretch/blocks.py", "file_name": "blocks.py", "file_ext": "py", "file_size_in_byte": 846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "eth.vm.forks.byzantium.blocks.ByzantiumBlock", "line_number": 23, "usage_type": "name"}, {"api_name": "eth.vm.forks.byzantium.transactions.ByzantiumTransaction", "line_number": 24, "usage_type": "name"}, {"api_name": "eth.vm.forks.stretch.xmessage.StretchXMessage", "line_number": 25, "usage_type": "name"}, {"api_name": "eth.vm.forks.stretch.xmessage.StretchXMessageReceived", "line_number": 26, "usage_type": "name"}, {"api_name": "eth.vm.forks.stretch.headers.StretchBlockHeader", "line_number": 28, "usage_type": "name"}, {"api_name": "rlp.sedes.CountableList", "line_number": 29, "usage_type": "call"}, {"api_name": "rlp.sedes.CountableList", "line_number": 30, "usage_type": "call"}, {"api_name": "rlp.sedes.CountableList", "line_number": 31, "usage_type": "call"}, {"api_name": "rlp.sedes.CountableList", "line_number": 32, "usage_type": "call"}, {"api_name": "eth.vm.forks.stretch.headers.StretchBlockHeader", "line_number": 32, "usage_type": "argument"}]} +{"seq_id": "131917497", "text": "# import tensorflow as tf \nimport tensorflow as tf\n# tf.disable_v2_behavior()\n\nimport numpy as np \nimport matplotlib.pyplot as plt \nimport data_process as dp\nimport argparse\n# Importing Dataset\nfrom ENet.enet import ENet \nimport utils as utils\n# import ENet as network\nimport logging\nimport cv2\nimport time\nimport json\nfrom datetime import datetime\nfrom config import global_config\n\ndef main():\n\tprint (\":::::::::::::::::::::Training Module:::::::::::::::::::::::::::\")\n\t# parser = argparse.ArgumentParser(description='Lane Net')\n\t\n\t# parser.add_argument('-d',help='data directory',\t\t\tdest='data_dir', \n\t# \t\t\t\t\ttype=str, \tdefault='../../../../dataset/carla/Town04_multilane/data.txt')\n\n\t# parser.add_argument('-i',help='Image directory',\t\tdest='img_dir',\n\t# \t\t\t\t\ttype=str,\tdefault='/home/docker_share/dataset/carla/') #dataset_store/lanes_5_color\n\t\n\t# parser.add_argument('-t', help='test size fraction',\tdest='test_size', \n\t# \t\t\t\t\ttype=float, default=0.1)\n\n\t# parser.add_argument('-k', help='drop out probability', dest='keep_prob', \n\t# \t\t\t\t\ttype=float, default=0.5)\n\n\t# parser.add_argument('-n', help='number of epochs', dest='nb_epoch', \n\t# \t\t\t\t\ttype=int, default=10)\n\n\t# parser.add_argument('-s', help='samples per epoch', dest='samples_per_epoch', \n\t# \t\t\t\t\ttype=int, default=2000)\n\n\t# parser.add_argument('-b', help='batch size', dest='batch_size', \n\t# \t\t\t\t\ttype=int, default=8)\n\n\t# parser.add_argument('-l', help='learning rate', dest='learning_rate', \n\t# \t\t\t\t\ttype=float, default=1.0e-4)\n\t# parser.add_argument('-r', help='restore flag', dest='restore', \n\t# \t\t\t\t\ttype=bool, default=False)\n\n\t# args = parser.parse_args()\n\t# _ =vars(args)\n\t\n\t# for i in _.keys():\n\t# \tprint (str(i)+\" : \"+str(_[i]))\n\n\tCFG = global_config.cfg\n\tX,Bin_Y,Ins_Y=dp.read_pandas_txt(CFG.TRAIN.DATA_DIR)\n\t# print (X)\n\tX,Bin_Y,Ins_Y = dp.randomize(X,Bin_Y,Ins_Y,is_list=True)\n\tprint(\"_________________________________data_____________________\")\n\tprint(X,Bin_Y,Ins_Y)\n\n\t# X=X[:40]\n\t# Y=Y[:40]\n\t# print (X)\n\n\timg_h= CFG.TRAIN.IMG_HEIGHT\n\timg_w= CFG.TRAIN.IMG_WIDTH #28x28\n\timg_flat_size = img_h*(img_w)\n\t# n_channels = 3\n\t# n_classes = 2\n\t# logs_path = \"log/\"\n\t# lr=0.01\n\t# epochs=1402\n\t# batch_size=8\n\t# display_freq = 20\n\tdataset=utils.image_data_read(X,Bin_Y,Ins_Y,CFG.TRAIN.IMG_DIR,img_h,img_w)\n\tdataset = dataset.shuffle(buffer_size = 100)\n\tdataset = dataset.repeat(CFG.TRAIN.EPOCHS)\n\tdataset = dataset.batch(CFG.TRAIN.BATCH_SIZE)\n\titerator = dataset.make_one_shot_iterator()\n\tfeatures = iterator.get_next()\n\t# iterator=iter(dataset)\n\t# features=next(iterator)\n\n\twith tf.name_scope('Input'):\n\t\tx = tf.placeholder(tf.float32,shape=[None,img_h,img_w,CFG.TRAIN.N_CHANNELS],name='X')\n\t\tbin_y = tf.placeholder(tf.float32,shape=[None,img_h,img_w,CFG.TRAIN.N_CLASSES],name='Bin_Y')\n\t\tins_y = tf.placeholder(tf.float32,shape=[None,img_h,img_w,1],name='Ins_Y')\n\n\tnetwork= ENet()\n\toutput_logits = network.model(x,skip_connection=True,\n\t\t\t\t\tbatch_size=CFG.TRAIN.BATCH_SIZE,stage_two_repeat=2,\n\t\t\t\t\tis_training=True,num_features_instance=8,\n\t\t\t\t\tnum_classes=CFG.TRAIN.N_CLASSES,scope=\"ENet\")\n\n\twith tf.name_scope(\"Loss\"):\n\t\tlosses = network.loss(output_logits,bin_y,ins_y)\n\n\twith tf.name_scope(\"Optimizer\"):\n\t\tglobal_step = tf.Variable(0,trainable=False)\n\t\ttrain_op = network.optimizer(losses,global_step,learning_rate = 1e-4)\n\t\n\t# with tf.name_scope(\"Evaluation\"):\n\t# \t# Add the Op to compare the logits to the labels during evaluation.\n\t# \teval_list = network.evaluation(y, output_logits, losses,n_classes)\n\t# logits, decoded_logits = model.build_model(x,train=True,num_classes=n_classes,random_init_fc8=True)\n\n\tinit = tf.global_variables_initializer()\n\tinit_l = tf.local_variables_initializer()\n\tmerged = tf.summary.merge_all()\n\tconfig = tf.ConfigProto()\n\tconfig.gpu_options.allow_growth = True\n\n\tsess = tf.Session(config=config)\n\tsess.run(init)\n\tsess.run(init_l)\n\tglobal_step = 0\n\tsummary_writer = tf.summary.FileWriter(CFG.TRAIN.LOG_PATH,sess.graph)\n\tnum_tr_iter = int((Bin_Y.shape[0])/CFG.TRAIN.BATCH_SIZE)\n\tprint (\"NUM Iter:\",num_tr_iter)\n\tsaver = tf.train.Saver()\n\n\tepochs=CFG.TRAIN.EPOCHS\n\trestore=CFG.TRAIN.RESTORE_FLAG\n\trestore_epoch=CFG.TRAIN.RESTORE_EPOCH\n\t# restore_epoch=200\n\n\tall_trainable_vars = tf.reduce_sum([tf.reduce_prod(v.shape) for v in tf.trainable_variables()])\n\tprint(\"Total Parameters:::::\",sess.run(all_trainable_vars))\n\tif restore:\n\n\t\ttemp_ = \"RUNS/model_\"+str(restore_epoch)+\".ckpt\"\n\t\tsaver.restore(sess, temp_)\n\n\tfor epoch in range(epochs):\n\t\tif restore:\n\t\t\tif (epoch-2) < restore_epoch:\n\t\t\t\tcontinue\n\t\t# hours, rem = divmod(time.time(), 3600)\n\t\t# minutes, seconds = divmod(rem, 60)\n\t\t# print(\"{:0>2}:{:0>2}:{:05.2f}\".format(int(hours%60),int(minutes),seconds))\n\t\tprint (datetime.now())\n\t\tprint('Training Epoch: {}'.format(epoch+1))\n\t\toverall_loss=0.0\n\t\tfor iteration in range(num_tr_iter):\n\t\t\tglobal_step+=1\n\t\t\t\n\t\t\t[x_batch_images,y_batch_labels,y_instance] = sess.run(features)\n\t\t\tprint((x_batch_images[0].shape),y_batch_labels.shape,y_instance.shape)\n\t\t\t# cv2.imshow(\"h\",x_batch_images[0])\n\t\t\t# cv2.waitKey()\n\t\t# \t# y_ins = y_instance[0,:,:]\n\t\t# \t# label=np.unique(y_ins)\n\t\t# \t# print (label)\n\t\t# \tfeed_dict_batch = {x:x_batch_images,bin_y:y_batch_labels,ins_y:y_instance}\n\t\t# \t# print(np.array(y_instance[0]).shape)\n\n\n\t\t# \t# cv2.imshow(\"instance\",np.array(y_instance[0]*50,dtype=np.int8))\n\t\t# \t# cv2.imshow(\"input\",np.array(x_batch_images[0]))\n\t\t# \t# cv2.waitKey(10)\n\t\t# \t_,loss_batch,out_,ins_prob= sess.run([train_op,losses,output_logits['binary_seg_prob'],\n\t\t# \t\t\t\t\t\t\t\t\t\t\toutput_logits['instance_seg_logits']],\n\t\t# \t\t\t\t\t\t\t\t\t\t\tfeed_dict=feed_dict_batch)\n\t\t# \toverall_loss+=loss_batch['total_loss']\n\t\t# \tif iteration % CFG.TRAIN.DISPLAY_STEP\t == 0:\n\t\t# \t\tprint(\"iter:\",iteration,\" Loss= \",loss_batch,\"\\n\")\n\t\t\t\n\t\t# if epoch%1==0:\n\n\t\t# \t# print('Training Epoch: {}'.format(epoch+1))\n\t\t# \t# print \"SHAPEPEE::\",out_.shape\n\t\t# \tfor out_write_i in range(CFG.TRAIN.N_CLASSES):\n\t\t# \t\tout11 = out_[:,:,:,out_write_i].reshape(CFG.TRAIN.BATCH_SIZE,img_h,img_w)\n\t\t\t\t\n\t\t# \t\tout_pro = np.array((out11[0]>0.9)*255,dtype=np.uint8)\n\t\t# \t\twrite_name_img = \"temp\"+\"_\"+str(out_write_i)+\".png\"\n\t\t# \t\tcv2.imwrite(\"RUNS/images/\"+write_name_img,out_pro)\n\t\t# \t\t# cv2.imshow(\"d\",out_pro)\n\t\t# \t\t# cv2.waitKey(10)\n\t\t\t\n\t\t# #print (\"iter {0:3d}:\\t Loss={1:.3f}\".format(iteration,loss_batch))\n\t\t# overall_loss/=float(num_tr_iter)\n\t\t# print (\"AVERAGE LOSS:\",overall_loss)\n\t\t# if ((epoch%200==0 and epoch<50 ) or (epoch%15==0)):\t\n\t\t\t\n\t\t# \tsave_path = saver.save(sess, \"RUNS/model_{0}.ckpt\".format(epoch))\n\t\t# \tprint (\"Model saved in path: %s\" % save_path)\n\n\t\t# \tfor out_write_i in range(CFG.TRAIN.N_CLASSES):\n\t\t# \t\tout11 = out_[:,:,:,out_write_i].reshape(CFG.TRAIN.BATCH_SIZE,img_h,img_w)\n\t\t\t\t\n\t\t# \t\tout_pro = np.array((out11[0]>0.9)*255,dtype=np.uint8)\n\t\t# \t\twrite_name_img = \"epoch_\"+str(epoch)+\"_\"+str(out_write_i)+\".png\"\n\t\t# \t\tcv2.imwrite(\"RUNS/images/\"+write_name_img,out_pro)\n\t\t# \tins_seg = np.squeeze(ins_prob[0,:,:,:])\n\t\t# \t# print (\"ins_seg:\",ins_seg.shape)\n\n\t\t# \t# print (\"y_ins_shape\",y_instance.shape)\n\t\t# \ty_ins = np.squeeze(y_instance[0,:,:,:])\n\t\t# \t# print (\"y_ins_shape\",y_ins.shape)\n\t\t# \t# cv2.imwrite(\"y_inst.png\",y_ins)\n\t\t# \t# cv2.imshow(\"d\",y_ins)\n\t\t# \t# cv2.waitKey(0)\n\n\t\t# \tlabel=np.unique(y_ins)\n\t\t# \tprint (label)\n\t\t# \tfeat=[]\n\t\t# \tfor i in range(len(label)-1):\n\t\t# \t\tselect= y_ins==[label[i+1]]\n\t\t# \t\tprint (select.shape)\n\t\t# \t\tprint(select)\n\t\t# \t\tfeat.append(ins_seg[select,:])\n\n\t\t# \tfile_='meta/meta_'+str(epoch)+'.tsv'\n\t\t# \tfile_open=open(file_,'w')\n\t\t# \tprint (len(feat))\n\t\t# \tfor i in range(len(feat)):\n\t\t# \t\tarr= np.array(feat[i])\n\t\t# \t\tprint (arr.shape)\n\t\t# \t\tfor ii in range(arr.shape[0]):\n\t\t# \t\t\tfeat_temp = arr[ii,:]\n\t\t# \t\t\tstring=''\n\t\t# \t\t\tfor iii in range(feat_temp.shape[0]):\n\t\t# \t\t\t\tstring+=str(feat_temp[iii])+\"\\t\"\n\t\t# \t\t\tstring=string[:-2]+\"\\n\"\n\t\t# \t\t\tfile_open.write(string)\n\t\t# \t\t\tfile_open.flush()\n\n\n\t\t# \tfile_1=\"meta/meta_label\"+str(epoch)+\".tsv\"\n\t\t# \tfile_open_1=open(file_1,'w')\n\n\t\t# \tfor i in range(len(feat)):\n\t\t# \t\tarr = np.array(feat[i]).shape[0]\n\t\t# \t\tstring=''\n\t\t# \t\tfor ii in range(arr):\n\t\t# \t\t\tstring=str(i)+\"\\n\"\n\t\t# \t\t\tfile_open_1.write(string)\n\t\t# \t\t\tfile_open_1.flush()\n\t\t\t\t\n\tsess.close()\n\t\nif __name__ == '__main__':\n\tmain()", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 8250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "config.global_config.cfg", "line_number": 56, "usage_type": "attribute"}, {"api_name": "config.global_config", "line_number": 56, "usage_type": "name"}, {"api_name": "data_process.read_pandas_txt", "line_number": 57, "usage_type": "call"}, {"api_name": "data_process.randomize", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.image_data_read", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 89, "usage_type": "attribute"}, {"api_name": "ENet.enet.ENet", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.local_variables_initializer", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 112, "usage_type": "call"}, {"api_name": "config.gpu_options", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.reduce_prod", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 143, "usage_type": "name"}]} +{"seq_id": "341563601", "text": "'''\nCreated on 29 Oct 2014\n\n@author: sm6110\n\nLow level methods for differentiation\n\n'''\n\nimport numpy as np\n\n\n\ndef dwgt(xv,xp,k=0):\n ''' \n Given a stencil of coordinates xv, the function returns a row vector wv\n such that, given a vector valued function fv=f(xv), the k-th derivative of\n f at the point xp is:\n \n d^k f = np.dot(wv,fv) \n \n If k is not passed in input, the function will perform by default a \n polynomial interpolation.\n \n Remark: the weight wv do not depend on the function value\n \n '''\n \n # initialise and check\n Nx=len(xv) \n if k>Nx:\n raise NameError('Derivation order not compatible with stencil dimension!')\n \n # change FoR\n xv = xv-xp\n \n # get base polynomials coefficients\n I=np.eye(Nx)\n C=np.zeros((Nx,Nx))\n for ii in range(Nx):\n C[ii,:]=np.polyfit(xv,I[ii,:],Nx-1)\n \n # get weights\n wv = C[:,Nx-k-1]*np.math.factorial(k)\n \n return wv\n\n\n\ndef difffun(f,x):\n '''\n Compute II order accuracy first derivative of a function f over the equally\n spaced vector x.\n \n The code is specifically set for II order accuracy 1st derivatives.\n To improve it:\n a. the boundary treatment should be modified.\n b. sarr variable is now specific for II order accuracy\n '''\n \n # get weights for II order polynomial derivative\n # remark: if a general order of derivation is required, special attention\n # should be put in evaluating the derivatives at the boundary \n kder=2\n KK=kder+1\n w0 = dwgt( x[:KK], x[ 0], k=1)\n wend = dwgt(x[-KK:], x[-1], k=1)\n wmid = dwgt( x[:KK], x[ 1], k=1)\n \n # \n NumSteps = len(f)-1\n df = np.zeros(NumSteps+1)\n \n # timestep 1 and final:\n df[ 0] = np.dot( w0,f[ :KK])\n df[-1] = np.dot(wend,f[-KK:])\n \n # timestep 2... NumSteps-1\n # sarr specific for II order derivative\n sarr = np.array([-1,0,1]) \n for tt in range(1,NumSteps,1):\n ttind = tt+sarr\n df[tt] = np.dot(wmid,f[ttind])\n \n return df\n\n\n\n\nif __name__=='__main__':\n \n import matplotlib.pyplot as plt\n \n \n \n \n #------------------------------------------------------------- check difffun\n x=np.linspace(0,2*np.pi,100)\n f=np.sin(x)\n \n df = difffun(f,x)\n plt.plot(x,f,'r')\n plt.plot(x,df,'b')\n plt.show()\n \n\n \n #---------------------------------------------------------------- check dwgt\n\n # analytical\n x = np.array([-1,0,1,2])\n f = 2.0*x**2 - x + 4.0\n df = 4.0*x - 1.0\n ddf = 4.0 + 0.0*x\n \n #numerical\n N=20\n xv=np.linspace(-2,3,N)\n fv=np.zeros(N)\n dfv=np.zeros(N)\n ddfv=np.zeros(N)\n \n for ii in range(N):\n fv[ii]=np.dot(dwgt(x,xv[ii],0),f)\n dfv[ii]=np.dot(dwgt(x,xv[ii],1),f) \n ddfv[ii]=np.dot(dwgt(x,xv[ii],2),f) \n \n plt.figure('interpolation')\n plt.plot(x,f,'ro')\n plt.plot(xv,fv,'k')\n plt.show()\n \n plt.figure('I der')\n plt.plot(x,df,'ro')\n plt.plot(xv,dfv,'k')\n plt.show() \n \n plt.figure('II der')\n plt.plot(x,ddf,'ro')\n plt.plot(xv,ddfv,'k')\n plt.show() \n \n ", "sub_path": "src/PyLibs/numerics/diff.py", "file_name": "diff.py", "file_ext": "py", "file_size_in_byte": 3167, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.eye", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.math.factorial", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.math", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}]} +{"seq_id": "529138327", "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 ('weekend', '0006_dates'),\n ('location', '0014_city_code'),\n ('flights', '0020_auto_20150411_1952'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Destinations',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('dates', models.ForeignKey(to='weekend.Dates')),\n ('destinations', models.ManyToManyField(related_name='destinations', to='location.City')),\n ('origin', models.ForeignKey(related_name='origin', to='location.City')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n", "sub_path": "flights/migrations/0021_destinations.py", "file_name": "0021_destinations.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "408771621", "text": "#!/usr/bin/python3\nimport time\nimport argparse\nimport requests\nimport schedule\n\ndef update_webhashcat(host, port, ssl):\n\n url = \"%s://%s:%d/api/update_hashfiles\" % (\"https\" if ssl else \"http\", host, port)\n\n try:\n res = requests.get(url)\n except requests.exceptions.ConnectionError:\n print(\"Unable to connect to webhashcat\")\n\nif __name__==\"__main__\":\n parser = argparse.ArgumentParser(description='WebHashcat cron', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('host', help='webhashcat hostname', nargs='?')\n parser.add_argument('port', help='webhashcat port', nargs='?')\n parser.add_argument('--ssl', help='https connection', action='store_true', dest='ssl')\n parser.add_argument('--standalone', help='use it without cron', action='store_true', dest='standalone')\n\n args = parser.parse_args()\n\n if not args.standalone:\n update_webhashcat(args.host, int(args.port), args.ssl)\n else:\n schedule.every(1).minutes.do(update_webhashcat, args.host, int(args.port), args.ssl)\n\n while True:\n schedule.run_pending()\n time.sleep(1)\n\n", "sub_path": "WebHashcat/cron.py", "file_name": "cron.py", "file_ext": "py", "file_size_in_byte": 1144, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 17, "usage_type": "attribute"}, {"api_name": "schedule.every", "line_number": 28, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "183979150", "text": "import sys\nimport unittest\nfrom os.path import join\n\nimport cv2\n\nimport prototype.tests as test\nfrom prototype.vision.feature2d.fast import FAST\nfrom prototype.vision.feature2d.lk_tracker import LKFeatureTracker\n\n\nclass LKFeatureTrackerTest(unittest.TestCase):\n def setUp(self):\n detector = FAST(threshold=150)\n self.tracker = LKFeatureTracker(detector=detector)\n\n data_path = join(test.TEST_DATA_PATH, \"vo\")\n self.img = []\n for i in range(10):\n img_filename = \"%d.png\" % i\n self.img.append(cv2.imread(join(data_path, img_filename)))\n\n def test_detect(self):\n self.tracker.detect(self.img[0])\n\n self.assertTrue(len(self.tracker.tracks) > 0)\n self.assertEqual(self.tracker.track_id,\n len(self.tracker.tracks))\n self.assertEqual(self.tracker.track_id,\n len(self.tracker.tracks_tracking))\n\n def test_last_keypoints(self):\n self.tracker.detect(self.img[0])\n keypoints = self.tracker.last_keypoints()\n\n self.assertEqual(len(self.tracker.tracks_tracking), keypoints.shape[0])\n self.assertEqual(2, keypoints.shape[1])\n\n def test_track_features(self):\n self.tracker.detect(self.img[0])\n tracks_tracking_before = len(self.tracker.tracks_tracking)\n\n self.tracker.track_features(self.img[0], self.img[1])\n tracks_tracking_after = len(self.tracker.tracks_tracking)\n\n self.assertTrue(tracks_tracking_after <= tracks_tracking_before)\n\n def test_draw_tracks(self):\n debug = False\n self.tracker.detect(self.img[0])\n self.tracker.track_features(self.img[0], self.img[1])\n self.tracker.draw_tracks(self.img[1], debug)\n\n if debug:\n cv2.waitKey(1000000)\n\n def test_update(self):\n debug = False\n tracks_tracked = []\n\n # Loop through images\n index = 0\n while index < len(self.img):\n # Index out of bounds guard\n index = 0 if index < 0 else index\n\n # Feature tracker update\n self.tracker.update(self.img[index], debug)\n tracks_tracked.append(len(self.tracker.tracks_tracking))\n\n # Display image\n if debug:\n cv2.imshow(\"Sequence \" + self.data.sequence, self.img[index])\n key = cv2.waitKey(0)\n if key == ord('q'): # Quit\n sys.exit(1)\n elif key == ord('p'): # Previous image\n index -= 1\n else:\n index += 1\n else:\n index += 1\n\n if debug:\n import matplotlib.pylab as plt\n plt.plot(range(len(tracks_tracked)), tracks_tracked)\n plt.show()\n plt.clf()\n", "sub_path": "prototype/tests/vision/feature2d/test_lk_tracker.py", "file_name": "test_lk_tracker.py", "file_ext": "py", "file_size_in_byte": 2803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "prototype.vision.feature2d.fast.FAST", "line_number": 14, "usage_type": "call"}, {"api_name": "prototype.vision.feature2d.lk_tracker.LKFeatureTracker", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "prototype.tests.TEST_DATA_PATH", "line_number": 17, "usage_type": "attribute"}, {"api_name": "prototype.tests", "line_number": 17, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pylab.plot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pylab.clf", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 88, "usage_type": "name"}]} +{"seq_id": "200669869", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\nfrom scipy import sparse\ntrain_data = pd.read_csv(\"/Users/vineevineela/Documents/Semester-2/CMPE-255/Assignments/PR3/train.dat\",header=None)\ntrain = train_data.values.tolist()\n\n\n# In[2]:\n\n\ntrain_int=[]\nind=[]\nval=[]\nptr=[]\nptr.append(0)\n\nfor l in train:\n str=l[0].split()\n train_int.append(str)\n \nfor sl in train_int:\n ptr.append(len(sl)/2)\n for i in range(len(sl)):\n if i % 2 != 0:\n val.append(int(sl[i]))\n else:\n ind.append(int(sl[i]))\n \nfor i in range(len(ptr)):\n if(i!=0):\n ptr[i]=ptr[i]+ptr[i-1]\n\nind=np.asarray(ind)\nval=np.asarray(val)\nptr=np.asarray(ptr)\nnrows=len(train)\nncols=max(ind)\n\nmat = sparse.csr_matrix((val, ind, ptr), shape=(nrows, ncols))\n\n\n# In[5]:\n\n\n# scale matrix and normalize its rows\nfrom collections import defaultdict\n\ndef csr_idf(mat, copy=False, **kargs):\n r\"\"\" Scale a CSR matrix by idf. \n Returns scaling factors as dict. If copy is True, \n returns scaled matrix and scaling factors.\n \"\"\"\n if copy is True:\n mat = mat.copy()\n nrows = mat.shape[0]\n nnz = mat.nnz\n ind, val, ptr = mat.indices, mat.data, mat.indptr\n # document frequency\n df = defaultdict(int)\n for i in ind:\n df[i] += 1\n # inverse document frequency\n for k,v in df.items():\n df[k] = np.log(nrows / float(v)) ## df turns to idf - reusing memory\n # scale by idf\n for i in range(0, nnz):\n val[i] *= df[ind[i]]\n \n return df if copy is False else mat\n \nmat_idf = csr_idf(mat, copy=True)\n\n\n# In[6]:\n\n\nmat = mat_idf\n\n\n# In[7]:\n\n\nfrom sklearn.feature_selection import VarianceThreshold\nvariance_selector = VarianceThreshold()\nvariance_selector.fit(mat)\nmat = variance_selector.transform(mat)\n\n\n# In[8]:\n\n\nfrom sklearn.neighbors import NearestNeighbors\nmin_points=[3,5,7,9,11,13,15,17,19,21]\ndistances=[]\nindices=[]\nfor i in min_points:\n n = NearestNeighbors(n_neighbors=i, algorithm='auto').fit(mat)\n dist, ind = n.kneighbors(mat)\n distances.append(dist)\n indices.append(ind)\n\n\n# In[9]:\n\n\nilist = indices[0][:,0].tolist()\ndistancelist=[]\npos = 0\nfor i in min_points:\n dlist = distances[pos][:,i-1].tolist()\n pos = pos+1\n dlist=sorted(dlist)\n distancelist.append(dlist)\n\n\n# In[10]:\n\n\nimport matplotlib \nfrom matplotlib import pyplot as plt\n\nx = ilist\nfor i in range(len(distancelist)):\n #plt.figure(figsize=(10,10))\n y = distancelist[i]\n plt.plot(x,y)\n plt.grid()\n #plt.yticks(np.arange(0,1200,50))\n plt.show()\n\n\n# In[ ]:\n\n\nimport sklearn.metrics.pairwise\n\ndef distance_calc(mat,nrows):\n distance_int = sklearn.metrics.pairwise.pairwise_distances(mat,mat)\n distance_ext = distance_int.tolist()\n return distance_ext\n\n# custom proximity function\n#def euclidean_distance(mat1,mat2):\n# euclidean_dist = np.linalg.norm(mat1 - mat2)\n# return euclidean_dist\n\n#def distance_calc(mat,nrows):\n# distance_int = [] \n# distance_ext = []\n# pts_count = nrows\n# mat_dense = mat.todense()\n# for i in range(pts_count):\n# for j in range(pts_count): \n# distance = euclidean_distance(mat_dense[i],mat_dense[j])\n# distance_int.append(distance)\n# distance_ext.append(distance_int)\n# distance_int = []\n \n# return distance_ext\n\ndef points_classifier(distance_ext,nrows,eps,min_pts):\n points_classification = []\n neighbor_count=[]\n for i in range(nrows):\n points_classification.append('N') \n for i in distance_ext:\n count = 0\n for j in i:\n if(j < eps):\n count = count + 1\n neighbor_count.append(count)\n for i in range(nrows):\n if(neighbor_count[i] > min_pts):\n points_classification[i] = 'C'\n for i in range(nrows):\n if(points_classification[i] == 'N'):\n for k in range(len(distance_ext[i])):\n if ((distance_ext[i][k] < eps) and (points_classification[k] == 'C' )):\n points_classification[i] = 'B'\n break\n return points_classification\n\n\n# In[ ]:\n\n\ndef dbscan(data, eps=150, min_pts=15):\n nrows = data.shape[0]\n ncols = data.shape[1]\n distance_list = []\n connected_points=[]\n clusters_list=[]\n noise_cluster=[]\n cluster_point_index=[]\n distance_ext = distance_calc(data,nrows)\n points_classification = points_classifier(distance_ext,nrows,eps,min_pts)\n for i in range(nrows):\n if(i not in connected_points):\n if(points_classification[i]=='C'):\n cluster=[]\n connected_points.append(i)\n clusterformation(i,points_classification,distance_ext,cluster,clusters_list,connected_points,nrows,eps)\n clusters_list.append(cluster)\n \n \n for i in range(nrows):\n if(i not in connected_points):\n if(points_classification[i]=='B'):\n connected_points.append(i)\n distance_sort = sorted(distance_ext[i]) \n for index in range(len(distance_sort)):\n if(distance_sort[index]!=0):\n nearest_neighbour = distance_sort[index]\n nn_index = distance_ext[i].index(nearest_neighbour)\n if(points_classification[nn_index]=='C'):\n break\n for c in clusters_list:\n for k in c:\n if(k == nn_index):\n c.append(i)\n \n \n for i in range(nrows):\n if(i not in connected_points):\n if(points_classification[i]=='N'):\n connected_points.append(i)\n noise_cluster.append(i)\n clusters_list.append(noise_cluster)\n \n \n \n for i in range(nrows):\n for j in range(len(clusters_list)):\n for k in clusters_list[j]:\n if(k==i):\n cluster_point_index.append(j)\n break\n \n return cluster_point_index \n \n \n \ndef clusterformation(i,points_classification,distance_ext,cluster,clusters_list,connected_points,nrows,eps):\n cluster.append(i)\n for j in range(nrows):\n if (distance_ext[i][j] < eps and j not in connected_points and points_classification[j]=='C'):\n cluster.append(j)\n connected_points.append(j)\n for k in range(nrows):\n if (distance_ext[j][k] < eps and k not in connected_points and points_classification[k]=='C'):\n connected_points.append(k)\n cluster.append(k)\n \n return\n \n\n\n# In[ ]:\n\n\ncluster_points = dbscan(mat,97,5)\n\n\n# In[ ]:\n\n\nf = open(\"/Users/vineevineela/Desktop/dbscan17.txt\", \"w\")\nfor item in cluster_points:\n f.write(\"%s\\n\" % item)\nf.close()\n\n\n# In[ ]:\n\n\nfrom sklearn.metrics import silhouette_score\neps_list=[80,97,150]\ncluster_pts_list = []\nsilhouette_score_lst=[]\n\nfor i in eps_list:\n for j in min_points:\n cluster_points=dbscan(mat,i,j)\n cluster_pts_list.append(cluster_points)\n silhouette_avg = silhouette_score(mat, cluster_points)\n silhouette_score_lst.append(silhouette_avg)\n \n\n\n# In[ ]:\n\n\ncluster_count=[]\nfor i in cluster_pts_list:\n cluster_count.append(max(i)+1)\n\n\n# In[ ]:\n\n\nx = cluster_count[0:10]\ny = silhouette_score_lst[0:10]\nplt.plot(x,y)\nplt.grid()\nplt.yticks(np.arange(0,1,0.05))\nplt.show()\n\nx = cluster_count[10:20]\ny = silhouette_score_lst[10:20]\nplt.plot(x,y)\nplt.grid()\nplt.yticks(np.arange(0,1,0.05))\nplt.show()\n\n\nx = cluster_count[20:30]\ny = silhouette_score_lst[20:30]\nplt.plot(x,y)\nplt.grid()\nplt.yticks(np.arange(0,1,0.05))\nplt.show()\n\n", "sub_path": "DbScan.py", "file_name": "DbScan.py", "file_ext": "py", "file_size_in_byte": 7788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 45, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.VarianceThreshold", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "sklearn.feature_selection.metrics.pairwise.pairwise_distances", "line_number": 144, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.metrics", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sklearn.feature_selection", "line_number": 144, "usage_type": "name"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 310, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 311, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 312, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 316, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 317, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 318, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}]} +{"seq_id": "449998533", "text": "from __future__ import unicode_literals\n\nfrom django.db import models\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.core.mail import EmailMessage, get_connection\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\n\nfrom wagtail.wagtailadmin.edit_handlers import (FieldPanel, MultiFieldPanel,\n PageChooserPanel)\nfrom wagtail.wagtailcore.models import Page\nfrom wagtail.wagtailcore.fields import RichTextField\nfrom wagtail.contrib.wagtailroutablepage.models import RoutablePageMixin, route\nfrom portal.services import limsfm_create_quote\nfrom .forms import QuoteRequestForm\n\n\nclass QuoteRequestFormPage(RoutablePageMixin, Page):\n \"\"\"\n A Quote Request Form Page that sends an email and calls LIMSfm API to create\n a draft quote.\n \"\"\"\n\n intro = RichTextField(blank=True)\n side_panel_title = models.CharField(max_length=255)\n side_panel_content = RichTextField(blank=True)\n success_message = models.CharField(max_length=255)\n thank_you_page = models.ForeignKey(\n 'formbuilder.FormThankYouPage',\n null=True,\n blank=True,\n on_delete=models.SET_NULL,\n related_name='+'\n )\n to_address = models.EmailField(\n max_length=255,\n help_text=\"Form submissions will be emailed to this address\")\n from_address = models.EmailField(\n max_length=255,\n help_text=\"Form submissions will show as having come from this\"\n \" address\")\n subject = models.CharField(max_length=255, blank=True)\n\n subpage_types = ['formbuilder.FormThankYouPage']\n\n content_panels = Page.content_panels + [\n FieldPanel('intro', classname='full'),\n MultiFieldPanel([\n FieldPanel('side_panel_title'),\n FieldPanel('side_panel_content', classname='full'),\n ], \"Side Panel\"),\n MultiFieldPanel([\n FieldPanel('to_address'),\n FieldPanel('from_address'),\n FieldPanel('subject', classname=\"full\"),\n ], \"Email\"),\n PageChooserPanel('thank_you_page'),\n ]\n\n\n def process_form_submission(self, form):\n try:\n quote_ref = limsfm_create_quote(form.cleaned_data)\n except Exception:\n quote_ref = ''\n\n # send email\n if self.to_address:\n addresses = [x.strip() for x in self.to_address.split(',')]\n content = []\n content.append('Quote Ref: {}'.format(quote_ref))\n\n for field in form:\n value = field.value()\n if isinstance(value, list):\n value = ', '.join(value)\n content.append('{}: {}'.format(field.label, value))\n content = '\\n'.join(content)\n\n reply_to = ([form.data['email']] if 'email' in form.data else None)\n subject = '%s [%s %s]' % (self.subject, quote_ref, form.data['name_last'])\n connection = get_connection()\n email = EmailMessage(subject, content, self.from_address, [self.to_address],\n connection=connection, reply_to=reply_to)\n email.send(fail_silently=False)\n\n\n # def serve(self, request):\n # if request.method == 'POST':\n # # honeypot\n # if len(request.POST.get('url_h', '')):\n # messages.success(request, self.success_message)\n # return HttpResponseRedirect(self.url)\n\n # form = QuoteRequestForm(request.POST)\n\n # if form.is_valid():\n # self.process_form_submission(form)\n # if self.thank_you_page:\n # return HttpResponseRedirect(self.thank_you_page.url)\n # else:\n # messages.success(request, self.success_message)\n # return HttpResponseRedirect(self.url)\n # else:\n # form = QuoteRequestForm()\n\n # return render(request, 'quote/quote_request_form.html', {\n # 'page': self,\n # 'form': form,\n # })\n\n\n @route(r'^$')\n @route(r'^(?P[\\w\\-]+)/$')\n def service_serve(self, request, service_string = None):\n if request.method == 'POST':\n # honeypot\n if len(request.POST.get('url_h', '')):\n messages.success(request, self.success_message)\n return HttpResponseRedirect(self.url)\n\n form = QuoteRequestForm(request.POST)\n\n if form.is_valid():\n self.process_form_submission(form)\n if self.thank_you_page:\n return HttpResponseRedirect(self.thank_you_page.url)\n else:\n messages.success(request, self.success_message)\n return HttpResponseRedirect(self.url)\n else:\n\n form = QuoteRequestForm()\n\n return render(request, 'quote/quote_request_form.html', {\n 'page': self,\n 'form': form,\n 'service_string': service_string\n })\n", "sub_path": "source/quote/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 5048, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "wagtail.contrib.wagtailroutablepage.models.RoutablePageMixin", "line_number": 19, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 19, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.fields.RichTextField", "line_number": 25, "usage_type": "call"}, {"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": "wagtail.wagtailcore.fields.RichTextField", "line_number": 27, "usage_type": "call"}, {"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.ForeignKey", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "wagtail.wagtailcore.models.Page.content_panels", "line_number": 47, "usage_type": "attribute"}, {"api_name": "wagtail.wagtailcore.models.Page", "line_number": 47, "usage_type": "name"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 48, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.MultiFieldPanel", "line_number": 49, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 50, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 51, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.MultiFieldPanel", "line_number": 53, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 54, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 55, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 56, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.PageChooserPanel", "line_number": 58, "usage_type": "call"}, {"api_name": "portal.services.limsfm_create_quote", "line_number": 64, "usage_type": "call"}, {"api_name": "django.core.mail.get_connection", "line_number": 83, "usage_type": "call"}, {"api_name": "django.core.mail.EmailMessage", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 120, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 121, "usage_type": "call"}, {"api_name": "forms.QuoteRequestForm", "line_number": 123, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 128, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 130, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 131, "usage_type": "call"}, {"api_name": "forms.QuoteRequestForm", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 136, "usage_type": "call"}, {"api_name": "wagtail.contrib.wagtailroutablepage.models.route", "line_number": 114, "usage_type": "call"}, {"api_name": "wagtail.contrib.wagtailroutablepage.models.route", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "306628718", "text": "#\n# Unary operator classes and methods\n#\nimport numpy as np\nimport pybamm\nfrom scipy.sparse import csr_matrix\n\n\nclass UnaryOperator(pybamm.Symbol):\n \"\"\"A node in the expression tree representing a unary operator\n (e.g. '-', grad, div)\n\n Derived classes will specify the particular operator\n\n **Extends:** :class:`Symbol`\n\n Parameters\n ----------\n name : str\n name of the node\n child : :class:`Symbol`\n child node\n\n \"\"\"\n\n def __init__(self, name, child):\n super().__init__(name, children=[child], domain=child.domain)\n self.child = self.children[0]\n\n def __str__(self):\n \"\"\" See :meth:`pybamm.Symbol.__str__()`. \"\"\"\n return \"{}({!s})\".format(self.name, self.child)\n\n def new_copy(self):\n \"\"\" See :meth:`pybamm.Symbol.new_copy()`. \"\"\"\n new_child = self.child.new_copy()\n return self._unary_new_copy(new_child)\n\n def _unary_new_copy(self, child):\n \"\"\"Make a new copy of the unary operator, with child `child`\"\"\"\n\n return self.__class__(child)\n\n def _unary_simplify(self, simplified_child):\n \"\"\"\n Simplify a unary operator. Default behaviour is to make a new copy, with\n simplified child.\n \"\"\"\n\n return self._unary_new_copy(simplified_child)\n\n def _unary_evaluate(self, child):\n \"\"\"Perform unary operation on a child. \"\"\"\n raise NotImplementedError\n\n def evaluate(self, t=None, y=None, known_evals=None):\n \"\"\" See :meth:`pybamm.Symbol.evaluate()`. \"\"\"\n if known_evals is not None:\n if self.id not in known_evals:\n child, known_evals = self.child.evaluate(t, y, known_evals)\n known_evals[self.id] = self._unary_evaluate(child)\n return known_evals[self.id], known_evals\n else:\n child = self.child.evaluate(t, y)\n return self._unary_evaluate(child)\n\n def evaluate_for_shape(self):\n \"\"\"\n Default behaviour: unary operator has same shape as child\n See :meth:`pybamm.Symbol.evaluate_for_shape()`\n \"\"\"\n return self.children[0].evaluate_for_shape()\n\n def evaluates_on_edges(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluates_on_edges()`. \"\"\"\n return self.child.evaluates_on_edges()\n\n\nclass Negate(UnaryOperator):\n \"\"\"A node in the expression tree representing a `-` negation operator\n\n **Extends:** :class:`UnaryOperator`\n \"\"\"\n\n def __init__(self, child):\n \"\"\" See :meth:`pybamm.UnaryOperator.__init__()`. \"\"\"\n super().__init__(\"-\", child)\n\n def __str__(self):\n \"\"\" See :meth:`pybamm.Symbol.__str__()`. \"\"\"\n return \"{}{!s}\".format(self.name, self.child)\n\n def _diff(self, variable):\n \"\"\" See :meth:`pybamm.Symbol._diff()`. \"\"\"\n return -self.child.diff(variable)\n\n def _jac(self, variable):\n \"\"\" See :meth:`pybamm.Symbol._jac()`. \"\"\"\n return -self.child.jac(variable)\n\n def _unary_evaluate(self, child):\n \"\"\" See :meth:`UnaryOperator._unary_evaluate()`. \"\"\"\n return -child\n\n\nclass AbsoluteValue(UnaryOperator):\n \"\"\"A node in the expression tree representing an `abs` operator\n\n **Extends:** :class:`UnaryOperator`\n \"\"\"\n\n def __init__(self, child):\n \"\"\" See :meth:`pybamm.UnaryOperator.__init__()`. \"\"\"\n super().__init__(\"abs\", child)\n\n def diff(self, variable):\n \"\"\" See :meth:`pybamm.Symbol.diff()`. \"\"\"\n # Derivative is not well-defined\n raise pybamm.UndefinedOperationError(\n \"Derivative of absolute function is not defined\"\n )\n\n def jac(self, variable):\n \"\"\" See :meth:`pybamm.Symbol.jac()`. \"\"\"\n # Derivative is not well-defined\n raise pybamm.UndefinedOperationError(\n \"Derivative of absolute function is not defined\"\n )\n\n def _unary_evaluate(self, child):\n \"\"\" See :meth:`UnaryOperator._unary_evaluate()`. \"\"\"\n return np.abs(child)\n\n\nclass Index(UnaryOperator):\n \"\"\"A node in the expression tree, which stores the index that should be\n extracted from its child after the child has been evaluated.\n\n Parameters\n ----------\n child : :class:`pybamm.Symbol`\n The symbol of which to take the index\n index : int or slice\n The index (if int) or indices (if slice) to extract from the symbol\n name : str, optional\n The name of the symbol\n \"\"\"\n\n def __init__(self, child, index, name=None):\n self.index = index\n if index == -1:\n self.slice = slice(index, None)\n if name is None:\n name = \"Index[-1]\"\n elif isinstance(index, int):\n self.slice = slice(index, index + 1)\n if name is None:\n name = \"Index[\" + str(index) + \"]\"\n elif isinstance(index, slice):\n self.slice = index\n if name is None:\n if index.start is None:\n name = \"Index[:{:d}]\".format(index.stop)\n else:\n name = \"Index[{:d}:{:d}]\".format(index.start, index.stop)\n else:\n raise TypeError(\"index must be integer or slice\")\n\n if self.slice in (slice(0, 1), slice(-1, None)):\n pass\n elif self.slice.stop > child.size:\n raise ValueError(\"slice size exceeds child size\")\n\n super().__init__(name, child)\n\n # no domain for integer value\n if isinstance(index, int):\n self.domain = []\n\n def _jac(self, variable):\n \"\"\" See :meth:`pybamm.Symbol._jac()`. \"\"\"\n\n # if child.jac returns a matrix of zeros, this subsequently gives a bug\n # when trying to simplify the node Index(child_jac). Instead, search the\n # tree for StateVectors and return a matrix of zeros of the correct size\n # if none are found.\n if all([not (isinstance(n, pybamm.StateVector)) for n in self.pre_order()]):\n variable_y_indices = np.arange(\n variable.y_slice.start, variable.y_slice.stop\n )\n jac = csr_matrix((1, np.size(variable_y_indices)))\n return pybamm.Matrix(jac)\n else:\n child_jac = self.child.jac(variable)\n return Index(child_jac, self.index)\n\n def set_id(self):\n \"\"\" See :meth:`pybamm.Symbol.set_id()` \"\"\"\n self._id = hash(\n (\n self.__class__,\n self.name,\n self.slice.start,\n self.slice.stop,\n self.children[0].id,\n )\n + tuple(self.domain)\n )\n\n def _unary_evaluate(self, child):\n \"\"\" See :meth:`UnaryOperator._unary_evaluate()`. \"\"\"\n return child[self.slice]\n\n def _unary_new_copy(self, child):\n \"\"\" See :meth:`UnaryOperator._unary_new_copy()`. \"\"\"\n\n return self.__class__(child, self.index)\n\n def evaluate_for_shape(self):\n return self.children[0].evaluate_for_shape()[self.slice]\n\n def evaluates_on_edges(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluates_on_edges()`. \"\"\"\n return False\n\n\nclass SpatialOperator(UnaryOperator):\n \"\"\"A node in the expression tree representing a unary spatial operator\n (e.g. grad, div)\n\n Derived classes will specify the particular operator\n\n This type of node will be replaced by the :class:`Discretisation`\n class with a :class:`Matrix`\n\n **Extends:** :class:`UnaryOperator`\n\n Parameters\n ----------\n\n name : str\n name of the node\n child : :class:`Symbol`\n child node\n\n \"\"\"\n\n def __init__(self, name, child):\n super().__init__(name, child)\n\n def diff(self, variable):\n \"\"\" See :meth:`pybamm.Symbol.diff()`. \"\"\"\n # We shouldn't need this\n raise NotImplementedError\n\n def jac(self, variable):\n \"\"\" See :meth:`pybamm.Symbol.jac()`. \"\"\"\n raise NotImplementedError\n\n def _unary_simplify(self, child):\n \"\"\" See :meth:`pybamm.UnaryOperator.simplify()`. \"\"\"\n\n # if there are none of these nodes in the child tree, then this expression\n # does not depend on space, and therefore the spatial operator result is zero\n search_types = (pybamm.Variable, pybamm.StateVector, pybamm.SpatialVariable)\n\n # do the search, return a scalar zero node if no relevent nodes are found\n if all([not (isinstance(n, search_types)) for n in self.pre_order()]):\n return pybamm.Scalar(0)\n else:\n return self.__class__(child)\n\n\nclass Gradient(SpatialOperator):\n \"\"\"A node in the expression tree representing a grad operator\n\n **Extends:** :class:`SpatialOperator`\n \"\"\"\n\n def __init__(self, child):\n super().__init__(\"grad\", child)\n\n def evaluates_on_edges(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluates_on_edges()`. \"\"\"\n return True\n\n\nclass Divergence(SpatialOperator):\n \"\"\"A node in the expression tree representing a div operator\n\n **Extends:** :class:`SpatialOperator`\n \"\"\"\n\n def __init__(self, child):\n super().__init__(\"div\", child)\n\n def evaluates_on_edges(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluates_on_edges()`. \"\"\"\n return False\n\n\nclass Laplacian(SpatialOperator):\n \"\"\"A node in the expression tree representing a laplacian operator. This is\n currently only implemeted in the weak form for finite element formulations.\n\n **Extends:** :class:`SpatialOperator`\n \"\"\"\n\n def __init__(self, child):\n super().__init__(\"laplacian\", child)\n\n def evaluates_on_edges(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluates_on_edges()`. \"\"\"\n return False\n\n\nclass Mass(SpatialOperator):\n \"\"\"Returns the mass matrix for a given symbol, accounting for boundary conditions\n where necessary (e.g. in the finite element formualtion)\n\n **Extends:** :class:`SpatialOperator`\n \"\"\"\n\n def __init__(self, child):\n super().__init__(\"mass\", child)\n\n def evaluate_for_shape(self):\n return pybamm.evaluate_for_shape_using_domain(self.domain, typ=\"matrix\")\n\n\nclass Integral(SpatialOperator):\n \"\"\"A node in the expression tree representing an integral operator\n\n .. math::\n I = \\\\int_{a}^{b}\\\\!f(u)\\\\,du,\n\n where :math:`a` and :math:`b` are the left-hand and right-hand boundaries of\n the domain respectively, and :math:`u\\\\in\\\\text{domain}`.\n Can be integration with respect to time or space.\n\n Parameters\n ----------\n function : :class:`pybamm.Symbol`\n The function to be integrated (will become self.children[0])\n integration_variable : :class:`pybamm.IndependentVariable`\n The variable over which to integrate\n\n **Extends:** :class:`SpatialOperator`\n \"\"\"\n\n def __init__(self, child, integration_variable):\n if not isinstance(integration_variable, list):\n integration_variable = [integration_variable]\n\n name = \"integral\"\n for var in integration_variable:\n if isinstance(var, pybamm.SpatialVariable):\n # Check that child and integration_variable domains agree\n if child.domain != var.domain:\n raise pybamm.DomainError(\n \"child and integration_variable must have the same domain\"\n )\n elif not isinstance(var, pybamm.IndependentVariable):\n raise ValueError(\n \"\"\"integration_variable must be of type pybamm.IndependentVariable,\n not {}\"\"\".format(\n type(var)\n )\n )\n name += \" d{}\".format(var.name)\n\n if any(isinstance(var, pybamm.SpatialVariable) for var in integration_variable):\n name += \" {}\".format(child.domain)\n\n self._integration_variable = integration_variable\n super().__init__(name, child)\n # integrating removes the domain\n self.domain = []\n\n @property\n def integration_variable(self):\n return self._integration_variable\n\n def set_id(self):\n \"\"\" See :meth:`pybamm.Symbol.set_id()` \"\"\"\n if not isinstance(self.integration_variable, list):\n self.integration_variable = [self.integration_variable]\n self._id = hash(\n (self.__class__, self.name)\n + tuple(\n [\n integration_variable.id\n for integration_variable in self.integration_variable\n ]\n )\n + (self.children[0].id,)\n + tuple(self.domain)\n )\n\n def _unary_simplify(self, simplified_child):\n \"\"\" See :meth:`UnaryOperator._unary_simplify()`. \"\"\"\n\n return self.__class__(simplified_child, self.integration_variable)\n\n def _unary_new_copy(self, child):\n \"\"\" See :meth:`UnaryOperator._unary_new_copy()`. \"\"\"\n\n return self.__class__(child, self.integration_variable)\n\n def evaluate_for_shape(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()` \"\"\"\n return pybamm.evaluate_for_shape_using_domain(self.domain)\n\n def evaluates_on_edges(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluates_on_edges()`. \"\"\"\n return False\n\n\nclass IndefiniteIntegral(Integral):\n \"\"\"A node in the expression tree representing an indefinite integral operator\n\n .. math::\n I = \\\\int_{x_\\text{min}}^{x}\\\\!f(u)\\\\,du\n\n where :math:`u\\\\in\\\\text{domain}` which can represent either a\n spatial or temporal variable.\n\n Parameters\n ----------\n function : :class:`pybamm.Symbol`\n The function to be integrated (will become self.children[0])\n integration_variable : :class:`pybamm.IndependentVariable`\n The variable over which to integrate\n\n **Extends:** :class:`Integral`\n \"\"\"\n\n def __init__(self, child, integration_variable):\n if isinstance(integration_variable, list):\n if len(integration_variable) > 1:\n raise NotImplementedError(\n \"Indefinite integral only implemeted w.r.t. one variable\"\n )\n else:\n integration_variable = integration_variable[0]\n super().__init__(child, integration_variable)\n # Overwrite the name\n self.name = \"{} integrated w.r.t {}\".format(\n child.name, integration_variable.name\n )\n if isinstance(integration_variable, pybamm.SpatialVariable):\n self.name += \" on {}\".format(integration_variable.domain)\n # the integrated variable has the same domain as the child\n self.domain = child.domain\n\n def evaluate_for_shape(self):\n return self.children[0].evaluate_for_shape()\n\n\nclass BoundaryOperator(SpatialOperator):\n \"\"\"A node in the expression tree which gets the boundary value of a variable.\n\n Parameters\n ----------\n name : str\n The name of the symbol\n child : :class:`pybamm.Symbol`\n The variable whose boundary value to take\n side : str\n Which side to take the boundary value on (\"left\" or \"right\")\n\n **Extends:** :class:`SpatialOperator`\n \"\"\"\n\n def __init__(self, name, child, side):\n self.side = side\n # Domain of Boundary must be ([]) so that expressions can be formed\n # of boundary values of variables in different domains\n super().__init__(name, child)\n self.domain = []\n\n def set_id(self):\n \"\"\" See :meth:`pybamm.Symbol.set_id()` \"\"\"\n self._id = hash(\n (self.__class__, self.name, self.side, self.children[0].id)\n + tuple(self.domain)\n )\n\n def _unary_simplify(self, simplified_child):\n \"\"\" See :meth:`UnaryOperator._unary_simplify()`. \"\"\"\n return self.__class__(simplified_child, self.side)\n\n def _unary_new_copy(self, child):\n \"\"\" See :meth:`UnaryOperator._unary_new_copy()`. \"\"\"\n return self.__class__(child, self.side)\n\n def evaluate_for_shape(self):\n \"\"\" See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()` \"\"\"\n return pybamm.evaluate_for_shape_using_domain(self.domain)\n\n\nclass BoundaryValue(BoundaryOperator):\n \"\"\"A node in the expression tree which gets the boundary value of a variable.\n\n Parameters\n ----------\n child : :class:`pybamm.Symbol`\n The variable whose boundary value to take\n side : str\n Which side to take the boundary value on (\"left\" or \"right\")\n\n **Extends:** :class:`BoundaryOperator`\n \"\"\"\n\n def __init__(self, child, side):\n super().__init__(\"boundary value\", child, side)\n\n\nclass BoundaryFlux(BoundaryOperator):\n \"\"\"A node in the expression tree which gets the boundary flux of a variable.\n\n Parameters\n ----------\n child : :class:`pybamm.Symbol`\n The variable whose boundary flux to take\n side : str\n Which side to take the boundary flux on (\"left\" or \"right\")\n\n **Extends:** :class:`BoundaryOperator`\n \"\"\"\n\n def __init__(self, child, side):\n super().__init__(\"boundary flux\", child, side)\n\n\n#\n# Methods to call Gradient and Divergence\n#\n\n\ndef grad(expression):\n \"\"\"convenience function for creating a :class:`Gradient`\n\n Parameters\n ----------\n\n expression : :class:`Symbol`\n the gradient will be performed on this sub-expression\n\n Returns\n -------\n\n :class:`Gradient`\n the gradient of ``expression``\n \"\"\"\n\n return Gradient(expression)\n\n\ndef div(expression):\n \"\"\"convenience function for creating a :class:`Divergence`\n\n Parameters\n ----------\n\n expression : :class:`Symbol`\n the divergence will be performed on this sub-expression\n\n Returns\n -------\n\n :class:`Divergence`\n the divergence of ``expression``\n \"\"\"\n\n return Divergence(expression)\n\n\ndef laplacian(expression):\n \"\"\"convenience function for creating a :class:`Laplacian`\n\n Parameters\n ----------\n\n expression : :class:`Symbol`\n the laplacian will be performed on this sub-expression\n\n Returns\n -------\n\n :class:`Laplacian`\n the laplacian of ``expression``\n \"\"\"\n\n return Laplacian(expression)\n\n\n#\n# Method to call SurfaceValue\n#\n\n\ndef surf(variable, set_domain=False):\n \"\"\"convenience function for creating a right :class:`BoundaryValue`, usually in the\n spherical geometry\n\n Parameters\n ----------\n\n variable : :class:`Symbol`\n the surface value of this variable will be returned\n\n Returns\n -------\n\n :class:`GetSurfaceValue`\n the surface value of ``variable``\n \"\"\"\n if variable.domain == [\"negative electrode\"] and isinstance(\n variable, pybamm.Broadcast\n ):\n child_surf = boundary_value(variable.orphans[0], \"right\")\n out = pybamm.Broadcast(\n child_surf, [\"negative electrode\"], broadcast_type=\"primary\"\n )\n elif variable.domain == [\"positive electrode\"] and isinstance(\n variable, pybamm.Broadcast\n ):\n child_surf = boundary_value(variable.orphans[0], \"right\")\n out = pybamm.Broadcast(\n child_surf, [\"positive electrode\"], broadcast_type=\"primary\"\n )\n else:\n out = boundary_value(variable, \"right\")\n if set_domain:\n if variable.domain == [\"negative particle\"]:\n out.domain = [\"negative electrode\"]\n elif variable.domain == [\"positive particle\"]:\n out.domain = [\"positive electrode\"]\n\n return out\n\n\ndef average(symbol):\n \"\"\"convenience function for creating an average\n\n Parameters\n ----------\n symbol : :class:`pybamm.Symbol`\n The function to be averaged\n\n Returns\n -------\n :class:`Symbol`\n the new averaged symbol\n \"\"\"\n # If symbol doesn't have a domain, its average value is itself\n if symbol.domain in [[], [\"current collector\"]]:\n new_symbol = symbol.new_copy()\n new_symbol.parent = None\n return new_symbol\n # If symbol is a Broadcast, its average value is its child\n elif isinstance(symbol, pybamm.Broadcast):\n return symbol.orphans[0]\n # If symbol is a concatenation of Broadcasts, its average value is its child\n elif (\n isinstance(symbol, pybamm.Concatenation)\n and all(isinstance(child, pybamm.Broadcast) for child in symbol.children)\n and symbol.domain == [\"negative electrode\", \"separator\", \"positive electrode\"]\n ):\n a, b, c = [orp.orphans[0] for orp in symbol.orphans]\n if a.id == b.id == c.id:\n return a\n else:\n l_n = pybamm.geometric_parameters.l_n\n l_s = pybamm.geometric_parameters.l_s\n l_p = pybamm.geometric_parameters.l_p\n return (l_n * a + l_s * b + l_p * c) / (l_n + l_s + l_p)\n # Otherwise, use Integral to calculate average value\n else:\n if symbol.domain == [\"negative electrode\"]:\n x = pybamm.standard_spatial_vars.x_n\n l = pybamm.geometric_parameters.l_n\n elif symbol.domain == [\"separator\"]:\n x = pybamm.standard_spatial_vars.x_s\n l = pybamm.geometric_parameters.l_s\n elif symbol.domain == [\"positive electrode\"]:\n x = pybamm.standard_spatial_vars.x_p\n l = pybamm.geometric_parameters.l_p\n elif symbol.domain == [\"negative electrode\", \"separator\", \"positive electrode\"]:\n x = pybamm.standard_spatial_vars.x\n l = pybamm.Scalar(1)\n elif symbol.domain == [\"negative particle\"]:\n x = pybamm.standard_spatial_vars.x_n\n l = pybamm.geometric_parameters.l_n\n elif symbol.domain == [\"positive particle\"]:\n x = pybamm.standard_spatial_vars.x_p\n l = pybamm.geometric_parameters.l_p\n else:\n raise pybamm.DomainError(\"domain '{}' not recognised\".format(symbol.domain))\n\n return Integral(symbol, x) / l\n\n\ndef boundary_value(symbol, side):\n \"\"\"convenience function for creating a :class:`Integral`\n\n Parameters\n ----------\n symbol : `pybamm.Symbol`\n The symbol whose boundary value to take\n side : str\n Which side to take the boundary value on (\"left\" or \"right\")\n\n Returns\n -------\n :class:`BoundaryValue`\n the new integrated expression tree\n \"\"\"\n # If symbol doesn't have a domain, its boundary value is itself\n if symbol.domain == []:\n new_symbol = symbol.new_copy()\n new_symbol.parent = None\n return new_symbol\n # If symbol is a Broadcast, its boundary value is its child\n if isinstance(symbol, pybamm.Broadcast):\n return symbol.orphans[0]\n # Otherwise, calculate boundary value\n else:\n return BoundaryValue(symbol, side)\n", "sub_path": "pybamm/expression_tree/unary_operators.py", "file_name": "unary_operators.py", "file_ext": "py", "file_size_in_byte": 22639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pybamm.Symbol", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pybamm.UndefinedOperationError", "line_number": 119, "usage_type": "call"}, {"api_name": "pybamm.UndefinedOperationError", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 132, "usage_type": "call"}, {"api_name": "pybamm.StateVector", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 188, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 191, "usage_type": "call"}, {"api_name": "pybamm.Matrix", "line_number": 192, "usage_type": "call"}, {"api_name": "pybamm.Variable", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pybamm.StateVector", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pybamm.SpatialVariable", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pybamm.Scalar", "line_number": 269, "usage_type": "call"}, {"api_name": "pybamm.evaluate_for_shape_using_domain", "line_number": 328, "usage_type": "call"}, {"api_name": "pybamm.SpatialVariable", "line_number": 357, "usage_type": "attribute"}, {"api_name": "pybamm.DomainError", "line_number": 360, "usage_type": "call"}, {"api_name": "pybamm.IndependentVariable", "line_number": 363, "usage_type": "attribute"}, {"api_name": "pybamm.SpatialVariable", "line_number": 372, "usage_type": "attribute"}, {"api_name": "pybamm.evaluate_for_shape_using_domain", "line_number": 412, "usage_type": "call"}, {"api_name": "pybamm.SpatialVariable", "line_number": 451, "usage_type": "attribute"}, {"api_name": "pybamm.evaluate_for_shape_using_domain", "line_number": 499, "usage_type": "call"}, {"api_name": "pybamm.Broadcast", "line_number": 620, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 623, "usage_type": "call"}, {"api_name": "pybamm.Broadcast", "line_number": 627, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 630, "usage_type": "call"}, {"api_name": "pybamm.Broadcast", "line_number": 663, "usage_type": "attribute"}, {"api_name": "pybamm.Concatenation", "line_number": 667, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 668, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 675, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 676, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 677, "usage_type": "attribute"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 682, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 683, "usage_type": "attribute"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 685, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 686, "usage_type": "attribute"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 688, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 689, "usage_type": "attribute"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 691, "usage_type": "attribute"}, {"api_name": "pybamm.Scalar", "line_number": 692, "usage_type": "call"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 694, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 695, "usage_type": "attribute"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 697, "usage_type": "attribute"}, {"api_name": "pybamm.geometric_parameters", "line_number": 698, "usage_type": "attribute"}, {"api_name": "pybamm.DomainError", "line_number": 700, "usage_type": "call"}, {"api_name": "pybamm.Broadcast", "line_number": 726, "usage_type": "attribute"}]} +{"seq_id": "472885949", "text": "### MISC ###\nfrom __future__ import print_function\nimport argparse\nimport time\nimport os\nimport shutil\nimport copy\n\n### PYTORCH STUFF ###\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\n\n### CLASSES ###\nfrom utils import loss_plot, batch_scatterplot as scatterplot, transforms, loader, logger\nfrom reinforcement import ReinforcementLearning as rl\nfrom omniglot import OMNIGLOT\n\nimport model\nimport validate\nimport train_target as train\n\n\n### IMPORTANT NOTICE ###\n\"\"\"\nIf train on 3 classes (or more):\n omniglot.py, line 64: img_classes = np.random.choice(3, self.classes, replace=False)\n\nIf train on whole dataset:\n omniglot.py, line 64: img_classes = np.random.choice(len(self.train_labels), self.classes, replace=False)\n\"\"\"\n\n\n# Training settings\nparser = argparse.ArgumentParser(description='PyTorch Reinforcement Learning LSTM', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n# Batch size:\nparser.add_argument('--batch-size', type=int, default=50, metavar='N',\n help='input batch size for training (default: 50)')\n\n# Mini-batch size:\nparser.add_argument('--mini-batch-size', type=int, default=50, metavar='N',\n help='How many episodes to train on at a time (default: 1)')\n\n# Episode size:\nparser.add_argument('--episode-size', type=int, default=30, metavar='N',\n help='input episode size for training (default: 30)')\n\n# Nof. classes in an episode:\nparser.add_argument('--class-vector-size', type=int, default=3, metavar='N',\n help='input class vector size for training (default: 3)')\n\n# Epochs:\nparser.add_argument('--epochs', type=int, default=0, metavar='N',\n help='number of epochs to train (default: 2000)')\n\n# Starting Epoch:\nparser.add_argument('--start_epoch', type=int, default=1, metavar='N',\n help='starting epoch (default: 1)')\n\n# CUDA:\nparser.add_argument('--no-cuda', action='store_true', default=True,\n help='enables CUDA training')\n\n# Checkpoint Loader:\nparser.add_argument('--load-checkpoint', default='pretrained/truncated_singlesum/checkpoint.pth.tar', type=str,\n help='path to latest checkpoint (default: none)')\n\n# Network Name:\nparser.add_argument('--name', default='truncated_singlesum', type=str,\n help='name of file')\n\n# Seed:\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n\n# Logging interval:\nparser.add_argument('--log-interval', type=int, default=50, metavar='N',\n help='how many batches to wait before logging training status')\n\n\n# Saves checkpoint to disk\ndef save_checkpoint(state, filename='checkpoint.pth.tar'):\n directory = \"pretrained/%s/\" % (args.name)\n if not os.path.exists(directory):\n os.makedirs(directory)\n filename = directory + filename\n torch.save(state, filename)\n print(\"Checkpoint successfully saved!\")\n\n\n# Writes a table to file:\ndef write_stats(requests, accuracy, penalty, folder, test=False):\n filename = \"results/plots/\" + str(folder) + \"table_file.txt\"\n stat_filename = \"results/plots/\" + str(folder) + \"stat_file.txt\"\n dimensions = [50, 20, 20]\n headers = [\"Method\", \"Accuracy (%)\", \"Requests (%)\"]\n method = \"RL Prediction\"\n if penalty == 0:\n method = \"Supervised\"\n else:\n if test:\n method += \"(Rinc = \" + str(penalty) + \") - Test\"\n else:\n method += \"(Rinc = \" + str(penalty) + \") - Training\"\n specs = [accuracy, requests]\n if (os.path.isfile(stat_filename)):\n stat_file = open(stat_filename, \"a\")\n else:\n stat_file = open(stat_filename, \"w\")\n stat_file.write(method + \"\\n\")\n\n # Averaging over 20 episodes:\n for s in specs:\n length = min(20, len(s))\n average = float(sum(s[len(s) - length:])/length)\n stat_file.write(str(average)[0:4] + \"\\n\")\n stat_file.close()\n\n # Reading from stat_file:\n stats = {}\n length = 3\n with open(stat_filename, \"r\") as statistics:\n i = 0\n current_key = \"\"\n for line in statistics:\n if (i == 0):\n if (line.rstrip() not in stats):\n stats[line.rstrip()] = [[], []]\n current_key = line.rstrip()\n elif (i < length):\n stats[current_key][i-1].append(float(line.rstrip()))\n i += 1\n if (i == length):\n i = 0\n\n stat_list = []\n for k in stats.keys():\n stat_list.append([])\n stat_list[-1].append(k)\n for v in stats[k]:\n stat_list[-1].append(sum(v)/len(v))\n\n print(stats)\n print(stat_list)\n\n # Creating Line:\n table = \"\"\n line = \"\\n+\"\n for d in dimensions:\n line += \"-\"*d + \"+\"\n line += \"\\n\"\n\n if (os.path.isfile(filename)):\n file = open(filename, \"a\")\n\n else:\n file = open(filename, \"w\")\n\n # HEADER\n table += line\n header = \"|\"\n for i, d in enumerate(dimensions):\n header += int((d/2) - int(len(headers[i])/2))*\" \" + headers[i] + int((d/2) - int(len(headers[i])/2))*\" \" + \"|\"\n table += header\n table += line\n\n # BODY\n for stat in stat_list:\n table += \"|\"\n for i, d in enumerate(dimensions):\n table += int((d/2) - int(len(str(stat[i]))/2))*\" \" + str(stat[i]) + int((d/2) - int(len(str(stat[i]))/2))*\" \" + \"|\"\n # END\n table += line\n\n \n print(table)\n file.write(table)\n print(\"Table successfully written!\")\n file.close()\n\n\n\nif __name__ == '__main__':\n\n ### SETTING UP TENSORBOARD LOGGER ###\n result_directory = 'results/'\n log_directory = 'results/logs/'\n if not os.path.exists(result_directory):\n os.makedirs(result_directory)\n if not os.path.exists(log_directory):\n os.makedirs(log_directory)\n\n logger = logger.Logger('results/logs/')\n\n ### PARSING ARGUMENTS ###\n args = parser.parse_args()\n args.cuda = not args.no_cuda and torch.cuda.is_available()\n\n #torch.manual_seed(args.seed)\n #if args.cuda:\n #torch.cuda.manual_seed(args.seed)\n\n kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n\n ### PARAMETERS ###\n\n # LSTM & Q Learning\n IMAGE_SCALE = 28\n IMAGE_SIZE = IMAGE_SCALE*IMAGE_SCALE\n HIDDEN_LAYERS = 1\n HIDDEN_NODES = 200\n OUTPUT_CLASSES = args.class_vector_size\n ##################\n\n train_transform = transforms.Compose([\n transforms.Resize((IMAGE_SCALE, IMAGE_SCALE)),\n transforms.ToTensor()\n ])\n test_transform = transforms.Compose([\n transforms.Resize((IMAGE_SCALE, IMAGE_SCALE)),\n transforms.ToTensor()\n ])\n\n print(\"Loading trainingsets...\")\n omniglot_loader = loader.OmniglotLoader('data/omniglot', classify=False, partition=0.8, classes=True)\n train_loader = torch.utils.data.DataLoader(\n OMNIGLOT('data/omniglot', train=True, transform=train_transform, download=True, omniglot_loader=omniglot_loader, batch_size=args.episode_size),\n batch_size=args.mini_batch_size, shuffle=True, **kwargs)\n print(\"Loading testset...\")\n test_loader = torch.utils.data.DataLoader(\n OMNIGLOT('data/omniglot', train=False, transform=test_transform, omniglot_loader=omniglot_loader, batch_size=args.episode_size),\n batch_size=args.mini_batch_size, shuffle=True, **kwargs)\n print(\"Done loading datasets!\")\n\n\n # LSTM Model and Target network:\n q_network = model.ReinforcedLSTM(IMAGE_SIZE, HIDDEN_NODES, HIDDEN_LAYERS, OUTPUT_CLASSES,\n args.batch_size, args.cuda)\n\n target_network = copy.deepcopy(q_network)\n\n # Modules:\n rl = rl(OUTPUT_CLASSES)\n\n if args.cuda:\n print(\"\\n---Activating GPU Training---\\n\")\n q_network.cuda()\n target_network.cuda()\n\n ### PRINTING AMOUNT OF PARAMETERS ###\n print('Number of model parameters: {}'.format(\n sum([p.data.nelement() for p in q_network.parameters()])))\n\n best_accuracy = 0.0\n\n req_dict = {1: [], 2: [], 5: [], 10: []}\n acc_dict = {1: [], 2: [], 5: [], 10: []}\n test_req_dict = {1: [], 2: [], 5: [], 10: []}\n test_acc_dict = {1: [], 2: [], 5: [], 10: []}\n total_requests = []\n total_accuracy = []\n total_prediction_accuracy= []\n total_loss = []\n total_reward = []\n\n\n ### LOADING PREVIOUS NETWORK ###\n if args.load_checkpoint:\n if os.path.isfile(args.load_checkpoint):\n print(\"=> loading checkpoint '{}'\".format(args.load_checkpoint))\n checkpoint = torch.load(args.load_checkpoint)\n args.start_epoch = checkpoint['epoch']\n episode = checkpoint['episode']\n req_dict = checkpoint['requests']\n acc_dict = checkpoint['accuracy']\n total_requests = checkpoint['tot_requests']\n total_accuracy = checkpoint['tot_accuracy']\n total_prediction_accuracy = checkpoint['tot_pred_acc']\n total_loss = checkpoint['tot_loss']\n total_reward = checkpoint['tot_reward']\n q_network.load_state_dict(checkpoint['state_dict'])\n target_network.load_state_dict(checkpoint['state_dict'])\n print(\"=> loaded checkpoint '{}' (epoch {})\"\n .format(args.load_checkpoint, checkpoint['epoch']))\n else:\n print(\"=> no checkpoint found at '{}'\".format(args.load_checkpoint))\n\n ### WEIGHT OPTIMIZER ###\n optimizer = optim.Adam(q_network.parameters())\n criterion = nn.MSELoss(reduce=True)\n epoch = 0\n episode = (args.start_epoch-1)*args.batch_size\n done = False\n start_time = time.time()\n update = False\n UPDATE_TARGET_NETWORK = 5\n\n\n while not done:\n ### TRAINING AND TESTING LOOP ###\n for epoch in range(args.start_epoch, args.epochs + 1):\n\n ### TRAINING ###\n print(\"\\n\\n--- Training epoch \" + str(epoch) + \" ---\\n\\n\")\n\n update = False\n if (epoch > 0 and epoch % UPDATE_TARGET_NETWORK == 0):\n update = True\n\n prediction_accuracy, requests, accuracy, loss, reward, req_dict, acc_dict = \\\n train.train(q_network, target_network, epoch, optimizer, \\\n train_loader, args, logger, rl, req_dict, acc_dict, episode, criterion, update)\n\n episode += args.batch_size\n\n # STATS:\n total_prediction_accuracy.append(prediction_accuracy)\n total_accuracy.append(accuracy)\n total_requests.append(requests)\n total_loss.append(loss)\n total_reward.append(reward)\n\n if (epoch % 1000 == 0):\n validate.validate(q_network, epoch, optimizer, test_loader, args, logger, rl, test_req_dict, test_acc_dict, episode)\n\n ### SAVING CHECKPOINT ###\n save_checkpoint({\n 'epoch': epoch + 1,\n 'episode': episode,\n 'state_dict': q_network.state_dict(),\n 'requests': req_dict,\n 'accuracy': acc_dict,\n 'tot_accuracy': total_accuracy,\n 'tot_requests': total_requests,\n 'tot_pred_acc': total_prediction_accuracy,\n 'tot_loss': total_loss,\n 'tot_reward': total_reward\n })\n\n ### ALSO SAVING BACKUP-CHECKPOINT ###\n if (epoch % 50 == 0):\n save_checkpoint({\n 'epoch': epoch + 1,\n 'episode': episode,\n 'state_dict': q_network.state_dict(),\n 'requests': req_dict,\n 'accuracy': acc_dict,\n 'tot_accuracy': total_accuracy,\n 'tot_requests': total_requests,\n 'tot_pred_acc': total_prediction_accuracy,\n 'tot_loss': total_loss,\n 'tot_reward': total_reward\n }, filename=\"backup.pth.tar\")\n\n elapsed_time = time.time() - start_time\n print(\"ELAPSED TIME = \" + str(elapsed_time) + \" seconds\")\n answer = input(\"How many more epochs to train: \")\n try:\n if int(answer) == 0:\n done = True\n else:\n args.start_epoch = args.epochs + 1\n args.epochs += int(answer)\n except:\n done = True\n\n \n # Plotting training accuracy:\n loss_plot.plot([total_accuracy, total_prediction_accuracy, total_requests], [\"Training Accuracy Percentage\", \"Training Prediction Accuracy\", \"Training Requests Percentage\"], \"training_stats\", args.name + \"/\", \"Percentage\")\n loss_plot.plot([total_loss], [\"Training Loss\"], \"training_loss\", args.name + \"/\", \"Average Loss\")\n loss_plot.plot([total_reward], [\"Training Average Reward\"], \"training_reward\", args.name + \"/\", \"Average Reward\")\n\n print(\"\\n\\n--- Training Done ---\\n\")\n val = input(\"\\nProceed to testing? \\n[Y/N]: \")\n\n if (val.lower() == \"y\"):\n test_accuracy = 0.0\n test_request = 0.0\n test_reward = 0.0\n test_epochs = 20\n test_stats = [[], []]\n training_stats = [[], []]\n\n # Validating on the test set for 100 epochs (5000 episodes):\n print_stats = False\n first = True\n for epoch in range(args.epochs + 1, args.epochs + 1 + test_epochs):\n\n # Validate the model:\n prediction, requests, accuracy, reward, req_dict, acc_dict = validate.validate(q_network, epoch, optimizer, test_loader, args, logger, rl, req_dict, acc_dict, episode)\n prediction, train_requests, train_accuracy, _, _, _ = validate.validate(q_network, epoch, optimizer, train_loader, args, logger, rl, req_dict, acc_dict, episode)\n\n # Increment episode count:\n episode += args.batch_size\n\n # Statistics:\n test_accuracy += accuracy\n test_request += requests\n test_reward += reward\n\n # For stat file:\n test_stats[0].append(accuracy)\n test_stats[1].append(requests)\n training_stats[0].append(train_accuracy)\n training_stats[1].append(train_requests)\n\n # Statistics:\n total_accuracy.append(accuracy)\n total_requests.append(requests)\n total_reward.append(reward)\n\n test_accuracy = float(test_accuracy/test_epochs)\n test_request = float(test_request/test_epochs)\n test_reward = float(test_reward/test_epochs)\n\n # Printing:\n print(\"\\nTesting Average Accuracy = \", str(test_accuracy) + \" %\")\n print(\"Testing Average Requests = \", str(test_request) + \" %\")\n print(\"Testing Average Reward = \", str(test_reward) + \" %\")\n loss_plot.plot([total_accuracy[args.epochs + 1:], total_requests[args.epochs + 1:]], [\"Accuracy Percentage\", \"Requests Percentage\"], \"testing_stats\", args.name + \"/\", \"Percentage\")\n loss_plot.plot([total_reward[args.epochs + 1:]], [\"Average Reward\"], \"test_reward\", args.name + \"/\", \"Average Reward\")\n\n\n scatterplot.plot(acc_dict, args.name + \"/\", title=\"Prediction Accuracy\")\n scatterplot.plot(req_dict, args.name + \"/\", title=\"Total Requests\")\n\n write_stats(training_stats[1], training_stats[0], rl.prediction_penalty, args.name + \"/\")\n write_stats(test_stats[1], test_stats[0], rl.prediction_penalty, args.name + \"/\", test=True)\n\n", "sub_path": "reinforcement/main_target.py", "file_name": "main_target.py", "file_ext": "py", "file_size_in_byte": 15405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 36, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 191, "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.makedirs", "line_number": 193, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 195, "usage_type": "name"}, {"api_name": "utils.logger.Logger", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 199, "usage_type": "attribute"}, {"api_name": "utils.transforms.Compose", "line_number": 217, "usage_type": "call"}, {"api_name": "utils.transforms", "line_number": 217, "usage_type": "name"}, {"api_name": "utils.transforms.Resize", "line_number": 218, "usage_type": "call"}, {"api_name": "utils.transforms", "line_number": 218, "usage_type": "name"}, {"api_name": "utils.transforms.ToTensor", "line_number": 219, "usage_type": "call"}, {"api_name": "utils.transforms", "line_number": 219, "usage_type": "name"}, {"api_name": "utils.transforms.Compose", "line_number": 221, "usage_type": "call"}, {"api_name": "utils.transforms", "line_number": 221, "usage_type": "name"}, {"api_name": "utils.transforms.Resize", "line_number": 222, "usage_type": "call"}, {"api_name": "utils.transforms", "line_number": 222, "usage_type": "name"}, {"api_name": "utils.transforms.ToTensor", "line_number": 223, "usage_type": "call"}, {"api_name": "utils.transforms", "line_number": 223, "usage_type": "name"}, {"api_name": "utils.loader.OmniglotLoader", "line_number": 227, "usage_type": "call"}, {"api_name": "utils.loader", "line_number": 227, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 228, "usage_type": "attribute"}, {"api_name": "omniglot.OMNIGLOT", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 232, "usage_type": "attribute"}, {"api_name": "omniglot.OMNIGLOT", "line_number": 233, "usage_type": "call"}, {"api_name": "model.ReinforcedLSTM", "line_number": 239, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 242, "usage_type": "call"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 245, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 291, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 291, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 292, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 292, "usage_type": "name"}, {"api_name": "time.time", "line_number": 296, "usage_type": "call"}, {"api_name": "train_target.train", "line_number": 313, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 314, "usage_type": "argument"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 314, "usage_type": "argument"}, {"api_name": "validate.validate", "line_number": 326, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 326, "usage_type": "argument"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 326, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 357, "usage_type": "call"}, {"api_name": "utils.loss_plot.plot", "line_number": 371, "usage_type": "call"}, {"api_name": "utils.loss_plot", "line_number": 371, "usage_type": "name"}, {"api_name": "utils.loss_plot.plot", "line_number": 372, "usage_type": "call"}, {"api_name": "utils.loss_plot", "line_number": 372, "usage_type": "name"}, {"api_name": "utils.loss_plot.plot", "line_number": 373, "usage_type": "call"}, {"api_name": "utils.loss_plot", "line_number": 373, "usage_type": "name"}, {"api_name": "validate.validate", "line_number": 392, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 392, "usage_type": "argument"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 392, "usage_type": "argument"}, {"api_name": "validate.validate", "line_number": 393, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 393, "usage_type": "argument"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 393, "usage_type": "argument"}, {"api_name": "utils.loss_plot.plot", "line_number": 422, "usage_type": "call"}, {"api_name": "utils.loss_plot", "line_number": 422, "usage_type": "name"}, {"api_name": "utils.loss_plot.plot", "line_number": 423, "usage_type": "call"}, {"api_name": "utils.loss_plot", "line_number": 423, "usage_type": "name"}, {"api_name": "utils.batch_scatterplot.plot", "line_number": 426, "usage_type": "call"}, {"api_name": "utils.batch_scatterplot", "line_number": 426, "usage_type": "name"}, {"api_name": "utils.batch_scatterplot.plot", "line_number": 427, "usage_type": "call"}, {"api_name": "utils.batch_scatterplot", "line_number": 427, "usage_type": "name"}, {"api_name": "reinforcement.ReinforcementLearning.prediction_penalty", "line_number": 429, "usage_type": "attribute"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 429, "usage_type": "name"}, {"api_name": "reinforcement.ReinforcementLearning.prediction_penalty", "line_number": 430, "usage_type": "attribute"}, {"api_name": "reinforcement.ReinforcementLearning", "line_number": 430, "usage_type": "name"}]} +{"seq_id": "623987319", "text": "\"\"\"\n Copyright 2018 Inmanta\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 Contact: code@inmanta.com\n\"\"\"\n\nfrom inmanta.config import Option, is_int, is_bool, is_time, is_list, is_str_opt\nfrom inmanta.config import state_dir, log_dir\nimport logging\n\n\nLOGGER = logging.getLogger(__name__)\n\n# flake8: noqa: H904\n\n#############################\n# Database\n#############################\n\ndb_host = Option(\"database\", \"host\", \"localhost\", \"Hostname or IP of the mongo server\")\ndb_port = Option(\"database\", \"port\", 27017, \"The port of the mongo server\", is_int)\ndb_name = Option(\"database\", \"name\", \"inmanta\", \"The name of the database on the mongo server\")\n\n#############################\n# server_rest_transport\n#############################\ntransport_port = Option(\"server_rest_transport\", \"port\", 8888, \"The port on which the server listens for connections\")\n\n\n#############################\n# server\n#############################\nserver_enable_auth = Option(\"server\", \"auth\", False, \"Enable authentication on the server API\", is_bool)\n\nserver_ssl_key = Option(\"server\", \"ssl_key_file\", None,\n \"Server private key to use for this server Leave blank to disable SSL\", is_str_opt)\n\nserver_ssl_cert = Option(\"server\", \"ssl_cert_file\", None,\n \"SSL certificate file for the server key. Leave blank to disable SSL\", is_str_opt)\n\nserver_ssl_ca_cert = Option(\"server\", \"ssl_ca_cert_file\", None,\n \"The CA cert file required to validate the server ssl cert. This setting is used by the server\"\n \"to correctly configure the compiler and agents that the server starts itself. If not set and \"\n \"SSL is enabled, the server cert should be verifiable with the CAs installed in the OS.\",\n is_str_opt)\n\nserver_fact_expire = Option(\"server\", \"fact-expire\", 3600,\n \"After how many seconds will discovered facts/parameters expire\", is_time)\n\n\ndef default_fact_renew():\n \"\"\" server.fact-expire/3 \"\"\"\n return int(server_fact_expire.get() / 3)\n\n\ndef validate_fact_renew(value):\n \"\"\" time; < server.fact-expire \"\"\"\n out = int(value)\n if not out < server_fact_expire.get():\n LOGGER.warn(\"can not set fact_renew to %d, must be smaller than fact-expire (%d), using %d instead\" %\n (out, server_fact_expire.get(), default_fact_renew()))\n out = default_fact_renew()\n return out\n\n\nserver_fact_renew = Option(\"server\", \"fact-renew\", default_fact_renew,\n \"\"\"After how many seconds will discovered facts/parameters be renewed?\n This value needs to be lower than fact-expire\"\"\", validate_fact_renew)\n\nserver_fact_resource_block = Option(\"server\", \"fact-resource-block\", 60,\n \"Minimal time between subsequent requests for the same fact\", is_time)\n\nserver_autrecompile_wait = Option(\"server\", \"auto-recompile-wait\", 10,\n \"\"\"The number of seconds to wait before the server may attempt to do a new recompile.\n Recompiles are triggered after facts updates for example.\"\"\", is_time)\n\nserver_purge_version_interval = Option(\"server\", \"purge-versions-interval\", 3600,\n \"\"\"The number of seconds between version purging,\n see :inmanta.config:option:`server.available-versions-to-keep`\"\"\", is_time)\n\nserver_version_to_keep = Option(\"server\", \"available-versions-to-keep\", 10,\n \"\"\"On boot and at regular intervals the server will purge older versions.\n This is the number of most recent versions to keep available.\"\"\", is_int)\n\nserver_address = Option(\"server\", \"server_address\", \"localhost\",\n \"\"\"The public ip address of the server.\n This is required for example to inject the inmanta agent in virtual machines at boot time.\"\"\")\n\nserver_wait_after_param = Option(\"server\", \"wait-after-param\", 5,\n \"Time to wait before recompile after new paramters have been received\", is_time)\n\nagent_timeout = Option(\"server\", \"agent-timeout\", 30,\n \"Time before an agent is considered to be offline\", is_time)\n\nserver_delete_currupt_files = Option(\"server\", \"delete_currupt_files\", True,\n \"The server logs an error when it detects a file got corrupted. When set to true, the \"\n \"server will also delete the file, so on subsequent compiles the missing file will be \"\n \"recreated.\", is_bool)\n\n#############################\n# Dashboard\n#############################\n\ndash_enable = Option(\"dashboard\", \"enabled\", True, \"Determines whether the server should host the dashboard or not\", is_bool)\n\ndash_path = Option(\"dashboard\", \"path\", \"/usr/share/inmanta/dashboard\",\n \"The path on the local file system where the dashboard can be found\")\n\ndash_realm = Option(\"dashboard\", \"realm\", \"inmanta\", \"The realm to use for keycloak authentication.\")\ndash_auth_url = Option(\"dashboard\", \"auth_url\", None, \"The auth url of the keycloak server to use.\")\ndash_client_id = Option(\"dashboard\", \"client_id\", None, \"The client id configured in keycloak for this application.\")\n# LCM support should move to a server extension\ndash_lcm_enable = Option(\"dashboard\", \"lcm\", False, \"Enable lifecycle manager in the dashboard\", is_bool)\n\n\ndef default_hangtime():\n \"\"\" server.agent-timeout*3/4 \"\"\"\n return str(int(agent_timeout.get() * 3 / 4))\n\n\nagent_hangtime = Option(\"server\", \"agent-hold\", default_hangtime,\n \"Maximal time the server will hold an agent heartbeat call\", is_time)\n", "sub_path": "src/inmanta/server/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 6403, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 32, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 33, "usage_type": "call"}, {"api_name": "inmanta.config.is_int", "line_number": 33, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 34, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 39, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 45, "usage_type": "call"}, {"api_name": "inmanta.config.is_bool", "line_number": 45, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 47, "usage_type": "call"}, {"api_name": "inmanta.config.is_str_opt", "line_number": 48, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 50, "usage_type": "call"}, {"api_name": "inmanta.config.is_str_opt", "line_number": 51, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 53, "usage_type": "call"}, {"api_name": "inmanta.config.is_str_opt", "line_number": 57, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 59, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 60, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 78, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 82, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 83, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 85, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 87, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 89, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 91, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 93, "usage_type": "call"}, {"api_name": "inmanta.config.is_int", "line_number": 95, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 97, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 101, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 102, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 104, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 105, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 107, "usage_type": "call"}, {"api_name": "inmanta.config.is_bool", "line_number": 110, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 116, "usage_type": "call"}, {"api_name": "inmanta.config.is_bool", "line_number": 116, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 118, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 121, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 122, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 123, "usage_type": "call"}, {"api_name": "inmanta.config.Option", "line_number": 125, "usage_type": "call"}, {"api_name": "inmanta.config.is_bool", "line_number": 125, "usage_type": "argument"}, {"api_name": "inmanta.config.Option", "line_number": 133, "usage_type": "call"}, {"api_name": "inmanta.config.is_time", "line_number": 134, "usage_type": "argument"}]} +{"seq_id": "483421862", "text": "\"\"\" rippleID.web.views\n\n This module defines the view functions for the \"web\" application within the\n RippleID system.\n\"\"\"\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, redirect\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\nfrom rippleID.shared.models import *\nfrom rippleID.web import sessions\n\n#############################################################################\n\ndef admin(request):\n \"\"\" Respond to the \"/admin\" URL.\n\n We let an administrator view and edit the list of rippleID identities.\n \"\"\"\n if not sessions.is_logged_in(request):\n return HttpResponseRedirect(\"/welcome\")\n\n cur_identity = sessions.get_identity(request)\n if not cur_identity.is_admin:\n return HttpResponseRedirect(\"/account\")\n if cur_identity.is_blocked:\n return HttpResponseRedirect(\"/welcome\")\n\n # Build the list of identities, and extract a single page of identities to\n # display, based on the \"page\" parameter.\n\n identities = Identity.objects.order_by(\"username\", \"full_name\")\n\n paginator = Paginator(identities, 10) # Show 10 identities at a time.\n\n if request.method == \"GET\":\n page_num = request.GET.get(\"page\", 0)\n elif request.method == \"POST\":\n page_num = request.POST.get(\"page\", 0)\n else:\n page_num = 0\n\n try:\n page = paginator.page(page_num)\n except (PageNotAnInteger, EmptyPage):\n page = paginator.page(1)\n\n # Prepare to show the page.\n\n if request.method == \"GET\":\n\n # We're displaying the page for the first time. Prepare our CGI\n # parameters.\n\n confirm = request.GET.get(\"confirm\")\n\n elif request.method == \"POST\":\n\n # Respond to the user clicking on one of our buttons. We start by\n # checking to see if the user clicked on the \"Done\" button.\n\n if request.POST.get(\"submit\") == \"Done\":\n return HttpResponseRedirect(\"/account\")\n\n # Did the user click on one of our \"Edit\" buttons? We redirect the\n # user to the \"admin\" page for the associated identity.\n\n for identity in identities:\n editValue = request.POST.get(\"edit-\" + str(identity.id))\n if editValue == \"Edit\":\n return redirect(\"/admin/edit/\" + str(identity.id))\n\n # Did the user click on one of our \"Delete\" buttons? We firstly\n # display the confirmation button beside the identity, and only delete\n # the identity if the user confirms.\n\n for identity in identities:\n deleteValue = request.POST.get(\"del-\" + str(identity.id))\n if deleteValue == \"Delete\":\n # The user clicked on the \"Delete\" button for the first time ->\n # redisplay the page with the confirmation buttons.\n return redirect(\"/admin?page=\" + str(page_num) +\n \"&confirm=\" + str(identity.id))\n elif deleteValue == \"Yes\":\n # The user click on our \"Yes\" confirmation button. Delete this\n # identity and redisplay the page.\n identity.delete()\n return redirect(\"/admin?page=\" + str(page_num))\n elif deleteValue == \"No\":\n # The user clicked on the \"No\" confirmation button. Redisplay\n # the page without the confirmation buttons.\n return redirect(\"/admin?page=\" + str(page_num))\n\n # If we get here, we're going to display the page again. Grab our\n # \"confirm\" CGI parameter so the page can display the appropriate\n # confirmation buttons.\n\n confirm = request.POST.get(\"confirm\")\n\n # Finally, display the page.\n\n banner_username = cur_identity.username\n\n banner_photo_url = None # initially.\n if cur_identity.default_photo_48x48 != None:\n if cur_identity.default_photo_48x48.name not in [\"\", None]:\n banner_photo_url = cur_identity.default_photo_48x48.url\n\n return render(request, \"web/admin.html\",\n {'banner_username' : banner_username,\n 'banner_photo_url' : banner_photo_url,\n 'page' : page,\n 'confirm' : confirm,\n 'cur_identity' : cur_identity})\n\n", "sub_path": "rippleID/web/views/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 4339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "rippleID.web.sessions.is_logged_in", "line_number": 20, "usage_type": "call"}, {"api_name": "rippleID.web.sessions", "line_number": 20, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 21, "usage_type": "call"}, {"api_name": "rippleID.web.sessions.get_identity", "line_number": 23, "usage_type": "call"}, {"api_name": "rippleID.web.sessions", "line_number": 23, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 25, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 34, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 45, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 45, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 92, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "454401633", "text": "#!/usr/bin/env python3\n\nIMAGE = '''\n\"${interface}
\n'''\n\nIFBLOCK = '''\n\\t
\n\\t\\t

${interface}

\n\\t\\t
\n\\t\\t\\t${images}\n\\t\\t
\n\\t
\n'''\n\ndef draw_traffic():\n from os import path\n from photon.util.locations import search_location\n from common import pinit\n from common.html import page\n\n photon, settings = pinit('draw_traffic', verbose=True)\n\n traffic = 'click to show or hide
'\n avail_if = photon.m(\n 'checking for available interfaces',\n cmdd=dict(\n cmd='sudo vnstat --iflist'\n )\n ).get('out', '')\n interfaces = settings['web']['traffic']['interfaces'] + [settings['fastd'][community]['interface'] for community in settings['fastd'].keys()]\n for interface in interfaces:\n if interface in avail_if:\n if not search_location(path.join(settings['web']['traffic']['dbdir'], interface)):\n photon.m(\n 'creating vnstat db for %s' %(interface),\n cmdd=dict(\n cmd='sudo vnstat -u -i %s' %(interface)\n ),\n verbose=True\n )\n\n images = ''\n for flag, itype in settings['web']['traffic']['types']:\n image = '%s-%s.png' %(interface, itype)\n photon.m(\n 'drawing %s graph for %s' %(itype, interface),\n cmdd=dict(\n cmd='vnstati -i %s -%s -o %s' %(interface, flag, path.join(settings['web']['output'], 'traffic', image))\n ),\n critical=False\n )\n\n images += photon.template_handler(\n IMAGE,\n fields=dict(\n interface=interface,\n itype=itype,\n image=image\n )\n ).sub\n\n traffic += photon.template_handler(\n IFBLOCK,\n fields=dict(\n interface=interface,\n images=images\n )\n ).sub\n\n page(photon, traffic, sub='traffic')\n\nif __name__ == '__main__':\n draw_traffic()\n", "sub_path": "draw_traffic_all.py", "file_name": "draw_traffic_all.py", "file_ext": "py", "file_size_in_byte": 2339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "photon.util.locations", "line_number": 22, "usage_type": "name"}, {"api_name": "common.pinit", "line_number": 22, "usage_type": "call"}, {"api_name": "photon.util.locations.m", "line_number": 25, "usage_type": "call"}, {"api_name": "photon.util.locations", "line_number": 25, "usage_type": "name"}, {"api_name": "photon.util.locations.search_location", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "name"}, {"api_name": "photon.util.locations.m", "line_number": 35, "usage_type": "call"}, {"api_name": "photon.util.locations", "line_number": 35, "usage_type": "name"}, {"api_name": "photon.util.locations.m", "line_number": 46, "usage_type": "call"}, {"api_name": "photon.util.locations", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "name"}, {"api_name": "photon.util.locations.template_handler", "line_number": 54, "usage_type": "call"}, {"api_name": "photon.util.locations", "line_number": 54, "usage_type": "name"}, {"api_name": "photon.util.locations.template_handler", "line_number": 63, "usage_type": "call"}, {"api_name": "photon.util.locations", "line_number": 63, "usage_type": "name"}, {"api_name": "common.html.page", "line_number": 71, "usage_type": "call"}, {"api_name": "photon.util.locations", "line_number": 71, "usage_type": "argument"}]} +{"seq_id": "325932543", "text": "from django.urls import path\n\nfrom zoomit_posts.views import (single_post, PostList, PostListByCategory,\n posts_sidebar, PostListSearch, PostListByTag,)\n\nurlpatterns = [\n path('posts', PostList.as_view(), name='posts'),\n path('posts/search', PostListSearch.as_view(), name='search'),\n path('post//', single_post),\n path('posts/category/', PostListByCategory.as_view()),\n path('posts/tag/', PostListByTag.as_view()),\n path('posts-sidebar', posts_sidebar, name='posts_sidebar'),\n]", "sub_path": "zoomit_posts/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostList.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostList", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostListSearch.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostListSearch", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "zoomit_posts.views.single_post", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostListByCategory.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostListByCategory", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostListByTag.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "zoomit_posts.views.PostListByTag", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "zoomit_posts.views.posts_sidebar", "line_number": 12, "usage_type": "argument"}]} +{"seq_id": "127817344", "text": "import os\nimport json\nimport threading\n\n\nclass FileManager:\n lock = threading.Lock()\n\n def __init__(self):\n self.status = 400\n self.content_type = 'text/plain'\n self.content = ''\n\n def get_all_files(self, dir_url):\n files = FileManager.files_list_in_dir(dir_url)\n output = {}\n f_list = []\n for f in files:\n f_list.append(f)\n output['dir_url'] = dir_url\n output['files'] = f_list\n self.status = 200\n self.content = json.dumps(output)\n self.content_type = 'application/json'\n\n def get_content(self, dir_url, file_name):\n if file_name.find('../') != -1:\n output = {}\n output['warning'] = 400\n output['message'] = 'Bad Request - Can not access to outer dictionary'\n self.status = 400\n self.content = json.dumps(output)\n self.content_type = 'application/json'\n else:\n files = FileManager.files_list_in_dir(dir_url)\n if file_name not in files:\n output = {}\n output['error'] = 404\n output['message'] = 'Not found'\n self.status = 404\n self.content = json.dumps(output)\n self.content_type = 'application/json'\n else:\n FileManager.lock.acquire()\n try:\n with open(dir_url + '/' + file_name, 'r', errors=\"ignore\") as file_obj:\n content = file_obj.read()\n finally:\n FileManager.lock.release()\n\n self.status = 200\n self.content = content\n self.content_type = FileManager.get_content_type(file_name)\n\n def post_content(self, dir_url, file_name, content):\n FileManager.lock.acquire()\n try:\n with open(dir_url + '/' + file_name, 'w') as f:\n f.write(content)\n finally:\n FileManager.lock.release()\n\n self.status = 200\n self.content = 'write in to ' + dir_url + '/' + file_name\n self.content_type = 'text/plain'\n\n @staticmethod\n def get_content_type(file_name):\n content_type = 'text/plain'\n suffix = os.path.splitext(file_name)[-1]\n if suffix == '.json':\n content_type = 'application/json'\n if suffix == '.html':\n content_type = 'text/html'\n if suffix == '.xml':\n content_type = 'text/xml'\n return content_type\n\n @staticmethod\n def files_list_in_dir(dir_url):\n files_lst = []\n\n for root, dirs, files in os.walk(dir_url):\n for file in files:\n temp = root + '/' + file\n files_lst.append(temp[(len(dir_url) + 1):])\n\n return files_lst\n", "sub_path": "A2/A2_V2/FileManager.py", "file_name": "FileManager.py", "file_ext": "py", "file_size_in_byte": 2804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "threading.Lock", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "295517546", "text": "# coding:utf-8\n\"\"\"\n Purpose: Compare CNN and Scatt + CNN models with different sizes of dataset\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\nfrom models.resnet18 import ResNet18, ScattResNet18\n\n\ndef train(model, device, train_loader, optimizer, epoch):\n model.train()\n nb_samples = 0\n for batch_idx, (data, target) in enumerate(train_loader):\n nb_samples += len(data)\n data, target = data.to(device), target.to(device)\n optimizer.zero_grad()\n output = model(data)\n loss = F.cross_entropy(output, target)\n loss.backward()\n optimizer.step()\n print('Train Epoch: {} [{}/{} ({:.0f}%)], Loss: {:.6f}'.format(\n epoch,\n nb_samples,\n len(train_loader.dataset),\n 100. * (batch_idx + 1) / len(train_loader),\n loss.item()), end='\\r')\n\n\ndef validate(model, device, test_loader):\n model.eval()\n test_loss = 0\n correct = 0\n with torch.no_grad():\n for data, target in test_loader:\n data, target = data.to(device), target.to(device)\n output = model(data)\n test_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss\n pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n test_loss /= len(test_loader.dataset)\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(\n test_loss,\n correct,\n len(test_loader.dataset),\n 100. * correct / len(test_loader.dataset)))\n return test_loss, 100. * correct / len(test_loader.dataset)\n\n\ndef main():\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n batch_size = 64\n epochs = 350\n max_images = 50000 # nbr of images available in CIFAR10\n nb_samples_train = 100 # nbr of images to pick from the training set (<= max_images)\n delta_epoch = 80\n\n train_loader = torch.utils.data.DataLoader(\n torch.utils.data.Subset(\n datasets.CIFAR10('../data/CIFAR10', train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(), # rescale between 0. and 1.\n # transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)) # CIFAR10\n ])),\n torch.multinomial(torch.ones(max_images), nb_samples_train)), # pick subset of training set\n batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n\n test_loader = torch.utils.data.DataLoader(\n datasets.CIFAR10('../data/CIFAR10', train=False,\n transform=transforms.Compose([\n transforms.ToTensor(), # rescale between 0. and 1.\n # transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)) # CIFAR10\n ])),\n batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n\n for model_class in [ResNet18, ScattResNet18]:\n model = model_class().to(device)\n\n optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0005)\n scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=delta_epoch, gamma=0.1)\n\n best_accuracy = 0.\n best_epoch = None\n for epoch in range(1, epochs + 1):\n scheduler.step()\n train(model, device, train_loader, optimizer, epoch)\n test_loss, accuracy = validate(model, device, test_loader)\n if best_accuracy < accuracy:\n best_accuracy = accuracy\n best_epoch = epoch\n else:\n if (epoch - best_epoch) > delta_epoch * 1.5:\n print(\"Early stopping!\")\n break\n\n print(\"/!\\\\ Model {} accuracy {}%\".format(model.__class__.__name__, best_accuracy))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Python/different_dataset_sizes/benchs_cifar10.py", "file_name": "benchs_cifar10.py", "file_ext": "py", "file_size_in_byte": 4088, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.nn.functional.cross_entropy", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Subset", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 64, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 64, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 65, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 65, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.multinomial", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 73, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 73, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 74, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 74, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 75, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 75, "usage_type": "name"}, {"api_name": "models.resnet18.ResNet18", "line_number": 80, "usage_type": "name"}, {"api_name": "models.resnet18.ScattResNet18", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "452157283", "text": "# Simple enough, just import everything from tkinter.\r\nfrom tkinter import *\r\n\r\nimport tkinter as tk\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox\r\nfrom tkinter import PhotoImage\r\nfrom tkinter import StringVar\r\n\r\nimport sys\r\nimport os\r\n\r\nimport json\r\n\r\n# Here, we are creating our class, Window, and inheriting from the Frame\r\n# class. Frame is a class from the tkinter module. (see Lib/tkinter/__init__)\r\nclass Window(Frame):\r\n\r\n # Define settings upon initialization. Here you can specify\r\n def __init__(self, master=None):\r\n\r\n #reference to the master widget, which is the tk window \r\n self.master = master\r\n \r\n # parameters that you want to send through the Frame class. \r\n Frame.__init__(self, self.master)\r\n \r\n #with that, we want to then run init_window, which doesn't yet exist\r\n self.init_window()\r\n\r\n #Creation of init_window\r\n def init_window(self):\r\n\r\n # changing the title of our master widget \r\n self.master.title(\"INRE\")\r\n\r\n # allowing the widget to take the full space of the root window\r\n self.grid()\r\n\r\n # creating a menu instance\r\n menu = Menu(self.master)\r\n self.master.config(menu=menu)\r\n\r\n # create the file object)\r\n file = Menu(menu)\r\n\r\n # adds a command to the menu option, calling it exit, and the\r\n # command it runs on event is client_exit\r\n file.add_command(label=\"Exit\", command=self.client_exit)\r\n\r\n # adds a command to the menu option, calling it exit, and the\r\n # command it runs on event is client_exit\r\n\r\n #added \"file\" to our menu\r\n menu.add_cascade(label=\"File\", menu=file)\r\n\r\n\r\n # create the file object)\r\n edit = Menu(menu)\r\n\r\n # adds a command to the menu option, calling it exit, and the\r\n # command it runs on event is client_exit\r\n # edit.add_command(label=\"Show Img\", command=self.showImg)\r\n edit.add_command(label=\"Serviços\", command=self.showServicos)\r\n\r\n #added \"file\" to our menu\r\n menu.add_cascade(label=\"INRE\", menu=edit)\r\n\r\n # create the file object)\r\n recic = Menu(menu)\r\n\r\n # adds a command to the menu option, calling it exit, and the\r\n # command it runs on event is client_exit\r\n # edit.add_command(label=\"Show Img\", command=self.showImg)\r\n recic.add_command(label=\"Cliente\", command=self.showPreCliente)\r\n\r\n #added \"file\" to our menu\r\n menu.add_cascade(label=\"Pesquisa Interna\", menu=recic)\r\n\r\n\r\n def client_exit(self):\r\n exit()\r\n\r\n def showServicos(self):\r\n\r\n self.master.destroy()\r\n\r\n self.__init__(master=None)\r\n\r\n label_1 = Label(self.master, text=\"INRE SERVIÇOS\",font=fonteC)\r\n label_2 = Label(self.master, text=(\"Bem vindo, deseja executar qual dos serviços\"),font=fonteC)\r\n button_2 = Button(self.master, width=35,pady = 10,text=\"Cadastrar cliente\",command=lambda:self.showCadastro())\r\n button_3 = Button(self.master, width=35,pady = 10,text=\"Orçamento Recic-Lar\",command=lambda:self.showReciclar())\r\n button_4 = Button(self.master, width=35,pady = 10,text=\"Orçamento Retirada PF\",command=lambda:fef())\r\n button_5 = Button(self.master, width=35,pady = 10,text=\"Orçamento Retirada PJ\",command=lambda:fe())\r\n label_1.grid(row=0,column=0,pady=10,padx=25)\r\n label_2.grid(row=1,column=0,pady=10,padx=25)\r\n button_2.grid()\r\n button_3.grid()\r\n button_4.grid()\r\n button_5.grid()\r\n\r\n def showCadastro(self):\r\n\r\n self.master.destroy()\r\n\r\n self.__init__(master=None)\r\n\r\n self.showCode()\r\n\r\n Label(self.master,text=\"Cadastro de cliente para o banco de dados\",font=fonteC).grid(row=0,column=0)\r\n\r\n Label(self.master,text=code).grid(row=3,column=2)\r\n\r\n self.code = code\r\n\r\n self.nome=Entry(self.master)\r\n self.nome.grid(row=4, column=2)\r\n\r\n self.cpf=Entry(self.master)\r\n self.cpf.grid(row=5, column=2)\r\n\r\n self.rg=Entry(self.master)\r\n self.rg.grid(row=6, column=2)\r\n\r\n self.mail=Entry(self.master)\r\n self.mail.grid(row=7, column=2)\r\n\r\n self.tel=Entry(self.master)\r\n self.tel.grid(row=8, column=2)\r\n\r\n self.end=Entry(self.master)\r\n self.end.grid(row=9, column=2)\r\n\r\n self.cep=Entry(self.master)\r\n self.cep.grid(row=10, column=2)\r\n\r\n self.bairro = Entry(self.master)\r\n self.bairro.grid(row=11,column=2)\r\n\r\n self.cid=Entry(self.master)\r\n self.cid.grid(row=12, column=2)\r\n\r\n self.uf = Entry(self.master)\r\n self.uf.grid(row=13,column=2)\r\n\r\n self.usuario =Entry(self.master)\r\n self.usuario.grid(row=14,column=2)\r\n\r\n self.contact =Entry(self.master)\r\n self.contact.grid(row=15,column=2)\r\n\r\n frase1 = Label(self.master, text=\"Dados do cliente\")\r\n frase1.grid(row=2 , column=2)\r\n\r\n frase0 = Label(self.master,text=\"Identificação - Código Cliente\")\r\n frase0.grid(row=3,column=0)\r\n\r\n frase2 = Label(self.master, text=\"Nome do cliente\")\r\n frase2.grid(row=4, column=0)\r\n\r\n frase4 = Label(self.master, text=\"CPF\")\r\n frase4.grid(row=5 , column=0)\r\n\r\n frase3 = Label(self.master, text=\"RG\")\r\n frase3.grid(row=6 , column=0)\r\n\r\n frase5 = Label(self.master, text=\"E-mail\")\r\n frase5.grid(row=7 , column=0)\r\n\r\n frase10 = Label(self.master, text=\"Contato Telefone/Celular\")\r\n frase10.grid(row=8 , column=0)\r\n\r\n frase20 = Label(self.master, text=\"Endereço\")\r\n frase20.grid(row=9, column=0)\r\n\r\n frase30 = Label(self.master, text=\"CEP\")\r\n frase30.grid(row=10 , column=0)\r\n\r\n frase40 = Label(self.master, text=\"Bairro\")\r\n frase40.grid(row=11 , column=0)\r\n\r\n frase60 = Label(self.master,text=\"Cidade\")\r\n frase60.grid(row=12,column=0)\r\n\r\n frase50 = Label(self.master, text=\"UF\")\r\n frase50.grid(row=13 , column=0)\r\n\r\n fraseU = Label(self.master, text=\"Usuário INRE\")\r\n fraseU.grid(row=14,column=0)\r\n\r\n frasec = Label(self.master, text=\"Forma de contato\")\r\n frasec.grid(row=15,column=0)\r\n\r\n butt = Button(self.master,text=\"Salvar dados\",command=self.showGet)\r\n butt.grid(row=16, column=2)\r\n\r\n butt_2 = Button(self.master,text=\"Concluir e voltar a tela dos serviços\",command=self.showServicos)\r\n butt_2.grid(row=17,column=0)\r\n\r\n def showGet(self):\r\n\r\n salvos = Label(self.master,text=\"Os dados do cliente foram salvos com sucesso!\",font=fonteA).grid(row=17,column=2)\r\n\r\n self.nome.real = self.nome.get()\r\n self.rg.real = self.rg.get()\r\n self.cpf.real = self.cpf.get()\r\n self.mail.real = self.mail.get()\r\n self.tel.real = self.tel.get()\r\n self.end.real = self.end.get()\r\n self.cep.real = self.cep.get()\r\n self.bairro.real = self.bairro.get()\r\n self.cid.real = self.cid.get()\r\n self.uf.real = self.uf.get()\r\n self.usuario.real = self.usuario.get()\r\n self.contact.real = self.contact.get()\r\n\r\n self.saveCliente()\r\n\r\n def saveCliente(self):\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n data[\"Clientes\"].append({\"Code\":str(self.code),\"Nome\": str(self.nome.real),\"RG\": str(self.rg.real),\"CPF\": str(self.cpf.real),\"E-mail\": str(self.mail.real),\\\r\n \"Telefone de contato\": str(self.tel.real),\"Endereco\": str(self.end.real),\"CEP\": str(self.cep.real),\"Bairro\": str(self.bairro.real),\"Cidade\": str(self.cid.real),\\\r\n \"UF\": str(self.uf.real),\"Usuario INRE\": str(self.usuario.real),\"Forma de contato\":str(self.contact.real), \"Pedidos\":[]\r\n })\r\n\r\n with open('arquivo.json', 'w') as C:\r\n json.dump(data, C)\r\n\r\n def showReciclar(self):\r\n\r\n self.master.destroy()\r\n\r\n self.__init__(master=None)\r\n\r\n Label(self.master,text=\"Recic-Lar\",font=fonteD).grid()\r\n\r\n self.scroll = ttk.Notebook(self.master)\r\n\r\n self.scroll_1 = ttk.Frame(self.scroll)\r\n self.scroll_2 = ttk.Frame(self.scroll)\r\n self.scroll_3 = ttk.Frame(self.scroll)\r\n self.scroll_4 = ttk.Frame(self.scroll)\r\n self.scroll_5 = ttk.Frame(self.scroll)\r\n self.scroll_6 = ttk.Frame(self.scroll)\r\n\r\n self.scroll.add(self.scroll_1, text='Charge')\r\n\r\n self.defineCliente()\r\n\r\n self.scroll.add(self.scroll_2, text='Orçamento')\r\n\r\n self.createOrcamento()\r\n\r\n self.scroll.add(self.scroll_3, text='Mudança')\r\n\r\n self.showMudanca()\r\n\r\n self.scroll.add(self.scroll_4, text='Endereço serviço')\r\n\r\n self.showEndereco()\r\n\r\n self.scroll.add(self.scroll_5, text='Dados cliente')\r\n\r\n Button(self.scroll_5, text=\"Mostrar cliente\",command=lambda:self.showClienteRecic()).grid(row=0,column=2)\r\n\r\n self.scroll.add(self.scroll_6, text=\"Finalização\")\r\n \r\n self.scroll.grid()\r\n\r\n def defineCliente(self):\r\n\r\n Label(self.scroll_1,text=\"Digite o código do cliente para iniciar o orçamento\",font=fonteC).grid(row=0,column=0)\r\n\r\n self.pre = Entry(self.scroll_1)\r\n self.pre.grid(row=1,column=0)\r\n\r\n Button(self.scroll_1,text=\"Verificar se existe no registro de clientes\",command=self.verifyClienteRecic).grid()\r\n\r\n def verifyClienteRecic(self):\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n self.pre.real = self.pre.get()\r\n\r\n self.searchClienteRecic()\r\n\r\n def searchClienteRecic(self):\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n for cliente in data[\"Clientes\"]:\r\n if cliente['Code'] == self.pre.real:\r\n\r\n self.chargeCliente(self.pre.real)\r\n\r\n Label(self.scroll_1,text=\"Cliente carregado prossiga com o orçamento!\",font=fonteC).grid(row=5,column=0)\r\n\r\n else:\r\n\r\n Label(self.scroll_1,text=\"Cliente não registrado\",font=fonteC)\r\n\r\n def createOrcamento(self):\r\n\r\n v = tk.IntVar()\r\n\r\n Label(self.scroll_2, text=\"Esvaziamento\", padx = 20,pady=20).grid(row=0,column=0)\r\n Radiobutton(self.scroll_2,text=\"Com movéis grandes\",padx = 20,pady=20,variable=v,value=1).grid(row=0,column=1)\r\n Radiobutton(self.scroll_2,text=\"Sem movéis grandes\",padx = 20,pady=20,variable=v,value=2).grid(row=0,column=2)\r\n\r\n scrollbar = Scrollbar(self.scroll_2)\r\n scrollbar.grid(row=3,column=1)\r\n\r\n Label(self.scroll_2,text=\"Lista de equipamentos\").grid(row=2,column=0)\r\n\r\n self.mylist = Listbox(self.scroll_2,yscrollcommand=scrollbar.set,width=40)\r\n self.mylist.insert(1, \"Equipamentos M3\")\r\n\r\n Button(self.scroll_2,text=\"ADD\",command=self.CurSelet).grid(row=5,column=1)\r\n\r\n self.mylist.grid(row=3,column=0)\r\n scrollbar.config(command=self.mylist.yview)\r\n\r\n self.mylist1 = Listbox(self.scroll_2,yscrollcommand=scrollbar.set,width=40)\r\n self.mylist1.grid(row=3,column=2)\r\n Label(self.scroll_2,text=\"Lista de equipamentos adicionados\").grid(row=2,column=2)\r\n\r\n self.qnt = Entry(self.scroll_2)\r\n self.qnt.grid(row=4,column=1)\r\n\r\n #def addEquipamento(self):\r\n\r\n\r\n def CurSelet(self):\r\n\r\n self.getQnt()\r\n\r\n value = str(self.mylist.get(self.mylist.curselection()))\r\n for i in range(0,int(self.qnt.new)):\r\n self.mylist1.insert(END,value)\r\n\r\n def getQnt(self):\r\n\r\n self.qnt.new = self.qnt.get()\r\n \r\n def showMudanca(self):\r\n\r\n Label(self.scroll_3,text=\"Data da mudança\").grid()\r\n\r\n self.data = Entry(self.scroll_3)\r\n self.data.grid()\r\n\r\n Label(self.scroll_3,text=\"Motivo da mudança\").grid()\r\n\r\n self.motivo = Entry(self.scroll_3)\r\n self.motivo.grid()\r\n\r\n def showEndereco(self):\r\n \r\n Label(self.scroll_4,text=\"Endereço do serviço\").grid()\r\n\r\n Button(self.scroll_4,text=\"Mesmo do cadastro\",command=lambda:self.showEnderecoRecic()).grid()\r\n\r\n Button(self.scroll_4,text=\"Outro endereço\",command=lambda:self.createEndereco()).grid()\r\n\r\n def showEnderecoRecic(self):\r\n\r\n Label(self.scroll_4,text=self.end,font=fonteB).grid(row=3,column=1)\r\n\r\n Label(self.scroll_4,text=self.cep,font=fonteB).grid(row=4,column=1)\r\n\r\n Label(self.scroll_4,text=self.bairro,font=fonteB).grid(row=5,column=1)\r\n\r\n Label(self.scroll_4,text=self.cid,font=fonteB).grid(row=6,column=1)\r\n\r\n Label(self.scroll_4,text=self.uf,font=fonteB).grid(row=7,column=1)\r\n\r\n frase20 = Label(self.scroll_4, text=\"Endereço\")\r\n frase20.grid(row=3,column=0)\r\n\r\n frase30 = Label(self.scroll_4, text=\"CEP\")\r\n frase30.grid(row=4,column=0)\r\n\r\n frase40 = Label(self.scroll_4, text=\"Bairro\")\r\n frase40.grid(row=5,column=0)\r\n\r\n frase60 = Label(self.scroll_4,text=\"Cidade\")\r\n frase60.grid(row=6,column=0)\r\n\r\n frase50 = Label(self.scroll_4, text=\"UF\")\r\n frase50.grid(row=7,column=0)\r\n\r\n def createEndereco(self):\r\n\r\n self.end_recic=Entry(self.scroll_4)\r\n self.end_recic.grid(row=3, column=1)\r\n\r\n self.cep_recic=Entry(self.scroll_4)\r\n self.cep_recic.grid(row=4, column=1)\r\n\r\n self.bairro_recic = Entry(self.scroll_4)\r\n self.bairro_recic.grid(row=5,column=1)\r\n\r\n self.cid_recic=Entry(self.scroll_4)\r\n self.cid_recic.grid(row=6, column=1)\r\n\r\n self.uf_recic = Entry(self.scroll_4)\r\n self.uf_recic.grid(row=7,column=1)\r\n\r\n frase20 = Label(self.scroll_4, text=\"Endereço\")\r\n frase20.grid(row=3, column=0)\r\n\r\n frase30 = Label(self.scroll_4, text=\"CEP\")\r\n frase30.grid(row=4 , column=0)\r\n\r\n frase40 = Label(self.scroll_4, text=\"Bairro\")\r\n frase40.grid(row=5 , column=0)\r\n\r\n frase60 = Label(self.scroll_4,text=\"Cidade\")\r\n frase60.grid(row=6,column=0)\r\n\r\n frase50 = Label(self.scroll_4, text=\"UF\")\r\n frase50.grid(row=7 , column=0)\r\n\r\n def showClienteRecic(self):\r\n\r\n Label(self.scroll_5,text=\"Dados do cliente para orçamento\",font=fonteC).grid(row=0,column=0)\r\n\r\n Label(self.scroll_5,text=self.code,font=fonteB).grid(row=3, column=2)\r\n\r\n Label(self.scroll_5,text=self.nome,font=fonteB).grid(row=4, column=2)\r\n\r\n Label(self.scroll_5,text=self.cpf,font=fonteB).grid(row=5, column=2)\r\n\r\n Label(self.scroll_5,text=self.rg,font=fonteB).grid(row=6, column=2)\r\n\r\n Label(self.scroll_5,text=self.mail,font=fonteB).grid(row=7, column=2)\r\n\r\n Label(self.scroll_5,text=self.tel,font=fonteB).grid(row=8, column=2)\r\n\r\n Label(self.scroll_5,text=self.end,font=fonteB).grid(row=9, column=2)\r\n\r\n Label(self.scroll_5,text=self.cep,font=fonteB).grid(row=10, column=2)\r\n\r\n Label(self.scroll_5,text=self.bairro,font=fonteB).grid(row=11, column=2)\r\n\r\n Label(self.scroll_5,text=self.cid,font=fonteB).grid(row=12, column=2)\r\n\r\n Label(self.scroll_5,text=self.uf,font=fonteB).grid(row=13, column=2)\r\n\r\n Label(self.scroll_5,text=self.usuario,font=fonteB).grid(row=14, column=2)\r\n\r\n Label(self.scroll_5,text=self.contact,font=fonteB).grid(row=15, column=2)\r\n\r\n frase1 = Label(self.scroll_5, text=\"Dados do cliente\")\r\n frase1.grid(row=2 , column=2)\r\n\r\n frase0 = Label(self.scroll_5,text=\"Identificação - Código Cliente\")\r\n frase0.grid(row=3,column=0)\r\n\r\n frase2 = Label(self.scroll_5, text=\"Nome de usuário\")\r\n frase2.grid(row=4, column=0)\r\n\r\n frase4 = Label(self.scroll_5, text=\"CPF\")\r\n frase4.grid(row=5 , column=0)\r\n\r\n frase4 = Label(self.scroll_5, text=\"RG\")\r\n frase4.grid(row=6 , column=0)\r\n\r\n frase5 = Label(self.scroll_5, text=\"E-mail\")\r\n frase5.grid(row=7 , column=0)\r\n\r\n frase10 = Label(self.scroll_5, text=\"Contato Telefone/Celular\")\r\n frase10.grid(row=8 , column=0)\r\n\r\n frase20 = Label(self.scroll_5, text=\"Endereço\")\r\n frase20.grid(row=9, column=0)\r\n\r\n frase30 = Label(self.scroll_5, text=\"CEP\")\r\n frase30.grid(row=10 , column=0)\r\n\r\n frase40 = Label(self.scroll_5, text=\"Bairro\")\r\n frase40.grid(row=11 , column=0)\r\n\r\n frase60 = Label(self.scroll_5,text=\"Cidade\")\r\n frase60.grid(row=12,column=0)\r\n\r\n frase50 = Label(self.scroll_5, text=\"UF\")\r\n frase50.grid(row=13,column=0)\r\n\r\n fraseU = Label(self.scroll_5, text=\"Usuário INRE\")\r\n fraseU.grid(row=14,column=0)\r\n\r\n fraseU = Label(self.scroll_5, text=\"Forma de contato\")\r\n fraseU.grid(row=15,column=0)\r\n\r\n def showPreCliente(self):\r\n\r\n self.master.destroy()\r\n\r\n self.__init__(master=None)\r\n\r\n self.showCode()\r\n\r\n Label(self.master,text=\"Digite o código do cliente para ver seus dados\",font=fonteC).grid(row=0,column=0)\r\n\r\n self.pre = Entry(self.master)\r\n self.pre.grid(row=1,column=0)\r\n\r\n Button(self.master,text=\"Verificar se existe no registro de clientes\",command=self.verifyCliente).grid()\r\n\r\n def showImg(self):\r\n load = Image.open(\"a.png\")\r\n render = ImageTk.PhotoImage(load)\r\n\r\n # labels can be text or images\r\n img = Label(self, image=render)\r\n img.image = render\r\n img.place(x=0, y=0)\r\n\r\n def verifyCliente(self):\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n self.pre.real = self.pre.get()\r\n\r\n self.searchCliente()\r\n\r\n def searchCliente(self):\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n for cliente in data[\"Clientes\"]:\r\n if cliente['Code'] == self.pre.real:\r\n\r\n self.chargeCliente(self.pre.real)\r\n\r\n Label(self.master,text=\"Cliente registrado!\",font=fonteC).grid(row=5,column=0)\r\n\r\n Button(self.master,text=\"Ver dados\",command=self.showCliente,font=fonteC).grid(row=6,column=0)\r\n\r\n else:\r\n\r\n Label(self.master,text=\"Cliente não registrado\",font=fonteC)\r\n\r\n def showBut(self):\r\n button_1 = Button(self, text=\"Cadastro Cliente\") \r\n button_1.grid()\r\n\r\n def showCliente(self):\r\n\r\n self.master.destroy()\r\n\r\n self.__init__(master=None)\r\n\r\n Label(self.master,text=\"Dados de cliente\",font=fonteC).grid(row=0,column=0)\r\n\r\n Label(self.master,text=self.code,font=fonteB).grid(row=3, column=2)\r\n\r\n Label(self.master,text=self.nome,font=fonteB).grid(row=4, column=2)\r\n\r\n Label(self.master,text=self.cpf,font=fonteB).grid(row=5, column=2)\r\n\r\n Label(self.master,text=self.rg,font=fonteB).grid(row=6, column=2)\r\n\r\n Label(self.master,text=self.mail,font=fonteB).grid(row=7, column=2)\r\n\r\n Label(self.master,text=self.tel,font=fonteB).grid(row=8, column=2)\r\n\r\n Label(self.master,text=self.end,font=fonteB).grid(row=9, column=2)\r\n\r\n Label(self.master,text=self.cep,font=fonteB).grid(row=10, column=2)\r\n\r\n Label(self.master,text=self.bairro,font=fonteB).grid(row=11, column=2)\r\n\r\n Label(self.master,text=self.cid,font=fonteB).grid(row=12, column=2)\r\n\r\n Label(self.master,text=self.uf,font=fonteB).grid(row=13, column=2)\r\n\r\n Label(self.master,text=self.usuario,font=fonteB).grid(row=14, column=2)\r\n\r\n Label(self.master,text=self.contact,font=fonteB).grid(row=15, column=2)\r\n\r\n frase1 = Label(self.master, text=\"Dados do cliente\")\r\n frase1.grid(row=2 , column=2)\r\n\r\n frase0 = Label(self.master,text=\"Identificação - Código Cliente\")\r\n frase0.grid(row=3,column=0)\r\n\r\n frase2 = Label(self.master, text=\"Nome de usuário\")\r\n frase2.grid(row=4, column=0)\r\n\r\n frase4 = Label(self.master, text=\"CPF\")\r\n frase4.grid(row=5 , column=0)\r\n\r\n frase4 = Label(self.master, text=\"RG\")\r\n frase4.grid(row=6 , column=0)\r\n\r\n frase5 = Label(self.master, text=\"E-mail\")\r\n frase5.grid(row=7 , column=0)\r\n\r\n frase10 = Label(self.master, text=\"Contato Telefone/Celular\")\r\n frase10.grid(row=8 , column=0)\r\n\r\n frase20 = Label(self.master, text=\"Endereço\")\r\n frase20.grid(row=9, column=0)\r\n\r\n frase30 = Label(self.master, text=\"CEP\")\r\n frase30.grid(row=10 , column=0)\r\n\r\n frase40 = Label(self.master, text=\"Bairro\")\r\n frase40.grid(row=11 , column=0)\r\n\r\n frase60 = Label(self.master,text=\"Cidade\")\r\n frase60.grid(row=12,column=0)\r\n\r\n frase50 = Label(self.master, text=\"UF\")\r\n frase50.grid(row=13,column=0)\r\n\r\n fraseU = Label(self.master, text=\"Usuário INRE\")\r\n fraseU.grid(row=14,column=0)\r\n\r\n fraseU = Label(self.master, text=\"Forma de contato\")\r\n fraseU.grid(row=15,column=0)\r\n\r\n def showClienteScroll(self):\r\n\r\n Label(self.scroll_5,text=self.code,font=fonteB).grid(row=3, column=2)\r\n\r\n Label(self.scroll_5,text=self.nome,font=fonteB).grid(row=4, column=2)\r\n\r\n Label(self.scroll_5,text=self.cpf,font=fonteB).grid(row=5, column=2)\r\n\r\n Label(self.scroll_5,text=self.rg,font=fonteB).grid(row=6, column=2)\r\n\r\n Label(self.scroll_5,text=self.mail,font=fonteB).grid(row=7, column=2)\r\n\r\n Label(self.scroll_5,text=self.tel,font=fonteB).grid(row=8, column=2)\r\n\r\n Label(self.scroll_5,text=self.end,font=fonteB).grid(row=9, column=2)\r\n\r\n Label(self.scroll_5,text=self.cep,font=fonteB).grid(row=10, column=2)\r\n\r\n Label(self.scroll_5,text=self.bairro,font=fonteB).grid(row=11, column=2)\r\n\r\n Label(self.scroll_5,text=self.cid,font=fonteB).grid(row=12, column=2)\r\n\r\n Label(self.scroll_5,text=self.uf,font=fonteB).grid(row=13, column=2)\r\n\r\n Label(self.scroll_5,text=self.usuario,font=fonteB).grid(row=14, column=2)\r\n\r\n frase1 = Label(self.scroll_5, text=\"Dados do cliente\")\r\n frase1.grid(row=2 , column=2)\r\n\r\n frase0 = Label(self.scroll_5,text=\"Identificação - Código Cliente\")\r\n frase0.grid(row=3,column=0)\r\n\r\n frase2 = Label(self.scroll_5, text=\"Nome de usuário\")\r\n frase2.grid(row=4, column=0)\r\n\r\n frase4 = Label(self.scroll_5, text=\"CPF\")\r\n frase4.grid(row=5 , column=0)\r\n\r\n frase4 = Label(self.scroll_5, text=\"RG\")\r\n frase4.grid(row=6 , column=0)\r\n\r\n frase5 = Label(self.scroll_5, text=\"E-mail\")\r\n frase5.grid(row=7 , column=0)\r\n\r\n frase10 = Label(self.scroll_5, text=\"Contato Telefone/Celular\")\r\n frase10.grid(row=8 , column=0)\r\n\r\n frase20 = Label(self.scroll_5, text=\"Endereço\")\r\n frase20.grid(row=9, column=0)\r\n\r\n frase30 = Label(self.scroll_5, text=\"CEP\")\r\n frase30.grid(row=10 , column=0)\r\n\r\n frase40 = Label(self.scroll_5, text=\"Bairro\")\r\n frase40.grid(row=11 , column=0)\r\n\r\n frase60 = Label(self.scroll_5,text=\"Cidade\")\r\n frase60.grid(row=12,column=0)\r\n\r\n frase50 = Label(self.scroll_5, text=\"UF\")\r\n frase50.grid(row=13,column=0)\r\n\r\n fraseU = Label(self.scroll_5, text=\"Usuário INRE\")\r\n fraseU.grid(row=14,column=0)\r\n\r\n def chargeCliente(self,cod):\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n for cliente in data[\"Clientes\"]:\r\n if cliente[\"Code\"] == cod:\r\n self.code = cod\r\n self.nome = cliente[\"Nome\"]\r\n self.rg = cliente[\"RG\"]\r\n self.cpf = cliente[\"CPF\"]\r\n self.tel = cliente[\"Telefone de contato\"]\r\n self.end = cliente[\"Endereco\"]\r\n self.mail = cliente[\"E-mail\"]\r\n self.cep = cliente[\"CEP\"]\r\n self.bairro = cliente[\"Bairro\"]\r\n self.cid = cliente[\"Cidade\"]\r\n self.uf = cliente[\"UF\"]\r\n self.usuario = cliente[\"Usuario INRE\"]\r\n self.contact = cliente[\"Forma de contato\"]\r\n\r\n def showCode(self):\r\n\r\n global code\r\n\r\n with open('arquivo.json') as C:\r\n data = json.load(C)\r\n\r\n co = len(data[\"Clientes\"])+1\r\n\r\n code = \"{0}/2018\".format(co)\r\n\r\n \r\n# Fontes\r\n\r\nfonteA = (\"Verdana\", 8)\r\nfonteB = (\"Verdana\", 10)\r\nfonteC = (\"Verdana\", 12)\r\nfonteD = (\"Verdana\", 16)\r\nfonteE = (\"Verdana\", 20)\r\n\r\n# root window created. Here, that would be the only window, but\r\n# you can later have windows within windows.\r\nroot = Tk()\r\n\r\nroot.geometry(\"600x400\")\r\n\r\n#creation of an instance\r\napp = Window(root)\r\n\r\n\r\n#mainloop \r\nroot.mainloop()", "sub_path": "Teste9.py", "file_name": "Teste9.py", "file_ext": "py", "file_size_in_byte": 24591, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "json.load", "line_number": 223, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 231, "usage_type": "call"}, {"api_name": "tkinter.ttk.Notebook", "line_number": 241, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 241, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 243, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 243, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 244, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 244, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 245, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 245, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 246, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 246, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 247, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 247, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 248, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 248, "usage_type": "name"}, {"api_name": "json.load", "line_number": 286, "usage_type": "call"}, {"api_name": "json.load", "line_number": 295, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 310, "usage_type": "call"}, {"api_name": "json.load", "line_number": 529, "usage_type": "call"}, {"api_name": "json.load", "line_number": 538, "usage_type": "call"}, {"api_name": "json.load", "line_number": 701, "usage_type": "call"}, {"api_name": "json.load", "line_number": 724, "usage_type": "call"}]} +{"seq_id": "323851836", "text": "# Copyright 2014 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\"\"\"Module to provide implicit behavior based on enviroment.\n\nActs as a mutable namespace to allow the datastore package to\nimply the current dataset ID and connection from the enviroment.\n\"\"\"\n\nimport socket\n\nfrom six.moves.http_client import HTTPConnection # pylint: disable=F0401\n\ntry:\n from google.appengine.api import app_identity\nexcept ImportError:\n app_identity = None\n\n\nDATASET_ID = None\n\"\"\"Module global to allow persistent implied dataset ID from enviroment.\"\"\"\n\nCONNECTION = None\n\"\"\"Module global to allow persistent implied connection from enviroment.\"\"\"\n\n\ndef app_engine_id():\n \"\"\"Gets the App Engine application ID if it can be inferred.\n\n :rtype: string or ``NoneType``\n :returns: App Engine application ID if running in App Engine,\n else ``None``.\n \"\"\"\n if app_identity is None:\n return None\n\n return app_identity.get_application_id()\n\n\ndef compute_engine_id():\n \"\"\"Gets the Compute Engine project ID if it can be inferred.\n\n Uses 169.254.169.254 for the metadata server to avoid request\n latency from DNS lookup.\n\n See https://cloud.google.com/compute/docs/metadata#metadataserver\n for information about this IP address. (This IP is also used for\n Amazon EC2 instances, so the metadata flavor is crucial.)\n\n See https://github.com/google/oauth2client/issues/93 for context about\n DNS latency.\n\n :rtype: string or ``NoneType``\n :returns: Compute Engine project ID if the metadata service is available,\n else ``None``.\n \"\"\"\n host = '169.254.169.254'\n uri_path = '/computeMetadata/v1/project/project-id'\n headers = {'Metadata-Flavor': 'Google'}\n connection = HTTPConnection(host, timeout=0.1)\n\n try:\n connection.request('GET', uri_path, headers=headers)\n response = connection.getresponse()\n if response.status == 200:\n return response.read()\n except socket.error: # socket.timeout or socket.error(64, 'Host is down')\n pass\n finally:\n connection.close()\n", "sub_path": "gcloud/datastore/_implicit_environ.py", "file_name": "_implicit_environ.py", "file_ext": "py", "file_size_in_byte": 2623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "google.appengine.api.app_identity", "line_number": 28, "usage_type": "name"}, {"api_name": "google.appengine.api.app_identity", "line_number": 45, "usage_type": "name"}, {"api_name": "google.appengine.api.app_identity.get_application_id", "line_number": 48, "usage_type": "call"}, {"api_name": "google.appengine.api.app_identity", "line_number": 48, "usage_type": "name"}, {"api_name": "six.moves.http_client.HTTPConnection", "line_number": 71, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 78, "usage_type": "attribute"}]} +{"seq_id": "72916437", "text": "from collections import Counter\n\ndef is_rect(pts, a, b):\n c1 = Counter(p[0] for p in pts)\n c1[a] += 1\n c2 = Counter(p[1] for p in pts)\n c2[b] += 1\n return len(c1) == 2 and len(c2) == 2 and all(y == 2 for x, y in c1.most_common()) and all(y == 2 for x, y in c2.most_common())\n\ndef main():\n pts = []\n for _ in range(3):\n a, b = map(int, input().split())\n pts.append((a, b))\n\n for xi in range(3):\n for yi in range(3):\n if is_rect(pts, pts[xi][0], pts[yi][1]):\n print(pts[xi][0], pts[yi][1])\n return\n\nmain()\n", "sub_path": "kattis/cetvrta.py", "file_name": "cetvrta.py", "file_ext": "py", "file_size_in_byte": 590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "collections.Counter", "line_number": 4, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "337878984", "text": "\"\"\"\nFedExSoapService contains classes used to communicate SOAP to FedEx Web Services.\n\"\"\"\nimport os, sys, socket\nimport urllib2\nfrom xml.dom.minidom import parseString\nfrom fedexservices import settings\n\n__author__ = 'scottumsted'\n\n\nclass FedExSoapService():\n \"\"\"\n Class provides interface to create soap request and submit request to soap endpoint.\n Takes in a xml template that represents the soap request and a dictionary of values to populate in\n template.\n Class parses response to determine return status and messages. These statuses along with the\n remaining doc are returned to the caller for additional service specific parsing\n \"\"\"\n\n def __init__(self):\n self._url = settings['SERVICES_URL']\n print ('FedExSoapService: %s' % (self._url))\n self._headers = {\n 'Content-Type': 'text/xml',\n 'Content-Length': None,\n }\n\n def submitRequest(self, requestTemplate, requestData):\n \"\"\"\n Populate template with request data, submit request and return status along with response.\n\n Attributes\n requestTemplate: file name without path, path determined at runtime\n requestData: values to place in template, augmented with FDX_CREDENTIALS\n\n Returns\n tuple of\n response: complete response that can be parsed by caller\n status dict: containing status code and message detail\n\n Raises\n FedExSoapServiceException\n \"\"\"\n try:\n payload = self.createPayload(requestTemplate, requestData)\n self._headers['Content-Length'] = len(payload)\n request = urllib2.Request(self._url, payload, self._headers)\n response = urllib2.urlopen(request, payload, settings['TIMEOUT']).read()\n return response, self._parseResponse(response)\n except FedExSoapServiceException as e:\n raise e\n except urllib2.URLError as e:\n raise FedExSoapServiceException('FedExSoapService.submitRequest(): Unable to submit request: ' + e.message)\n except Exception as e:\n raise FedExSoapServiceException('FedExSoapService.submitRequest(): Unable to submit request: ' + e.message)\n\n def createPayload(self, xmlFileName, inTemplateValues):\n \"\"\"\n Populate content supplied in the xml template with the supplied dictionary values.\n\n Attributes\n xmlFileName: file name without path, path determined at runtime\n inTemplateValues: values to place in template, augmented with FDX_CREDENTIALS\n\n Returns\n Complete SOAP request\n\n Raises\n FedExSoapServiceException\n \"\"\"\n payload = ''\n try:\n templateValues = {}\n templateValues.update(settings)\n templateValues.update(inTemplateValues)\n path = os.path.join(settings['XML_TEMPLATE_DIR'], xmlFileName)\n payload = self._renderTemplate(path, templateValues)\n except FedExSoapServiceException as e:\n raise e\n except Exception as e:\n detail = 'xmlFileName:' + (str(xmlFileName) if xmlFileName else 'unknown') + '\\n'\n detail += 'templateValues:' + (str(inTemplateValues) if inTemplateValues else 'unknown')\n raise FedExSoapServiceException(\n 'FedExSoapService.createPayload(): Unable to create SOAP payload\\n' + detail + '\\n' + e.message)\n return payload\n\n\n def _parseResponse(self, response):\n \"\"\"\n Parse basic status detail from SOAP response.\n\n Attributes\n SOAP response, xml\n\n Returns\n Dictionary of status detail.\n\n Raises def test_checkAvailability_success(self):\n ca = FedExServiceAvailability()\n test_data = {\n 'shipperPostalCode':'38128',\n 'shipperCountryCode':'US',\n 'recipientPostalCode':'97224',\n 'recipientCountryCode':'US',\n 'shipDate':'2015-10-11'\n }\n result = ca.checkAvailability(test_data)\n print str(result)\n self.assertTrue(result['Severity'] in ['SUCCESS','NOTE'], 'Unable to determine service availability')\n\n FedExSoapServiceException\n \"\"\"\n responseValues = {}\n try:\n doc = parseString(response)\n responseValues['HighestSeverity'] = self.getNodeValue('HighestSeverity', 'string', doc)[0]\n responseValues['Severity'] = self.getNodeValue('Severity', 'string', doc)[0]\n responseValues['Source'] = str(self.getNodeValue('Source', 'string', doc)[0])\n responseValues['Code'] = str(self.getNodeValue('Code', 'string', doc)[0])\n responseValues['Message'] = self.getNodeValue('Message', 'string', doc)[0]\n responseValues['LocalizedMessage'] = self.getNodeValue('LocalizedMessage', 'string', doc)[0]\n except FedExSoapServiceException as e:\n raise e\n except Exception as e:\n message = responseValues['Message'] if ('Message' in responseValues and len(responseValues['Message']) > 0) else e.message\n raise FedExSoapServiceException('FedExSoapService._parseResponse(): Unable to parse response: ' + message)\n return responseValues\n\n def getNodeValue(self, name, type, doc, default=None):\n \"\"\"\n Pulls a node value from the supplied doc(xml). Can be used to pull a specific value or section from doc.\n If a section is pulled it can be passed back to getNodeValue to pull additional attributes. In this way\n getNodeValue can be used to walk through collections of data in a doc.\n\n Note that namespace is disregarded as\n FedEx SOAP services use version as ns in response. Using ns makes it difficult to create common parser as\n different FedEx services are at different versions.\n\n Attributes\n name: item by name to look for, name space is not expected\n type: string, boolean, int, datetime - can translate for caller\n doc: document to parse\n default: value if not found\n\n Returns\n node value\n\n Raises\n FedExSoapServiceException\n \"\"\"\n values = []\n try:\n for node in doc.getElementsByTagNameNS('*', name):\n value = ''\n firstNode = node.firstChild\n if firstNode is not None:\n if firstNode.nodeType == firstNode.TEXT_NODE:\n if type == 'string':\n value = firstNode.nodeValue\n elif type == 'boolean':\n value = True if (str(firstNode.nodeValue).upper() == 'TRUE') else False\n elif type == 'int':\n value = int(firstNode.nodeValue)\n elif type == 'datetime':\n tempValue = str(firstNode.nodeValue)\n if tempValue is not None and len(tempValue) > 17:\n value = tempValue[5:10] + '-' + tempValue[0:4] + ' ' + tempValue[11:19]\n elif firstNode.nodeType == firstNode.ELEMENT_NODE:\n value = node\n values.append(value)\n except FedExSoapServiceException as e:\n raise e\n except Exception as e:\n raise FedExSoapServiceException(\n 'FedExSoapService.getNodeValue(): unable to locate node value: ' + e.message)\n finally:\n if default is not None and len(values) == 0:\n values.append(default)\n return values\n\n\n def _renderTemplate(self, templateName, vars):\n \"\"\" returns content from template using vars dict \"\"\"\n content = ''\n try:\n content = open(templateName, 'r').read() % vars\n except Exception as e:\n raise FedExSoapServiceException(\n 'FedExSoapService._renderTemplate(): Unable to render template: ' + e.message)\n return content\n\n\nclass FedExSoapServiceException(Exception):\n \"\"\" Exception specific to problem raised in FedExSoapService class. \"\"\"\n\n def __init__(self, value):\n self.value = value\n self.response = {\n 'HighestSeverity': 'ERROR',\n 'Severity': 'ERROR',\n 'Source': 'SERVICE',\n 'Code': '-1',\n 'Message': value,\n 'LocalizeMessage': value\n }\n\n def __str__(self):\n return repr(self.value)\n", "sub_path": "fedexservices/FedExSoapService.py", "file_name": "FedExSoapService.py", "file_ext": "py", "file_size_in_byte": 8468, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "fedexservices.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "urllib2.Request", "line_number": 48, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 49, "usage_type": "call"}, {"api_name": "fedexservices.settings", "line_number": 49, "usage_type": "name"}, {"api_name": "urllib2.URLError", "line_number": 53, "usage_type": "attribute"}, {"api_name": "fedexservices.settings", "line_number": 75, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "fedexservices.settings", "line_number": 77, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "355798204", "text": "# Copyright [yyyy] [name of copyright owner]\n# Copyright 2020 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\n\n\nimport random\n\nimport Polygon as plg\nimport cv2\nimport numpy as np\nimport pyclipper\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom torch.utils import data\n\nimport util\n\nrandom.seed(123456)\n\n\ndef get_img(img_path):\n try:\n img = cv2.imread(img_path)\n img = img[:, :, [2, 1, 0]]\n except Exception as e:\n print(img_path)\n raise\n return img\n\n\ndef get_bboxes(img, gt_path):\n h, w = img.shape[0:2]\n lines = util.io.read_lines(gt_path)\n bboxes = []\n tags = []\n for line in lines:\n line = util.str.remove_all(line, '\\xef\\xbb\\xbf')\n gt = util.str.split(line, ',')\n if gt[-1][0] == '#':\n tags.append(False)\n else:\n tags.append(True)\n box = [int(gt[i]) for i in range(8)]\n box = np.asarray(box) / ([w * 1.0, h * 1.0] * 4)\n bboxes.append(box)\n return np.array(bboxes), tags\n\n\ndef random_horizontal_flip(imgs):\n if random.random() < 0.5:\n for i in range(len(imgs)):\n imgs[i] = np.flip(imgs[i], axis=1).copy()\n return imgs\n\n\ndef random_rotate(imgs):\n max_angle = 10\n angle = random.random() * 2 * max_angle - max_angle\n for i in range(len(imgs)):\n img = imgs[i]\n w, h = img.shape[:2]\n rotation_matrix = cv2.getRotationMatrix2D((h / 2, w / 2), angle, 1)\n img_rotation = cv2.warpAffine(img, rotation_matrix, (h, w))\n imgs[i] = img_rotation\n return imgs\n\n\ndef scale(img, long_size=2240):\n h, w = img.shape[0:2]\n scale = long_size * 1.0 / max(h, w)\n img = cv2.resize(img, dsize=None, fx=scale, fy=scale)\n return img\n\n\ndef random_scale(img, min_size):\n h, w = img.shape[0:2]\n if max(h, w) > 1280:\n scale = 1280.0 / max(h, w)\n img = cv2.resize(img, dsize=None, fx=scale, fy=scale)\n\n h, w = img.shape[0:2]\n random_scale = np.array([0.5, 1.0, 2.0, 3.0])\n scale = np.random.choice(random_scale)\n if min(h, w) * scale <= min_size:\n scale = (min_size + 10) * 1.0 / min(h, w)\n img = cv2.resize(img, dsize=None, fx=scale, fy=scale)\n return img\n\n\ndef random_crop(imgs, img_size):\n h, w = imgs[0].shape[0:2]\n th, tw = img_size\n if w == tw and h == th:\n return imgs\n\n if random.random() > 3.0 / 8.0 and np.max(imgs[1]) > 0:\n tl = np.min(np.where(imgs[1] > 0), axis=1) - img_size\n tl[tl < 0] = 0\n br = np.max(np.where(imgs[1] > 0), axis=1) - img_size\n br[br < 0] = 0\n br[0] = min(br[0], h - th)\n br[1] = min(br[1], w - tw)\n\n i = random.randint(tl[0], br[0])\n j = random.randint(tl[1], br[1])\n else:\n i = random.randint(0, h - th)\n j = random.randint(0, w - tw)\n\n # return i, j, th, tw\n for idx in range(len(imgs)):\n if len(imgs[idx].shape) == 3:\n imgs[idx] = imgs[idx][i:i + th, j:j + tw, :]\n else:\n imgs[idx] = imgs[idx][i:i + th, j:j + tw]\n return imgs\n\n\ndef dist(a, b):\n return np.sqrt(np.sum((a - b) ** 2))\n\n\ndef perimeter(bbox):\n peri = 0.0\n for i in range(bbox.shape[0]):\n peri += dist(bbox[i], bbox[(i + 1) % bbox.shape[0]])\n return peri\n\n\ndef shrink(bboxes, rate, max_shr=20):\n rate = rate * rate\n shrinked_bboxes = []\n for bbox in bboxes:\n area = plg.Polygon(bbox).area()\n peri = perimeter(bbox)\n\n pco = pyclipper.PyclipperOffset()\n pco.AddPath(bbox, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)\n offset = min((int)(area * (1 - rate) / (peri + 0.001) + 0.5), max_shr)\n\n shrinked_bbox = pco.Execute(-offset)\n if len(shrinked_bbox) == 0:\n shrinked_bboxes.append(bbox)\n continue\n\n shrinked_bbox = np.array(shrinked_bbox)[0]\n if shrinked_bbox.shape[0] <= 2:\n shrinked_bboxes.append(bbox)\n continue\n\n shrinked_bboxes.append(shrinked_bbox)\n\n return np.array(shrinked_bboxes)\n\n\nclass IC15Loader(data.Dataset):\n def __init__(self, args, is_transform=False, img_size=None, kernel_num=7, min_scale=0.4):\n self.args = args\n self.is_transform = is_transform\n self.img_size = img_size if (img_size is None or isinstance(img_size, tuple)) else (img_size, img_size)\n self.kernel_num = kernel_num\n self.min_scale = min_scale\n root_dir = self.args.data_dir\n if self.args.train_data == 'ICDAR2015':\n train_data_dir = root_dir + '/ICDAR/Challenge/ch4_training_images/'\n validation_data_dir = root_dir + '/ICDAR/Challenge/ch4_test_images/'\n train_gt_dir = root_dir + '/ICDAR/Challenge/ch4_training_localization_transcription_gt/'\n validation_gt_dir = root_dir + '/ICDAR/Challenge/ch4_test_localization_transcription_gt/'\n elif self.args.train_data == 'ICDAR2017':\n train_data_dir = root_dir + '/ICDAR/Challenge/ch8_training_images/'\n validation_data_dir = root_dir + '/ICDAR/Challenge/ch8_validation_images/'\n train_gt_dir = root_dir + '/ICDAR/Challenge/ch8_training_localization_transcription_gt/'\n validation_gt_dir = root_dir + '/ICDAR/Challenge/ch8_validation_localization_transcription_gt/'\n else:\n raise Exception('wrong data input')\n\n data_dirs = [train_data_dir] # set the training data data_dirs = [train_data_dir, validation_data_dir]\n gt_dirs = [train_gt_dir] # set the GT labels gt_dirs = [train_gt_dir, validation_gt_dir]\n\n self.img_paths = []\n self.gt_paths = []\n\n for data_dir, gt_dir in zip(data_dirs, gt_dirs):\n img_names = util.io.ls(data_dir, '.jpg')\n img_names.extend(util.io.ls(data_dir, '.png'))\n # img_names.extend(util.io.ls(data_dir, '.gif'))\n\n img_paths = []\n gt_paths = []\n for idx, img_name in enumerate(img_names):\n img_path = data_dir + img_name\n img_paths.append(img_path)\n\n gt_name = 'gt_' + img_name.split('.')[0] + '.txt'\n gt_path = gt_dir + gt_name\n gt_paths.append(gt_path)\n\n self.img_paths.extend(img_paths)\n self.gt_paths.extend(gt_paths)\n\n def __len__(self):\n return len(self.img_paths)\n\n def __getitem__(self, index):\n img_path = self.img_paths[index]\n gt_path = self.gt_paths[index]\n\n img = get_img(img_path)\n bboxes, tags = get_bboxes(img, gt_path)\n\n if self.is_transform:\n img = random_scale(img, self.img_size[0])\n\n gt_text = np.zeros(img.shape[0:2], dtype='uint8')\n training_mask = np.ones(img.shape[0:2], dtype='uint8')\n if bboxes.shape[0] > 0:\n bboxes = np.reshape(bboxes * ([img.shape[1], img.shape[0]] * 4),\n (bboxes.shape[0], int(bboxes.shape[1] / 2), 2)).astype('int32')\n for i in range(bboxes.shape[0]):\n cv2.drawContours(gt_text, [bboxes[i]], -1, i + 1, -1)\n if not tags[i]:\n cv2.drawContours(training_mask, [bboxes[i]], -1, 0, -1)\n\n gt_kernels = []\n for i in range(1, self.kernel_num):\n rate = 1.0 - (1.0 - self.min_scale) / (self.kernel_num - 1) * i\n gt_kernel = np.zeros(img.shape[0:2], dtype='uint8')\n kernel_bboxes = shrink(bboxes, rate)\n for i in range(bboxes.shape[0]):\n cv2.drawContours(gt_kernel, [kernel_bboxes[i]], -1, 1, -1)\n gt_kernels.append(gt_kernel)\n\n if self.is_transform:\n imgs = [img, gt_text, training_mask]\n imgs.extend(gt_kernels)\n\n imgs = random_horizontal_flip(imgs)\n imgs = random_rotate(imgs)\n imgs = random_crop(imgs, self.img_size)\n\n img, gt_text, training_mask, gt_kernels = imgs[0], imgs[1], imgs[2], imgs[3:]\n\n gt_text[gt_text > 0] = 1\n gt_kernels = np.array(gt_kernels)\n\n # '''\n if self.is_transform:\n img = Image.fromarray(img)\n img = img.convert('RGB')\n img = transforms.ColorJitter(brightness=32.0 / 255, saturation=0.5)(img)\n else:\n img = Image.fromarray(img)\n img = img.convert('RGB')\n\n img = transforms.ToTensor()(img)\n img = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)\n\n gt_text = torch.from_numpy(gt_text).float()\n gt_kernels = torch.from_numpy(gt_kernels).float()\n training_mask = torch.from_numpy(training_mask).float()\n # '''\n\n return img, gt_text, gt_kernels, training_mask\n", "sub_path": "PyTorch/built-in/cv/detection/PSENet_for_PyTorch/NPU/src/data_loader.py", "file_name": "data_loader.py", "file_ext": "py", "file_size_in_byte": 9277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "random.seed", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "util.io.read_lines", "line_number": 47, "usage_type": "call"}, {"api_name": "util.io", "line_number": 47, "usage_type": "attribute"}, {"api_name": "util.str.remove_all", "line_number": 51, "usage_type": "call"}, {"api_name": "util.str", "line_number": 51, "usage_type": "attribute"}, {"api_name": "util.str.split", "line_number": 52, "usage_type": "call"}, {"api_name": "util.str", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "random.random", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 66, "usage_type": "call"}, {"api_name": "random.random", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 97, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 100, "usage_type": "call"}, {"api_name": "random.random", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 113, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 118, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 119, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 121, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 134, "usage_type": "call"}, {"api_name": "Polygon.Polygon", "line_number": 148, "usage_type": "call"}, {"api_name": "pyclipper.PyclipperOffset", "line_number": 151, "usage_type": "call"}, {"api_name": "pyclipper.JT_ROUND", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pyclipper.ET_CLOSEDPOLYGON", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 170, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 170, "usage_type": "name"}, {"api_name": "util.io.ls", "line_number": 198, "usage_type": "call"}, {"api_name": "util.io", "line_number": 198, "usage_type": "attribute"}, {"api_name": "util.io.ls", "line_number": 199, "usage_type": "call"}, {"api_name": "util.io", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 231, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 234, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 241, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 258, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 262, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 262, "usage_type": "name"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 264, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 264, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 266, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 266, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 269, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 269, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 270, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 270, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "452621114", "text": "# -*- coding: utf-8 -*-\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nimport re\r\n\r\nname = '경기도 고양시 맘스터치 대화점' #검색어 앱에서 검색하면됨\r\nsplt_name = name.split()\r\n\r\na_name=[]\r\ng=0\r\nwhile(1):\r\n a_name.append(splt_name[g].encode('utf-8'))\r\n g=g+1\r\n if(g==len(splt_name)):\r\n break\r\n#print(len(a_name[0]))\r\n\r\nm_list=[]\r\nmm_list=[]\r\nh=0\r\ni=0\r\n\r\nwhile(1):\r\n mm_list.append(hex(a_name[h][i])[2:4])\r\n i=i+1\r\n if(i==len(a_name[h])):\r\n mm_list_str='%'+'%'.join(mm_list)\r\n mm_list=[]\r\n m_list.append(mm_list_str)\r\n i=0\r\n h=h+1\r\n if(h==len(a_name)):\r\n break\r\nprint(m_list)\r\nlist_str = '+'.join(m_list)\r\nprint(list_str)\r\n\r\n\"\"\"\r\nwhile(1):\r\n mm_list.append(hex(a_name[h][i])[2:4])\r\n i=i+1\r\n if(i==len(splt_name[h]*3)):\r\n mm_list_str='%'+'%'.join(mm_list)\r\n mm_list=[]\r\n m_list.append(mm_list_str)\r\n i=0\r\n h=h+1\r\n if(h==len(splt_name)):\r\n break\r\nprint(m_list)\r\nlist_str = '+'.join(m_list)\r\nprint(list_str)\r\n\"\"\"\r\ndomain=\"https://search.daum.net/search?nil_suggest=btn&nil_ch=&rtupcoll=&w=tot&m=&f=&lpp=&DA=SBC&sug=&sq=&o=&sugo==&q=\"\r\nreq = requests.get(domain+list_str)\r\n\r\nhtml = req.content\r\nsoup = BeautifulSoup(html, 'lxml') # pip install lxml\r\n\r\nlist_restr = soup.dd\r\nprint(list_restr.string)\r\n\r\n\r\n", "sub_path": "Python/prototypetest.py", "file_name": "prototypetest.py", "file_ext": "py", "file_size_in_byte": 1364, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "444334700", "text": "from django.db import models\nfrom django.db.models import Max, Sum\nfrom django.conf import settings\n\nfrom taggit.managers import TaggableManager\n\n\n# from lazysignup.signals import converted\n# from django.dispatch import receiver\n#\n# @receiver(converted)\n# def my_callback(sender, **kwargs):\n# print \"New user account: %s!\" % kwargs['user'].username\n\nfrom django.db import models\nfrom django.contrib.auth.models import (\n BaseUserManager, AbstractBaseUser\n)\n\n\nclass MyUserManager(BaseUserManager):\n def create_user(self, email, password=None):\n \"\"\"\n Creates and saves a User with the given email, date of\n birth and password.\n \"\"\"\n if not email:\n raise ValueError('Users must have an email address')\n\n user = self.model(\n email=self.normalize_email(email),\n\n )\n\n\n user.set_password(password)\n user.save(using=self._db)\n return user\n\n def create_superuser(self, email, password):\n \"\"\"\n Creates and saves a superuser with the given email, date of\n birth and password.\n \"\"\"\n user = self.create_user(email,\n password=password,\n\n )\n user.is_admin = True\n user.save(using=self._db)\n return user\n\n\nclass MyUser(AbstractBaseUser):\n email = models.EmailField(\n verbose_name='email address',\n max_length=255,\n unique=True,\n )\n username = models.CharField(max_length=150, default='')\n is_active = models.BooleanField(default=True)\n is_admin = models.BooleanField(default=False)\n\n objects = MyUserManager()\n\n USERNAME_FIELD = 'email'\n\n def save(self, *args, **kwargs):\n self.username = self.email\n super(MyUser, self).save(*args, **kwargs)\n\n def get_full_name(self):\n # The user is identified by their email address\n return self.email\n\n def get_short_name(self):\n # The user is identified by their email address\n return self.email\n\n def __str__(self): # __unicode__ on Python 2\n return self.email\n\n def has_perm(self, perm, obj=None):\n \"Does the user have a specific permission?\"\n # Simplest possible answer: Yes, always\n return True\n\n def has_module_perms(self, app_label):\n \"Does the user have permissions to view the app `app_label`?\"\n # Simplest possible answer: Yes, always\n return True\n\n @property\n def is_staff(self):\n \"Is the user a member of staff?\"\n # Simplest possible answer: All admins are staff\n return self.is_admin\n\nclass Answer(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField()\n help_text = models.TextField(blank=True, null=True)\n score = models.IntegerField(default=0)\n order = models.IntegerField(default=0)\n question = models.ForeignKey('Question', related_name=\"answers\")\n live = models.BooleanField(default=True)\n\n is_default = models.BooleanField(default=False)\n\n class Meta:\n ordering = ('question', 'order')\n\n def __unicode__(self):\n return u\"%s: %s\" % (self.question.label, self.title)\n\n\n\nclass GroupedAnswer(Answer):\n group = models.CharField(max_length=255)\n\n class Meta:\n ordering = ('group', 'order',)\n\nclass Question(models.Model):\n QUESTION_TYPES = [\n ('S', 'Single-choice question'),\n ('M', 'Multi-choice question'),\n ('F', 'Free-text question'),\n ('P', 'Prioritise question'),\n ]\n\n\n tags = TaggableManager(blank=True)\n label = models.CharField(max_length=512, blank=True)\n help_text = models.CharField(max_length=512, blank=True)\n question_type = models.CharField(max_length=1, choices=QUESTION_TYPES)\n optional = models.BooleanField(\n default=False,\n help_text=\"Only applies to free text questions\",\n )\n\n depends_on_answer = models.ForeignKey(\n Answer, null=True, blank=True, related_name='trigger_questions')\n\n def save(self, *args, **kwargs):\n\n super(Question, self).save(*args, **kwargs)\n\n def copy_relations(self, oldinstance):\n for answer in oldinstance.answers.all():\n answer.pk = None\n answer.question = self\n answer.save()\n\n self.depends_on_answer = oldinstance.depends_on_answer\n\n # @staticmethod\n # def all_in_tree(page):\n # root = page.get_root()\n # # Remember that there might be questions on the root page as well!\n # tree = root.get_descendants() | Page.objects.filter(id=root.id)\n # placeholders = Placeholder.objects.filter(page__in=tree)\n # return Question.objects.filter(placeholder__in=placeholders)\n #\n # @staticmethod\n # def all_in_page(page):\n # placeholders = Placeholder.objects.filter(page=page)\n # return Question.objects.filter(placeholder__in=placeholders)\n\n def score(self, answers):\n if self.question_type == 'F':\n return 0\n elif self.question_type == 'S':\n return self.answers.get(slug=answers).score\n elif self.question_type == 'M':\n answers_list = answers.split(',')\n return sum([self.answers.get(slug=a).score for a in answers_list])\n\n @property\n def max_score(self):\n if not hasattr(self, '_max_score'):\n if self.question_type == \"S\":\n self._max_score = self.answers.aggregate(\n Max('score'))['score__max']\n elif self.question_type == \"M\":\n self._max_score = self.answers.aggregate(\n Sum('score'))['score__sum']\n else:\n self._max_score = None # don't score free-text answers\n return self._max_score\n\n def percent_score_for_user(self, user):\n if self.max_score:\n try:\n score = Submission.objects.get(\n question=self.slug,\n user=user,\n ).score\n except Submission.DoesNotExist:\n return 0\n return 100.0 * score / self.max_score\n else:\n return None\n\n def __unicode__(self):\n return self.label\n\n\n\nclass SubmissionSet(models.Model):\n \"\"\" A set of submissions stored and associated with a particular user to\n provide a mechanism through which a single user can provide repeated\n sets of answers to the same questionnaire.\n \"\"\"\n slug = models.SlugField(blank=True)\n tag = models.SlugField(blank=True)\n user = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='saq_submissions_sets')\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n\n\n\n def save(self, *args, **kwargs):\n\n super(SubmissionSet, self).save(*args, **kwargs)\n\n\nclass Submission(models.Model):\n question = models.ForeignKey(Question)\n answer = models.ForeignKey(Answer)\n answer_text = models.TextField(blank=True)\n score = models.IntegerField(default=0)\n user = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='saq_submissions')\n\n submission_set = models.ForeignKey(\n SubmissionSet, related_name='submissions', null=True)\n\n class Meta:\n ordering = ('submission_set', 'user', 'question', 'score')\n\n\n\n def __unicode__(self):\n return u\"%s answer to %s (%s)\" % (\n self.user, self.question, self.submission_set.slug\n if self.submission_set else \"default\")\n\n def save(self, *args, **kwargs):\n\n super(Submission, self).save(*args, **kwargs)\n\ndef aggregate_score_for_user_by_questions(user, questions):\n scores = []\n for question in questions:\n score = question.percent_score_for_user(user)\n if score is not None:\n scores.append(score)\n if len(scores):\n return sum(scores) / len(scores)\n else:\n return 0\n\n\ndef aggregate_score_for_user_by_tags(user, tags):\n questions = Question.objects.filter(tags__name__in=tags).distinct()\n scores = []\n for question in questions:\n score = question.percent_score_for_user(user)\n if score is not None:\n scores.append(score)\n if len(scores):\n return sum(scores) / len(scores)\n else:\n return 0", "sub_path": "web/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 8240, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.contrib.auth.models.BaseUserManager", "line_number": 21, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AbstractBaseUser", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 61, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 100, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 100, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 101, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 101, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 102, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 103, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 103, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 104, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 104, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 105, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 105, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 106, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 106, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 119, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 124, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 124, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 133, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 134, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 134, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 135, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 135, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 136, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 136, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 142, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 142, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 184, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 187, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 210, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 210, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 215, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 215, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 216, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 216, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 217, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 217, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 217, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 217, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 219, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 219, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 220, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 220, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 229, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 229, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 230, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 230, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 231, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 231, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 232, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 232, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 233, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 233, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 234, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 234, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 234, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 234, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 236, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 236, "usage_type": "name"}]} +{"seq_id": "195272431", "text": "__author__ = 'diegoguaman'\n\n'''\n\n\n QUITO\n==============\n'''\nimport couchdb\nimport sys\nimport urllib2\nimport re\nimport json\n\nregex = re.compile(r'[A-z a-z 0-9]+')\n\nfrom couchdb import view\n\nURL = 'localhost'\ndb_name = 'tunez'\n\n\n'''========couchdb'=========='''\nserver = couchdb.Server('http://localhost:5984/') #('http://245.106.43.184:5984/') poner la url de su base de datos\ntry:\n print (db_name)\n db = server[db_name]\n print ('success')\n\nexcept:\n sys.stderr.write(\"Error: DB not found. Closing...\\n\")\n sys.exit()\n\n\n\nurl = 'http://127.0.0.1:5984/tunez/_design/tunez28JuneCoordenadas/_view/tunez28JuneCoordenadas'\nreq = urllib2.Request(url)\nf = urllib2.urlopen(req)\nd = json.loads(f.read())\ndata = {}\ndata ['people'] = []\nfor x in d['rows']:\n\ta = x ['value']\n\n\tdata ['people'].append({'text': x })\n\twith open ('CoordenadasTunez.txt' , 'w') as outfile:\n\t\tjson.dump(data,outfile)\n\t\tprint (data)\nf.close()\n", "sub_path": "Proyecto/Analisis/partidos 28-Junio/Panama-Tunez/TunezCoordenadas.py", "file_name": "TunezCoordenadas.py", "file_ext": "py", "file_size_in_byte": 919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "couchdb.Server", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 32, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "4070456", "text": "# allows for web-scrapping data\nfrom urllib.request import urlopen\n# from urllib2 import urlopen\nfrom bs4 import BeautifulSoup as soup\n\n# Anime class for different animes\nclass Anime:\n def __init__(self):\n self.data = []\n\n# List to store anime\nanimeList = []\n# Clears the anime data file\nopen('AnimeData.txt','w').close()\n# Opens the anime data file for append\nanimeFile = open('AnimeData.txt', 'a', encoding='utf-8')\nanimeFile.write('ANIME DATA TAKEN FROM ANIMENEWSNETWORK.COM')\nanimeFile.write('\\n\\n')\n\n# data URL\ndataUrl = 'https://www.animenewsnetwork.com/encyclopedia/ratings-anime.php?top50=best_bayesian&n=500'\n# opens the URL\ndataClient = urlopen(dataUrl)\n# takes the HTML from the URL\ndataHTML = dataClient.read()\n# closes the client\ndataClient.close()\n# parses the HTML\npageSoup = soup(dataHTML, 'html.parser')\n# Gets the html for each anime\ncontainers = pageSoup.findAll('tr')\n\n# There are two irrelevant items at the beginning of containers\n# and one irrelevant item at the end of containers.\n# Iterates over the top 500 anime.\n\ndef getAnime():\n for index in range(2, 502):\n \n # gets anime statistics from HTML\n container = containers[index]\n ranking = container.td.text\n name = container.findAll('td', {'class', 't'})\n link = 'https://www.animenewsnetwork.com' + name[0].a['href']\n name = name[0].text\n statistics = container.findAll('td', {'class', 'r'})\n rating = statistics[0].text\n numVotes = statistics[1].text\n # prints out basic anime stats to console\n print('name: ' + name)\n print('ranking: ' + ranking) \n # prints out anime stats to file\n currentAnime = Anime()\n animeFile.write('\\nname: ' + name)\n animeFile.write('\\nlink: ' + link)\n animeFile.write('\\nranking: ' + ranking)\n animeFile.write('\\nrating: ' + rating)\n animeFile.write('\\nvotes: ' + numVotes)\n\n # Goes to the webpage for the current anime\n success = False\n while success == False:\n try: \n animeClient = urlopen(link, timeout=5)\n animeHTML = animeClient.read()\n success = True\n except: \n pass\n animeClient.close()\n pageSoup = soup(animeHTML, 'html.parser')\n\n # Genres of the current anime\n try:\n genreDiv = pageSoup.find(id='infotype-30')\n genres = genreDiv.findAll('span')\n genreList = []\n for genre in genres:\n genreList.append(genre.a.text)\n currentAnime.genres = genreList\n except:\n currentAnime.genres = 'unknown'\n\n # Themes of the current anime\n try:\n themes = pageSoup.find(id='infotype-31').findAll('span')\n themeList = []\n for theme in themes:\n themeList.append(theme.a.text)\n currentAnime.themes = themeList\n except:\n currentAnime.themes = 'unknown'\n\n # Premiere date of the current anime\n try:\n date = pageSoup.find(id='infotype-9').div.text\n currentAnime.premiereDate = date\n except:\n currentAnime.premiereDate = 'unknown'\n\n # Director of the current anime\n try:\n director = pageSoup.find('b', text='Director').parent.a.text\n currentAnime.director = director\n except:\n currentAnime.director = 'unknown'\n\n # Production Studio of the current anime\n try:\n productionStudio = pageSoup.find('b', text='Production').parent.a.text\n currentAnime.studio = productionStudio\n except:\n currentAnime.studio = 'unknown'\n\n # Prints the genres\n animeFile.write('\\ngenres: ')\n for genre in currentAnime.genres:\n animeFile.write(genre + ',')\n # Prints the themes\n animeFile.write('\\nthemes: ')\n for theme in currentAnime.themes:\n animeFile.write(theme + ',')\n \n # Prints the premiere date, director, and studio\n animeFile.write('\\npremiere date: ' + currentAnime.premiereDate)\n animeFile.write('\\ndirector: ' + currentAnime.director)\n animeFile.write('\\nproduction studio: ' + currentAnime.studio)\n \n #closing instructions\n # animeList.append(currentAnime)\n print()\n animeFile.write('\\n')\n \ngetAnime() \nanimeFile.close()", "sub_path": "random/animeScrape.py", "file_name": "animeScrape.py", "file_ext": "py", "file_size_in_byte": 4273, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "urllib.request.urlopen", "line_number": 23, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 64, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "383279333", "text": "import pygame\r\nimport sys\r\nimport random\r\nimport copy\r\n\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\nGREEN_BLUE = (0, 153, 153)\r\nLIGHT_GRAY = (192, 192, 192)\r\nRED = (255, 0, 0)\r\n\r\nblock_size = 50\r\nleft_margin = 5 * block_size\r\nupper_margin = 2 * block_size\r\n# 30 = 2x10 blocks width in two grids + hard-coded 5*blocks gap after each grid!\r\nsize = (left_margin + 30 * block_size, upper_margin + 15 * block_size)\r\nLETTERS = \"ABCDEFGHIJ\"\r\n\r\npygame.init()\r\n\r\nscreen = pygame.display.set_mode(size)\r\npygame.display.set_caption(\"BATTLESHIP\")\r\n# This ratio is purely for scaling the font according to the block size\r\nfont_size = int(block_size / 1.5)\r\nfont = pygame.font.SysFont('notosans', font_size)\r\ngame_over_font_size = 3 * block_size\r\ngame_over_font = pygame.font.SysFont('notosans', game_over_font_size)\r\n\r\n### COMPUTER DATA ###\r\ncomputer_available_to_fire_set = {(x, y)\r\n for x in range(16, 26) for y in range(1, 11)}\r\naround_last_computer_hit_set = set()\r\ndotted_set_for_computer_not_to_shoot = set()\r\nhit_blocks_for_computer_not_to_shoot = set()\r\nlast_hits_list = []\r\n###################\r\n\r\nhit_blocks = set()\r\ndotted_set = set()\r\ndestroyed_computer_ships = []\r\n\r\n\r\nclass Grid:\r\n \"\"\"\r\n Class to draw the grids and add title, numbers and letters to them\r\n ----------\r\n Attributes:\r\n title (str): Players' name to be displayed on the top of his grid\r\n offset (int): Where the grid starts (in number of blocks)\r\n (typically 0 for computer and 15 for human)\r\n ----------\r\n Methods:\r\n __draw_grid(): Draws two grids for both players\r\n __add_nums_letters_to_grid(): Draws numbers 1-10 along vertical and adds letters below horizontal\r\n lines for both grids\r\n __sign_grid(): Puts players' names (titles) in the center above the grids\r\n \"\"\"\r\n\r\n def __init__(self, title, offset):\r\n \"\"\"\r\n title(str): Players' name to be displayed on the top of his grid\r\n offset (int): Where the grid starts (in number of blocks)\r\n (typically 0 for computer and 15 for human)\r\n \"\"\"\r\n self.title = title\r\n self.offset = offset\r\n self.__draw_grid()\r\n self.__add_nums_letters_to_grid()\r\n self.__sign_grid()\r\n\r\n def __draw_grid(self):\r\n \"\"\"\r\n Draws two grids for both players\r\n \"\"\"\r\n for i in range(11):\r\n # Horizontal lines\r\n pygame.draw.line(screen, BLACK, (left_margin + self.offset * block_size, upper_margin + i * block_size),\r\n (left_margin + (10 + self.offset) * block_size, upper_margin + i * block_size), 1)\r\n # Vertical lines\r\n pygame.draw.line(screen, BLACK, (left_margin + (i + self.offset) * block_size, upper_margin),\r\n (left_margin + (i + self.offset) * block_size, upper_margin + 10 * block_size), 1)\r\n\r\n def __add_nums_letters_to_grid(self):\r\n \"\"\"\r\n Draws numbers 1-10 along vertical and adds letters below horizontal\r\n lines for both grids\r\n \"\"\"\r\n for i in range(10):\r\n num_ver = font.render(str(i + 1), True, BLACK)\r\n letters_hor = font.render(LETTERS[i], True, BLACK)\r\n num_ver_width = num_ver.get_width()\r\n num_ver_height = num_ver.get_height()\r\n letters_hor_width = letters_hor.get_width()\r\n\r\n # Numbers (vertical)\r\n screen.blit(num_ver, (left_margin - (block_size // 2 + num_ver_width // 2) + self.offset * block_size,\r\n upper_margin + i * block_size + (block_size // 2 - num_ver_height // 2)))\r\n # Letters (horizontal)\r\n screen.blit(letters_hor, (left_margin + i * block_size + (block_size // 2 -\r\n letters_hor_width // 2) + self.offset * block_size, upper_margin + 10 * block_size))\r\n\r\n def __sign_grid(self):\r\n \"\"\"\r\n Puts players' names (titles) in the center above the grids\r\n \"\"\"\r\n player = font.render(self.title, True, BLACK)\r\n sign_width = player.get_width()\r\n screen.blit(player, (left_margin + 5 * block_size - sign_width // 2 +\r\n self.offset * block_size, upper_margin - block_size // 2 - font_size))\r\n\r\n\r\nclass Button:\r\n \"\"\"\r\n Creates buttons and prints explanatory message for them\r\n ----------\r\n Attributes:\r\n __title (str): Button's name (title)\r\n __message (str): explanatory message to print on screen\r\n __x_start (int): horizontal offset where to start drawing button\r\n __y_start (int): vertical offset where to start drawing button\r\n rect_for_draw (tuple of four ints): button's rectangle to be drawn\r\n rect (pygame Rect): pygame Rect object\r\n __rect_for_button_title (tuple of two ints): rectangle within button to print text in it\r\n __color (tuple): color of button (Default is BLACK, hovered is GREEN_BLUE, disabled is LIGHT_GRAY)\r\n ----------\r\n Methods:\r\n draw_button(): Draws button as a rectangle of color (default is BLACK)\r\n change_color_on_hover(): Draws button as a rectangle of GREEN_BLUE color\r\n print_message_for_button(): Prints explanatory message next to button\r\n \"\"\"\r\n\r\n def __init__(self, x_offset, button_title, message_to_show):\r\n self.__title = button_title\r\n self.__title_width, self.__title_height = font.size(self.__title)\r\n self.__message = message_to_show\r\n self.__button_width = self.__title_width + block_size\r\n self.__button_height = self.__title_height + block_size\r\n self.__x_start = x_offset\r\n self.__y_start = upper_margin + 10 * block_size + self.__button_height\r\n self.rect_for_draw = self.__x_start, self.__y_start, self.__button_width, self.__button_height\r\n self.rect = pygame.Rect(self.rect_for_draw)\r\n self.__rect_for_button_title = self.__x_start + self.__button_width / 2 - \\\r\n self.__title_width / 2, self.__y_start + \\\r\n self.__button_height / 2 - self.__title_height / 2\r\n self.__color = BLACK\r\n\r\n def draw_button(self, color=None):\r\n \"\"\"\r\n Draws button as a rectangle of color (default is BLACK)\r\n Args:\r\n color (tuple, optional): Button's color. Defaults to None (BLACK).\r\n \"\"\"\r\n if not color:\r\n color = self.__color\r\n pygame.draw.rect(screen, color, self.rect_for_draw)\r\n text_to_blit = font.render(self.__title, True, WHITE)\r\n screen.blit(text_to_blit, self.__rect_for_button_title)\r\n\r\n def change_color_on_hover(self):\r\n \"\"\"\r\n Draws button as a rectangle of GREEN_BLUE color\r\n \"\"\"\r\n mouse = pygame.mouse.get_pos()\r\n if self.rect.collidepoint(mouse):\r\n self.draw_button(GREEN_BLUE)\r\n\r\n def print_message_for_button(self):\r\n \"\"\"\r\n Prints explanatory message next to button\r\n \"\"\"\r\n message_width, message_height = font.size(self.__message)\r\n rect_for_message = self.__x_start / 2 - message_width / \\\r\n 2, self.__y_start + self.__button_height / 2 - message_height / 2\r\n text = font.render(self.__message, True, BLACK)\r\n screen.blit(text, rect_for_message)\r\n\r\n\r\nclass AutoShips:\r\n \"\"\"\r\n Randomly create all player's ships on a grid\r\n ----------\r\n Attributes:\r\n offset (int): Where the grid starts (in number of blocks)\r\n (typically 0 for computer and 15 for human)\r\n available_blocks (set of tuples): coordinates of all blocks\r\n that are avaiable for creating ships (updated every time a ship is created)\r\n ships_set (set of tuples): all blocks that are occupied by ships\r\n ships (list of lists): list of all individual ships (as lists)\r\n ----------\r\n Methods:\r\n __create_start_block(available_blocks):\r\n Randomly chooses a block from which to start creating a ship.\r\n Randomly chooses horizontal or vertical type of a ship\r\n Randomly chooses direction (from the start block) - straight or reverse\r\n Returns three randomly chosen values\r\n __create_ship(number_of_blocks, available_blocks):\r\n Creates a ship of given length (number_of_blocks) starting from the start block\r\n returned by the previous method, using type of ship and direction (changing it\r\n if going outside of grid) returned by previous method.\r\n Checks if the ship is valid (not adjacent to other ships and within the grid)\r\n and adds it to the list of ships.\r\n Returns: a list of tuples with a new ship's coordinates\r\n __get_new_block_for_ship(self, coor, direction, orientation, ship_coordinates):\r\n Checks if new individual blocks that are being added to a ship in the previous method\r\n are within the grid, otherwise changes the direction.\r\n Returns:\r\n direction (int): straight or reverse\r\n incremented/decremented coordinate of the last/first block in a ship under construction\r\n __is_ship_valid(new_ship):\r\n Check if all of a ship's coordinates are within the available blocks set.\r\n Returns: True or False\r\n __add_new_ship_to_set(new_ship):\r\n Adds all blocks in a ship's list to the ships_set\r\n __update_available_blocks_for_creating_ships(new_ship):\r\n Removes all blocks occupied by a ship and around it from the available blocks set\r\n __populate_grid():\r\n Creates needed number of each type of ships by calling the create_ship method.\r\n Adds every ship to the ships list, ships_set and updates the available blocks.\r\n Returns: the list of all ships\r\n \"\"\"\r\n\r\n def __init__(self, offset):\r\n \"\"\"\r\n Parameters:\r\n offset (int): Where the grid starts (in number of blocks)\r\n (typically 0 for computer and 15 for human)\r\n available_blocks (set of tuples): coordinates of all blocks\r\n that are avaiable for creating ships (updated every time a ship is created)\r\n ships_set (set of tuples): all blocks that are occupied by ships\r\n ships (list of lists): list of all individual ships (as lists)\"\"\"\r\n\r\n self.offset = offset\r\n self.available_blocks = {(x, y) for x in range(\r\n 1 + self.offset, 11 + self.offset) for y in range(1, 11)}\r\n self.ships_set = set()\r\n self.ships = self.__populate_grid()\r\n self.orientation = None\r\n self.direction = None\r\n\r\n def __create_start_block(self, available_blocks):\r\n \"\"\"\r\n Randomly chooses a block from which to start creating a ship.\r\n Randomly chooses horizontal or vertical type of a ship\r\n Randomly chooses direction (from the start block) - straight or reverse\r\n Args:\r\n available_blocks (set of tuples): coordinates of all blocks\r\n that are avaiable for creating ships (updated every time a ship is created)\r\n Returns:\r\n int: x coordinate of a random block\r\n int: y coordinate of a random block\r\n int: 0=horizontal (change x), 1=vertical (change y)\r\n int: 1=straight, -1=reverse\r\n \"\"\"\r\n self.orientation = random.randint(0, 1)\r\n # -1 is left or down, 1 is right or up\r\n self.direction = random.choice((-1, 1))\r\n x, y = random.choice(tuple(available_blocks))\r\n return x, y, self.orientation, self.direction\r\n\r\n def __create_ship(self, number_of_blocks, available_blocks):\r\n \"\"\"\r\n Creates a ship of given length (number_of_blocks) starting from the start block\r\n returned by the previous method, using type of ship and direction (changing it\r\n if going outside of grid) returned by previous method.\r\n Checks if the ship is valid (not adjacent to other ships and within the grid)\r\n and adds it to the list of ships.\r\n Args:\r\n number_of_blocks (int): length of a needed ship\r\n available_blocks (set): free blocks for creating ships\r\n Returns:\r\n list: a list of tuples with a new ship's coordinates\r\n \"\"\"\r\n ship_coordinates = []\r\n x, y, self.orientation, self.direction = self.__create_start_block(\r\n available_blocks)\r\n for _ in range(number_of_blocks):\r\n ship_coordinates.append((x, y))\r\n if not self.orientation:\r\n self.direction, x = self.__get_new_block_for_ship(\r\n x, self.direction, self.orientation, ship_coordinates)\r\n else:\r\n self.direction, y = self.__get_new_block_for_ship(\r\n y, self.direction, self.orientation, ship_coordinates)\r\n if self.__is_ship_valid(ship_coordinates):\r\n return ship_coordinates\r\n return self.__create_ship(number_of_blocks, available_blocks)\r\n\r\n def __get_new_block_for_ship(self, coor, direction, orientation, ship_coordinates):\r\n \"\"\"\r\n Checks if new individual blocks that are being added to a ship in the previous method\r\n are within the grid, otherwise changes the direction.\r\n Args:\r\n coor (int): x or y coordinate to increment/decrement\r\n direction (int): 1 or -1\r\n orientation (int): 0 or 1\r\n ship_coordinates (list): coordinates of unfinished ship\r\n Returns:\r\n direction (int): straight or reverse\r\n incremented/decremented coordinate of the last/first block in a ship under construction (int)\r\n \"\"\"\r\n self.direction = direction\r\n self.orientation = orientation\r\n if (coor <= 1 - self.offset * (self.orientation - 1) and self.direction == -1) or (\r\n coor >= 10 - self.offset * (self.orientation - 1) and self.direction == 1):\r\n self.direction *= -1\r\n return self.direction, ship_coordinates[0][self.orientation] + self.direction\r\n else:\r\n return self.direction, ship_coordinates[-1][self.orientation] + self.direction\r\n\r\n def __is_ship_valid(self, new_ship):\r\n \"\"\"\r\n Check if all of a ship's coordinates are within the available blocks set.\r\n Args:\r\n new_ship (list): list of tuples with a newly created ship's coordinates\r\n Returns:\r\n bool: True or False\r\n \"\"\"\r\n ship = set(new_ship)\r\n return ship.issubset(self.available_blocks)\r\n\r\n def __add_new_ship_to_set(self, new_ship):\r\n \"\"\"\r\n Adds all blocks in a ship's list to the ships_set\r\n Args:\r\n new_ship (list): list of tuples with a newly created ship's coordinates\r\n \"\"\"\r\n self.ships_set.update(new_ship)\r\n\r\n def __update_available_blocks_for_creating_ships(self, new_ship):\r\n \"\"\"\r\n Removes all blocks occupied by a ship and around it from the available blocks set\r\n Args:\r\n new_ship ([type]): list of tuples with a newly created ship's coordinates\r\n \"\"\"\r\n for elem in new_ship:\r\n for k in range(-1, 2):\r\n for m in range(-1, 2):\r\n if self.offset < (elem[0] + k) < 11 + self.offset and 0 < (elem[1] + m) < 11:\r\n self.available_blocks.discard(\r\n (elem[0] + k, elem[1] + m))\r\n\r\n def __populate_grid(self):\r\n \"\"\"\r\n Creates needed number of each type of ships by calling the create_ship method.\r\n Adds every ship to the ships list, ships_set and updates the available blocks.\r\n Returns:\r\n list: the 2d list of all ships\r\n \"\"\"\r\n ships_coordinates_list = []\r\n for number_of_blocks in range(4, 0, -1):\r\n for _ in range(5 - number_of_blocks):\r\n new_ship = self.__create_ship(\r\n number_of_blocks, self.available_blocks)\r\n ships_coordinates_list.append(new_ship)\r\n self.__add_new_ship_to_set(new_ship)\r\n self.__update_available_blocks_for_creating_ships(new_ship)\r\n return ships_coordinates_list\r\n\r\n# ===========Shooting section==============\r\n\r\n\r\ndef computer_shoots(set_to_shoot_from):\r\n \"\"\"\r\n Randomly chooses a block from available to shoot from set\r\n \"\"\"\r\n pygame.time.delay(500)\r\n computer_fired_block = random.choice(tuple(set_to_shoot_from))\r\n computer_available_to_fire_set.discard(computer_fired_block)\r\n return computer_fired_block\r\n\r\n\r\ndef check_hit_or_miss(fired_block, opponents_ships_list, computer_turn, opponents_ships_list_original_copy,\r\n opponents_ships_set):\r\n \"\"\"\r\n Checks whether the block that was shot at either by computer or by human is a hit or a miss.\r\n Updates sets with dots (in missed blocks or in diagonal blocks around hit block) and 'X's\r\n (in hit blocks).\r\n Removes destroyed ships from the list of ships.\r\n \"\"\"\r\n for elem in opponents_ships_list:\r\n diagonal_only = True\r\n if fired_block in elem:\r\n # This is to put dots before and after a destroyed ship\r\n # and to draw computer's destroyed ships (which are hidden until destroyed)\r\n ind = opponents_ships_list.index(elem)\r\n if len(elem) == 1:\r\n diagonal_only = False\r\n update_dotted_and_hit_sets(\r\n fired_block, computer_turn, diagonal_only)\r\n elem.remove(fired_block)\r\n # This is to check who loses - if ships_set is empty\r\n opponents_ships_set.discard(fired_block)\r\n if computer_turn:\r\n last_hits_list.append(fired_block)\r\n update_around_last_computer_hit(fired_block, True)\r\n # If the ship is destroyed\r\n if not elem:\r\n update_destroyed_ships(\r\n ind, computer_turn, opponents_ships_list_original_copy)\r\n if computer_turn:\r\n last_hits_list.clear()\r\n around_last_computer_hit_set.clear()\r\n else:\r\n # Add computer's destroyed ship to the list to draw it (computer ships are hidden)\r\n destroyed_computer_ships.append(computer.ships[ind])\r\n return True\r\n add_missed_block_to_dotted_set(fired_block)\r\n if computer_turn:\r\n update_around_last_computer_hit(fired_block, False)\r\n return False\r\n\r\n\r\ndef update_destroyed_ships(ind, computer_turn, opponents_ships_list_original_copy):\r\n \"\"\"\r\n Adds blocks before and after a ship to dotted_set to draw dots on them.\r\n Adds all blocks in a ship to hit_blocks set to draw 'X's within a destroyed ship.\r\n \"\"\"\r\n ship = sorted(opponents_ships_list_original_copy[ind])\r\n for i in range(-1, 1):\r\n update_dotted_and_hit_sets(ship[i], computer_turn, False)\r\n\r\n\r\ndef update_around_last_computer_hit(fired_block, computer_hits):\r\n \"\"\"\r\n Updates around_last_computer_hit_set (which is used to choose for computer to fire from) if it\r\n hit the ship but not destroyed it). Adds to this set vertical or horizontal blocks around the\r\n block that was last hit. Then removes those block from that set which were shot at but missed.\r\n around_last_computer_hit_set makes computer choose the right blocks to quickly destroy the ship\r\n instead of just randomly shooting at completely random blocks.\r\n \"\"\"\r\n global around_last_computer_hit_set, computer_available_to_fire_set\r\n if computer_hits and fired_block in around_last_computer_hit_set:\r\n around_last_computer_hit_set = computer_hits_twice()\r\n elif computer_hits and fired_block not in around_last_computer_hit_set:\r\n computer_first_hit(fired_block)\r\n elif not computer_hits:\r\n around_last_computer_hit_set.discard(fired_block)\r\n\r\n around_last_computer_hit_set -= dotted_set_for_computer_not_to_shoot\r\n around_last_computer_hit_set -= hit_blocks_for_computer_not_to_shoot\r\n computer_available_to_fire_set -= around_last_computer_hit_set\r\n computer_available_to_fire_set -= dotted_set_for_computer_not_to_shoot\r\n\r\n\r\ndef computer_first_hit(fired_block):\r\n \"\"\"\r\n Adds blocks above, below, to the right and to the left from the block hit\r\n by computer to a temporary set for computer to choose its next shot from.\r\n Args:\r\n fired_block (tuple): coordinates of a block hit by computer\r\n \"\"\"\r\n x_hit, y_hit = fired_block\r\n if x_hit > 16:\r\n around_last_computer_hit_set.add((x_hit - 1, y_hit))\r\n if x_hit < 25:\r\n around_last_computer_hit_set.add((x_hit + 1, y_hit))\r\n if y_hit > 1:\r\n around_last_computer_hit_set.add((x_hit, y_hit - 1))\r\n if y_hit < 10:\r\n around_last_computer_hit_set.add((x_hit, y_hit + 1))\r\n\r\n\r\ndef computer_hits_twice():\r\n \"\"\"\r\n Adds blocks before and after two or more blocks of a ship to a temporary list\r\n for computer to finish the ship faster.\r\n Returns:\r\n set: temporary set of blocks where potentially a human ship should be\r\n for computer to shoot from\r\n \"\"\"\r\n last_hits_list.sort()\r\n new_around_last_hit_set = set()\r\n for i in range(len(last_hits_list) - 1):\r\n x1 = last_hits_list[i][0]\r\n x2 = last_hits_list[i + 1][0]\r\n y1 = last_hits_list[i][1]\r\n y2 = last_hits_list[i + 1][1]\r\n if x1 == x2:\r\n if y1 > 1:\r\n new_around_last_hit_set.add((x1, y1 - 1))\r\n if y2 < 10:\r\n new_around_last_hit_set.add((x1, y2 + 1))\r\n elif y1 == y2:\r\n if x1 > 16:\r\n new_around_last_hit_set.add((x1 - 1, y1))\r\n if x2 < 25:\r\n new_around_last_hit_set.add((x2 + 1, y1))\r\n return new_around_last_hit_set\r\n\r\n\r\ndef update_dotted_and_hit_sets(fired_block, computer_turn, diagonal_only=True):\r\n \"\"\"\r\n Puts dots in center of diagonal or all around a block that was hit (either by human or\r\n by computer). Adds all diagonal blocks or all-around chosen block to a separate set\r\n block: hit block (tuple)\r\n \"\"\"\r\n global dotted_set\r\n x, y = fired_block\r\n a = 15 * computer_turn\r\n b = 11 + 15 * computer_turn\r\n # Adds a block hit by computer to the set of his hits to later remove\r\n # them from the set of blocks available for it to shoot from\r\n hit_blocks_for_computer_not_to_shoot.add(fired_block)\r\n # Adds hit blocks on either grid1 (x:1-10) or grid2 (x:16-25)\r\n hit_blocks.add(fired_block)\r\n # Adds blocks in diagonal or all-around a block to repsective sets\r\n for i in range(-1, 2):\r\n for j in range(-1, 2):\r\n if (not diagonal_only or i != 0 and j != 0) and a < x + i < b and 0 < y + j < 11:\r\n add_missed_block_to_dotted_set((x + i, y + j))\r\n dotted_set -= hit_blocks\r\n\r\n\r\ndef add_missed_block_to_dotted_set(fired_block):\r\n \"\"\"\r\n Adds a fired_block to the set of missed shots (if fired_block is a miss then) to then draw dots on them.\r\n Also needed for computer to remove these dotted blocks from the set of available blocks for it to shoot from.\r\n \"\"\"\r\n dotted_set.add(fired_block)\r\n dotted_set_for_computer_not_to_shoot.add(fired_block)\r\n\r\n\r\n# ===========DRAWING SECTION==============\r\n\r\ndef draw_ships(ships_coordinates_list):\r\n \"\"\"\r\n Draws rectangles around the blocks that are occupied by a ship\r\n Args:\r\n ships_coordinates_list (list of tuples): a list of ships's coordinates\r\n \"\"\"\r\n for elem in ships_coordinates_list:\r\n ship = sorted(elem)\r\n x_start = ship[0][0]\r\n y_start = ship[0][1]\r\n # Horizontal and 1block ships\r\n ship_width = block_size * len(ship)\r\n ship_height = block_size\r\n # Vertical ships\r\n if len(ship) > 1 and ship[0][0] == ship[1][0]:\r\n ship_width, ship_height = ship_height, ship_width\r\n x = block_size * (x_start - 1) + left_margin\r\n y = block_size * (y_start - 1) + upper_margin\r\n pygame.draw.rect(\r\n screen, BLACK, ((x, y), (ship_width, ship_height)), width=block_size // 10)\r\n\r\n\r\ndef draw_from_dotted_set(dotted_set_to_draw_from):\r\n \"\"\"\r\n Draws dots in the center of all blocks in the dotted_set\r\n \"\"\"\r\n for elem in dotted_set_to_draw_from:\r\n pygame.draw.circle(screen, BLACK, (block_size * (\r\n elem[0] - 0.5) + left_margin, block_size * (elem[1] - 0.5) + upper_margin), block_size // 6)\r\n\r\n\r\ndef draw_hit_blocks(hit_blocks_to_draw_from):\r\n \"\"\"\r\n Draws 'X' in the blocks that were successfully hit either by computer or by human\r\n \"\"\"\r\n for block in hit_blocks_to_draw_from:\r\n x1 = block_size * (block[0] - 1) + left_margin\r\n y1 = block_size * (block[1] - 1) + upper_margin\r\n pygame.draw.line(screen, BLACK, (x1, y1),\r\n (x1 + block_size, y1 + block_size), block_size // 6)\r\n pygame.draw.line(screen, BLACK, (x1, y1 + block_size),\r\n (x1 + block_size, y1), block_size // 6)\r\n\r\n\r\ndef show_message_at_rect_center(message, rect, which_font=font, color=RED):\r\n \"\"\"\r\n Prints message to screen at a given rect's center.\r\n Args:\r\n message (str): Message to print\r\n rect (tuple): rectangle in (x_start, y_start, width, height) format\r\n which_font (pygame font object, optional): What font to use to print message. Defaults to font.\r\n color (tuple, optional): Color of the message. Defaults to RED.\r\n \"\"\"\r\n message_width, message_height = which_font.size(message)\r\n message_rect = pygame.Rect(rect)\r\n x_start = message_rect.centerx - message_width / 2\r\n y_start = message_rect.centery - message_height / 2\r\n background_rect = pygame.Rect(\r\n x_start - block_size / 2, y_start, message_width + block_size, message_height)\r\n message_to_blit = which_font.render(message, True, color)\r\n screen.fill(WHITE, background_rect)\r\n screen.blit(message_to_blit, (x_start, y_start))\r\n\r\n\r\ndef ship_is_valid(ship_set, blocks_for_manual_drawing):\r\n \"\"\"\r\n Checks if ship is not touching other ships\r\n Args:\r\n ship_set (set): Set with tuples of new ships' coordinates\r\n blocks_for_manual_drawing (set): Set with all used blocks for other ships, including all blocks around ships.\r\n\r\n Returns:\r\n Bool: True if ships are not touching, False otherwise.\r\n \"\"\"\r\n return ship_set.isdisjoint(blocks_for_manual_drawing)\r\n\r\n\r\ndef check_ships_numbers(ship, num_ships_list):\r\n \"\"\"\r\n Checks if a ship of particular length (1-4) does not exceed necessary quantity (4-1).\r\n\r\n Args:\r\n ship (list): List with new ships' coordinates\r\n num_ships_list (list): List with numbers of particular ships on respective indexes.\r\n\r\n Returns:\r\n Bool: True if the number of ships of particular length is not greater than needed, \r\n False if there are enough of such ships.\r\n \"\"\"\r\n return (5 - len(ship)) > num_ships_list[len(ship)-1]\r\n\r\n\r\ndef update_used_blocks(ship, method):\r\n for block in ship:\r\n for i in range(-1, 2):\r\n for j in range(-1, 2):\r\n method((block[0]+i, block[1]+j))\r\n\r\n\r\n# Create computer ships\r\ncomputer = AutoShips(0)\r\ncomputer_ships_working = copy.deepcopy(computer.ships)\r\n\r\n# Create AUTO and MANUAL buttons and explanatory message for them\r\nauto_button_place = left_margin + 17 * block_size\r\nmanual_button_place = left_margin + 20 * block_size\r\nhow_to_create_ships_message = \"How do you want to create your ships? Click the button\"\r\nauto_button = Button(auto_button_place, \"AUTO\", how_to_create_ships_message)\r\nmanual_button = Button(manual_button_place, \"MANUAL\",\r\n how_to_create_ships_message)\r\n\r\n# Create UNDO message and button\r\nundo_message = \"To undo the last ship click the button\"\r\nundo_button_place = left_margin + 12 * block_size\r\nundo_button = Button(undo_button_place, \"UNDO LAST SHIP\", undo_message)\r\n\r\n# Create PLAY AGAIN and QUIT buttons and message for them\r\nplay_again_message = \"Do you want to play again or quit?\"\r\nplay_again_button = Button(\r\n left_margin + 15 * block_size, \"PLAY AGAIN\", play_again_message)\r\nquit_game_button = Button(manual_button_place, \"QUIT\", play_again_message)\r\n\r\n\r\ndef main():\r\n ships_creation_not_decided = True\r\n ships_not_created = True\r\n drawing = False\r\n game_over = False\r\n computer_turn = False\r\n start = (0, 0)\r\n ship_size = (0, 0)\r\n\r\n rect_for_grids = (0, 0, size[0], upper_margin + 12 * block_size)\r\n rect_for_messages_and_buttons = (\r\n 0, upper_margin + 11 * block_size, size[0], 5 * block_size)\r\n message_rect_for_drawing_ships = (undo_button.rect_for_draw[0] + undo_button.rect_for_draw[2], upper_margin + 11 * block_size, size[0]-(\r\n undo_button.rect_for_draw[0] + undo_button.rect_for_draw[2]), 4 * block_size)\r\n message_rect_computer = (left_margin - 2 * block_size, upper_margin +\r\n 11 * block_size, 14 * block_size, 4 * block_size)\r\n message_rect_human = (left_margin + 15 * block_size, upper_margin +\r\n 11 * block_size, 10 * block_size, 4 * block_size)\r\n\r\n human_ships_to_draw = []\r\n human_ships_set = set()\r\n used_blocks_for_manual_drawing = set()\r\n num_ships_list = [0, 0, 0, 0]\r\n\r\n screen.fill(WHITE)\r\n computer_grid = Grid(\"COMPUTER\", 0)\r\n human_grid = Grid(\"HUMAN\", 15)\r\n\r\n while ships_creation_not_decided:\r\n auto_button.draw_button()\r\n manual_button.draw_button()\r\n auto_button.change_color_on_hover()\r\n manual_button.change_color_on_hover()\r\n auto_button.print_message_for_button()\r\n\r\n mouse = pygame.mouse.get_pos()\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n game_over = True\r\n ships_creation_not_decided = False\r\n ships_not_created = False\r\n # If AUTO button is pressed - create human ships automatically\r\n elif event.type == pygame.MOUSEBUTTONDOWN and auto_button.rect.collidepoint(mouse):\r\n human = AutoShips(15)\r\n human_ships_to_draw = human.ships\r\n human_ships_working = copy.deepcopy(human.ships)\r\n human_ships_set = human.ships_set\r\n ships_creation_not_decided = False\r\n ships_not_created = False\r\n elif event.type == pygame.MOUSEBUTTONDOWN and manual_button.rect.collidepoint(mouse):\r\n ships_creation_not_decided = False\r\n\r\n pygame.display.update()\r\n screen.fill(WHITE, rect_for_messages_and_buttons)\r\n\r\n while ships_not_created:\r\n screen.fill(WHITE, rect_for_grids)\r\n computer_grid = Grid(\"COMPUTER\", 0)\r\n human_grid = Grid(\"HUMAN\", 15)\r\n undo_button.draw_button()\r\n undo_button.print_message_for_button()\r\n undo_button.change_color_on_hover()\r\n mouse = pygame.mouse.get_pos()\r\n if not human_ships_to_draw:\r\n undo_button.draw_button(LIGHT_GRAY)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n ships_not_created = False\r\n game_over = True\r\n elif undo_button.rect.collidepoint(mouse) and event.type == pygame.MOUSEBUTTONDOWN:\r\n if human_ships_to_draw:\r\n screen.fill(WHITE, message_rect_for_drawing_ships)\r\n deleted_ship = human_ships_to_draw.pop()\r\n num_ships_list[len(deleted_ship) - 1] -= 1\r\n update_used_blocks(\r\n deleted_ship, used_blocks_for_manual_drawing.discard)\r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n drawing = True\r\n x_start, y_start = event.pos\r\n start = x_start, y_start\r\n ship_size = (0, 0)\r\n elif drawing and event.type == pygame.MOUSEMOTION:\r\n x_end, y_end = event.pos\r\n end = x_end, y_end\r\n ship_size = x_end - x_start, y_end - y_start\r\n elif drawing and event.type == pygame.MOUSEBUTTONUP:\r\n x_end, y_end = event.pos\r\n drawing = False\r\n ship_size = (0, 0)\r\n start_block = ((x_start - left_margin) // block_size + 1,\r\n (y_start - upper_margin) // block_size + 1)\r\n end_block = ((x_end - left_margin) // block_size + 1,\r\n (y_end - upper_margin) // block_size + 1)\r\n if start_block > end_block:\r\n start_block, end_block = end_block, start_block\r\n temp_ship = []\r\n if 15 < start_block[0] < 26 and 0 < start_block[1] < 11 and 15 < end_block[0] < 26 and 0 < end_block[1] < 11:\r\n screen.fill(WHITE, message_rect_for_drawing_ships)\r\n if start_block[0] == end_block[0] and (end_block[1] - start_block[1]) < 4:\r\n for block in range(start_block[1], end_block[1]+1):\r\n temp_ship.append((start_block[0], block))\r\n elif start_block[1] == end_block[1] and (end_block[0] - start_block[0]) < 4:\r\n for block in range(start_block[0], end_block[0]+1):\r\n temp_ship.append((block, start_block[1]))\r\n else:\r\n show_message_at_rect_center(\r\n \"SHIP IS TOO LARGE! Try again!\", message_rect_for_drawing_ships)\r\n else:\r\n show_message_at_rect_center(\r\n \"SHIP IS BEYOND YOUR GRID! Try again!\", message_rect_for_drawing_ships)\r\n if temp_ship:\r\n temp_ship_set = set(temp_ship)\r\n if ship_is_valid(temp_ship_set, used_blocks_for_manual_drawing):\r\n if check_ships_numbers(temp_ship, num_ships_list):\r\n num_ships_list[len(temp_ship) - 1] += 1\r\n human_ships_to_draw.append(temp_ship)\r\n human_ships_set |= temp_ship_set\r\n update_used_blocks(\r\n temp_ship, used_blocks_for_manual_drawing.add)\r\n else:\r\n show_message_at_rect_center(\r\n f\"There already are enough of {len(temp_ship)} ships!\", message_rect_for_drawing_ships)\r\n else:\r\n show_message_at_rect_center(\r\n \"SHIPS ARE TOUCHING! Try again\", message_rect_for_drawing_ships)\r\n if len(human_ships_to_draw) == 10:\r\n ships_not_created = False\r\n human_ships_working = copy.deepcopy(human_ships_to_draw)\r\n screen.fill(WHITE, rect_for_messages_and_buttons)\r\n pygame.draw.rect(screen, BLACK, (start, ship_size), 3)\r\n draw_ships(human_ships_to_draw)\r\n pygame.display.update()\r\n\r\n while not game_over:\r\n draw_ships(destroyed_computer_ships)\r\n draw_ships(human_ships_to_draw)\r\n if not (dotted_set | hit_blocks):\r\n show_message_at_rect_center(\r\n \"GAME STARTED! YOUR MOVE!\", message_rect_computer)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n game_over = True\r\n elif not computer_turn and event.type == pygame.MOUSEBUTTONDOWN:\r\n x, y = event.pos\r\n if (left_margin < x < left_margin + 10 * block_size) and (\r\n upper_margin < y < upper_margin + 10 * block_size):\r\n fired_block = ((x - left_margin) // block_size + 1,\r\n (y - upper_margin) // block_size + 1)\r\n computer_turn = not check_hit_or_miss(fired_block, computer_ships_working, False, computer.ships,\r\n computer.ships_set)\r\n draw_from_dotted_set(dotted_set)\r\n draw_hit_blocks(hit_blocks)\r\n screen.fill(WHITE, message_rect_computer)\r\n show_message_at_rect_center(\r\n f\"Your last shot: {LETTERS[fired_block[0]-1] + str(fired_block[1])}\", message_rect_computer, color=BLACK)\r\n else:\r\n show_message_at_rect_center(\r\n \"Your shot is outside of grid! Try again\", message_rect_computer)\r\n if computer_turn:\r\n set_to_shoot_from = computer_available_to_fire_set\r\n if around_last_computer_hit_set:\r\n set_to_shoot_from = around_last_computer_hit_set\r\n fired_block = computer_shoots(set_to_shoot_from)\r\n computer_turn = check_hit_or_miss(\r\n fired_block, human_ships_working, True, human_ships_to_draw, human_ships_set)\r\n draw_from_dotted_set(dotted_set)\r\n draw_hit_blocks(hit_blocks)\r\n screen.fill(WHITE, message_rect_human)\r\n show_message_at_rect_center(\r\n f\"Computer's last shot: {LETTERS[fired_block[0] - 16] + str(fired_block[1])}\", message_rect_human, color=BLACK)\r\n if not computer.ships_set:\r\n show_message_at_rect_center(\r\n \"YOU WON!\", (0, 0, size[0], size[1]), game_over_font)\r\n game_over = True\r\n if not human_ships_set:\r\n show_message_at_rect_center(\r\n \"YOU LOST!\", (0, 0, size[0], size[1]), game_over_font)\r\n game_over = True\r\n pygame.display.update()\r\n\r\n while game_over:\r\n screen.fill(WHITE, rect_for_messages_and_buttons)\r\n play_again_button.draw_button()\r\n play_again_button.print_message_for_button()\r\n play_again_button.change_color_on_hover()\r\n quit_game_button.draw_button()\r\n quit_game_button.change_color_on_hover()\r\n\r\n mouse = pygame.mouse.get_pos()\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n elif event.type == pygame.MOUSEBUTTONDOWN and play_again_button.rect.collidepoint(mouse):\r\n main()\r\n elif event.type == pygame.MOUSEBUTTONDOWN and quit_game_button.rect.collidepoint(mouse):\r\n pygame.quit()\r\n sys.exit()\r\n pygame.display.update()\r\n\r\n\r\nmain()\r\n", "sub_path": "Battleship.py", "file_name": "Battleship.py", "file_ext": "py", "file_size_in_byte": 38697, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.init", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 80, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 141, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 155, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 163, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 163, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 254, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 256, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 257, "usage_type": "call"}, {"api_name": "pygame.time.delay", "line_number": 366, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 366, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 367, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 543, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 543, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 552, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 552, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 563, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 563, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 565, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 565, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 579, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 582, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 626, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 683, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 683, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 684, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 684, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 685, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 690, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 693, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 697, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 700, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 700, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 710, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 710, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 713, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 713, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 714, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 717, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 724, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEMOTION", "line_number": 729, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONUP", "line_number": 733, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 775, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 777, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 777, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 779, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 779, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 787, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 787, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 788, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 790, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 826, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 826, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 836, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 836, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 837, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 837, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 838, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 839, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 840, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 841, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 843, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 844, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 845, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 846, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 846, "usage_type": "attribute"}]} +{"seq_id": "364301608", "text": "from django.contrib.auth import authenticate, login\nfrom django.core.mail import send_mail\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render, redirect\nfrom django.views.generic.base import TemplateView\nfrom django.views.generic import View\n\n\nfrom seadssite.forms import UserForm\nfrom seadssite.models import Device\n\n\nclass IndexView(TemplateView):\n \"\"\"\n load main page as \"index\"\n \"\"\"\n template_name = 'index.html'\n\nclass DashboardTest(TemplateView):\n \"\"\"\n load main page as \"index\"\n \"\"\"\n template_name = 'dashboard_test.html'\n\n\nclass RegisterView(View):\n \"\"\"\n registration page controller\n sends a user to the registration page\n \"\"\"\n form_class = UserForm\n template_name = 'registration/register.html'\n\n def get(self, request):\n if request.user.is_authenticated():\n return redirect('/dashboard')\n user_form = self.form_class()\n return render(request, 'registration/register.html', {'form': user_form})\n\n def post(self, request):\n user_form = UserForm(data=request.POST)\n if user_form.is_valid():\n # Creating a new user\n user = user_form.save(commit=False) #don't save to db, we do this after setting the password\n user.set_password(user.password)\n user.save()\n # log the user in and send them to the homepage\n user = authenticate(username=request.POST['username'], password=request.POST['password'])\n login(request, user)\n return HttpResponseRedirect('/dashboard')\n # TODO: sending a welcome email to the new user needs to be implemented here\n else:\n return render(request, self.template_name, {'form': user_form})\n\n'''\ndevice dashboard page controller\nTODO: users can delete each others devices I think\n'''\n\ndef DashboardView(request):\n # get needed variables set up, and try to make sure only the users devices are shown\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n current_user = request.user\n\n print(request.POST)\n\n # if the user clicked register (and dashboard -- that is register a device)\n # we set the new id and new name as what was submitted in the form\n # if there are any alerts (invalid id etc), they will get appened to alert\n if request.POST.get('device_id'):\n new_device_id = request.POST.get('device_id')\n new_device_name = request.POST.get('device_name')\n Device.objects.register_device(new_device_id, new_device_name, current_user)\n # if the user clicked delete\n # we delete the specified device\n elif request.POST.get('delete'):\n device_id = request.POST.get('delete')\n device = Device.objects.get(device_id=device_id)\n device.deactivate_device()\n\n connected_user_devices = Device.objects.filter(user=current_user, is_active=True)\n return render(request, 'dashboard.html', {'devices': connected_user_devices})\n\ndef TimerView(request):\n # get needed variables set up, and try to make sure only the users devices are shown\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n current_user = request.user\n\n connected_user_devices = Device.objects.filter(user=current_user, is_active=True)\n\n return render(request, 'timer.html', {'devices': connected_user_devices})\n\ndef DevicesView(request):\n # get needed variables set up, and try to make sure only the users devices are shown\n current_user = request.user\n\n # if the user clicked the editable field and submitted an edit\n # changes the edited field to the new submission\n if request.POST.get('name') == \"modify\":\n device_id = request.POST.get('pk')\n device = Device.objects.get(device_id=device_id)\n new_name = request.POST.get('value')\n device.name = new_name\n device.save()\n # if the user clicked register\n # we set the new id and new name as what was submitted in the form\n # if there are any alerts (invalid id etc), they will get appened to alert\n elif request.POST.get('register'):\n new_device_id = request.POST.get('device_id')\n new_device_name = request.POST.get('device_name')\n Device.objects.register_device(new_device_id, new_device_name, current_user)\n\n # if the user clicked delete\n # fetch request, process data package\n elif request.POST.get('delete'):\n device_id = request.POST.get('delete')\n device = Device.objects.get(device_id=device_id)\n device.deactivate_device()\n\n user_devices = Device.objects.filter(user=current_user, is_active=True)\n return render(request, 'devices.html', {'devices': user_devices})\n\ndef graph(request):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n current_user = request.user\n\n connected_user_devices = Device.objects.filter(user=current_user, is_active=True)\n\n return render(request, 'graph.html', {'devices': connected_user_devices})\n\n\n### WEB2PY API ###\n\ndef get_data():\n today = datetime.utcnow()\n if request.get_vars.start is not None:\n start = request.vars.start\n start = start.split(',')\n start = datetime(int(start[0]),int(start[1]),int(start[2]))\n print(start)\n else:\n raise HTTP(400, \"No room specified\")\n if request.get_vars.end is not None:\n end = request.vars.end\n end = end.split(',')\n end = datetime(int(end[0]), int(end[1]), int(end[2]))\n print(end)\n else:\n end = None\n if request.get_vars.room is not None:\n room = request.vars.room\n else:\n raise HTTP(400,\"No room specified\")\n if request.get_vars.device is not None:\n device = request.vars.device\n else:\n raise HTTP(400, \"No device specified\")\n\n # text = 'connection success'\n data = []\n time = []\n if request.get_vars.end is None: #get one day from device usage\n rows = get_oneday(room, device, start)\n for i, r in enumerate(rows):\n data.append(r.use_time)\n time.append(r.on_off)\n else: # get a time period from daily usage\n rows = get_period(room, device, start, end)\n for i, r in enumerate(rows):\n data.append(r.total_usage)\n time.append(r.use_day)\n\n return response.json(dict(\n device=device,\n data=data,\n time=time,\n ))\n\n\ndef get_room():\n rows = db(db.user_info.user_id==request.vars.user_id).select() #user_info\n rooms = ['Reserve For Home']\n row = rows[0]\n rowDict = json.loads(row['rooms'])\n roomsDict = rowDict['rooms']\n for r in roomsDict:\n rooms.append(\n dict(\n room=r,\n icon_path=roomsDict[r]['icon_path'],\n device=roomsDict[r]['device'],\n mod_header=roomsDict[r]['mod_header'],\n )\n )\n print(rooms)\n return response.json(dict(\n rooms=rooms,\n ))\n\n\n# @auth.requires_signature()\ndef add_room():\n action_room = db(db.demo_rooms).select().first()\n action_room.room = \",\".join(request.post_vars.rooms)\n action_room.update_record()\n print(\"Insert success\")\n return get_room()\n\n\ndef get_oneday(room, dev, day):\n r = (db.device_usage.room == room)\n d = (db.device_usage.device == dev)\n s = (db.device_usage.use_time >= day)\n e = (db.device_usage.use_time < day+timedelta(days=1))\n rows = db(r & d & s & e).select(orderby=db.device_usage.use_time)\n return rows\n\ndef get_period(room, dev, start, end):\n r = (db.daily_usage.room == room)\n d = (db.daily_usage.device == dev)\n s = (db.daily_usage.use_day >= start)\n e = (db.daily_usage.use_day <= end)\n rows = db(r & d & s & e).select(orderby=db.daily_usage.use_day)\n return rows\n", "sub_path": "seadssite/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7893, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.views.generic.base.TemplateView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateView", "line_number": 19, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 26, "usage_type": "name"}, {"api_name": "seadssite.forms.UserForm", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "seadssite.forms.UserForm", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 49, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 63, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects.register_device", "line_number": 74, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 74, "usage_type": "name"}, {"api_name": "seadssite.models.Device.objects.get", "line_number": 79, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 79, "usage_type": "name"}, {"api_name": "seadssite.models.Device.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 82, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 88, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects.filter", "line_number": 91, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 91, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects.get", "line_number": 103, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 103, "usage_type": "name"}, {"api_name": "seadssite.models.Device.objects.register_device", "line_number": 113, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 113, "usage_type": "name"}, {"api_name": "seadssite.models.Device.objects.get", "line_number": 119, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 119, "usage_type": "name"}, {"api_name": "seadssite.models.Device.objects.filter", "line_number": 122, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 122, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 123, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 127, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects.filter", "line_number": 130, "usage_type": "call"}, {"api_name": "seadssite.models.Device.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "seadssite.models.Device", "line_number": 130, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "512091339", "text": "import os\nimport re\n\n\n# from wordsegment import load, segment\n# load()\nclass NltkFactory:\n lemm = stem = stop = None\n \n @staticmethod\n def lemmatize(word):\n if not NltkFactory.lemm:\n from nltk.stem import WordNetLemmatizer\n NltkFactory.lemm = WordNetLemmatizer()\n return NltkFactory.lemm.lemmatize(word)\n \n @staticmethod\n def stemming(word):\n if not NltkFactory.stem:\n from nltk.stem.snowball import EnglishStemmer\n NltkFactory.stem = EnglishStemmer()\n return NltkFactory.stem.stem(word)\n \n @staticmethod\n def get_stop_words():\n if not NltkFactory.stop:\n from nltk.corpus import stopwords\n NltkFactory.stop = set(stopwords.words('english'))\n return NltkFactory.stop\n\n\nlemmatize = NltkFactory.lemmatize\nstemming = NltkFactory.stemming\n\nfile_path = os.path.abspath(os.path.dirname(__file__))\nmy_stop_words_file = os.path.join(file_path, \"stopwords.txt\")\nnltk_stop_words = my_stop_words = None\n# nltk_stop_words = NltkFactory.get_stop_words()\n# my_stop_words = set(fu.read_lines(my_stop_words_file, newline='\\r\\n'))\ntokenize_pattern = r\"[a-zA-Z0-9]+(?:[_-][a-zA-Z0-9]+)*\"\n\n\ndef tokenize(text, pattern): return re.findall(pattern, text)\n\n\ndef has_enough_alpha(text, threshold):\n text = re.sub('\\s', '', text)\n if len(text) == 0:\n return False\n alphas = re.findall('[a-zA-Z]', text)\n return len(alphas) / len(text) >= threshold\n\n\nif __name__ == '__main__':\n print(my_stop_words)\n", "sub_path": "utils/pattern_utils.py", "file_name": "pattern_utils.py", "file_ext": "py", "file_size_in_byte": 1534, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer'}.lemm", "line_number": 14, "usage_type": "attribute"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 14, "usage_type": "call"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer'}.lemm.lemmatize", "line_number": 15, "usage_type": "call"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer'}.lemm", "line_number": 15, "usage_type": "attribute"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer'}.stem", "line_number": 19, "usage_type": "attribute"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer'}.stem", "line_number": 21, "usage_type": "attribute"}, {"api_name": "nltk.stem.snowball.EnglishStemmer", "line_number": 21, "usage_type": "call"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer'}.stem.stem", "line_number": 22, "usage_type": "call"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer'}.stem", "line_number": 22, "usage_type": "attribute"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer'}.stop", "line_number": 26, "usage_type": "attribute"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer', 'stopwords': 'nltk.corpus.stopwords'}.stop", "line_number": 28, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 28, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 28, "usage_type": "name"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer', 'stopwords': 'nltk.corpus.stopwords'}.stop", "line_number": 29, "usage_type": "attribute"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer', 'stopwords': 'nltk.corpus.stopwords'}.lemmatize", "line_number": 32, "usage_type": "attribute"}, {"api_name": "{'WordNetLemmatizer': 'nltk.stem.WordNetLemmatizer', 'EnglishStemmer': 'nltk.stem.snowball.EnglishStemmer', 'stopwords': 'nltk.corpus.stopwords'}.stemming", "line_number": 33, "usage_type": "attribute"}, {"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.dirname", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "438743639", "text": "import openpyxl\nclass DoExcel:\n def __init__(self,excel_name,sheet_name):\n self.workbook = openpyxl.load_workbook(excel_name)\n self.sheet = self.workbook[sheet_name]\n\n def get_case(self):\n max_row = self.sheet.max_row # 最大行数\n # max_col = self.sheet.max_column #最大列数\n\n # case = []\n case_set = set()\n for r in range(2,max_row+1):\n case_time = self.sheet.cell(r,1).value\n # case_data = self.sheet.cell(r,2).value\n case_set.add(case_time) #去重的时间\n return case_set\n\n def get_time(self):\n max_row = self.sheet.max_row\n case = []\n for r in range(2,max_row+1):\n case_time = self.sheet.cell(r,1).value\n case.append(case_time) #所有时间\n return case\n\n\nif __name__ == '__main__':\n from openpyxl import load_workbook\n from configparser import ConfigParser\n\n cf = ConfigParser()\n cf.read('wifi.conf', encoding='utf-8')\n excel = cf.get('wifi_data', 'excel')\n url = \"C:\\\\Users\\\\t\\Desktop\\WiFi-data\\\\time_data\\\\\" + excel\n print(url)\n do_excel1 = DoExcel(url, 'old')\n do_excel2 = DoExcel(url, 'new')\n #去重时间\n old_set = list(do_excel1.get_case())\n new_set = list(do_excel2.get_case())\n #所有时间\n old_case = do_excel1.get_time()\n\n wb = load_workbook(url)\n sheet3 = wb['old_repetition']\n # sheet4 = wb['new_repetition']\n\n a = 1\n for item in old_set:\n a+=1\n sheet3.cell(1,1).value = \"老版本时间去重复\"\n sheet3.cell(a,1).value = item\n wb.save(url)\n\n # for item in new_set:\n # a+=1\n # sheet4.cell(a,1).value = item\n\n # b = 0\n # for i in old_set:\n # b +=1\n # if i in old_case:\n # old_data = do_excel1.sheet.cell(b,3).value\n # # old_data_cases.append(old_data)\n # sheet3.cell(b,2).value = old_data\n # wb.save(url)\n\n\n\n\n\n", "sub_path": "wifi_data/time_old.py", "file_name": "time_old.py", "file_ext": "py", "file_size_in_byte": 1949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 4, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 32, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "51831945", "text": "import hypothesis.strategies as st\n\ndef selection_sort(data):\n \"\"\"Sort contents of data in place.\n\nThis is a generator which yields each algorithm step (comparison or\nswap), allow for visualization or instrumentation. The caller is\nresponsible for performing swaps.\n\"\"\"\n for dest in range(len(data) - 1):\n yield 'label', 'Find Smallest'\n smallest_i = dest\n yield 'focus', dest\n for i in range(dest + 1, len(data)):\n yield 'cmp', smallest_i, i\n if data[i] < data[smallest_i]:\n smallest_i = i\n yield 'focus', i\n yield 'label', 'Move to Front'\n yield 'swap', dest, smallest_i\n\n\ndef merge(data, left, mid, right):\n # Copy each sub-list, reverse them so we can pop from the end, and\n # merge back into the main list. This isn't terribly efficient but\n # it's convenient.\n yield 'merge', (left, mid), (mid, right)\n sub_left = data[left:mid]\n sub_right = data[mid:right]\n for i in range(left, right):\n if not sub_right or (sub_left and sub_left[0] < sub_right[0]):\n yield 'set', i, sub_left[0]\n del sub_left[0]\n else:\n yield 'set', i, sub_right[0]\n del sub_right[0]\n assert len(sub_left) == 0\n assert len(sub_right) == 0\n\n\ndef merge_sort(data, left=None, right=None):\n \"\"\"Merge sort in place. Like selection_sort, this is a generator.\"\"\"\n if left is None:\n left = 0\n if right is None:\n right = len(data)\n if right <= left + 1:\n return\n\n mid = (left + right) // 2\n yield 'subdivide', left, mid\n yield from merge_sort(data, left, mid)\n yield 'subdivide', mid + 1, right\n yield from merge_sort(data, mid, right)\n yield from merge(data, left, mid, right)\n\n\ndef quicksort(data, left=None, right=None):\n \"\"\"Quick sort in place. Like selection_sort, this is a generator.\"\"\"\n if left is None:\n left = 0\n if right is None:\n right = len(data) - 1\n if right <= left:\n return\n\n mid = None\n def partition(data, left, right):\n pi = (left + right) // 2\n pivot = data[pi]\n yield 'label', 'Partition, pivot=%s' % pivot\n i = left\n j = right\n while True:\n yield 'focus', pi\n while True:\n yield 'cmp', i, pi\n if i == pi or data[i] > pivot:\n yield 'focus', i, pi\n break\n i += 1\n while True:\n yield 'cmp', j, pi\n if j == pi or data[j] < pivot:\n yield 'focus', i, j\n break\n j -= 1\n\n nonlocal mid\n if i >= j:\n mid = j\n return\n elif data[i] == pivot and data[j] == pivot:\n mid = i\n return\n\n yield 'swap', i, j\n if i == pi:\n pi = j\n elif j == pi:\n pi = i\n\n yield from partition(data, left, right)\n assert(mid is not None)\n\n yield 'label', 'Sort Left Side'\n yield 'subdivide', left, mid + 1\n yield from quicksort(data, left, mid)\n yield 'label', 'Sort Right Side'\n yield 'subdivide', mid + 1, right + 1\n yield from quicksort(data, mid + 1, right)\n yield 'subdivide', left, right + 1\n yield 'label', 'Sorted from %d to %d' % (left, right)\n\n\ndef perform_effect(effect, data):\n \"\"\"Apply an effect to a list, modifying the data argument.\"\"\"\n kind = effect[0]\n if kind == 'swap':\n a, b = effect[1:]\n data[a], data[b] = data[b], data[a]\n if kind == 'set':\n a, b = effect[1:]\n data[a] = b\n\n\ndef run(sort, data, callback=None):\n \"\"\"Run a sorting algorithm, passing all effects to optional callback.\"\"\"\n for effect in sort(data):\n if callback:\n callback(*effect)\n perform_effect(effect, data)\n\n\ndef print_effects(sort, data):\n \"\"\"Run a sorting algorithm, printing all steps.\"\"\"\n run(sort, data, lambda *e: print(*e))\n\n\ndef count_steps(sort, data):\n \"\"\"Run a sorting algorithm, returning the number of effects performed.\"\"\"\n\n def inc_count(effect, *args):\n nonlocal count\n if effect in ['cmp', 'swap', 'set']:\n count = count + 1\n count = 0\n\n run(sort, data, inc_count)\n return count\n\n\ndef random_list(length, max_value=None):\n return st.lists(st.integers(min_value=1, max_value=max_value),\n min_size=length, max_size=length).example()\n\n\nif __name__ == '__main__':\n lst = [-2, -2]\n mysort = quicksort\n\n print_effects(mysort, lst)\n print(lst)\n print(count_steps(mysort, lst), 'steps')\n", "sub_path": "sort.py", "file_name": "sort.py", "file_ext": "py", "file_size_in_byte": 4695, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "hypothesis.strategies.lists", "line_number": 154, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 154, "usage_type": "name"}, {"api_name": "hypothesis.strategies.integers", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "131282232", "text": "#!/usr/bin/python2.7\n\nimport os\nimport sys\n\nimport argparse\nimport logging\n\ndef argument_parser():\n parser = argparse.ArgumentParser()\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\"-v\", \"--verbose\", action=\"store_true\")\n group.add_argument(\"-q\", \"--quite\", action=\"store_true\")\n parser.add_argument(\"-l\", \"--listen\", action=\"store_true\", help=\"Listen to the incoming connection\")\n parser.add_argument(\"-p\", \"--port\", required=True, type=int, help=\"Port to listen to\")\n args = parser.parse_args()\n return args\n\ndef main():\n # Get all arguments first\n args = argument_parser()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "nc_alt/nc.py", "file_name": "nc.py", "file_ext": "py", "file_size_in_byte": 667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "88292329", "text": "from aiohttp.client import ClientSession\n\nfrom baas.conf import settings\nfrom baas.models import Account\n\n\nclass AccountClient:\n @classmethod\n async def get_by_id(cls, acc_id) -> Account:\n async with ClientSession() as session:\n async with session.get(\n f\"{settings.ACCOUNT_SERVICE_ADDRESS}/accounts/{acc_id}\"\n ) as resp:\n data = await resp.json()\n return Account(**data)\n", "sub_path": "baas/clients/account.py", "file_name": "account.py", "file_ext": "py", "file_size_in_byte": 452, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "aiohttp.client.ClientSession", "line_number": 10, "usage_type": "call"}, {"api_name": "baas.conf.settings.ACCOUNT_SERVICE_ADDRESS", "line_number": 12, "usage_type": "attribute"}, {"api_name": "baas.conf.settings", "line_number": 12, "usage_type": "name"}, {"api_name": "baas.models.Account", "line_number": 15, "usage_type": "call"}, {"api_name": "baas.models.Account", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "385146439", "text": "# coding: utf-8\n\nfrom __future__ import unicode_literals\n\nfrom xml.etree import ElementTree as ET\n\nfrom django.contrib.auth.models import User\nfrom django.core.urlresolvers import set_urlconf, get_resolver, get_urlconf\nfrom django.db import IntegrityError, transaction\nfrom django.test import TestCase, RequestFactory\nfrom django.test.utils import override_settings\nfrom django.utils import six\n\nfrom debug_toolbar.middleware import DebugToolbarMiddleware, show_toolbar\nfrom debug_toolbar.panels.request_vars import RequestVarsDebugPanel\n\nfrom .base import BaseTestCase\n\n\nrf = RequestFactory()\n\n\n@override_settings(DEBUG=True)\nclass DebugToolbarTestCase(BaseTestCase):\n\n urls = 'tests.urls'\n\n def test_show_toolbar(self):\n self.assertTrue(show_toolbar(self.request))\n\n def test_show_toolbar_DEBUG(self):\n with self.settings(DEBUG=False):\n self.assertFalse(show_toolbar(self.request))\n\n def test_show_toolbar_INTERNAL_IPS(self):\n with self.settings(INTERNAL_IPS=[]):\n self.assertFalse(show_toolbar(self.request))\n\n def test_request_urlconf_string(self):\n request = rf.get('/')\n set_urlconf('tests.urls')\n middleware = DebugToolbarMiddleware()\n\n middleware.process_request(request)\n\n patterns = get_resolver(get_urlconf()).url_patterns\n self.assertTrue(hasattr(patterns[1], '_callback_str'))\n self.assertEqual(patterns[-1]._callback_str, 'tests.views.execute_sql')\n\n def test_request_urlconf_string_per_request(self):\n request = rf.get('/')\n set_urlconf('debug_toolbar.urls')\n middleware = DebugToolbarMiddleware()\n\n middleware.process_request(request)\n set_urlconf('tests.urls')\n middleware.process_request(request)\n\n patterns = get_resolver(get_urlconf()).url_patterns\n self.assertTrue(hasattr(patterns[1], '_callback_str'))\n self.assertEqual(patterns[-1]._callback_str, 'tests.views.execute_sql')\n\n def test_request_urlconf_module(self):\n request = rf.get('/')\n request.urlconf = __import__('tests.urls').urls\n middleware = DebugToolbarMiddleware()\n\n middleware.process_request(request)\n\n self.assertFalse(isinstance(request.urlconf, six.string_types))\n\n patterns = request.urlconf.urlpatterns\n self.assertTrue(hasattr(patterns[1], '_callback_str'))\n self.assertEqual(patterns[-1]._callback_str, 'tests.views.execute_sql')\n\n def test_tuple_urlconf(self):\n request = rf.get('/')\n urls = __import__('tests.urls').urls\n urls.urlpatterns = tuple(urls.urlpatterns)\n request.urlconf = urls\n middleware = DebugToolbarMiddleware()\n\n middleware.process_request(request)\n\n self.assertFalse(isinstance(request.urlconf, six.string_types))\n\n def _resolve_stats(self, path):\n # takes stats from RequestVars panel\n self.request.path = path\n panel = self.toolbar.get_panel(RequestVarsDebugPanel)\n panel.process_request(self.request)\n panel.process_response(self.request, self.response)\n return self.toolbar.stats['requestvars']\n\n def test_url_resolving_positional(self):\n stats = self._resolve_stats('/resolving1/a/b/')\n self.assertEqual(stats['view_urlname'], 'positional-resolving')\n self.assertEqual(stats['view_func'], 'tests.views.resolving_view')\n self.assertEqual(stats['view_args'], ('a', 'b'))\n self.assertEqual(stats['view_kwargs'], {})\n\n def test_url_resolving_named(self):\n stats = self._resolve_stats('/resolving2/a/b/')\n self.assertEqual(stats['view_args'], ())\n self.assertEqual(stats['view_kwargs'], {'arg1': 'a', 'arg2': 'b'})\n\n def test_url_resolving_mixed(self):\n stats = self._resolve_stats('/resolving3/a/')\n self.assertEqual(stats['view_args'], ('a',))\n self.assertEqual(stats['view_kwargs'], {'arg2': 'default'})\n\n def test_url_resolving_bad(self):\n stats = self._resolve_stats('/non-existing-url/')\n self.assertEqual(stats['view_urlname'], 'None')\n self.assertEqual(stats['view_args'], 'None')\n self.assertEqual(stats['view_kwargs'], 'None')\n self.assertEqual(stats['view_func'], '')\n\n\n@override_settings(DEBUG=True)\nclass DebugToolbarIntegrationTestCase(TestCase):\n\n urls = 'tests.urls'\n\n def test_middleware(self):\n response = self.client.get('/execute_sql/')\n self.assertEqual(response.status_code, 200)\n\n @override_settings(DEFAULT_CHARSET='iso-8859-1')\n def test_non_utf8_charset(self):\n response = self.client.get('/regular/ASCII/')\n self.assertContains(response, 'ASCII') # template\n self.assertContains(response, 'djDebug') # toolbar\n\n response = self.client.get('/regular/LÀTÍN/')\n self.assertContains(response, 'LÀTÍN') # template\n self.assertContains(response, 'djDebug') # toolbar\n\n def test_non_ascii_bytes_in_db_params(self):\n response = self.client.get('/non_ascii_bytes_in_db_params/')\n if six.PY3:\n self.assertContains(response, 'djàngó')\n else:\n self.assertContains(response, 'dj\\\\xe0ng\\\\xf3')\n\n def test_non_ascii_session(self):\n response = self.client.get('/set_session/')\n if six.PY3:\n self.assertContains(response, 'où')\n else:\n self.assertContains(response, 'o\\\\xf9')\n self.assertContains(response, 'l\\\\xc3\\\\xa0')\n\n def test_object_with_non_ascii_repr_in_context(self):\n response = self.client.get('/non_ascii_context/')\n self.assertContains(response, 'nôt åscíì')\n\n def test_object_with_non_ascii_repr_in_request_vars(self):\n response = self.client.get('/non_ascii_request/')\n self.assertContains(response, 'nôt åscíì')\n\n def test_xml_validation(self):\n response = self.client.get('/regular/XML/')\n ET.fromstring(response.content) # shouldn't raise ParseError\n\n def test_view_executed_once(self):\n with self.settings(\n DEBUG_TOOLBAR_PANELS=['debug_toolbar.panels.profiling.ProfilingDebugPanel']):\n\n self.assertEqual(User.objects.count(), 0)\n\n response = self.client.get('/new_user/')\n self.assertContains(response, 'Profiling')\n self.assertEqual(User.objects.count(), 1)\n\n with self.assertRaises(IntegrityError):\n if hasattr(transaction, 'atomic'): # Django >= 1.6\n with transaction.atomic():\n response = self.client.get('/new_user/')\n else:\n response = self.client.get('/new_user/')\n self.assertEqual(User.objects.count(), 1)\n", "sub_path": "tests/test_integration.py", "file_name": "test_integration.py", "file_ext": "py", "file_size_in_byte": 6766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.test.RequestFactory", "line_number": 20, "usage_type": "call"}, {"api_name": "base.BaseTestCase", "line_number": 24, "usage_type": "name"}, {"api_name": "debug_toolbar.middleware.show_toolbar", "line_number": 29, "usage_type": "call"}, {"api_name": "debug_toolbar.middleware.show_toolbar", "line_number": 33, "usage_type": "call"}, {"api_name": "debug_toolbar.middleware.show_toolbar", "line_number": 37, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.set_urlconf", "line_number": 41, "usage_type": "call"}, {"api_name": "debug_toolbar.middleware.DebugToolbarMiddleware", "line_number": 42, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.get_resolver", "line_number": 46, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.get_urlconf", "line_number": 46, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.set_urlconf", "line_number": 52, "usage_type": "call"}, {"api_name": "debug_toolbar.middleware.DebugToolbarMiddleware", "line_number": 53, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.set_urlconf", "line_number": 56, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.get_resolver", "line_number": 59, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.get_urlconf", "line_number": 59, "usage_type": "call"}, {"api_name": "debug_toolbar.middleware.DebugToolbarMiddleware", "line_number": 66, "usage_type": "call"}, {"api_name": "django.utils.six.string_types", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.utils.six", "line_number": 70, "usage_type": "name"}, {"api_name": "debug_toolbar.middleware.DebugToolbarMiddleware", "line_number": 81, "usage_type": "call"}, {"api_name": "django.utils.six.string_types", "line_number": 85, "usage_type": "attribute"}, {"api_name": "django.utils.six", "line_number": 85, "usage_type": "name"}, {"api_name": "debug_toolbar.panels.request_vars.RequestVarsDebugPanel", "line_number": 90, "usage_type": "argument"}, {"api_name": "django.test.utils.override_settings", "line_number": 23, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 121, "usage_type": "name"}, {"api_name": "django.test.utils.override_settings", "line_number": 129, "usage_type": "call"}, {"api_name": "django.utils.six.PY3", "line_number": 141, "usage_type": "attribute"}, {"api_name": "django.utils.six", "line_number": 141, "usage_type": "name"}, {"api_name": "django.utils.six.PY3", "line_number": 148, "usage_type": "attribute"}, {"api_name": "django.utils.six", "line_number": 148, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 164, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 164, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.count", "line_number": 170, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 170, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 170, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.count", "line_number": 174, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 174, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 174, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 176, "usage_type": "argument"}, {"api_name": "django.db.transaction", "line_number": 177, "usage_type": "argument"}, {"api_name": "django.db.transaction.atomic", "line_number": 178, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 178, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.count", "line_number": 182, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 182, "usage_type": "name"}, {"api_name": "django.test.utils.override_settings", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "192252547", "text": "import pandas as pd \r\nimport numpy as np\r\nimport networkx as nx\r\n\r\ndef correlation(prices, reverse=False):\r\n \"\"\"\r\n Calculates the correlation matrix from asset prices\r\n\r\n :param prices: (pd.DataFrame) Asset price data\r\n :param reverse: (bool) When True, the data order of the data is\r\n reversed to cope with different data \r\n indexing. (False by default)\r\n :return: (pd.DataFrame) Correlation matrix\r\n \"\"\"\r\n \r\n #Calculate returns\r\n #Take reverse order of index into account\r\n if reverse == False:\r\n returns = np.log(prices.divide(prices.shift(1))).iloc[1:,:]\r\n elif reverse == True:\r\n returns = np.log(prices.divide(prices.shift(-1))).iloc[:-1,:]\r\n\r\n #Calculate correlation matrix\r\n correlation = returns.corr()\r\n\r\n return correlation\r\n\r\ndef distance(correlation):\r\n \"\"\"\r\n Calculates the distance matrix from a correlation matrix\r\n\r\n :param prices: (pd.DataFrame) Correlation matrix\r\n :return: (pd.DataFrame) Distance matrix\r\n \"\"\"\r\n\r\n #Create pandas DataFrame of ones\r\n ones = pd.DataFrame(\r\n np.repeat(\r\n 1, correlation.shape[1]**2\r\n ).reshape(\r\n correlation.shape[1], \r\n correlation.shape[1]\r\n ), \r\n index=correlation.columns, columns=correlation.columns\r\n )\r\n\r\n #Calculate distance\r\n distance = np.power(ones - correlation, 0.5) * 2**0.5\r\n\r\n return distance\r\n\r\ndef MST(distance):\r\n \"\"\"\r\n Calculates the MST according to Kruskal's algorithm\r\n\r\n :param distance: (pd.DataFrame) Distance matrix\r\n :return: (networkx.Graph) Minimum Spanning Tree\r\n \"\"\"\r\n\r\n #List all distances and their corresponding tickers\r\n node_1, node_2, index = [], [], []\r\n\r\n #Iterate over tickers\r\n for i in range(0, distance.shape[1]):\r\n\r\n #Iterating over tickers, skipping half the matrix due to symmetry \r\n for j in range(i, distance.shape[1]):\r\n\r\n #Skip distance of oneself, since this distance is zero\r\n if i != j:\r\n node_1.append(distance.columns[i])\r\n node_2.append(distance.columns[j])\r\n index.append(distance.iat[i,j])\r\n\r\n #Put lists in a DataFrame and sort by ascending distance\r\n dis_sort = pd.DataFrame(\r\n {'0': node_1, '1': node_2}, index=index\r\n ).sort_index(\r\n ascending=True\r\n )\r\n\r\n #Initialize graph and nodes\r\n G = nx.Graph()\r\n G.add_nodes_from(distance.columns)\r\n\r\n #Create edges based on ascending distance according to Kruskal's algorithm\r\n for i in range(0, len(dis_sort.index)):\r\n \r\n #For each distance, check if a path between tickers already exists, \r\n #if not then create an edge between the tickers\r\n if (\r\n dis_sort.iat[i, 0] not in \r\n list(nx.algorithms.descendants(G, dis_sort.iat[i, 1]))\r\n ):\r\n #Add edge between nodes\r\n G.add_edge(dis_sort.iat[i, 0], dis_sort.iat[i, 1])\r\n\r\n return G\r\n", "sub_path": "Open-Source-Soldier-of-Fortune/robert_submission/kruskal.py", "file_name": "kruskal.py", "file_ext": "py", "file_size_in_byte": 3166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.log", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 76, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 83, "usage_type": "call"}, {"api_name": "networkx.algorithms.descendants", "line_number": 93, "usage_type": "call"}, {"api_name": "networkx.algorithms", "line_number": 93, "usage_type": "attribute"}]} +{"seq_id": "577195878", "text": "\nfrom pymongo import MongoClient \n\nclient = MongoClient(\"mongodb+srv://dicson:gestionhc@cluster0-0pchh.azure.mongodb.net/test?retryWrites=true&w=majority\")\n\ndb = client.get_database('test')\n\nrecords = db.test\n\ncount = records.count_documents({})\n\nprint (count)", "sub_path": "src/database/bd.py", "file_name": "bd.py", "file_ext": "py", "file_size_in_byte": 260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pymongo.MongoClient", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "638274004", "text": "#!/usr/bin/env python3\n#\n# Title: sts_getcreds.py\n#\n# Description: sts_getcreds.py is used in tandem with okta_aws to populate a hidden text file\n# so that the sts creds acquired from okta_aws can be exported as bash shell env variables.\n# AWS CLI and Boto3 reference shell env variables first and then looks for creds in ~/.aws/credentials.\n# sts_getcreds.py parses the ~/.aws/credentials file and breaks the creds out by account.\n#\n# Usage: sts_getcreds.py [ account profile ]\n# sts_getcreds.py -h for help\n#\n# Prereqs:\n# - AWS CLI installed properly\n# - Python 3. Preferrable >=3.7.2\n# - Okta-based AWS role that can be assumed\n# - okta_aws installed to assume an okta-based role and then populate the ~/.aws/credentials\n# file (https://github.com/chef/okta_aws)\n#\n###############################################\n# imports\nimport boto3\nimport argparse\nimport os\nimport configparser\nfrom configparser import RawConfigParser\n\n# define the profile you want to work with\nparser = argparse.ArgumentParser(description='Fetch the AWS account creds keys and tokens')\nparser.add_argument('account', help='declare the AWS account as an argument, either sandbox, prod, or nonprod',\n action='store', default='nonprod')\nargs = parser.parse_args()\n\n# capture the account name\naccount = args.account\n\n# initialize the aws keys and tokens\nAWS_ACCESS_KEY_ID = ''\nAWS_SECRET_ACCESS_KEY = ''\nAWS_SESSION_TOKEN = ''\n\n# get the creds file\npath = os.path.join(os.path.expanduser('~'), '.aws/credentials')\n\n# parse the creds file\nconfig = RawConfigParser()\nconfig.read(path)\n\n# get values for the variables. key - value\nAWS_ACCESS_KEY_ID = config.get(account, 'aws_access_key_id')\nAWS_SECRET_ACCESS_KEY = config.get(account, 'aws_secret_access_key')\nAWS_SESSION_TOKEN = config.get(account, 'aws_session_token')\n\n# open a file to house the sts creds\nstspath = os.path.join(os.path.expanduser('~'), '.sts'+account)\nstsf = open(stspath, 'w')\n\n# write the sts creds file for the account\nprint('export AWS_ACCESS_KEY_ID = ' + AWS_ACCESS_KEY_ID + '\\n' + 'export AWS_SECRET_ACCESS_KEY = ' +\n AWS_SECRET_ACCESS_KEY + '\\n' + 'export AWS_SESSION_TOKEN = ' + AWS_SESSION_TOKEN, file=stsf)\n\n# close the file handle to the sts creds file\nstsf.close()\n", "sub_path": "iam/sts_getcreds.py", "file_name": "sts_getcreds.py", "file_ext": "py", "file_size_in_byte": 2251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 43, "usage_type": "call"}, {"api_name": "configparser.RawConfigParser", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "647646461", "text": "\"\"\"\nSetup file for your personal prelude.\n\nThis file is responsible for \"installing\" your prelude file onto your\nand making it available to Python. Note that you will need to install\nthis on every computer that you will use for writing Python code.\n\nThe main purpose of having this file is to make it easier to set up\nyour Python installation with the packages that you require. To this\nend, the list `THINGS_I_NEED_INSTALLED` can be amended as necessary\nto install all of the basic packages that you need.\n\nOnce you have this file, you can use pip to install your file on a\ncomputer, and if this is hosted on Github (or similar), then you can\neven install it directly from the web.\n\"\"\"\n\n\nfrom setuptools import setup\n\n\n# Feel free to add things to this list if you feel you need them\n# in your day-to-day Pythoning.\nTHINGS_I_NEED_INSTALLED = [\n 'numpy', # High-performance array types\n 'scipy', # Scientific computation routines\n 'pandas', # Data handling and analysis\n 'matplotlib', # Plotting tools.\n]\n\n# You will need to update the version number if you make changes your\n# prelude file and want to update them using pip install --update ...\n# You should change the minor version number for the best effect.\nMAJOR = 1\nMINOR = 0\nPATCH = 0\nVERSION = f\"{MAJOR}.{MINOR}.{PATCH}\"\n# This is the so-called \"semantic versioning\" scheme, and it is fairly\n# common throughout the programming world.\n\nDESCRIPTION = \"Basic package for making importing common packages easy\"\n\nsetup(\n name=\"prelude\",\n author=\"Anonymous\",\n version=VERSION,\n description=DESCRIPTION,\n \n install_requires=THINGS_I_NEED_INSTALLED,\n\n # You should be using a fairly recent version of Python!\n python_requires=\">=3.6.0\",\n py_modules = [\"prelude\"]\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "setuptools.setup", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "430836503", "text": "# Copyright 2020 LMNT, 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\nimport numpy as np\nimport torch\nimport torchaudio as T\nimport torchaudio.transforms as TT\n\nfrom argparse import ArgumentParser\nfrom concurrent.futures import ProcessPoolExecutor\nfrom glob import glob\nfrom tqdm import tqdm\n\nfrom wavegrad.params import params\n\nfrom wavegrad.mozilla_tts_audio import AudioProcessor\n\nap = AudioProcessor( \n sample_rate=params.sample_rate,\n num_mels=params.num_mels,\n min_level_db=params.min_level_db,\n frame_shift_ms=params.frame_length_ms,\n frame_length_ms=params.frame_length_ms,\n hop_length=params.hop_length,\n win_length=params.win_length,\n ref_level_db=params.ref_level_db,\n fft_size=params.fft_size,\n power=params.power,\n preemphasis=params.preemphasis,\n signal_norm=params.signal_norm,\n symmetric_norm=params.symmetric_norm,\n max_norm=params.max_norm,\n mel_fmin=params.mel_fmin,\n mel_fmax=params.mel_fmax,\n spec_gain=params.spec_gain,\n stft_pad_mode=params.stft_pad_mode,\n clip_norm=params.clip_norm,\n griffin_lim_iters=params.griffin_lim_iters,\n do_trim_silence=params.do_trim_silence,\n trim_db=params.trim_db)\n\ndef transform(filename):\n audio, sr = T.load_wav(filename)\n if params.sample_rate != sr:\n raise ValueError(f'Invalid sample rate {sr}.')\n audio = torch.clamp(audio[0] / 32767.5, -1.0, 1.0)\n\n #hop = params.hop_length\n #win = hop * 4\n #n_fft = 2**((win-1).bit_length())\n #f_max = sr / 2.0\n #mel_spec_transform = TT.MelSpectrogram(sample_rate=sr, n_fft=n_fft, win_length=win, hop_length=hop, f_min=20.0, f_max=f_max, power=1.0, normalized=True)\n\n with torch.no_grad():\n #spectrogram = mel_spec_transform(audio)\n #spectrogram = 20 * torch.log10(torch.clamp(spectrogram, min=1e-5)) - 20\n #spectrogram = torch.clamp((spectrogram + 100) / 100, 0.0, 1.0)\n spectrogram = np.float32(ap.melspectrogram(audio.detach().cpu().numpy()))\n np.save(f'{filename}.spec.npy', spectrogram)\n\n\ndef main(args):\n filenames = glob(f'{args.dir}/**/*.wav', recursive=True)\n with ProcessPoolExecutor() as executor:\n list(tqdm(executor.map(transform, filenames), desc='Preprocessing', total=len(filenames)))\n\n\nif __name__ == '__main__':\n parser = ArgumentParser(description='prepares a dataset to train WaveGrad')\n parser.add_argument('dir',\n help='directory containing .wav files for training')\n main(parser.parse_args())\n", "sub_path": "src/wavegrad/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 3063, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "wavegrad.mozilla_tts_audio.AudioProcessor", "line_number": 30, "usage_type": "call"}, {"api_name": "wavegrad.params.params.sample_rate", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 31, "usage_type": "name"}, {"api_name": "wavegrad.params.params.num_mels", "line_number": 32, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 32, "usage_type": "name"}, {"api_name": "wavegrad.params.params.min_level_db", "line_number": 33, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 33, "usage_type": "name"}, {"api_name": "wavegrad.params.params.frame_length_ms", "line_number": 34, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 34, "usage_type": "name"}, {"api_name": "wavegrad.params.params.frame_length_ms", "line_number": 35, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 35, "usage_type": "name"}, {"api_name": "wavegrad.params.params.hop_length", "line_number": 36, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 36, "usage_type": "name"}, {"api_name": "wavegrad.params.params.win_length", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 37, "usage_type": "name"}, {"api_name": "wavegrad.params.params.ref_level_db", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 38, "usage_type": "name"}, {"api_name": "wavegrad.params.params.fft_size", "line_number": 39, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 39, "usage_type": "name"}, {"api_name": "wavegrad.params.params.power", "line_number": 40, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 40, "usage_type": "name"}, {"api_name": "wavegrad.params.params.preemphasis", "line_number": 41, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 41, "usage_type": "name"}, {"api_name": "wavegrad.params.params.signal_norm", "line_number": 42, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 42, "usage_type": "name"}, {"api_name": "wavegrad.params.params.symmetric_norm", "line_number": 43, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 43, "usage_type": "name"}, {"api_name": "wavegrad.params.params.max_norm", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 44, "usage_type": "name"}, {"api_name": "wavegrad.params.params.mel_fmin", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 45, "usage_type": "name"}, {"api_name": "wavegrad.params.params.mel_fmax", "line_number": 46, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 46, "usage_type": "name"}, {"api_name": "wavegrad.params.params.spec_gain", "line_number": 47, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 47, "usage_type": "name"}, {"api_name": "wavegrad.params.params.stft_pad_mode", "line_number": 48, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 48, "usage_type": "name"}, {"api_name": "wavegrad.params.params.clip_norm", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 49, "usage_type": "name"}, {"api_name": "wavegrad.params.params.griffin_lim_iters", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 50, "usage_type": "name"}, {"api_name": "wavegrad.params.params.do_trim_silence", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 51, "usage_type": "name"}, {"api_name": "wavegrad.params.params.trim_db", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 52, "usage_type": "name"}, {"api_name": "torchaudio.load_wav", "line_number": 55, "usage_type": "call"}, {"api_name": "wavegrad.params.params.sample_rate", "line_number": 56, "usage_type": "attribute"}, {"api_name": "wavegrad.params.params", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.clamp", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 71, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 75, "usage_type": "call"}, {"api_name": "concurrent.futures.ProcessPoolExecutor", "line_number": 76, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 77, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "313421778", "text": "\n#\n# Team 1\n# My Weekend in Miami\n#\n\nimport sqlite3\nimport random\n\n\"\"\"\n\nPURPOSE:\nUsed by the Itinerary class to select activities for the user from a database\nof activities based on the user's maximum funds available for activities.\nThis class then stores these selections to make them available to the\nItinerary class.\n\nThe user will be in Miami from Friday afternoon through Sunday afternoon.\nThere will therefore be no Friday morning activity nor any Sunday evening\nactvitiy. The Friday afternoon activity will be the user's arrival in Miami\nand the Sunday afternoon activity will be the user's departure from Miami.\nThe five remaining values must be set by the program based on the user's\npreferences:\n\n Friday Evening\n Saturday Morning\n Saturday Afternoon\n Saturday Evening\n Sunday Morning\n\n\n\"\"\"\n\n\nclass Activities:\n\n def __init__(self, activities_budget, dbname):\n \"\"\"\n Activities Constructor\n Parameters:\n activities_budget (int)\n maximum dollar amt (per person) available for activities\n dbname (str)\n name of database containing activities to choose from\n Return:\n Activities instance\n \"\"\"\n # Type-check 'activities_budget' parameter\n if (type(activities_budget) != int):\n raise TypeError(\"Activities param 'activities_budget' must be int\")\n self.activities_budget = activities_budget\n\n # Type-check 'dbname' parameter\n if (type(dbname) != str):\n raise TypeError(\"Activities param 'dbname' must be string\")\n self.dbname = dbname\n\n # Initialize activities itinerary attributes\n self.friday_mor = \"N/A\"\n self.friday_aft = \"Arrival in Miami\"\n self.friday_eve = \"\"\n self.saturday_mor = \"\"\n self.saturday_aft = \"\"\n self.saturday_eve = \"\"\n self.sunday_mor = \"\"\n self.sunday_aft = \"Departure from Miami\"\n self.sunday_eve = \"N/A\"\n\n\n # Helper method for pick_activities_helper method\n # Deprecated -- replaced by select_activity_rand()\n def select_activity(self, max_price, activity_list):\n for activity in activity_list:\n if (int(activity[8]) <= max_price):\n return activity\n raise Exception(\"No match found in Activities.select_activity method\")\n\n\n # Helper method for pick_activities_helper method\n # This is a randomized version of the select_activity method\n def select_activity_rand(self, max_price, activity_list):\n act = random.choice(activity_list)\n act_price = int(act[8])\n while (act_price > max_price):\n act = random.choice(activity_list)\n act_price = int(act[8])\n return act\n\n\n # Helper method for pick_activities method\n def pick_activities_helper(self, activity_list):\n search_list = activity_list\n ret_list = []\n\n # Get first activity\n available_balance = self.activities_budget\n item_max = available_balance / 5\n activity = self.select_activity_rand(item_max, search_list)\n activity_price = int(activity[8])\n ret_list.append(activity)\n search_list.remove(activity)\n\n # Get second activity\n available_balance -= activity_price\n item_max = available_balance / 4\n activity = self.select_activity_rand(item_max, search_list)\n activity_price = int(activity[8])\n ret_list.append(activity)\n search_list.remove(activity)\n\n # Get third activity\n available_balance -= activity_price\n item_max = available_balance / 3\n activity = self.select_activity_rand(item_max, search_list)\n activity_price = int(activity[8])\n ret_list.append(activity)\n search_list.remove(activity)\n\n # Get fourth activity\n available_balance -= activity_price\n item_max = available_balance / 2\n activity = self.select_activity_rand(item_max, search_list)\n activity_price = int(activity[8])\n ret_list.append(activity)\n search_list.remove(activity)\n\n # Get fifth activity\n available_balance -= activity_price\n item_max = available_balance\n activity = self.select_activity_rand(item_max, search_list)\n activity_price = int(activity[8])\n ret_list.append(activity)\n search_list.remove(activity)\n\n return ret_list\n\n\n # Populates empty Activites attributes\n def pick_activities(self):\n # Connect to database\n conn = sqlite3.connect(self.dbname)\n c = conn.cursor()\n\n # Retrieve activities data from database\n output_ptr = c.execute(\"SELECT * FROM Entertainment;\")\n output_lst = output_ptr.fetchall()\n\n # Retrieve list of suitable activities and populate empty\n # activities attributes\n activity_list = self.pick_activities_helper(output_lst)\n self.friday_eve = activity_list[0]\n self.saturday_mor = activity_list[1]\n self.saturday_aft = activity_list[2]\n self.saturday_eve = activity_list[3]\n self.sunday_mor = activity_list[4]\n\n # Save and close database\n conn.commit()\n conn.close()\n\n\n \"\"\"\n activity_list KEY to determine output\n\n first index\n 0 : Friday evening\n 1 : Saturday morning\n 2 : Saturday afternoon\n 3 : Saturday evening\n 4 : Sunday morning\n\n second index\n 0 : Entertainment ID\n 1 : Description\n 2 : Includes\n 3 : Contact\n 4 : Street Address\n 5 : City\n 6 : State\n 7 : Zip\n 8 : Price Per Person\n 9 : Price Total\n 10: Phone Number\n 11: Google Rating\n 12: URL\n 13: Opening Time\n 14: Closing Time\n 15: Morning\t Boolean: open in the mornings?\n 16: Afternoon Boolean: open in the afternoons?\n 17: Evening\t Boolean: open in the evenings?\n 18: Child Friendly Boolean: can children participate?\n \"\"\"\n", "sub_path": "activities.py", "file_name": "activities.py", "file_ext": "py", "file_size_in_byte": 6105, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "random.choice", "line_number": 82, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "272352077", "text": "from django.core.management.base import BaseCommand, CommandError\nfrom optparse import make_option\nfrom order.models import Transaction, DEBIT, CREDIT\nfrom datetime import datetime\nimport logging\n\nlogger = logging.getLogger('django.request')\n\nclass Command(BaseCommand):\n\n option_list = BaseCommand.option_list + (\n make_option(\n '--dry-run',\n action='store_true',\n dest='dry-run',\n default=False,\n help=\"List all transactions to be processed, but don't actually transfer money. Use first as a double check to know what's going to be processed\"\n ),\n )\n\n def handle(self, *args, **options):\n dry_run = options['dry-run']\n \n now = datetime.now() \n unprocessed_transactions = Transaction.objects.filter(\n apply_on__lte=now, \n charge_id='', \n cancelled=False, \n apply_only_on_payment_update=False\n )\n if unprocessed_transactions.count() > 0:\n self.stdout.write(\"Processing %s vendor deposit%s ...\\n\" % (\n unprocessed_transactions.count(),\n \"s\" if unprocessed_transactions.count() != 1 else \"\"\n ))\n for trans in unprocessed_transactions:\n if trans.type == DEBIT:\n #check to make sure the corresponding credit has been applied so the money is available to be paid out\n if trans.related_trans.type == CREDIT and trans.related_trans.charge_id != '':\n self.stdout.write('Transaction %s to be processed...\\n' % trans.pk)\n try:\n if not dry_run:\n trans.apply_through_eft()\n except:\n logger.exception(\"Failed to process Transaction %s\" % (trans.pk))\n self.stdout.write(\"Transaction %s failed to process\\n\" % (trans.pk))\n else:\n if not dry_run:\n self.stdout.write('Transaction %s processed: DEBIT of $%s\\n' % (trans.pk, trans.amount))\n", "sub_path": "order/management/commands/sweep_transactions.py", "file_name": "sweep_transactions.py", "file_ext": "py", "file_size_in_byte": 2104, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 9, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand.option_list", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 11, "usage_type": "name"}, {"api_name": "optparse.make_option", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}, {"api_name": "order.models.Transaction.objects.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "order.models.Transaction.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "order.models.Transaction", "line_number": 25, "usage_type": "name"}, {"api_name": "order.models.DEBIT", "line_number": 37, "usage_type": "name"}, {"api_name": "order.models.CREDIT", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "280735822", "text": "# -*- encoding: utf-8 -*-\nimport requests\nimport logging\n\n# Modules in Watchman\nfrom conf import load_endpoints, LOG_FILENAME\n\nENDPOINTS = load_endpoints()\n\n# Logger Creds\nlogging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)\nlog = logging.getLogger(\"watchman.pigeon\")\n\ndef post_folder_creation(src):\n log.debug(\"post_folder_creation\")\n \n options = { 'path': src }\n requests.post(ENDPOINTS['folder_create'], params=options)\n\ndef post_file_creation(src):\n log.debug(\"post_file_creation\")\n \n options = { 'path': src }\n requests.post(ENDPOINTS['file_create'], params=options)\n\ndef post_folder_destroy(src):\n log.debug(\"post_folder_destroy\")\n \n options = { 'path': src }\n requests.post(ENDPOINTS['folder_destroy'], params=options)\n\ndef post_file_destroy(src):\n log.debug(\"post_file_destroy\")\n \n options = { 'path': src }\n requests.post(ENDPOINTS['file_destroy'], params=options)\n\ndef post_folder_move(src, dest):\n log.debug(\"post_folder_move\")\n \n options = { 'oldpath': src, 'newpath': dest }\n requests.post(ENDPOINTS['folder_move'], params=options)\n\ndef post_file_move(src, dest):\n log.debug(\"post_file_move\")\n \n options = { 'oldpath': src, 'newpath': dest }\n requests.post(ENDPOINTS['file_move'], params=options)\n", "sub_path": "watchman/pigeon.py", "file_name": "pigeon.py", "file_ext": "py", "file_size_in_byte": 1293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "conf.load_endpoints", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "conf.LOG_FILENAME", "line_number": 11, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 36, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "592732495", "text": "\nfrom kivy.app import App\nfrom kivy.lang import Builder\nfrom kivy.uix.label import Label\n\nclass DynamicLabelsApp(App):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.names = ['jai', 'jordan', 'paul', 'paula', 'steve', 'helen']\n\n def build(self):\n self.title = 'Dynamic lists'\n self.root = Builder.load_file('dynamic_labels.kv')\n self.create_labels()\n return self.root\n\n def create_labels(self):\n for name in self.names:\n temp_label = Label(text=name)\n self.root.ids.labels_box.add_widget(temp_label)\n\n\nDynamicLabelsApp().run()\n", "sub_path": "prac_07/dynamic_labels.py", "file_name": "dynamic_labels.py", "file_ext": "py", "file_size_in_byte": 626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "kivy.app.App", "line_number": 6, "usage_type": "name"}, {"api_name": "kivy.lang.Builder.load_file", "line_number": 14, "usage_type": "call"}, {"api_name": "kivy.lang.Builder", "line_number": 14, "usage_type": "name"}, {"api_name": "kivy.uix.label.Label", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "568696733", "text": "# -*- coding: utf-8 -*-\n\nimport datetime\nfrom requests.exceptions import MissingSchema\nimport struct\nimport time\nimport urllib.request\nimport os\nfrom requests_html import HTMLSession\n\n__all__ = ['ImgSession', 'streaming']\n\n\nclass ImgSession:\n def __init__(self, _url):\n session = self.get_htmlsession(_url)\n self.url = _url\n self.title = self.get_title(session)\n self.urls = self.get_urls(session)\n\n @staticmethod\n def get_htmlsession(url):\n session = HTMLSession()\n return session.get(url).html\n\n @staticmethod\n def get_urls(session) -> list:\n elements = session.find('img')\n img_urls = [i.element.get('src') for i in elements if i.element.get('src')]\n return img_urls\n\n @staticmethod\n def get_title(session) -> str:\n return session.find('title', first=True).text\n\n\ndef streaming(_urls, interval, debug=False,\n create_folder=True, title=None, save_directory=os.getcwd(), name_shorten=True,\n restriction=True, restrict_size=(500, 400), logger=False):\n\n if create_folder:\n folder_path = _create_folder(title, save_directory, name_shorten=name_shorten)\n else:\n folder_path = save_directory\n\n if restriction:\n urls, invalid_urls = _restriction(_urls, restrict_size)\n else:\n urls = _urls\n invalid_urls = []\n\n for i, img_url in enumerate(urls):\n\n if len(img_url) <= 200:\n image_path = os.path.join(folder_path, img_url.split('/')[-1])\n else:\n image_path = os.path.join(folder_path, str(i))\n\n if not debug:\n with open(image_path, 'wb') as file:\n try:\n file.write(HTMLSession().get(img_url).content)\n except MissingSchema:\n pass\n\n time.sleep(interval)\n\n if logger:\n with open(os.path.join(folder_path, '.log'), 'a') as file:\n file.write(title + '\\n\\tvalid urls\\n\\t' + '\\n\\t'.join(urls) +\n '\\n\\tinvalid urls\\n\\t' + \"\\n\\t\".join(invalid_urls)+'\\n\\n')\n\n\ndef _title_shorten(name) -> str:\n for _ in range(1, 3):\n c_right = name.find('【')\n c_left = name.find('】')\n if c_right != -1 and c_left != -1:\n name = name[:c_right]+name[c_left+1:]\n\n bar = name.find('|')\n if bar != -1:\n name = name[:bar]\n\n minus = name.find('-')\n if minus != -1:\n name = name[:minus]\n\n c_right = name.find('【')\n c_left = name.find('】')\n if c_right != -1 and c_left != -1:\n name = name[:c_right] + name[c_left + 1:]\n return name\n\n\ndef _create_folder(title, save_directory, name_shorten=True) -> str:\n date = datetime.date.today().strftime('%Y%m%d')\n if name_shorten:\n title = _title_shorten(title)\n folder_path = os.path.join(save_directory, date, title)\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)\n return folder_path\n\n\ndef _restriction(urls, restrict_size=(500, 400)) -> (list, list):\n def parse_jpeg(res):\n while not res.closed:\n (marker, size) = struct.unpack('>2sH', res.read(4))\n if marker == b'\\xff\\xc0':\n (_, height, width, _) = struct.unpack('>chh10s', res.read(size-2))\n return width, height\n else:\n res.read(size-2)\n\n def get_image_size(_url):\n try:\n with urllib.request.urlopen(_url) as res:\n _size = (-1, -1)\n if res.status == 200:\n signature = res.read(2)\n if signature == b'\\xff\\xd8':\n _size = parse_jpeg(res)\n return _size\n except ValueError:\n pass\n\n confirmed = []\n unconfirmed = []\n for url in urls:\n img_size = get_image_size(url)\n try:\n if (restrict_size[0] < img_size[0]) and (restrict_size[1] < img_size[1]):\n confirmed.append(url)\n else:\n unconfirmed.append(url)\n except TypeError:\n unconfirmed.append(url)\n\n return confirmed, unconfirmed\n", "sub_path": "olds/oldfiles/Kraken_rumps/streaming.py", "file_name": "streaming.py", "file_ext": "py", "file_size_in_byte": 4170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests_html.HTMLSession", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "requests_html.HTMLSession", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.exceptions.MissingSchema", "line_number": 63, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 102, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 109, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 118, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 118, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "487847583", "text": "# pylint: disable=missing-docstring,redefined-outer-name,protected-access\nimport pytest\nimport torch\nfrom ray.rllib import SampleBatch\n\nfrom raylab.modules.svg_module import SVGModule\n\n\n@pytest.fixture\ndef module_batch(module_and_batch_fn):\n return module_and_batch_fn(SVGModule, {})\n\n\ndef test_model_params(module_batch):\n module, batch = module_batch\n\n params = module.model(batch[SampleBatch.CUR_OBS], batch[SampleBatch.ACTIONS])\n assert \"loc\" in params\n assert \"scale\" in params\n\n loc, scale = params[\"loc\"], params[\"scale\"]\n assert loc.shape == batch[SampleBatch.NEXT_OBS].shape\n assert scale.shape == batch[SampleBatch.NEXT_OBS].shape\n assert loc.dtype == torch.float32\n assert scale.dtype == torch.float32\n\n parameters = set(module.model.parameters())\n for par in parameters:\n par.grad = None\n loc.mean().backward()\n assert any(p.grad is not None for p in parameters)\n assert all(p.grad is None for p in set(module.parameters()) - parameters)\n\n for par in parameters:\n par.grad = None\n module.model(batch[SampleBatch.CUR_OBS], batch[SampleBatch.ACTIONS])[\n \"scale\"\n ].mean().backward()\n assert any(p.grad is not None for p in parameters)\n assert all(p.grad is None for p in set(module.parameters()) - parameters)\n", "sub_path": "tests/modules/test_svg_module.py", "file_name": "test_svg_module.py", "file_ext": "py", "file_size_in_byte": 1301, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "raylab.modules.svg_module.SVGModule", "line_number": 11, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch.CUR_OBS", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch", "line_number": 17, "usage_type": "name"}, {"api_name": "ray.rllib.SampleBatch.ACTIONS", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch.NEXT_OBS", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch", "line_number": 22, "usage_type": "name"}, {"api_name": "ray.rllib.SampleBatch.NEXT_OBS", "line_number": 23, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch.CUR_OBS", "line_number": 36, "usage_type": "attribute"}, {"api_name": "ray.rllib.SampleBatch", "line_number": 36, "usage_type": "name"}, {"api_name": "ray.rllib.SampleBatch.ACTIONS", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "540442892", "text": "#\n# Copyright 2016 iXsystems, Inc.\n# All rights reserved\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted providing that the following conditions\n# are met:\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR\n# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY\n# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS\n# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\n# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING\n# IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n#\n#####################################################################\n\nimport os\nimport shutil\nimport errno\nfrom task import Task, TaskException, Provider\nfrom freenas.dispatcher.rpc import RpcException, private, accepts, returns, generator\nfrom freenas.utils import query as q\n\n\nclass LocalDatastoreProvider(Provider):\n @private\n @generator\n def discover(self):\n return\n\n @private\n def get_filesystem_path(self, datastore_id, datastore_path):\n ds = self.datastore.get_by_id('vm.datastores', datastore_id)\n if ds['type'] != 'local':\n raise RpcException(errno.EINVAL, 'Invalid datastore type')\n\n return os.path.join(q.get(ds, 'properties.path'), datastore_path)\n\n @private\n def directory_exists(self, datastore_id, datastore_path):\n ds = self.datastore.get_by_id('vm.datastores', datastore_id)\n if ds['type'] != 'local':\n raise RpcException(errno.EINVAL, 'Invalid datastore type')\n\n return os.path.exists(os.path.join(q.get(ds, 'properties.path'), datastore_path))\n\n @private\n def get_resources(self, datastore_id):\n return ['system']\n\n\nclass LocalDirectoryCreateTask(Task):\n def run(self, id, path):\n path = self.dispatcher.call_sync('vm.datastore.get_filesystem_path', id, path)\n os.mkdir(path)\n\n\nclass LocalDirectoryDeleteTask(Task):\n def run(self, id, path):\n path = self.dispatcher.call_sync('vm.datastore.get_filesystem_path', id, path)\n shutil.rmtree(path, ignore_errors=True)\n\n\nclass LocalDirectoryRenameTask(Task):\n def run(self, id, old_path, new_path):\n old_path = self.dispatcher.call_sync('vm.datastore.get_filesystem_path', id, old_path)\n new_path = self.dispatcher.call_sync('vm.datastore.get_filesystem_path', id, new_path)\n os.rename(old_path, new_path)\n\n\ndef _metadata():\n return {\n 'type': 'datastore',\n 'driver': 'local',\n 'block_devices': False,\n 'clones': False,\n 'snapshots': False\n }\n\n\ndef _init(dispatcher, plugin):\n plugin.register_schema_definition('vm-datastore-properties-local', {\n 'type': 'object',\n 'additionalProperties': False,\n 'properties': {\n '%type': {'enum': ['vm-datastore-local']},\n 'path': {'type': 'string'}\n }\n })\n\n plugin.register_provider('vm.datastore.local', LocalDatastoreProvider)\n plugin.register_task_handler('vm.datastore.local.create_directory', LocalDirectoryCreateTask)\n plugin.register_task_handler('vm.datastore.local.delete_directory', LocalDirectoryDeleteTask)\n plugin.register_task_handler('vm.datastore.local.rename_directory', LocalDirectoryRenameTask)\n", "sub_path": "src/dispatcher/plugins/datastore/LocalDatastorePlugin.py", "file_name": "LocalDatastorePlugin.py", "file_ext": "py", "file_size_in_byte": 3980, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "task.Provider", "line_number": 36, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.private", "line_number": 37, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.generator", "line_number": 38, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.RpcException", "line_number": 46, "usage_type": "call"}, {"api_name": "errno.EINVAL", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "freenas.utils.query.get", "line_number": 48, "usage_type": "call"}, {"api_name": "freenas.utils.query", "line_number": 48, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.private", "line_number": 42, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.RpcException", "line_number": 54, "usage_type": "call"}, {"api_name": "errno.EINVAL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "freenas.utils.query.get", "line_number": 56, "usage_type": "call"}, {"api_name": "freenas.utils.query", "line_number": 56, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.private", "line_number": 50, "usage_type": "name"}, {"api_name": "freenas.dispatcher.rpc.private", "line_number": 58, "usage_type": "name"}, {"api_name": "task.Task", "line_number": 63, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 66, "usage_type": "call"}, {"api_name": "task.Task", "line_number": 69, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 72, "usage_type": "call"}, {"api_name": "task.Task", "line_number": 75, "usage_type": "name"}, {"api_name": "os.rename", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "529789048", "text": "import itertools as it\nfrom cluster_lib import *\n\n\ndef merge(clusters, link):\n combo = list(it.combinations(enumerate(clusters), 2))\n min_clusters = []\n least = float('inf')\n for i in range(len(combo)):\n dist = link(combo[i][0][1], combo[i][1][1])\n if least > dist:\n least = dist\n min_clusters = [combo[i][0][0], combo[i][1][0]]\n\n removed = clusters[min_clusters[1]]\n clusters.pop(min_clusters[1])\n clusters[min_clusters[0]] += removed\n return clusters\n\n\ndef single_link(arr1, arr2):\n least = float('inf')\n for i in range(len(arr1)):\n for j in range(len(arr2)):\n dist = euclidean_distance(arr1[i], arr2[j])\n if dist < least:\n least = dist\n\n return least\n\n\ndef complete_link(arr1, arr2):\n greatest = float(0)\n for i in range(len(arr1)):\n for j in range(len(arr2)):\n dist = euclidean_distance(arr1[i], arr2[j])\n if dist > greatest:\n greatest = dist\n\n return greatest\n\n\ndef mean_link(arr1, arr2):\n arr1mid = get_midpoint_mean(arr1)\n arr2mid = get_midpoint_mean(arr2)\n dist = euclidean_distance(arr1mid, arr2mid)\n return dist\n\n\ndef reduce_clusters(count, clusters, link):\n while len(clusters) > count:\n merge(clusters, link)\n\n\ndef run_hierarchical():\n # x = [[[1, 1]], [[-3, 3]], [[-10, -10]], [[2, 1]]]\n #\n # y = [[2, 2], [-2, -2]]\n # z = [[-10, -10], [-10, -11], [4, 2]]\n # a = [[-1, 0], [2, 2]]\n # x.append(y)\n # x.append(z)\n # x.append(a)\n # print(x)\n # merge(x)\n\n\n ################################################\n names = [\"Single-Link\\nHierarchical Clustering\", \"Complete-Link\\nHierarchical Clustering\",\n \"Mean-Link\\nHierarchical Clustering\"]\n funcs = [single_link, complete_link, mean_link]\n cluster_count = 4\n for i in range(3):\n x = read_file('day_change.txt', 2)\n points = x[:]\n reduce_clusters(cluster_count, points, funcs[i])\n # print(points)\n\n\n graph_clusters((points, None), names[i])\n\n # colors = ['b', 'r', 'm', 'c', 'g']\n # for i in range(cluster_count):\n # xvalues = x_values(points[i])\n # yvalues = y_values(points[i])\n # plt.scatter(xvalues, yvalues, s=70, c=colors[i], edgecolors='w')\n #\n # plt.show()\n #####################################################################\n # reduce_clusters(4, points, mean_link)\n # print(points)\n # colors = ['b', 'r', 'g', 'c']\n # for i in range(4):\n # xvalues = x_values(points[i])\n # yvalues = y_values(points[i])\n # plt.scatter(xvalues, yvalues, s=70, c=colors[i], edgecolors='w')\n #\n # plt.show()\n\n", "sub_path": "2016_01_Spring/cs5140/a03_new/a03_code/hierarchical.py", "file_name": "hierarchical.py", "file_ext": "py", "file_size_in_byte": 2739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "itertools.combinations", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "126626192", "text": "from appraisal.models import Appraisal\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom django.db import transaction\nfrom django.contrib.auth.models import User, Group\nfrom .forms import EditProfileForm, UserForm\nfrom .models import UserProfile\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom region.models import Region\nfrom commune.models import Commune\n\n\n@login_required(login_url='user/login')\ndef view_profile(request, pk=None):\n if pk:\n user = User.objects.get(pk=pk)\n userprofile = UserProfile.objects.get(pk=pk)\n else:\n user = request.user\n userprofile = UserProfile.objects.get(user=request.user)\n\n context = {'user': user, 'userprofile': userprofile}\n return render(request, 'user/profile.html', context)\n\n@login_required\n@transaction.atomic\ndef edit_profile(request):\n try:\n user = UserProfile.objects.get(user=request.user)\n except ObjectDoesNotExist:\n user = UserProfile.objects.create(user=request.user, first_name=request.user.first_name,\n last_name=request.user.last_name, email=request.user.email)\n if request.method == 'POST':\n profile_form = EditProfileForm(request.POST, instance=user)\n if profile_form.is_valid():\n profile_form.save()\n _first_name = profile_form.cleaned_data['first_name']\n _last_name = profile_form.cleaned_data['last_name']\n _email = profile_form.cleaned_data['email']\n _addressRegion = profile_form.cleaned_data['addressRegion']\n _addressCommune = profile_form.cleaned_data['addressCommune']\n _addressStreet = profile_form.cleaned_data['addressStreet']\n _addressNumber = profile_form.cleaned_data['addressNumber']\n UserProfile.objects.filter(user=request.user).update(first_name=_first_name,\n last_name=_last_name,\n email=_email,\n addressRegion=_addressRegion,\n addressCommune=_addressCommune,\n addressStreet=_addressStreet,\n addressNumber=_addressNumber)\n #messages.success(request, _('Your profile was successfully updated!'))\n return redirect('user:profile')\n else:\n #messages.error(request, _('Please correct the error below.'))\n print('error')\n else:\n profile_form = EditProfileForm(instance=user)\n\n return render(request, 'user/profile_edit.html', {\n 'profile_form': profile_form\n })\n\n\n@login_required(login_url='user/login')\ndef userAppraisals(request):\n try:\n if request.user.groups.values_list('name',flat=True)[0]=='tasador':\n appraisals_active = Appraisal.objects.filter(tasadorUser=request.user).filter(state=Appraisal.STATE_ACTIVE).order_by('timeCreated')\n appraisals_finished = Appraisal.objects.filter(tasadorUser=request.user).filter(state=Appraisal.STATE_FINISHED).order_by('timeCreated')\n elif request.user.groups.values_list('name',flat=True)[0]=='visador':\n appraisals_active = Appraisal.objects.filter(visadorUser=request.user).filter(\n state=Appraisal.STATE_ACTIVE).order_by('timeCreated')\n appraisals_finished = Appraisal.objects.filter(visadorUser=request.user).filter(\n state=Appraisal.STATE_FINISHED).order_by('timeCreated')\n else:\n appraisals_active = Appraisal.objects.filter(state=Appraisal.STATE_ACTIVE).order_by('timeCreated')\n appraisals_finished = Appraisal.objects.filter(state=Appraisal.STATE_FINISHED).order_by('timeCreated')\n except IndexError:\n appraisals_active = Appraisal.objects.filter(state=Appraisal.STATE_ACTIVE).order_by('timeCreated')\n appraisals_finished = Appraisal.objects.filter(state=Appraisal.STATE_FINISHED).order_by('timeCreated')\n\n return [appraisals_active,appraisals_finished]\n\n@login_required(login_url='user/login')\ndef load_communes(request):\n region_id = int(request.GET.get('region'))\n region = Region.objects.get(pk=region_id)\n communes = list(Commune.objects.filter(region=region.code).order_by('name'))\n return render(request,\n 'hr/commune_dropdown_list_options.html',\n {'communes': communes})\n", "sub_path": "web/user/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 18, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 19, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 15, "usage_type": "call"}, {"api_name": "models.UserProfile.objects.get", "line_number": 31, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 31, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 32, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.create", "line_number": 33, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 33, "usage_type": "name"}, {"api_name": "forms.EditProfileForm", "line_number": 36, "usage_type": "call"}, {"api_name": "models.UserProfile.objects.filter", "line_number": 46, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 46, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "forms.EditProfileForm", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 28, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 70, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_ACTIVE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 71, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_FINISHED", "line_number": 71, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 73, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_ACTIVE", "line_number": 74, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 74, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 75, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 75, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_FINISHED", "line_number": 76, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 76, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 78, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 78, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_ACTIVE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 79, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 79, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_FINISHED", "line_number": 79, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 81, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 81, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_ACTIVE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "appraisal.models.Appraisal.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "appraisal.models.Appraisal", "line_number": 82, "usage_type": "name"}, {"api_name": "appraisal.models.Appraisal.STATE_FINISHED", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 66, "usage_type": "call"}, {"api_name": "region.models", "line_number": 89, "usage_type": "name"}, {"api_name": "region.models.Region.objects.get", "line_number": 89, "usage_type": "call"}, {"api_name": "region.models.Region.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "region.models.Region", "line_number": 89, "usage_type": "name"}, {"api_name": "commune.models.Commune.objects.filter", "line_number": 90, "usage_type": "call"}, {"api_name": "commune.models.Commune.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "commune.models.Commune", "line_number": 90, "usage_type": "name"}, {"api_name": "region.models.code", "line_number": 90, "usage_type": "attribute"}, {"api_name": "region.models", "line_number": 90, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 91, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "100786043", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nProduction Configurations\n\n- Use djangosecure\n- Use mailgun to send emails\n- Use Redis on Heroku\n\n\n\"\"\"\nfrom __future__ import absolute_import, unicode_literals\nfrom celery.schedules import crontab\nfrom datetime import timedelta\n\nfrom .common import * # noqa\n\n# SECRET CONFIGURATION\n# ------------------------------------------------------------------------------\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#secret-key\n# Raises ImproperlyConfigured exception if DJANGO_SECRET_KEY not in os.environ\nSECRET_KEY = env('DJANGO_SECRET_KEY')\n\n# This ensures that Django will be able to detect a secure connection\n# properly on Heroku.\nSECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n\n# django-secure\n# ------------------------------------------------------------------------------\nINSTALLED_APPS += ('djangosecure', )\n\nSECURITY_MIDDLEWARE = (\n 'djangosecure.middleware.SecurityMiddleware',\n)\n# Use Whitenoise to serve static files\n# See: https://whitenoise.readthedocs.io/\nWHITENOISE_MIDDLEWARE = (\n 'whitenoise.middleware.WhiteNoiseMiddleware',\n)\nMIDDLEWARE_CLASSES = WHITENOISE_MIDDLEWARE + MIDDLEWARE_CLASSES\n\n\n# Make sure djangosecure.middleware.SecurityMiddleware is listed first\nMIDDLEWARE_CLASSES = SECURITY_MIDDLEWARE + MIDDLEWARE_CLASSES\n\n\n# set this to 60 seconds and then to 518400 when you can prove it works\nSECURE_HSTS_SECONDS = 60\nSECURE_HSTS_INCLUDE_SUBDOMAINS = env.bool(\n 'DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS', default=True)\nSECURE_CONTENT_TYPE_NOSNIFF = env.bool(\n 'DJANGO_SECURE_CONTENT_TYPE_NOSNIFF', default=True)\nSECURE_BROWSER_XSS_FILTER = True\nSESSION_COOKIE_SECURE = False\nSESSION_COOKIE_HTTPONLY = True\nSECURE_SSL_REDIRECT = env.bool('DJANGO_SECURE_SSL_REDIRECT', default=True)\n\n# SITE CONFIGURATION\n# ------------------------------------------------------------------------------\n# Hosts/domain names that are valid for this site\n# See https://docs.djangoproject.com/en/1.6/ref/settings/#allowed-hosts\n#ALLOWED_HOSTS = env.list('DJANGO_ALLOWED_HOSTS', default=['lessdoing.com'])\nALLOWED_HOSTS = ['*', ]\n# END SITE CONFIGURATION\n\nINSTALLED_APPS += ('gunicorn', 'opbeat.contrib.django',)\n\n'''\n# STORAGE CONFIGURATION\n# ------------------------------------------------------------------------------\n# Uploaded Media Files\n# ------------------------\n# See: http://django-storages.readthedocs.io/en/latest/index.html\nINSTALLED_APPS += (\n 'storages',\n)\n\nAWS_ACCESS_KEY_ID = env('DJANGO_AWS_ACCESS_KEY_ID')\nAWS_SECRET_ACCESS_KEY = env('DJANGO_AWS_SECRET_ACCESS_KEY')\nAWS_STORAGE_BUCKET_NAME = env('DJANGO_AWS_STORAGE_BUCKET_NAME')\nAWS_AUTO_CREATE_BUCKET = True\nAWS_QUERYSTRING_AUTH = False\nAWS_S3_CALLING_FORMAT = OrdinaryCallingFormat()\n\n# AWS cache settings, don't change unless you know what you're doing:\nAWS_EXPIRY = 60 * 60 * 24 * 7\n\n# TODO See: https://github.com/jschneier/django-storages/issues/47\n# Revert the following and use str after the above-mentioned bug is fixed in\n# either django-storage-redux or boto\nAWS_HEADERS = {\n 'Cache-Control': six.b('max-age=%d, s-maxage=%d, must-revalidate' % (\n AWS_EXPIRY, AWS_EXPIRY))\n}\n\n# URL that handles the media served from MEDIA_ROOT, used for managing\n# stored files.\nMEDIA_URL = 'https://s3.amazonaws.com/%s/' % AWS_STORAGE_BUCKET_NAME\n'''\n\n# Static Assets\n# ------------------------\nSTATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'\n\n\n# EMAIL\n# ------------------------------------------------------------------------------\nDEFAULT_FROM_EMAIL = env('DJANGO_DEFAULT_FROM_EMAIL',\n default='lessdoing ')\n#EMAIL_BACKEND = 'django_mailgun.MailgunBackend'\nMAILGUN_ACCESS_KEY = env('DJANGO_MAILGUN_API_KEY')\nMAILGUN_SERVER_NAME = env('DJANGO_MAILGUN_SERVER_NAME')\nEMAIL_SUBJECT_PREFIX = env('DJANGO_EMAIL_SUBJECT_PREFIX', default='[lessdoing] ')\nSERVER_EMAIL = env('DJANGO_SERVER_EMAIL', default=DEFAULT_FROM_EMAIL)\n\n\n# TEMPLATE CONFIGURATION\n# ------------------------------------------------------------------------------\n# See:\n# https://docs.djangoproject.com/en/dev/ref/templates/api/#django.template.loaders.cached.Loader\nTEMPLATES[0]['OPTIONS']['loaders'] = [\n ('django.template.loaders.cached.Loader', [\n 'django.template.loaders.app_directories.Loader', 'django.template.loaders.filesystem.Loader', ]),\n]\n\n# DATABASE CONFIGURATION\n# ------------------------------------------------------------------------------\n# Raises ImproperlyConfigured exception if DATABASE_URL not in os.environ\nDATABASES['default'] = env.db('DATABASE_URL')\n\n# CACHING\n# ------------------------------------------------------------------------------\n# Heroku URL does not pass the DB number, so we parse it in\nCACHES = {\n 'default': {\n 'BACKEND': 'django_redis.cache.RedisCache',\n 'LOCATION': '{0}/{1}'.format(env('REDIS_URL', default='redis://127.0.0.1:6379'), 0),\n 'OPTIONS': {\n 'CLIENT_CLASS': 'django_redis.client.DefaultClient',\n 'IGNORE_EXCEPTIONS': True, # mimics memcache behavior.\n # http://niwinz.github.io/django-redis/latest/#_memcached_exceptions_behavior\n }\n }\n}\n\n\n# LOGGING CONFIGURATION\n# ------------------------------------------------------------------------------\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#logging\n# A sample logging configuration. The only tangible logging\n# performed by this configuration is to send an email to\n# the site admins on every HTTP 500 error when DEBUG=False.\n# See http://docs.djangoproject.com/en/dev/topics/logging for\n# more details on how to customize your logging configuration.\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'filters': {\n 'require_debug_false': {\n '()': 'django.utils.log.RequireDebugFalse'\n }\n },\n 'formatters': {\n 'verbose': {\n 'format': '%(levelname)s %(asctime)s %(module)s '\n '%(process)d %(thread)d %(message)s'\n },\n },\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'filters': ['require_debug_false'],\n 'class': 'django.utils.log.AdminEmailHandler'\n },\n 'console': {\n 'level': 'DEBUG',\n 'class': 'logging.StreamHandler',\n 'formatter': 'verbose',\n },\n },\n 'loggers': {\n 'django.request': {\n 'handlers': ['mail_admins'],\n 'level': 'ERROR',\n 'propagate': True\n },\n 'django.security.DisallowedHost': {\n 'level': 'ERROR',\n 'handlers': ['console', 'mail_admins'],\n 'propagate': True\n },\n 'debug': {\n 'level': 'DEBUG',\n 'handlers': ['console']\n }\n }\n}\n\n# Custom Admin URL, use {% url 'admin:index' %}\nADMIN_URL = env('DJANGO_ADMIN_URL')\n\n\nOPBEAT = {\n 'ORGANIZATION_ID': '6690c1267fa34ef19fc2d7f3b2f665ce',\n 'APP_ID': '4e161945ca',\n 'SECRET_TOKEN': 'e33b5435341f23b4802c52380786d619ac3649e1',\n}\n\nOPBEAT_MIDDLEWARE_CLASSES = (\n 'opbeat.contrib.django.middleware.OpbeatAPMMiddleware',\n # ...\n)\n\nMIDDLEWARE_CLASSES = OPBEAT_MIDDLEWARE_CLASSES + MIDDLEWARE_CLASSES\n\n\nINSTALLED_APPS += ('djrill',)\nMANDRILL_API_KEY = env('MANDRILL_API_KEY')\nEMAIL_BACKEND = \"djrill.mail.backends.djrill.DjrillBackend\"\n\n# Your production stuff: Below this line define 3rd party library settings\nTRELLO_KEY = env('TRELLO_KEY') #'8fe14d2cc290f4dedd5a286532bf40fe'\nTRELLO_TOKEN = env('TRELLO_TOKEN') #'160c373edb861475fdf2c6b17b8bc0be0c236fee4e8836695ae1eeea8de4d44c'\nTRELLO_SECRET = env('TRELLO_SECRET') #'c274b9e393a5c1ae69749590d1128602903e271de0a1b15e3b806ebce7291806'\nTRELLO_ORGANIZATION_ID = env('TRELLO_ORGANIZATION_ID') #'560ffc987966347a92eb8982'\nTRELLO_NO_NEED_CHECK_BOARDS = ['0_Validated', 'VA Resources', 'BPO', 'Events', '0_Dev_internal', '0_TopTasks']\nTRELLO_INTERNAL_PROJECT_LIST_ID = env('TRELLO_INTERNAL_PROJECT_LIST_ID') #'55cf5dc1b18a75e2a0a612be' # '5693c35254ac7c6113d228fd'\nTRELLO_INTERNAL_ARI_LIST_ID = env('TRELLO_INTERNAL_ARI_LIST_ID') #'5714f1f200490a1c329d9847' # '5693c35254ac7c6113d228fd'\nTRELLO_WEBHOOK_URL = env('TRELLO_WEBHOOK_URL') #'https://lessdoing.herokuapp.com/trello/webhook/'\nTRELLO_Z_FORMER_ID = env('TRELLO_Z_FORMER_ID') #'563aab534c1938d32b37aaab'\nTRELLO_Z_ON_HOLD_ID = env('TRELLO_Z_ON_HOLD_ID') #'5688742dcd48b86b5222bf1a'\nTRELLO_PREMIUM_ID = env('TRELLO_PREMIUM_ID') #'5637df9ec0b223f01d134c74'\nTRELLO_STANDARD_ID = env('TRELLO_STANDARD_ID') #'563a9ec034d7adc54ab879ed'\nTRELLO_PREMIUM2_ID = env('TRELLO_PREMIUM2_ID') #'5739893e5cf0d024f296b46e'\nTRELLO_TAGS = {\n TRELLO_PREMIUM_ID: \"Premium\",\n TRELLO_STANDARD_ID: \"Standard\",\n TRELLO_PREMIUM2_ID: \"Premium2\"\n}\n\nCHARGIFY_PRODUCT_HANDLE = env('CHARGIFY_PRODUCT_HANDLE') #'acme-online' # 'va'\nCHARGIFY_SHARE_KEY = env('CHARGIFY_SHARE_KEY') #'pUn732AjiSW4gJLXdS7oaxeTYKnjGEMqkcrMAO9FA'\n\nSLACK_INCOME_WEBHOOK_URL = env('SLACK_INCOME_WEBHOOK_URL') #'https://hooks.slack.com/services/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'\nSLACK_INCOME_WEBHOOK_URL_LDVA = env('SLACK_INCOME_WEBHOOK_URL_LDVA') #'https://hooks.slack.com/services/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'\nSLACK_INCOME_WEBHOOK_URL_STATE_CHANGE = env('SLACK_INCOME_WEBHOOK_URL_STATE_CHANGE')# 'https://hooks.slack.com/services/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'\nSLACK_INCOME_MANAGER_WEBHOOK_URL = env('SLACK_INCOME_MANAGER_WEBHOOK_URL')\n\nLESSDOING_RUN_TRELLO_URL = env('LESSDOING_RUN_TRELLO_URL') # 'https://lessdoing.herokuapp.com/trello/run/?board_id=%s'\nDASHBOARD_URL = env('LESSDOING_DASHBOARD_URL')\nSLACK_API_ACCESS_TOKEN = env('SLACK_API_ACCESS_TOKEN')\nDASHBOARD_ADMIN_EMAIL = env('DASHBOARD_ADMIN_EMAIL')\n\nMONGO_DB_URL = env('MONGO_DB_URL')\nMONGO_DB_NAME = env('MONGO_DB_NAME')\nMONGO_DB_COLLECTION = env('MONGO_DB_COLLECTION')\n\nFIREBASE_URL = env('FIREBASE_URL')\nFIREBASE_DATABASE_SECRET = env('FIREBASE_DATABASE_SECRET')\n\n\n\nCELERYBEAT_SCHEDULE = {\n 'run_report_every_day_8am_est': {\n 'task': 'src.trello.tasks.send_dm_red_member_task',\n 'schedule': crontab(hour=12, minute=00),\n },\n 'send_dm_for_waiting_cards_every_day_9am_est': {\n 'task': 'src.trello.tasks.send_dm_for_waiting_cards',\n 'schedule': crontab(hour=13, minute=00),\n },\n 'upd': {\n 'task': 'src.trello.tasks.daily_stats_task',\n 'schedule': crontab(hour=4, minute=00),\n },\n\n 'run_send_no_response_dm_task_every_5_mins': {\n 'task': 'src.trello.tasks.send_no_response_dm_task',\n 'schedule': timedelta(minutes=5)\n },\n\n 'run_create_user_firebase_token_task': {\n 'task': 'src.users.tasks.create_user_firebase_token_task',\n 'schedule': timedelta(minutes=55)\n },\n\n #update_all_customer_toggl_task\n 'run_update_all_customer_toggl_task': {\n 'task': 'src.chargify.tasks.update_all_customer_toggl_task',\n 'schedule': timedelta(minutes=30)\n },\n}\n\nFIREBASE_SERVICE_ACCOUNT_EMAIL = env('FIREBASE_SERVICE_ACCOUNT_EMAIL')\nFIREBASE_PRIVATE_KEY = env('FIREBASE_PRIVATE_KEY')\n\nTOGGL_WORKSPACE_ID=env('TOGGL_WORKSPACE_ID')", "sub_path": "config/settings/production.py", "file_name": "production.py", "file_ext": "py", "file_size_in_byte": 11096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "celery.schedules.crontab", "line_number": 265, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 269, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 273, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 278, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 289, "usage_type": "call"}]} +{"seq_id": "156160841", "text": "from db.hotel_db import HotelInDB\nfrom db.hotel_db import get_hotel, create_hotel, delete_hotel, update_hotel\nfrom db.referencia_db import get_hotel\nfrom models.hotel_models import HotelIn, HotelOut\nfrom models.referencia_models import RefIn, RefOut\n\n\nfrom fastapi import FastAPI, HTTPException\nfrom fastapi.middleware.cors import CORSMiddleware\n\napp = FastAPI()\n\norigins = [\n \"http://localhost.tiangolo.com\", \"https://localhost.tiangolo.com\",\n \"http://localhost\", \"http://localhost:8080\", \"http://localhost:8000\", \"http://localhost:8082\",\n]\n\napp.add_middleware(\n CORSMiddleware, allow_origins=origins,\n allow_credentials=True, allow_methods=[\"*\"], allow_headers=[\"*\"],\n)\n\n@app.get(\"/test/\")\nasync def check_conexion():\n return {\"LA APLICACION ESTA CONECTADA\"}\n\n@app.post(\"/hotel/verification/\")\nasync def check_hotel(check: HotelIn):\n hotel_in_db = get_hotel(check.nombre)\n if hotel_in_db == None:\n raise HTTPException(status_code=404, detail=\"EL REGISTRO NO EXISTE EN LA BASE DE DATOS\")\n return {\"EL REGISTRO EXISTE EN LA BASE DE DATOS\"}\n\n@app.get(\"/hotel/search/{nombre}\")\nasync def get_Hotel(name: str):\n hotel_in_db = get_hotel(name)\n if hotel_in_db == None:\n raise HTTPException(status_code=404, detail=\"EL REGISTRO NO EXISTE EN LA BASE DE DATOS\")\n hotel_out = HotelOut(**hotel_in_db.dict())\n return hotel_out\n\n@app.post(\"/hotel/create\")\nasync def create_new_hotel(newHotel: HotelOut):\n hotel_in_db = get_hotel(newHotel.nombre)\n if hotel_in_db == None:\n create_hotel(newHotel)\n return {\"EL REGISTRO SE CREO EN LA BASE DE DATOS\"}\n else:\n raise HTTPException(status_code=404, detail=\"EL REGISTRO NO SE PUEDE CREAR YA EXISTE EN LA BASE DE DATOS\")\n\n@app.delete(\"/hotel/delete/{nombre}\")\nasync def delete_this_hotel(nombre: str):\n hotel_in_db = get_hotel(nombre)\n if hotel_in_db ==None:\n raise HTTPException(status_code=404, detail=\"EL REGISTRO NO SE PUEDE BORRAR NO EXISTE EN LA BASE DE DATOS\")\n delete_hotel(nombre)\n return{\"EL REGISTRO SE BORRRO DE LA BASE DE DATOS\"}\n\n@app.put(\"/hotel/update/\")\nasync def update_this_hotel(updateHotel: HotelOut):\n hotel_in_db = get_hotel(updateHotel.nombre)\n if hotel_in_db == None:\n raise HTTPException(status_code=404, detail=\"EL REGISTRO NO EXISTE EN LA BASE DE DATOS NO SE PUEDE ACTUALIZAR\")\n update_hotel(updateHotel)\n return {\"EL REGISTRO SE ACTUALIZO DE MANERA CORRECTA\"}", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "fastapi.FastAPI", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 19, "usage_type": "argument"}, {"api_name": "models.hotel_models.HotelIn", "line_number": 28, "usage_type": "name"}, {"api_name": "db.referencia_db.get_hotel", "line_number": 29, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 31, "usage_type": "call"}, {"api_name": "db.referencia_db.get_hotel", "line_number": 36, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 38, "usage_type": "call"}, {"api_name": "models.hotel_models.HotelOut", "line_number": 39, "usage_type": "call"}, {"api_name": "models.hotel_models.HotelOut", "line_number": 43, "usage_type": "name"}, {"api_name": "db.referencia_db.get_hotel", "line_number": 44, "usage_type": "call"}, {"api_name": "db.hotel_db.create_hotel", "line_number": 46, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 49, "usage_type": "call"}, {"api_name": "db.referencia_db.get_hotel", "line_number": 53, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 55, "usage_type": "call"}, {"api_name": "db.hotel_db.delete_hotel", "line_number": 56, "usage_type": "call"}, {"api_name": "models.hotel_models.HotelOut", "line_number": 60, "usage_type": "name"}, {"api_name": "db.referencia_db.get_hotel", "line_number": 61, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 63, "usage_type": "call"}, {"api_name": "db.hotel_db.update_hotel", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "543445358", "text": "import random\nimport pygame\nimport pygame.gfxdraw\n# local files\nimport const\nfrom const import Colors\nfrom field import Field\n# utilities\nfrom imgutil import ImageLoader\n\n\nclass Background:\n \"\"\" Background drawer (Start screen)\"\"\"\n\n # title image\n TITLE_IMAGE_SIZE = 256\n # title header\n TITLE_IMAGE_SIZE_XY = (TITLE_IMAGE_SIZE, TITLE_IMAGE_SIZE)\n # title image y position\n TITLE_IMAGE_Y = 100\n # title text y position\n TITLE_TEXT_Y = 50\n\n # change level y position\n CHANGE_LEVEL_Y = 400\n\n # press enter y position\n PRESS_ENTER_Y = 440\n\n # normal star speed\n NORMAL_STAR_SPEED = 0.8\n\n # number of stars\n NUMBER_OF_STARS = 100\n\n # credit text position(x)\n CREDITS_TEXT_X = 20\n # credit text position(y)\n CREDITS_TEXT_Y = 470\n # credit text line spacing\n LINE_SPACING = 8\n\n # change level arrow text\n CHANGE_ARROW_TEXT = \"< >\"\n\n # press enter text\n PRESS_ENTER_TEXT = \" - Press Enter - \"\n\n def __init__(self, infofont, titlefont, largefont):\n self.largefont = largefont\n # init start screen\n # create start screen\n ss_surf = pygame.Surface(Field.XY)\n ss_surf.fill(Colors.WHITE)\n\n # add title image\n title_img = ImageLoader.load_with_trans(\n \"titile_img.png\", Background.TITLE_IMAGE_SIZE_XY)\n xy = ((Field.x - Background.TITLE_IMAGE_SIZE) / 2,\n Background.TITLE_IMAGE_Y)\n ss_surf.blit(title_img, xy)\n\n # add title text\n title_text = titlefont.render(const.GAME_TITLE_TEXT,\n True, Colors.BLACK)\n sizew, sizeh = titlefont.size(const.GAME_TITLE_TEXT)\n xy = ((Field.x - sizew) / 2, Background.TITLE_TEXT_Y - sizeh)\n ss_surf.blit(title_text, xy)\n\n # add credit text\n for i, line in enumerate(const.CREDITS_TEXT.split(\"\\n\")):\n text = infofont.render(line, True, Colors.BLACK)\n y = Background.CREDITS_TEXT_Y + Background.LINE_SPACING * i\n ss_surf.blit(text, (Background.CREDITS_TEXT_X, y))\n\n # init change level arrow\n arrow_text = largefont.render(Background.CHANGE_ARROW_TEXT,\n True, Colors.BLACK)\n sizew, sizeh = largefont.size(Background.CHANGE_ARROW_TEXT)\n xy = ((Field.x - sizew) / 2, Background.CHANGE_LEVEL_Y - sizeh)\n ss_surf.blit(arrow_text, xy)\n\n self.start_screen_surf = ss_surf\n\n # init press enter blinking text\n self.press_enter_message = largefont.render(\n Background.PRESS_ENTER_TEXT, True, Colors.BLACK)\n sizew, sizeh = largefont.size(Background.PRESS_ENTER_TEXT)\n self.press_enter_xy = ((Field.x - sizew) / 2,\n Background.PRESS_ENTER_Y - sizeh)\n\n # init game screen background\n gs_surf = pygame.Surface(Field.XY)\n gs_surf.fill(Colors.WHITE)\n max_range = max(Field.x, Field.y) * 2\n diff_max = 75\n variant = (float(diff_max) / float(max_range))\n for i in range(max_range):\n gray = int(254 - diff_max + i * variant)\n color = (gray, gray, gray)\n pygame.draw.circle(gs_surf, color, (0, 0), max_range - i)\n self.game_screen_surf = gs_surf\n\n # init background stars\n self.stars = [(random.randint(0, Field.x),\n (random.randint(0, Field.y)))\n for _ in range(Background.NUMBER_OF_STARS)]\n\n def draw_starrysky(self, screen, game): # background\n # screen.fill(Colors.WHITE)\n screen.blit(self.game_screen_surf, (0, 0))\n if game.over:\n speed = Background.NORMAL_STAR_SPEED + 1\n elif game.pause:\n speed = 0\n else:\n speed = Background.NORMAL_STAR_SPEED\n # move star\n for i, (x, y) in enumerate(self.stars):\n self.stars[i] = (x, (y + speed) % Field.y)\n # draw background\n for (x_d, y_d) in self.stars:\n x, y = int(x_d), int(y_d)\n pygame.gfxdraw.line(screen, x - 1, y, x + 1, y, Colors.BLACK)\n pygame.gfxdraw.line(screen, x, y - 1, x, y + 1, Colors.BLACK)\n\n def draw_start_screen(self, screen, blinking_state, gamelevel):\n screen.blit(self.start_screen_surf, (0, 0))\n\n # selected game level description\n gamelevel_text = self.largefont.render(gamelevel, True, Colors.BLACK)\n sizew, sizeh = self.largefont.size(gamelevel)\n xy = ((Field.x - sizew) / 2, Background.CHANGE_LEVEL_Y - sizeh)\n screen.blit(gamelevel_text, xy)\n\n if blinking_state:\n screen.blit(self.press_enter_message, self.press_enter_xy)\n", "sub_path": "contoh space game/pygame-simple-shooting-master/game/background.py", "file_name": "background.py", "file_ext": "py", "file_size_in_byte": 4698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pygame.Surface", "line_number": 53, "usage_type": "call"}, {"api_name": "field.Field.XY", "line_number": 53, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 53, "usage_type": "name"}, {"api_name": "const.Colors.WHITE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 54, "usage_type": "name"}, {"api_name": "imgutil.ImageLoader.load_with_trans", "line_number": 57, "usage_type": "call"}, {"api_name": "imgutil.ImageLoader", "line_number": 57, "usage_type": "name"}, {"api_name": "field.Field.x", "line_number": 59, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 59, "usage_type": "name"}, {"api_name": "const.GAME_TITLE_TEXT", "line_number": 64, "usage_type": "attribute"}, {"api_name": "const.Colors.BLACK", "line_number": 65, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 65, "usage_type": "name"}, {"api_name": "const.GAME_TITLE_TEXT", "line_number": 66, "usage_type": "attribute"}, {"api_name": "field.Field.x", "line_number": 67, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 67, "usage_type": "name"}, {"api_name": "const.CREDITS_TEXT.split", "line_number": 71, "usage_type": "call"}, {"api_name": "const.CREDITS_TEXT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "const.Colors.BLACK", "line_number": 72, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 72, "usage_type": "name"}, {"api_name": "const.Colors.BLACK", "line_number": 78, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 78, "usage_type": "name"}, {"api_name": "field.Field.x", "line_number": 80, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 80, "usage_type": "name"}, {"api_name": "const.Colors.BLACK", "line_number": 87, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 87, "usage_type": "name"}, {"api_name": "field.Field.x", "line_number": 89, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 89, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 93, "usage_type": "call"}, {"api_name": "field.Field.XY", "line_number": 93, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 93, "usage_type": "name"}, {"api_name": "const.Colors.WHITE", "line_number": 94, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 94, "usage_type": "name"}, {"api_name": "field.Field.x", "line_number": 95, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 95, "usage_type": "name"}, {"api_name": "field.Field.y", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 101, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 101, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 105, "usage_type": "call"}, {"api_name": "field.Field.x", "line_number": 105, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 105, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 106, "usage_type": "call"}, {"api_name": "field.Field.y", "line_number": 106, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 106, "usage_type": "name"}, {"api_name": "field.Field.y", "line_number": 120, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 120, "usage_type": "name"}, {"api_name": "pygame.gfxdraw.line", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 124, "usage_type": "attribute"}, {"api_name": "const.Colors.BLACK", "line_number": 124, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 124, "usage_type": "name"}, {"api_name": "pygame.gfxdraw.line", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 125, "usage_type": "attribute"}, {"api_name": "const.Colors.BLACK", "line_number": 125, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 125, "usage_type": "name"}, {"api_name": "const.Colors.BLACK", "line_number": 131, "usage_type": "attribute"}, {"api_name": "const.Colors", "line_number": 131, "usage_type": "name"}, {"api_name": "field.Field.x", "line_number": 133, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 133, "usage_type": "name"}]} +{"seq_id": "38441743", "text": "from keras.models import Sequential, Model, load_model\r\nfrom keras.layers import Dense, Activation, GaussianNoise, Dropout, BatchNormalization\r\nfrom keras import initializers\r\nfrom keras.optimizers import SGD, Nadam, RMSprop\r\nfrom keras.constraints import maxnorm\r\nfrom keras.regularizers import l2\r\nfrom keras.layers.advanced_activations import LeakyReLU, PReLU\r\n#from keras.wrappers.scikit_learn import KerasRegressor\r\n#from sklearn import preprocessing \r\n#from sklearn.preprocessing import MinMaxScaler\r\n#from sklearn.pipeline import Pipeline\r\n#from scipy.stats.stats import pearsonr\r\nfrom scikit_learn.model_selection import train_test_split\r\n#from scikit_learn.metrics import mean_absolute_error\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport keras.backend as kb\r\nimport keras.backend as K\r\nimport os\r\nimport threading\r\nimport random\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '1' #use GPU with ID=0\r\nconfig = tf.ConfigProto()\r\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.5 # maximun alloc gpu50% of MEM\r\nconfig.gpu_options.allow_growth = True #allocate dynamically\r\n\r\n#from netCDF4 import Dataset\r\n\r\ndef rae(y_true, y_pred):\r\n return tf.reduce_sum(tf.abs(y_pred-y_true)) / tf.reduce_sum(tf.abs(y_true))\r\n\r\ndef mae(y_true, y_pred):\r\n return tf.reduce_mean(tf.abs(y_pred-y_true))\r\n\r\ndef mape(y_true, y_pred):\r\n diff = tf.abs((y_true - y_pred) / (tf.abs(y_true)))\r\n return 100. * tf.reduce_mean(diff)\r\n\r\ndef mean_absolute_percentage_error(y_true, y_pred):\r\n diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))\r\n return 100. * K.mean(diff)\r\n\r\ndef rmse(y_true, y_pred):\r\n return K.sqrt(K.mean(K.square(y_pred - y_true)))\r\n\r\n# reading the input data\r\n\r\n#SGSDATA = np.genfromtxt('NUSGS.dat')\r\nSGSDATA = np.load('RANDOMDATA/NUSGSTRAIN.npy')\r\n#SGSDATA2 = np.load('RANDOMDATA/TESTING_DATA.npy')\r\n#random.shuffle(SGSDATA)\r\nprint (SGSDATA.shape)\r\n#print (SGSDATA2.shape)\r\n\r\n#np.save('NUSGS.npy',SGSDATA)\r\n\r\n#X_train0 = SGSDATA[:,0:9]\r\nY_train0 = -SGSDATA[:,13]\r\n#Y_test = -SGSDATA2[:,13]\r\nSGSDATA = np.delete(SGSDATA,3,1)\r\n#SGSDATA2 = np.delete(SGSDATA2,3,1)\r\n\r\nprint (SGSDATA.shape)\r\n#print (SGSDATA2.shape)\r\n\r\nX_train0 = SGSDATA[:,0:9]\r\n#X_test = SGSDATA2[:,0:9]\r\n\r\nprint (Y_train0,X_train0)\r\n\r\nX_train, X_test, Y_train, Y_test = train_test_split(X_train0,Y_train0, test_size=0.1, random_state=42)\r\nprint (X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)\r\n\r\nXMEAN=np.max(X_train,axis=0) #np.mean(X_train,axis=0)\r\nXSTDD=np.min(X_train,axis=0)\r\n\r\n#print XMEAN, XSTDD\r\nnp.savetxt('xnmax.dat',XMEAN)\r\nnp.savetxt('xnmin.dat',XSTDD)\r\n\r\nfor j in range(9):\r\n X_train[:,j]= (X_train[:,j]-XSTDD[j])/(XMEAN[j]-XSTDD[j]) #(X_train[:,j]-XMEAN[j])/XSTDD[j]\r\n X_test[:,j] = (X_test[:,j]-XSTDD[j])/(XMEAN[j]-XSTDD[j]) #(X_test[:,j]-XMEAN[j])/XSTDD[j]\r\n\r\n#print XMEAN,XSTDD\r\n\r\nYMEAN=np.max(Y_train,axis=0) #np.mean(Y_train,axis=0)\r\nYSTDD=np.min(Y_train,axis=0) #np.std(Y_train,axis=0)\r\nYDATA = np.zeros((2))\r\nYDATA[0] = YMEAN\r\nYDATA[1] = YSTDD\r\nprint (YDATA.shape)\r\nprint (YDATA)\r\n\r\nnp.savetxt('ynmax.dat',YDATA)\r\n\r\nY_train[:]=(Y_train[:]-YSTDD)/(YMEAN-YSTDD) #(Y_train[:]-YMEAN)/YSTDD\r\nY_test[:] =(Y_test[:]-YSTDD)/(YMEAN-YSTDD)\r\n\r\nprint (YMEAN,YSTDD)\r\n\r\n#fix random seed for reproducibility\r\nseed = 7\r\nnp.random.seed(seed)\r\n\r\nmodel = Sequential()\r\nmodel.add(Dense(16, input_dim=9, kernel_initializer='uniform', activation = 'relu'))\r\nmodel.add(Dense(16, kernel_initializer='uniform', activation = 'relu'))\r\nmodel.add(Dense(16, kernel_initializer='uniform', activation = 'relu'))\r\nmodel.add(Dense(1, kernel_initializer='uniform'))\r\n# Compile model\r\nmodel.compile(loss='mean_squared_error', optimizer='rmsprop')\r\nhistory = model.fit(X_train,Y_train, epochs=100, batch_size=240,validation_split=0.1)\r\n\r\nscore = model.predict(X_test)\r\nscore[:]=score[:]*(YMEAN-YSTDD)+YSTDD #score[:]*YSTDD+YMEAN\r\n\r\nprint(\"Larger: %.2f (%.2f) MSE\" % (score.mean(), score.std()))\r\n\r\nW_Input_Hidden0 = model.layers[0].get_weights()[0]; print (W_Input_Hidden0.shape)\r\nbiases0 = model.layers[0].get_weights()[1]; print (biases0.shape)\r\nW_Input_Hidden1 = model.layers[1].get_weights()[0]; print (W_Input_Hidden1.shape)\r\nbiases1 = model.layers[1].get_weights()[1]; print (biases1.shape)\r\nW_Input_Hidden2 = model.layers[2].get_weights()[0]; print (W_Input_Hidden2.shape)\r\nbiases2 = model.layers[2].get_weights()[1]; print (biases2.shape)\r\nW_Input_Hidden3 = model.layers[3].get_weights()[0]; print (W_Input_Hidden3.shape)\r\nbiases3 = model.layers[3].get_weights()[1]; print (biases3.shape)\r\n\r\nnp.save('SWNHidden003.npy',W_Input_Hidden0)\r\nnp.save('SWNbiases003.npy',biases0)\r\nnp.save('SWNHidden013.npy',W_Input_Hidden1)\r\nnp.save('SWNbiases013.npy',biases1)\r\nnp.save('SWNHidden023.npy',W_Input_Hidden2)\r\nnp.save('SWNbiases023.npy',biases2)\r\nnp.save('SWNHidden033.npy',W_Input_Hidden3)\r\nnp.save('SWNbiases033.npy',biases3)\r\nnp.save('SWNX_test3.npy', X_test)\r\nnp.save('SWNY_test3.npy', Y_test)\r\nnp.save('SWNScore3.npy', score)\r\n\r\nprint(history.history.keys())\r\ntrain_loss = history.history['loss']\r\nval_loss = history.history['val_loss']\r\nnp.save('train_lossN.npy',train_loss)\r\nnp.save('val_lossN.npy',val_loss)\r\n", "sub_path": "NUSGSTRAIN2.py", "file_name": "NUSGSTRAIN2.py", "file_ext": "py", "file_size_in_byte": 5193, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.abs", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.abs", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.abs", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.backend.abs", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 41, "usage_type": "name"}, {"api_name": "keras.backend.clip", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.backend.epsilon", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 41, "usage_type": "attribute"}, {"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.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 61, "usage_type": "call"}, {"api_name": "scikit_learn.model_selection.train_test_split", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 105, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "222222304", "text": "from __future__ import unicode_literals, print_function, division\nfrom io import open\nimport unicodedata\nimport string\nimport re\nimport random\nimport time\nimport math\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch import optim\nimport torch.nn.functional as F\n\nfrom Seq2SeqModel import EncoderRNN, DecoderRNN, AttnDecoderRNN\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nclass Lang:\n def __init__(self, name):\n self.name = name\n self.word2index = dict()\n self.word2count = dict()\n self.index2word = {0 : 'SOS', 1 : 'EOS'}\n self.n_words = 2\n \n def addSentence(self, sentence):\n for word in sentence.split(' '):\n self.addWord(word)\n \n def addWord(self, word):\n if word not in self.word2index:\n self.word2index[word] = self.n_words\n self.word2count[word] = 1\n self.index2word[self.n_words] = word\n self.n_words +=1\n else:\n self.word2count[word] += 1\n\ndef unicodeToAscii(s):\n return ''.join(\n c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn'\n )\n\ndef normalizeString(s):\n s = unicodeToAscii(s.lower().strip())\n s = re.sub(r\"([.!?])\", r\" \\1\", s)\n s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\n return s\n\ndef readLangs(lang1, lang2, reverse = False):\n print('starting ...')\n lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').read().strip().split('\\n')\n pairs = [[normalizeString(s) for s in l.split('\\t')] for l in lines]\n\n if reverse:\n pairs = [list(reversed(p)) for p in pairs]\n input_lang = Lang(lang2)\n output_lang = Lang(lang1)\n else:\n input_lang = Lang(lang1)\n output_lang = Lang(lang2)\n return input_lang, output_lang, pairs\n\ndef filterPair(p):\n return len(p[0].split(' ')) < MAX_LENGTH and \\\n len(p[1].split(' ')) < MAX_LENGTH and \\\n p[1].startswith(eng_prefixes)\n\ndef filterPairs(pairs):\n return [pair for pair in pairs if filterPair(pair)]\n\ndef prepareData(lang1, lang2, reverse = False):\n input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)\n print(\"Read %s sentence pairs\" % len(pairs))\n pairs = filterPairs(pairs)\n print(\"Trimmed to %s sentence pairs\" % len(pairs))\n print(\"Counting words...\")\n for pair in pairs:\n input_lang.addSentence(pair[0])\n output_lang.addSentence(pair[1])\n print(\"Counted words:\")\n print(input_lang.name, input_lang.n_words)\n print(output_lang.name, output_lang.n_words)\n return input_lang, output_lang, pairs\n\ndef indexesFromSentence(lang,sentence):\n return [lang.word2index[word] for word in sentence.split(' ')]\n\ndef tensorFromSentence(lang, sentence):\n indexes = indexesFromSentence(lang, sentence)\n indexes.append(EOS_Token)\n return torch.tensor(indexes, dtype = torch.long, device = device).view(-1,1)\n\ndef tensorsFromPair(pair):\n input_tensor = tensorFromSentence(input_lang, pair[0])\n target_tensor = tensorFromSentence(output_lang, pair[1])\n return (input_tensor, target_tensor)\n\ndef train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length = 10):\n encoder_hidden = encoder.initHidden(device)\n \n encoder_optimizer.zero_grad()\n decoder_optimizer.zero_grad()\n \n input_length = input_tensor.size(0)\n target_length = target_tensor.size(0)\n\n encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device= device)\n\n loss = 0\n\n for ei in range(input_length):\n encoder_ouput, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n encoder_outputs[ei] = encoder_ouput[0,0]\n\n decoder_input = torch.tensor([[SOS_Token]], device=device)\n\n decoder_hidden = encoder_hidden\n\n use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n\n if use_teacher_forcing:\n for di in range(target_length):\n decoder_output, decoder_hidden, decoder_attn = decoder(decoder_input, decoder_hidden, encoder_outputs)\n \n loss += criterion(decoder_output, target_tensor[di])\n decoder_input = target_tensor[di] # teacher forcing\n \n else:\n for di in range(target_length):\n decoder_output, decoder_hidden, decoder_attn = decoder(decoder_input, decoder_hidden, encoder_outputs)\n topv, topi = decoder_output.topk(1)\n decoder_input = topi.squeeze().detach() # remove from history as input\n\n loss += criterion(decoder_output, target_tensor[di])\n if decoder_input.item() == EOS_Token:\n break\n\n loss.backward()\n encoder_optimizer.step()\n decoder_optimizer.step()\n\n return loss.item() / target_length\n\ndef asMinutes(s):\n m = math.floor(s / 60)\n s -= m * 60\n return '%dm %ds' % (m, s)\n\ndef timeSince(since, percent):\n now = time.time()\n s = now - since\n es = s / (percent)\n rs = es - s\n return '%s (- %s)' % (asMinutes(s), asMinutes(rs))\n\ndef trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=.01):\n start = time.time()\n plot_losses = []\n print_loss_total = 0\n plot_loss_total = 0\n\n encoder_optimizer = optim.SGD(encoder.parameters(), lr = learning_rate)\n decoder_optimizer = optim.SGD(decoder.parameters(), lr = learning_rate)\n training_pairs = [tensorsFromPair(random.choice(pairs)) for i in range(n_iters)]\n criterion = nn.NLLLoss()\n\n for iter in range(1, n_iters + 1):\n training_pair = training_pairs[iter - 1]\n input_tensor = training_pair[0]\n target_tensor = training_pair[1]\n\n loss = train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)\n print_loss_total += loss\n plot_loss_total += loss\n\n if iter % print_every == 0:\n print_loss_avg = print_loss_total / print_every\n print_loss_total = 0\n print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),\n iter, iter / n_iters * 100, print_loss_avg))\n \n if iter % plot_every == 0:\n plot_loss_avg = plot_loss_total / plot_every\n plot_losses.append(plot_loss_avg)\n plot_loss_total = 0\n \n showPlot(plot_losses)\n\ndef showPlot(points):\n plt.figure()\n fig, ax = plt.subplots()\n loc = ticker.MultipleLocator(base=0.2)\n ax.yaxis.set_major_locator(loc)\n plt.plot(points)\n plt.show()\n\ndef evaluate(encoder, decoder, sentence, max_length = 10):\n with torch.no_grad():\n input_tensor = tensorFromSentence(input_lang, sentence)\n input_lenght = input_tensor.size()[0]\n encoder_hidden = encoder.initHidden(device)\n\n encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n\n for ei in range(input_lenght):\n encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n encoder_outputs[ei] += encoder_output[0,0]\n\n decoder_input = torch.tensor([[SOS_Token]], device=device)\n\n decoder_hidden = encoder_hidden\n\n decoded_words = []\n decoder_attns = torch.zeros(max_length, max_length)\n\n for di in range(max_length):\n decoder_output, decoder_hidden, decoder_attn = decoder(decoder_input, decoder_hidden, encoder_outputs)\n decoder_attns[di] = decoder_attn.data\n topv, topi = decoder_output.data.topk(1)\n if topi.item() == EOS_Token:\n decoded_words.append('')\n break\n else:\n decoded_words.append(output_lang.index2word[topi.item()])\n \n decoder_input = topi.squeeze().detach()\n\n return decoded_words, decoder_attns[:di + 1]\n\ndef evaluateRandomly(encoder, decoder, n=10):\n for i in range(n):\n pair = random.choice(pairs)\n print('>', pair[0])\n print('=', pair[1])\n output_words, attentions = evaluate(encoder,decoder, pair[0])\n output_sentence = ' '.join(output_words)\n print('<', output_sentence)\n print('')\n\ndef showAttention(input_sentence, output_words, attentions):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n cax = ax.matshow(attentions.numpy(), cmap='bone')\n fig.colorbar(cax)\n\n # Set up axes\n ax.set_xticklabels([''] + input_sentence.split(' ') +\n [''], rotation=90)\n ax.set_yticklabels([''] + output_words)\n\n # Show label at every tick\n ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n\n plt.show()\n\ndef evaluateAndShowAttention(input_sentence):\n output_words, attentions = evaluate(encoder1, attn_decoder1, input_sentence)\n print('input =', input_sentence)\n print('output =', ' '.join(output_words))\n showAttention(input_sentence, output_words, attentions)\n\nif __name__ == \"__main__\":\n SOS_Token = 0\n EOS_Token = 0\n MAX_LENGTH = 10\n eng_prefixes = (\n \"i am \", \"i m \",\n \"he is\", \"he s \",\n \"she is\", \"she s \",\n \"you are\", \"you re \",\n \"we are\", \"we re \",\n \"they are\", \"they re \"\n )\n\n teacher_forcing_ratio = .5\n\n input_lang, output_lang, pairs = prepareData('eng', 'fra', True)\n # print(random.choice(pairs))\n\n hidden_size = 256\n encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)\n attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words).to(device)\n trainIters(encoder1, attn_decoder1, 75000, print_every=5000)\n evaluateRandomly(encoder1, attn_decoder1)\n\n evaluateAndShowAttention(\"elle a cinq ans de moins que moi .\")\n\n evaluateAndShowAttention(\"elle est trop petit .\")\n\n evaluateAndShowAttention(\"je ne crains pas de mourir .\")\n\n evaluateAndShowAttention(\"c est un jeune directeur plein de talent .\")", "sub_path": "nlp/Seq2SeqTrans.py", "file_name": "Seq2SeqTrans.py", "file_ext": "py", "file_size_in_byte": 9976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "torch.device", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 21, "usage_type": "attribute"}, {"api_name": "unicodedata.normalize", "line_number": 46, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 47, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 53, "usage_type": "call"}, {"api_name": "io.open", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 122, "usage_type": "call"}, {"api_name": "random.random", "line_number": 126, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 152, "usage_type": "call"}, {"api_name": "time.time", "line_number": 157, "usage_type": "call"}, {"api_name": "time.time", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 170, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"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.subplots", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "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": "torch.no_grad", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 221, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "Seq2SeqModel.EncoderRNN", "line_number": 289, "usage_type": "call"}, {"api_name": "Seq2SeqModel.AttnDecoderRNN", "line_number": 290, "usage_type": "call"}]} +{"seq_id": "185263260", "text": "\"\"\"\nPART 1 - Making and Populating a Database\n\"\"\"\n\nimport sqlite3\n\nprint('Part 1: Making and Populating a Database')\nprint('-'*80)\n\n# Give database a name\n\ndb_name = 'demo_data.sqlite3'\n\n# Try connecting\n\nconn = sqlite3.connect(db_name)\ncurs = conn.cursor()\n\n# Create the Table and Insert Data\n\ncurs.execute(\"\"\"CREATE TABLE demo(\n s CHAR(1) PRIMARY KEY,\n x INT NOT NULL,\n y INT NOT NULL\n );\"\"\")\n\ncurs.execute(\"\"\"INSERT INTO demo (s,x,y) VALUES ('g', 3, 9);\"\"\")\ncurs.execute(\"\"\"INSERT INTO demo (s,x,y) VALUES ('v', 5, 7);\"\"\")\ncurs.execute(\"\"\"INSERT INTO demo (s,x,y) VALUES ('f', 8, 7);\"\"\")\n\n# Answer questions\n\n# Count number of Rows\n\ncurs.execute(\"\"\"SELECT COUNT(*) FROM demo;\"\"\")\nprint('Number of Rows:')\nprint(curs.fetchall())\n\n# Minimum 5 in x and y\n\ncurs.execute(\"\"\"SELECT COUNT(*) FROM demo\n WHERE x >= 5 and y >= 5;\"\"\")\nprint('Rows where x and y are 5 or greater:')\nprint(curs.fetchall())\n\n# unique values of y\n\ncurs.execute(\"\"\"SELECT COUNT(DISTINCT y) FROM demo\"\"\")\nprint(\"Unique values of y:\")\nprint(curs.fetchall())\n", "sub_path": "SC/demo_data.py", "file_name": "demo_data.py", "file_ext": "py", "file_size_in_byte": 1051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sqlite3.connect", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "145508227", "text": "import numpy as np # a conventional alias\n\nimport sklearn.feature_extraction.text as text\n\nimport os\n\nCORPUS_PATH = os.path.join('C:/', 'word2vec/test')\n\nfilenames = sorted([os.path.join(CORPUS_PATH, fn) for fn in os.listdir(CORPUS_PATH)])\n\nvectorizer = text.CountVectorizer(input='filename', stop_words='english', min_df=1)\n\ndtm = vectorizer.fit_transform(filenames).toarray()\n\nvocab = np.array(vectorizer.get_feature_names())\n\nprint (dtm.shape)\n\nprint (len(vocab))\n\nfrom sklearn import decomposition\n\nnum_topics = 5\n\nnum_top_words = 5\n\nclf = decomposition.NMF(n_components=num_topics, random_state=1)\n\ndoctopic = clf.fit_transform(dtm)\n\ntopic_words = []\n\nfor topic in clf.components_:\n word_idx = np.argsort(topic)[::-1][0:num_top_words]\n\n topic_words.append([vocab[i] for i in word_idx])\n\ndoctopic = doctopic / np.sum(doctopic, axis=1, keepdims=True)\n\nnovel_names = []\n\nfor fn in filenames:\n basename = os.path.basename(fn)\n name, ext = os.path.splitext(basename)\n name = name.rstrip('0123456789')\n novel_names.append(name)\n\n\n# turn this into an array so we can use NumPy functions\nnovel_names = np.asarray(novel_names)\n\ndoctopic_orig = doctopic.copy()\n\n# use method described in preprocessing section\nnum_groups = len(set(novel_names))\n\ndoctopic_grouped = np.zeros((num_groups, num_topics))\n\nfor i, name in enumerate(sorted(set(novel_names))):\n doctopic_grouped[i, :] = np.mean(doctopic[novel_names == name, :], axis=0)\n\n\ndoctopic = doctopic_grouped\n\nnovels = sorted(set(novel_names))\n\nprint(\"Top NMF topics in...\")\n\n\nfor i in range(len(doctopic)):\n top_topics = np.argsort(doctopic[i,:])[::-1][0:3]\n top_topics_str = ' '.join(str(t) for t in top_topics)\n print(\"{}: {}\".format(novels[i], top_topics_str))", "sub_path": "app/topicModelling.py", "file_name": "topicModelling.py", "file_ext": "py", "file_size_in_byte": 1750, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.decomposition.NMF", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.decomposition", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "44667539", "text": "from django import forms\nfrom .models import Order, Delivery_method\n\nclass OrderForm(forms.ModelForm):\n # delivery_field = forms.ModelChoiceField(queryset=Delivery_method.objects.all(),\n # to_field_name='method')\n class Meta:\n model = Order\n fields = ['delivery_method', 'name', 'email', 'phone_number',\n 'address', 'post_index', 'region', 'private_info_agreement',\n 'comment']\n\n\nclass Delivery_methodForm(forms.ModelForm):\n class Meta:\n model = Delivery_method\n fields = ['method', 'price']\n", "sub_path": "cart/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 602, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "models.Order", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "models.Delivery_method", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "214720612", "text": "import discord\nimport re\nimport requests\nimport random\nimport time\nimport threading\nimport asyncio\n\nclient = discord.Client()\n\npingchannel = client.get_channel(\"763821567695126588\")\n\npinginterval = 2 # seconds\n\npinging = False\n\n# async def _pingloop():\n# while pinging:\n# print('============ BRUH')\n# await pingchannel.send('@Eon#8669')\n# time.sleep(pinginterval)\n\n# loop = asyncio.new_event_loop()\n# asyncio.set_event_loop(loop)\n\nasync def pingloop():\n # loop = asyncio.get_event_loop()\n # loop = asyncio.new_event_loop()\n # asyncio.set_event_loop(loop)\n\n while pinging:\n print(\"========== BRUH\")\n fuck = await pingchannel.send('@Eon#8669')\n # loop.create_task(pingchannel.send('@Eon#8669'))\n # asyncio.run(pingchannel.send('@Eon#8669'))\n # asyncio.run_coroutine_threadsafe(pingchannel.send('@Eon#8669'), loop)\n time.sleep(pinginterval)\n \n # loop.close()\n\n\npinger = 0#threading.Thread(target = pingloop)#asyncio.run, args = (pingloop(),))\n\ndef startPinging():\n global pinger, pinging\n pinging = True\n # loop.create_task(pingloop())\n # loop.run_forever()\n # pinger = threading.Thread(target = pingloop)#asyncio.run, args = (pingloop(),))\n # pinger.start()\n\ndef stopPinging():\n pinging = False\n # pinger.join()\n\n\n@client.event\nasync def on_ready():\n print('We have logged in as {0.user}'.format(client))\n\n@client.event\nasync def on_message(message):\n global pinging, pinginterval, pingchannel\n\n if message.author == \"Eon#8669\":\n stopPinging()\n\n if message.author == client.user:\n return\n\n if \"Eon#8669\" in [str(mention) for mention in message.mentions]:\n pingean()\n \n if message.content.startswith(\"hey bot, ping ean!\"):\n pingchannel = message.channel\n # startPinging()\n print('fucing bitch fuck cunt')\n pinging = True\n await pingloop()\n \n if message.content.startswith(\"hey bot, stop!\"):\n stopPinging()\n\n # await message.channel.send(makepenis(random.randint(0,10)))\n \n\n\nwith open('apikey.txt', 'r') as apikeytxt:\n api_key = apikeytxt.read()\napi_url = 'https://www.alphavantage.co/query'\ndef getQuote(symbol):\n data = {\n 'function': 'GLOBAL_QUOTE',\n 'symbol': symbol,\n 'apikey': api_key\n }\n return requests.get(api_url, params=data).json()['Global Quote']\n\n\n\nwith open('token.txt', 'r') as tokentxt:\n client.run(tokentxt.read())", "sub_path": ".history/main_20210320180432.py", "file_name": "main_20210320180432.py", "file_ext": "py", "file_size_in_byte": 2484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "discord.Client", "line_number": 9, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "261753756", "text": "from google.appengine.ext import db\n\nclass Book(db.Model):\n \"\"\"Models an individual Book entry with an author, delta, participants, and date.\"\"\"\n one = db.TextProperty()\n delta = db.FloatProperty()\n many = db.TextProperty()\n author = db.StringProperty()\n date = db.DateTimeProperty(auto_now_add=True)\n\n\ndef book_key(book_name=None):\n \"\"\"Constructs a datastore key for a Book entity with book_name.\"\"\"\n return db.Key.from_path('Book', book_name or 'default_book')\n\n", "sub_path": "lib/dbmodel.py", "file_name": "dbmodel.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "google.appengine.ext.db.Model", "line_number": 3, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 3, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.TextProperty", "line_number": 5, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 5, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.FloatProperty", "line_number": 6, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 6, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.TextProperty", "line_number": 7, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 7, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.StringProperty", "line_number": 8, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 8, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.DateTimeProperty", "line_number": 9, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 9, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.Key.from_path", "line_number": 14, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.Key", "line_number": 14, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "323256264", "text": "from django.http import HttpResponse\nfrom django.shortcuts import redirect,render\nfrom lists.models import Item, List\nfrom django.core.exceptions import ValidationError\n\n\ndef home_page(request):\n# if request.method == 'POST': \n# Item.objects.create(text=request.POST['item_text']) #2\n# return redirect('/lists/the-only-list-in-the-world/')\n\n comment = 'yey, waktunya berlibur'\n\t\n items = Item.objects.all()\n return render(request, 'home.html', {'comment': comment})\n\ndef view_list(request, list_id):\n list_ = List.objects.get(id=list_id)\n error = None\n\n if request.method == 'POST':\n try:\n item = Item(text=request.POST['item_text'], list=list_)\n item.full_clean() \n item.save()\n return redirect(list_)\n except ValidationError:\n error = \"You can't have an empty list item\"\n\n \n\n comment = ''\n countlist = Item.objects.filter(list_id=list_.id).count()\n if countlist == 0 :\n comment = 'yey, waktunya berlibur'\n elif (countlist > 0) and (countlist < 5) :\n comment = 'sibuk tapi santai'\n else :\n comment = 'oh tidak'\n\n return render(request, 'list.html', {'list': list_, 'comment':comment, 'error':error})\n\t\ndef new_list(request):\n list_ = List.objects.create()\n item = Item(text=request.POST['item_text'], list=list_)\n try:\n item.full_clean()\n item.save()\n except ValidationError:\n list_.delete()\n error = \"You can't have an empty list item\"\n return render(request, 'home.html', {\"error\": error})\n return redirect(list_)\n #return redirect('view_list', list_.id)\n\n#def add_item(request, list_id):\n # list_ = List.objects.get(id=list_id)\n # Item.objects.create(text=request.POST['item_text'], list=list_)\n # return redirect('/lists/%d/' % (list_.id,))\n \n\n", "sub_path": "lists/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "lists.models.Item.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "lists.models.Item.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "lists.models.Item", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "lists.models.List.objects.get", "line_number": 18, "usage_type": "call"}, {"api_name": "lists.models.List.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "lists.models.List", "line_number": 18, "usage_type": "name"}, {"api_name": "lists.models.Item", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 27, "usage_type": "name"}, {"api_name": "lists.models.Item.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "lists.models.Item.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "lists.models.Item", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "lists.models.List.objects.create", "line_number": 44, "usage_type": "call"}, {"api_name": "lists.models.List.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "lists.models.List", "line_number": 44, "usage_type": "name"}, {"api_name": "lists.models.Item", "line_number": 45, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 49, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "128890445", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 24 15:58:49 2017\n\n@author: m.leclech\n\"\"\"\n\n\nimport requests\n\n\ndef getMagicToken():\n \"\"\"Return the magic token\"\"\" \n \n with open(\"cred/tokenMagic.txt\",\"r\") as f:\n magicToken = f.readline()\n return magicToken\n\n\ndef requestSeanceToken(url, idSeance):\n \"\"\"Request a token from the server to access a specific seance\n \n Args:\n url (string): the url of the server\n idSeance (int): the id of the seance\n Returns:\n the Token needed (string)\n \n \"\"\" \n \n seanceToken = requests.get(\"http://\"+url+\"/api/seance/\"\n +str(idSeance)+\"/token?token=\"\n +getMagicToken()).text\n return seanceToken\n\n\ndef requestGoogleToken(url):\n \"\"\"Request a token to use with Google API Auth\n \n Args:\n url (string): the server you'll ask from\n idevaluation.estia.fr or neptune2.estia.fr\n Returns:\n a token to use Natural Language API\n \n \"\"\" \n \n magicToken = getMagicToken()\n tokenRequest = requests.get(\"http://\"+url+\"/api/googleToken?token=\"\n +magicToken).json()\n googleToken = tokenRequest['token']['access_token']\n return googleToken", "sub_path": "Token.py", "file_name": "Token.py", "file_ext": "py", "file_size_in_byte": 1328, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "592688561", "text": "from kafka import KafkaProducer\nimport time\nimport speech_recognition as sr\nimport constants\n\nr = sr.Recognizer()\nm = sr.Microphone()\nwith m as source:\n r.adjust_for_ambient_noise(source) # we only need to calibrate once, before we start listening\n\nproducer = KafkaProducer(bootstrap_servers=constants.KAFKA_HOST)\n\ndef callback(recognizer, audio):\n\tprint(\"Volume threshold reached, sending data\")\n\tproducer.send('audio', audio.get_raw_data())\n\nstop_listening = r.listen_in_background(m, callback)\n\nwhile True:\n\ttime.sleep(600)", "sub_path": "kafka_clients/laptop-audio-producer.py", "file_name": "laptop-audio-producer.py", "file_ext": "py", "file_size_in_byte": 530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "speech_recognition.Recognizer", "line_number": 6, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 7, "usage_type": "call"}, {"api_name": "kafka.KafkaProducer", "line_number": 11, "usage_type": "call"}, {"api_name": "constants.KAFKA_HOST", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "646683376", "text": "# GET ALL GAMES ON STEAM (INCLUDES TITLE):\n# http://api.steampowered.com/ISteamApps/GetAppList/v0002/?key=STEAMKEY&format=json\n#\n# SEARCH FOR GAME INFO (LIMITED TO 200 REQUESTS PER 5 MIN):\n# http://store.steampowered.com/api/appdetails?appids={APP_ID}\n#\n# *THE STORE API IS NOT INTENDED FOR PUBLIC USE AND THEREFORE HAS NO OFFICIAL DOCUMENTATION*\n\n# THIS IS A TESTING SCRIPT MADE TO DEVELOP A METHOD FOR SEARCHING STEAMS API\n# TO USE THIS, MANUALY DOWNLOAD THE ALL APP ID JSON USING ABOVE METHOD AND PLACE IN SAME FOLDER TITLED \"steamgames.json\"\n\nimport json\nimport urllib.request\nimport time\n\n#open file\ngamesfile = open('steamgames.json')\n\n#parse to dictionary\ndata = json.load(gamesfile)\ndata = data['applist']\ndata = data['apps']\n\n#test it loaded\n#print(data)\n\n#count total games\nprint('There are ' + str(len(data)) + ' games on steam')\n\n# count = 0\n# for game in data:\n# if game['metacritic']:\n# count = count + 1\n# print('There are ' + str(count) + ' games with metacritic scores')\n\n# # search for factorio ID\n# for game in data:\n# if game['name'] == 'Celeste':\n# gID = str(game['appid'])\n# break\n\n# # print g ID\n# print('g ID: ' + gID)\n\n# #pull data from the g store page\n# gPage = urllib.request.urlopen(\"http://store.steampowered.com/api/appdetails?appids=\" + gID)\n# gData = json.loads(gPage.read())\n\n# # print raw g data\n# print(gData)\n\n# # print g tags\n# gData = gData[gID]\n# gData = gData['data']\n# gGenres = gData['genres']\n# gTags = gData['categories']\n\n# tags = []\n# for tag in gTags:\n# tags.append(tag['description'])\n\n# for genre in gGenres:\n# tags.append(genre['description'])\n\n# print('g\\'s tags:')\n# for tag in tags:\n# print(' ' + tag)\n\n# -------------------------------------------READER-------------------------------------------------------------------\nreading = False\ncount = 0\nfor game in data:\n count += 1\n\n if not reading:\n if game['appid'] == 252670:\n reading = True\n else:\n loadedPage = False\n while not loadedPage:\n try:\n gPage = urllib.request.urlopen(\"http://store.steampowered.com/api/appdetails?appids=\" + str(game['appid']))\n loadedPage = True\n except:\n print(\"Hit request limit.... waiting\") \n time.sleep(310)\n\n gData = json.loads(gPage.read())\n gData = gData[str(game['appid'])]\n print(\"Reading appid: \" + str(game['appid']) + \" This is game \" + str(count))\n #print(gData)\n if gData['success']:\n # print(\"API success\")\n try:\n gData = gData['data']\n gGenres = gData['genres']\n gTags = gData['categories']\n\n score = gData['metacritic']\n score = score['score']\n\n tags = []\n for tag in gTags:\n tags.append(tag['description'])\n\n for genre in gGenres:\n tags.append(genre['description'])\n\n date = gData['release_date']\n date = date['date']\n\n fgame = {'id':game['appid'], 'name':game['name'],'date':date, 'tags':tags, 'score':score}\n \n print(\"------GAME ADDED TO JSON-------\")\n with open(\"database.json\", \"a\") as outfile: \n json.dump(fgame, outfile) \n outfile.write('\\n')\n \n except:\n #print(\"Game has no metacritic OR is missing data\")\n pass\n\n\n\n\n# Use this to recover if the read crashes \n# count = 0\n# for game in data:\n# count += 1\n# if game['appid'] == 252670:\n# #print('Stopped on game: ' + str(count))\n# break\n# print('Stopped on game: ' + str(count))", "sub_path": "GameRecommendationWebsite/rawdatabase/pyscripts/pullgames.py", "file_name": "pullgames.py", "file_ext": "py", "file_size_in_byte": 3776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 82, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 82, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 82, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 86, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 88, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "158583525", "text": "import time\n\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.http import JsonResponse\n\nfrom ubskin_site.column_manage import models as column_models\nfrom ubskin_site.extends_manage import models as extends_models\n\n# Create your views here.\n\ndef my_render(request, templater_path, **kwargs):\n return render(request, templater_path, dict(**kwargs))\n\ndef index(request):\n if request.method == \"GET\":\n column_data = column_models.Columns.build_column_links()\n team_work_data = extends_models.TeamWork.get_team_work_for_index()\n campany_news = column_models.Article.get_campany_news()\n return my_render(\n request,\n 'web/index.html',\n column_data = column_data,\n team_work_data = team_work_data,\n campany_news = campany_news,\n ad_dict = extends_models.Ad.get_ad_dict_for_page(),\n )\n\n\ndef public_page(request, data_id):\n '''\n (1, '导航栏目'),\n (2, '单网页'),\n (3, '菜单'),\n -----\n (1, '文本图片'),\n (2, '留言页面'),\n (3, '物流查询'),\n (4, '文章列表类型'),\n (5, '重点店铺')\n '''\n model_obj = column_models.get_model_by_pk(\n column_models.Columns,\n data_id\n )\n page = request.GET.get('page', 1)\n column_data_list, select_columns_ids = column_models.Columns.get_page_columns_list(data_id)\n page_type = None\n page_content = None\n data_count = 1\n photo_dict = None\n article_obj = None\n if model_obj.columns_type == 2:\n if model_obj.page_type == 1:\n page_type = 1\n page_content = column_models.Article.get_article_obj_by_columns_id(data_id)\n elif model_obj.page_type == 4:\n article_id = request.GET.get('article_id')\n if article_id:\n article_obj = column_models.get_model_by_pk(\n column_models.Article,\n article_id\n )\n page_type = 4\n page_content = column_models.Article.get_article_list_by_columns_id(data_id, page)\n data_count = column_models.Article.get_article_count_by_columns_id(data_id)\n elif model_obj.page_type == 3:\n return redirect(reverse('shop_search', kwargs = {'data_id': model_obj.columns_id}))\n elif model_obj.page_type == 5:\n page_type = 5\n page_content = column_models.get_data_list(\n column_models.CompanyAddr,\n current_page=page,\n )\n data_count = column_models.get_data_count(column_models.CompanyAddr)\n elif model_obj.page_type == 6:\n page_type = 6\n page_content = column_models.get_data_list(\n column_models.FocusShop,\n current_page=page,\n search_value={'columns_id': model_obj.columns_id}\n )\n data_count = column_models.get_data_count(\n column_models.FocusShop,\n search_value={'columns_id': model_obj.columns_id}\n )\n elif model_obj.page_type == 2:\n return redirect(reverse('message', kwargs = {'data_id': model_obj.columns_id}))\n photo_dict = {\n 'photo_id': model_obj.photo_id,\n 'thumb_photo_id': model_obj.thumb_photo_id\n }\n else:\n photo_dict = column_models.Columns.get_prent_photo(model_obj.parent_id)\n page_content = []\n \n column_data = column_models.Columns.build_column_links()\n return my_render(\n request,\n 'web/public_page.html',\n model_obj = model_obj,\n column_data_list = column_data_list,\n page_type = page_type,\n page_content = page_content,\n photo_dict = photo_dict,\n column_data = column_data,\n current_page = page,\n data_count = data_count,\n select_columns_ids = select_columns_ids,\n article_obj = article_obj,\n )\n \n\ndef shop_search(request, data_id):\n column_data = column_models.Columns.build_column_links()\n model_obj = column_models.get_model_by_pk(\n column_models.Columns,\n data_id\n )\n shop_data_dict = column_models.ShopManage.get_all_shop_for_search()\n photo_dict = {\n 'photo_id': model_obj.photo_id,\n 'thumb_photo_id': model_obj.thumb_photo_id\n }\n return my_render(\n request,\n 'web/shop_search.html',\n column_data = column_data,\n photo_dict = photo_dict,\n shop_data_dict = shop_data_dict,\n )\n\ndef message(request, data_id):\n column_data = column_models.Columns.build_column_links()\n column_data_list, select_columns_ids = column_models.Columns.get_page_columns_list(data_id)\n model_obj = extends_models.get_model_by_pk(\n column_models.Columns,\n data_id,\n )\n photo_dict = {\n 'photo_id': model_obj.photo_id,\n 'thumb_photo_id': model_obj.thumb_photo_id\n }\n if request.method == 'GET':\n return my_render(\n request,\n 'web/message.html',\n column_data = column_data,\n column_data_list = column_data_list,\n select_columns_ids = select_columns_ids,\n photo_dict = photo_dict,\n )\n else:\n filds_list = [\n 'user_name', 'gender', 'phone_number',\n 'message_text'\n ]\n p_get = request.POST.get\n form_data = { i: p_get(i) for i in filds_list }\n form_data.update(\n {'create_time': int(time.time()), 'user_ip': request.META.get('REMOTE_ADDR')}\n )\n extends_models.create_model_data(\n extends_models.Message,\n form_data\n )\n return JsonResponse({'status': 'success'})\n ", "sub_path": "ubskin_site/web/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns.build_column_links", "line_number": 17, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 17, "usage_type": "name"}, {"api_name": "ubskin_site.extends_manage.models.TeamWork.get_team_work_for_index", "line_number": 18, "usage_type": "call"}, {"api_name": "ubskin_site.extends_manage.models.TeamWork", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ubskin_site.extends_manage.models", "line_number": 18, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Article.get_campany_news", "line_number": 19, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Article", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 19, "usage_type": "name"}, {"api_name": "ubskin_site.extends_manage.models.Ad.get_ad_dict_for_page", "line_number": 26, "usage_type": "call"}, {"api_name": "ubskin_site.extends_manage.models.Ad", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ubskin_site.extends_manage.models", "line_number": 26, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.get_model_by_pk", "line_number": 42, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 42, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 43, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns.get_page_columns_list", "line_number": 47, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 47, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Article.get_article_obj_by_columns_id", "line_number": 56, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Article", "line_number": 56, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 56, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.get_model_by_pk", "line_number": 60, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 60, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Article", "line_number": 61, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 61, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Article.get_article_list_by_columns_id", "line_number": 65, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Article", "line_number": 65, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 65, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Article.get_article_count_by_columns_id", "line_number": 66, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Article", "line_number": 66, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 68, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.get_data_list", "line_number": 71, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 71, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.CompanyAddr", "line_number": 72, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 72, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.get_data_count", "line_number": 75, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 75, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.CompanyAddr", "line_number": 75, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models.get_data_list", "line_number": 78, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 78, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.FocusShop", "line_number": 79, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 79, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.get_data_count", "line_number": 83, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 83, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.FocusShop", "line_number": 84, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 88, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns.get_prent_photo", "line_number": 94, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 94, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 94, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns.build_column_links", "line_number": 97, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 97, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 97, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns.build_column_links", "line_number": 115, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 115, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 115, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.get_model_by_pk", "line_number": 116, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 116, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 117, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 117, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.ShopManage.get_all_shop_for_search", "line_number": 120, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.ShopManage", "line_number": 120, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 120, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns.build_column_links", "line_number": 134, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 134, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 134, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns.get_page_columns_list", "line_number": 135, "usage_type": "call"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 135, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 135, "usage_type": "name"}, {"api_name": "ubskin_site.extends_manage.models.get_model_by_pk", "line_number": 136, "usage_type": "call"}, {"api_name": "ubskin_site.extends_manage.models", "line_number": 136, "usage_type": "name"}, {"api_name": "ubskin_site.column_manage.models.Columns", "line_number": 137, "usage_type": "attribute"}, {"api_name": "ubskin_site.column_manage.models", "line_number": 137, "usage_type": "name"}, {"api_name": "time.time", "line_number": 161, "usage_type": "call"}, {"api_name": "ubskin_site.extends_manage.models.create_model_data", "line_number": 163, "usage_type": "call"}, {"api_name": "ubskin_site.extends_manage.models", "line_number": 163, "usage_type": "name"}, {"api_name": "ubskin_site.extends_manage.models.Message", "line_number": 164, "usage_type": "attribute"}, {"api_name": "ubskin_site.extends_manage.models", "line_number": 164, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "200343069", "text": "from django.conf.urls import include, patterns, url\nfrom .views import Autocomplete, Flyedit\n\n\nurlpatterns = patterns(\n # pylint: disable=E1101\n # Instance of has no \n\n '',\n\n url(r'^$', Flyedit.as_view(),\n name='flyedit'),\n url(r'^autocomplete/$', Autocomplete.as_view(),\n name='flyedit-autocomplete')\n)\n", "sub_path": "flyedit/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "views.Flyedit.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.Flyedit", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.Autocomplete.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.Autocomplete", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "171739205", "text": "from src.common import util\n\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine(util.get_config_value(\"DB\", \"MYSQL\"))\nBase = automap_base()\n\nclass GlobalWine(Base):\n\n __tablename__ = \"global_wine\"\n __mapper_args__ = {\n \"exclude_properties\" : [\"foodpairing\", \"winery\", \"region\"]\n }\n\nBase.prepare(engine, reflect=True)\nSession = sessionmaker(bind=engine)\nsession = Session()\n\nCwWine = Base.classes.cw_wine\nGlobalVintageWine = Base.classes.global_vintage_wine\n\nif __name__ == \"__main__\":\n\n query_resault = session.query(\n CwWine.member_id,\n GlobalWine.display_name,\n GlobalVintageWine.vintage\n ).join(\n GlobalVintageWine, CwWine.global_vintage_wine_id == GlobalVintageWine.id\n ).join(\n GlobalWine, GlobalVintageWine.global_wine_id == GlobalWine.id\n ).filter(\n CwWine.member_id == 3\n )\n\n for item in query_resault:\n print(item)", "sub_path": "src/common/mysql.py", "file_name": "mysql.py", "file_ext": "py", "file_size_in_byte": 1004, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 7, "usage_type": "call"}, {"api_name": "src.common.util.get_config_value", "line_number": 7, "usage_type": "call"}, {"api_name": "src.common.util", "line_number": 7, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "363807040", "text": "#\n#\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\nwindow_sizes = ['3','5','7']\n\npdb_code = \"PED6AAC-k18_pool\"\n#pdb_code = \"PED7AAC-ntail_pool\"\n\nfor window_size in window_sizes:\n\tinfile = open(pdb_code+\"_local_rmsd_\"+window_size+\"aa-S.txt\", 'r')\n\tmedians = []\n\tuppers = []\n\tlowers = []\n\tstdevs = []\n\tfor line in infile:\n\t\tline = line.strip()\n\t\tif len(line)>1:\n\t\t\tarray = line.split(\";\")\n\t\t\trmsd_array = array[2][1:-1].split(\", \")\n\t\t\tarray2 = []\n\t\t\tfor rmsd in rmsd_array:\n\t\t\t\tarray2.append( float(rmsd) )\n\t\t\trmsd_array = array2\n\t\t\tmedians.append( np.median(rmsd_array) )\n\t\t\tstdevs.append( np.std(rmsd_array) )\n\t\t\tuppers.append( np.percentile(rmsd_array, 95) )\n\t\t\tlowers.append( np.percentile(rmsd_array, 5) )\n\tinfile.close()\n\n\tx_pos = np.arange(len(medians))\n\tax = plt.subplot(1,1,1)\n\tplt.fill_between(x_pos, uppers, lowers, color='gray', alpha=0.6)\n\tplt.plot(x_pos, medians, 'k', label=\"medians\")\n\tplt.plot(x_pos, uppers, 'gray', label=\"95% percentiles\")\n\tplt.xlim(1,len(medians)-1)\n\tplt.ylim(0, 6)\n\tplt.title(\"Local superimposability of \"+pdb_code)\n\thandles, labels = ax.get_legend_handles_labels()\n\tax.legend(handles, labels)\n\tplt.xlabel(\"residue number\")\n\tplt.ylabel(window_size+\"-residue local backbone RMSD (Angstrom)\")\n\tplt.savefig(pdb_code+\"_local_rmsd_\"+window_size+\"aa-S.png\", dpi=600)\n\tplt.clf()\n", "sub_path": "plot_local_RMSDs_pool.py", "file_name": "plot_local_RMSDs_pool.py", "file_ext": "py", "file_size_in_byte": 1323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.median", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "462474744", "text": "from bs4 import BeautifulSoup\nfrom .sessionData import SessionData\n\n\nclass CoursesPage:\n CONSTANT_PARAMS = {\n \"lbDays\": \"1-7;1;2;3;4;5;6;7\",\n \"dlPeriod\": \"1-32;1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18;19;20;21;22;23;24;25;26;27;28;29;30;31;32;\",\n \"RadioType\": \"module_list;cyon_reports_list_url;dummy\",\n \"bGetTimetable\": \"View+Timetable\"\n }\n def __init__(self, response):\n self.soup = BeautifulSoup(response.content, 'html.parser')\n self.courseList = self._courseList()\n self.semList = self._getSems()\n def getBody(self, semseter):\n body = {}\n body.update(self.CONSTANT_PARAMS)\n body['lbWeeks'] = self.semList[semseter-1]\n return body\n def _courseList(self):\n list = []\n select = self.soup.find(id=\"dlObject\")\n for c in select.find_all('option'):\n list.append((c.string, c[\"value\"]))\n return list\n\n def _getSems(self):\n list = []\n select = self.soup.find(id=\"lbWeeks\")\n for c in select.find_all('option'):\n if c.string == \"Semester 1\" or c.string == \"Semester 2\":\n list.append(c[\"value\"])\n return list\n\n\ndef Chunk(coursesPage, chunk):\n max = len(coursesPage.courseList)\n n = 0\n while n + chunk < max:\n s = slice(n, n+chunk)\n yield coursesPage.courseList[s]\n n += chunk\n if n < max:\n s = slice(n, max)\n yield coursesPage.courseList[s]\n\n\n ", "sub_path": "archive/2020S1/js/anuscrape/classes/coursesPage.py", "file_name": "coursesPage.py", "file_ext": "py", "file_size_in_byte": 1479, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "570215699", "text": "#!/usr/bin/env python3\nimport webbrowser\nimport requests\nimport json # for prety json output of transactions\nimport sys # to force close programme\n\n# stuff needed to get use API of SEB\n# information about registered app in sandbox enviroment\nGLOBAL_CLIENT_SECRET = \"VEgtGle6ZT53ylOg4bgv\" # apps client secret\nGLOBAL_REDIRECT_URL = \"https://example.com/\"\nGLOBAL_CLIENT_ID = \"1lRpPVFcNNJiRM3mYd2z\"\n\n# means key to get access token\nGLOBAL_ACCESS_CODE = \"VZsOs1RFYcynKqKMTAL2nvns83cy5I\"\n\n# The associated scopes requested with the authorization grant.\nGLOBAL_REQUEST_SCOPE = [\"psd2_accounts\", \"psd2_payments\"]\n\nGLOBAL_SANDBOX_ID = \"9311219589\"\n\n# for whom will be shown all transactions of specific client\nGLOBAL_CARD_CLIENT_ID = \"301019000264028\"\n\nGLOBAL_TIMEOUT_PERIOD = 3 # in seconds\n\n\ndef open_code_webpage():\n \"\"\" opens URL of SEB API where need to enter sandbox api and press button 'submit'.\n As result redirects into GLOBAL_REDIRECT_URL with additional parameter in URL.\n In that URL need to copy string part of URL after 'code='\n this part is 'acces code' to request access_token\n \"\"\"\n\n url = \"https://api-sandbox.sebgroup.com/mga/sps/oauth/oauth20/authorize\"\n\n query_params = {\"response_type\": \"code\",\n \"redirect_uri\": GLOBAL_REDIRECT_URL,\n \"scope\": GLOBAL_REQUEST_SCOPE,\n \"client_id\": GLOBAL_CLIENT_ID\n }\n\n try:\n # output of request is htmp page but here only need URL\n r = requests.get(url, params=query_params, timeout=GLOBAL_TIMEOUT_PERIOD)\n\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n raise SystemExit(err)\n\n # open web browser and in new tab go into this url\n print(\"STEP 1/3 done open url in webbrowser\")\n webbrowser.open(r.url)\n\n\ndef get_access_token(access_code: str) -> str:\n \"\"\" REST request to bank API where input param is access_code\n on succesions of request returns json file what contains tokens\n if not colapses the programme\n\n :returns access_token in string format\"\"\"\n\n query_header = {\"grant_type\": \"authorization_code\",\n \"code\": access_code,\n \"scope\": GLOBAL_REQUEST_SCOPE,\n \"client_id\": GLOBAL_CLIENT_ID,\n \"client_secret\": GLOBAL_CLIENT_SECRET,\n \"redirect_uri\": GLOBAL_REDIRECT_URL\n }\n\n try:\n r = requests.post(\"https://api-sandbox.sebgroup.com/mga/sps/oauth/oauth20/token\", data=query_header,\n timeout=GLOBAL_TIMEOUT_PERIOD)\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n\n print(\"word is cruel place and to it I say goodbye, probably bad access_code was entered\")\n raise SystemExit(err)\n\n print(\"STEP 2/3 done, got access_token\")\n return r.json()[\"access_token\"]\n\n\ndef get_all_transactions(access_token: str, card_transaction_id: str):\n \"\"\"shows all transactions of specific person by GET request\n :param access_token - to acces API\n :param card_transaction_id whose transactions will be shows\n :return json\"\"\"\n\n # TODO show only for specific person data not all transactions\n\n query_headers = {'Authorization': 'Bearer ' + access_token,\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n\n query_params = {\"dateFrom\": \" \",\n \"dateTo\": \" \",\n \"bookingStatus\": \" \"\n }\n\n try:\n r = requests.get(\n f\"https://api-sandbox.sebgroup.com/ais/v1/identified2/branded-card-accounts/{card_transaction_id}/transactions\",\n headers=query_headers, params=query_params, timeout=GLOBAL_TIMEOUT_PERIOD)\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n raise SystemExit(err)\n\n print(\"STEP 3/3 done, got list of transactions\")\n\n return json.dumps(r.json())\n\n\ndef main():\n print(\"Gets out from SEB API SANDBOX all transactions\")\n print(\"This is done by three steps\")\n print(f\"STEP 1 - if you continue script will open your web browser in new tab webpage and in input field enter \"\n f\"sandbox ID this one -> '{GLOBAL_SANDBOX_ID}' and press submit \")\n print(\"STEP 2 - ENTER Authorization code redirected from earlier request in promt\")\n print(\"STEP 3 - if previous steps are passed you will see dumped json in console with transactions\")\n\n while True:\n i = input(\"any text entered means quit from programme (or Enter to continue): \")\n if not i:\n break\n sys.exit(0)\n\n open_code_webpage()\n print(\"input ENTER Authorization code redirected from earlier request in promt, code without 'code='\")\n code = str(input())\n\n i = 3\n while len(code) > 30:\n if i == 0:\n sys.exit(0)\n print(\n f\"error, length of code must be 30 symbols, try again , you have {i} trys left otherwise quit\")\n code = str(input())\n i -= 1\n\n access_token = get_access_token(code)\n transactions = get_all_transactions(access_token, GLOBAL_CARD_CLIENT_ID)\n print(transactions)\n\n\nif __name__ == \"__main__\":\n print(\"Show all transactions from SEB bank API\")\n main()\n", "sub_path": "seb_requests.py", "file_name": "seb_requests.py", "file_ext": "py", "file_size_in_byte": 5287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 47, "usage_type": "attribute"}, {"api_name": "webbrowser.open", "line_number": 52, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 74, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 106, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 111, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "368408101", "text": "# -*- coding: UTF-8 -*- \n\nimport re, time\nimport simplejson as json\nfrom threading import Thread\nfrom collections import OrderedDict\n\nfrom django.db.models import Q, F\nfrom django.db import connection, transaction\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom .dao import Dao\nfrom .api import ServerError, pages\nfrom .const import Const, WorkflowDict\nfrom .inception import InceptionDao\nfrom .aes_decryptor import Prpcrypt\nfrom .models import users, UserGroup, master_config, AliyunRdsConfig, workflow, slave_config, QueryPrivileges, Group, \\\n QueryPrivilegesApply, ProjectResource, GroupQueryPrivileges\nfrom .workflow import Workflow\nfrom .permission import role_required, superuser_required\nfrom .sqlreview import getDetailUrl, execute_call_back, execute_skipinc_call_back\nfrom .jobs import job_info, del_sqlcronjob\nfrom .pycrypt import MyCrypt\nfrom .projectresource import integration_resource, get_resource , PermissionVerification, get_query_permisshion\nfrom .query import get_query_clustername\nfrom archer.settings import HASH_KEY\nimport logging\n\nlogger = logging.getLogger('default')\n\ndao = Dao()\ninceptionDao = InceptionDao()\nprpCryptor = Prpcrypt()\nworkflowOb = Workflow()\n\n\n# 登录\ndef login(request):\n access_itom_addr = settings.ACCESS_ITOM_ADDR\n return HttpResponseRedirect('%s/login/'%(access_itom_addr))\n # return render(request, 'login.html')\n\n\n# 退出登录\ndef logout(request):\n access_itom_addr = settings.ACCESS_ITOM_ADDR\n if request.session.get('login_username', False):\n del request.session['login_username']\n if request.session.get('resource_status', False):\n del request.session['resource_status']\n return HttpResponseRedirect('%s/logout/'%(access_itom_addr))\n # return render(request, 'login.html')\n\n\n# SQL上线工单页面\ndef allworkflow(request):\n context = {'currentMenu': 'allworkflow'}\n return render(request, 'allWorkflow.html', context)\n\n\n# 提交SQL的页面\ndef submitSql(request):\n # 获取数据连接信息\n masters = master_config.objects.all().order_by('cluster_name')\n if len(masters) == 0:\n return HttpResponseRedirect('/admin/sql/master_config/add/')\n\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n\n pv = PermissionVerification(loginUser, loginUserOb)\n # 获取用户所属项目组信息\n context = pv.get_group_info()\n if context[\"status\"] == 1:\n group_list = context[\"data\"]\n else:\n errMsg = context[\"msg\"]\n return render(request, 'error.html', {'errMsg': errMsg})\n\n # 获取用户所属项目组拥有权限的实列信息\n context = pv.get_cluster_info(masters)\n if context[\"status\"] == 1:\n listAllClusterName = context[\"data\"]\n else:\n errMsg = context[\"msg\"]\n return render(request, 'error.html', {'errMsg': errMsg})\n\n # 获取所有有效用户，通知对象\n active_user = users.objects.filter(is_active=1)\n\n context = {'currentMenu': 'allworkflow', 'listAllClusterName': listAllClusterName,\n 'active_user': active_user, 'group_list': group_list}\n return render(request, 'submitSql.html', context)\n\n\n# 提交SQL给inception进行解析\ndef autoreview(request):\n workflowid = request.POST.get('workflowid')\n sqlContent = request.POST['sql_content']\n workflowName = request.POST['workflow_name']\n group_name = request.POST['group_name']\n group_id = Group.objects.get(group_name=group_name).group_id\n clusterName = request.POST['cluster_name']\n db_name = request.POST.get('db_name')\n isBackup = request.POST['is_backup']\n reviewMan = request.POST.get('workflow_auditors')\n notify_users = request.POST.getlist('notify_users')\n\n # 服务器端参数验证\n if sqlContent is None or workflowName is None or clusterName is None or db_name is None or isBackup is None or reviewMan is None:\n context = {'errMsg': '页面提交参数可能为空'}\n return render(request, 'error.html', context)\n\n # 删除注释语句\n sqlContent = ''.join(\n map(lambda x: re.compile(r'(^--.*|^/\\*.*\\*/;[\\f\\n\\r\\t\\v\\s]*$)').sub('', x, count=1),\n sqlContent.splitlines(1))).strip()\n # 去除空行\n sqlContent = re.sub('[\\r\\n\\f]{2,}', '\\n', sqlContent)\n\n if sqlContent[-1] != \";\":\n context = {'errMsg': \"SQL语句结尾没有以;结尾，请后退重新修改并提交！\"}\n return render(request, 'error.html', context)\n\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n pv = PermissionVerification(loginUser, loginUserOb)\n\n # 检测用户资源权限\n if loginUserOb.is_superuser:\n reviewResult = pv.check_resource_priv(sqlContent, clusterName, db_name, 1)\n else:\n reviewResult = pv.check_resource_priv(sqlContent, clusterName, db_name, 0)\n\n result = reviewResult[\"data\"]\n if reviewResult[\"status\"] == 1:\n context = {'errMsg': reviewResult[\"msg\"]}\n return render(request, 'error.html', context)\n\n if result is None or len(result) == 0:\n context = {'errMsg': 'inception返回的结果集为空！可能是SQL语句有语法错误'}\n return render(request, 'error.html', context)\n\n # 要把result转成JSON存进数据库里，方便SQL单子详细信息展示\n jsonResult = json.dumps(result)\n\n # 遍历result，看是否有任何自动审核不通过的地方，一旦有，则为自动审核不通过；没有的话，则为等待人工审核状态\n workflowStatus = Const.workflowStatus['manreviewing']\n for row in result:\n if row[2] == 2:\n # 状态为2表示严重错误，必须修改\n workflowStatus = Const.workflowStatus['autoreviewwrong']\n break\n elif re.match(r\"\\w*comments\\w*\", row[4]):\n workflowStatus = Const.workflowStatus['autoreviewwrong']\n break\n\n # 调用工作流生成工单\n # 使用事务保持数据一致性\n try:\n with transaction.atomic():\n # 存进数据库里\n engineer = request.session.get('login_username', False)\n if not workflowid:\n Workflow = workflow()\n Workflow.create_time = timezone.now()\n else:\n Workflow = workflow.objects.get(id=int(workflowid))\n Workflow.workflow_name = workflowName\n Workflow.group_id = group_id\n Workflow.group_name = group_name\n Workflow.engineer = engineer\n Workflow.review_man = reviewMan\n Workflow.status = workflowStatus\n Workflow.is_backup = isBackup\n Workflow.review_content = jsonResult\n Workflow.cluster_name = clusterName\n Workflow.db_name = db_name\n Workflow.sql_content = sqlContent\n Workflow.execute_result = ''\n Workflow.audit_remark = ''\n Workflow.save()\n workflowId = Workflow.id\n # 自动审核通过了，才调用工作流\n if workflowStatus == Const.workflowStatus['manreviewing']:\n # 调用工作流插入审核信息, 查询权限申请workflow_type=2\n # 抄送通知人\n listCcAddr = [email['email'] for email in\n users.objects.filter(username__in=notify_users).values('email')]\n workflowOb.addworkflowaudit(request, WorkflowDict.workflow_type['sqlreview'], workflowId,\n listCcAddr=listCcAddr)\n except Exception as msg:\n context = {'errMsg': msg}\n return render(request, 'error.html', context)\n\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':workflowId, 'workflowType':0}))\n\n\n# 展示SQL工单详细内容，以及可以人工审核，审核通过即可执行\ndef detail(request, workflowId, workflowType):\n workflowDetail = get_object_or_404(workflow, pk=workflowId)\n if workflowDetail.status in (Const.workflowStatus['finish'], Const.workflowStatus['exception']) \\\n and workflowDetail.is_manual == 0:\n listContent = json.loads(workflowDetail.execute_result)\n else:\n listContent = json.loads(workflowDetail.review_content)\n\n # 获取审核人\n reviewMan = workflowDetail.review_man\n reviewMan = reviewMan.split(',')\n\n # 获取当前审核人\n try:\n current_audit_user = workflowOb.auditinfobyworkflow_id(workflow_id=workflowId,\n workflow_type=WorkflowDict.workflow_type['sqlreview']\n ).current_audit_user\n except Exception:\n current_audit_user = None\n\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n\n # 获取定时执行任务信息\n if workflowDetail.status == Const.workflowStatus['tasktiming']:\n job_id = Const.workflowJobprefix['sqlreview'] + '-' + str(workflowId)\n job = job_info(job_id)\n if job:\n run_date = job.next_run_time\n else:\n run_date = ''\n else:\n run_date = ''\n\n # sql结果\n column_list = ['ID', 'stage', 'errlevel', 'stagestatus', 'errormessage', 'SQL', 'Affected_rows', 'sequence',\n 'backup_dbname', 'execute_time', 'sqlsha1']\n rows = []\n for row_index, row_item in enumerate(listContent):\n row = {}\n row['ID'] = row_index + 1\n row['stage'] = row_item[1]\n row['errlevel'] = row_item[2]\n row['stagestatus'] = row_item[3]\n row['errormessage'] = row_item[4]\n row['SQL'] = row_item[5]\n row['Affected_rows'] = row_item[6]\n row['sequence'] = row_item[7]\n row['backup_dbname'] = row_item[8]\n row['execute_time'] = row_item[9]\n row['sqlsha1'] = row_item[10]\n rows.append(row)\n\n if workflowDetail.status == '执行中':\n row['stagestatus'] = ''.join(\n [\"
\",\n \"
\",\n \"
\",\n \" \",\n \"
\",\n \"
\",\n \"
\",\n \"
\",\n \" \",\n \" \",\n \"
\",\n \"
\",\n \"
\"])\n context = {'currentMenu': 'allworkflow', 'workflowDetail': workflowDetail, 'column_list': column_list, 'rows': rows,\n 'reviewMan': reviewMan, 'current_audit_user': current_audit_user, 'loginUserOb': loginUserOb,\n 'run_date': run_date}\n\n if int(workflowType) == 1:\n return render(request, 'detailhash.html', context)\n else:\n return render(request, 'detail.html', context)\n\n\n# 审核通过，不执行\ndef passonly(request):\n workflowId = request.POST['workflowid']\n workflowType = request.POST.get('workflowtype',0)\n if workflowId == '' or workflowId is None:\n context = {'errMsg': 'workflowId参数为空.'}\n return render(request, 'error.html', context)\n workflowId = int(workflowId)\n workflowDetail = workflow.objects.get(id=workflowId)\n\n # 获取审核人\n reviewMan = workflowDetail.review_man\n reviewMan = reviewMan.split(',')\n\n # 服务器端二次验证，正在执行人工审核动作的当前登录用户必须为审核人. 避免攻击或被接口测试工具强行绕过\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n if loginUser is None or (loginUser not in reviewMan and loginUserOb.is_superuser != 1):\n context = {'errMsg': '当前登录用户不是审核人，请重新登录.'}\n return render(request, 'error.html', context)\n\n # 服务器端二次验证，当前工单状态必须为等待人工审核\n if workflowDetail.status != Const.workflowStatus['manreviewing']:\n context = {'errMsg': '当前工单状态不是等待人工审核中，请刷新当前页面！'}\n return render(request, 'error.html', context)\n\n # 使用事务保持数据一致性\n try:\n with transaction.atomic():\n # 调用工作流接口审核\n # 获取audit_id\n audit_id = workflowOb.auditinfobyworkflow_id(workflow_id=workflowId,\n workflow_type=WorkflowDict.workflow_type['sqlreview']).audit_id\n auditresult = workflowOb.auditworkflow(request, audit_id, WorkflowDict.workflow_status['audit_success'],\n loginUser, '')\n\n # 按照审核结果更新业务表审核状态\n if auditresult['data']['workflow_status'] == WorkflowDict.workflow_status['audit_success']:\n # 将流程状态修改为审核通过，并更新reviewok_time字段\n workflowDetail.status = Const.workflowStatus['pass']\n workflowDetail.reviewok_time = timezone.now()\n workflowDetail.audit_remark = ''\n workflowDetail.save()\n except Exception as msg:\n context = {'errMsg': msg}\n if int(workflowType) == 1:\n return HttpResponse(context['errMsg'])\n else:\n return render(request, 'error.html', context)\n\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':workflowId, 'workflowType':workflowType}))\n\n\n# 仅执行SQL\ndef executeonly(request):\n workflowId = request.POST['workflowid']\n if workflowId == '' or workflowId is None:\n context = {'errMsg': 'workflowId参数为空.'}\n return render(request, 'error.html', context)\n\n workflowId = int(workflowId)\n workflowDetail = workflow.objects.get(id=workflowId)\n clusterName = workflowDetail.cluster_name\n db_name = workflowDetail.db_name\n url = getDetailUrl(request) + str(workflowId) + '/'\n\n # 获取审核人\n reviewMan = workflowDetail.review_man\n reviewMan = reviewMan.split(',')\n\n # 服务器端二次验证，正在执行人工审核动作的当前登录用户必须为审核人或者提交人. 避免攻击或被接口测试工具强行绕过\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n if loginUser is None or (loginUser not in reviewMan and loginUser != workflowDetail.engineer and loginUserOb.role != 'DBA'):\n context = {'errMsg': '当前登录用户不是审核人或者提交人，请重新登录.'}\n return render(request, 'error.html', context)\n\n # 服务器端二次验证，当前工单状态必须为审核通过状态\n if workflowDetail.status != Const.workflowStatus['pass']:\n context = {'errMsg': '当前工单状态不是审核通过，请刷新当前页面！'}\n return render(request, 'error.html', context)\n\n # 将流程状态修改为执行中，并更新reviewok_time字段\n workflowDetail.status = Const.workflowStatus['executing']\n workflowDetail.reviewok_time = timezone.now()\n # 执行之前重新split并check一遍，更新SHA1缓存；因为如果在执行中，其他进程去做这一步操作的话，会导致inception core dump挂掉\n try:\n splitReviewResult = inceptionDao.sqlautoReview(workflowDetail.sql_content, workflowDetail.cluster_name, db_name,\n isSplit='yes')\n except Exception as msg:\n context = {'errMsg': msg}\n return render(request, 'error.html', context)\n workflowDetail.review_content = json.dumps(splitReviewResult)\n try:\n workflowDetail.save()\n except Exception:\n # 关闭后重新获取连接，防止超时\n connection.close()\n workflowDetail.save()\n\n # 采取异步回调的方式执行语句，防止出现持续执行中的异常\n t = Thread(target=execute_call_back, args=(workflowId, clusterName, url))\n t.start()\n\n return HttpResponseRedirect(reverse('sql:detail', kwargs={ 'workflowId':workflowId, 'workflowType':0 }))\n\n\n# 跳过inception直接执行SQL，只是为了兼容inception不支持的语法，谨慎使用\n@role_required(('DBA',))\ndef execute_skipinc(request):\n workflowId = request.POST['workflowid']\n\n # 获取工单信息\n workflowId = int(workflowId)\n workflowDetail = workflow.objects.get(id=workflowId)\n sql_content = workflowDetail.sql_content\n clusterName = workflowDetail.cluster_name\n url = getDetailUrl(request) + str(workflowId) + '/'\n\n # 服务器端二次验证，当前工单状态必须为自动审核不通过\n if workflowDetail.status not in [Const.workflowStatus['manreviewing'], Const.workflowStatus['pass'],\n Const.workflowStatus['autoreviewwrong']]:\n context = {'errMsg': '当前工单状态不是自动审核不通过，请刷新当前页面！'}\n return render(request, 'error.html', context)\n\n # 更新工单状态为执行中\n workflowDetail = workflow.objects.get(id=workflowId)\n workflowDetail.status = Const.workflowStatus['executing']\n workflowDetail.reviewok_time = timezone.now()\n workflowDetail.save()\n\n # 采取异步回调的方式执行语句，防止出现持续执行中的异常\n t = Thread(target=execute_skipinc_call_back, args=(workflowId, clusterName, sql_content, url))\n t.start()\n\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':workflowId, 'workflowType':0}))\n\n\n# 终止流程\ndef cancel(request):\n workflowId = request.POST['workflowid']\n workflowType = request.POST.get('workflowtype', 0)\n if workflowId == '' or workflowId is None:\n context = {'errMsg': 'workflowId参数为空.'}\n return render(request, 'error.html', context)\n\n workflowId = int(workflowId)\n workflowDetail = workflow.objects.get(id=workflowId)\n\n # 获取审核人\n reviewMan = workflowDetail.review_man\n reviewMan = reviewMan.split(',')\n\n audit_remark = request.POST.get('audit_remark')\n if audit_remark is None:\n context = {'errMsg': '驳回原因不能为空'}\n return render(request, 'error.html', context)\n\n # 服务器端二次验证，如果正在执行终止动作的当前登录用户，不是提交人也不是审核人，则异常.\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n if loginUser is None or (loginUser not in reviewMan and loginUser != workflowDetail.engineer and loginUserOb.role != 'DBA'):\n context = {'errMsg': '当前登录用户不是审核人也不是提交人，请重新登录.'}\n return render(request, 'error.html', context)\n\n # 服务器端二次验证，如果当前单子状态是结束状态，则不能发起终止\n if workflowDetail.status in (\n Const.workflowStatus['abort'], Const.workflowStatus['finish'], Const.workflowStatus['autoreviewwrong'],\n Const.workflowStatus['exception']):\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':workflowId, 'workflowType':workflowType}))\n\n # 使用事务保持数据一致性\n try:\n with transaction.atomic():\n # 调用工作流接口取消或者驳回\n # 获取audit_id\n audit_id = workflowOb.auditinfobyworkflow_id(workflow_id=workflowId,\n workflow_type=WorkflowDict.workflow_type['sqlreview']).audit_id\n if loginUser == workflowDetail.engineer:\n auditresult = workflowOb.auditworkflow(request, audit_id, WorkflowDict.workflow_status['audit_abort'],\n loginUser, audit_remark)\n else:\n auditresult = workflowOb.auditworkflow(request, audit_id, WorkflowDict.workflow_status['audit_reject'],\n loginUser, audit_remark)\n # 删除定时执行job\n if workflowDetail.status == Const.workflowStatus['tasktiming']:\n job_id = Const.workflowJobprefix['sqlreview'] + '-' + str(workflowId)\n del_sqlcronjob(job_id)\n # 按照审核结果更新业务表审核状态\n if auditresult['data']['workflow_status'] in (\n WorkflowDict.workflow_status['audit_abort'], WorkflowDict.workflow_status['audit_reject']):\n # 将流程状态修改为人工终止流程\n workflowDetail.status = Const.workflowStatus['abort']\n workflowDetail.audit_remark = audit_remark\n workflowDetail.save()\n except Exception as msg:\n context = {'errMsg': msg}\n if int(workflowType) == 1:\n return HttpResponse(context['errMsg'])\n else:\n return render(request, 'error.html', context)\n\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':workflowId, 'workflowType':workflowType}))\n\n\n# 展示回滚的SQL\ndef rollback(request):\n workflowId = request.GET['workflowid']\n if workflowId == '' or workflowId is None:\n context = {'errMsg': 'workflowId参数为空.'}\n return render(request, 'error.html', context)\n workflowId = int(workflowId)\n try:\n listBackupSql = inceptionDao.getRollbackSqlList(workflowId)\n except Exception as msg:\n context = {'errMsg': msg}\n return render(request, 'error.html', context)\n workflowDetail = workflow.objects.get(id=workflowId)\n workflowName = workflowDetail.workflow_name\n rollbackWorkflowName = \"【回滚工单】原工单Id:%s ,%s\" % (workflowId, workflowName)\n context = {'listBackupSql': listBackupSql, 'currentMenu': 'sqlworkflow', 'workflowDetail': workflowDetail,\n 'rollbackWorkflowName': rollbackWorkflowName}\n return render(request, 'rollback.html', context)\n\n\n# SQL审核必读\ndef dbaprinciples(request):\n context = {'currentMenu': 'dbaprinciples'}\n return render(request, 'dbaprinciples.html', context)\n\n\n# 图表展示\ndef charts(request):\n context = {'currentMenu': 'charts'}\n return render(request, 'charts.html', context)\n\n\n# SQL在线查询\ndef sqlquery(request):\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n\n # 获取所有从库实例名称\n slaves = slave_config.objects.all().order_by('cluster_name')\n if len(slaves) == 0:\n return HttpResponseRedirect('/admin/sql/slave_config/add/')\n\n #判断是否为管理员\n if loginUserOb.is_superuser:\n listAllClusterName = [ slave.cluster_name for slave in slaves ]\n else:\n listAllClusterName = get_query_clustername(loginUser)\n\n context = {'currentMenu': 'sqlquery', 'listAllClusterName': listAllClusterName}\n return render(request, 'sqlquery.html', context)\n\n\n# SQL慢日志\ndef slowquery(request):\n # 获取所有实例主库名称\n masters = master_config.objects.all().order_by('cluster_name')\n if len(masters) == 0:\n return HttpResponseRedirect('/admin/sql/master_config/add/')\n cluster_name_list = [master.cluster_name for master in masters]\n\n context = {'currentMenu': 'slowquery', 'tab': 'slowquery', 'cluster_name_list': cluster_name_list}\n return render(request, 'slowquery.html', context)\n\n\n# SQL优化工具\ndef sqladvisor(request):\n # 获取所有实例主库名称\n masters = master_config.objects.all().order_by('cluster_name')\n if len(masters) == 0:\n return HttpResponseRedirect('/admin/sql/master_config/add/')\n cluster_name_list = [master.cluster_name for master in masters]\n\n context = {'currentMenu': 'sqladvisor', 'listAllClusterName': cluster_name_list}\n return render(request, 'sqladvisor.html', context)\n\n\n# 查询权限申请列表\ndef queryapplylist(request):\n slaves = slave_config.objects.all().order_by('cluster_name')\n # 获取用户所属项目组信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n groupname_list = [ group['group_name'] for group in UserGroup.objects.filter(user_name=loginUser).values('group_name') ]\n\n # 获取所有实例从库名称\n listAllClusterName = [slave.cluster_name for slave in slaves]\n if len(slaves) == 0:\n return HttpResponseRedirect('/admin/sql/slave_config/add/')\n\n # 获取所有项组名称\n # group_list = Group.objects.all().annotate(id=F('group_id'),\n # name=F('group_name'),\n # parent=F('group_parent_id'),\n # level=F('group_level')\n # ).values('id', 'name', 'parent', 'level')\n group_list = Group.objects.filter(group_name__in=groupname_list).annotate(id=F('group_id'),\n name=F('group_name'),\n parent=F('group_parent_id'),\n level=F('group_level')\n ).values('id', 'name', 'parent', 'level')\n\n group_list = [group for group in group_list]\n if len(group_list) == 0 and loginUserOb.is_superuser == False:\n errMsg = '您尚未属于任何项目组，请与管理员联系.'\n return render(request, 'error.html', {'errMsg': errMsg})\n # elif len(group_list) == 0 and loginUserOb.is_superuser == True:\n # return HttpResponseRedirect('/config/')\n\n context = {'currentMenu': 'queryapply', 'listAllClusterName': listAllClusterName,\n 'group_list': group_list}\n return render(request, 'queryapplylist.html', context)\n\n\n# 查询权限申请详情\ndef queryapplydetail(request, apply_id, audit_type):\n workflowDetail = QueryPrivilegesApply.objects.get(apply_id=apply_id)\n # 获取当前审核人\n audit_info = workflowOb.auditinfobyworkflow_id(workflow_id=apply_id,\n workflow_type=WorkflowDict.workflow_type['query'])\n\n context = {'currentMenu': 'queryapply', 'workflowDetail': workflowDetail, 'audit_info': audit_info}\n if int(audit_type) == 1:\n return render(request, 'queryapplydetailhash.html', context)\n else:\n return render(request, 'queryapplydetail.html', context)\n\n\n# 用户的查询权限管理\ndef queryuserprivileges(request):\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n # 获取所有用户\n user_list_person = [ user['user_name'] for user in QueryPrivileges.objects.filter(is_deleted=0).values('user_name').distinct() ]\n group_name_list = [ group['group_name'] for group in GroupQueryPrivileges.objects.all().values('group_name').distinct() ]\n user_list_group = [ user['user_name'] for user in UserGroup.objects.filter(group_name__in=group_name_list).values('user_name').distinct() ]\n user_list = user_list_person + user_list_group\n # 排序去重\n user_list = sorted(list(set(user_list)))\n context = {'currentMenu': 'queryapply', 'user_list': user_list, 'loginUserOb': loginUserOb}\n return render(request, 'queryuserprivileges.html', context)\n\n\n# 用户的执行权限管理\ndef executeuserprivileges(request):\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n # 获取所有用户\n user_list = users.objects.all().values(\"username\").distinct()\n context = {'currentMenu': 'queryapply', 'user_list': user_list, 'loginUserOb': loginUserOb}\n return render(request, 'executeuserprivileges.html', context)\n\n\n# 问题诊断--进程\ndef diagnosis_process(request):\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n\n # 获取所有实例名称\n masters = AliyunRdsConfig.objects.all().order_by('cluster_name')\n cluster_name_list = [master.cluster_name for master in masters]\n\n context = {'currentMenu': 'diagnosis', 'tab': 'process', 'cluster_name_list': cluster_name_list,\n 'loginUserOb': loginUserOb}\n return render(request, 'diagnosis.html', context)\n\n\n# 问题诊断--空间\ndef diagnosis_sapce(request):\n # 获取所有实例名称\n masters = AliyunRdsConfig.objects.all().order_by('cluster_name')\n cluster_name_list = [master.cluster_name for master in masters]\n\n context = {'currentMenu': 'diagnosis', 'tab': 'space', 'cluster_name_list': cluster_name_list}\n return render(request, 'diagnosis.html', context)\n\n\n# 获取工作流审核列表\ndef workflows(request):\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n context = {'currentMenu': 'workflow', \"loginUserOb\": loginUserOb}\n return render(request, \"workflow.html\", context)\n\n\n# 工作流审核列表\ndef workflowsdetail(request, audit_id):\n # 按照不同的workflow_type返回不同的详情\n auditInfo = workflowOb.auditinfo(audit_id)\n if auditInfo.workflow_type == WorkflowDict.workflow_type['query']:\n return HttpResponseRedirect(reverse('sql:queryapplydetail', kwargs={'apply_id':auditInfo.workflow_id, 'audit_type':0}))\n elif auditInfo.workflow_type == WorkflowDict.workflow_type['sqlreview']:\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':auditInfo.workflow_id, 'workflowType':0}))\n\n\n# 工作流审核列表HASH认证审核\ndef workflowsdetailhash(request):\n # 用户免登录更加HASH认证快速审核\n # http://192.168.123.110:8080/workflowshash/?timestamp=454545&hash=kkkkkkkk\n timestamp, uuid, audit_id = None, None, None\n dbom_host = request.scheme + \"://\" + request.get_host() + \"/login/\"\n timestamp_before = request.GET.get('timestamp', '')\n hash_encode = request.GET.get('hash', '')\n timestamp_after = int(time.time())\n # 解密哈希\n try:\n crypter = MyCrypt(HASH_KEY)\n hash_text = crypter.decrypt(hash_encode)\n hash_text_list = hash_text.split(',')\n timestamp = hash_text_list[0]\n uuid = hash_text_list[1]\n audit_id = hash_text_list[2]\n except Exception as e:\n errMsg = \"HASH鉴权失败，请确保HASH值正常。\"\n return HttpResponse(errMsg)\n\n if int(timestamp_before) != int(timestamp) or (int(timestamp_after) - int(timestamp)) > 3600:\n errMsg = \"链接已经超过1小时或TIMESTAMP被修改，请登录DBOM(%s)进行审核。\" % (dbom_host)\n return HttpResponse(errMsg)\n\n # 获取用户信息\n loginUserOb = users.objects.get(uuid=uuid)\n login_username = loginUserOb.username\n if not loginUserOb:\n errMsg = \"用户鉴权失败，请登录DBOM(%s)进行审核。\" % (dbom_host)\n return HttpResponse(errMsg)\n else:\n request.session['login_username'] = login_username\n request.session.set_expiry(300)\n\n # 按照不同的workflow_type返回不同的详情\n auditInfo = workflowOb.auditinfo(audit_id)\n\n if auditInfo.workflow_type == WorkflowDict.workflow_type['query']:\n return HttpResponseRedirect(reverse('sql:queryapplydetail', kwargs={'apply_id':auditInfo.workflow_id, 'audit_type':1}))\n elif auditInfo.workflow_type == WorkflowDict.workflow_type['sqlreview']:\n return HttpResponseRedirect(reverse('sql:detail', kwargs={'workflowId':auditInfo.workflow_id, 'workflowType':1}))\n\n\n# 配置管理\n@superuser_required\ndef config(request):\n # 获取所有项组名称\n group_list = Group.objects.all().annotate(id=F('group_id'),\n name=F('group_name'),\n parent=F('group_parent_id'),\n level=F('group_level'),\n leader=F('group_leader')\n ).values('id', 'name', 'parent', 'level', 'leader')\n # 获取组的成员数\n for group_name in group_list:\n members_num = UserGroup.objects.filter(group_name=group_name['name']).count()\n group_name['members_num'] = members_num\n\n group_list = [group for group in group_list]\n\n # 获取所有用户\n user_list = users.objects.filter(is_active=1).values('username', 'display')\n context = {'currentMenu': 'config', 'group_list': group_list, 'user_list': user_list,\n 'WorkflowDict': WorkflowDict}\n group_list, p, groups, page_range, current_page, show_first, show_end, contacts = pages(group_list, request)\n return render(request, 'config.html', locals())\n\n# 配置项目组信息\n@csrf_exempt\ndef configGroup(request):\n context = { 'status': 1, 'msg':'', 'data': {}} # 1是成功，0是失败\n if request.method == \"POST\":\n operation_type = request.POST.get('operation_type', None)\n project_name = request.POST.get('project_name', None)\n project_auditors = request.POST.get('project_auditors', None)\n\n if operation_type == \"project_add\":\n try:\n if not project_name or len(project_name) == 0:\n msg = u'项目名称不能为空'\n raise ServerError(msg)\n elif not project_auditors or len(project_auditors) == 0:\n msg = u'请选择项目负责人'\n raise ServerError(msg)\n except ServerError as e:\n context['status'] = 0\n context['msg'] = e.message\n logger.error('项目添加出错:%s'%e.message)\n else:\n try:\n # 添加组信息\n group_default_dict = { 'group_name': project_name, 'group_leader': project_auditors }\n group_obj, group_created = Group.objects.get_or_create(group_name=project_name, group_leader=project_auditors, defaults=group_default_dict)\n logger.info('project add obj: %s created: %s' % (group_obj, group_created))\n # 添加用户与组对应关系表\n usergroup_default_dict = { 'group_name': project_name, 'user_name': project_auditors }\n usergroup_obj, usergroup_created = UserGroup.objects.get_or_create(group_name=project_name, user_name=project_auditors, defaults=usergroup_default_dict)\n logger.info('Relationship between the project and the user add obj: %s created: %s' % (usergroup_obj, usergroup_created))\n # 配置项目成员\n users_list_select_web = request.POST.getlist('users_selected', [])\n configGroupMembers(project_name, users_list_select_web)\n\n context['status'] = 1\n context['msg'] = '项目组添加成功'\n logger.info('Project add %s is success.'%project_name)\n except Exception as e:\n context['status'] = 0\n serache_result = re.search('Duplicate entry',str(e))\n if serache_result:\n context['msg'] = '项目组已经存在'\n else:\n context['msg'] = '项目组添加失败'\n logger.info('Project add %s is failed. { %s }'%(project_name, e))\n\n elif operation_type == \"project_del\":\n project_id = request.POST.get('project_id', None)\n project_name = Group.objects.get(group_id=project_id)\n try:\n # 删除组信息\n Group.objects.filter(group_id=project_id).delete()\n # 删除组对应的用户信息\n UserGroup.objects.filter(group_name=project_name.group_name).delete()\n context['status'] = 1\n context['msg'] = '项目组删除成功'\n logger.info('Project %s delete success.' % project_name.group_name)\n except Exception as e:\n context['status'] = 0\n context['msg'] = '项目组删除失败'\n logger.info('Project %s delete failed. { %s }' %(project_name.group_name, e))\n\n elif operation_type == \"get_project\":\n project_dic = {}\n get_type = request.POST.get('get_type', None)\n project_id = request.POST.get('project_id', None)\n try:\n if get_type == 'edit':\n # 项目组信息\n project_info = Group.objects.get(group_id=project_id)\n group_name = project_info.group_name\n user_list = list(users.objects.filter(is_active=1).values('username'))\n project_dic[\"group_id\"] = project_info.group_id\n project_dic[\"group_name\"] = group_name\n project_dic[\"group_leader\"] = project_info.group_leader\n project_dic[\"user_list\"] = user_list\n else:\n group_name = ''\n\n # 项目组成员信息\n user_list_all = [user['username'] for user in list(users.objects.values('username'))]\n user_list_select = [user['user_name'] for user in list(UserGroup.objects.filter(group_name=group_name).values('user_name'))]\n user_list_noselect = [user for user in user_list_all if user not in user_list_select]\n project_dic[\"user_list_select\"] = user_list_select\n project_dic[\"user_list_noselect\"] = user_list_noselect\n\n context['data'] = project_dic\n context['status'] = 1\n context['msg'] = '获取项目信息成功'\n logger.info('Get project %s info success.' %group_name)\n except Exception as e:\n context['status'] = 0\n context['msg'] = '获取项目信息失败'\n logger.info('Get project info failed. { %s }' %e)\n\n elif operation_type == \"project_edit\":\n edit_group_id = request.POST.get('edit_group_id', None)\n edit_project_name = request.POST.get('edit_project_name', None)\n edit_project_auditors = request.POST.get('edit_project_auditors', None)\n try:\n if not edit_project_name or len(edit_project_name) == 0:\n msg = u'项目名称不能为空'\n raise ServerError(msg)\n elif not edit_project_auditors or len(edit_project_auditors) == 0:\n msg = u'请选择项目负责人'\n raise ServerError(msg)\n except ServerError as e:\n context['status'] = 0\n context['msg'] = e.message\n logger.error('项目更新出错:%s'%e.message)\n else:\n try:\n # 更新组信息\n obj, created = Group.objects.update_or_create(group_id=edit_group_id, defaults={\"group_name\":edit_project_name, \"group_leader\":edit_project_auditors})\n logger.info('project update obj: %s created: %s' % (obj, created))\n # 配置项目成员\n users_list_select_web = request.POST.getlist('users_selected', [])\n configGroupMembers(edit_project_name, users_list_select_web)\n context['status'] = 1\n context['msg'] = '项目组更新成功'\n logger.info('Project ID %s update success.' % edit_group_id)\n except Exception as e:\n context['status'] = 0\n serache_result = re.search('Duplicate entry', str(e))\n if serache_result:\n context['msg'] = '项目组已经存在'\n else:\n context['msg'] = '项目组更新失败'\n logger.info('Project ID %s update failed. { %s }' %(edit_group_id, e))\n\n return HttpResponse(json.dumps(context), content_type=\"application/x-www-form-urlencoded\")\n\n\n# 配置项目成员\n@csrf_exempt\ndef configGroupMembers(group_name, users_list_select_web):\n\n user_list_select = [ user['user_name'] for user in list(UserGroup.objects.filter(group_name=group_name).values('user_name')) ]\n insert_users_list = [ user for user in users_list_select_web if user not in user_list_select ]\n del_users_list = [ user for user in user_list_select if user not in users_list_select_web ]\n # 插入新增\n for user in insert_users_list:\n obj, created = UserGroup.objects.get_or_create(group_name=group_name, user_name=user, defaults={'group_name':group_name, 'user_name':user})\n logger.info('group members insert obj: %s created: %s'%(obj, created))\n logger.info('group members insert data %s'%insert_users_list)\n # 删除剔除\n for user in del_users_list:\n UserGroup.objects.filter(group_name=group_name, user_name=user).delete()\n logger.info('group members delete data %s' % del_users_list)\n\n\n\n# 获取项目资源\n@csrf_exempt\ndef projectresource(request):\n currentMenu = 'projectresource'\n context = {'status': 1, 'msg': '', 'data': {}} # 1是成功，0是失败\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n\n # 获取项目集群\n listAllCluster = slave_config.objects.all().order_by('cluster_name')\n listAllClusterName = [ str(cluster.cluster_name) for cluster in listAllCluster ]\n\n if request.session.get('resource_status', 0) == 0:\n logger.debug('异步整合现网表资源信息中...')\n # 采取异步回调的方式进行资源整合，防止出现持续执行中的异常\n t = Thread(target=integration_resource, args=(listAllClusterName,))\n t.start()\n request.session['resource_status'] = 1\n\n # 获取当前用户所管理的项目列表\n if loginUserOb.is_superuser:\n user_project_list = [ group[\"group_name\"] for group in Group.objects.all().values(\"group_name\").distinct() ]\n else:\n user_project_list = [ group[\"group_name\"] for group in Group.objects.filter(group_leader=loginUser).values(\"group_name\").distinct() ]\n\n if request.method == \"POST\":\n limitStart = int(request.POST.get('offset',0))\n pageSize = int(request.POST.get('pageSize',0))\n project_name = request.POST.get('project_name',None)\n cluster_name = request.POST.get('cluster_name',None)\n db_name = request.POST.get('db_name',None)\n search = request.POST.get('search',None)\n config_type = request.POST.get('config_type',None)\n\n if config_type == \"change_cluster\":\n listDatabase = []\n if cluster_name:\n # 获取实列所有库信息\n listDatabase = [ row['db_name'] for row in list(ProjectResource.objects.filter(cluster_name=cluster_name).values('db_name').distinct()) ]\n return HttpResponse(json.dumps(listDatabase), content_type=\"application/x-www-form-urlencoded\")\n\n elif config_type == \"get_resource\":\n resource_id = request.POST.get('resource_id',None)\n project_name = request.POST.get('project_name',None)\n if not project_name or len(project_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择需要获取权限的项目'\n else:\n try:\n group_list_str = ProjectResource.objects.get(id=resource_id).group_list\n if len(group_list_str) > 0:\n group_list_tmp = group_list_str.split(\",\")\n else:\n group_list_tmp = []\n group_list_tmp.append(project_name)\n group_list = ','.join(group_list_tmp)\n # 更新资源列表信息\n ProjectResource.objects.update_or_create(id=resource_id, defaults={'group_list':group_list})\n context['status'] = 1\n context['data'] = group_list\n logger.info('Get resource ID %s is success.'%resource_id)\n except Exception as e:\n context['status'] = 0\n context['msg'] = '资源获取失败'\n logger.error('Get resource ID %s is filed. { %s }' %(resource_id, e))\n return HttpResponse(json.dumps(context), content_type=\"application/x-www-form-urlencoded\")\n\n elif config_type == \"get_db_all_resource\":\n group_name = request.POST.get('group_name', None)\n cluster_name = request.POST.get('cluster_name',None)\n db_name = request.POST.get('db_name', None)\n if not group_name or len(group_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择项目组'\n elif not cluster_name or len(cluster_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择数据库实例'\n elif not db_name or len(db_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择数据库'\n else:\n try:\n group_info_list = list(ProjectResource.objects.filter(cluster_name=cluster_name, db_name=db_name).values('id', 'group_list'))\n for group_info in group_info_list:\n resource_id = group_info['id']\n group_list_str = group_info['group_list']\n if len(group_list_str) > 0:\n group_list_tmp = group_list_str.split(\",\")\n else:\n group_list_tmp = []\n if group_name not in group_list_tmp:\n group_list_tmp.append(group_name)\n group_list = ','.join(group_list_tmp)\n # 更新资源列表信息\n ProjectResource.objects.update_or_create(id=resource_id, defaults={'group_list':group_list})\n context['status'] = 1\n context['data'] = group_list\n logger.info('Get resource ID %s is success.'%resource_id)\n logger.info('Get whole database %s resource is success.' % db_name)\n except Exception as e:\n context['status'] = 0\n context['msg'] = '整库资源获取失败'\n logger.error('Get whole database %s resource is filed. { %s }' %(db_name, e))\n return HttpResponse(json.dumps(context), content_type=\"application/x-www-form-urlencoded\")\n\n elif config_type == \"del_resource\":\n resource_id = request.POST.get('resource_id',None)\n project_name = request.POST.get('project_name',None)\n if not project_name or len(project_name) == 0:\n context['status'] = 0\n context['msg'] = '请先选择项目'\n else:\n try:\n group_list_tmp = (ProjectResource.objects.get(id=resource_id).group_list).split(\",\")\n group_list_tmp.remove(project_name)\n group_list = ','.join(group_list_tmp)\n ProjectResource.objects.update_or_create(id=resource_id, defaults={'group_list':group_list})\n context['status'] = 1\n context['data'] = group_list\n logger.info('Delete resource ID %s is success.'%resource_id)\n except Exception as e:\n context['status'] = 0\n context['msg'] = '资源清除失败'\n logger.error('Delete resource ID %s is filed. { %s }' %(resource_id, e))\n return HttpResponse(json.dumps(context), content_type=\"application/x-www-form-urlencoded\")\n\n elif config_type == \"del_db_all_resource\":\n group_name = request.POST.get('group_name', None)\n cluster_name = request.POST.get('cluster_name', None)\n db_name = request.POST.get('db_name', None)\n if not group_name or len(group_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择项目组'\n elif not cluster_name or len(cluster_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择数据库实例'\n elif not db_name or len(db_name) == 0:\n context['status'] = 0\n context['msg'] = '请选择数据库'\n else:\n try:\n group_info_list = list(ProjectResource.objects.filter(cluster_name=cluster_name, db_name=db_name).values('id','group_list'))\n for group_info in group_info_list:\n resource_id = group_info['id']\n group_list_str = group_info['group_list']\n if len(group_list_str) > 0:\n group_list_tmp = group_list_str.split(\",\")\n else:\n group_list_tmp = []\n if group_name in group_list_tmp:\n group_list_tmp.remove(group_name)\n group_list = ','.join(group_list_tmp)\n # 更新资源列表信息\n ProjectResource.objects.update_or_create(id=resource_id, defaults={'group_list': group_list})\n context['status'] = 1\n context['data'] = group_list\n logger.info('Delete resource ID %s is success.' % resource_id)\n logger.info('Delete whole database %s resource is success.' % db_name)\n except Exception as e:\n context['status'] = 0\n context['msg'] = '整库资源清除失败'\n logger.error('Delete whole database %s resource is filed. { %s }' % (db_name, e))\n return HttpResponse(json.dumps(context), content_type=\"application/x-www-form-urlencoded\")\n\n else:\n where_list = ['1=1']\n if cluster_name:\n where_list.append('AND cluster_name=\"%s\"'%cluster_name)\n if db_name:\n where_list.append('AND db_name=\"%s\"'%db_name)\n if search:\n where_list.append('AND ( table_name LIKE \"%%%s%%\" OR group_list LIKE \"%%%s%%\" )'%(search, search))\n\n if len(where_list) > 0:\n where_value = ' '.join(where_list)\n table = 'project_resource'\n count_sql = \"SELECT COUNT(1) AS rowcount FROM %s WHERE %s;\"%(table, where_value)\n row_sql = \"SELECT id,cluster_name,db_name,table_name,group_list FROM %s WHERE %s ORDER by id ASC LIMIT %s,%s;\"%(table, where_value, limitStart, pageSize)\n # 获取资源信息\n resource_data = get_resource(count_sql, row_sql, project_name)\n else:\n table = 'project_resource'\n count_sql = \"SELECT COUNT(1) AS rowcount FROM %s;\"%(table)\n row_sql = \"SELECT id,cluster_name,db_name,table_name,group_list FROM %s ORDER by id ASC LIMIT %s,%s;\"%(table, limitStart, pageSize)\n # 获取资源信息\n resource_data = get_resource(count_sql, row_sql , project_name)\n\n return HttpResponse(json.dumps(resource_data), content_type=\"application/x-www-form-urlencoded\")\n\n group_list = Group.objects.all().annotate(id=F('group_id'),\n name=F('group_name'),\n parent=F('group_parent_id'),\n level=F('group_level')\n ).values('id', 'name', 'parent', 'level')\n\n group_list = [group for group in group_list]\n\n return render(request, 'project_config/get_project_group_resource.html', locals())\n\n\n# 设置项目组的查询权限\n@csrf_exempt\ndef groupQueryPermission(request):\n currentMenu = 'projectresource'\n context = {'status': 1, 'msg': '', 'data': {}} # 1是成功，0是失败\n # 获取用户信息\n loginUser = request.session.get('login_username', False)\n loginUserOb = users.objects.get(username=loginUser)\n\n # 获取项目集群\n listAllCluster = slave_config.objects.all().order_by('cluster_name')\n listAllClusterName = [ str(cluster.cluster_name) for cluster in listAllCluster ]\n\n # 获取当前用户所管理的项目列表\n if loginUserOb.is_superuser:\n user_group_list = [ group[\"group_name\"] for group in Group.objects.all().values(\"group_name\").distinct() ]\n else:\n user_group_list = [ group[\"group_name\"] for group in Group.objects.filter(group_leader=loginUser).values(\"group_name\").distinct() ]\n\n if request.method == \"POST\":\n limitStart = int(request.POST.get('offset',0))\n pageSize = int(request.POST.get('pageSize',0))\n group_name = request.POST.get('group_name',None)\n cluster_name = request.POST.get('cluster_name',None)\n db_name = request.POST.get('db_name',None)\n search = request.POST.get('search',None)\n\n user_group_text = '\\\"' + '\\\",\\\"'.join(user_group_list) + '\\\"'\n where_list = ['1=1']\n if group_name:\n where_list.append('AND group_name=\"%s\"' % group_name)\n else:\n where_list.append('AND group_name IN (%s)' % user_group_text)\n if cluster_name:\n where_list.append('AND cluster_name=\"%s\"' % cluster_name)\n if db_name:\n where_list.append('AND db_name=\"%s\"' % db_name)\n if search:\n where_list.append('AND ( table_name LIKE \"%%%s%%\" OR group_name LIKE \"%%%s%%\" )' % (search, search))\n\n where_value = ' '.join(where_list)\n table = 'group_query_privileges'\n count_sql = \"SELECT COUNT(1) AS rowcount FROM %s WHERE %s;\" % (table, where_value)\n row_sql = \"SELECT privilege_id,group_name,cluster_name,db_name,table_name,valid_date,limit_num FROM %s WHERE %s ORDER by privilege_id ASC LIMIT %s,%s;\" % (\n table, where_value, limitStart, pageSize)\n # 获取资源信息\n resource_data = get_query_permisshion(count_sql, row_sql)\n # logger.debug('获取权限资源信息:%s.'%resource_data)\n\n return HttpResponse(json.dumps(resource_data), content_type=\"application/x-www-form-urlencoded\")\n\n return render(request, 'project_config/set_group_query_permission.html', locals())\n\n\n# 设置项目组的查询权限\n@csrf_exempt\ndef getGroupQueryPermission(request):\n context = {'status': 1, 'msg': '', 'data': {}} # 1是成功，0是失败\n group_name = request.POST.get('group_name', None)\n cluster_name = request.POST.get('cluster_name', None)\n db_name = request.POST.get('db_name', None)\n operation_type = request.POST.get('operation_type', None)\n valid_date = request.POST.get('valid_date', None)\n limit_num = request.POST.get('limit_num', 1000)\n\n table_resource_list = [ table['table_name'] for table in ProjectResource.objects.filter(cluster_name=cluster_name,db_name=db_name).values('table_name') ]\n\n permission_table_list = [ table['table_name'] for table in GroupQueryPrivileges.objects.filter(group_name=group_name,cluster_name=cluster_name,db_name=db_name).values('table_name') ]\n no_permission_table_list = [ table_name for table_name in table_resource_list if table_name not in permission_table_list ]\n\n if operation_type == 'resource_save':\n try:\n if not group_name or len(group_name) == 0:\n msg = u'请选择项目组'\n raise ServerError(msg)\n elif not cluster_name or len(cluster_name) == 0:\n msg = u'请选择数据库实列'\n raise ServerError(msg)\n elif not db_name or len(db_name) == 0:\n msg = u'请选择数据库'\n raise ServerError(msg)\n elif not valid_date or len(valid_date) == 0:\n msg = u'请选择授权时间'\n raise ServerError(msg)\n elif not limit_num or len(limit_num) == 0:\n msg = u'请选择查询限制行数'\n raise ServerError(msg)\n except ServerError as e:\n context['status'] = 0\n context['msg'] = e.message\n logger.error('Group premission set error:%s' % e.message)\n else:\n try:\n web_permission_table_list = request.POST.getlist('tables_selected', [])\n new_permission_table_list = [ table_name for table_name in web_permission_table_list if table_name not in permission_table_list ]\n del_permission_table_list = [ table_name for table_name in permission_table_list if table_name not in web_permission_table_list ]\n defaults_data = {'group_name': group_name, 'cluster_name': cluster_name, 'db_name': db_name, 'valid_date': valid_date, 'limit_num': limit_num}\n # 添加新增数据\n for table_name in new_permission_table_list:\n defaults_data['table_name'] = table_name\n # 插入数据\n GroupQueryPrivileges.objects.create(**defaults_data)\n logger.debug('Insert group query permission %s.' % new_permission_table_list)\n # 删除排除的数据\n for table_name in del_permission_table_list:\n # 删除数据\n GroupQueryPrivileges.objects.filter(group_name=group_name,cluster_name=cluster_name,db_name=db_name,table_name=table_name).delete()\n logger.debug('Delete group query permission %s.' % del_permission_table_list)\n logger.debug('Save group query permission success.%s'%web_permission_table_list)\n except Exception as e:\n context['status'] = 0\n context['msg'] = e\n logger.error('Save group query permission error {%s}.'%e)\n elif operation_type == 'del_premission':\n privilege_id = request.POST.get('privilege_id', None)\n try:\n # 删除对应权限数据\n GroupQueryPrivileges.objects.filter(privilege_id=privilege_id).delete()\n logger.info(\"Delete group query permission sucdess.\")\n except Exception as e:\n context['status'] = 0\n context['msg'] = e\n logger.error('Group premission delete error,:%s' % e)\n\n\n table_resource = {}\n table_resource['permission_table_list'] = permission_table_list\n table_resource['no_permission_table_list'] = no_permission_table_list\n context['data'] = table_resource\n\n return HttpResponse(json.dumps(context), content_type=\"application/x-www-form-urlencoded\")\n\n", "sub_path": "sql/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 60378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "dao.Dao", "line_number": 36, "usage_type": "call"}, {"api_name": "inception.InceptionDao", "line_number": 37, "usage_type": "call"}, {"api_name": "aes_decryptor.Prpcrypt", "line_number": 38, "usage_type": "call"}, {"api_name": "workflow.Workflow", "line_number": 39, "usage_type": "call"}, {"api_name": "django.conf.settings.ACCESS_ITOM_ADDR", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 44, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.conf.settings.ACCESS_ITOM_ADDR", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 51, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "models.master_config.objects.all", "line_number": 69, "usage_type": "call"}, {"api_name": "models.master_config.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.master_config", "line_number": 69, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 71, "usage_type": "call"}, {"api_name": "models.users.objects.get", "line_number": 75, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 75, "usage_type": "name"}, {"api_name": "projectresource.PermissionVerification", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 92, "usage_type": "call"}, {"api_name": "models.users.objects.filter", "line_number": 95, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 95, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Group.objects.get", "line_number": 108, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 108, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 118, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 122, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 125, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 129, "usage_type": "call"}, {"api_name": "models.users.objects.get", "line_number": 133, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 133, "usage_type": "name"}, {"api_name": "projectresource.PermissionVerification", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 145, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 149, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 152, "usage_type": "call"}, {"api_name": "const.Const.workflowStatus", "line_number": 155, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 155, "usage_type": "name"}, {"api_name": "const.Const.workflowStatus", "line_number": 159, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 159, "usage_type": "name"}, {"api_name": "re.match", "line_number": 161, "usage_type": "call"}, {"api_name": "const.Const.workflowStatus", "line_number": 162, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 162, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 168, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 168, "usage_type": "name"}, {"api_name": "workflow.Workflow", "line_number": 172, "usage_type": "name"}, {"api_name": "models.workflow", "line_number": 172, "usage_type": "call"}, {"api_name": "workflow.Workflow.create_time", "line_number": 173, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 173, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 173, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 173, "usage_type": "name"}, {"api_name": "workflow.Workflow", "line_number": 175, "usage_type": "name"}, {"api_name": "models.workflow.objects.get", "line_number": 175, "usage_type": "call"}, {"api_name": "models.workflow.objects", "line_number": 175, "usage_type": "attribute"}, {"api_name": "models.workflow", "line_number": 175, "usage_type": "name"}, {"api_name": "workflow.Workflow.workflow_name", "line_number": 176, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 176, "usage_type": "name"}, {"api_name": "workflow.Workflow.group_id", "line_number": 177, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 177, "usage_type": "name"}, {"api_name": "workflow.Workflow.group_name", "line_number": 178, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 178, "usage_type": "name"}, {"api_name": "workflow.Workflow.engineer", "line_number": 179, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 179, "usage_type": "name"}, {"api_name": "workflow.Workflow.review_man", "line_number": 180, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 180, "usage_type": "name"}, {"api_name": "workflow.Workflow.status", "line_number": 181, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 181, "usage_type": "name"}, {"api_name": "workflow.Workflow.is_backup", "line_number": 182, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 182, "usage_type": "name"}, {"api_name": "workflow.Workflow.review_content", "line_number": 183, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 183, "usage_type": "name"}, {"api_name": "workflow.Workflow.cluster_name", "line_number": 184, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 184, "usage_type": "name"}, {"api_name": "workflow.Workflow.db_name", "line_number": 185, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 185, "usage_type": "name"}, {"api_name": "workflow.Workflow.sql_content", "line_number": 186, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 186, "usage_type": "name"}, {"api_name": "workflow.Workflow.execute_result", "line_number": 187, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 187, "usage_type": "name"}, {"api_name": "workflow.Workflow.audit_remark", "line_number": 188, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 188, "usage_type": "name"}, {"api_name": "workflow.Workflow.save", "line_number": 189, "usage_type": "call"}, {"api_name": "workflow.Workflow", "line_number": 189, "usage_type": "name"}, {"api_name": "workflow.Workflow.id", "line_number": 190, "usage_type": "attribute"}, {"api_name": "workflow.Workflow", "line_number": 190, "usage_type": "name"}, {"api_name": "const.Const.workflowStatus", "line_number": 192, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 192, "usage_type": "name"}, {"api_name": "models.users.objects.filter", "line_number": 196, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 196, "usage_type": "name"}, {"api_name": "const.WorkflowDict.workflow_type", "line_number": 197, "usage_type": "attribute"}, {"api_name": "const.WorkflowDict", "line_number": 197, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 201, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 203, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 203, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 208, "usage_type": "call"}, {"api_name": "models.workflow", "line_number": 208, "usage_type": "argument"}, {"api_name": "const.Const.workflowStatus", "line_number": 209, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 209, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 211, "usage_type": "call"}, {"api_name": "simplejson.loads", "line_number": 213, "usage_type": "call"}, {"api_name": "const.WorkflowDict.workflow_type", "line_number": 222, "usage_type": "attribute"}, {"api_name": "const.WorkflowDict", "line_number": 222, "usage_type": "name"}, {"api_name": "models.users.objects.get", "line_number": 229, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 229, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 229, "usage_type": "name"}, {"api_name": "const.Const.workflowStatus", "line_number": 232, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 232, "usage_type": "name"}, {"api_name": "const.Const.workflowJobprefix", "line_number": 233, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 233, "usage_type": "name"}, {"api_name": "jobs.job_info", "line_number": 234, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 286, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 288, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 297, "usage_type": "call"}, {"api_name": "models.workflow.objects.get", "line_number": 299, "usage_type": "call"}, {"api_name": "models.workflow.objects", "line_number": 299, "usage_type": "attribute"}, {"api_name": "models.workflow", "line_number": 299, "usage_type": "name"}, {"api_name": "models.users.objects.get", "line_number": 307, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 307, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 307, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 310, "usage_type": "call"}, {"api_name": "const.Const.workflowStatus", "line_number": 313, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 313, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 315, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 319, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 319, "usage_type": "name"}, {"api_name": "const.WorkflowDict.workflow_type", "line_number": 323, "usage_type": "attribute"}, {"api_name": "const.WorkflowDict", "line_number": 323, "usage_type": "name"}, {"api_name": "const.WorkflowDict.workflow_status", "line_number": 324, "usage_type": "attribute"}, {"api_name": "const.WorkflowDict", "line_number": 324, "usage_type": "name"}, {"api_name": "const.WorkflowDict.workflow_status", "line_number": 328, "usage_type": "attribute"}, {"api_name": "const.WorkflowDict", "line_number": 328, "usage_type": "name"}, {"api_name": "const.Const.workflowStatus", "line_number": 330, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 330, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 331, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 331, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 337, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 339, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 341, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 341, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 349, "usage_type": "call"}, {"api_name": "models.workflow.objects.get", "line_number": 352, "usage_type": "call"}, {"api_name": "models.workflow.objects", "line_number": 352, "usage_type": "attribute"}, {"api_name": "models.workflow", "line_number": 352, "usage_type": "name"}, {"api_name": "sqlreview.getDetailUrl", "line_number": 355, "usage_type": "call"}, {"api_name": "models.users.objects.get", "line_number": 363, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 363, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 363, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 366, "usage_type": "call"}, {"api_name": "const.Const.workflowStatus", "line_number": 369, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 369, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 371, "usage_type": "call"}, {"api_name": "const.Const.workflowStatus", "line_number": 374, "usage_type": "attribute"}, {"api_name": "const.Const", "line_number": 374, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 375, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 375, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 382, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 383, "usage_type": "call"}, {"api_name": "django.db.connection.close", "line_number": 388, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 388, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 392, "usage_type": "call"}, {"api_name": "sqlreview.execute_call_back", "line_number": 392, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 395, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 395, "usage_type": "call"}, {"api_name": "models.workflow.objects.get", "line_number": 405, "usage_type": "call"}, {"api_name": "models.workflow.objects", "line_number": 405, 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{"api_name": "simplejson.dumps", "line_number": 894, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 764, "usage_type": "name"}, {"api_name": "models.UserGroup.objects.filter", "line_number": 901, "usage_type": "call"}, {"api_name": "models.UserGroup.objects", "line_number": 901, "usage_type": "attribute"}, {"api_name": "models.UserGroup", "line_number": 901, "usage_type": "name"}, {"api_name": "models.UserGroup.objects.get_or_create", "line_number": 906, "usage_type": "call"}, {"api_name": "models.UserGroup.objects", "line_number": 906, "usage_type": "attribute"}, {"api_name": "models.UserGroup", "line_number": 906, "usage_type": "name"}, {"api_name": "models.UserGroup.objects.filter", "line_number": 911, "usage_type": "call"}, {"api_name": "models.UserGroup.objects", "line_number": 911, "usage_type": "attribute"}, {"api_name": "models.UserGroup", "line_number": 911, "usage_type": "name"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 898, "usage_type": "name"}, {"api_name": "models.users.objects.get", "line_number": 923, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 923, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 923, "usage_type": "name"}, {"api_name": "models.slave_config.objects.all", "line_number": 926, "usage_type": "call"}, {"api_name": "models.slave_config.objects", "line_number": 926, "usage_type": "attribute"}, {"api_name": "models.slave_config", "line_number": 926, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 932, "usage_type": "call"}, {"api_name": "projectresource.integration_resource", "line_number": 932, "usage_type": "name"}, {"api_name": "models.Group.objects.all", "line_number": 938, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 938, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 938, "usage_type": "name"}, {"api_name": "models.Group.objects.filter", "line_number": 940, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 940, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 940, "usage_type": "name"}, {"api_name": "models.ProjectResource.objects.filter", "line_number": 955, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 955, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 955, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 956, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 956, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects.get", "line_number": 966, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 966, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 966, "usage_type": "name"}, {"api_name": "models.ProjectResource.objects.update_or_create", "line_number": 974, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 974, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 974, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 982, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 982, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects.filter", "line_number": 999, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 999, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 999, "usage_type": "name"}, {"api_name": "models.ProjectResource.objects.update_or_create", "line_number": 1011, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 1011, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 1011, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 1020, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 1020, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects.get", "line_number": 1030, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 1030, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 1030, "usage_type": "name"}, {"api_name": "models.ProjectResource.objects.update_or_create", "line_number": 1033, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 1033, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 1033, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 1041, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 1041, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects.filter", "line_number": 1058, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 1058, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 1058, "usage_type": "name"}, {"api_name": "models.ProjectResource.objects.update_or_create", "line_number": 1070, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 1070, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 1070, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 1079, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 1079, "usage_type": "call"}, {"api_name": "projectresource.get_resource", "line_number": 1096, "usage_type": "call"}, {"api_name": "projectresource.get_resource", "line_number": 1102, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 1104, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 1104, "usage_type": "call"}, {"api_name": "models.Group.objects.all", "line_number": 1106, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 1106, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 1106, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 1106, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 1107, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 1108, "usage_type": "call"}, {"api_name": "django.db.models.F", "line_number": 1109, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 1114, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 917, "usage_type": "name"}, {"api_name": "models.users.objects.get", "line_number": 1124, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 1124, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 1124, "usage_type": "name"}, {"api_name": "models.slave_config.objects.all", "line_number": 1127, "usage_type": "call"}, {"api_name": "models.slave_config.objects", "line_number": 1127, "usage_type": "attribute"}, {"api_name": "models.slave_config", "line_number": 1127, "usage_type": "name"}, {"api_name": "models.Group.objects.all", "line_number": 1132, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 1132, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 1132, "usage_type": "name"}, {"api_name": "models.Group.objects.filter", "line_number": 1134, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 1134, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 1134, "usage_type": "name"}, {"api_name": "projectresource.get_query_permisshion", "line_number": 1163, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 1166, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 1166, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 1168, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 1118, "usage_type": "name"}, {"api_name": "models.ProjectResource.objects.filter", "line_number": 1182, "usage_type": "call"}, {"api_name": "models.ProjectResource.objects", "line_number": 1182, "usage_type": "attribute"}, {"api_name": "models.ProjectResource", "line_number": 1182, "usage_type": "name"}, {"api_name": "models.GroupQueryPrivileges.objects.filter", "line_number": 1184, "usage_type": "call"}, {"api_name": "models.GroupQueryPrivileges.objects", "line_number": 1184, "usage_type": "attribute"}, {"api_name": "models.GroupQueryPrivileges", "line_number": 1184, "usage_type": "name"}, {"api_name": "api.ServerError", "line_number": 1191, "usage_type": "call"}, {"api_name": "api.ServerError", "line_number": 1194, "usage_type": "call"}, {"api_name": "api.ServerError", "line_number": 1197, "usage_type": "call"}, {"api_name": "api.ServerError", "line_number": 1200, "usage_type": "call"}, {"api_name": "api.ServerError", "line_number": 1203, "usage_type": "call"}, {"api_name": "api.ServerError", "line_number": 1204, "usage_type": "name"}, {"api_name": "models.GroupQueryPrivileges.objects.create", "line_number": 1218, "usage_type": "call"}, {"api_name": "models.GroupQueryPrivileges.objects", "line_number": 1218, "usage_type": "attribute"}, {"api_name": "models.GroupQueryPrivileges", "line_number": 1218, "usage_type": "name"}, {"api_name": "models.GroupQueryPrivileges.objects.filter", "line_number": 1223, "usage_type": "call"}, {"api_name": "models.GroupQueryPrivileges.objects", "line_number": 1223, "usage_type": "attribute"}, {"api_name": "models.GroupQueryPrivileges", "line_number": 1223, "usage_type": "name"}, {"api_name": "models.GroupQueryPrivileges.objects.filter", "line_number": 1234, "usage_type": "call"}, {"api_name": "models.GroupQueryPrivileges.objects", "line_number": 1234, "usage_type": "attribute"}, {"api_name": "models.GroupQueryPrivileges", "line_number": 1234, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 1247, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 1247, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 1172, "usage_type": "name"}]} +{"seq_id": "565029082", "text": "from flask import Blueprint, session, request, redirect, url_for, render_template\nfrom . import db\nfrom .auth import userLoggedIn, userType\n\nstaff = Blueprint('staff',__name__)\n\n\n\n@staff.route('/staffDashboard')\ndef dashboard():\n if not(userLoggedIn() and userType('employee')):\n return\n return render_template('staff/dashboard.html')\n\n\n@staff.route('/staffClientInfo')\ndef dashboardClient():\n if not(userLoggedIn() and userType('employee')):\n return\n return render_template('staff/clientInfo.html')\n\n@staff.route('/staffInsuranceInfo')\ndef dashboardInsurance():\n if not(userLoggedIn() and userType('employee')):\n return\n return render_template('staff/insurance.html', staffClientQuery=False)\n\n\n@staff.context_processor\ndef viewStaffProfile():\n\tif not(userLoggedIn() and userType('employee')):\n\t\treturn\n\tdbCursor = db.cursor()\n\tsql = \"SELECT A.employee_name, \\\n\tA.employee_aadhar, A.employee_PAN, B.branch_name,A.department, A.position, A.salary, A.employee_ph \\\n\tFROM staff A, branch B \\\n\tWHERE employee_ID = %s AND A.branch_ID=B.branch_ID\"\n\temployee_ID = session['id']\n\tval = (employee_ID,)\n\tdbCursor.execute(sql, val)\n\tres = dbCursor.fetchone()\n\tdbCursor.close()\n\treturn {'staffProfile' : [session['username'], res[7], session['email'], res[0], res[1], res[2],res[3], res[4],res[5],res[6]]}\n\n@staff.route('/viewClientDetails', methods = [\"POST\"])\ndef viewClientDetails():\n\tif not(userLoggedIn() and userType(\"employee\")):\n\t\treturn\n\tdbCursor = db.cursor()\n\tsql = \"SELECT client_name,client_ph,client_email,branch_ID,client_aadhar,client_PAN, \"\\\n\t\"client_DOB,client_sex,agent_name,agent_ph,agent_email FROM clients c, agents a WHERE \"\\\n\t\"c.client_ID = %s AND c.agent_ID=a.agent_ID\"\n\tval = (request.form['clientID'],)\n\tdbCursor.execute(sql, val)\n\tres = dbCursor.fetchone()\n\tsql = \"SELECT Unique_Ins_ID, ins_type, DATE_ADD(start_date, INTERVAL duration YEAR) as end_date FROM \\\n\tinsurances, policies WHERE policies.policy_key=insurances.policy_key AND client_ID = %s\"\n\tval = (request.form['clientID'],)\n\tdbCursor.execute(sql, val)\n\tres2 = dbCursor.fetchall()\n\tdbCursor.close()\n\tif res:\n\t\treturn render_template('staff/clientInfo.html',staffClientQuery=True, clientInfo=res, staffClientIns=res2)\n\telse:\n\t\treturn render_template('staff/clientInfo.html',staffClientQuery=False)\n\n@staff.route(\"/viewStaffInsurance\", methods = ['POST'])\ndef viewInsurance():\n\tif not(userLoggedIn() and userType('employee')):\n\t\treturn\n\tdbCursor = db.cursor()\n\tsql = \"SELECT c.client_name, c.client_ID,c.agent_ID,ins_type, \" \\\n\t\"policy_name,coverage_amt, ppm, start_date, DATE_ADD(start_date, INTERVAL duration YEAR), \"\\\n\t\"dues, Unique_Ins_id FROM clients c, insurances i, policies p WHERE \"\\\n\t\"i.Unique_Ins_id = %s AND c.client_ID=i.client_ID AND p.policy_key=i.policy_key\"\n\tval = (request.form['insID'],)\n\tdbCursor.execute(sql, val)\n\tres = dbCursor.fetchone()\n\tdbCursor.close()\n\tif res:\n\t\treturn render_template('staff/insurance.html',staffInsQuery=True, insInfo=res)\n\telse:\n\t\treturn render_template('staff/insurance.html',staffInsQuery=False)\n\t\n\t", "sub_path": "MainApp/app/staff.py", "file_name": "staff.py", "file_ext": "py", "file_size_in_byte": 3067, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.Blueprint", "line_number": 5, "usage_type": "call"}, {"api_name": "auth.userLoggedIn", "line_number": 11, "usage_type": "call"}, {"api_name": "auth.userType", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "auth.userLoggedIn", "line_number": 18, "usage_type": "call"}, {"api_name": "auth.userType", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "auth.userLoggedIn", "line_number": 24, "usage_type": "call"}, {"api_name": "auth.userType", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "auth.userLoggedIn", "line_number": 31, "usage_type": "call"}, {"api_name": "auth.userType", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 43, "usage_type": "name"}, {"api_name": "auth.userLoggedIn", "line_number": 47, "usage_type": "call"}, {"api_name": "auth.userType", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "auth.userLoggedIn", "line_number": 69, "usage_type": "call"}, {"api_name": "auth.userType", "line_number": 69, "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.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "517132870", "text": "from django.test import LiveServerTestCase\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import WebDriverException\nfrom django.contrib.auth.models import User\nimport time\n\nMAX_WAIT = 10\n\nclass EditCVTest (LiveServerTestCase):\n\n\tdef setUp (self):\n\t\tself.browser = webdriver.Firefox()\n\t\t\t\n\t\t#login\n\t\tUser.objects.create_user('dan', 'dan.turner.djt@gmail.com', 'test')\n\t\tself.client.login (username='dan', password='test')\n\t\tcookie = self.client.cookies['sessionid']\n\t\tself.browser.get(self.live_server_url + '/admin/') #selenium will set cookie domain based on current page domain\n\t\tself.browser.add_cookie({'name': 'sessionid', 'value': cookie.value, 'secure': False, 'path': '/'})\n\t\tself.browser.refresh() #need to update page for logged in user\n\t\tself.browser.get(self.live_server_url + '/admin/')\n\n\tdef tearDown (self):\n\t\tself.browser.quit()\n\n\n\n\tdef test_can_add_an_entry (self):\n\t\t# Dan wants to visit his website to update his online CV. First he visits its home page\n\t\tself.browser.get(self.live_server_url)\n\t\t\n\n\t\t# He notices the CV page and naviagates to it, noticing that the new page title mentions CVs\n\t\tself.browser.find_element_by_link_text(\"CV\").click()\n\t\ttime.sleep (1)\n\t\theader_text = self.browser.find_element_by_class_name ('page-title').text\n\t\tself.assertIn ('CV', header_text)\n\n\t\t# He tries to create a new entry for the projects section\n\t\tplus_button = self.browser.find_element_by_css_selector ('span.glyphicon-plus')\n\t\tplus_button.click()\n\t\t\n\t\t\n\t\t# He selects projects from the section selection list\n\t\ttime.sleep (1)\n\t\tself.browser.find_element_by_xpath(\"//select[@id='id_section']/option[text()='Projects']\").click()\n\n\t\t# He types \"Website\" into the title box and \"Made a website\" into the text box \n\t\ttitleBox = self.browser.find_element_by_id('id_title')\n\t\ttitleBox.send_keys ('Website')\n\t\ttextBox = self.browser.find_element_by_id('id_text')\n\t\ttextBox.send_keys ('Made a website')\n\n\t\t#Finally he preses the \"Save\" button to submit what he has entered\n\t\tsave_button = self.browser.find_element_by_class_name ('btn-default')\n\t\tsave_button.click()\n\t\t\n\t\t# After submitting, he sees that the page has redirected to the drafts list page where he see can what he just submitted\n\t\ttime.sleep(1)\n\t\tentry = self.browser.find_element_by_css_selector ('div.entry')\n\t\ttitle_text = entry.find_element_by_tag_name('h2').text\n\t\ttext_text = entry.find_element_by_tag_name('p').text\n\t\tself.assertIn ('Website', title_text)\n\t\tself.assertIn ('Made a website', text_text)\n\t\t\n\t\t\n\t\t# Looking at it, he decides to be a little more specific, so clicks on the edit button in order to expand on it\n\t\tedit_button = self.browser.find_element_by_css_selector ('span.glyphicon-pencil')\n\t\tedit_button.click()\n\n\t\t\n\t\t# He adds \"using django\" into the text box and presses save again\n\t\ttime.sleep (1)\n\t\ttextBox = self.browser.find_element_by_id('id_text')\n\t\ttextBox.send_keys (' using django')\n\n\t\tsave_button = self.browser.find_element_by_class_name ('btn-default')\n\t\tsave_button.click()\n\n\t\t\t\n\t\t#Back on the drafts list page, he can see that his entry has been updated with the extra text he entered\n\t\ttime.sleep(1)\n\t\tentry = self.browser.find_element_by_css_selector ('div.entry')\n\t\ttext_text = entry.find_element_by_tag_name('p').text\n\t\tself.assertIn ('Made a website using django', text_text)\n\n\n\t\t#Happy with his entry, he presses the \"Publish\" button to publish it on the public page\n\t\tpublish_button = self.browser.find_element_by_link_text ('Publish')\n\t\tpublish_button.click()\t\t\n\n\n\t\t# After publishing, he returns to the main CV page where he can see his published entry\n\t\ttime.sleep (1)\n\t\tself.browser.find_element_by_link_text(\"CV\").click()\n\t\ttime.sleep (1)\n\t\tentry = self.browser.find_element_by_css_selector ('div.entry')\n\t\ttext_text = entry.find_element_by_tag_name('p').text\n\t\tself.assertIn ('Made a website using django', text_text)\n\n\n\t\t# He decides to add another entry, this time to the education section, so presses the plus button, selects education from the section list, types in the details, save it, and submits it like last time\n\t\tplus_button = self.browser.find_element_by_css_selector ('span.glyphicon-plus')\n\t\tplus_button.click()\n\t\ttime.sleep (1)\n\t\tself.browser.find_element_by_xpath(\"//select[@id='id_section']/option[text()='Education']\").click()\n\t\ttitleBox = self.browser.find_element_by_id('id_title')\n\t\ttitleBox.send_keys ('Nursery')\n\t\ttextBox = self.browser.find_element_by_id('id_text')\n\t\ttextBox.send_keys ('I went to nursery')\n\t\tsave_button = self.browser.find_element_by_class_name ('btn-default')\n\t\tsave_button.click()\n\t\ttime.sleep (1)\n\t\tpublish_button = self.browser.find_element_by_link_text ('Publish')\n\t\tpublish_button.click()\n\t\t\t\n\t\t\n\t\t# After publishing, he returns to the main CV page where he can see both the first entry and his new second published entry\n\t\ttime.sleep (1)\n\t\tself.browser.find_element_by_link_text(\"CV\").click()\n\t\ttime.sleep (1)\n\t\tentries = self.browser.find_elements_by_css_selector ('div.entry')\n\t\tself.assertIn ('nursery', entries[0].find_element_by_tag_name('p').text)\n\t\tself.assertIn ('website', entries[1].find_element_by_tag_name('p').text)\n\n\n\t\t#He decides that he doesn't like his new entry, and wants to delete, so he goes to the edit-list, clicks the entry and then clicks the delete button\n\t\tedit_list_button = self.browser.find_element_by_css_selector ('span.glyphicon-edit')\n\t\tedit_list_button.click()\n\t\ttime.sleep (1)\n\t\tself.browser.find_element_by_link_text(\"Nursery\").click()\n\t\ttime.sleep (1)\n\t\t\n\t\tedit_button = self.browser.find_element_by_css_selector ('span.glyphicon-remove')\n\t\tedit_button.click()\n\t\t\n\n\t\t# Returning to the main list page, he can see that the only entry still there is his orignal entry\n\t\ttime.sleep (1)\n\t\tentries = self.browser.find_elements_by_css_selector ('div.entry')\n\t\tself.assertNotIn ('nursery', entries[0].find_element_by_tag_name('p').text)\n\t\tself.assertIn ('website', entries[0].find_element_by_tag_name('p').text)\n\t\t\n\n\n\n\n", "sub_path": "functional_tests/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 5988, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.test.LiveServerTestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 95, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 120, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 133, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "114618725", "text": "#import library\nimport logging\nimport logging.handlers\nfrom datetime import datetime\nfrom decouple import config\n\n\nclass logs:\n\n # file logs\n def file_logs(self, logggername):\n # create log file\n log_file = config('dockerlogfiles')+'{}'.format(datetime.now().strftime('%Y%m%d')+'.log')\n\n # create logger console\n logger = logging.getLogger(logggername)\n if not logger.hasHandlers():\n logger.setLevel(logging.DEBUG)\n\n # create console handler and set level to debug\n handler = logging.handlers.RotatingFileHandler(\n filename=log_file, maxBytes=12000000, backupCount=10)\n\n # create formatter\n formatter = logging.Formatter(\n \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", \"%Y-%m-%d %H:%M:%S\")\n handler.setFormatter(formatter)\n\n # add handler\n logger.addHandler(handler)\n\n return logger\n", "sub_path": "infrastructure/utilities/app_logs.py", "file_name": "app_logs.py", "file_ext": "py", "file_size_in_byte": 960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "decouple.config", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "433134217", "text": "#coding=utf-8\r\nimport os\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom osgeo import gdal\r\n\r\ndef output_tiff(df, val_index, nrow, ncol, nodata_value):\r\n data = np.zeros((nrow, ncol))\r\n data[:] = nodata_value\r\n for index, row in df.iterrows():\r\n pos = str(int(row[0]))\r\n irow = int(pos[:-3])\r\n icol = int(pos[-3:])\r\n data[irow][icol] = row[val_index]\r\n \r\n file_name = r'F:\\crop-climate\\TIFF\\maize_polyfit_%d.tiff' % (val_index,)\r\n #file_name = 'maize_decade_%d.tiff' % (val_index,)\r\n if os.path.exists(file_name):\r\n os.remove(file_name)\r\n driver = gdal.GetDriverByName(\"GTiff\")\r\n ds_out = driver.Create(file_name, ncol, nrow, 1, gdal.GDT_Float32)#1是波段数\r\n band_out = ds_out.GetRasterBand(1)\r\n band_out.SetNoDataValue(nodata_value)\r\n band_out.WriteArray(data)\r\n ds_out.FlushCache()\r\n ds_out = None\r\n\r\nif __name__ == '__main__':\r\n nrow, ncol = 360, 720\r\n nodata_value = -9999\r\n\r\n csvfile = r'F:\\crop-climate\\regression\\mlr\\polyfit-additive.csv'\r\n df = pd.read_csv(csvfile,index_col=False)\r\n\r\n for val_index in [2,3,4]:\r\n print(val_index)\r\n output_tiff(df, val_index, nrow, ncol, nodata_value)\r\n\r\n\r\n", "sub_path": "csv_to_grid_linear.py", "file_name": "csv_to_grid_linear.py", "file_ext": "py", "file_size_in_byte": 1208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.zeros", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 19, "usage_type": "call"}, {"api_name": "osgeo.gdal.GetDriverByName", "line_number": 20, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 20, "usage_type": "name"}, {"api_name": "osgeo.gdal.GDT_Float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "osgeo.gdal", "line_number": 21, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "416375635", "text": "import time\nfrom PIL import Image\nimport hashlib\nimport numbers\n\nfrom google.cloud import pubsub_v1\n\nfrom typing import List\nfrom fastapi import APIRouter, Depends, UploadFile, File, Form, HTTPException\nfrom starlette.requests import Request\nfrom func_timeout import func_set_timeout\nfrom sqlalchemy.orm import Session\nfrom google.cloud import storage\n\n\nfrom image_upload import settings\nfrom image_upload.utils import fallback\nfrom image_upload.database import crud, models, schemas, get_db, engine\n\ntry:\n models.Base.metadata.create_all(bind=engine, checkfirst=True)\nexcept:\n pass\n\nrouter = APIRouter()\n\n\n# Instantiates a client\nstorage_client = storage.Client()\nbucket_name = \"super_skrivni_bozickov_zaklad\"\nbucket = storage_client.bucket(bucket_name)\n\n# Pub/sub.\npublisher = pubsub_v1.PublisherClient()\nsubscriber = pubsub_v1.SubscriberClient()\n\ntopic_name = 'projects/{project_id}/topics/{topic}'.format(\n project_id='forward-leaf-258910',\n topic='image_to_process',\n)\nsubscription_name = 'projects/{project_id}/subscriptions/{sub}'.format(\n project_id='forward-leaf-258910',\n sub='image-upload',\n)\n\n\ndef callback(message):\n print(message)\n\n tags = message.attributes[\"image_tags\"]\n image_id = message.attributes[\"image_id\"]\n\n crud.update_tags(db=next(get_db()), image_id=int(image_id), tags=tags)\n\n message.ack()\n\nfuture = subscriber.subscribe(subscription_name, callback)\n\n\n\n\n@router.post('/images', response_model=schemas.Image)\ndef upload(*, user_id: int = Form(...), file: UploadFile = File(...), db: Session = Depends(get_db)):\n try:\n Image.open(file.file)\n except:\n raise HTTPException(status_code=400, detail='Uploaded file is not an image.')\n\n if not isinstance(user_id, numbers.Number):\n raise HTTPException(status_code=400, detail='user_id is not a number.')\n\n # Get hash.\n file_hash = hashlib.sha1(file.filename.encode('utf-8')).hexdigest() + \".\" + file.filename.split(\".\")[-1]\n\n # Save to DB.\n new_image = crud.create_image(db=db, file_name=file.filename, file_hash=file_hash, user_id=user_id)\n iid = new_image.id\n\n # Upload to GC, append file ID to hash.\n file.file.seek(0)\n try:\n blob = bucket.blob(str(iid) + file_hash)\n blob.upload_from_file(file.file)\n except:\n crud.delete_image(db=db, image_id=iid)\n raise HTTPException(status_code=400, detail='Upload to gCloud failed.')\n\n # Send to image processor.\n url_r = str(iid) + file_hash\n url_l = \"https://storage.googleapis.com/super_skrivni_bozickov_zaklad/\"\n publisher.publish(topic_name, b'', image_id=str(iid), image_url=url_l + url_r)\n\n return new_image\n\n\n@router.delete('/images/{image_id}', response_model=schemas.Image)\ndef delete_image(image_id: int, db: Session = Depends(get_db)):\n db_image = crud.delete_image(db=db, image_id=image_id)\n if db_image is None:\n raise HTTPException(status_code=404, detail='Image not found')\n return db_image\n\n\n@router.get('/settings')\nasync def test_configs(request: Request):\n return {\"Config for X:\": f\"{settings.config_x}\", \"Config for Y:\": f\"{settings.config_y}\"}\n", "sub_path": "image_upload/routers/upload.py", "file_name": "upload.py", "file_ext": "py", "file_size_in_byte": 3132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "image_upload.database.models.Base.metadata.create_all", "line_number": 21, "usage_type": "call"}, {"api_name": "image_upload.database.models.Base", "line_number": 21, "usage_type": "attribute"}, {"api_name": "image_upload.database.models", "line_number": 21, "usage_type": "name"}, {"api_name": "image_upload.database.engine", "line_number": 21, "usage_type": "name"}, {"api_name": "fastapi.APIRouter", "line_number": 25, "usage_type": "call"}, {"api_name": "google.cloud.storage.Client", "line_number": 29, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 29, "usage_type": "name"}, {"api_name": "google.cloud.pubsub_v1.PublisherClient", "line_number": 34, "usage_type": "call"}, {"api_name": "google.cloud.pubsub_v1", "line_number": 34, "usage_type": "name"}, {"api_name": "google.cloud.pubsub_v1.SubscriberClient", "line_number": 35, "usage_type": "call"}, {"api_name": "google.cloud.pubsub_v1", "line_number": 35, "usage_type": "name"}, {"api_name": "image_upload.database.crud.update_tags", "line_number": 53, "usage_type": "call"}, {"api_name": "image_upload.database.crud", "line_number": 53, "usage_type": "name"}, {"api_name": "image_upload.database.get_db", "line_number": 53, "usage_type": "call"}, {"api_name": "fastapi.UploadFile", "line_number": 63, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 63, "usage_type": "name"}, {"api_name": "fastapi.Form", "line_number": 63, "usage_type": "call"}, {"api_name": "fastapi.File", "line_number": 63, "usage_type": "call"}, {"api_name": "fastapi.Depends", "line_number": 63, "usage_type": "call"}, {"api_name": "image_upload.database.get_db", "line_number": 63, "usage_type": "argument"}, {"api_name": "PIL.Image.open", "line_number": 65, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 65, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 67, "usage_type": "call"}, {"api_name": "numbers.Number", "line_number": 69, "usage_type": "attribute"}, {"api_name": "fastapi.HTTPException", "line_number": 70, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 73, "usage_type": "call"}, {"api_name": "image_upload.database.crud.create_image", "line_number": 76, "usage_type": "call"}, {"api_name": "image_upload.database.crud", "line_number": 76, "usage_type": "name"}, {"api_name": "image_upload.database.crud.delete_image", "line_number": 85, "usage_type": "call"}, {"api_name": "image_upload.database.crud", "line_number": 85, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 86, "usage_type": "call"}, {"api_name": "image_upload.database.schemas.Image", "line_number": 62, "usage_type": "attribute"}, {"api_name": "image_upload.database.schemas", "line_number": 62, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 97, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 97, "usage_type": "call"}, {"api_name": "image_upload.database.get_db", "line_number": 97, "usage_type": "argument"}, {"api_name": "image_upload.database.crud.delete_image", "line_number": 98, "usage_type": "call"}, {"api_name": "image_upload.database.crud", "line_number": 98, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 100, "usage_type": "call"}, {"api_name": "image_upload.database.schemas.Image", "line_number": 96, "usage_type": "attribute"}, {"api_name": "image_upload.database.schemas", "line_number": 96, "usage_type": "name"}, {"api_name": "starlette.requests.Request", "line_number": 105, "usage_type": "name"}, {"api_name": "image_upload.settings.config_x", "line_number": 106, "usage_type": "attribute"}, {"api_name": "image_upload.settings", "line_number": 106, "usage_type": "name"}, {"api_name": "image_upload.settings.config_y", "line_number": 106, "usage_type": "attribute"}]} +{"seq_id": "69389351", "text": "from BeautifulSoup import BeautifulSoup\nimport urllib2\nimport string\nimport json\n\ndef get_en_name (link) :\n str = string.split(link, '/')\n str = str[-1]\n return str\n\ncountries = ['Germany', 'Italy', 'Spain', 'Bulgaria', 'France']\nresult = []\n\n\ni = 0;\nfor country in countries :\n\n country_result = {}\n country_result['country_' + country] = country\n country_result['country_submission_' + country] = []\n\n page = urllib2.urlopen('http://redigo.ru/geo/Europe/' + country + '/cities')\n soup = BeautifulSoup(page)\n\n main_el = soup.findAll('div', {\"class\":\"cityList\"})\n\n districts_start = 50\n j = 0\n for child_node in main_el[0].contents :\n\n if (child_node.name == 'p'):\n continue\n\n try:\n\n if (child_node.attrMap['class'] != 'ym-grid cityItem'):\n print('Dist')\n\n if (child_node.attrMap['class'] == 'horizontal_vitrine_one') :\n continue\n\n districts_start = 1\n parent_region_el = child_node.findAll('a')\n parent_region_name = parent_region_el[0].text\n\n parent_region_link = get_en_name(parent_region_el[0].attrs[0][1])\n # parent_region_link = string.split(parent_region_link, '/')\n # parent_region_link = parent_region_link[-1]\n country_result[parent_region_link] = []\n j += 1\n # country_result['region_' + j] = parent_region_name\n\n else:\n if (districts_start == 50) :\n region_el = child_node.findAll('a', {\"class\":\"cityItem_title\"})\n region_name = region_el[0].text\n region_name_en = get_en_name(region_el[0].attrs[0][1])\n country_result['country_submission_' + country].append((region_name, country))\n elif (districts_start == 1) :\n region_el = child_node.findAll('a', {\"class\":\"cityItem_title\"})\n if (not region_el):\n continue\n region_name = region_el[0].text\n region_name_en = get_en_name(region_el[0].attrs[0][1])\n country_result[parent_region_link].append((region_name, parent_region_name, parent_region_link))\n # districts_start = 0\n print('ad')\n except:\n print ('error')\n continue\n\n result.append(country_result)\n\n i += 1\n\n# json_file = json.dumps(result)\nwith open('data.json', 'w') as outfile:\n json.dump(result, outfile)\n\ns = 0", "sub_path": "parsegeo.py", "file_name": "parsegeo.py", "file_ext": "py", "file_size_in_byte": 2597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "string.split", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 22, "usage_type": "call"}, {"api_name": "BeautifulSoup.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "405313362", "text": "from __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nimport os\n\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch import optim\n\nfrom dataset import ScalarDataset, build_dataset\nfrom model import Model, NoiseModel\nfrom mutual_information_estimator_discrete import MutualInformationEstimator\nfrom utils import UnivariateGaussian, UniformDataDistribution\n\n\ndef sample(m, num_samp=1000):\n X, _ = build_dataset(num_samp)\n output_noise, output = m(torch.from_numpy(X).float().to(device))\n return output_noise, output\n\n\ndef test_model(m, test_loader):\n avg_acc = 0\n num_batch = 0\n for _, (test_x, test_y) in enumerate(test_loader):\n test_out_noise, _ = m(test_x.to(device))\n avg_acc += compute_acc(test_out_noise.detach().cpu().numpy(), test_y.detach().cpu().numpy())\n num_batch += 1\n\n avg_acc /= num_batch\n assert num_batch == 1\n\n return avg_acc, test_out_noise, test_y\n\n\ndef compute_acc(model_out, true_out):\n # model_out = tanh values\n assert model_out.shape[0] == true_out.shape[0]\n model_out[model_out<=0] = -1\n model_out[model_out>0] = 1\n correct = np.sum((model_out==true_out).astype(int))\n return correct / true_out.shape[0]\n\n\nif __name__ == \"__main__\":\n # python train.py --epochs 200 --beta 0.05 --noise True --lr 0.001\n\n # Sample size\n N = 30\n\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--epochs', type=int, default=1000)\n parser.add_argument('--batch_size', type=int, default=25)\n parser.add_argument('--seed', type=int, default=0)\n parser.add_argument('--beta', type=float, default=0.05)\n parser.add_argument('--noise', action='store_true')\n parser.add_argument('--lr', type=float, default=0.001)\n parser.add_argument('--results_dir', type=str, default=\"\")\n args = parser.parse_args()\n\n # Results dir\n if args.results_dir == \"\":\n assert 0, \"Need results directory.\"\n if not os.path.exists(args.results_dir):\n os.makedirs(args.results_dir)\n print(\"Results directory:\", args.results_dir)\n\n # Use GPU or not\n cuda = torch.cuda.is_available()\n device = torch.device('cuda' if cuda else 'cpu')\n print(\"Using cuda:\", cuda)\n print(\"Device:\", device)\n # Noise or no noise\n print(\"Using noise model:\", args.noise)\n\n # Reproducibility\n torch.manual_seed(args.seed)\n np.random.seed(args.seed)\n\n # Learning rate\n print(\"Learning rate:\", args.lr)\n\n # Datasets\n train_dataset = ScalarDataset(N, test=False)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=False)\n\n test_dataset = ScalarDataset(N, test=False)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=N, shuffle=False)\n\n # Model\n if not args.noise:\n m = Model() # Default dim=1\n else:\n m = NoiseModel(beta=args.beta) # Add noise to layer\n m = m.to(device)\n\n # Optimizer\n optimizer = optim.SGD(m.parameters(), lr=args.lr)\n loss_func = nn.MSELoss()\n\n # Set up probability distributions\n noise_distr = UnivariateGaussian(0, np.square(args.beta))\n # The '4' is hard coded since the dataset is {-3,-1,1,3}\n data_distr = UniformDataDistribution(4) \n\n # Mutual information estimator\n mi = MutualInformationEstimator(\n m,\n device,\n noise_distr.compute_probability,\n data_distr.compute_probability,\n np.array([-3,-1,1,3]).reshape(4,1)\n )\n\n # Start the training\n accs = np.zeros((args.epochs,))\n mutual_info = np.zeros((args.epochs,))\n for i in xrange(args.epochs):\n if (i+1) % 1 == 0:\n m.eval()\n acc, test_out_noise, test_y = test_model(m, test_loader)\n accs[i] = acc\n #np.save(\"results/epoch_{}_outputs.npy\".format(i+1), test_out_noise.detach().cpu().numpy())\n\n # Get noise samples\n gen_noise_outputs, gen_outputs = sample(m, num_samp=1000)\n np.save(args.results_dir + \"/epoch_{}_outputs_noise.npy\".format(i+1), gen_noise_outputs.detach().cpu().numpy())\n\n # Compute MI\n curr_mutual_info = mi.compute_mutual_information(gen_outputs[\"output\"].detach().cpu().numpy(), 1000, \"output\")\n mutual_info[i] = curr_mutual_info\n\n print(\"Epoch {}; MI {}; Acc {}\".format(i+1, curr_mutual_info, acc))\n\n m.train()\n for batch_idx, (data_x, data_y) in enumerate(train_loader):\n data_x, data_y = data_x.to(device), data_y.to(device) \n\n output_noise, _ = m(data_x)\n loss = loss_func(output_noise, data_y)\n loss.backward()\n optimizer.step()\n\n\n # Diagnostics\n for param in m.parameters():\n print(\"Weight:\", param.data)\n\n new_set_x, new_set_y = build_dataset(10)\n model_y = np.tanh(6.9393*new_set_x - 14.2929)\n print(\"True out:\", new_set_y.T)\n print(\"Model out:\", model_y.T)\n new_set_x, new_set_y = build_dataset(10)\n print(\"Random out:\", new_set_x.T)\n print(\"Random out label:\", new_set_y.T)\n\n # Save stuff\n np.save(args.results_dir + \"/accuracies.npy\", accs)\n np.save(args.results_dir + \"/mutual_information.npy\", mutual_info)\n\n\n\n", "sub_path": "code/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 5246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "dataset.build_dataset", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 82, "usage_type": "attribute"}, {"api_name": "dataset.ScalarDataset", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 89, "usage_type": "attribute"}, {"api_name": "dataset.ScalarDataset", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 92, "usage_type": "attribute"}, {"api_name": "model.Model", "line_number": 96, "usage_type": "call"}, {"api_name": "model.NoiseModel", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "utils.UnivariateGaussian", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 106, "usage_type": "call"}, {"api_name": "utils.UniformDataDistribution", "line_number": 108, "usage_type": "call"}, {"api_name": "mutual_information_estimator_discrete.MutualInformationEstimator", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 131, "usage_type": "call"}, {"api_name": "dataset.build_dataset", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.tanh", "line_number": 154, "usage_type": "call"}, {"api_name": "dataset.build_dataset", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 163, "usage_type": "call"}]} +{"seq_id": "157627419", "text": "import matplotlib.pyplot as plt \r\nimport matplotlib.dates as mdates \r\nimport pandas as pd\r\nimport numpy as np\r\nfrom scipy import stats\r\nfrom datetime import datetime, timedelta\r\n\r\n# import data\r\ndf = pd.read_csv('C:/Users/usuari/Documents/idaea/MSY_2017/MSY_campaign_all_data.csv', sep=';')\r\n# subset days\r\n#df = df[(df['date'] >= '01/06/2017') & (df['date'] < '04/07/2017')]\r\n\r\ntime = np.asarray(pd.to_datetime(df['date'], format='%d/%m/%Y %H:%M'))\r\nt = np.asarray(df['T'])\r\nws = np.asarray(df['ws'])\r\nwd = np.asarray(df['wd'])\r\nrh = np.asarray(df['RH'])\r\nppt = np.asarray(df['Precipitation'])\r\np = np.asarray(df['Pressure'])\r\nso2 = np.asarray(df['SO2'])\r\nno = np.asarray(df['NO'])\r\nno2 = np.asarray(df['NO2'])\r\no3 = np.asarray(df['O3'])\r\nco = np.asarray(df['CO'])\r\npm10 = np.asarray(df['PM10'])\r\npm25 = np.asarray(df['PM2.5'])\r\npm1 = np.asarray(df['PM1'])\r\nbc = np.asarray(df['BC'])\r\nn25 = np.asarray(df['N2.5'])\r\nnsub25 = np.asarray(df['Nsub25'])\r\nn = np.asarray(df['N9-855'])\r\n\r\nnox=no+no2\r\nox=o3+no2\r\n\r\n\r\n#%% \r\n#Plots diaris 3 variables\r\ndate0=pd.to_datetime('12/06/2017', format='%d/%m/%Y')\r\ndate1=pd.to_datetime('31/07/2017', format='%d/%m/%Y')\r\ndd = pd.date_range(start=date0, end=date1, freq='D', tz='UTC')\r\n\r\nfor i in dd:\r\n l0 = i.strftime('%Y-%m-%d')\r\n d0 = datetime.strptime(l0, '%Y-%m-%d')\r\n d1 = d0 + timedelta(days=1)\r\n l1 = d1.strftime('%Y-%m-%d')\r\n\r\n fig, axarr = plt.subplots(3, sharex=True)\r\n axarr[0].plot(time, bc,linewidth=1)\r\n axarr[1].plot(time, nsub25,linewidth=1)\r\n axarr[2].plot(time, ws,linewidth=1)\r\n axarr[2].xaxis_date()\r\n \r\n axarr[2].set_xlim(['{}'.format(l0),'{}'.format(l1)])\r\n axarr[2].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\r\n axarr[0].set_yticks(np.arange(0, 1600, 500))\r\n \r\n axarr[2].set_xlabel(\"Time UTC\")\r\n axarr[0].set_ylabel(\"$BC\\ (ng\\ m^{-3})$\")\r\n axarr[1].set_ylabel(\"$N_{9-25}\\ (cm^{-3})$\")\r\n axarr[2].set_ylabel(\"$WS\\ (m\\ s^{-1})$\")\r\n plt.suptitle(l0)\r\n\r\n axarr[0].ticklabel_format(style='sci', axis='y', scilimits=(0,0))\r\n axarr[1].ticklabel_format(style='sci', axis='y', scilimits=(0,0))\r\n axarr[2].ticklabel_format(style='sci', axis='y', scilimits=(0,0))\r\n \r\n plt.show()\r\n\r\n plt.savefig('C:/Users/usuari/Documents/idaea/MSY_2017/BC/BC_daily/BC_Nsub25_WS_{}.png'.format(l0), dpi=300)\r\n plt.close()\r\n\r\n\r\nexit()\r\n\r\n#%% Scatter plots amb colors\r\n\r\nx=bc\r\ny=nsub25\r\n#reg. lineal\r\nmask = ~np.isnan(x) & ~np.isnan(y)\r\nslope, intercept, r_value, p_value, std_err = stats.linregress(x[mask],y[mask])\r\nline = slope*x+intercept\r\n\r\nplt.scatter(x,y,c=ws,s=10,lw=0,cmap='jet')\r\n#plt.plot(x,line, lw=0.5, c='r')\r\n#plt.scatter(bc,nsub25,s=10,lw=0)\r\ncb = plt.colorbar()\r\ncb.ax.set_title(\"$WS\\ (m\\ s^{-1})$\")\r\n#plt.xlabel(\"$T (°C)$\")\r\nplt.xlabel(\"$BC\\ (ng\\ m^{-3})$\")\r\n#plt.ylabel(\"$O_3\\ (\\mu g\\ m^{-3})$\")\r\n#plt.xlabel(\"$N_{2.5}\\ (cm^{-3})$\")\r\n#plt.ylabel(\"$O_3\\ (\\mu g\\ m^{-3})$\")\r\nplt.ylabel(\"$N_{9-25}\\ (cm^{-3})$\")\r\n\r\nexit()\r\n\r\n#%% \r\n \r\n#v = [t, pm1, n25, no2, o3, bc]\r\n#L=len(v)\r\n#fig, axarr = plt.subplots(L, sharex=True)\r\n#for i in range(0,len(v)):\r\n# axarr[i].plot(time, v[i], linewidth=1)\r\n# \r\n#axarr[L-1].xaxis_date()\r\n#fig.set_size_inches(12,6)\r\n#\r\n#\r\n#axarr[L-1].xaxis.set_minor_locator(mdates.HourLocator(byhour=range(0,24,12)))\r\n#axarr[L-1].xaxis.set_major_locator(mdates.DayLocator(interval=2))\r\n#axarr[L-1].xaxis.set_major_formatter(mdates.DateFormatter('%d%b'))\r\n#\r\n#exit()\r\n\r\n# Two subplots, the axes array is 1-d\r\nfig, axarr = plt.subplots(4, sharex=True)\r\nl=len(axarr)-1\r\naxarr[0].plot(time, t,linewidth=1)\r\naxarr[1].plot(time, o3,linewidth=1)\r\naxarr[2].plot(time, bc,linewidth=1)\r\naxarr[3].plot(time, n,linewidth=1)\r\n#axarr[4].plot(time, p, linewidth=1)\r\n#axarr[3].bar(time, ppt)\r\naxarr[l].xaxis_date()\r\n\r\nplt.show()\r\nfig.set_size_inches(12,6)\r\n\r\n\r\n\r\n###\r\naxarr[l].xaxis.set_minor_locator(mdates.HourLocator(byhour=range(0,24,6)))\r\naxarr[l].xaxis.set_major_locator(mdates.DayLocator(interval=2))\r\naxarr[l].xaxis.set_major_formatter(mdates.DateFormatter('%d%b'))\r\n\r\n#axarr[1].yaxis.tick_right()\r\n#axarr[1].yaxis.set_label_position(\"right\")\r\n#axarr[3].yaxis.tick_right()\r\n#axarr[3].yaxis.set_label_position(\"right\")\r\n#axarr[5].yaxis.tick_right()\r\n#axarr[5].yaxis.set_label_position(\"right\")\r\n\r\n#axarr[0].set_yticks(np.arange(15, 35+1, 10))\r\n#axarr[1].set_yticks(np.arange(25, 100+1, 25))\r\n#axarr[2].set_yticks(np.arange(0, 6+1, 2))\r\n##axarr[3].set_yticks(np.arange(0,360+1,90))\r\n##axarr[4].set_yticks(np.arange(930,950+1,10))\r\n#axarr[3].set_yticks(np.arange(0,8+1,2))\r\naxarr[l].set_xlim(['2017-06-30 00:00:00','2017-07-23 00:00:00'])\r\n\r\naxarr[l].set_xlabel(\"Date\")\r\naxarr[1].set_ylabel(r'$O_3\\ (\\mu g\\ m^{-3})$')\r\naxarr[2].set_ylabel(\"$BC\\ (ng\\ m^{-3})$\")\r\n#axarr[0].set_ylabel(r'$N_{9-25} (cm^{-3})$')\r\naxarr[0].set_ylabel('T (°C)')\r\n#axarr[3].set_ylabel(\"WD (°)\")\r\n#axarr[4].set_ylabel(\"P (hPa)\")\r\naxarr[3].set_ylabel(r'$N_{9-885}\\ (cm^{-3})$')\r\n\r\n#make subplots close to each other and hide x ticks for all but bottom plot\r\n#fig.subplots_adjust(hspace=0)\r\n#plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)\r\n\r\nplt.savefig('C:/Users/usuari/Documents/idaea/MSY_2017/20170710-20170715_o3_bc_nsub25_N.tiff', dpi=500)", "sub_path": "plots.py", "file_name": "plots.py", "file_ext": "py", "file_size_in_byte": 5192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 82, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.dates.HourLocator", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.dates.DayLocator", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}]} +{"seq_id": "85856481", "text": "#!/usr/bin/env python3\nimport os\nimport subprocess\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor, optim\nimport torch.distributions as D\nfrom combinators.stochastic import Trace, RandomVariable\nfrom typing import Callable, Any, Tuple, Optional, Set\nfrom copy import deepcopy\nfrom typeguard import typechecked\nfrom combinators.out import Out\nfrom combinators.program import check_passable_kwarg, Out\nimport combinators.tensor.utils as tensor_utils\nimport combinators.trace.utils as trace_utils\nimport inspect\n\n\ndef save_models(models, filename, weights_dir=\"./weights\")->None:\n checkpoint = {k: v.state_dict() for k, v in models.items()}\n\n if not os.path.exists(weights_dir):\n os.makedirs(weights_dir)\n\n torch.save(checkpoint, f'{weights_dir}/{filename}')\n\n\ndef load_models(model, filename, weights_dir=\"./weights\", **kwargs)->None:\n path = os.path.normpath(f'{weights_dir}/{filename}')\n\n checkpoint = torch.load(path, **kwargs)\n\n {k: v.load_state_dict(checkpoint[k]) for k, v in model.items()}\n\n\ndef models_as_dict(model_iter, names):\n \"\"\" (for annealing) given a list of list of targets and kernels -- flatten for save_models and load_models, above \"\"\"\n assert isinstance(model_iter, (tuple, list)) or all(map(lambda ms: isinstance(ms, (tuple,list)), model_iter.values())), \"takes a list or dict of lists\"\n assert len(names) == len(model_iter), 'names must exactly align with model lists'\n\n model_dict = dict()\n for i, (name, models) in enumerate(zip(names, model_iter) if isinstance(model_iter, (tuple, list)) else model_iter.items()):\n for j, m in enumerate(models):\n model_dict[f'{str(i)}_{name}_{str(j)}'] = m\n return model_dict\n\n\ndef adam(models, **kwargs):\n \"\"\" Adam for dicts or iterables of models \"\"\"\n iterable = models.values() if isinstance(models, dict) else models\n return optim.Adam([dict(params=x.parameters()) for x in iterable], **kwargs)\n\n\ndef git_root()->str:\n \"\"\" print the root of the project \"\"\"\n return subprocess.check_output('git rev-parse --show-toplevel', shell=True).decode(\"utf-8\").rstrip()\n\n\ndef ppr_show(a:Any, m:str='dv', debug:bool=False, **kkwargs:Any):\n \"\"\" show instance of a prettified object \"\"\"\n if debug:\n print(type(a))\n if isinstance(a, Tensor):\n return tensor_utils.show(a)\n elif isinstance(a, D.Distribution):\n return trace_utils.showDist(a)\n elif isinstance(a, list):\n return \"[\" + \", \".join(map(ppr_show, a)) + \"]\"\n elif isinstance(a, (Trace, RandomVariable)):\n args = []\n kwargs = dict()\n if m is not None:\n if 'v' in m or m == 'a':\n args.append('value')\n if 'p' in m or m == 'a':\n args.append('log_prob')\n if 'd' in m or m == 'a':\n kwargs['dists'] = True\n showinstance = trace_utils.showall if isinstance(a, Trace) else trace_utils.showRV\n if debug:\n print(\"showinstance\", showinstance)\n print(\"args\", args)\n print(\"kwargs\", kwargs)\n return showinstance(a, args=args, **kwargs, **kkwargs)\n elif isinstance(a, Out):\n print(f\"got type {type(a)}, guessing you want the trace:\")\n return ppr_show(a.trace)\n elif isinstance(a, dict):\n return repr({k: ppr_show(v) for k, v in a.items()})\n else:\n return repr(a)\n\n\ndef ppr(a:Any, m='dv', debug=False, desc='', **kkwargs):\n \"\"\" a pretty printer that relies ppr_show \"\"\"\n print(desc, ppr_show(a, m=m, debug=debug, **kkwargs))\n", "sub_path": "combinators/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 51, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 56, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 63, "usage_type": "argument"}, {"api_name": "combinators.tensor.utils.show", "line_number": 64, "usage_type": "call"}, {"api_name": "combinators.tensor.utils", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.distributions.Distribution", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.distributions", "line_number": 65, "usage_type": "name"}, {"api_name": "combinators.trace.utils.showDist", "line_number": 66, "usage_type": "call"}, {"api_name": "combinators.trace.utils", "line_number": 66, "usage_type": "name"}, {"api_name": "combinators.stochastic.Trace", "line_number": 69, "usage_type": "name"}, {"api_name": "combinators.stochastic.RandomVariable", "line_number": 69, "usage_type": "name"}, {"api_name": "combinators.stochastic.Trace", "line_number": 79, "usage_type": "argument"}, {"api_name": "combinators.trace.utils.showall", "line_number": 79, "usage_type": "attribute"}, {"api_name": "combinators.trace.utils", "line_number": 79, "usage_type": "name"}, {"api_name": "combinators.trace.utils.showRV", "line_number": 79, "usage_type": "attribute"}, {"api_name": "combinators.program.Out", "line_number": 85, "usage_type": "argument"}, {"api_name": "typing.Any", "line_number": 94, "usage_type": "name"}]} +{"seq_id": "288368885", "text": "# -*- coding:utf-8 -*-\n'''\nCreated on 2015年8月29日\n\n@author: jiansong\n'''\nimport datetime\nimport pytz\n\n# local = pytz.country_timezones('cn')[0]\nlocal = u'Asia/Shanghai'\nlocal_tz = pytz.timezone(local)\nutc_tz = pytz.timezone('UTC')\ndef utc2local(str_utc_time):\n utc_time = datetime.datetime.strptime(str_utc_time[0:19], '%Y-%m-%d %H:%M:%S')\n\n utc_time = utc_time.replace(tzinfo=utc_tz)\n local_time = utc_time.astimezone(local_tz)\n\n return local_time.strftime('%Y-%m-%d %H:%M:%S')\n\ndef local2utc(str_local_time):\n local_time = datetime.datetime.strptime(str_local_time[0:19], '%Y-%m-%d %H:%M:%S')\n\n local_time = local_tz.localize(local_time, is_dst=None)\n utc_time = local_time.astimezone(utc_tz)\n\n return utc_time.strftime('%Y-%m-%d %H:%M:%S')\n\n#'U'代表utc,'L'代表当地 tz表示传入格式,return_tz表示返回格式\ndef get_today_margin(time_str,tz='U',return_tz ='U'):\n if tz == 'U':\n time_str = utc2local(time_str)\n start_time = '%s 00:00:00'%time_str[:10]\n end_time = '%s 23:59:59'%time_str[:10]\n return {\n 'start_time':local2utc(start_time) if return_tz == 'U' else start_time,\n 'end_time':local2utc(end_time) if return_tz == 'U' else end_time\n }\n\n", "sub_path": "test/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1238, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pytz.timezone", "line_number": 12, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "448616209", "text": "import math\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions import Independent\nfrom torch.distributions import Normal\n\nfrom .module import Module\nfrom .init import ortho_init\n\n\nclass DiagGaussianHead(Module):\n r\"\"\"Defines a module for a diagonal Gaussian (continuous) action distribution which\n the standard deviation is state independent. \n \n The network outputs the mean :math:`\\mu(x)` and the state independent logarithm of variance \n :math:`\\log\\sigma^2` (allowing to optimize in log-space, i.e. both negative and positive). \n \n There are several options for modelling the variance term:\n \n * std_style='exp': regard raw features as log-variance, and variance is obtained by applying\n exponential function :math:`\\epsilon + \\exp(x)` where :math:`\\epsilon=1e-4` is a lower bound to \n avoid numerical instability, e.g. producing ``NaN``. \n \n * std_style='softplus': the variance is obtained by applying softplus function\n :math:`\\epsilon + \\log(1 + \\exp(x))` where :math:`\\epsilon=1e-4` is a lower bound to \n avoid numerical instability, e.g. producing ``NaN``. \n \n * std_style='sigmoidal': the variance is obtained by applying sigmoidal function\n :math:`\\sigma(x, \\beta) = \\frac{1}{1 + \\beta\\exp(-x)}` where :math:`\\beta` is a\n scaling coefficient. And the function is bounded above and below by applying\n the transformation :math:`\\min + (\\max - \\min)\\sigma(x, \\beta)`\n \n Then the standard deviation is obtained by taking the square root. \n \n Example:\n \n >>> import torch\n >>> action_head = DiagGaussianHead(10, 4, 'cpu', 0.45, 'sigmoidal', [0.01, 1.0], 1.0)\n >>> action_dist = action_head(torch.randn(2, 10))\n >>> action_dist.base_dist\n Normal(loc: torch.Size([2, 4]), scale: torch.Size([2, 4]))\n >>> action_dist.base_dist.stddev\n tensor([[0.4500, 0.4500, 0.4500, 0.4500],\n [0.4500, 0.4500, 0.4500, 0.4500]], grad_fn=)\n \n Args:\n feature_dim (int): number of input features\n action_dim (int): flat dimension of actions\n device (torch.device): PyTorch device\n std0 (float): initial standard deviation\n std_style (str): specifies the transformation mapping from raw features to variance:\n ['exp', 'softplus', 'sigmoidal']. Note that except for 'sigmoidal', both :attr:`std_range` \n and :attr:`beta` should be ``None``.\n std_range (tuple/list, optional): lower and upper bound for sigmoid function\n beta (float, optional): scaling coefficient of sigmoid function\n **kwargs: keyword arguments for more specifications.\n \n \"\"\"\n def __init__(self, \n feature_dim, \n action_dim, \n device, \n std0,\n std_style, \n std_range=None, \n beta=None, \n **kwargs):\n super().__init__(**kwargs)\n \n assert std0 > 0\n assert std_style in ['exp', 'softplus', 'sigmoidal']\n if std_style == 'sigmoidal':\n assert len(std_range) == 2\n assert std_range[0] > 0 and std_range[1] > 0\n assert std_range[0] < std_range[1]\n assert std0 >= std_range[0] and std0 <= std_range[1]\n assert beta is not None and isinstance(beta, float)\n else:\n assert std_range is None, f'for std_style!=sigmoidal, expected None, got {std_range}'\n assert beta is None, f'for std_style!=sigmoidal, expected None, got {beta}'\n \n self.feature_dim = feature_dim\n self.action_dim = action_dim\n self.device = device\n self.std0 = std0\n self.std_style = std_style\n self.std_range = std_range\n self.beta = beta\n \n self.eps = 1e-4 # used for default min-variance to avoid numerical instability\n \n self.mean_head = nn.Linear(self.feature_dim, self.action_dim)\n # 0.01 -> almost zeros initially\n ortho_init(self.mean_head, weight_scale=0.01, constant_bias=0.0)\n self.logvar_head = nn.Parameter(torch.full((self.action_dim,), \n self._get_logvar0(self.std0), \n requires_grad=True))\n \n self.to(self.device)\n \n def forward(self, x):\n mean = self.mean_head(x)\n logvar = self.logvar_head.expand_as(mean)\n var = self._get_var(logvar)\n std = torch.sqrt(var)\n action_dist = Independent(Normal(loc=mean, scale=std), 1)\n return action_dist\n \n def _get_logvar0(self, std0):\n var0 = std0**2\n if self.std_style == 'exp':\n return math.log(var0 - self.eps)\n elif self.std_style == 'softplus':\n return math.log(math.exp(var0 - self.eps) - 1)\n else: # bounded beta-sigmoid\n min_std, max_std = self.std_range\n min_var = min_std**2\n max_var = max_std**2\n x = (var0 - min_var)/(max_var - min_var)\n x = float(np.clip(x, 1e-4, 0.9999)) # avoid too large +/- values\n x = -math.log((1/x - 1)/self.beta)\n return x\n \n def _get_var(self, logvar):\n if self.std_style == 'exp':\n return self.eps + torch.exp(logvar)\n elif self.std_style == 'softplus':\n return self.eps + F.softplus(logvar)\n else: # bounded beta-sigmoid\n var = 1/(1 + self.beta*torch.exp(-logvar))\n min_std, max_std = self.std_range\n min_var = min_std**2\n max_var = max_std**2\n return min_var + (max_var - min_var)*var\n", "sub_path": "lagom/networks/diag_gaussian_head.py", "file_name": "diag_gaussian_head.py", "file_ext": "py", "file_size_in_byte": 5776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "module.Module", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "init.ortho_init", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.full", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.distributions.Independent", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.distributions.Normal", "line_number": 109, "usage_type": "call"}, {"api_name": "math.log", "line_number": 115, "usage_type": "call"}, {"api_name": "math.log", "line_number": 117, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 123, "usage_type": "call"}, {"api_name": "math.log", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "578004505", "text": "#!/usr/bin/python\n\nimport os\nimport re\nimport csv\nimport unittest\nimport tempfile\n\nfrom vcf import Vcf, VcfConverter\nfrom vcf.test.base_test import BaseTestCase\n\ninclude = True\n\nclass SingleAllelesTestCase(BaseTestCase):\n TEST_DIR = os.path.dirname(os.path.abspath(__file__))\n REFERENCE_VCF_DIRECTORY = os.path.join(TEST_DIR, 'reference_vcfs')\n CONVERTED_REF_VCF_DIRECTORY = None\n\n tests = []\n setup_called = False\n\n @classmethod\n def create_tests(cls):\n for test_file in os.listdir(cls.CONVERTED_REF_VCF_DIRECTORY):\n if test_file.endswith('.tsv'):\n testmethodname = 'test_{filename}'.format(filename=test_file)\n test_func = cls.factory(test_file, cls.CONVERTED_REF_VCF_DIRECTORY)\n setattr(cls, testmethodname, test_func)\n cls.tests.append(test_func)\n\n @classmethod\n def setUpClass(cls):\n super(SingleAllelesTestCase, cls).setUpClass()\n if not cls.setup_called:\n cls.CONVERTED_REF_VCF_DIRECTORY = os.path.join(cls.OUTPUT_DIR, 'converted_reference_vcfs')\n os.makedirs(cls.CONVERTED_REF_VCF_DIRECTORY)\n cls.convert_vcfs_in_directory(cls.REFERENCE_VCF_DIRECTORY, cls.CONVERTED_REF_VCF_DIRECTORY)\n cls.create_tests()\n cls.setup_called = True\n\n @classmethod\n def tearDownClass(cls):\n cls.remove_tsvs_in_directory(cls.CONVERTED_REF_VCF_DIRECTORY)\n\n @staticmethod\n def remove_tsvs_in_directory(directory):\n for tsv_file in os.listdir(directory):\n if tsv_file.endswith('.tsv'):\n os.remove(os.path.join(directory, tsv_file))\n\n def verify_converter_output(self, reference_filename, reference_directory):\n with open(os.path.join(reference_directory, reference_filename), 'r') as reference_file:\n \n alt_index = re.findall('\\d+', reference_filename)[0]\n expected_genotype = \"0/\" + alt_index\n\n csv_reader = csv.DictReader(reference_file, delimiter='\\t')\n for line in csv_reader:\n if line['FORMAT.1.GT'] not in [\"./.\",\"0/0\"]:\n if line['FORMAT.1.GT'] != expected_genotype:\n raise RuntimeError('Unexpected genotype found.')\n elif line['ALT.idx'] == alt_index and line['ID'] != \".\":\n if line['FUNC1.oncomineGeneClass'] not in ['Gain-of-Function','Loss-of-Function']:\n raise RuntimeError('ERROR: Expected OVAT Annotation not present.')\n return True\n\n @staticmethod\n def convert_vcfs_in_directory(vcf_dir, tsv_dir):\n for vcf_file in os.listdir(vcf_dir):\n if vcf_file.endswith('.vcf'):\n vcf_object = Vcf.create_from_vcf_file(os.path.join(vcf_dir, vcf_file))\n converter = VcfConverter()\n records = converter.convert(vcf_object, keep_all_genes=True)\n converter.write_to_file(\n os.path.join(tsv_dir, os.path.basename(vcf_object.file_name).replace('.vcf', '.tsv')),\n records,\n vcf_object.metadata_raw,\n [])\n\n @classmethod\n def factory(cls, reference_filename, reference_directory):\n def test(self):\n self.assertTrue(self.verify_converter_output(reference_filename, reference_directory))\n return test\n\ndef suite():\n SingleAllelesTestCase.setUpClass()\n return unittest.makeSuite(SingleAllelesTestCase, 'test')\n\nif __name__ == '__main__':\n SingleAllelesTestCase.setUpClass()\n unittest.main()\n", "sub_path": "oncomine-vcf-converter-1.5.1/vcf/test/test_single_alleles.py", "file_name": "test_single_alleles.py", "file_ext": "py", "file_size_in_byte": 3584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "vcf.test.base_test.BaseTestCase", "line_number": 14, "usage_type": "name"}, {"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.listdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 54, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 57, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 69, "usage_type": "call"}, {"api_name": "vcf.Vcf.create_from_vcf_file", "line_number": 71, "usage_type": "call"}, {"api_name": "vcf.Vcf", "line_number": 71, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "vcf.VcfConverter", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 75, "usage_type": "call"}, {"api_name": "unittest.makeSuite", "line_number": 88, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "559435216", "text": "\"\"\"Language encoder for dictionary definitions.\n\nFor training, takes (target-word, dictionary-definition) pairs and\noptimises the encoder to produce a single vector for each definition\nwhich is close to the vector for the corresponding target word.\n\nThe definitions encoder can be either a bag-of-words or an RNN model.\n\nThe vectors for the target words, and the words making up the\ndefinitions, can be either pre-trained or learned as part of the\ntraining process.\n\nSometimes the definitions are referred to as \"glosses\", and the target\nwords as \"heads\".\n\nInspiration from Tensorflow documentation (www.tensorflow.org).\nThe data reading functions were taken from\nhttps://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport pickle\nimport os\nimport sys\nfrom time import time\nfrom datetime import datetime\nfrom collections import namedtuple\n\nfrom tqdm import tqdm\nimport numpy as np\nimport scipy.spatial.distance as dist\nimport tensorflow as tf\n\nimport data_utils\n\ntf.app.flags.DEFINE_integer(\"max_seq_len\", 20, \"Maximum length (in words) of a\"\n \"definition processed by the model\")\n\ntf.app.flags.DEFINE_integer(\"batch_size\", 128, \"batch size\")\n\ntf.app.flags.DEFINE_float(\"learning_rate\", 0.001,\n \"Learning rate applied in TF optimiser\")\n\ntf.app.flags.DEFINE_integer(\"lr_decay_epochs\", 10,\n \"How many epochs before decay_factor\")\n\ntf.app.flags.DEFINE_float(\"lr_decay_rate\", None,\n \"LR decay rate factor. Typical value might be 0.5 or 0.9\")\n\ntf.app.flags.DEFINE_float(\"grad_clip_val\", 5.0,\n \"Gradient clipping limit\")\n\ntf.app.flags.DEFINE_integer(\"embedding_size\", 500,\n \"Number of units in word representation.\")\n\ntf.app.flags.DEFINE_integer(\"vocab_size\", 100000, \"Number of words the model\"\n \"knows and stores representations for\")\n\ntf.app.flags.DEFINE_integer(\"num_epochs\", 500, \"Train for this number of\"\n \"sweeps through the training set\")\n\ntf.app.flags.DEFINE_string(\"data_dir\", \"../data/definitions/\", \"Directory for finding\"\n \"training data and dumping processed data.\")\n\ntf.app.flags.DEFINE_string(\"train_file\", \"train.definitions.ids100000\",\n \"File with dictionary definitions for training.\")\n\ntf.app.flags.DEFINE_string(\"dev_file\", \"'dev.definitions.ids100000\",\n \"File with dictionary definitions for dev testing.\")\n\ntf.app.flags.DEFINE_string(\"save_dir\", \"./models/part2\", \"Directory for saving model.\"\n \"If using restore=True, directory to restore from.\")\ntf.app.flags.DEFINE_string(\"exp_tag\", \"\",\"Experiment tag\")\n\ntf.app.flags.DEFINE_boolean(\"restore\", False, \"Restore a trained model\"\n \"instead of training one.\")\n\ntf.app.flags.DEFINE_boolean(\"evaluate\", False, \"Evaluate model (needs\"\n \"Restore==True).\")\n\ntf.app.flags.DEFINE_string(\"vocab_file\", None, \"Path to vocab file\")\n\ntf.app.flags.DEFINE_boolean(\"pretrained_target\", True,\n \"Use pre-trained embeddings for head words.\")\n\ntf.app.flags.DEFINE_boolean(\"pretrained_input\", False,\n \"Use pre-trained embeddings for gloss words.\")\n\ntf.app.flags.DEFINE_boolean(\"train_gloss_vocabulary\", False,\n \"Given a pretrained_input, allow gradients to flow into embedding matrix?\")\n\ntf.app.flags.DEFINE_string(\"embeddings_path\",\n \"../embeddings/GoogleWord2Vec.clean.normed.pkl\",\n \"Path to pre-trained (.pkl) word embeddings.\")\n\ntf.app.flags.DEFINE_string(\"glove_path\",\n \"../embeddings/glove.840B.300d.4.pkl\",\n \"Path to GloVe embeddings .pkl\")\n\ntf.app.flags.DEFINE_boolean(\"use_glove\", False, \"Conditional to select which embeddings to load [W2V or Glove]\")\n\ntf.app.flags.DEFINE_string(\"encoder_type\", \"recurrent\", \"BOW or recurrent.\")\n\ntf.app.flags.DEFINE_string(\"model_name\", \"recurrent\", \"BOW or recurrent.\")\n\ntf.app.flags.DEFINE_string(\"out_form\", \"cosine\", \"Type of output loss FOR EVAL ONLY\")\n\ntf.app.flags.DEFINE_string(\"optimizer\",\"Adam\", \"Type of optimizer for tf.train.optimize_loss \"\n \"['Adagrad','Adam','Ftrl','Momentum','RMSProp','SGD']\")\n\ntf.app.flags.DEFINE_float(\"lstm_dropout_prob\", 0.7, \"LSTM dropout probability\")\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.logging.set_verbosity(tf.logging.INFO)\n\ndef read_data(data_path, vocab_size, phase=\"train\"):\n \"\"\"Read data from gloss and head files.\n \n Args:\n data_path: path to the definition .gloss and .head files.\n vocab_size: total number of word types in the data.\n phase: used to locate definitions (train or dev).\n \n Returns:\n a tuple (gloss, head)\n where gloss is an np array of encoded glosses and head is an\n encoded array of head words; len(gloss) == len(head).\n \"\"\"\n glosses, heads = [], []\n \n gloss_path = os.path.join(\n data_path, \"%s.definitions.ids%s.gloss\" % (phase, vocab_size))\n \n head_path = os.path.join(\n data_path, \"%s.definitions.ids%s.head\" % (phase, vocab_size))\n \n tf.logging.info(\"Reading data file from %s...\" % data_path)\n \n with tf.gfile.GFile(gloss_path, mode=\"r\") as gloss_file:\n with tf.gfile.GFile(head_path, mode=\"r\") as head_file:\n gloss, head = gloss_file.readline(), head_file.readline()\n counter = 0\n while gloss and head:\n counter += 1\n if counter % 100000 == 0:\n tf.logging.info(\"-> Reading data line %d\" % counter)\n sys.stdout.flush()\n gloss_ids = np.array([int(x) for x in gloss.split()], dtype=np.int32)\n glosses.append(gloss_ids)\n heads.append(int(head))\n gloss, head = gloss_file.readline(), head_file.readline()\n tf.logging.info(\"Loaded %d gloss/head pairs...\" % counter)\n return np.asarray(glosses), np.array(heads, dtype=np.int32)\n\n\ndef load_pretrained_embeddings(embeddings_file_path):\n \"\"\"Loads pre-trained word embeddings.\n \n Args:\n embeddings_file_path: path to the pickle file with the embeddings.\n \n Returns:\n tuple of (dictionary of embeddings, length of each embedding).\n \"\"\"\n tf.logging.info(\"Loading pretrained embeddings from %s...\" % embeddings_file_path)\n \n # Open embedding dict\n with open(embeddings_file_path, \"rb\") as input_file:\n pre_embs_dict = pickle.load(input_file, encoding='bytes')\n \n iter_keys = iter(pre_embs_dict.keys())\n first_key = next(iter_keys)\n embedding_length = len(pre_embs_dict[first_key])\n \n tf.logging.info(\"Loaded %d embeddings; each embedding is length %d\" % (len(pre_embs_dict.keys()), embedding_length))\n\n return pre_embs_dict, embedding_length\n\n\ndef get_embedding_matrix(embedding_dict, vocab, emb_dim):\n tf.logging.info(\"Creating %d-dim embedding matrix for %d words\" % (emb_dim, len(vocab)))\n emb_matrix = np.random.normal(size=[len(vocab), emb_dim])\n rejected_words=0\n for word, ii in vocab.items():\n if word in embedding_dict:\n emb_matrix[ii] = embedding_dict[word]\n else:\n rejected_words+=1\n tf.logging.info(\"Rejected %d words\" % rejected_words)\n return np.asarray(emb_matrix)\n\n\ndef gen_batch(raw_data, batch_size):\n raw_x, raw_y = raw_data\n data_length = len(raw_x)\n num_batches = data_length // batch_size\n data_x, data_y = [], []\n for i in range(num_batches):\n data_x = raw_x[batch_size * i:batch_size * (i + 1)]\n data_y = raw_y[batch_size * i:batch_size * (i + 1)]\n yield (data_x, data_y)\n\n\ndef gen_epochs(data_path, total_epochs, batch_size, vocab_size, phase=\"train\"):\n # Read all of the glosses and heads into two arrays.\n raw_data = read_data(data_path, vocab_size, phase)\n # Return a generator over the data.\n for _ in range(total_epochs):\n yield gen_batch(raw_data, batch_size)\n\n\ndef build_model(max_seq_len, vocab_size, emb_size, learning_rate, encoder_type,\n pretrained_target=True, pretrained_input=False, pre_embs=None):\n \"\"\"Build the dictionary model including loss function.\n \n Args:\n max_seq_len: maximum length of gloss.\n vocab_size: number of words in vocab.\n emb_size: size of the word embeddings.\n learning_rate: learning rate for the optimizer.\n encoder_type: method of encoding (RRN or BOW).\n pretrained_target: Boolean indicating pre-trained head embeddings.\n pretrained_input: Boolean indicating pre-trained gloss word embeddings.\n pre_embs: pre-trained embedding matrix.\n \n Returns:\n tuple of (gloss_in, head_in, total_loss, train_step, output_form)\n \n Creates the embedding matrix for the input, which is split into the\n glosses (definitions) and the heads (targets). So checks if there are\n pre-trained embeddings for the glosses or heads, and if not sets up\n some trainable embeddings. The default is to have pre-trained\n embeddings for the heads but not the glosses.\n \n The encoder for the glosses is either an RNN (with LSTM cell) or a\n bag-of-words model (in which the word vectors are simply\n averaged). For the RNN, the output is the output vector for the\n final state.\n \n If the heads are pre-trained, the output of the encoder is put thro'\n a non-linear layer, and the loss is the cosine distance. Without\n pre-trained heads, a linear layer on top of the encoder output is\n used to predict logits for the words in the vocabulary, and the loss\n is cross-entropy.\n \"\"\"\n # Build the TF graph on the GPU.\n with tf.device(\"/device:GPU:0\"):\n tf.logging.info(\"Building device on GPU:0...\")\n tf.reset_default_graph()\n \n # Batch of input definitions (glosses).\n gloss_in = tf.placeholder(\n tf.int32, [None, max_seq_len], name=\"input_placeholder\")\n \n # Batch of the corresponding targets (heads).\n head_in = tf.placeholder(tf.int32, [None], name=\"labels_placeholder\")\n with tf.variable_scope(\"embeddings\"):\n if pretrained_input:\n assert pre_embs is not None, \"Must include pre-trained embedding matrix\"\n \n tf.logging.info(\"Using pre-trained embeddings, learning embeddings? %s \" % FLAGS.train_gloss_vocabulary)\n # embedding_matrix is pre-trained embeddings.\n embedding_matrix = tf.get_variable(\n name=\"inp_emb\",\n shape=[vocab_size, emb_size],\n initializer=tf.constant_initializer(pre_embs),\n trainable=FLAGS.train_gloss_vocabulary)\n tf.logging.info(\"Getting trainable embedding matrix for input...\")\n \n else:\n # embedding_matrix is learned.\n embedding_matrix = tf.get_variable(\n name=\"inp_emb\",\n shape=[vocab_size, emb_size])\n tf.logging.info(\"Creating trainable embedding matrix for input...\")\n \n # embeddings for the batch of definitions (glosses).\n embs = tf.nn.embedding_lookup(embedding_matrix, gloss_in)\n \n if pretrained_target:\n out_size = pre_embs.shape[-1]\n else:\n out_size = emb_size\n \n tf.logging.info(\"out_size is %d\" % out_size)\n \n # RNN encoder for the definitions.\n if encoder_type == \"recurrent\":\n cell = tf.nn.rnn_cell.LSTMCell(emb_size)\n cell = tf.contrib.rnn.DropoutWrapper(\n cell,\n input_keep_prob=FLAGS.lstm_dropout_prob,\n output_keep_prob=FLAGS.lstm_dropout_prob)\n \n # state is the final state of the RNN.\n _, state = tf.nn.dynamic_rnn(cell, embs, dtype=tf.float32)\n # state is a pair: (hidden_state, output)\n core_out = state[0]\n tf.logging.info(\"Building LSTM encoder...\")\n else:\n core_out = tf.reduce_mean(embs, axis=1)\n tf.logging.info(\"Building BOW encoder...\")\n \n # core_out is the output from the gloss encoder.\n output_form = \"cosine\"\n if pretrained_target:\n # Create a loss based on cosine distance for pre-trained heads.\n if pretrained_input:\n # Already have the pre-trained embedding matrix, so use that.\n out_emb_matrix = embedding_matrix\n tf.logging.info(\"Using the same non-trainable embedding matrix from input for output...\")\n else:\n # Target embeddings are pre-trained.\n out_emb_matrix = tf.get_variable(\n name=\"out_emb\",\n shape=[vocab_size, out_size],\n initializer=tf.constant_initializer(pre_embs),\n trainable=False)\n \n tf.logging.info(\"Getting trainable embedding matrix for output...\")\n \n # Put core_out through a final non-linear layer.\n core_out = tf.contrib.layers.fully_connected(\n core_out,\n out_size,\n activation_fn=tf.nn.relu)\n \n tf.logging.info(\"Encoder is passed through a fc layer to size %d...\" % out_size)\n \n # Embeddings for the batch of targets/heads.\n targets = tf.nn.embedding_lookup(out_emb_matrix, head_in)\n \n # cosine_distance assumes the arguments are unit normalized.\n tf.logging.info(\"L2 normalised cosine distance for loss...\")\n losses = tf.losses.cosine_distance(\n tf.nn.l2_normalize(targets, 1),\n tf.nn.l2_normalize(core_out, 1),\n dim=1)\n else:\n tf.logging.info(\"Do not use pretrained head word embeddings, creating softmax output...\")\n \n # Create a softmax loss when no pre-trained heads.\n out_emb_matrix = tf.get_variable(\n name=\"out_emb\", shape=[emb_size, vocab_size])\n logits = tf.matmul(core_out, out_emb_matrix)\n pred_dist = tf.nn.softmax(logits, name=\"predictions\")\n \n tf.logging.info(\"Sparse_softmax_cross_entropy for loss...\")\n losses = tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=head_in, logits=pred_dist)\n \n output_form = \"softmax\"\n\n # Average loss across batch.\n global_step = tf.Variable(\n initial_value=0,\n name=\"global_step\",\n trainable=False,\n collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES])\n\n steps_per_epoch = int(367347/FLAGS.batch_size)\n \n # Learning rate decay\n def _learning_rate_decay_fn(learning_rate, global_step):\n return tf.train.exponential_decay(\n learning_rate,\n global_step,\n decay_steps=FLAGS.lr_decay_epochs * steps_per_epoch,\n decay_rate=FLAGS.lr_decay_rate,\n staircase=True)\n\n learning_rate_decay_fn = _learning_rate_decay_fn if FLAGS.lr_decay_rate else None\n \n total_loss = tf.reduce_mean(losses, name=\"total_loss\")\n train_step = tf.contrib.layers.optimize_loss(\n loss=total_loss,\n global_step=global_step,\n learning_rate=learning_rate,\n optimizer=FLAGS.optimizer,\n clip_gradients=FLAGS.grad_clip_val,\n learning_rate_decay_fn=learning_rate_decay_fn\n )\n\n return gloss_in, head_in, total_loss, train_step, output_form, learning_rate, global_step\n\n\ndef train_network(model, num_epochs, batch_size, data_dir, save_dir, eval_save_dir, best_save_dir,\n vocab, rev_vocab, vocab_size, name=\"model\", eval_embs=None, verbose=True):\n \n tf.logging.info(\"Model checkpoints to be saved in %s...\" % save_dir)\n tf.logging.info(\"Beginning training at %s\" % (datetime.now()))\n start_time = time()\n \n # Running count of the number of training instances.\n num_training = 0\n \n # saver object for saving the model after each epoch.\n saver = tf.train.Saver()\n writer = tf.summary.FileWriter(graph=tf.get_default_graph(),\n logdir=save_dir)\n\n # Get Tensor handles from model\n gloss_in, head_in, total_loss, train_step, out_form, learning_rate, global_step = model\n\n # Declare summaries to be saved\n #for var in tf.trainable_variables():\n # tf.summary.histogram(\"params/\"+var.op.name, var)\n summary_op = tf.summary.merge_all()\n \n #end_of_epoch_step = 367232 // batch_size\n logstep = 1000\n \n for var in tf.trainable_variables():\n print(\"VAR -> %s\" % var.op.name)\n \n minimal_median = 100\n \n with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:\n \n tf.logging.info(\"Getting default graph...\")\n graph = tf.get_default_graph()\n if out_form == \"softmax\":\n predictions = graph.get_tensor_by_name(\"predictions:0\")\n else:\n predictions = graph.get_tensor_by_name(\"fully_connected/Relu:0\")\n \n tf.logging.info(\"Extracted tensor handle for output %s\" % out_form)\n tf.logging.info(\"Tensor found -> %s\" % predictions)\n \n # Initialize the model parameters.\n sess.run(tf.global_variables_initializer())\n \n # epoch is a generator of batches which passes over the data once.\n for idx, epoch in enumerate(\n gen_epochs(\n data_dir, num_epochs, batch_size, vocab_size, phase=\"train\")):\n \n # Running total for training loss reset every 500 steps.\n training_loss = 0\n if verbose:\n print(\"-> Epoch: \", idx)\n \n for step, (gloss, head) in enumerate(tqdm(epoch)):\n \n num_training += len(gloss)\n \n if (step % logstep == 0) and step > 0:\n training_loss_, _, summaries = sess.run(fetches=[total_loss, train_step, summary_op],\n feed_dict={gloss_in: gloss,\n head_in: head})\n writer.add_summary(summaries, global_step=tf.train.global_step(sess, global_step))\n writer.flush()\n else:\n training_loss_, _ = sess.run(fetches=[total_loss, train_step],\n feed_dict={gloss_in: gloss,\n head_in: head\n })\n \n # Run evaluation after each epoch\n new_median = evaluate_model(sess=sess,\n data_dir=data_dir,\n input_node=gloss_in,\n target_node=head_in,\n prediction=predictions,\n loss=total_loss,\n rev_vocab=rev_vocab,\n vocab=vocab,\n embs=eval_embs,\n save_dir=eval_save_dir,\n global_step=idx,\n out_form=out_form,\n verbose=True)\n \n if new_median < minimal_median:\n tf.logging.info(\"Saving new best model with rank %d\" % new_median)\n saver.save(sess, best_save_dir, global_step=tf.train.global_step(sess, global_step))\n minimal_median = new_median\n\n print(\"Elapsed training time %.2f hours\" % ((time()-start_time)/(60*60)))\n print(\"Total data points seen during training: %s or %d epochs of %d datapoints\" % (num_training,\n num_epochs,\n num_training/num_epochs))\n return save_dir, saver\n\n\ndef evaluate_model(sess, data_dir, input_node, target_node, prediction, loss,\n rev_vocab, vocab, embs, save_dir, global_step, out_form=\"cosine\", verbose=True):\n \"\"\"\n Thomas Sherborne trs46\n R228 Deep Learning for Natural Language Processing Assignment 1\n April 2018\n \n Evaluate reverse dictionary model in terms of median rank on dev set and validation loss\n \n input\n ------\n :param sess: TF session with loaded model checkpoint or current training checkpoint\n :param data_dir: directory to load development data from\n :param input_node: tf.placeholder to feed in dev set glosses for head word encoding and loss calculation\n :param target_node: tf.placeholder to feed in dev set head words for validation loss computation in sess.graph\n :param prediction: tf operation to fetch the prediction for head word embedding using input_node\n :param loss: tf operation for validation loss\n :param rev_vocab: reverse dict to look up word strings from vocab index keys\n :param vocab: vocab dict to look up vocab indices from word string keys\n :param embs: pretrained W2V/GloVe embeddings for all head words to rank across\n :param save_dir: directory to log summaries to\n :param global_step: nth epoch to log summaries for\n :param out_form: string to condition the type of assessment as 'cosine' for cdist or 'softmax' for simple ranking\n :param verbose: boolean True for output logging or False for silent\n ------\n :return:None\n \"\"\"\n \n if out_form not in ['cosine', 'softmax']:\n raise NotImplementedError(\"output forms of only 'cosine' and 'softmax' supported.\")\n\n # Keep start time\n start_time = time()\n\n RankedWord = namedtuple(\"RankedWord\", [\"word\", \"idx\", \"rank\"])\n\n # Accumulators for rankings and validation loss\n ranks_ = []\n loss_ = []\n \n if verbose:\n tf.logging.info(\"Running evaluation for epoch %d\\nBeginning eval at %s\" % (global_step, datetime.now()))\n \n # Batch size is set by FLAGS unless greater than validation set size\n #dev_batch_size = FLAGS.batch_size if FLAGS.batch_size < 200 else 1\n dev_batch_size = 1\n # Get epoch\n epoch = next(gen_epochs(data_path=data_dir, total_epochs=1, batch_size=dev_batch_size,\n vocab_size=FLAGS.vocab_size, phase=\"dev\"))\n\n # Get ranks over the batch\n for b_idx, (glosses, heads) in enumerate(epoch):\n \n if verbose:\n print('Evaluation step %d...' % (b_idx + 1))\n \n # Get the predictions and the batch validation loss from the session graph\n batch_pred, batch_val_loss = sess.run(fetches=[prediction, loss],\n feed_dict={input_node: glosses,\n target_node: heads})\n # Accumulate loss over dev set words\n loss_ = np.concatenate((loss_, [batch_val_loss]))\n \n # Over each word in dev set\n for head_, prediction_ in zip(heads, batch_pred):\n # If learning using cosine loss and embeddings for head words\n if out_form == \"cosine\":\n # Get cosine distance across the vocabulary\n prediction_ = np.expand_dims(prediction_, 0)\n cosine_distance = 1 - np.squeeze(dist.cdist(prediction_, embs, metric=\"cosine\"))\n cosine_distance = np.nan_to_num(cosine_distance)\n # Rank vocabulary by cosine distance\n rank_cands = np.argsort(cosine_distance)[::-1]\n \n # Else learning with softmax as labels not embeddings for heads\n else:\n # Rank by the softmax prediction\n rank_cands = np.squeeze(prediction_)[2:].argsort()[::-1] + 2\n # Get the rank of the ground-truth head word by index\n head_rank = np.asscalar(np.where(rank_cands == head_)[0].squeeze())\n # Append to list of rankings\n ranks_.append(RankedWord(word=rev_vocab[head_], idx=head_, rank=head_rank))\n \n if verbose:\n tf.logging.info(\"----------------------------\\n\"\n \"HEAD -> %s\\nRANK -> %d\\n\" % (ranks_[-1].word, ranks_[-1].rank))\n\n if verbose:\n tf.logging.info(\"Elapsed evaluation time %s\\nCompleted evaluation at epoch %d\" %\n (time()-start_time, global_step))\n\n # Get metrics over the validation set\n rank_avg_median = np.median([word.rank for word in ranks_])\n rank_avg_mad = np.median(np.abs([word.rank - rank_avg_median for word in ranks_]))\n loss_avg_mean = np.mean(loss_)\n loss_avg_std = np.std(loss_)\n \n if verbose:\n tf.logging.info('Median rank %.1f ± %.4f / Validation loss %.5f ± %.4f' % (rank_avg_median, rank_avg_mad,\n loss_avg_mean, loss_avg_std))\n # Tensorboard logging\n writer = tf.summary.FileWriter(logdir=save_dir)\n eval_summaries = tf.Summary()\n eval_summaries.value.add(tag=\"eval/loss\", simple_value=loss_avg_mean)\n eval_summaries.value.add(tag=\"eval/loss_stddev\", simple_value=loss_avg_std)\n eval_summaries.value.add(tag=\"eval/median_rank\", simple_value=rank_avg_median)\n eval_summaries.value.add(tag=\"eval/rank_mad\", simple_value=rank_avg_mad)\n writer.add_summary(eval_summaries, global_step)\n writer.flush()\n\n # File logging\n ranks_str_file = save_dir + os.sep + 'ranks_step_%d.txt' % global_step\n with open(ranks_str_file, 'w') as f:\n f.writelines(\n ['%d,%s,%d\\n' % (w.idx, w.word, w.rank) for w in ranks_]\n )\n f.write(\"rank,%.1f,mad,%.1f,loss,%.5f,stddev,%.4f\\n\" %\n (rank_avg_median, rank_avg_mad, loss_avg_mean, loss_avg_std))\n \n return rank_avg_median\n \n\ndef restore_model(sess, save_dir, vocab_file, out_form):\n # Get checkpoint in dir\n model_path = tf.train.latest_checkpoint(save_dir)\n\n global_step = int(os.path.basename(model_path).split(\"_\")[1].split(\".\")[0])\n\n tf.logging.info(\"Loading checkpoint from global step %d\" % global_step)\n\n # restore the model from the meta graph\n saver = tf.train.import_meta_graph(model_path + \".meta\")\n saver.restore(sess, model_path)\n \n graph = tf.get_default_graph()\n \n # get the names of input and output tensors\n input_node = graph.get_tensor_by_name(\"input_placeholder:0\")\n target_node = graph.get_tensor_by_name(\"labels_placeholder:0\")\n \n if out_form == \"softmax\":\n predictions = graph.get_tensor_by_name(\"predictions:0\")\n else:\n predictions = graph.get_tensor_by_name(\"fully_connected/Relu:0\")\n \n # Get loss tensor\n loss = graph.get_tensor_by_name(\"total_loss:0\")\n \n # vocab is mapping from words to ids, rev_vocab is the reverse.\n vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_file)\n \n return input_node, target_node, predictions, loss, vocab, rev_vocab, global_step\n\n\ndef query_model(sess, input_node, predictions, vocab, rev_vocab,\n max_seq_len, saver=None, embs=None, out_form=\"cosine\"):\n while True:\n # Get input feats\n sys.stdout.write(\"Type a definition: \")\n sys.stdout.flush()\n sentence = sys.stdin.readline()\n sys.stdout.write(\"Number of candidates: \")\n sys.stdout.flush()\n top = int(sys.stdin.readline())\n \n # Get token-ids for the input gloss.\n token_ids = data_utils.sentence_to_token_ids(sentence, vocab)\n \n # Pad out (or truncate) the input gloss ids.\n padded_ids = np.asarray(data_utils.pad_sequence(token_ids, max_seq_len))\n input_data = np.asarray([padded_ids])\n \n # Single vector encoding the input gloss.\n model_preds = sess.run(predictions, feed_dict={input_node: input_data})\n \n # Softmax already provides scores over the vocab.\n if out_form == \"softmax\":\n # Exclude padding and _UNK tokens from the top-k calculation.\n candidate_ids = np.squeeze(model_preds)[2:].argsort()[-top:][::-1] + 2\n # Replace top-k ids with corresponding words.\n candidates = [rev_vocab[idx] for idx in candidate_ids]\n # Cosine requires sim to be calculated for each vocab word.\n else:\n sims = 1 - np.squeeze(dist.cdist(model_preds, embs, metric=\"cosine\"))\n # replace nans with 0s.\n sims = np.nan_to_num(sims)\n candidate_ids = sims.argsort()[::-1][:top]\n candidates = [rev_vocab[idx] for idx in candidate_ids]\n \n # get baseline candidates from the raw embedding space.\n base_rep = np.asarray([np.mean(embs[token_ids], axis=0)])\n sims_base = 1 - np.squeeze(dist.cdist(base_rep, embs, metric=\"cosine\"))\n sims_base = np.nan_to_num(sims_base)\n \n candidate_ids_base = sims_base.argsort()[::-1][:top]\n candidates_base = [rev_vocab[idx] for idx in candidate_ids_base]\n \n print(\"Top %s baseline candidates:\" % top)\n for ii, cand in enumerate(candidates_base):\n print(\"%s: %s\" % (ii + 1, cand))\n \n print(\"\\n Top %s candidates from the model:\" % top)\n for ii, cand in enumerate(candidates):\n print(\"%s: %s\" % (ii + 1, cand))\n \n old_model_preds = model_preds\n sys.stdout.flush()\n sentence = sys.stdin.readline()\n\n\ndef main(_):\n \"\"\"Calls train and test routines for the dictionary model.\n \n If restore FLAG is true, loads an existing model and runs test\n routine. If restore FLAG is false, builds a model and trains it.\n \"\"\"\n assert FLAGS.exp_tag, \"Must give experiment tag\"\n \n if FLAGS.vocab_file is None:\n vocab_file = os.path.join(FLAGS.data_dir,\n \"definitions_%s.vocab\" % FLAGS.vocab_size)\n else:\n vocab_file = FLAGS.vocab_file\n \n train_save_dir = FLAGS.save_dir + os.sep + FLAGS.exp_tag + os.sep + \"train\"\n eval_save_dir = FLAGS.save_dir + os.sep + FLAGS.exp_tag + os.sep + \"eval\"\n best_save_dir = FLAGS.save_dir + os.sep + FLAGS.exp_tag + os.sep + \"best\"\n \n if not tf.gfile.IsDirectory(train_save_dir):\n tf.logging.info(\"Creating save_dir in %s...\" % train_save_dir)\n tf.gfile.MakeDirs(train_save_dir)\n \n if not tf.gfile.IsDirectory(eval_save_dir):\n tf.logging.info(\"Creating save_dir in %s...\" % eval_save_dir)\n tf.gfile.MakeDirs(eval_save_dir)\n\n # Build and train a dictionary model.\n if not FLAGS.restore:\n emb_size = FLAGS.embedding_size\n\n # Load any pre-trained word embeddings.\n if FLAGS.pretrained_input or FLAGS.pretrained_target:\n\n # embs_dict is a dictionary from words to vectors.\n emb_path = FLAGS.glove_path if FLAGS.use_glove else FLAGS.embeddings_path\n \n embs_dict, pre_emb_dim = load_pretrained_embeddings(emb_path)\n if FLAGS.pretrained_input:\n emb_size = pre_emb_dim\n else:\n pre_embs, embs_dict = None, None\n \n # Create vocab file, process definitions (if necessary).\n data_utils.prepare_dict_data(\n FLAGS.data_dir,\n FLAGS.train_file,\n FLAGS.dev_file,\n vocabulary_size=FLAGS.vocab_size,\n max_seq_len=FLAGS.max_seq_len)\n\n # vocab is a dictionary from strings to integers.\n vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_file)\n pre_embs = None\n\n if FLAGS.pretrained_input or FLAGS.pretrained_target:\n # pre_embs is a numpy array with row vectors for words in vocab.\n # for vocab words not in embs_dict, vector is all zeros.\n pre_embs = get_embedding_matrix(embs_dict, vocab, pre_emb_dim)\n \n # Build the TF graph for the dictionary model.\n model = build_model(\n max_seq_len=FLAGS.max_seq_len,\n vocab_size=FLAGS.vocab_size,\n emb_size=emb_size,\n learning_rate=FLAGS.learning_rate,\n encoder_type=FLAGS.encoder_type,\n pretrained_target=FLAGS.pretrained_target,\n pretrained_input=FLAGS.pretrained_input,\n pre_embs=pre_embs)\n \n # Run the training for specified number of epochs.\n save_path, saver = train_network(\n model,\n FLAGS.num_epochs,\n FLAGS.batch_size,\n FLAGS.data_dir,\n train_save_dir,\n eval_save_dir,\n best_save_dir,\n vocab=vocab,\n rev_vocab=rev_vocab,\n vocab_size=FLAGS.vocab_size,\n eval_embs=pre_embs,\n name=FLAGS.model_name)\n \n # Load an existing model.\n else:\n # Note cosine loss output form is hard coded here. For softmax output\n # change \"cosine\" to \"softmax\"\n \n # Get pretrained objects\n if FLAGS.pretrained_input or FLAGS.pretrained_target:\n emb_path = FLAGS.glove_path if FLAGS.use_glove else FLAGS.embeddings_path\n embs_dict, pre_emb_dim = load_pretrained_embeddings(emb_path)\n vocab, _ = data_utils.initialize_vocabulary(vocab_file)\n pre_embs = get_embedding_matrix(embs_dict, vocab, pre_emb_dim)\n \n # Always run on CPU for no resource contention\n with tf.device(\"/cpu:0\"):\n with tf.Session() as sess:\n (input_node, target_node, predictions, loss, vocab,\n rev_vocab, global_step) = restore_model(sess, train_save_dir, vocab_file,\n out_form=FLAGS.out_form)\n \n if FLAGS.evaluate:\n evaluate_model(sess=sess,\n data_dir=FLAGS.data_dir,\n input_node=input_node,\n target_node=target_node,\n prediction=predictions,\n loss=loss,\n rev_vocab=rev_vocab,\n vocab=vocab,\n embs=pre_embs,\n out_form=FLAGS.out_form,\n save_dir=eval_save_dir,\n global_step=global_step)\n \n # Load the final saved model and run querying routine.\n \n query_model(sess, input_node, predictions,\n vocab, rev_vocab, FLAGS.max_seq_len, embs=pre_embs,\n out_form=FLAGS.out_form)\n\n\nif __name__ == \"__main__\":\n tf.app.run()\n", "sub_path": "train_definition_model.py", "file_name": "train_definition_model.py", "file_ext": "py", "file_size_in_byte": 35482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_float", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_float", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_float", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_boolean", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_boolean", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_boolean", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_boolean", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_boolean", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_boolean", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_float", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.app", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.set_verbosity", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 140, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 149, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 150, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 156, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 193, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 250, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 251, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 251, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 252, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 255, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 256, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 259, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 259, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 260, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 266, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 269, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 271, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 271, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 275, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 278, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 281, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 281, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 288, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 288, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMCell", "line_number": 292, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 292, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.rnn.DropoutWrapper", "line_number": 293, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 293, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dynamic_rnn", "line_number": 299, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 299, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 299, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 302, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 302, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 304, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 305, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 305, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 314, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 314, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 317, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 320, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 323, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 323, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 326, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 326, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 329, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 331, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 331, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 334, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 334, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 337, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 337, "usage_type": "attribute"}, {"api_name": "tensorflow.losses.cosine_distance", "line_number": 338, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 338, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 339, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 339, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 340, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 340, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 343, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 343, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 346, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 348, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 349, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 349, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 351, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 351, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.sparse_softmax_cross_entropy_with_logits", "line_number": 352, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 352, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 358, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 362, "usage_type": "attribute"}, {"api_name": "tensorflow.train.exponential_decay", "line_number": 368, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 368, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 377, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers.optimize_loss", "line_number": 378, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 378, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 393, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 393, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 394, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 394, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 394, "usage_type": "name"}, {"api_name": "time.time", "line_number": 395, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 401, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 401, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 402, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 402, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 402, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 411, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 411, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 416, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 421, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 421, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 423, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 423, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 424, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 430, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 430, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 431, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 431, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 434, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 446, "usage_type": "call"}, {"api_name": "tensorflow.train.global_step", "line_number": 454, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 454, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 478, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 478, "usage_type": "attribute"}, {"api_name": "tensorflow.train.global_step", "line_number": 479, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 479, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 482, "usage_type": "call"}, {"api_name": "time.time", "line_number": 521, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 523, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 530, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 530, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 530, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 530, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 550, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 557, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 558, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 558, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 558, "usage_type": "name"}, {"api_name": "numpy.nan_to_num", "line_number": 559, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 561, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 566, "usage_type": "call"}, {"api_name": "numpy.asscalar", "line_number": 568, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 568, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 573, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 573, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 577, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 577, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 578, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 581, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 583, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 584, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 587, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 587, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 590, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 590, "usage_type": "attribute"}, {"api_name": "tensorflow.Summary", "line_number": 591, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 600, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 613, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 613, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 615, "usage_type": "call"}, {"api_name": "os.path", "line_number": 615, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 617, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 617, "usage_type": "attribute"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 620, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 620, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 623, "usage_type": "call"}, {"api_name": "data_utils.initialize_vocabulary", "line_number": 638, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 647, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 647, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 648, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 648, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 649, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 649, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 650, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 650, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 651, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 651, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 652, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 652, "usage_type": "attribute"}, {"api_name": "data_utils.sentence_to_token_ids", "line_number": 655, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 658, "usage_type": "call"}, {"api_name": "data_utils.pad_sequence", "line_number": 658, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 659, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 667, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 672, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 672, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 672, "usage_type": "name"}, {"api_name": "numpy.nan_to_num", "line_number": 674, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 679, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 679, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 680, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 680, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 680, "usage_type": "name"}, {"api_name": "numpy.nan_to_num", "line_number": 681, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 695, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 695, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 696, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 696, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 708, "usage_type": "call"}, {"api_name": "os.path", "line_number": 708, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 713, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 714, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 715, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.IsDirectory", "line_number": 717, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 717, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 718, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 718, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.MakeDirs", "line_number": 719, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 719, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.IsDirectory", "line_number": 721, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 721, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.info", "line_number": 722, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 722, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.MakeDirs", "line_number": 723, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 723, "usage_type": "attribute"}, {"api_name": "data_utils.prepare_dict_data", "line_number": 742, "usage_type": "call"}, {"api_name": "data_utils.initialize_vocabulary", "line_number": 750, "usage_type": "call"}, {"api_name": "data_utils.initialize_vocabulary", "line_number": 793, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 797, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 798, "usage_type": "call"}, {"api_name": "tensorflow.app.run", "line_number": 825, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 825, "usage_type": "attribute"}]} +{"seq_id": "361822673", "text": "# -*- coding: utf-8 -*-\n\nfrom flask import render_template, flash, redirect, url_for, g\nfrom .forms import AdminForm, EmployeeForm, ProductForm, StoreForm\nfrom zhejin import app, db\nfrom flask.ext.login import login_user, logout_user, current_user, login_required\nfrom datetime import datetime\nfrom models import Admin, Personnel, Product, Store, Stock, Stockdetail\nfrom methods import admin_required\nfrom collections import defaultdict\n\n\n@app.route('/product', methods=['GET', 'POST'])\n@login_required\ndef product():\n form = ProductForm()\n productlist = Product.query.order_by(Product.cate, Product.number).all()\n cate_list = defaultdict(list)\n for i in Product.query.all():\n cate_list[i.cate].append(int(i.number))\n catelist = {i: max(cate_list[i])+1 for i in cate_list}\n form.unit.choices = [('箱', '箱'), ('件', '件'), ('瓶', '瓶'), ('盒', '盒'), ('坛', '坛')]\n if form.validate_on_submit():\n id = form.id.data\n cate = form.cate.data\n product = form.product.data\n index = form.index.data\n unit = form.unit.data\n format = form.format.data\n defaultprice = float(form.defaultprice.data) * 10\n time = datetime.now().replace(microsecond=0)\n number = form.number.data\n if id:\n user = Product.query.get(int(id))\n for i in Stock.query.filter_by(product=user.product).all():\n i.product=product\n db.session.add(i)\n\n for i in Stockdetail.query.filter_by(product=user.product).all():\n i.product=product\n db.session.add(i)\n\n user.cate = cate\n user.product = product\n user.number = number\n user.index = index\n user.unit = unit\n user.format = format\n user.defaultprice = defaultprice\n user.time = time\n db.session.add(user)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n else:\n db.session.add(Product(cate=cate, product=product, unit=unit, format=format,\n index=index, defaultprice=defaultprice, time=time, number=number, buyprice=0))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('product'))\n return render_template('somehtml/product.html',\n form=form,\n productlist=productlist,\n catelist=catelist)\n\n\n# 成功\n@app.route('/delete/product/', methods=['GET', 'POST'])\n@login_required\ndef delete_product(id):\n db.session.delete(Product.query.get(id))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('product'))\n\n\n# 成功\n@app.route('/admin', methods=['GET', 'POST'])\n@login_required\ndef admin():\n form = AdminForm()\n form.role.choices = [('普通', '普通'), ('超级', '超级')]\n adminlist = Admin.query.order_by(Admin.username).all()\n if form.validate_on_submit():\n id = form.id.data\n username = form.username.data\n password = form.password.data\n warehouse = form.warehouse.data\n person = form.person.data\n role = form.role.data\n telephone = form.telephone.data\n address = form.address.data\n if id:\n user = Admin.query.get(int(id))\n user.password=password\n user.username=username\n user.person=person\n user.telephone=telephone\n for i in Stock.query.filter_by(warehouse=user.warehouse).all():\n i.warehouse = warehouse\n db.session.add(i)\n for i in Stockdetail.query.filter_by(warehouse=user.warehouse).all():\n i.warehouse = warehouse\n db.session.add(i)\n user.warehouse=warehouse\n user.role=role\n user.address=address\n db.session.add(user)\n\n if Personnel.query.filter_by(employee=person).first():\n pass\n else:\n db.session.add(Personnel(employee=person, employeeadmin=user, telephone=telephone))\n\n try:\n db.session.commit()\n except:\n db.session.rollback()\n else:\n db.session.add(Admin(username=username, password=password, address=address,\n warehouse=warehouse, person=person, role=role, telephone=telephone,\n stock_detail_start_date=datetime.today().date()))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n\n if not Personnel.query.filter_by(employee=person).first():\n belong = Admin.query.filter_by(username=username).first()\n db.session.add(Personnel(employee=person, employeeadmin=belong, telephone=telephone))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('admin'))\n return render_template('somehtml/admin.html',\n form=form,\n adminlist=adminlist)\n\n\n# 成功\n@app.route('/delete/admin/', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef delete_admin(id):\n a = Admin.query.get(id)\n if len(Admin.query.all()) == 1:\n flash('只有一个账户了，不能再删除！')\n elif a==g.user:\n flash('不能删除自己，您可以修改您的密码！')\n else:\n p = Personnel.query.filter_by(employee=a.person).first()\n if p:\n db.session.delete(p)\n db.session.delete(a)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('admin'))\n\n\n# 成功\n@app.route('/employee', methods=['GET', 'POST'])\n@login_required\ndef employee():\n form = EmployeeForm()\n employeelist = Personnel.query.join(Personnel.employeeadmin).order_by(Admin.warehouse).all()\n form.warehouse.choices = [(i.warehouse, i.warehouse) for i in Admin.query.filter(Admin.warehouse!='所有').all()]\n if form.validate_on_submit():\n id = form.id.data\n username = form.employee.data\n warehouse = form.warehouse.data\n telephone = form.telephone.data\n belong = Admin.query.filter_by(warehouse=warehouse).first()\n if id:\n user = Personnel.query.get(int(id))\n user.employee = username\n user.employeeadmin = belong\n user.telephone = telephone\n db.session.add(user)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n else:\n db.session.add(Personnel(employee=username, employeeadmin=belong, telephone=telephone))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('employee'))\n return render_template('somehtml/employee.html',\n form=form,\n employeelist=employeelist)\n\n\n# 成功\n@app.route('/delete/employee/', methods=['GET', 'POST'])\n@login_required\ndef delete_employee(id):\n db.session.delete(Personnel.query.get(id))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('employee'))\n\n\n\n# 成功\n@app.route('/store', methods=['GET', 'POST'])\n@login_required\ndef store():\n form = StoreForm()\n storelist = Store.query.join(Store.storeadmin).filter(Admin.warehouse==g.user.warehouse).all()\n form.warehouse.choices = [(g.user.warehouse, g.user.warehouse)]\n\n if form.validate_on_submit():\n id = int(form.id.data or 0)\n storename = form.storename.data\n person = form.person.data\n telephone = form.telephone.data\n address = form.address.data\n index = form.index.data\n storeadmin = Admin.query.filter_by(warehouse=form.warehouse.data).first()\n if id:\n user = Store.query.get(id)\n user.storename = storename\n user.person = person\n user.telephone = telephone\n user.storeadmin = storeadmin\n user.index = index\n user.address = address\n db.session.add(user)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n else:\n db.session.add(Store(storename=storename, telephone=telephone, storeadmin=storeadmin, person=person,\n address=address, index=index))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('store'))\n return render_template('somehtml/store.html',\n form=form,\n storelist=storelist)\n\n\n@app.route('/store_admin', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef store_admin():\n storelist = Store.query.all()\n return render_template('somehtml/store_admin.html',\n storelist=storelist)\n\n# 成功\n@app.route('/delete/store/', methods=['GET', 'POST'])\n@login_required\ndef delete_store(id):\n db.session.delete(Store.query.get(id))\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return redirect(url_for('store'))\n\n\n@app.route('/print_store', methods=['GET', 'POST'])\n@login_required\ndef print_store():\n time = datetime.now().replace(microsecond=0)\n storelist = Store.query.join(Store.storeadmin).filter(Admin.warehouse==g.user.warehouse).all()\n return render_template('print/print_store.html',\n storelist=storelist,\n time=time)\n\n", "sub_path": "zhejin/sometable.py", "file_name": "sometable.py", "file_ext": "py", "file_size_in_byte": 9761, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "forms.ProductForm", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Product.query.order_by", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Product.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Product.cate", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Product.number", "line_number": 17, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Product.query.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Product.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 19, "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": "models.Product.query.get", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Product.query", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Stock.query.filter_by", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Stock.query", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Stock", "line_number": 35, "usage_type": "name"}, {"api_name": "zhejin.db.session.add", "line_number": 37, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Stockdetail.query.filter_by", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Stockdetail.query", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Stockdetail", "line_number": 39, "usage_type": "name"}, {"api_name": "zhejin.db.session.add", "line_number": 41, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 41, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 41, "usage_type": "name"}, {"api_name": "zhejin.db.session.add", "line_number": 51, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 51, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 51, "usage_type": "name"}, {"api_name": "zhejin.db.session.commit", "line_number": 53, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 53, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 53, "usage_type": "name"}, {"api_name": "zhejin.db.session.rollback", "line_number": 55, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 55, "usage_type": "name"}, {"api_name": "zhejin.db.session.add", "line_number": 57, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 57, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 57, "usage_type": "name"}, {"api_name": "models.Product", "line_number": 57, "usage_type": "call"}, {"api_name": "zhejin.db.session.commit", "line_number": 60, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 60, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 60, "usage_type": "name"}, {"api_name": "zhejin.db.session.rollback", "line_number": 62, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 62, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": 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258, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 259, "usage_type": "name"}, {"api_name": "methods.admin_required", "line_number": 260, "usage_type": "name"}, {"api_name": "zhejin.db.session.delete", "line_number": 270, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 270, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 270, "usage_type": "name"}, {"api_name": "models.Store.query.get", "line_number": 270, "usage_type": "call"}, {"api_name": "models.Store.query", "line_number": 270, "usage_type": "attribute"}, {"api_name": "models.Store", "line_number": 270, "usage_type": "name"}, {"api_name": "zhejin.db.session.commit", "line_number": 272, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 272, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 272, "usage_type": "name"}, {"api_name": "zhejin.db.session.rollback", "line_number": 274, "usage_type": "call"}, {"api_name": "zhejin.db.session", "line_number": 274, "usage_type": "attribute"}, {"api_name": "zhejin.db", "line_number": 274, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 275, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 275, "usage_type": "call"}, {"api_name": "zhejin.app.route", "line_number": 267, "usage_type": "call"}, {"api_name": "zhejin.app", "line_number": 267, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 268, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 281, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 281, "usage_type": "name"}, {"api_name": "models.Store.query.join", "line_number": 282, "usage_type": "call"}, {"api_name": "models.Store.query", "line_number": 282, "usage_type": "attribute"}, {"api_name": "models.Store", "line_number": 282, "usage_type": "name"}, {"api_name": "models.Store.storeadmin", "line_number": 282, "usage_type": "attribute"}, {"api_name": "models.Admin.warehouse", "line_number": 282, "usage_type": "attribute"}, {"api_name": "models.Admin", "line_number": 282, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 282, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 282, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 283, "usage_type": "call"}, {"api_name": "zhejin.app.route", "line_number": 278, "usage_type": "call"}, {"api_name": "zhejin.app", "line_number": 278, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 279, "usage_type": "name"}]} +{"seq_id": "303971105", "text": "from random import randint\nfrom typing import Any, Union\n\n\ndef rand_num_gen(choice: int) -> int:\n \"\"\"Get a random number to simulate a d6, d10, or d100 roll.\"\"\"\n die: Union[int, int] = 1\n if choice == 6: # d6 roll\n die = randint(1, 6)\n elif choice == 10: # d10 roll\n die = randint(1, 10)\n elif choice == 100: # d100 roll\n die = randint(1, 100)\n elif choice == 4: # d4 roll\n die = randint(1, 4)\n elif choice == 8: # d8 roll\n die = randint(1, 8)\n elif choice == 12: # d12 roll\n die = randint(1, 12)\n elif choice == 20: # d20 roll\n die = randint(1, 20)\n return die\n\n\ndef multi_die(dice_number: int, die_type: int) -> int:\n \"\"\"Add die rolls together, e.g. 2d6, 4d10, etc.\"\"\"\n final_roll: int = 0\n val: int = 0\n\n while val < dice_number:\n final_roll += rand_num_gen(die_type)\n val += 1\n return final_roll\n\n\ndef test():\n \"\"\"Test criteria to show script works.\"\"\"\n _1d6: int = multi_die(1, 6) # 1d6\n print(\"1d6 = \", _1d6, )\n _2d6: int = multi_die(2, 6) # 2d6\n print(\"\\n2d6 = \", _2d6, )\n _3d6: int = multi_die(3, 6) # 3d6\n print(\"\\n3d6 = \", _3d6, )\n _4d6: int = multi_die(4, 6) # 4d6\n print(\"\\n4d6 = \", _4d6, )\n _1d10: int = multi_die(1, 10) # 1d10\n print(\"\\n1d10 = \", _1d10, )\n _2d10: int = multi_die(2, 10) # 2d10\n print(\"\\n2d10 = \", _2d10, )\n _3d10: int = multi_die(2, 10) # 3d10\n print(\"\\n3d10 = \", _3d10, )\n _d100: int = multi_die(1, 100) # d100\n print(\"\\n1d100 = \", _d100, )\n\n\nif __name__ == \"__main__\": # run test() if calling as a separate program\n test()\n", "sub_path": "Adv_Dark_Deep/dice_roller.py", "file_name": "dice_roller.py", "file_ext": "py", "file_size_in_byte": 1634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "typing.Union", "line_number": 7, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 9, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "314704723", "text": "\"\"\"\nCreated on Fri Jul 19 21:21:26 2019\n\n@author: Steven Belcher (stevellen)\nEVA functions for use in Boolean Networks course University of Nebraska Omaha\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\nimport matplotlib.animation as animation\n\n\ndef generate_ca(ru=np.random.randint(1, 256), it=250, n=51, k=1, **kwargs):\n \"\"\"\n Cellular automata generator designed to create plots for rules in the range\n [1,255] with any number of iterations by default but is generalized and \n can create rules up to 2^2k+1 for some value of k, which can be passed \n as an argument to the function. \n\n Additional argument options include:\n rand[bool] - If true, creates a random first generation;\n col[pair of str] - two color choices to indicate [dead, alive] cells,\n random_parents[bool] - determines if random parents are to be chosen \n when building a new generation;\n save[bool] - flag to indicate if the image should be saved;\n filename[str] - self explanatory.\n \"\"\"\n # Assign default values to optional arguments\n rand = False\n random_parents = False\n col = ['white', 'black']\n save = False\n filename = 'eca.png'\n\n # Checking kwargs\n if 'rand' in kwargs:\n rand = kwargs['rand']\n if 'col' in kwargs:\n col = kwargs['col']\n if 'random_parents' in kwargs:\n random_parents = kwargs['random_parents']\n if 'save' in kwargs:\n save = kwargs['save']\n if 'filename' in kwargs:\n if kwargs['filename'][-4:] == '.png' or kwargs['filename'][-4:] == '.jpg':\n filename = kwargs['filename']\n else:\n filename = kwargs['filename'] + '.png'\n \n\n density = np.zeros(it)\n rows = np.zeros((it, n), dtype=int)\n assert rows is not None, \"Number of nodes (n) and number of iterations (it) must be defined to build matrix.\"\n\n def __get_rule():\n out = format(ru, f'0{2**(2*k+1)}b')\n return {format(2**(2*k+1)-1-i, f'0{2*k+1}b'): int(out[i]) for i in range(2**(2*k+1))}\n\n def __plot_automaton():\n col_map = colors.ListedColormap(col)\n plt.figure(figsize=(16, 7))\n ax = plt.subplot(2, 1, 1)\n ax.set_title(\"ECA Rule {:,}\".format(ru), fontsize=20)\n ax.set_ylabel(\"Nodes\")\n plt.matshow(rows.T, cmap=col_map, interpolation='nearest',\n aspect='auto', fignum=False)\n ax.xaxis.set_ticks_position('bottom')\n ax = plt.subplot(2, 1, 2)\n ax.set_title('Iterations')\n ax.set_ylabel(\"Density\")\n plt.plot(np.arange(0, it, 1), density)\n plt.xlabel(f'Mean Density {np.round(np.mean(density), 3)}')\n \n if save:\n fig = plt.gcf()\n fig.savefig(filename)\n plt.show()\n\n def __apply_rule():\n fr = np.zeros(n, dtype=int) # Create first row\n if rand:\n fr = np.random.randint(0, 2, n)\n else:\n fr[n//2] = 1\n rows[0] = pr = fr # previous row\n nr = np.zeros(n, dtype=int) # next row\n rule = __get_rule() # fetch rule dict\n\n for i in range(1, it): # Need to find a cleaner way to do this\n density[i-1] = np.mean(rows[i-1])\n for j in range(n):\n if random_parents:\n nc = pr[np.random.randint(0,n,2*k+1)]\n else:\n if j - k < 0:\n nc = np.concatenate([pr[j-k:], pr[:j+k+1]])\n elif j + k >= n:\n nc = np.concatenate([pr[j-k:], pr[:(k+j-n+1)]])\n else:\n nc = pr[j-k:j+k+1]\n r = ''.join([str(v) for v in nc])\n nr[j] = rule[r]\n pr = np.copy(nr)\n rows[i] = np.copy(nr)\n density[-1] = np.mean(rows[-1])\n\n __apply_rule()\n __plot_automaton()\n\n\ndef game_of_life(n=30, it=50, col=['black', 'white'], show=True, save=False):\n \"\"\"\n The game_of_life function is a quick numpy implementation of Conway's game of life.\n The function takes the grid length and number of iterations as arguments and produces\n an animation for as long as the window remains open. If shown, the game will continue until \n the window is closed. If saved, a file is generated in the CWD named 'life.mp4'.\n \"\"\"\n try:\n n = int(n)\n it = int(it)\n except Exception as e:\n print(e)\n\n def __iterate(framenum, img, world, n):\n \"\"\" Perform animation iteration; returns an Artist. \"\"\"\n grid = np.copy(world)\n for i in range(1, n+1):\n for j in range(1, n+1):\n window = world[i-1:i+2, j-1:j+2]\n total = np.sum(window)\n if world[i, j] == 1: # Live cell case\n if total-1 not in [2, 3]:\n grid[i, j] = 0\n else: # Cell is dead\n if total == 3:\n grid[i, j] = 1\n img.set_array(grid)\n world[:, :] = grid[:, :]\n return img,\n\n \"\"\" Play the game of life \"\"\"\n col_map = colors.ListedColormap(col)\n world = np.zeros((n+2, n+2), dtype=np.uint8) # Generate empty world\n adam, eve = np.random.randint(\n 1, n, n**2//10), np.random.randint(1, n, n**2//10) # sow the seeds of life\n world[adam, eve] = 1\n fig = plt.figure(figsize=(8, 8)) # assign figure\n # assign image (Artist)\n im = plt.imshow(world, cmap=col_map, animated=True)\n anim = animation.FuncAnimation(fig, __iterate, fargs=(\n im, world, n), frames=it, interval=50, blit=True)\n\n if save:\n show = False\n anim.save('life.mp4', fps=25)\n if show:\n plt.show()\n plt.close()\n\n\nif __name__ == \"__main__\":\n \"\"\" A quick, simple demo for if the file is called directly \"\"\"\n rule = np.random.randint(1, 256)\n generate_ca(ru=rule, n=51, k=1, it=200, col=['yellow', 'black'])\n generate_ca(ru=rule, n=51, k=1, it=200, col=['yellow', 'black'], random_parents=True)\n # game_of_life(60)\n", "sub_path": "CellularAutomata/ECA.py", "file_name": "ECA.py", "file_ext": "py", "file_size_in_byte": 6263, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.random.randint", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.matshow", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 143, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 144, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 164, "usage_type": "attribute"}]} +{"seq_id": "39298598", "text": "#!/home/cimatori/installed/anaconda/bin/python\n\"\"\"\nStructure function of various order of temperature increments\n\"\"\"\n\nimport numpy as np\nimport numexpr as ne\n\nimport matplotlib.pyplot as ppl\n\nfrom cPickle import load as cLoad\nfrom gzip import GzipFile\nfrom sys import path\npath.append('/home/cimatori/installed/nioz-hst/Python/Trunk')\nfrom NIOZhst.String import String\n\n# Load paramters\nimport ConfigMoments\nreload(ConfigMoments)\nfrom ConfigMoments import *\n\nppl.close('all')\n\n# Compute stuff only if results files is not available\nimport os\nif not os.path.exists(OutFileNoF):\n print ('Load data...')\n\n f = GzipFile(DetailFile, 'r')\n S = cLoad(f)\n f.close()\n\n from socket import gethostname\n if gethostname()=='sboron2':\n for T in S.Thermistors:\n path = T.Source['Dir']\n T.Source['Dir'] = path.replace('media/scratch', \\\n 'run/media/sambarluc/Cimatoribus1')\n T.Source['FullPath'] = T.Source['Dir'] + '/' + \\\n T.Source['SourceFile']\n\n time, Tmp = S.ToArray(Range=(Start,End), Convention='yearday', \\\n Fill='interp', Skip=0, ColInt=False, Ind=Thms)\n\n Z = S.Depths\n dz = np.mean(np.diff(Z))\n dt = 1.0\n\n print ('Data loaded.')\n\n print ('Load tidal phase.')\n\n pfile = np.load(OutDir+'results/TidalPhase.npz')\n ph = pfile['phase']\n\n print ('Done.')\n\n # Compute time series of increments\n print ('Compute temperature increments moments...')\n # We add one extra dimension at the beginning, for the thermistor number,\n # this way we can split the computation in smaller pieces and average\n # everything at the end\n def ComputeMoments(th):\n ne.set_num_threads(1)\n Out = np.zeros((nM,nT,ndTs))\n print ('Computing thermistor {}'.format(th))\n for i,inc in enumerate(dTs):\n tmp = ne.evaluate(\"a-b\", \\\n local_dict={'a':Tmp[th,inc:],'b':Tmp[th,:-inc]})\n for pn,p in enumerate(ph):\n data = tmp[p[:-inc]]\n bad = np.isnan(data)\n if bad.sum()>0:\n data = data[~bad]\n data = data.ravel()\n data = ne.evaluate(\"abs(data)\")\n for mn,m in enumerate(Moms):\n Out[mn,pn,i] = ne.evaluate(\"sum(data**m)\") / data.size\n return Out\n # Compute different thermistors in parallel\n from multiprocessing import Pool\n pool = Pool(3)\n tmp = pool.map(ComputeMoments, range(nTh))\n SF = np.zeros((nTh,nM,nT,ndTs))\n for th in xrange(nTh):\n SF[th,:,:,:] = tmp[th]\n SF = SF.mean(axis=0)\n print ('Done.')\n\n # Save results\n np.savez(OutFileNoF, \\\n dTs=dTs,SF=SF,Moms=Moms)\nelse:\n print ('Load previously computed results for plotting only.')\n Data = np.load(OutFileNoF)\n for k,v in Data.iteritems():\n exec('{}=v'.format(k))\n del Data\n\n# Plot dependence of variance on time increment\nF = ppl.figure()\nF.subplots_adjust(left=0.12, bottom=0.11, right=0.98, top=0.98)\nax = F.add_subplot(111)\n\nmn = np.where(np.array(Moms)==2)[0][0]\n\npl = []\nfor p in xrange(nT):\n s, = ax.plot(dTs, SF[mn,p,:], marker=markT[p], \\\n c=colorsT[p], mec=colorsT[p], mfc=colorsT[p], \\\n **pStyle)\n pl.append(s)\n\nax.legend(pl, namT, numpoints=1, fontsize=14, ncol=2, loc='lower right')\n\nax.set_xscale('log')\nax.set_yscale('log')\nax.set_xlim(8e-1,1e4)\n\nax.set_xlabel(spacing, fontsize='xx-large')\nax.set_ylabel('$R_2(\\\\tau)$', fontsize='xx-large')\nF.savefig(OutDir+'figures/R2_{}_NoFilter_Tide_day_{}_{}.pdf' \\\n .format(setName,Start,End))\n\n# Plot dependence of normalised R1\nF = ppl.figure()\nF.subplots_adjust(left=0.12, bottom=0.11, right=0.98, top=0.98)\nax = F.add_subplot(111)\n\nmn2 = np.where(np.array(Moms)==2)[0][0]\nmn1 = np.where(np.array(Moms)==1)[0][0]\n\npl = []\nfor p in xrange(nT):\n s, = ax.plot(dTs, SF[mn1,p,:]*SF[mn2,p,:]**-0.5, marker=markT[p], \\\n c=colorsT[p], mec=colorsT[p], mfc=colorsT[p], \\\n **pStyle)\n pl.append(s)\n\nax.legend(pl, namT, numpoints=1, fontsize=14, ncol=2, loc='lower right')\n\nax.set_xscale('log')\nax.set_xlim(8e-1,1e4)\n\nax.set_xlabel(spacing, fontsize='xx-large')\nax.set_ylabel('$R_1(\\\\tau)\\,R_2(\\\\tau)^{-1/2}$', fontsize='xx-large')\nF.savefig(OutDir+'figures/R1R2_{}_NoFilter_Tide_day_{}_{}.pdf' \\\n .format(setName,Start,End))\n\n# Plot dependence Rm on the separation\nfor p in xrange(nT):\n F = ppl.figure()\n F.subplots_adjust(left=0.13, bottom=0.11, right=0.98, top=0.98)\n ax = F.add_subplot(111)\n\n x = np.array([0.1,10000])\n for y0 in 10.**np.arange(-20,2,1):\n for zeta in (0.8,1.4):\n y = x**zeta; y *= y0/y.min()\n ax.plot(x,y, 'k:', lw=0.5)\n \n pl = []\n for mn in xrange(nM):\n s, = ax.plot(dTs, SF[mn,p,:], marker='o', \\\n c=colorsM[mn], mec=colorsM[mn], mfc=colorsM[mn], \\\n **pStyle)\n pl.append(s)\n\n ax.legend(pl, Moms, numpoints=1, fontsize=14, ncol=4, loc='lower right')\n\n ax.set_xscale('log')\n ax.set_yscale('log')\n ax.set_xlim(8e-1,1e4)\n ax.set_ylim(5e-15,1e-1)\n\n ax.set_xlabel(spacing, fontsize='xx-large')\n ax.set_ylabel('$R_m(\\\\tau)$', fontsize='xx-large')\n F.savefig(OutDir+'figures/Rm_{}_NoFilter_Tide_{}_day_{}_{}.pdf' \\\n .format(setName,namT[p],Start,End))\n\n# Plot dependence Rm normalised by R2\nfor p in xrange(nT):\n F = ppl.figure()\n F.subplots_adjust(left=0.13, bottom=0.11, right=0.98, top=0.98)\n ax = F.add_subplot(111)\n \n pl = []\n for mn,mom in enumerate(Moms):\n s, = ax.plot(dTs, SF[mn,p,:]/SF[1,p,:]**(mom/2.), marker='o', \\\n c=colorsM[mn], mec=colorsM[mn], mfc=colorsM[mn], \\\n **pStyle)\n pl.append(s)\n\n ax.legend(pl, Moms, numpoints=1, fontsize=14, ncol=4, loc='upper right')\n\n ax.set_xscale('log')\n ax.set_yscale('log')\n ax.set_xlim(8e-1,1e4)\n #ax.set_ylim(5e-15,1e-1)\n\n ax.set_xlabel(spacing, fontsize='xx-large')\n ax.set_ylabel('$R_m(\\\\tau)$', fontsize='xx-large')\n F.savefig(OutDir+'figures/RmR2_{}_NoFilter_Tide_{}_day_{}_{}.pdf' \\\n .format(setName,namT[p],Start,End))\n", "sub_path": "LIS131/TemperatureDissipation/Moments_tide_noFilter.py", "file_name": "Moments_tide_noFilter.py", "file_ext": "py", "file_size_in_byte": 6289, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "gzip.GzipFile", "line_number": 29, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 36, "usage_type": "name"}, {"api_name": "sys.path.replace", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 53, "usage_type": "call"}, {"api_name": "numexpr.set_num_threads", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numexpr.evaluate", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 72, "usage_type": "call"}, {"api_name": "numexpr.evaluate", "line_number": 76, "usage_type": "call"}, {"api_name": "numexpr.evaluate", "line_number": 78, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}]} +{"seq_id": "591366664", "text": "\"\"\"BashOperator with lazy environment variables\"\"\"\nfrom airflow import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom datetime import datetime\n\ndefault_args = {\n 'start_date': datetime.now()\n}\n\ndag = DAG(\n 'lazy_bash_operator',\n default_args=default_args)\n\nclass LazyDict(dict):\n def __init__(self, lambda_dict: dict):\n super().__init__(lambda_dict)\n\n def items(self):\n return [(k, self.get(k)()) for k in self.keys()]\n\n\ndef get_title():\n print(\"Get title\")\n return 'Mr'\n\n\norigin_lambda_dict={'TITLE': lambda: get_title()}\nenv_lazy_dict = LazyDict(origin_lambda_dict)\n\n\nlazy_bash_operator_task = BashOperator(\n task_id='lazy_bash_operator_task',\n bash_command='echo Title is ${TITLE}! ',\n env=env_lazy_dict,\n dag=dag)\n", "sub_path": "Building+/Docker+/DockerImage+/Application+/AirFlow/dags/lazy_bash_operator.py", "file_name": "lazy_bash_operator.py", "file_ext": "py", "file_size_in_byte": 784, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "datetime.datetime.now", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}, {"api_name": "airflow.DAG", "line_number": 10, "usage_type": "call"}, {"api_name": "airflow.operators.bash_operator.BashOperator", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "39439675", "text": "import sys\nimport matplotlib.pyplot as plt\n#import sqlite3\nsys.path.append('/afs/ipp-garching.mpg.de/home/i/ianf/codes/python/TRIPPy-master')\nimport TRIPPy.beam\nimport TRIPPy.geometry\nimport TRIPPy.invert\nimport scipy\nimport scipy.interpolate\nimport scipy.io\nimport eqtools\nimport GIWprofiles\nimport dd\nimport sqlite3 \nimport multiprocessing\n\n\n_dbname = 'SIFweight2015.db'\n_dbname2 = 'SIFreduced2015.db'\n\ndef genSIF(tokamak):\n \"\"\" Use the TRIPPy system to yield the spectrometer sightline \"\"\"\n\n #set up two points for extracting\n vec1 = TRIPPy.geometry.Vecr([1.785,0.,1.08])\n pt1 = TRIPPy.geometry.Point(vec1,tokamak)\n\n vec2 = TRIPPy.geometry.Vecr([1.785,0.,1.06])\n pt2 = TRIPPy.geometry.Point(vec2,tokamak)\n \n beam = TRIPPy.beam.Ray(pt1,pt2)\n tokamak.trace(beam) #modify beam to yield the proper in and exit points in the tokamak\n\n return beam #this contains the functionality to do the analysis.\n\ndef genInp(num=1e-3,shot=34228):\n tok = TRIPPy.Tokamak(eqtools.AUGDDData(34228))\n ray = genSIF(tok)\n print(ray.norm.s)\n #inp = scipy.linspace(ray.norm.s[-2],ray.norm.s[-1],num)\n inp = scipy.mgrid[ray.norm.s[-2]:ray.norm.s[-1]:num]\n r = ray(inp).r()[0]\n z = ray(inp).r()[2]\n l = inp - inp[0]\n return [r,z,l]\n\n\ndef goodGIW(time, shot, name=\"c_W\"):\n \"\"\"extract values which are only valid, otherwise place in zeros\"\"\"\n temp = GIWData(shot, data=name)\n interp = scipy.interpolate.interp1d(temp.time,\n temp.data,\n bounds_error=False)\n output = interp(time)\n output[ scipy.logical_not(scipy.isfinite(output))] = 0. #force all bad values to zero\n return output\n\n\ndef GIWData(shot, filename=\"GIW\", data=\"c_W\"):\n shotfile = dd.shotfile(filename,shot)\n return shotfile(data)\n\n\ndef calcArealDens(l, te, halfte, rho, tped, tcore, nped, ncore):\n \n minrho = scipy.argmin(rho)\n spline1 = scipy.interpolate.interp1d(rho[:minrho+1],\n l[:minrho+1],\n bounds_error=False,\n kind='linear')\n \n spline2 = scipy.interpolate.interp1d(rho[minrho:],\n l[minrho:],\n bounds_error=False,\n kind='linear')\n \n # step 3, find te rho locations\n ne = GIWprofiles.ne(GIWprofiles.te2rho2(te, tcore, tped), ncore, nped)\n\n rhohalfte = GIWprofiles.te2rho2(halfte, tcore, tped)\n \n bounds =scipy.array([rho[minrho],1.])\n boundte = GIWprofiles.te(bounds, tcore, tped)\n bndidx = scipy.searchsorted(halfte, boundte) #add proper endpoints to the temperature array\n rhohalfte = scipy.insert(rhohalfte, bndidx, bounds) #assumes that Te is positively increasing\n \n #step 4, find l location for those 1/2 te locations AND rho=1 for endpoints \n l1 = spline1(rhohalfte)\n deltal1 = abs(l1[:-1] - l1[1:])\n deltal1[scipy.logical_not(scipy.isfinite(deltal1))] = 0.\n\n \n l2 = spline2(rhohalfte)\n deltal2 = abs(l2[:-1] - l2[1:])\n deltal2[scipy.logical_not(scipy.isfinite(deltal2))] = 0.\n \n #plt.semilogx(te,ne*(deltal1*deltal2)/1e19, '.')\n #plt.xlabel('deltal2')\n #plt.show()\n\n return pow(ne,2)*(deltal1+deltal2)\n\n\ndef weights(inp, te, shot, time):\n \"\"\" pull out data vs time necessary to calculate the weights\"\"\"\n #condition initialized values\n\n output = scipy.zeros((len(time), len(te)))\n r = inp[0]\n z = inp[1]\n l = inp[2]\n \n #load GIW ne, Te data\n \n tped = goodGIW(time, shot, name=\"t_e_ped\")\n tcore = goodGIW(time, shot, name=\"t_e_core\")\n\n nped = goodGIW(time, shot, name=\"n_e_ped\")\n ncore = goodGIW(time, shot, name=\"n_e_core\")\n\n good = scipy.arange(len(time))[scipy.logical_and(ncore !=0, tcore != 0)] #take only good data\n\n #use spline of the GIW data to solve for the proper Te, otherwise dont evaluate\n\n logte = scipy.log(te)\n halfte = scipy.exp(logte[1:]/2. + logte[:-1]/2.) #not going to worry about endpoints\n # because I trust te is big enough to be larger than the range of the profile\n \n #step 1, use array of r,z values to solve for rho\n \n eq = eqtools.AUGDDData(shot)\n rho = eq.rz2rho('psinorm', r, z, time[good], each_t=True, sqrt=True) #solve at each time\n idx = 0\n\n #step 2, construct 2 splines of l(rho)\n for i in good:\n output[i] = calcArealDens(l, te, halfte, rho[idx], tped[i], tcore[i], nped[i], ncore[i])\n output[i][scipy.logical_not(scipy.isfinite(output[i]))] = 0.\n idx+=1\n #print(idx),\n\n #step 8, return values for storage\n return output\n\n\ndef weights2(inp, te, shot, time):\n \"\"\" pull out data vs time necessary to calculate the weights\"\"\"\n #condition initialized values\n\n output = scipy.zeros((len(time),2))\n r = inp[0]\n z = inp[1]\n l = inp[2]\n \n #load GIW ne, Te data\n \n tped = goodGIW(time, shot, name=\"t_e_ped\")\n tcore = goodGIW(time, shot, name=\"t_e_core\")\n\n nped = goodGIW(time, shot, name=\"n_e_ped\")\n ncore = goodGIW(time, shot, name=\"n_e_core\")\n\n good = scipy.arange(len(time))[scipy.logical_and(ncore !=0, tcore != 0)] #take only good data\n\n #use spline of the GIW data to solve for the proper Te, otherwise dont evaluate\n\n logte = scipy.log(te)\n halfte = scipy.exp(logte[1:]/2. + logte[:-1]/2.) #not going to worry about endpoints\n # because I trust te is big enough to be larger than the range of the profile\n \n #step 1, use array of r,z values to solve for rho\n \n eq = eqtools.AUGDDData(shot)\n rho = eq.rz2rho('psinorm', r, z, time[good], each_t=True, sqrt=True) #solve at each time\n rhomin = scipy.nanmin(rho,axis=1)\n temax = GIWprofiles.te(rhomin, tcore[good], tped[good])\n\n idx = 0\n\n #step 2, construct 2 splines of l(rho)\n for i in good:\n temp = calcArealDens(l, te, halfte, rho[idx], tped[i], tcore[i], nped[i], ncore[i])\n temp[scipy.logical_not(scipy.isfinite(temp))] = 0.\n temp = scipy.sum(temp)\n output[i,0] = temp\n output[i,1] = temax[idx]\n idx+=1\n #print(idx),\n\n #step 8, return values for storage\n return output\n\n\ndef loadTe(name='W_Abundances_grid_puestu_adpak_fitscaling_74_0.00000_5.00000_1000_idlsave'):\n data = scipy.io.readsav(name)\n return data['en']\n\n\ndef run(shotlist, timelist, idx, name='shots2016'):\n conn = sqlite3.connect(_dbname)\n\n unishots = scipy.unique(shotlist)\n te = loadTe()\n rzl = genInp()\n\n\n for i in unishots:\n print(i)\n inp = shotlist == i\n results = weights(rzl, te, i, timelist[inp])\n\n writeData(idx[inp], results, conn, name)\n\n conn.close()\n\n\ndef run2(shotlist, timelist, idx, name='shots2016', serial=True):\n conn = sqlite3.connect(_dbname)\n\n unishots = scipy.unique(shotlist)\n te = loadTe()\n rzl = genInp()\n\n if serial:\n for i in unishots:\n print(i)\n inp = shotlist == i\n results = weights(rzl, te, i, timelist[inp])\n\n writeData(idx[inp], results, conn, name)\n\n conn.close()\n else: \n\n index=0 \n lim = len(unishots)\n while index < lim:\n num =35\n if lim-index < num:\n num = lim-index\n print(num)\n\n pool = multiprocessing.Pool(num)\n output = {}\n indexout = {}\n for i in xrange(num):\n inp = shotlist == unishots[index]\n if index < lim:\n indexout[i] = idx[inp]\n output[i] = pool.apply_async(weights,(rzl, te, unishots[index], timelist[inp]))\n index += 1\n\n\n pool.close()\n pool.join()\n results = scipy.array([output[i].get() for i in output]) #breakdown the 100 shot chunks and write the data to the sql database\n\n #return indexout, results, output\n print(' ')\n print('writing to shot: '+str(inp))\n for i in xrange(len(results)):\n writeData(indexout[i], results[i], conn, name)\n\n conn.close()\n\n\ndef run3(shotlist, timelist, idx, name='shots2016', serial=True):\n conn = sqlite3.connect(_dbname2)\n\n unishots = scipy.unique(shotlist)\n te = loadTe()\n rzl = genInp()\n\n if serial:\n for i in unishots:\n print(i)\n inp = shotlist == i\n results = weights2(rzl, te, i, timelist[inp])\n\n writeData(idx[inp], results, conn, name)\n\n conn.close()\n else: \n\n index=0 \n lim = len(unishots)\n while index < lim:\n num =35\n if lim-index < num:\n num = lim-index\n print(num)\n\n pool = multiprocessing.Pool(num)\n output = {}\n indexout = {}\n for i in xrange(num):\n inp = shotlist == unishots[index]\n if index < lim:\n indexout[i] = idx[inp]\n output[i] = pool.apply_async(weights2,(rzl, te, unishots[index], timelist[inp]))\n index += 1\n\n\n pool.close()\n pool.join()\n results = scipy.array([output[i].get() for i in output]) #breakdown the 100 shot chunks and write the data to the sql database\n\n #return indexout, results, output\n print(' ')\n print('writing to shot: '+str(inp))\n for i in xrange(len(results)):\n writeData(indexout[i], results[i], conn, name)\n\n conn.close()\n\n\n\n\ndef genTable(name='shots2015'):\n\n te = loadTe()\n #print(len(temp))\n\n conn = sqlite3.connect(_dbname)\n c = conn.cursor()\n temp = 'CREATE TABLE '+name+' (id INTEGER PRIMARY KEY ASC '\n for i in xrange(len(te)):\n temp += ', val'+str(i)+' REAL'\n\n temp += ')'\n\n #print(temp)\n c.execute(temp)\n conn.commit()\n conn.close()\n\ndef genTable2(name='shots2016'):\n\n conn = sqlite3.connect(_dbname2)\n c = conn.cursor()\n temp = 'CREATE TABLE '+name+' (id INTEGER PRIMARY KEY ASC , sumM REAL, maxT REAL)'\n\n #print(temp)\n c.execute(temp)\n conn.commit()\n conn.close()\n\n\ndef writeData(idx, results, conn, name):\n \"\"\" this function writes to the SQL database the new data derived from fitData\n the format is as follow:\n\n RMSE of fit, shotnumber, time, params (with peak val, offset, width)\"\"\"\n c = conn.cursor()\n text = 'INSERT INTO '+name+' VALUES('\n\n for i in xrange(len(results)):\n newstr = text+str(idx[i])\n for j in results[i]:\n newstr +=', '+str(j)\n \n newstr += ')'\n #print(newstr)\n c.execute(newstr)\n\n conn.commit() # write data to database\n", "sub_path": "2015/SIFweightings.py", "file_name": "SIFweightings.py", "file_ext": "py", "file_size_in_byte": 10800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "TRIPPy.beam.geometry.Vecr", "line_number": 25, "usage_type": "call"}, {"api_name": "TRIPPy.beam.geometry", "line_number": 25, "usage_type": "attribute"}, {"api_name": "TRIPPy.beam", "line_number": 25, "usage_type": "name"}, {"api_name": "TRIPPy.beam.geometry.Point", "line_number": 26, "usage_type": "call"}, {"api_name": "TRIPPy.beam.geometry", "line_number": 26, "usage_type": "attribute"}, {"api_name": "TRIPPy.beam", "line_number": 26, "usage_type": "name"}, {"api_name": "TRIPPy.beam.geometry.Vecr", "line_number": 28, "usage_type": "call"}, {"api_name": "TRIPPy.beam.geometry", "line_number": 28, "usage_type": "attribute"}, {"api_name": "TRIPPy.beam", "line_number": 28, "usage_type": "name"}, {"api_name": "TRIPPy.beam.geometry.Point", "line_number": 29, "usage_type": "call"}, {"api_name": "TRIPPy.beam.geometry", "line_number": 29, "usage_type": "attribute"}, {"api_name": "TRIPPy.beam", "line_number": 29, "usage_type": "name"}, {"api_name": "TRIPPy.beam.beam.Ray", "line_number": 31, "usage_type": "call"}, {"api_name": "TRIPPy.beam.beam", "line_number": 31, "usage_type": "attribute"}, {"api_name": "TRIPPy.beam", "line_number": 31, "usage_type": "name"}, {"api_name": "TRIPPy.beam.Tokamak", "line_number": 37, "usage_type": "call"}, {"api_name": "TRIPPy.beam", "line_number": 37, "usage_type": "name"}, {"api_name": "eqtools.AUGDDData", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.mgrid", "line_number": 41, "usage_type": "attribute"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 51, "usage_type": "attribute"}, {"api_name": "scipy.logical_not", "line_number": 55, "usage_type": "call"}, {"api_name": "scipy.isfinite", "line_number": 55, "usage_type": "call"}, {"api_name": "dd.shotfile", "line_number": 60, "usage_type": "call"}, {"api_name": "scipy.argmin", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 67, "usage_type": "attribute"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 72, "usage_type": "attribute"}, {"api_name": "GIWprofiles.ne", "line_number": 78, "usage_type": "call"}, {"api_name": "GIWprofiles.te2rho2", "line_number": 78, "usage_type": "call"}, {"api_name": "GIWprofiles.te2rho2", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "GIWprofiles.te", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.searchsorted", "line_number": 84, "usage_type": "call"}, {"api_name": "scipy.insert", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.logical_not", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.isfinite", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.logical_not", "line_number": 95, "usage_type": "call"}, {"api_name": "scipy.isfinite", "line_number": 95, "usage_type": "call"}, {"api_name": "scipy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "scipy.arange", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.logical_and", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.log", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.exp", "line_number": 126, "usage_type": "call"}, {"api_name": "eqtools.AUGDDData", "line_number": 131, "usage_type": "call"}, {"api_name": "scipy.logical_not", "line_number": 138, "usage_type": "call"}, {"api_name": "scipy.isfinite", "line_number": 138, "usage_type": "call"}, {"api_name": "scipy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "scipy.arange", "line_number": 163, "usage_type": "call"}, {"api_name": "scipy.logical_and", "line_number": 163, "usage_type": "call"}, {"api_name": "scipy.log", "line_number": 167, "usage_type": "call"}, {"api_name": "scipy.exp", "line_number": 168, "usage_type": "call"}, {"api_name": "eqtools.AUGDDData", "line_number": 173, "usage_type": "call"}, {"api_name": "scipy.nanmin", "line_number": 175, "usage_type": "call"}, {"api_name": "GIWprofiles.te", "line_number": 176, "usage_type": "call"}, {"api_name": "scipy.logical_not", "line_number": 183, "usage_type": "call"}, {"api_name": "scipy.isfinite", "line_number": 183, "usage_type": "call"}, {"api_name": "scipy.sum", "line_number": 184, "usage_type": "call"}, {"api_name": "scipy.io.readsav", "line_number": 195, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 195, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 200, "usage_type": "call"}, {"api_name": "scipy.unique", "line_number": 202, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 218, "usage_type": "call"}, {"api_name": "scipy.unique", "line_number": 220, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 243, "usage_type": "call"}, {"api_name": "scipy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 268, "usage_type": "call"}, {"api_name": "scipy.unique", "line_number": 270, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 293, "usage_type": "call"}, {"api_name": "scipy.array", "line_number": 306, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 324, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 339, "usage_type": "call"}]} +{"seq_id": "572367339", "text": "\"\"\"\nDefinition of views.\n\"\"\"\n\nfrom django.shortcuts import render\n\nfrom django.views.generic import FormView,CreateView,TemplateView, ListView,UpdateView\nfrom django.core.urlresolvers import reverse_lazy\nfrom .models import *\nfrom .forms import *\nfrom django.core import serializers\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom allauth.account.views import *\nfrom allauth.account.models import *\nfrom allauth.socialaccount.models import SocialAccount\n\nfrom django.db import connection,transaction\nfrom django.contrib.auth.models import User\n#1.7 reportes\nfrom django.db.models import Q\n#1.8.1 CIrculos\nimport math\nimport time\n\ndef Distancia(x1,y1,x2,y2):\n\tdistancia=math.sqrt(abs(((x2**2)-(x1**2))+((y2**2)-(y1**2))))\n\treturn distancia\n\ndef coordenadas(queryset):\n\tdenuncia=[]\n\tfor x in queryset:\n\t\tdenuncia.append([x.id,x.titulo, x.latitud,x.longitud])\n\tV=[]\n\tVv=[]\n\tVmax=[]\n\tV2=[]\n\tmismo=False\n\tfor x in denuncia:\n\t\tVv=[]\n\t\tfor y in denuncia:\n\t\t\tif Distancia(y[2],y[3],x[2],x[3])<=1.6:\n\t\t\t\n\t\t\t\n\t\t\t\tif denuncia.index(y)!=denuncia.index(x):\n\t\t\t\t\tVv.append(y)\n\t\tif len(Vv)!=0:\n\t\t\tV.append(Vv)\n\n\tfor x in V:\n\t\n\t\tVmax.append(len(x))\n\tm=max(Vmax)\n\n\tfor x in V:\n\t\tif (len(x)==m):\n\t\t\tV2.append(x)\n\tcoord_x=0\n\tcoord_y=0\n\tv_promedios=[]\n\t#obtener prom de cada v2\n\tfor a in V2:\n\t\tfor b in a:\n\t\t\tcoord_x+=b[2]\n\t\t\tcoord_y+=b[3]\n\t\tentre=len(a)\n\t\tv_promedios+=[[(coord_x/len(a)),(coord_y/len(a))]]\n\t\tcoord_x=0\n\t\tcoord_y=0\n\treturn v_promedios, m\n\n\ndef crear_circulo():\n\tcirculos=Circulos_m.objects.filter(fecha=(time.strftime(\"%Y-%m-%d\")))\n\tif len(circulos)==0:\n\t\ta=Denuncia_m.objects.filter(Q(estado=True)|Q(estado=None))\n\t\tv,m=coordenadas(a)\n\t\tfor Vv in v:\n\t\t\tCirculos_m.objects.create(x=Vv[0], y=Vv[1], cantidad=m)\n#add\n@login_required(login_url=reverse_lazy('account_login'), redirect_field_name=None)\ndef Zonas(request):\t\n\tif request.POST:\n\t\tfechaDesde= request.POST['fechaDesde']\n\t\tfechaHasta= request.POST['fechaHasta']\n\t\tcirculos=Circulos_m.objects.filter(fecha__range=[fechaDesde,fechaHasta])\n\telse:\n\t\tcirculos=Circulos_m.objects.filter(fecha=(time.strftime(\"%Y-%m-%d\")))\n\treturn render(request, 'app/zonas.html', {'circulos':circulos})\n#fin 1.8.1\n\n@login_required(login_url=reverse_lazy('account_login'), redirect_field_name=None)\ndef Denuncia(request):\n form=Denuncia_Form(request.POST or None)\n form2=imagenes_f(request.POST or None,request.FILES or None)\n form3=videos_f(request.POST or None,request.FILES or None)\n if(request.method=='POST' and form.is_valid):\n formResult=form.save()\n if(form2.is_valid()):\n idDenuncia=formResult.id\n \n for x in request.FILES.getlist('imagenes'):\n imagenes_m.objects.create(imagen=x, denunciaA_id=idDenuncia)\n \n if(form3.is_valid()):\n formResult3=form3.save(commit=False)\n formResult3.denunciaB_id=formResult.id\n formResult3.save()\n return HttpResponseRedirect(reverse(\"mapa_view\"))\n return render(request, 'app/denuncia.html',{'form':form,'form2':form2, 'form3':form3})\n \n\n#1.7 Reportes\ndef MasVotadas():\n object_list=Denuncia_m.objects.raw(\"select count(f.denuncia_id) as x, f.id as y, f.denuncia_id as w,f.usuario_id as z, f.fecha as q,d.* from app_favor_m f, app_denuncia_m d where f.denuncia_id=d.id group by f.denuncia_id order by x desc,f.fecha desc limit 0,10\")\n ctx=Denuncia_m.objects.raw(\"select * from app_denuncia_m o where o.id='%s'\"%(10))\n return object_list\n#Fin 1.7\n\n#1.6 Filtros\n\ndef Filtro(request, cat):\n\tif cat==\"100\":\n\t\tobject_list=MasVotadas()\n\telse:\n\t\tobject_list=Denuncia_m.objects.filter(categoria=cat)\n\treturn render(request, 'app/mapa.html', {'object_list':object_list})\n\n#Fin ver 1.6\n\n\nclass Mapa(ListView):\n\ttemplate_name='app/mapa.html'\n\tmodel=Denuncia_m\n\t#1.7 reportes\n\tdef get_queryset(self):\n\t\tDenuncia_m.objects.filter(hasta__lt=(time.strftime(\"%Y-%m-%d\"))).update(estado=False)\n\t\tDenuncia_m.objects.filter(hasta__gte=(time.strftime(\"%Y-%m-%d\"))).update(estado=True)\n\t\tcrear_circulo()\n\t\tqueryset = super(Mapa, self).get_queryset()\n\t\treturn queryset.filter(Q(estado=True)|Q(estado=None))\n\nclass contador(ListView):\n template_name='app/contador.html'\n model=Denuncia_m\n\n#add\n@login_required(login_url=reverse_lazy('account_login'), redirect_field_name=None)\ndef Noticia(request,pk):\n ctx=Denuncia_m.objects.raw(\"select * from app_denuncia_m o where o.id='%s'\"%(pk))\n ctx2=imagenes_m.objects.raw(\"select * from app_imagenes_m o where o.denunciaA_id='%s'\"%(pk))\n ctx3=videos_m.objects.raw(\"select * from app_videos_m o where o.denunciaB_id='%s'\"%(pk))\n ctx4=Comentario_m.objects.raw(\"select * from app_comentario_m o where o.denuncia_id='%s'\"%(pk))\n #ctx5=Usuario_m.objects.raw(\"select * from app_usuario_m\")\n ctx5=Usuario_m.objects.raw(\"select u.id,c.id,a.id, u.username,c.comentario,a.Avatar,a.Nombre_id from auth_user u, app_comentario_m c,app_usuario_m a where c.denuncia_id='%s' and a.Nombre_id=c.user_id and u.id=a.Nombre_id\"%(pk))\n cont=0\n\n form=Comentario_form(request.POST or None,request.FILES or None)\n if request.method == \"POST\":\n if form.is_valid():\n instance=form.save(commit=False)\n #user=user.id\n user = form.cleaned_data.get(\"user\")\n denuncia = form.cleaned_data.get(\"denuncia\")\n #denuncia=pk\n comentario = form.cleaned_data.get(\"comentario\")\n form.save()\n #1.6.3 Reportes\n reputacion(request, pk)\n #fin reportes\n #1.6.1 Likes, rangos\n\n x,ninguno,favor,ob_favor, ob_contra, reportado=consultaVotos(request, pk)\n\n favor_form=Favor_f(request.POST or None)\n contra_form=Contra_f(request.POST or None)\n #1.6.3 Reportes\n reportes_form=Reportados_f(request.POST or None)\n #Fin 1.6.3\n if request.method == \"POST\":\n user = User.objects.get(id=request.user.id)\n\n if 'favor' in request.POST:\n if favor_form.is_valid():\n if not ob_contra:\n f=favor_form.save(commit=False)\n f.denuncia_id=pk\n f.usuario_id=request.user.id\n f.save()\n else:\n Contra_m.objects.filter(usuario=request.user.id, denuncia=pk).delete() \n f=favor_form.save(commit=False)\n f.denuncia_id=pk\n f.usuario_id=request.user.id\n f.save()\n if 'contra' in request.POST:\n if contra_form.is_valid():\n if not ob_favor:\n\n c=contra_form.save(commit=False)\n c.denuncia_id=pk\n c.usuario_id=request.user.id\n c.save()\n else:\n Favor_m.objects.filter(usuario=request.user.id, denuncia=pk).delete()\n c=contra_form.save(commit=False)\n c.denuncia_id=pk\n c.usuario_id=request.user.id\n c.save()\n if 'reportar' in request.POST:\n if reportes_form.is_valid():\n r=reportes_form.save(commit=False)\n r.denuncia_id=pk\n r.usuario_id=request.user.id\n r.save()\n Denuncia_m.objects.filter(pk=pk).update(reportado=True)\n x,ninguno,favor,ob_favor, ob_contra, reportado=consultaVotos(request, pk)\n #fin 1.6.2\n #1.6.3 Reportes\n nivel=reputacion(request, pk)\n #fin reportes\n return render(request,'app/noticia.html',{'ctx':ctx,'ctx2':ctx2,'ctx3':ctx3,'ctx4':ctx4,'ctx5':ctx5,'form':form,'pk':pk, 'favor_f':favor_form, 'contra_f':contra_form, 'x':x, 'ninguno':ninguno, 'favor':favor, 'reportado':reportado, 'reportes_f':reportes_form,'nivel':nivel})\n#1.6.2 Votos\n\n return render(request,'app/noticia.html',{'ctx':ctx,'ctx2':ctx2,'ctx3':ctx3,'ctx4':ctx4,'ctx5':ctx5,'form':form,'pk':pk,'cont':cont})\n\ndef consultaVotos(request, pk):\n #1.6.3 Reportes\n ob_reportes=Reportados_m.objects.filter(usuario=request.user.id, denuncia=pk)\n #fin 1.6.3\n ob_favor=Favor_m.objects.filter(usuario=request.user.id, denuncia=pk)\n ob_contra=Contra_m.objects.filter(usuario=request.user.id, denuncia=pk)\n ninguno=True\n favor=True\n reportado=False\n if not ob_favor and not ob_contra:\n x=1\n ninguno=False\n else:\n if not ob_contra:\n if ob_favor[0].usuario_id == request.user.id:\n x=2\n favor=True\n else:\n if ob_contra[0].usuario_id == request.user.id:\n x=3\n favor=False\n if not ob_reportes:\n reportado=False\n elif ob_reportes[0].usuario_id == request.user.id:\n reportado=True\n \n return x,ninguno, favor, ob_favor, ob_contra, reportado\n#Fin 1.6.2\n#1.6.3 Reportes\ndef reputacion(request, pk):\n usuario_reportado=Denuncia_m.objects.filter(id=pk)\n usuario_r_id=usuario_reportado[0].user_id\n buenos=Favor_m.objects.filter(denuncia__user=usuario_r_id).count()\n malos_contra=Contra_m.objects.filter(denuncia__user=usuario_r_id).count()\n malos_reportados=Reportados_m.objects.filter(denuncia__user=usuario_r_id).count()\n malos=malos_contra+(malos_reportados*10)\n\n try:\n nivel=buenos*100/(malos+buenos)\n except:\n nivel=0\n return nivel\n#fin 1.6.3\n\ndef reputacion_perfil(request, pk):\n #usuario_reportado=Denuncia_m.objects.filter(id=pk)\n usuario_r_id=pk\n buenos=Favor_m.objects.filter(denuncia__user=usuario_r_id).count()\n malos_contra=Contra_m.objects.filter(denuncia__user=usuario_r_id).count()\n malos_reportados=Reportados_m.objects.filter(denuncia__user=usuario_r_id).count()\n malos=malos_contra+(malos_reportados*10)\n\n try:\n nivel=buenos*100/(malos+buenos)\n except:\n nivel=0\n return nivel\n#fin 1.6.3\n#add\n@login_required(login_url=reverse_lazy('account_login'), redirect_field_name=None)\ndef PerfilUser(request):\n ctx=Usuario_m.objects.raw(\"select * from app_usuario_m o where o.Nombre_id='%s'\"%(request.user.id))\n actualizar=Usuario_m.objects.filter(Nombre_id=request.user.id)\n rep=reputacion_perfil(request,request.user.id)\n denuncia=Denuncia_m.objects.raw(\"select * from app_denuncia_m d where d.user_id='%s'\"%(request.user.id))\n\t\n return render(request,'app/perfiluser.html',{'ctx':ctx,'actualizar':actualizar,'rep':rep,'denuncia':denuncia})\n \n#\n@login_required(login_url=reverse_lazy('account_login'), redirect_field_name=None)\ndef Perfil(request,pk): \n if str(pk)==str(request.user.id):\n return HttpResponseRedirect(reverse(\"perfilUser_view\"))\n\n usuario=Usuario_m.objects.raw(\"select o.id,o.first_name,o.last_name,o.email,o.username,a.id,a.Avatar from auth_user o, app_usuario_m a where o.id='%s' and a.Nombre_id='%s'\"%(pk,pk))\n\n publicaciones=Denuncia_m.objects.raw(\"select * from app_denuncia_m d, app_imagenes_m i where d.user_id='%s' and i.denunciaA_id=d.id\"%(pk))\n\n denuncia=Denuncia_m.objects.raw(\"select * from app_denuncia_m d where d.user_id='%s'\"%(pk))\n\n return render(request,'app/perfil.html',{'usuario':usuario,'publicaciones':publicaciones,'denuncia':denuncia})\n\n\ndef Perfil_respaldo(request,pk):\n ctx=Comentario_m.objects.raw(\"select * from auth_user o where o.id='%s'\"%(pk))\n ctx2=Usuario_m.objects.raw(\"select * from app_usuario_m o where o.nombre_id='%s'\"%(pk))\n ctx3=Denuncia_m.objects.all()\n \n \n desicion=False\t\t\n \n for a in ctx:\n for b in ctx2:\n if str(a.id)==str(pk) and str(b.Nombre_id)==str(pk):\n desicion=True #Actualizar\n else:\n desicion=False # Registrar\n\n \n return render(request,'app/perfil.html',{'ctx':ctx,'ctx2':ctx2,'ctx3':ctx3,'desicion':desicion})\n\n\n#Sirve para actualizar el perfil pero ya debe de estar creado\nclass UpdatePerfil(UpdateView):\n template_name='app/actualizar.html'\n model=Usuario_m\n fields=['Telefono','Direccion','Avatar']\n success_url = reverse_lazy(\"perfilUser_view\")\n\n\n''' class RegisterPerfil(CreateView):\n template_name='app/registrar.html'\n model=Usuario_m\n fields= \"__all__\"\n success_url = reverse_lazy(\"mapa_view\") '''\n\ndef insertar(request):\n id_usuario=request.user.id\n consulta=Usuario_m.objects.filter(Nombre=id_usuario)\n \n if not consulta:\n cursor = connection.cursor()\n cursor.execute(\"INSERT INTO app_usuario_m(Telefono,Direccion,Avatar,Nombre_id) VALUES ('%s','%s','%s','%s')\"%( None,None ,None ,id_usuario))\n\n return HttpResponseRedirect(reverse(\"mapa_view\"))\n \n\n''' \n\ndef insertar_respaldo(request):\n id_usuario=request.user.id\n consulta=Usuario_m.objects.filter(Nombre=id_usuario)\n \n if not consulta:\n cursor = connection.cursor()\n cursor.execute(\"INSERT INTO app_usuario_m(Telefono,Direccion,Avatar,Nombre_id) VALUES ('%s','%s','%s','%s')\"%('NULL','NULL','NULL',id_usuario))\n\n return HttpResponseRedirect(reverse(\"mapa_view\"))\n\n\n '''\n \n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 12998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "math.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 75, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 88, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 80, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 108, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 126, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 131, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 136, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 140, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 142, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 180, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 180, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 180, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 221, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 224, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 147, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 292, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 285, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 298, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 306, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 295, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 325, "usage_type": "call"}, {"api_name": "django.views.generic.UpdateView", "line_number": 329, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse_lazy", "line_number": 333, "usage_type": "call"}, {"api_name": "django.db.connection.cursor", "line_number": 347, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 347, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 350, "usage_type": "call"}]} +{"seq_id": "608014908", "text": "import math\nimport time, sys\nfrom bresenham import bresenham\nfrom enum import Enum\n\nclass Direction(Enum):\n RIGHT = 0\n UP = 1\n LEFT = 2\n DOWN = 3\n\nclass State(Enum):\n STRAIGHT_LINE = 0\n SURROUND = 1\n BEST_SURROUNDED = 2\n\n\nclass PathPlanning:\n\n def __init__(self, start, goal, map):\n self.start = start\n self.goal = goal\n self.current_pos = start\n self.map = map\n \n \n def finished(self):\n return self.current_pos == self.goal\n \n \n def next_step(self):\n # Placeholder\n return self.current_pos\n \n \n def distance(self, p0, p1):\n return math.sqrt((p0[0] - p1[0])**2 + (p0[1] - p1[1])**2)\n \n \n def neighbors(self, p):\n return [(p[0]-1, p[1]), (p[0]+1, p[1]), (p[0], p[1]-1), (p[0], p[1]+1)]\n \n \n def corner_neighbors(self, p):\n return [(p[0]-1, p[1]-1), (p[0]-1, p[1]+1), (p[0]+1, p[1]-1), (p[0]+1, p[1]+1)]\n \n \n def closest_neighbor(self, p):\n min_dist = math.inf\n closest_neighbor = None\n for n in self.neighbors(p):\n if self.distance(n, self.goal) < min_dist:\n min_dist = self.distance(n, self.goal)\n closest_neighbor = n\n return closest_neighbor\n\n\nclass Bug(PathPlanning):\n \n def __init__(self, start, goal, map):\n super().__init__(start, goal, map)\n self.line = list(bresenham(start[0], start[1], goal[0], goal[1]))\n self.last_wall = Direction.RIGHT\n self.current_state = State.STRAIGHT_LINE\n self.left_hit = False\n \n \n def straight_line(self):\n if self.map[self.line[0][1]][self.line[0][0]] == 255:\n self.current_pos = self.line[0]\n self.line.pop(0)\n else:\n self.current_state = State.SURROUND\n if self.map[self.current_pos[1]][self.current_pos[0] + 1] == 0:\n self.last_wall = Direction.RIGHT\n elif self.map[self.current_pos[1]][self.current_pos[0] - 1] == 0:\n self.last_wall = Direction.LEFT\n elif self.map[self.current_pos[1] + 1][self.current_pos[0]] == 0:\n self.last_wall = Direction.DOWN\n elif self.map[self.current_pos[1] - 1][self.current_pos[0]] == 0:\n self.last_wall = Direction.UP\n else:\n # It touched in a corner\n if self.map[self.current_pos[1] + 1][self.current_pos[0] + 1] == 0:\n self.current_pos = (self.current_pos[0] + 1, self.current_pos[1])\n self.last_wall = Direction.DOWN\n elif self.map[self.current_pos[1] + 1][self.current_pos[0] - 1] == 0:\n self.current_pos = (self.current_pos[0], self.current_pos[1] + 1)\n self.last_wall = Direction.LEFT\n elif self.map[self.current_pos[1] - 1][self.current_pos[0] - 1] == 0:\n self.current_pos = (self.current_pos[0] - 1, self.current_pos[1])\n self.last_wall = Direction.UP\n elif self.map[self.current_pos[1] - 1][self.current_pos[0] + 1] == 0:\n self.current_pos = (self.current_pos[0], self.current_pos[1] - 1)\n self.last_wall = Direction.RIGHT\n else:\n # This never happens\n pass\n \n \n def surround(self):\n x, y = self.current_pos\n if self.last_wall == Direction.RIGHT:\n if self.map[y][x + 1] == 255:\n self.current_pos = (x + 1, y)\n self.last_wall = Direction.DOWN\n self.left_hit = True\n else:\n self.last_wall = Direction.UP\n elif self.last_wall == Direction.UP:\n if self.map[y - 1][x] == 255:\n self.current_pos = (x, y - 1)\n self.last_wall = Direction.RIGHT\n self.left_hit = True\n else:\n self.last_wall = Direction.LEFT\n elif self.last_wall == Direction.LEFT:\n if self.map[y][x - 1] == 255:\n self.current_pos = (x - 1, y)\n self.last_wall = Direction.UP\n self.left_hit = True\n else:\n self.last_wall = Direction.DOWN\n elif self.last_wall == Direction.DOWN:\n if self.map[y + 1][x] == 255:\n self.current_pos = (x, y + 1)\n self.last_wall = Direction.LEFT\n self.left_hit = True\n else:\n self.last_wall = Direction.RIGHT\n\n\nclass Bug1(Bug):\n \n def __init__(self, start, goal, map):\n super().__init__(start, goal, map)\n self.hit_point = self.start\n self.surroundings = {}\n \n \n def next_step(self):\n if self.current_state == State.STRAIGHT_LINE:\n if self.map[self.line[0][1]][self.line[0][0]] == 255:\n self.current_pos = self.line[0]\n self.line.pop(0)\n else:\n self.straight_line()\n self.hit_point = self.current_pos\n self.left_hit = False\n \n elif self.current_state == State.SURROUND:\n self.surround()\n self.surroundings[self.current_pos] = self.distance(self.current_pos, self.goal)\n \n if self.current_pos == self.hit_point and self.left_hit:\n self.current_state = State.BEST_SURROUNDED\n \n elif self.current_state == State.BEST_SURROUNDED:\n self.current_pos = min(self.surroundings, key=self.surroundings.get)\n self.current_state = State.STRAIGHT_LINE\n self.line = list(bresenham(self.current_pos[0], self.current_pos[1], self.goal[0], self.goal[1]))\n \n \n return self.current_pos\n\n\nclass Bug2(Bug):\n \n def __init__(self, start, goal, map):\n super().__init__(start, goal, map)\n \n \n def next_step(self):\n if self.current_state == State.STRAIGHT_LINE:\n self.straight_line()\n \n elif self.current_state == State.SURROUND:\n self.surround()\n \n if (self.current_pos in self.line) and self.left_hit:\n while self.line[0] != self.current_pos:\n self.line.pop(0)\n self.current_state = State.STRAIGHT_LINE\n \n return self.current_pos\n\n\n\nclass ValueIteration(PathPlanning):\n \n def __init__(self, start, goal, map):\n super().__init__(start, goal, map)\n self.neighbors_graph = {}\n self.generate_neighbors()\n self.distances = {}\n self.generate_distances()\n self.dist_goal_start = self.distances[self.start]\n \n \n def generate_distances(self):\n for key in self.neighbors_graph.keys():\n self.distances[key] = math.inf\n self.distances[(self.goal[0],self.goal[1])] = 0\n \n queue = [self.goal]\n \n # Breadth first approach\n while queue:\n current = queue.pop(0)\n \n for neighbor in self.neighbors_graph[current]:\n is_corner_neighbor = (((current[0] - neighbor[0]) * (current[1] - neighbor[1])) != 0)\n dist = self.distances[current]\n if is_corner_neighbor:\n dist += math.sqrt(2)\n else:\n dist += 1\n \n if dist < self.distances[neighbor] and dist < self.distances[self.start]:\n queue.append(neighbor)\n self.distances[neighbor] = dist\n \n \n def generate_neighbors(self):\n # for each point\n for y in range(len(self.map)):\n for x in range(len(self.map[0])):\n # if it is not a border\n if self.map[y][x] != 0:\n neighbors = []\n # for each of its neighbors\n for i in range(-1,2):\n if x+i >= 0 and x+i < len(self.map[0]):\n for j in range(-1,2):\n if y+j >= 0 and y+j < len(self.map):\n # if they are not borders or the same node\n if i != 0 or j != 0:\n if self.map[y+j][x+i] != 0:\n neighbors.append((x+i, y+j))\n self.neighbors_graph[(x,y)] = neighbors\n \n \n def next_step(self):\n min_dist = math.inf\n min_neigh = None\n for neighbor in self.neighbors_graph[self.current_pos]:\n dist = self.distances[neighbor]\n if dist < min_dist:\n min_dist = dist\n min_neigh = neighbor\n self.current_pos = min_neigh\n return min_neigh\n \n \n def distance_map(self):\n dist_map = {}\n dist_mul = self.dist_goal_start/254\n for key in self.distances.keys():\n if self.distances[key] > self.dist_goal_start:\n dist_map[key] = 255\n else:\n dist_map[key] = self.distances[key]//dist_mul\n return dist_map\n", "sub_path": "PathPlanning.py", "file_name": "PathPlanning.py", "file_ext": "py", "file_size_in_byte": 9166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "enum.Enum", "line_number": 6, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 12, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 49, "usage_type": "attribute"}, {"api_name": "bresenham.bresenham", "line_number": 62, "usage_type": "call"}, {"api_name": "bresenham.bresenham", "line_number": 161, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 202, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 215, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 244, "usage_type": "attribute"}]} +{"seq_id": "373515497", "text": "from django.db.models import Count, Q, Subquery, OuterRef\nfrom django_filters import FilterSet, filters\nfrom rest_framework import mixins, serializers\nfrom rest_framework.decorators import action\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import GenericViewSet\n\nfrom ..models import Discussion\nfrom ..models import Like\nfrom ..models import Question\nfrom ..serializers.discussion import DiscussionSerializer\n\n__all__ = ['DiscussionsViewSet']\n\n\nclass CommentParser(serializers.Serializer):\n text = serializers.CharField(min_length=1)\n\n\nclass LikeParser(serializers.Serializer):\n value = serializers.BooleanField()\n\n\nclass AnswerParser(serializers.Serializer):\n question = serializers.PrimaryKeyRelatedField(queryset=Question.objects)\n answer = serializers.CharField(min_length=1)\n\n def validate(self, attrs):\n def boolean(value):\n if value not in ('true', 'false'):\n raise ValueError()\n\n def single(value):\n if not question.answer_variants.filter(id=int(value)).exists():\n raise ValueError()\n\n def multiple(value):\n values = list(map(int, value.split(',')))\n count = question.answer_variants.filter(id__in=values).count()\n if len(values) != count:\n raise ValueError()\n\n question = attrs['question']\n validators = {\n Question.TEXT: lambda x: x,\n Question.BOOL: boolean,\n Question.NUMBER: lambda x: float(x.replace(',', '.')),\n Question.SINGLE: single,\n Question.MULTIPLE: multiple,\n }\n try:\n validators[question.answer_type](attrs['answer'])\n except ValueError:\n raise ValidationError('Неверный формат ответа')\n return attrs\n\n\nclass DiscussionsFilterSet(FilterSet):\n topic = filters.NumberFilter()\n\n\nclass DiscussionsViewSet(GenericViewSet, mixins.ListModelMixin, mixins.RetrieveModelMixin):\n queryset = Discussion.objects.annotate(likes_count=Count('likes', filter=Q(likes__value=True)),\n dislikes_count=Count('likes', filter=Q(likes__value=False))\n ).order_by('name')\n serializer_class = DiscussionSerializer\n filterset_class = DiscussionsFilterSet\n\n def get_queryset(self):\n user = self.request.user\n return super().get_queryset().filter(type=user.type).annotate(\n like=Subquery(Like.objects.filter(discussion=OuterRef('id'),\n user=user).values('value')[:1]))\n\n @action(methods=('post',), detail=True)\n def like(self, request, *args, **kwargs):\n context = self.get_serializer_context()\n parser = LikeParser(data=request.data, context=context)\n parser.is_valid(raise_exception=True)\n\n obj = self.get_object()\n user = request.user\n\n obj.likes.filter(user=user).delete()\n obj.likes.create(user=user, value=parser.validated_data['value'])\n return Response()\n\n @action(methods=('post',), detail=True)\n def comment(self, request, *args, **kwargs):\n context = self.get_serializer_context()\n parser = CommentParser(data=request.data, context=context)\n parser.is_valid(raise_exception=True)\n\n obj = self.get_object()\n user = request.user\n\n obj.comments.create(user=user, text=parser.validated_data['text'])\n return Response()\n\n @action(methods=('post',), detail=True)\n def answer(self, request, *args, **kwargs):\n context = self.get_serializer_context()\n parser = AnswerParser(data=request.data, many=True, context=context)\n parser.is_valid(raise_exception=True)\n user = request.user\n\n for answer in parser.validated_data:\n answers = answer['question'].answers\n answers.filter(user=user).delete()\n answers.create(user=user, answer=answer['answer']) # need more answers...\n\n return Response()\n", "sub_path": "questionnaire/views/discussions.py", "file_name": "discussions.py", "file_ext": "py", "file_size_in_byte": 4112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 21, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.serializers.BooleanField", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.serializers.PrimaryKeyRelatedField", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Question.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Question.TEXT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Question.BOOL", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Question.NUMBER", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 48, "usage_type": "name"}, {"api_name": "models.Question.SINGLE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 49, "usage_type": "name"}, {"api_name": "models.Question.MULTIPLE", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 50, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 55, "usage_type": "call"}, {"api_name": "django_filters.FilterSet", "line_number": 59, "usage_type": "name"}, {"api_name": "django_filters.filters.NumberFilter", "line_number": 60, "usage_type": "call"}, {"api_name": "django_filters.filters", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 63, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Discussion.objects.annotate", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Discussion.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Discussion", "line_number": 64, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 65, "usage_type": "call"}, {"api_name": "serializers.discussion.DiscussionSerializer", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.Subquery", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Like.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Like.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Like", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.OuterRef", "line_number": 73, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 87, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 76, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 99, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 89, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 113, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "205855007", "text": "import operator\nimport random\nimport math\nimport json\nimport threading\nimport numpy as np\nimport tensorflow as tf\nimport pickle\nimport util\n# import coref_ops\n# import conll\nimport metrics\n\nclass CorefModel(object):\n def __init__(self, config):\n self.config = config\n self.embedding_info = [(emb[\"size\"], emb[\"lowercase\"]) for emb in config[\"embeddings\"]]\n self.embedding_size = sum(size for size, _ in self.embedding_info)\n self.char_embedding_size = config[\"char_embedding_size\"]\n self.char_dict = util.load_char_dict(config[\"char_vocab_path\"])\n self.embedding_dicts = [util.load_embedding_dict(emb[\"path\"], emb[\"size\"], emb[\"format\"]) for emb in config[\"embeddings\"]]\n self.max_mention_width = config[\"max_mention_width\"]\n self.genres = { g:i for i,g in enumerate(config[\"genres\"]) }\n self.eval_data = None # Load eval data lazily.\n\n input_props = []\n input_props.append((tf.float32, [None, None, self.embedding_size])) # Question embeddings.\n input_props.append((tf.float32, [None, None, self.embedding_size])) # Transcript embeddings.\n input_props.append((tf.int32, [None])) # Labels.\n input_props.append((tf.int32, [None])) # q_len.\n input_props.append((tf.int32, [None])) # s_len.\n input_props.append((tf.bool, [])) # Is training.\n\n self.queue_input_tensors = [tf.placeholder(dtype, shape) for dtype, shape in input_props]\n dtypes, shapes = zip(*input_props)\n queue = tf.PaddingFIFOQueue(capacity=10, dtypes=dtypes, shapes=shapes)\n self.enqueue_op = queue.enqueue(self.queue_input_tensors)\n self.input_tensors = queue.dequeue()\n\n self.predictions, self.loss = self.get_predictions_and_loss(*self.input_tensors)\n self.global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n self.reset_global_step = tf.assign(self.global_step, 0)\n learning_rate = tf.train.exponential_decay(self.config[\"learning_rate\"], self.global_step,\n self.config[\"decay_frequency\"], self.config[\"decay_rate\"], staircase=True)\n trainable_params = tf.trainable_variables()\n gradients = tf.gradients(self.loss, trainable_params)\n gradients, _ = tf.clip_by_global_norm(gradients, self.config[\"max_gradient_norm\"])\n optimizers = {\n \"adam\" : tf.train.AdamOptimizer,\n \"sgd\" : tf.train.GradientDescentOptimizer\n }\n optimizer = optimizers[self.config[\"optimizer\"]](learning_rate)\n self.train_op = optimizer.apply_gradients(zip(gradients, trainable_params), global_step=self.global_step)\n\n def start_enqueue_thread(self, session):\n with open(self.config[\"train_path\"]) as f:\n train_examples = [json.loads(jsonline) for jsonline in f.readlines()]\n def _enqueue_loop():\n while True:\n print('*' * 89)\n random.shuffle(train_examples)\n for i in range(0, len(train_examples), 16):\n examples = train_examples[i:i + 16]\n tensorized_example = [self.tensorize_example(x, True, 40, 200) for x in examples]\n ques_emb = np.concatenate([x[0] for x in tensorized_example], axis=0)\n trans_emb = np.concatenate([x[1] for x in tensorized_example], axis=0)\n labels = np.array([x[2] for x in tensorized_example])\n q_len = np.array([x[3] for x in tensorized_example])\n s_len = np.array([x[4] for x in tensorized_example])\n feed_dict = dict(zip(self.queue_input_tensors, [ques_emb, trans_emb, labels, q_len, s_len, True]))\n session.run(self.enqueue_op, feed_dict=feed_dict)\n enqueue_thread = threading.Thread(target=_enqueue_loop)\n enqueue_thread.daemon = True\n enqueue_thread.start()\n \n def tensorize_mentions(self, mentions):\n if len(mentions) > 0:\n starts, ends = zip(*mentions)\n else:\n starts, ends = [], []\n return np.array(starts), np.array(ends)\n\n def tensorize_example(self, example, is_training, mlq, mlt, oov_counts=None):\n label = example['label']\n q_len = example['q_len']\n s_len = example['s_len']\n question = example['question'].split(' ')\n trans = example['script'].split(' ')\n \n ques_emb = np.zeros([1, mlq, self.embedding_size])\n trans_emb = np.zeros([1, mlt, self.embedding_size])\n # char_index = np.zeros([len(sentences), max_sentence_length, max_word_length])\n ques_len = np.array([len(question)])\n trans_len = np.array([len(trans)])\n for j, word in enumerate(question):\n if j > mlq - 1:\n break\n current_dim = 0\n for k, (d, (s,l)) in enumerate(zip(self.embedding_dicts, self.embedding_info)):\n if l:\n current_word = word.lower()\n else:\n current_word = word\n if oov_counts is not None and current_word not in d:\n oov_counts[k] += 1\n ques_emb[0, j, current_dim:current_dim + s] = util.normalize(d[current_word])\n current_dim += s\n # char_index[i, j, :len(word)] = [self.char_dict[c] for c in word]\n \n for j, word in enumerate(trans):\n if j > mlt - 1:\n break\n current_dim = 0\n for k, (d, (s,l)) in enumerate(zip(self.embedding_dicts, self.embedding_info)):\n if l:\n current_word = word.lower()\n else:\n current_word = word\n if oov_counts is not None and current_word not in d:\n oov_counts[k] += 1\n trans_emb[0, j, current_dim:current_dim + s] = util.normalize(d[current_word])\n current_dim += s\n return ques_emb, trans_emb, label, q_len, s_len, is_training # char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids\n\n\n def get_predictions_and_loss(self, ques_emb, trans_emb, label, ques_len, trans_len, is_training):\n self.dropout = 1 - (tf.to_float(is_training) * self.config[\"dropout_rate\"])\n self.lexical_dropout = 1 - (tf.to_float(is_training) * self.config[\"lexical_dropout_rate\"])\n\n num_questions = tf.shape(ques_emb)[0]\n num_sentences = tf.shape(trans_emb)[0]\n # num_sentences = tf.shape(ques_emb)[0]\n # max_sentence_length = tf.shape(ques_emb)[1]\n\n # ques_len = tf.shape(ques_emb)[1]\n # trans_len = tf.shape(trans_emb)[1]\n\n # ques_len_lstm = tf.reshape(ques_len, [1])\n # trans_len_lstm = tf.reshape(trans_len, [1])\n\n # text_emb_list = [word_emb]\n\n # if self.config[\"char_embedding_size\"] > 0:\n # char_emb = tf.gather(tf.get_variable(\"char_embeddings\", [len(self.char_dict), self.config[\"char_embedding_size\"]]), char_index) # [num_sentences, max_sentence_length, max_word_length, emb]\n # flattened_char_emb = tf.reshape(char_emb, [num_sentences * max_sentence_length, util.shape(char_emb, 2), util.shape(char_emb, 3)]) # [num_sentences * max_sentence_length, max_word_length, emb]\n # flattened_aggregated_char_emb = util.cnn(flattened_char_emb, self.config[\"filter_widths\"], self.config[\"filter_size\"]) # [num_sentences * max_sentence_length, emb]\n # aggregated_char_emb = tf.reshape(flattened_aggregated_char_emb, [num_sentences, max_sentence_length, util.shape(flattened_aggregated_char_emb, 1)]) # [num_sentences, max_sentence_length, emb]\n # text_emb_list.append(aggregated_char_emb)\n\n # text_emb = tf.concat(text_emb_list, 2)\n # ques_emb = tf.matmul(ques_emb, tf.zeros([100, 5]))\n with tf.variable_scope('ques_emb'):\n ques_emb = util.projection(ques_emb, 600)\n # ques_emb = tf.tanh(ques_emb)\n with tf.variable_scope('trans_emb'):\n trans_emb = util.projection(trans_emb, 600)\n # trans_emb = tf.tanh(trans_emb)\n \n ques_emb = tf.nn.dropout(ques_emb, self.lexical_dropout)\n trans_emb = tf.nn.dropout(trans_emb, self.lexical_dropout)\n\n ques_len_mask = tf.sequence_mask(ques_len, maxlen=40, dtype=tf.float32)\n ques_len_mask = tf.reshape(ques_len_mask, [num_questions, -1, 1])\n\n trans_len_mask = tf.sequence_mask(trans_len, maxlen=200, dtype=tf.float32)\n trans_len_mask = tf.reshape(trans_len_mask, [num_sentences, -1, 1])\n\n '''\n with tf.variable_scope(\"question_lstm\"):\n ques_outputs = self.encode_sentences(ques_emb, ques_len_lstm, ques_len_mask)\n ques_outputs = tf.nn.dropout(ques_outputs, self.dropout)\n\n with tf.variable_scope(\"transcript_lstm\"):\n trans_outputs = self.encode_sentences(trans_emb, trans_len_lstm, trans_len_mask)\n trans_outputs = tf.nn.dropout(trans_outputs, self.dropout)\n '''\n # '''\n # with tf.variable_scope(\"question_cnn5\"):\n ques_outputs = tf.layers.conv1d(ques_emb, 600, 5, padding=\"same\", activation=tf.nn.tanh)\n ques_outputs = tf.nn.dropout(ques_outputs, self.dropout)\n \n trans_outputs = tf.layers.conv1d(trans_emb, 600, 5, padding=\"same\", activation=tf.nn.tanh)\n trans_outputs = tf.nn.dropout(trans_outputs, self.dropout)\n \n # ques_outputs = tf.matmul(ques_outputs, tf.zeros([100, 5]))\n \n # with tf.variable_scope(\"question_cnn3\"):\n # ques_outputs = tf.layers.conv1d(ques_outputs, 600, 3, padding=\"same\", activation=tf.nn.relu)\n # ques_outputs = tf.nn.dropout(ques_outputs, self.dropout)[0]\n \n # ques_outputs = tf.matmul(ques_outputs, tf.zeros([100, 5]))\n\n # ques_outputs = tf.matmul(ques_outputs, tf.zeros([100, 5]))\n with tf.variable_scope(\"qh_score\"):\n head_scores = util.projection(ques_outputs, 1) + tf.log(ques_len_mask)\n head_att = tf.nn.softmax(head_scores, dim=1)\n ques_emb2 = tf.reduce_sum(head_att * ques_outputs, 1)\n # ques_query = tf.reduce_sum(head_att * ques_outputs, 1, keepdims=True)\n \n with tf.variable_scope(\"th_score\"):\n head_scores = util.projection(trans_outputs, 1) + tf.log(trans_len_mask)\n head_att = tf.nn.softmax(head_scores, dim=1)\n trans_emb2 = tf.reduce_sum(head_att * trans_outputs, 1)\n # trans_query = tf.reduce_sum(head_att * trans_outputs, 1, keepdims=True)\n\n # ques_lstm_emb = tf.gather(tf.squeeze(ques_outputs, 0), [ques_len - 1])\n # trans_lstm_emb = tf.squeeze(trans_outputs, 0)\n\n '''\n ques_tiled = tf.tile(ques_query, [1, 200, 1])\n\n hist_att_emb = tf.concat([ques_tiled, trans_outputs], 2)\n with tf.variable_scope(\"context_att\"):\n att_logits = util.projection(hist_att_emb, 1) + tf.log(trans_len_mask)\n \n context_att = tf.nn.softmax(att_logits, dim=1)\n hist_emb2 = tf.reduce_sum(context_att * trans_outputs, 1, keepdims=True)\n\n hist_tiled = tf.tile(hist_emb2, [1, 40, 1])\n ques_att_emb = tf.concat([hist_tiled, ques_outputs], 2)\n with tf.variable_scope(\"question_att\"):\n att_logits = util.projection(ques_att_emb, 1) + tf.log(ques_len_mask)\n\n question_att = tf.nn.softmax(att_logits, dim=1)\n ques_emb2 = tf.reduce_sum(question_att * ques_outputs, 1)\n\n # pair_emb = tf.concat([ques_emb2, hist_emb2, ques_emb2 * hist_emb2], 1)\n\n # logits = util.ffnn(pair_emb, 2, 150, 1, self.dropout)\n trans_emb2 = tf.squeeze(hist_emb2, 1)\n '''\n \n logits = tf.matmul(ques_emb2, tf.transpose(trans_emb2, [1, 0]))\n label = tf.eye(num_sentences)\n # loss = tf.nn.softmax_cross_entropy_with_logits(labels=label, logits=logits)\n loss = tf.cond(is_training, lambda: tf.nn.softmax_cross_entropy_with_logits(labels=label, logits=logits), lambda: 0.)\n\n # logits = tf.reshape(tf.reduce_sum(ques_emb2 * hist_emb2), [])\n \n # score = tf.nn.sigmoid(logits)\n # score = tf.reduce_sum(score)\n # label = tf.reduce_sum(label)\n # label = tf.cast(label, tf.float32)\n # self.label = label\n # self.score = score\n # loss = tf.reduce_sum((label - score) * (label - score))\n\n # loss = tf.cond(tf.cast(tf.reshape(label, []), tf.bool), lambda: 1 - score, lambda: score)\n self.score = logits\n loss = tf.reduce_sum(loss)\n return logits, loss\n\n def softmax_loss(self, antecedent_scores, antecedent_labels):\n gold_scores = antecedent_scores + tf.log(tf.to_float(antecedent_labels)) # [num_mentions, max_ant + 1]\n marginalized_gold_scores = tf.reduce_logsumexp(gold_scores, [1]) # [num_mentions]\n log_norm = tf.reduce_logsumexp(antecedent_scores, [1]) # [num_mentions]\n return log_norm - marginalized_gold_scores # [num_mentions]\n\n def encode_sentences(self, text_emb, text_len, text_len_mask):\n num_sentences = tf.shape(text_emb)[0]\n max_sentence_length = tf.shape(text_emb)[1]\n\n # Transpose before and after for efficiency.\n inputs = tf.transpose(text_emb, [1, 0, 2]) # [max_sentence_length, num_sentences, emb]\n\n with tf.variable_scope(\"fw_cell\"):\n cell_fw = util.CustomLSTMCell(self.config[\"lstm_size\"], num_sentences, self.dropout)\n preprocessed_inputs_fw = cell_fw.preprocess_input(inputs)\n with tf.variable_scope(\"bw_cell\"):\n cell_bw = util.CustomLSTMCell(self.config[\"lstm_size\"], num_sentences, self.dropout)\n preprocessed_inputs_bw = cell_bw.preprocess_input(inputs)\n preprocessed_inputs_bw = tf.reverse_sequence(preprocessed_inputs_bw,\n seq_lengths=text_len,\n seq_dim=0,\n batch_dim=1)\n state_fw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_fw.initial_state.c, [num_sentences, 1]), tf.tile(cell_fw.initial_state.h, [num_sentences, 1]))\n state_bw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_bw.initial_state.c, [num_sentences, 1]), tf.tile(cell_bw.initial_state.h, [num_sentences, 1]))\n with tf.variable_scope(\"lstm\"):\n with tf.variable_scope(\"fw_lstm\"):\n fw_outputs, fw_states = tf.nn.dynamic_rnn(cell=cell_fw,\n inputs=preprocessed_inputs_fw,\n sequence_length=text_len,\n initial_state=state_fw,\n time_major=True)\n with tf.variable_scope(\"bw_lstm\"):\n bw_outputs, bw_states = tf.nn.dynamic_rnn(cell=cell_bw,\n inputs=preprocessed_inputs_bw,\n sequence_length=text_len,\n initial_state=state_bw,\n time_major=True)\n\n bw_outputs = tf.reverse_sequence(bw_outputs,\n seq_lengths=text_len,\n seq_dim=0,\n batch_dim=1)\n\n text_outputs = tf.concat([fw_outputs, bw_outputs], 2)\n return tf.transpose(text_outputs, [1, 0, 2]) # [num_sentences, max_sentence_length, emb]\n # return self.flatten_emb_by_sentence(text_outputs, text_len_mask)\n\n def evaluate_mentions(self, candidate_starts, candidate_ends, mention_starts, mention_ends, mention_scores, gold_starts, gold_ends, example, evaluators):\n text_length = sum(len(s) for s in example[\"sentences\"])\n gold_spans = set(zip(gold_starts, gold_ends))\n\n if len(candidate_starts) > 0:\n sorted_starts, sorted_ends, _ = zip(*sorted(zip(candidate_starts, candidate_ends, mention_scores), key=operator.itemgetter(2), reverse=True))\n else:\n sorted_starts = []\n sorted_ends = []\n\n for k, evaluator in evaluators.items():\n if k == -3:\n predicted_spans = set(zip(candidate_starts, candidate_ends)) & gold_spans\n else:\n if k == -2:\n predicted_starts = mention_starts\n predicted_ends = mention_ends\n elif k == 0:\n is_predicted = mention_scores > 0\n predicted_starts = candidate_starts[is_predicted]\n predicted_ends = candidate_ends[is_predicted]\n else:\n if k == -1:\n num_predictions = len(gold_spans)\n else:\n num_predictions = (k * text_length) / 100\n predicted_starts = sorted_starts[:num_predictions]\n predicted_ends = sorted_ends[:num_predictions]\n predicted_spans = set(zip(predicted_starts, predicted_ends))\n evaluator.update(gold_set=gold_spans, predicted_set=predicted_spans)\n\n def get_predicted_antecedents(self, antecedents, antecedent_scores):\n predicted_antecedents = []\n for i, index in enumerate(np.argmax(antecedent_scores, axis=1) - 1):\n if index < 0:\n predicted_antecedents.append(-1)\n else:\n predicted_antecedents.append(antecedents[i, index])\n return predicted_antecedents\n\n def get_predicted_clusters(self, mention_starts, mention_ends, predicted_antecedents):\n mention_to_predicted = {}\n predicted_clusters = []\n for i, predicted_index in enumerate(predicted_antecedents):\n if predicted_index < 0:\n continue\n assert i > predicted_index\n predicted_antecedent = (int(mention_starts[predicted_index]), int(mention_ends[predicted_index]))\n if predicted_antecedent in mention_to_predicted:\n predicted_cluster = mention_to_predicted[predicted_antecedent]\n else:\n predicted_cluster = len(predicted_clusters)\n predicted_clusters.append([predicted_antecedent])\n mention_to_predicted[predicted_antecedent] = predicted_cluster\n\n mention = (int(mention_starts[i]), int(mention_ends[i]))\n predicted_clusters[predicted_cluster].append(mention)\n mention_to_predicted[mention] = predicted_cluster\n\n predicted_clusters = [tuple(pc) for pc in predicted_clusters]\n mention_to_predicted = { m:predicted_clusters[i] for m,i in mention_to_predicted.items() }\n\n return predicted_clusters, mention_to_predicted\n\n def evaluate_coref(self, mention_starts, mention_ends, predicted_antecedents, gold_clusters, evaluator):\n gold_clusters = [tuple(tuple(m) for m in gc) for gc in gold_clusters]\n mention_to_gold = {}\n for gc in gold_clusters:\n for mention in gc:\n mention_to_gold[mention] = gc\n\n predicted_clusters, mention_to_predicted = self.get_predicted_clusters(mention_starts, mention_ends, predicted_antecedents)\n evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold)\n return predicted_clusters\n\n def load_eval_data(self):\n if self.eval_data is None:\n oov_counts = [0 for _ in self.embedding_dicts]\n with open(self.config[\"eval_path\"]) as f:\n test_examples = [json.loads(jsonline) for jsonline in f.readlines()]\n return test_examples\n\n def load_test_set(self):\n with open(r'test_set.jsonlines') as f:\n test_set = [json.loads(jsonline) for jsonline in f.readlines()]\n return test_set\n\n def eval_enqueue_thread(self, session, official_stdout=False):\n oov_counts = [0 for _ in self.embedding_dicts]\n test_samples = self.load_eval_data()\n test_set = self.load_test_set()\n num_samples = len(test_samples)\n num_videos = len(test_set)\n def _enqueue_loop():\n # q, _, _, q_len, _, _ = self.tensorize_example(test_samples[i], False, 40, 200, oov_counts=oov_counts)\n ques = [self.tensorize_example(test_samples[j], False, 40, 200, oov_counts=oov_counts) for j in range(num_samples)]\n ques = [(x[0], x[3]) for x in ques]\n \n trans = [self.tensorize_example(test_set[j], False, 40, 200, oov_counts=oov_counts) for j in range(num_videos)]\n trans = [(x[1], x[4]) for x in trans]\n \n labels = np.zeros(num_videos)\n q_len = np.array([x[1] for x in ques])\n s_len = np.array([x[1] for x in trans])\n q = np.concatenate([x[0] for x in ques], axis=0)\n # q = np.concatenate([q for i in range(num_videos)], axis=0)\n t = np.concatenate([x[0] for x in trans], axis=0)\n print(q.shape)\n print(t.shape)\n # print(q_len.shape)\n # print(s_len.shape)\n feed_dict = dict(zip(self.queue_input_tensors, [q, t, labels, q_len, s_len, False]))\n session.run(self.enqueue_op, feed_dict=feed_dict)\n enqueue_thread = threading.Thread(target=_enqueue_loop)\n enqueue_thread.daemon = True\n enqueue_thread.start()\n \n def eval_enqueue_thread2(self, session, official_stdout=False):\n oov_counts = [0 for _ in self.embedding_dicts]\n test_samples = self.load_eval_data()\n test_set = self.load_test_set()\n num_samples = len(test_samples)\n num_videos = len(test_set)\n def _enqueue_loop():\n for i in range(num_samples):\n q, _, _, q_len, _, _ = self.tensorize_example(test_samples[i], False, 40, 200, oov_counts=oov_counts)\n trans = [self.tensorize_example(test_set[j], False, 40, 200, oov_counts=oov_counts) for j in range(num_videos)]\n trans = [(x[1], x[4]) for x in trans]\n labels = np.zeros(num_videos)\n q_len = np.array([q_len])\n # q_len = np.array([q_len] * num_videos)\n s_len = np.array([x[1] for x in trans])\n q = np.concatenate([q,], axis=0)\n # q = np.concatenate([q for i in range(num_videos)], axis=0)\n t = np.concatenate([x[0] for x in trans], axis=0)\n # print(q.shape)\n # print(t.shape)\n # print(q_len.shape)\n # print(s_len.shape)\n feed_dict = dict(zip(self.queue_input_tensors, [q, t, labels, q_len, s_len, False]))\n session.run(self.enqueue_op, feed_dict=feed_dict)\n enqueue_thread = threading.Thread(target=_enqueue_loop)\n enqueue_thread.daemon = True\n enqueue_thread.start()\n\n def evaluate_raw(self, session, official_stdout=False):\n ques_emb, trans_emb, labels, q_len, s_len = self.load_eval_data()\n scores_list = []\n for i, t in enumerate(trans_emb):\n feed_dict = dict(zip(self.queue_input_tensors, [ques_emb, t, labels, q_len, s_len, True]))\n score = session.run(self.predictions, feed_dict=feed_dict)\n scores_list.append(score)\n scores_list = np.concatenate(scores_list, axis=1)\n pickle.dump([[], [], scores_list], open('scores.pkg', 'wb'))\n\n '''\n coref_predictions = {}\n coref_evaluator = metrics.CorefEvaluator()\n\n scores_list = []\n eval_size = len(self.eval_data)\n\n for example_num, (tensorized_example, example) in enumerate(self.eval_data):\n if example_num % 100 == 0:\n print('%d / %d pairs evaluated' % (example_num, eval_size))\n ques_emb, trans_emb, label, is_training = tensorized_example\n\n feed_dict = {i:t for i,t in zip(self.input_tensors, tensorized_example)}\n score = session.run(self.predictions, feed_dict=feed_dict)\n\n scores_list.append([example['q_id'], example['v_id'], score])\n \n pickle.dump([[], [], scores_list], open('scores.pkg', 'wb'))\n '''\n", "sub_path": "ques_trans_model.py", "file_name": "ques_trans_model.py", "file_ext": "py", "file_size_in_byte": 22229, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "util.load_char_dict", "line_number": 20, "usage_type": "call"}, {"api_name": "util.load_embedding_dict", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.bool", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.PaddingFIFOQueue", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.train.exponential_decay", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_global_norm", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.train", "line_number": 50, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.concatenate", "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": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "util.normalize", "line_number": 106, "usage_type": "call"}, {"api_name": "util.normalize", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 152, "usage_type": "call"}, {"api_name": "util.projection", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 155, "usage_type": "call"}, {"api_name": "util.projection", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.nn.dropout", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tensorflow.sequence_mask", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 162, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.sequence_mask", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.layers.conv1d", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.conv1d", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 182, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 182, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 183, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 194, "usage_type": "call"}, {"api_name": "util.projection", "line_number": 195, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 195, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 196, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 196, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 197, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 200, "usage_type": "call"}, {"api_name": "util.projection", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 202, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 203, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 233, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 233, "usage_type": "call"}, {"api_name": "tensorflow.eye", "line_number": 234, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 236, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 236, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 236, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 250, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.reduce_logsumexp", "line_number": 255, "usage_type": "call"}, {"api_name": "tensorflow.reduce_logsumexp", "line_number": 256, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 260, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 261, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 266, "usage_type": "call"}, {"api_name": "util.CustomLSTMCell", "line_number": 267, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 269, "usage_type": "call"}, {"api_name": "util.CustomLSTMCell", "line_number": 270, "usage_type": "call"}, {"api_name": "tensorflow.reverse_sequence", "line_number": 272, "usage_type": "call"}, {"api_name": "tensorflow.contrib.rnn.LSTMStateTuple", "line_number": 276, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 276, "usage_type": "attribute"}, {"api_name": "tensorflow.tile", "line_number": 276, "usage_type": "call"}, {"api_name": "tensorflow.contrib.rnn.LSTMStateTuple", "line_number": 277, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 277, "usage_type": "attribute"}, {"api_name": "tensorflow.tile", "line_number": 277, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 279, "usage_type": "call"}, {"api_name": "tensorflow.nn.dynamic_rnn", "line_number": 280, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 280, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 285, "usage_type": "call"}, {"api_name": "tensorflow.nn.dynamic_rnn", "line_number": 286, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 286, "usage_type": "attribute"}, {"api_name": "tensorflow.reverse_sequence", "line_number": 292, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 298, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 334, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 380, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 407, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 430, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 432, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 435, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 453, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 454, "usage_type": "call"}]} +{"seq_id": "432025716", "text": "import pandas as pd\nimport numpy as np\nimport sys\nimport matplotlib.pyplot as plt\nfrom sklearn import metrics\n\n\ndataPath = sys.argv[1]\n#rankingMethod = sys.argv[2]\n\ndata = pd.read_csv(dataPath)\nprint(data.columns)\n\ndataName = dataPath.replace('.csv','')\n\ngrp = data.groupby([\"SerieName\"])\n\n###########\n# RocDist #\n###########\ndataList = []\ndataListROC = []\nfor serieName,data in grp:\n # for each volume\n groupedData = data.groupby([\"VolumeName\"])\n \n for name,dataFiltered in groupedData:\n # for each parameter set\n TruePositiveRate = dataFiltered['sensitivity'].values\n FalsePositiveRate = 1 - dataFiltered['specificity'].values\n # auc curve\n auc = metrics.auc(FalsePositiveRate, TruePositiveRate)\n dataListROC.append([serieName,name,TruePositiveRate,FalsePositiveRate])\n\ndf_rocAUC = pd.DataFrame(dataList,columns=[\"SerieName\",\"VolumeName\",\"AUC\"]).sort_values(by=[\"SerieName\",\"AUC\"],ascending=True)\nprint(df_rocAUC)\n\nsaveName = dataName + \"_Best_RocAUC.csv\"\ndf_rocAUC.to_csv(saveName,index=False)\n\n\n###################\n# ranking methods #\n###################\n\nfor rankingMethod in [\"MCC\",\"Dice\"]:\n bestParameterSetPerVolume = []\n dataList = []\n for serieName,data in grp:\n # for each volume\n groupedData = data.groupby([\"VolumeName\"])\n \n \n for name,dataFiltered in groupedData:\n # for each parameter set\n index = np.argmax(dataFiltered[rankingMethod].values)\n maxValue = np.max(dataFiltered[rankingMethod].values)\n dataList.append(dataFiltered.iloc[index,:])\n \n \n temp_df = pd.DataFrame(dataList).sort_values(by=[\"SerieName\",rankingMethod],ascending=False)\n print(temp_df)\n \n saveName = dataName+\"_Best_\" + rankingMethod + \".csv\"\n print(saveName)\n temp_df.to_csv(saveName,index=False)\n\n print(\"------------\")\n\n", "sub_path": "scripts/Analyse_results/parseCSV.py", "file_name": "parseCSV.py", "file_ext": "py", "file_size_in_byte": 1943, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 32, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "356567544", "text": "from flask import jsonify, make_response\nfrom ons_ras_common import ons_env\nimport requests\nimport json\n\nbusinesses = [{\n \"id\": \"3b136c4b-7a14-4904-9e01-13364dd7b972\",\n \"address\": {\n \"saon\": \"Office 2a\",\n \"paon\": \"Unit 5\",\n \"street\": \"Milton Street\",\n \"locality\": \"Green Industrial Park\",\n \"town\": \"New Town\",\n \"postcode\": \"NT23 7TN\"\n },\n \"contactName\": \"John Doe\",\n \"employeeCount\": 50,\n \"enterpriseName\": \"ABC Limited\",\n \"facsimile\": \"+44 1234 567890\",\n \"fulltimeCount\": 35,\n \"legalStatus\": \"Private Limited Company\",\n \"name\": \"Bolts and Ratchets Ltd\",\n \"businessRef\": \"0123456789\",\n \"sic2003\": \"2520\",\n \"sic2007\": \"2520\",\n \"telephone\": \"+44 1234 567890\",\n \"tradingName\": \"ABC Trading Ltd\",\n \"turnover\": 350\n}]\n\nrespondents = [{\n \"id\": \"3b136c4b-7a14-4904-9e01-13364dd7b972\",\n \"emailAddress\": \"john.doe@abc-ltd.com\",\n \"firstName\": \"Jacky\",\n \"lastName\": \"Turner\",\n \"telephone\": \"+44 1234 567890\",\n \"status\": \"ACTIVE\"\n}]\n\n#\n# /respondents/{id}\n#\ndef respondents_id_get(id):\n \"\"\"\n Get respondent by id\n \n :param id: Respondent identifier (uuid)\n :type id: str\n\n :rtype: None\n \"\"\"\n if id == 'id_example':\n id = '3b136c4b-7a14-4904-9e01-13364dd7b972'\n for doc in respondents:\n if doc['id'] == id:\n return make_response(jsonify(doc), 200)\n return make_response(jsonify('Not found'), 404)\n\n\n\n#\n# /businesses/id/{id}\n#\ndef businesses_id_id_get(id):\n \"\"\"\n Get business by id\n \n :param id: Business identifier (uuid)\n :type id: str\n\n :rtype: None\n \"\"\"\n if id == 'id_example':\n id = '3b136c4b-7a14-4904-9e01-13364dd7b972'\n for doc in businesses:\n if doc['id'] == id:\n ru_ref = {\"RU_REF\": \"{}\".format(doc['businessRef'])}\n ss = json.dumps(ru_ref)\n proto = ons_env.protocol\n gateway = ons_env.gateway\n port = 443 if proto == 'https' else 8080\n GET_CI_ID=\"{}://{}:{}/collection-instrument-api/1.0.2/collectioninstrument?searchString={}\"\\\n .format(proto, gateway, port, ss)\n ons_env.logger.info(GET_CI_ID)\n resp = requests.get(GET_CI_ID, verify=False)\n if resp.status_code == 200:\n print(\"** CI Lookup Worked\")\n print(resp.json())\n ci = resp.json()[0]\n doc['collectionInstrumentId'] = ci['id']\n print(\"** CI id is {}\".format(ci['id']))\n else:\n print(\"** WARNING: Unable to lookup CI with RU_REF={}\".format(doc['businessRef']))\n return make_response(jsonify(doc), 200)\n return make_response(jsonify('Not found'), 404)\n", "sub_path": "swagger_server/controllers/partyapi_controller.py", "file_name": "partyapi_controller.py", "file_ext": "py", "file_size_in_byte": 2669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.make_response", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 57, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}, {"api_name": "ons_ras_common.ons_env.protocol", "line_number": 79, "usage_type": "attribute"}, {"api_name": "ons_ras_common.ons_env", "line_number": 79, "usage_type": "name"}, {"api_name": "ons_ras_common.ons_env.gateway", "line_number": 80, "usage_type": "attribute"}, {"api_name": "ons_ras_common.ons_env", "line_number": 80, "usage_type": "name"}, {"api_name": "ons_ras_common.ons_env.logger.info", "line_number": 84, "usage_type": "call"}, {"api_name": "ons_ras_common.ons_env.logger", "line_number": 84, "usage_type": "attribute"}, {"api_name": "ons_ras_common.ons_env", "line_number": 84, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "637999836", "text": "# -*- coding: utf-8 -*-\nimport io\nimport os\nfrom unittest import TestCase\n\nfrom minio.error import S3Error\n\nfrom platiagro import download_artifact\nfrom platiagro.util import BUCKET_NAME, MINIO_CLIENT\n\n\nclass TestArtifacts(TestCase):\n\n def setUp(self):\n self.make_bucket()\n buffer = io.BytesIO(b\"mock\")\n MINIO_CLIENT.put_object(\n bucket_name=BUCKET_NAME,\n object_name=\"artifacts/mock.txt\",\n data=buffer,\n length=buffer.getbuffer().nbytes,\n )\n\n def tearDown(self):\n MINIO_CLIENT.remove_object(\n bucket_name=BUCKET_NAME,\n object_name=\"artifacts/mock.txt\",\n )\n\n def make_bucket(self):\n try:\n MINIO_CLIENT.make_bucket(BUCKET_NAME)\n except S3Error as err:\n if err.code == \"BucketAlreadyOwnedByYou\":\n pass\n if err.code == \"NoSuchBucket\" or err.code == \"NoSuchKey\":\n raise FileNotFoundError(\"The specified artifact does not exist\")\n\n def test_download_artifact(self):\n with self.assertRaises(FileNotFoundError):\n download_artifact(\"unk.zip\", \"./unk.zip\")\n\n download_artifact(\"mock.txt\", \"./mock-dest.txt\")\n self.assertTrue(os.path.exists(\"./mock-dest.txt\"))\n\n try:\n MINIO_CLIENT.remove_object(\n bucket_name=BUCKET_NAME,\n object_name=\"artifacts/mock.txt\",\n )\n except S3Error as err:\n err.code == \"NoSuchBucket\"\n self.assertEqual(err.code, S3Error)\n", "sub_path": "tests/test_artifacts.py", "file_name": "test_artifacts.py", "file_ext": "py", "file_size_in_byte": 1557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 16, "usage_type": "call"}, {"api_name": "platiagro.util.MINIO_CLIENT.put_object", "line_number": 17, "usage_type": "call"}, {"api_name": "platiagro.util.MINIO_CLIENT", "line_number": 17, "usage_type": "name"}, {"api_name": "platiagro.util.BUCKET_NAME", "line_number": 18, "usage_type": "name"}, {"api_name": "platiagro.util.MINIO_CLIENT.remove_object", "line_number": 25, "usage_type": "call"}, {"api_name": "platiagro.util.MINIO_CLIENT", "line_number": 25, "usage_type": "name"}, {"api_name": "platiagro.util.BUCKET_NAME", "line_number": 26, "usage_type": "name"}, {"api_name": "platiagro.util.MINIO_CLIENT.make_bucket", "line_number": 32, "usage_type": "call"}, {"api_name": "platiagro.util.BUCKET_NAME", "line_number": 32, "usage_type": "argument"}, {"api_name": "platiagro.util.MINIO_CLIENT", "line_number": 32, "usage_type": "name"}, {"api_name": "minio.error.S3Error", "line_number": 33, "usage_type": "name"}, {"api_name": "platiagro.download_artifact", "line_number": 41, "usage_type": "call"}, {"api_name": "platiagro.download_artifact", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "platiagro.util.MINIO_CLIENT.remove_object", "line_number": 47, "usage_type": "call"}, {"api_name": "platiagro.util.MINIO_CLIENT", "line_number": 47, "usage_type": "name"}, {"api_name": "platiagro.util.BUCKET_NAME", "line_number": 48, "usage_type": "name"}, {"api_name": "minio.error.S3Error", "line_number": 51, "usage_type": "name"}, {"api_name": "minio.error.S3Error", "line_number": 53, "usage_type": "argument"}]} +{"seq_id": "484362482", "text": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. 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\"\"\"BERT finetuning runner.\"\"\"\n\nfrom __future__ import absolute_import, division, print_function\n\nimport argparse\nimport csv\nimport glob\nimport logging\nimport os\nimport random\nimport sys\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,\n TensorDataset)\nfrom torch.utils.data.distributed import DistributedSampler\nfrom tqdm import tqdm, trange\n\nfrom torch.nn import CrossEntropyLoss, MSELoss\nfrom scipy.stats import pearsonr, spearmanr\nfrom sklearn.metrics import matthews_corrcoef, f1_score\n\nfrom pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME\nfrom pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig\nfrom pytorch_pretrained_bert.tokenization import BertTokenizer\nfrom pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule\nfrom smallfry import compress\nfrom smallfry import utils\n\n\nlogger = logging.getLogger(__name__)\nconfig = {}\n\nclass InputExample(object):\n \"\"\"A single training/test example for simple sequence classification.\"\"\"\n\n def __init__(self, guid, text_a, text_b=None, label=None):\n \"\"\"Constructs a InputExample.\n\n Args:\n guid: Unique id for the example.\n text_a: string. The untokenized text of the first sequence. For single\n sequence tasks, only this sequence must be specified.\n text_b: (Optional) string. The untokenized text of the second sequence.\n Only must be specified for sequence pair tasks.\n label: (Optional) string. The label of the example. This should be\n specified for train and dev examples, but not for test examples.\n \"\"\"\n self.guid = guid\n self.text_a = text_a\n self.text_b = text_b\n self.label = label\n\n\nclass InputFeatures(object):\n \"\"\"A single set of features of data.\"\"\"\n\n def __init__(self, input_ids, input_mask, segment_ids, label_id):\n self.input_ids = input_ids\n self.input_mask = input_mask\n self.segment_ids = segment_ids\n self.label_id = label_id\n\n\nclass DataProcessor(object):\n \"\"\"Base class for data converters for sequence classification data sets.\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"Gets a collection of `InputExample`s for the train set.\"\"\"\n raise NotImplementedError()\n\n def get_dev_examples(self, data_dir):\n \"\"\"Gets a collection of `InputExample`s for the dev set.\"\"\"\n raise NotImplementedError()\n\n def get_labels(self):\n \"\"\"Gets the list of labels for this data set.\"\"\"\n raise NotImplementedError()\n\n @classmethod\n def _read_tsv(cls, input_file, quotechar=None):\n \"\"\"Reads a tab separated value file.\"\"\"\n with open(input_file, \"r\", encoding=\"utf-8\") as f:\n reader = csv.reader(f, delimiter=\"\\t\", quotechar=quotechar)\n lines = []\n for line in reader:\n if sys.version_info[0] == 2:\n line = list(unicode(cell, 'utf-8') for cell in line)\n lines.append(line)\n return lines\n\n\nclass MrpcProcessor(DataProcessor):\n \"\"\"Processor for the MRPC data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n logger.info(\"LOOKING AT {}\".format(os.path.join(data_dir, \"train.tsv\")))\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"0\", \"1\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, i)\n text_a = line[3]\n text_b = line[4]\n label = line[0]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\nclass MnliProcessor(DataProcessor):\n \"\"\"Processor for the MultiNLI data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev_matched.tsv\")),\n \"dev_matched\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"contradiction\", \"entailment\", \"neutral\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n text_a = line[8]\n text_b = line[9]\n label = line[-1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\nclass MnliMismatchedProcessor(MnliProcessor):\n \"\"\"Processor for the MultiNLI Mismatched data set (GLUE version).\"\"\"\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev_mismatched.tsv\")),\n \"dev_matched\")\n\n\nclass ColaProcessor(DataProcessor):\n \"\"\"Processor for the CoLA data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"0\", \"1\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n guid = \"%s-%s\" % (set_type, i)\n text_a = line[3]\n label = line[1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=None, label=label))\n return examples\n\n\nclass Sst2Processor(DataProcessor):\n \"\"\"Processor for the SST-2 data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"0\", \"1\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, i)\n text_a = line[0]\n label = line[1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=None, label=label))\n return examples\n\n\nclass StsbProcessor(DataProcessor):\n \"\"\"Processor for the STS-B data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [None]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n text_a = line[7]\n text_b = line[8]\n label = line[-1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\nclass QqpProcessor(DataProcessor):\n \"\"\"Processor for the STS-B data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"0\", \"1\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n try:\n text_a = line[3]\n text_b = line[4]\n label = line[5]\n except IndexError:\n continue\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\nclass QnliProcessor(DataProcessor):\n \"\"\"Processor for the STS-B data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \n \"dev_matched\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"entailment\", \"not_entailment\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n text_a = line[1]\n text_b = line[2]\n label = line[-1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\nclass RteProcessor(DataProcessor):\n \"\"\"Processor for the RTE data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"entailment\", \"not_entailment\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n text_a = line[1]\n text_b = line[2]\n label = line[-1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\nclass WnliProcessor(DataProcessor):\n \"\"\"Processor for the WNLI data set (GLUE version).\"\"\"\n\n def get_train_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n \"\"\"See base class.\"\"\"\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"dev.tsv\")), \"dev\")\n\n def get_labels(self):\n \"\"\"See base class.\"\"\"\n return [\"0\", \"1\"]\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n if i == 0:\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n text_a = line[1]\n text_b = line[2]\n label = line[-1]\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples\n\n\ndef convert_examples_to_features(examples, label_list, max_seq_length,\n tokenizer, output_mode):\n \"\"\"Loads a data file into a list of `InputBatch`s.\"\"\"\n\n label_map = {label : i for i, label in enumerate(label_list)}\n\n features = []\n for (ex_index, example) in enumerate(examples):\n if ex_index % 10000 == 0:\n logger.info(\"Writing example %d of %d\" % (ex_index, len(examples)))\n\n tokens_a = tokenizer.tokenize(example.text_a)\n\n tokens_b = None\n if example.text_b:\n tokens_b = tokenizer.tokenize(example.text_b)\n # Modifies `tokens_a` and `tokens_b` in place so that the total\n # length is less than the specified length.\n # Account for [CLS], [SEP], [SEP] with \"- 3\"\n _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)\n else:\n # Account for [CLS] and [SEP] with \"- 2\"\n if len(tokens_a) > max_seq_length - 2:\n tokens_a = tokens_a[:(max_seq_length - 2)]\n\n # The convention in BERT is:\n # (a) For sequence pairs:\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n # (b) For single sequences:\n # tokens: [CLS] the dog is hairy . [SEP]\n # type_ids: 0 0 0 0 0 0 0\n #\n # Where \"type_ids\" are used to indicate whether this is the first\n # sequence or the second sequence. The embedding vectors for `type=0` and\n # `type=1` were learned during pre-training and are added to the wordpiece\n # embedding vector (and position vector). This is not *strictly* necessary\n # since the [SEP] token unambiguously separates the sequences, but it makes\n # it easier for the model to learn the concept of sequences.\n #\n # For classification tasks, the first vector (corresponding to [CLS]) is\n # used as as the \"sentence vector\". Note that this only makes sense because\n # the entire model is fine-tuned.\n tokens = [\"[CLS]\"] + tokens_a + [\"[SEP]\"]\n segment_ids = [0] * len(tokens)\n\n if tokens_b:\n tokens += tokens_b + [\"[SEP]\"]\n segment_ids += [1] * (len(tokens_b) + 1)\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding = [0] * (max_seq_length - len(input_ids))\n input_ids += padding\n input_mask += padding\n segment_ids += padding\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n\n if output_mode == \"classification\":\n label_id = label_map[example.label]\n elif output_mode == \"regression\":\n label_id = float(example.label)\n else:\n raise KeyError(output_mode)\n\n if ex_index < 5:\n logger.info(\"*** Example ***\")\n logger.info(\"guid: %s\" % (example.guid))\n logger.info(\"tokens: %s\" % \" \".join(\n [str(x) for x in tokens]))\n logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n logger.info(\n \"segment_ids: %s\" % \" \".join([str(x) for x in segment_ids]))\n logger.info(\"label: %s (id = %d)\" % (example.label, label_id))\n\n features.append(\n InputFeatures(input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n label_id=label_id))\n return features\n\n\ndef _truncate_seq_pair(tokens_a, tokens_b, max_length):\n \"\"\"Truncates a sequence pair in place to the maximum length.\"\"\"\n\n # This is a simple heuristic which will always truncate the longer sequence\n # one token at a time. This makes more sense than truncating an equal percent\n # of tokens from each, since if one sequence is very short then each token\n # that's truncated likely contains more information than a longer sequence.\n while True:\n total_length = len(tokens_a) + len(tokens_b)\n if total_length <= max_length:\n break\n if len(tokens_a) > len(tokens_b):\n tokens_a.pop()\n else:\n tokens_b.pop()\n\n\ndef simple_accuracy(preds, labels):\n return (preds == labels).mean()\n\n\ndef acc_and_f1(preds, labels):\n acc = simple_accuracy(preds, labels)\n f1 = f1_score(y_true=labels, y_pred=preds)\n return {\n \"acc\": acc,\n \"f1\": f1,\n \"acc_and_f1\": (acc + f1) / 2,\n }\n\n\ndef pearson_and_spearman(preds, labels):\n pearson_corr = pearsonr(preds, labels)[0]\n spearman_corr = spearmanr(preds, labels)[0]\n return {\n \"pearson\": pearson_corr,\n \"spearmanr\": spearman_corr,\n \"corr\": (pearson_corr + spearman_corr) / 2,\n }\n\ndef compute_metrics(task_name, preds, labels):\n assert len(preds) == len(labels)\n if task_name == \"cola\":\n return {\"mcc\": matthews_corrcoef(labels, preds)}\n elif task_name == \"sst-2\":\n return {\"acc\": simple_accuracy(preds, labels)}\n elif task_name == \"mrpc\":\n return acc_and_f1(preds, labels)\n elif task_name == \"sts-b\":\n return pearson_and_spearman(preds, labels)\n elif task_name == \"qqp\":\n return acc_and_f1(preds, labels)\n elif task_name == \"mnli\":\n return {\"acc\": simple_accuracy(preds, labels)}\n elif task_name == \"qnli\":\n return {\"acc\": simple_accuracy(preds, labels)}\n elif task_name == \"rte\":\n return {\"acc\": simple_accuracy(preds, labels)}\n elif task_name == \"wnli\":\n return {\"acc\": simple_accuracy(preds, labels)}\n else:\n raise KeyError(task_name)\n\ndef get_processor(task_name):\n processors = {\n \"cola\": ColaProcessor,\n \"mnli\": MnliProcessor,\n \"mnli-mm\": MnliMismatchedProcessor,\n \"mrpc\": MrpcProcessor,\n \"sst-2\": Sst2Processor,\n \"sts-b\": StsbProcessor,\n \"qqp\": QqpProcessor,\n \"qnli\": QnliProcessor,\n \"rte\": RteProcessor,\n \"wnli\": WnliProcessor,\n }\n return processors[task_name]\n\ndef get_output_mode(task_name):\n output_modes = {\n \"cola\": \"classification\",\n \"mnli\": \"classification\",\n \"mrpc\": \"classification\",\n \"sst-2\": \"classification\",\n \"sts-b\": \"regression\",\n \"qqp\": \"classification\",\n \"qnli\": \"classification\",\n \"rte\": \"classification\",\n \"wnli\": \"classification\",\n }\n return output_modes[task_name]\n\ndef get_upper_case_task_name(task_name):\n upper_case_tasks = {\n \"cola\": \"CoLA\",\n \"mnli\": \"MNLI\",\n \"mrpc\": \"MRPC\",\n \"sst-2\": \"SST-2\",\n \"sts-b\": \"STS-B\",\n \"qqp\": \"QQP\",\n \"qnli\": \"QNLI\",\n \"rte\": \"RTE\",\n \"wnli\": \"WNLI\",\n }\n return upper_case_tasks[task_name]\n\ndef get_dca_embedding_path(b, seed):\n path_regex = '/proj/smallfry/embeddings/bert-base-cased/2019-05-09-BertDcclFiveSeeds/embeddim,768_compresstype,dca_bitrate,{}.0_seed,{}_k,*_lr,0.0003/*_compressed_embeds.txt'\n path_regex = path_regex.format(int(b),int(seed))\n file_list = glob.glob(path_regex)\n assert len(file_list) == 1, 'There should only be one embedding matching this regex'\n return file_list[0]\n\ndef compress_embeddings(X, b, compress_type, seed):\n logger.info('Beginning to compress embeddings')\n if compress_type == 'uniform':\n Xq, frob_squared_error, elapsed = compress.compress_uniform(X, b, adaptive_range=True)\n elif compress_type == 'kmeans':\n Xq, frob_squared_error, elapsed = compress.compress_kmeans(X, b, random_seed=seed)\n elif compress_type == 'pca':\n pca_dim = int(X.shape[1] * b / 32.0)\n Xq, frob_squared_error, elapsed = compress.compress_pca(X, pca_dim, keep_v=True)\n elif compress_type == 'dca':\n Xq,_ = utils.load_embeddings(get_dca_embedding_path(b, seed))\n frob_squared_error = np.linalg.norm(X-Xq)**2\n elapsed = 0\n else:\n raise Exception('Other compress types not yet supported.')\n logger.info('Done compressing embeddings. Elapsed = {}, Frob-squared-error = {}'.format(elapsed, frob_squared_error))\n return Xq, frob_squared_error, elapsed\n\ndef init_parser():\n parser = argparse.ArgumentParser()\n\n ## Required parameters\n parser.add_argument(\"--task_name\",\n default=None,\n type=str,\n required=True,\n choices=['cola', 'mnli', 'mrpc', 'sst-2', 'sts-b', 'qqp', 'qnli', 'rte', 'wnli'],\n help=\"The name of the task to train.\")\n parser.add_argument(\"--rungroup\",\n default=None,\n type=str,\n required=True,\n help=\"The run group for organizing results.\")\n\n ## Important parameters\n parser.add_argument(\"--bert_model\",\n default=\"bert-base-cased\",\n type=str,\n choices=[\"bert-base-uncased\", \"bert-large-uncased\", \"bert-base-cased\", \"bert-large-cased\",\n \"bert-base-multilingual-uncased\", \"bert-base-multilingual-cased\", \"bert-base-chinese\"],\n help=\"Bert pre-trained model selected in the list: bert-base-uncased, \"\n \"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, \"\n \"bert-base-multilingual-cased, bert-base-chinese.\")\n parser.add_argument(\"--data_dir\",\n default='/proj/smallfry/glue_data',\n type=str,\n help=\"The base directory of the input data. Data files should be under data_dir/task-name folder.\")\n parser.add_argument(\"--output_dir\",\n default='/proj/smallfry/results/glue/',\n type=str,\n help=\"The base directory for where the model predictions and checkpoints will be written. \"\n \"Results will be written to output_dir/task-name/run-group/run-name folder.\")\n parser.add_argument(\"--git_repo_dir\",\n default='/proj/smallfry/git/smallfry',\n type=str,\n help=\"The directory of the git repo. Used for getting git hash and diff strings.\")\n parser.add_argument('--seed',\n type=int,\n default=1,\n help=\"random seed for initialization\")\n parser.add_argument(\"--checkpoint_metric\",\n default='eval_loss_min',\n type=str,\n help=\"The metric to use to determine best performing epoch. Append '_min'/'_max' to metric name if minimium/maximum value determines best epoch.\")\n parser.add_argument(\"--learning_rate\",\n default=5e-5,\n type=float,\n help=\"The initial learning rate for Adam.\")\n parser.add_argument(\"--weight_decay\",\n default=0.01,\n type=float,\n help=\"The L2 regularization parameter (not applied to LayerNorm or bias parameters).\")\n parser.add_argument(\"--num_train_epochs\",\n default=3.0,\n type=float,\n help=\"Total number of training epochs to perform.\")\n parser.add_argument('--debug', action='store_true',\n help='If true, can have local git changes when running experiments.')\n\n # Compression parameters\n parser.add_argument('--freeze_embeddings',\n action='store_true',\n help=\"Specifies if to freeze the WordPiece embeddings in the BERT model during training.\")\n parser.add_argument('--compresstype',\n type=str,\n default='nocompress',\n choices=['nocompress','uniform','kmeans','pca','dca','tt'],\n help='Name of compression method to use.')\n parser.add_argument('--bitrate',\n type=float,\n default=32,\n help='The number of bits per entry of embedding matrix.')\n\n # Other parameters\n parser.add_argument(\"--cache_dir\",\n default='/proj/smallfry/bert_pretrained_models',\n type=str,\n help=\"Where do you want to store the pre-trained models downloaded from s3\")\n parser.add_argument(\"--max_seq_length\",\n default=128,\n type=int,\n help=\"The maximum total input sequence length after WordPiece tokenization. \\n\"\n \"Sequences longer than this will be truncated, and sequences shorter \\n\"\n \"than this will be padded.\")\n parser.add_argument(\"--train_batch_size\",\n default=32,\n type=int,\n help=\"Total batch size for training.\")\n parser.add_argument(\"--eval_batch_size\",\n default=8,\n type=int,\n help=\"Total batch size for eval.\")\n parser.add_argument(\"--warmup_proportion\",\n default=0.1,\n type=float,\n help=\"Proportion of training to perform linear learning rate warmup for. \"\n \"E.g., 0.1 = 10%% of training.\")\n parser.add_argument(\"--no_cuda\",\n action='store_true',\n help=\"Whether not to use CUDA when available\")\n parser.add_argument('--gradient_accumulation_steps',\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\")\n return parser\n\ndef get_filename(suffix):\n return os.path.join(config['output_dir'], config['full_run_name'] + suffix)\n\ndef validate_config():\n if config['compresstype'] != 'nocompress' and not config['freeze_embeddings']:\n raise ValueError('Can only do compression if freezing embeddings.')\n\n if config['compresstype'] not in ('nocompress','pca') and config['bitrate'] >= 32:\n raise ValueError('If compressing, must specify bitrate < 32.')\n\n if config['gradient_accumulation_steps'] < 1:\n raise ValueError(\"Invalid gradient_accumulation_steps parameter: {}, should be >= 1\".format(\n config['gradient_accumulation_steps']))\n\ndef get_runname_suffix(parser):\n # Find all arguments (other than those in \"to_skip\" list) different from the default values, and concatenate them in a string\n runname_suffix = ''\n to_skip = ('rungroup', 'task_name', 'freeze_embeddings', 'compresstype', 'bitrate', 'learning_rate', 'seed', 'output_dir', 'data_dir', 'git_repo_dir', 'cache_dir')\n for key,val in utils.non_default_args(parser, config):\n if key not in to_skip:\n runname_suffix += '_{},{}'.format(key,val)\n return runname_suffix\n\ndef init_config(parser):\n global config\n config = vars(parser.parse_args())\n orig_config = config.copy()\n validate_config()\n\n # 1) Set up run directory and run names\n config['data_dir'] = os.path.join(config['data_dir'], get_upper_case_task_name(config['task_name']))\n config['rungroup'] = '{}-{}'.format(utils.get_date_str(), config['rungroup'])\n config['short_run_name'] = 'freeze,{}_compresstype,{}_bitrate,{}_lr,{}_seed,{}{}'.format(\n config['freeze_embeddings'], config['compresstype'], config['bitrate'], config['learning_rate'], config['seed'],\n get_runname_suffix(parser))\n config['full_run_name'] = '{}_task,{}_{}'.format(config['rungroup'], config['task_name'], config['short_run_name'])\n config['output_dir'] = os.path.join(config['output_dir'], config['task_name'], config['rungroup'], config['short_run_name'])\n utils.ensure_dir(config['output_dir']) # Make the output directory if it doesn't exist\n\n # 2) Add important entries into final config dictionary\n git_hash, git_diff = utils.get_git_hash_and_diff(config['git_repo_dir'], log=False, debug=config['debug'])\n config['git_hash'] = git_hash\n config['git_diff'] = git_diff\n config['train_batch_size'] = config['train_batch_size'] // config['gradient_accumulation_steps']\n\n # 3) save the original config, and the current config.\n utils.save_to_json(orig_config, get_filename('_orig_config.json'))\n utils.save_to_json(config, get_filename('_config.json'))\n\ndef init_logging(log_filename):\n # Log to file in output directory as well as to stdout.\n logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',\n datefmt = '%m/%d/%Y %H:%M:%S',\n level = logging.DEBUG,\n handlers=[\n logging.FileHandler(log_filename, mode='w'),\n logging.StreamHandler()])\n\ndef init_random_seeds(seed, n_gpu):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n if n_gpu > 0:\n torch.cuda.manual_seed_all(seed)\n\ndef init():\n parser = init_parser()\n init_config(parser)\n init_logging(get_filename('.log'))\n device = torch.device('cuda' if torch.cuda.is_available() and not config['no_cuda'] else 'cpu')\n n_gpu = torch.cuda.device_count()\n init_random_seeds(config['seed'], n_gpu)\n logger.info(\"device: {} n_gpu: {}\".format(device, n_gpu))\n return device, n_gpu\n\ndef get_data():\n processor = get_processor(config['task_name'])()\n if config['task_name'] == 'mnli':\n processor_mm = get_processor('mnli-mm')()\n eval_examples_mm = processor_mm.get_dev_examples(config['data_dir'])\n else:\n eval_examples_mm = None\n train_examples = processor.get_train_examples(config['data_dir'])\n eval_examples = processor.get_dev_examples(config['data_dir'])\n label_list = processor.get_labels()\n return train_examples, eval_examples, eval_examples_mm, label_list\n\ndef get_num_train_optimization_steps(num_train_examples):\n num_train_optimization_steps = int(\n num_train_examples / config['train_batch_size'] / config['gradient_accumulation_steps']) * config['num_train_epochs']\n return num_train_optimization_steps\n\ndef get_optimizer(model, num_train_examples):\n param_optimizer = list(model.named_parameters())\n no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n optimizer_grouped_parameters = [\n {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': config['weight_decay']},\n {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n ]\n optimizer = BertAdam(optimizer_grouped_parameters,\n lr=config['learning_rate'],\n warmup=config['warmup_proportion'],\n t_total=get_num_train_optimization_steps(num_train_examples))\n return optimizer\n\ndef freeze_and_compress_embeddings(model, device):\n X = model.bert.embeddings.word_embeddings.weight.detach().cpu().numpy().copy()\n dummy_word_list = ['x'] * X.shape[0]\n utils.save_embeddings(get_filename('_orig_embeddings.txt'), X, dummy_word_list)\n Xq = X\n compression_results = {}\n # Freeze and perhaps compress embeddings\n if config['freeze_embeddings']:\n # \"freeze\" WordPiece embbedings by setting requires_grad to False.\n model.bert.embeddings.word_embeddings.weight.requires_grad = False\n # perform compression of the WordPiece embeddings.\n if config['compresstype'] != 'nocompress':\n Xq,_,elapsed = compress_embeddings(X, config['bitrate'], config['compresstype'], config['seed'])\n # copy compressed WordPiece embeddings into the BERT model.\n model.bert.embeddings.word_embeddings.weight.copy_(torch.from_numpy(Xq.copy()).to(device))\n # Measure compression quality (reconstruction error, PIP, deltas, overlap).\n compression_results = utils.compute_basic_compression_results(X, Xq)\n compression_results['elapsed'] = elapsed\n utils.save_embeddings(get_filename('_compressed_embeddings.txt'), Xq, dummy_word_list)\n return Xq, compression_results\n\ndef save_model_and_tokenizer(model, tokenizer):\n # Save a trained model, configuration and tokenizer\n model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self\n\n # If we save using the predefined names, we can load using `from_pretrained`\n output_model_file = os.path.join(config['output_dir'], WEIGHTS_NAME)\n output_config_file = os.path.join(config['output_dir'], CONFIG_NAME)\n\n torch.save(model_to_save.state_dict(), output_model_file)\n model_to_save.config.to_json_file(output_config_file)\n tokenizer.save_vocabulary(config['output_dir'])\n\n # *** Avner removed the lines below. Unclear why they had this code in the original run_classifier.py. ***\n # # Load a trained model and vocabulary that you have fine-tuned\n # model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)\n # tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)\n\ndef get_model(num_labels, device, n_gpu):\n model = BertForSequenceClassification.from_pretrained(config['bert_model'],\n cache_dir=config['cache_dir'],\n num_labels=num_labels)\n model.to(device)\n if n_gpu > 1:\n model = torch.nn.DataParallel(model)\n return model\n\ndef get_dataloader(examples, label_list, tokenizer, output_mode, train=True):\n batch_size = config['train_batch_size'] if train else config['eval_batch_size']\n features = convert_examples_to_features(\n examples, label_list, config['max_seq_length'], tokenizer, output_mode)\n logger.info(\"***** Running {} *****\".format('training' if train else 'evaluation'))\n logger.info(\" Num examples = %d\", len(examples))\n logger.info(\" Batch size = %d\", batch_size)\n all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n dtype = torch.long if output_mode == \"classification\" else torch.float\n all_label_ids = torch.tensor([f.label_id for f in features], dtype=dtype)\n data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)\n sampler = RandomSampler(data) if train else SequentialSampler(data)\n return DataLoader(data, sampler=sampler, batch_size=batch_size), all_label_ids\n\ndef run_train_epoch(model, train_dataloader, optimizer, output_mode, n_gpu, device, num_labels):\n tr_loss = 0\n num_steps = 0\n for step, batch in enumerate(tqdm(train_dataloader, desc=\"Iteration\")):\n batch = tuple(t.to(device) for t in batch)\n input_ids, input_mask, segment_ids, label_ids = batch\n\n # define a new function to compute loss values for both output_modes\n logits = model(input_ids, segment_ids, input_mask, labels=None)\n\n if output_mode == \"classification\":\n loss_fct = CrossEntropyLoss()\n loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))\n elif output_mode == \"regression\":\n loss_fct = MSELoss()\n loss = loss_fct(logits.view(-1), label_ids.view(-1))\n\n if n_gpu > 1:\n loss = loss.mean() # mean() to average on multi-gpu.\n if config['gradient_accumulation_steps'] > 1:\n loss = loss / config['gradient_accumulation_steps']\n\n loss.backward()\n tr_loss += loss.item()\n if (step + 1) % config['gradient_accumulation_steps'] == 0:\n optimizer.step()\n optimizer.zero_grad()\n num_steps += 1\n return tr_loss/num_steps\n\ndef run_evaluation(model, eval_dataloader, eval_label_ids, output_mode, device, num_labels):\n eval_loss = 0\n num_steps = 0\n preds = []\n\n for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc=\"Evaluating\"):\n input_ids = input_ids.to(device)\n input_mask = input_mask.to(device)\n segment_ids = segment_ids.to(device)\n label_ids = label_ids.to(device)\n\n with torch.no_grad():\n logits = model(input_ids, segment_ids, input_mask, labels=None)\n\n # create eval loss and other metric required by the task\n if output_mode == \"classification\":\n loss_fct = CrossEntropyLoss()\n tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))\n elif output_mode == \"regression\":\n loss_fct = MSELoss()\n tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))\n \n eval_loss += tmp_eval_loss.mean().item()\n num_steps += 1\n if len(preds) == 0:\n preds.append(logits.detach().cpu().numpy())\n else:\n preds[0] = np.append(\n preds[0], logits.detach().cpu().numpy(), axis=0)\n\n eval_loss = eval_loss / num_steps\n preds = preds[0]\n if output_mode == \"classification\":\n preds = np.argmax(preds, axis=1)\n elif output_mode == \"regression\":\n preds = np.squeeze(preds)\n result = compute_metrics(config['task_name'], preds, eval_label_ids.numpy())\n result['eval_loss'] = eval_loss\n return result\n\ndef update_full_results(full_results, result, epoch, mismatch=False):\n is_best_epoch = False\n mm_str = '_mm' if mismatch else ''\n for k,v in result.items():\n k = k + mm_str\n if k not in full_results:\n full_results[k] = []\n full_results[k].append(v)\n if v == min(full_results[k]):\n full_results[k + '_min'] = v\n if k + '_min' == config['checkpoint_metric']:\n is_best_epoch = True\n if v == max(full_results[k]):\n full_results[k + '_max'] = v\n if k + '_max' == config['checkpoint_metric']:\n is_best_epoch = True\n if is_best_epoch:\n full_results['checkpoint' + mm_str] = result\n full_results['best_epoch' + mm_str] = epoch\n return convert_numpy_to_float(full_results)\n\ndef convert_numpy_to_float(obj):\n if type(obj) in (np.float64, np.float32):\n obj = float(obj)\n elif type(obj) is list:\n for i,x in enumerate(obj):\n obj[i] = convert_numpy_to_float(x)\n elif type(obj) is dict:\n for k,v in obj.items():\n obj[k] = convert_numpy_to_float(v)\n return obj\n\ndef main():\n device, n_gpu = init()\n # eval_examples_mm is only used when task_name = 'mnli', to evaluate on both matched and mismatched dev sets.\n train_examples, eval_examples, eval_examples_mm, label_list = get_data()\n output_mode = get_output_mode(config['task_name'])\n tokenizer = BertTokenizer.from_pretrained(\n config['bert_model'],\n do_lower_case=('uncased' in config['bert_model'])\n )\n\n # Prepare model and optimizer\n model = get_model(len(label_list), device, n_gpu)\n optimizer = get_optimizer(model, len(train_examples))\n\n # if config['freeze_embeddings'] is true, freeze and then optionally compress embeddings.\n Xq, full_results = freeze_and_compress_embeddings(model, device)\n\n train_dataloader,_ = get_dataloader(train_examples, label_list, tokenizer, output_mode, train=True)\n eval_dataloader, eval_label_ids = get_dataloader(eval_examples, label_list, tokenizer, output_mode, train=False)\n if config['task_name'] == 'mnli':\n eval_dataloader_mm, eval_label_ids_mm = get_dataloader(eval_examples_mm, label_list, tokenizer, output_mode, train=False)\n for epoch in trange(int(config['num_train_epochs']), desc=\"Epoch\"):\n # Do one epoch of training\n model.train()\n logger.info('Epoch #{}: Begin training'.format(epoch))\n tr_loss = run_train_epoch(model, train_dataloader, optimizer, output_mode, n_gpu, device, len(label_list))\n logger.info('Epoch #{}: Finished training'.format(epoch))\n\n # Run evaluation\n model.eval()\n logger.info('Epoch #{}: Begin evaluation'.format(epoch))\n result = run_evaluation(model, eval_dataloader, eval_label_ids, output_mode, device, len(label_list))\n logger.info('Epoch #{}: Finished evaluation'.format(epoch))\n result['train_loss'] = tr_loss\n full_results = update_full_results(full_results, result, epoch)\n if config['task_name'] == 'mnli':\n logger.info('Epoch #{}: Begin evaluation (mismatch)'.format(epoch))\n result_mm = run_evaluation(model, eval_dataloader_mm, eval_label_ids_mm, output_mode, device, len(label_list))\n logger.info('Epoch #{}: Finished evaluation (mismatch)'.format(epoch))\n result_mm['acc_avg'] = (result['acc'] + result_mm['acc'])/2.0\n update_full_results(full_results, result_mm, epoch, mismatch=True)\n utils.save_to_json(full_results, get_filename('_results.json'))\n\n # Assert that after training has completed, embeddings didn't change if config['freeze_embeddings'] is True\n if config['freeze_embeddings']:\n X_final = model.bert.embeddings.word_embeddings.weight.detach().cpu().numpy()\n logger.info('Difference between embeddings at beginning vs end of training: {}'.format(np.linalg.norm(Xq-X_final)))\n assert np.allclose(Xq, X_final), 'Embeddings changed during training.'\n\n # Save model, tokenizer, final results, and final config.\n save_model_and_tokenizer(model, tokenizer)\n # Save results to config so that everything from this run is easiliy accessible in config_final.json\n config['results'] = full_results\n utils.save_to_json(full_results, get_filename('_results_final.json'))\n utils.save_to_json(config, get_filename('_config_final.json'))\n logging.info('Run complete. Exiting compress.py main method.')\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "examples/run_classifier.py", "file_name": "run_classifier.py", "file_ext": "py", "file_size_in_byte": 44285, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.getLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "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.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "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": "os.path.join", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path", "line_number": 283, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path", "line_number": 323, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 351, "usage_type": "call"}, {"api_name": "os.path", "line_number": 351, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 356, "usage_type": "call"}, {"api_name": "os.path", "line_number": 356, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 383, "usage_type": "call"}, {"api_name": "os.path", "line_number": 383, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path", "line_number": 388, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 524, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 533, "usage_type": "call"}, {"api_name": "scipy.stats.spearmanr", "line_number": 534, "usage_type": "call"}, {"api_name": "sklearn.metrics.matthews_corrcoef", "line_number": 544, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 610, "usage_type": "call"}, {"api_name": "smallfry.compress.compress_uniform", "line_number": 617, "usage_type": "call"}, {"api_name": "smallfry.compress", "line_number": 617, "usage_type": "name"}, {"api_name": "smallfry.compress.compress_kmeans", "line_number": 619, "usage_type": "call"}, {"api_name": "smallfry.compress", "line_number": 619, "usage_type": "name"}, {"api_name": "smallfry.compress.compress_pca", "line_number": 622, "usage_type": "call"}, {"api_name": "smallfry.compress", "line_number": 622, "usage_type": "name"}, {"api_name": "smallfry.utils.load_embeddings", "line_number": 624, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 624, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 625, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 625, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 633, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 741, "usage_type": "call"}, {"api_name": "os.path", "line_number": 741, "usage_type": "attribute"}, {"api_name": "smallfry.utils.non_default_args", "line_number": 758, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 758, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 770, "usage_type": "call"}, {"api_name": "os.path", "line_number": 770, "usage_type": "attribute"}, {"api_name": "smallfry.utils.get_date_str", "line_number": 771, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 771, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 776, "usage_type": "call"}, {"api_name": "os.path", "line_number": 776, "usage_type": "attribute"}, {"api_name": "smallfry.utils.ensure_dir", "line_number": 777, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 777, "usage_type": "name"}, {"api_name": "smallfry.utils.get_git_hash_and_diff", "line_number": 780, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 780, "usage_type": "name"}, {"api_name": "smallfry.utils.save_to_json", "line_number": 786, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 786, "usage_type": "name"}, {"api_name": "smallfry.utils.save_to_json", "line_number": 787, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 787, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 791, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 793, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 795, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 796, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 799, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 800, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 800, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 801, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 803, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 803, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 809, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 809, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 809, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 810, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 810, "usage_type": "attribute"}, {"api_name": "pytorch_pretrained_bert.optimization.BertAdam", "line_number": 839, "usage_type": "call"}, {"api_name": "smallfry.utils.save_embeddings", "line_number": 848, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 848, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 859, "usage_type": "call"}, {"api_name": "smallfry.utils.compute_basic_compression_results", "line_number": 861, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 861, "usage_type": "name"}, {"api_name": "smallfry.utils.save_embeddings", "line_number": 863, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 863, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 871, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.file_utils.WEIGHTS_NAME", "line_number": 871, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 871, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 872, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.file_utils.CONFIG_NAME", "line_number": 872, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 872, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 874, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.modeling.BertForSequenceClassification.from_pretrained", "line_number": 884, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.modeling.BertForSequenceClassification", "line_number": 884, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 889, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 889, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 899, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 899, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 900, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 900, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 901, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 901, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 902, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 902, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 903, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 904, "usage_type": "call"}, {"api_name": "torch.utils.data.RandomSampler", "line_number": 905, "usage_type": "call"}, {"api_name": "torch.utils.data.SequentialSampler", "line_number": 905, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 906, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 911, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 919, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 922, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 943, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 949, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 954, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 957, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 965, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 971, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 973, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "pytorch_pretrained_bert.tokenization.BertTokenizer.from_pretrained", "line_number": 1015, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.tokenization.BertTokenizer", "line_number": 1015, "usage_type": "name"}, {"api_name": "tqdm.trange", "line_number": 1031, "usage_type": "call"}, {"api_name": "smallfry.utils.save_to_json", "line_number": 1051, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 1051, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 1056, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 1056, "usage_type": "attribute"}, {"api_name": "numpy.allclose", "line_number": 1057, "usage_type": "call"}, {"api_name": "smallfry.utils.save_to_json", "line_number": 1063, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 1063, "usage_type": "name"}, {"api_name": "smallfry.utils.save_to_json", "line_number": 1064, "usage_type": "call"}, {"api_name": "smallfry.utils", "line_number": 1064, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 1065, "usage_type": "call"}]} +{"seq_id": "508597559", "text": "import json\nfrom functools import wraps\nfrom json.decoder import JSONDecodeError\n\nfrom requests import Session\n\nimport constants\nfrom services.config import ConfigService\nfrom services.http import HttpService\nfrom services.mixins import EventEmitterMixin\nfrom services.session import SessionService\nfrom utils.url import (create_url, get_path_from_url,\n get_query_params_from_url, succ_status)\n\n\ndef _attach_token(f):\n \"\"\"\n Attach access token if exists\n \"\"\"\n @wraps(f)\n def decorated(*args, **kwargs):\n if SessionService.access_token:\n token = 'Bearer {token}'.format(token=SessionService.access_token)\n if kwargs.get('headers'):\n kwargs['headers']['Authorization'] = token\n else:\n kwargs['headers'] = {'Authorization': token}\n return f(*args, **kwargs)\n return decorated\n\n\nclass ApiService(EventEmitterMixin, HttpService):\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Extend the Functionality of by Handling Unsuccessful Requests\n\n Return , \n If successful message is None\n If not successful json is None\n \"\"\"\n super().__init__(*args, **kwargs)\n\n @_attach_token\n def get(self, path, params={}, **kwargs):\n url = self._get_api_url(path=path)\n r = super().get(url=url, params=params, **kwargs)\n return self._handle_request(r)\n\n @_attach_token\n def post(self, path, params={}, json=None, **kwargs):\n url = self._get_api_url(path=path)\n r = super().post(url=url, params=params, json=json, **kwargs)\n return self._handle_request(r)\n\n @_attach_token\n def put(self, path, params={}, json=None, **kwargs):\n url = self._get_api_url(path=path)\n r = super().put(url=url, params=params, json=json, **kwargs)\n return self._handle_request(r)\n\n @_attach_token\n def patch(self, path, params={}, json=None, **kwargs):\n url = self._get_api_url(path=path)\n r = super().patch(url=url, params=params, json=json, **kwargs)\n return self._handle_request(r)\n\n @_attach_token\n def delete(self, path, params={}, **kwargs):\n url = self._get_api_url(path=path)\n r = super().delete(url=url, params=params, **kwargs)\n return self._handle_request(r)\n\n @_attach_token\n def resend_request(self, r, *args, **kwargs):\n path = get_path_from_url(r.url)\n params = get_query_params_from_url(r.url)\n method = getattr(self, r.method.lower())\n body = getattr(r, 'body', None)\n if body:\n kwargs['json'] = json.loads(body)\n return method(path=path, params=params, *args, **kwargs)\n\n # Private Methods\n def _get_api_url(self, path):\n return create_url(\n protocol=ConfigService.get_protocol(),\n host=ConfigService.get_host(),\n port=ConfigService.get_port(),\n path=path)\n\n def _handle_request(self, r):\n error = 'Error Occured When Connecting to Server'\n if r is None:\n return None, error\n try:\n if succ_status(r.status_code):\n return r.json(), None\n json = r.json()\n if json and json.get('expired'):\n if r.url == self._get_api_url(constants.API_AUTH_REFRESH):\n self.__class__._emit_event(constants.EVENT_SESSION_EXPIRE)\n return None, json[constants.API_DEFAULT_KEY]\n else:\n if self.refresh_session():\n return self.resend_request(r.request)\n else:\n return None, json[constants.API_DEFAULT_KEY]\n return None, json[constants.API_DEFAULT_KEY]\n except JSONDecodeError:\n return None, error\n return None, error\n\n def refresh_session(self):\n refresh_token = SessionService.refresh_token\n if refresh_token:\n json = {'refresh_token': refresh_token}\n json, error = self.post(path=constants.API_AUTH_REFRESH, json=json)\n if not error:\n SessionService.refresh(access_token=json['access_token'])\n return True\n else:\n return False\n else:\n return False\n", "sub_path": "app/services/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 4352, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "services.session.SessionService.access_token", "line_number": 22, "usage_type": "attribute"}, {"api_name": "services.session.SessionService", "line_number": 22, "usage_type": "name"}, {"api_name": "services.session.SessionService.access_token", "line_number": 23, "usage_type": "attribute"}, {"api_name": "services.session.SessionService", "line_number": 23, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 20, "usage_type": "call"}, {"api_name": "services.mixins.EventEmitterMixin", "line_number": 32, "usage_type": "name"}, {"api_name": "services.http.HttpService", "line_number": 32, "usage_type": "name"}, {"api_name": "utils.url.get_path_from_url", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.url.get_query_params_from_url", "line_number": 77, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.url.create_url", "line_number": 86, "usage_type": "call"}, {"api_name": "services.config.ConfigService.get_protocol", "line_number": 87, "usage_type": "call"}, {"api_name": "services.config.ConfigService", "line_number": 87, "usage_type": "name"}, {"api_name": "services.config.ConfigService.get_host", "line_number": 88, "usage_type": "call"}, {"api_name": "services.config.ConfigService", "line_number": 88, "usage_type": "name"}, {"api_name": "services.config.ConfigService.get_port", "line_number": 89, "usage_type": "call"}, {"api_name": "services.config.ConfigService", "line_number": 89, "usage_type": "name"}, {"api_name": "utils.url.succ_status", "line_number": 97, "usage_type": "call"}, {"api_name": "json.get", "line_number": 100, "usage_type": "call"}, {"api_name": "constants.API_AUTH_REFRESH", "line_number": 101, "usage_type": "attribute"}, {"api_name": "constants.EVENT_SESSION_EXPIRE", "line_number": 102, "usage_type": "attribute"}, {"api_name": "constants.API_DEFAULT_KEY", "line_number": 103, "usage_type": "attribute"}, {"api_name": "constants.API_DEFAULT_KEY", "line_number": 108, "usage_type": "attribute"}, {"api_name": "constants.API_DEFAULT_KEY", "line_number": 109, "usage_type": "attribute"}, {"api_name": "json.decoder.JSONDecodeError", "line_number": 110, "usage_type": "name"}, {"api_name": "services.session.SessionService.refresh_token", "line_number": 115, "usage_type": "attribute"}, {"api_name": "services.session.SessionService", "line_number": 115, "usage_type": "name"}, {"api_name": "constants.API_AUTH_REFRESH", "line_number": 118, "usage_type": "attribute"}, {"api_name": "services.session.SessionService.refresh", "line_number": 120, "usage_type": "call"}, {"api_name": "services.session.SessionService", "line_number": 120, "usage_type": "name"}]} +{"seq_id": "41388330", "text": "import uuid\nimport logging\nfrom flask import render_template, redirect, flash\nfrom . import app, piastrix, db\nfrom .exceptions import PiastrixApiException\nfrom .forms import BasePaymentForm, PayMethodForm, InvoiceMethodForm, Currency\nfrom .models import Payment\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n form = BasePaymentForm()\n\n if form.validate_on_submit():\n shop_order_id = str(uuid.uuid4())\n amount = form.amount.data\n currency = form.currency.data\n description = form.description.data\n\n if currency == Currency.EUR.value:\n form_data, url = piastrix.pay(amount=amount, currency=currency,\n description=description,\n shop_order_id=shop_order_id)\n\n payment = Payment(shop_order_id=shop_order_id, amount=amount,\n currency=currency, description=description)\n db.session.add(payment)\n db.session.commit()\n\n new_form = PayMethodForm(data=form_data)\n return render_template('pay.html', form=new_form, url=url)\n elif currency == Currency.USD.value:\n try:\n data = piastrix.bill(shop_amount=amount,\n shop_currency=currency,\n payer_currency=currency,\n shop_order_id=shop_order_id,\n description=description)\n except PiastrixApiException as err:\n flash('Something went wrong. Please try again later', 'danger')\n logging.warning(f\"Failed to create bill: {err}\")\n return render_template('home.html', form=form)\n\n payment = Payment(shop_order_id=shop_order_id, amount=amount,\n currency=currency, description=description)\n db.session.add(payment)\n db.session.commit()\n\n return redirect(data['url'])\n elif currency == Currency.RUB.value:\n try:\n data = piastrix.invoice(amount=amount,\n currency=currency,\n shop_order_id=shop_order_id)\n except PiastrixApiException as err:\n flash('Something went wrong. Please try again later', 'danger')\n logging.warning(f\"Failed to create invoice: {err}\")\n return render_template('home.html', form=form)\n\n payment = Payment(shop_order_id=shop_order_id, amount=amount,\n currency=currency, description=description)\n db.session.add(payment)\n db.session.commit()\n\n new_form = InvoiceMethodForm(data=data['data'])\n return render_template('invoice.html', form=new_form,\n url=data['url'], method=data['method'])\n\n return render_template('home.html', form=form)\n", "sub_path": "main/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 3000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "forms.BasePaymentForm", "line_number": 12, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 15, "usage_type": "call"}, {"api_name": "forms.Currency.EUR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "forms.Currency", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Payment", "line_number": 25, "usage_type": "call"}, {"api_name": "forms.PayMethodForm", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "forms.Currency.USD", "line_number": 32, "usage_type": "attribute"}, {"api_name": "forms.Currency", "line_number": 32, "usage_type": "name"}, {"api_name": "exceptions.PiastrixApiException", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Payment", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "forms.Currency.RUB", "line_number": 50, "usage_type": "attribute"}, {"api_name": "forms.Currency", "line_number": 50, "usage_type": "name"}, {"api_name": "exceptions.PiastrixApiException", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Payment", "line_number": 60, "usage_type": "call"}, {"api_name": "forms.InvoiceMethodForm", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "570806992", "text": "import pandas as pd\nimport glob\nimport xlrd\nimport csv\nimport re\n\n# Be aware: CD to the right dir first!\n\n# Define the Import and Export folder\n# Needs changing when used with Linux\nimport_path = 'Import\\\\'\nexport_path = 'Export\\\\'\n\n# Removes HTML tags when called\n\n\ndef remove_html_tags(data):\n p = re.compile(r'<.*?>')\n return p.sub('', data)\n\n# Removes lone linebreaks (without /r) when called\n\n\ndef remove_n_without_r(data):\n p = re.compile(r'(? min(self.levels):\n raise ValueError(\"Level sequence has value %s which is smaller than level density's domainMin of %s\" %\n (str(min(self.levels)), str(self.level_density.domainMin)))\n if self.level_density.domainMax < max(self.levels):\n raise ValueError(\"Level sequence has value %s which is larger than level density's domainMax of %s\" %\n (str(max(self.levels)), str(self.level_density.domainMax)))\n self.Lmin = Lmin\n self.Lmax = Lmax\n\n @property\n def num_levels(self):\n return len(self.levels)\n\n @property\n def unfolded_levels(self):\n theCLD = self.level_density.indefiniteIntegral()\n return [theCLD.evaluate(x) for x in self.levels]\n\n @property\n def spacings(self):\n return [self.levels[i + 1] - self.levels[i] for i in range(self.num_levels - 1)]\n\n @property\n def num_spacings(self):\n return len(self.spacings)\n\n @property\n def ave_spacing(self):\n return numpy.average(self.spacings)\n\n @property\n def mean_spacing(self):\n return numpy.mean(self.spacings)\n\n @property\n def stddev_spacing(self):\n return numpy.std(self.spacings)\n\n @property\n def var_spacing(self):\n return numpy.var(self.spacings)\n\n @property\n def normalized_spacings(self):\n result = []\n for i in range(self.num_levels - 1):\n D = self.levels[i + 1] - self.levels[i]\n E = 0.5 * (self.levels[i + 1] + self.levels[i])\n result.append(D / self.mean_spacing_at_E(E))\n return result\n\n def mean_spacing_at_E(self, E):\n try:\n return 1.0 / self.level_density.evaluate(E)\n except TypeError as err:\n raise TypeError(str(err) + \" for E=\" + str(E))\n\n def getAverageSpacingLevelDensity(self):\n \"\"\"\n Generate a level density corresponding to the average spacing\n\n :return: An XYs1d containing the level density\n \"\"\"\n myAxes = axesModule.Axes(2, labelsUnits={0: ('Level density', '1/' + self.energyUnit),\n 1: ('Excitation energy', self.energyUnit)})\n theData = []\n for thisEnergy in self.levels:\n if len(theData) > 0 and abs(thisEnergy - theData[-1][0]) < 1e-6 * thisEnergy:\n continue # Deal with corner case where two levels are degenerate\n else:\n theData.append([thisEnergy, 1.0 / self.mean_spacing])\n return XYs1dModule.XYs1d(axes=myAxes, data=theData)\n\n def getCumulativeLevelDistribution(self, c0=1.0, domainMin=None, domainMax=None):\n \"\"\"\n Generate a cummulative level distribution from the levels in self.levels\n\n :param c0: 1st bin's starting number of levels (if this one is tacked onto a lower energy sequence)\n :param domainMin:\n :param domainMax:\n :return: An XYs1d containing the histogram of the cummulative level distribution\n \"\"\"\n myAxes = axesModule.Axes(2, labelsUnits={0: ('Number of levels', ''), 1: ('Excitation energy', self.energyUnit)})\n theData = []\n if domainMin is not None and domainMin < self.levels[0]:\n theData.append([domainMin, c0])\n theData.append([self.levels[0], c0 + 1])\n else:\n theData.append([self.levels[0], c0])\n for thisEnergy in self.levels[1:]:\n if abs(thisEnergy - theData[-1][0]) < 1e-6 * thisEnergy:\n theData[-1][1] = theData[-1][1] + 1 # Deal with corner case where two levels are degenerate\n else:\n theData.append([thisEnergy, theData[-1][1] + 1])\n if domainMax is not None and domainMax > self.levels[-1]:\n theData.append([domainMax, theData[-1][1]])\n return XYs1dModule.XYs1d(axes=myAxes, data=theData, interpolation=xDataEnumsModule.Interpolation.flat).domainSlice(\n domainMin=domainMin, domainMax=domainMax)\n\n def getNNSDist(self, normalizeByMeanLevelSpacing=True, normalizeDistribution=True, numBins=None):\n if normalizeByMeanLevelSpacing:\n if numBins is not None:\n raw_histogram = numpy.histogram(self.normalized_spacings, bins=numBins, density=normalizeDistribution)\n else:\n raw_histogram = numpy.histogram(self.normalized_spacings, density=normalizeDistribution)\n else:\n if numBins is not None:\n raw_histogram = numpy.histogram(self.spacings, bins=numBins, density=normalizeDistribution)\n else:\n raw_histogram = numpy.histogram(self.spacings, density=normalizeDistribution)\n table = []\n for i in range(len(raw_histogram[0])):\n table.append([0.5 * (raw_histogram[1][i] + raw_histogram[1][i + 1]), raw_histogram[0][i]])\n return table, raw_histogram\n\n def getDysonMehtaDelta3_vs_L(self):\n if self.num_levels < self.Lmin:\n raise ValueError(\n \"Not enough levels (%i) to compute Delta_3, need at least %i\" % (self.num_levels, self.Lmin))\n Lmax = min(self.num_levels, self.Lmax)\n P_L = [0.0 for L in range(Lmax + 1)]\n P2_L = [0.0 for L in range(Lmax + 1)]\n N_L = [0.0 for L in range(Lmax + 1)]\n levelSequence = self.unfolded_levels\n # Get all the L-dists for each energy\n for iE in range(self.num_levels - Lmax):\n for iL, p in enumerate(self.getDysonMehtaDelta3_vs_L_at_E(iE, levelSequence)):\n P_L[iL + self.Lmin] += p\n P2_L[iL + self.Lmin] += p * p\n N_L[iL + self.Lmin] += 1\n # Normalize the results\n for L in range(Lmax + 1):\n if N_L[L] > 0.0:\n P_L[L] /= N_L[L]\n # Compute variance\n for L in range(Lmax + 1):\n if N_L[L] > 1.0:\n P2_L[L] = (P2_L[L] - P_L[L] * P_L[L]) / N_L[L] / (N_L[L] - 1)\n return P_L, P2_L\n\n def getDysonMehtaDelta3_vs_L_at_E(self, iE, levelSequence):\n if self.num_levels < self.Lmin:\n raise ValueError(\n \"Not enough levels (%i) to compute Delta_3, need at least %i\" % (self.num_levels, self.Lmin))\n Lmax = min(self.num_levels, self.Lmax)\n return [self.getDysonMehtaDelta3(L, iE, levelSequence) for L in range(self.Lmin, Lmax + 1)]\n\n def getDysonMehtaDelta3(self, L, iE, levelSequence):\n \"\"\"\n Compute the Dyson Mehta Delta3 statistic using Declan's algorithm (D. Mulhall, Phys. Rev. 83, 05321 (2011))\n\n :return:\n \"\"\"\n C, W, Y = 0.0, 0.0, 0.0\n Z = (levelSequence[iE + L] - levelSequence[iE])\n X = Z * (levelSequence[iE + L] + levelSequence[iE])\n V = (pow(levelSequence[iE + L], 3.0) - pow(levelSequence[iE], 3.0)) / 3.0\n for j in range(iE, iE + L):\n jEdiff = j * (levelSequence[j + 1] - levelSequence[j])\n C += j * jEdiff\n W += -jEdiff * (levelSequence[j + 1] + levelSequence[j])\n Y += -2.0 * jEdiff\n den = (4.0 * V * Z - X * X)\n A = (X * Y - 2.0 * W * Z) / den\n B = (W * X - 2.0 * V * Y) / den\n return (C + V * A * A + W * A + X * A * B + Y * B + Z * B * B) / (L + 1)\n\n def get2SpacingCorrelation(self, epsD=1e-9):\n \"\"\"\n Get the spacing-spacing correlation function.\n\n ..math::\n D_i=E_{i+1}-E_i\n\n ..math::\n\n \\rho(D_i,D_{i+1}) = \\frac{\\sum_i(D_i-\\overline{D})(D_{i+1}-\\overline{D})}{\\left[\\sum_i\n (D_i-\\overline{D})^2\\sum_j(D_{j+1}-\\overline{D})^2\\right]^{1/2}}\n\n If you stare at this for a while, it is just the level-level level spacing correlation coefficient.\n\n For GOE level spacings, should see :math:`\\rho=-0.27`\n\n :returns : value and variance in a tuple\n \"\"\"\n diff_spacings = []\n for i in range(self.num_levels - 1):\n aveE = 0.5 * (self.levels[i + 1] + self.levels[i])\n diff_spacings.append(self.spacings[i] - self.mean_spacing_at_E(aveE))\n if len([x for x in diff_spacings if x > epsD]) < 2:\n raise ValueError(\"Level-level spacing correlation undefined, is this a picket fence?\")\n correlation_matrix = numpy.corrcoef(\n numpy.array([[diff_spacings[i], diff_spacings[i + 1]] for i in range(self.num_spacings - 1)]).T)\n return correlation_matrix[0, 1]\n\n def getThermodynamicEnergyU(self):\n \"\"\"\n Get Dyson-Mehta's estimate of the thermodynamic energy of the system, U\n\n Use Eqs. (19-21) from G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n L = self.num_levels / 2.0\n\n # normalize energies to average spacing\n # dE=self.energies[-1]-self.energies[0]\n # eps=[(2*L-1)*E/dE for E in self.levels] # normalize to average spacing (option 1)\n eps = [E / self.mean_spacing for E in self.levels] # normalize to average spacing (option 2)\n\n # center energies on interval\n E0 = eps[0] + L - 0.5\n eps = [E - E0 for E in eps]\n\n # \"picket fence\" energies\n peps = [-L + (i + 1) - 0.5 for i in\n range(self.num_levels)] # note the \"1\" index offset -- Mitchell and Shriner use Fortran style indexing\n\n # define \"potential energy\"\n def V(E):\n return (L - E) * (0.5 + math.log((L - E) / (2.0 * L))) + (L + E) * (0.5 + math.log((L + E) / (2.0 * L)))\n\n # Now compute the U statistic\n term1 = 0.0\n term2 = 0.0\n for i in range(self.num_levels):\n term2 += V(eps[i]) - V(peps[i])\n for j in range(i + 1, self.num_levels):\n term1 += math.log((eps[j] - eps[i]) / (j - i))\n return -(term1 + term2) / self.num_levels\n\n def getDysonMehtaQ(self):\n \"\"\"\n Get Dyson-Mehta's Q statistic (billed as a lower-variance version of the thermodynamic energy of the system)\n\n Use Eqs. (25-32) from G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n # Mitchell and Shriner pick M=4, so ...\n M = 4\n R = M * self.mean_spacing\n\n # renormalize energies\n Eave = 0.5 * (self.levels[0] + self.levels[-1])\n eps = [E - Eave for E in self.levels]\n\n # compute scaling variable\n Emax = max(self.levels) # FIXME: check this, not clearly defined in section D\n Emin = min(self.levels) # FIXME: check this, not clearly defined in section D\n if Emax - self.levels[-1] < self.levels[0] - Emin:\n L = 0.5 * (self.levels[-1] + Emax)\n else:\n L = 0.5 * (Emin + self.levels[0])\n\n def f(x, y):\n \"\"\"Eq. (29)\"\"\"\n if abs(x - y) < R and abs(x) < L and abs(y) < L:\n return 1\n return 0\n\n # Eq. (30)\n U0 = -R / L + R * R / 8.0 / L / L\n\n def F(x):\n \"\"\"Eq. (30)\"\"\"\n if abs(x) < L:\n return 1\n return 0\n\n def U(x):\n \"\"\"Eq. (30)\"\"\"\n if abs(x) < L - R:\n return -R / L\n if L > abs(x) > L - R:\n return -(R + (L - abs(x)) * (1.0 - math.log((L - abs(x)) / R))) / 2.0 / L\n raise ValueError(\"shouldn't get here, x=%s, L=%s, R=%s\" % (str(x), str(L), str(R)))\n\n # Compute Q statistic\n term1 = 0.0\n term2 = 0.0\n term3 = 0.0\n term4 = 0.0\n sumF = 0.0\n intF = 2.0 * L\n for i in range(self.num_levels):\n sumF += F(eps[i])\n for j in range(self.num_levels):\n if i < j:\n term1 -= f(eps[i], eps[j]) * math.log((eps[j] - eps[\n i]) / R) # suspect typo in term #1 of Eq. (25), i & j should be swapped in logrithm\n term2 += F(eps[i]) * U(eps[j])\n term3 -= 0.5 * U0 * F(eps[i]) * F(eps[j])\n term4 = 0.5 * sumF * math.log(2.0 * math.pi * R * sumF / intF)\n n = 2.0 * L / self.mean_spacing\n return (term1 + term2 + term3 + term4) / n\n\n def getFractionMissing(self):\n \"\"\"Estimate the fraction of missing levels using an inference based on all of the metrics given below\"\"\"\n raise NotImplementedError(\"write me\")\n\n def getFractionMissingFromDysonMehtaQ(self):\n \"\"\"\n See G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n raise NotImplementedError(\"write me\")\n\n def getFractionMissingFromDysonMehtaDelta3(self, use_b_covariance=False):\n \"\"\"\n See G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n d3, dd3 = self.getDysonMehtaDelta3_vs_L()\n Ls = numpy.array(list(range(self.Lmin, self.Lmax + 1)))\n d3 = numpy.array(d3[5:])\n dd3 = numpy.array(dd3[5:])\n bmean = numpy.array([0.920, 0.978])\n bcov = numpy.array([[6.64e-3, -9.99e-4], [-9.99e-4, 4.15e-3]])\n\n def d3func(L, *f, **kwrds):\n f1 = 1. - f[0]\n LL = L # /2.0 #don't need factor of 2 used in Mitchell and Shriner Eq. (39)\n return kwrds['oparms'][0] * f[0] * LL / 15.0 + kwrds['oparms'][1] * f1 * f1 * (\n numpy.log(LL / f1) - 0.687) / math.pi / math.pi\n\n if use_b_covariance:\n result = bounded_nonlinear_curve_fit(func=d3func, xdata=Ls, ydata=d3,\n yerr=dd3, fitparms0=[0.8], oparms=bmean, oparmscov=bcov,\n bounds=([0.0], [1.0]), size=100)\n else:\n result = scipy.optimize.curve_fit(lambda L, *f: d3func(L, *f, oparms=bmean), Ls, d3, p0=[0.8], sigma=dd3,\n bounds=([0.0], [1.0]))\n return float(result[0]), math.sqrt(float(result[1]))\n\n def getFractionMissingThermodynamicEnergyU(self):\n \"\"\"\n See G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n raise NotImplementedError(\"write me\")\n\n def getFractionMissing2SpacingCorrelation(self):\n \"\"\"\n See G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n rho = self.get2SpacingCorrelation()\n emean = numpy.array([-0.251, 0.428])\n ecov = numpy.array([[2.67e-4, -7.44e-4], [-7.44e-4, 3.22e-3]])\n dfde0 = -1.0 / emean[1]\n dfde1 = -(rho - emean[0]) / emean[1] / emean[1]\n f = (rho - emean[0]) / emean[1]\n df = math.sqrt(dfde0 * dfde0 * ecov[0][0] + dfde1 * dfde1 * ecov[1][1] + 2.0 * dfde0 * dfde1 * ecov[0][1])\n return max(min(1.0, f), 0.0), df\n\n def getFractionMissingNNSDist(self):\n \"\"\"\n See G.E. Mitchell, J.F. Shriner, \"Missing Level Corrections using Neutron Spacings\",\n IAEA NDS Report INDC(NDS)-0561 (2009)\n \"\"\"\n raise NotImplementedError(\"write me\")\n", "sub_path": "brownies/BNL/restools/level_analysis.py", "file_name": "level_analysis.py", "file_ext": "py", "file_size_in_byte": 16853, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "math.log", "line_number": 18, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.average", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 89, "usage_type": "call"}, {"api_name": "xData.axes.Axes", "line_number": 112, "usage_type": "call"}, {"api_name": "xData.axes", "line_number": 112, "usage_type": "name"}, {"api_name": "xData.XYs1d.XYs1d", "line_number": 120, "usage_type": "call"}, {"api_name": "xData.XYs1d", "line_number": 120, "usage_type": "name"}, {"api_name": "xData.axes.Axes", "line_number": 131, "usage_type": "call"}, {"api_name": "xData.axes", "line_number": 131, "usage_type": "name"}, {"api_name": "xData.XYs1d.XYs1d", "line_number": 145, "usage_type": "call"}, {"api_name": "xData.XYs1d", "line_number": 145, "usage_type": "name"}, {"api_name": "xData.enums.Interpolation", "line_number": 145, "usage_type": "attribute"}, {"api_name": "xData.enums", "line_number": 145, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 241, "usage_type": "call"}, {"api_name": "math.log", "line_number": 268, "usage_type": "call"}, {"api_name": "math.log", "line_number": 276, "usage_type": "call"}, {"api_name": "math.log", "line_number": 322, "usage_type": "call"}, {"api_name": "math.log", "line_number": 336, "usage_type": "call"}, {"api_name": "math.log", "line_number": 340, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 340, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 371, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 371, "usage_type": "attribute"}, {"api_name": "brownies.BNL.utilities.fitting.bounded_nonlinear_curve_fit", "line_number": 374, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize.curve_fit", "line_number": 378, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize", "line_number": 378, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 378, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 396, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 400, "usage_type": "call"}]} +{"seq_id": "532253546", "text": "'''\nCreated on 17 Apr 2017\n\n@author: rweffercifue\n'''\n\nfrom facial_measures import AxialFace, FrontalFace\nfrom geometry import Point, Line, Mark, distance, Rect, intersects\nfrom utils import colors as cs\nfrom PIL import Image\nfrom PIL.ImageTk import PhotoImage\nfrom workAreas.state_manager import get_patient\nfrom facial_measures.order import AxialOrder, FrontalOrder\n\nMIN_DIST = 20\nMAX_V = 700\nMAX_H = 900\n\n\nclass Workspace:\n\n def __init__(self):\n self.screen = None\n self.rect = None\n self.patient = get_patient()\n self.guideline = None\n self.img_obj = None\n self.show_lines = True\n self.pil_img = Image.open(self.patient.photo)\n self.order = None\n self.marks = []\n self.additional_marks = []\n\n def get_image_size(self):\n w, h = self.pil_img.size\n proportion = w / h\n h = min(MAX_V, h)\n w = int(h * proportion)\n if w > MAX_H:\n w = MAX_H\n h = int(w // proportion)\n return w, h\n\n def load_screen(self, screen, left, top, right, bottom):\n self.screen = screen\n self.rect = Rect(left, top, right, bottom)\n self.img_obj = Image.open(self.patient.photo)\n self.img_obj = self.img_obj.resize((right - left, bottom - top), Image.ANTIALIAS)\n self.img_obj = PhotoImage(self.img_obj)\n self.screen.create_image(self.rect.left, self.rect.top, image=self.img_obj, anchor=\"nw\")\n\n def in_box(self, p):\n return p.x >= self.rect.left and p.x < self.rect.right and p.y >= self.rect.top and p.y < self.rect.bottom\n\n def create_line(self, line):\n return self.screen.create_line(line.p1.x, line.p1.y, line.p2.x, line.p2.y,\n fill=line.color, width=line.w, dash=line.dash)\n\n def add_mark_to_screen(self, mark):\n return self.screen.create_oval(mark.p.x - mark.r, mark.p.y - mark.r, mark.p.x + mark.r,\n mark.p.y + mark.r, fill=mark.color)\n\n def _check_for_too_close_neighbors(self, p):\n for m in self.marks:\n if self._check_distance(m.p, p) < MIN_DIST:\n return True\n return False\n\n def _check_distance(self, p1, p2):\n if p1 is not None and p2 is not None:\n return distance(p1, p2)\n else:\n return True\n\n def create_guideline(self, face):\n pass\n\n def remove_guideline(self):\n if self.guideline is not None:\n self.screen.delete(self.guideline.screen_ref)\n self.guideline = None\n\n def process_move(self, p):\n pass\n\n def restart(self):\n self.clean()\n\n def undo_previous_action(self):\n self.order.delete_last_processed()\n self._delete_last_mark()\n self.remove_additional_marks()\n if self.order.is_empty():\n self.remove_guideline()\n self._delete_all_lines()\n\n def _delete_all_lines(self):\n for line in self.lines:\n self.screen.delete(line.screen_ref)\n self.lines.clear()\n\n def clean(self):\n self.remove_guideline()\n self.remove_additional_marks()\n self._delete_all_marks()\n self._delete_all_lines()\n self.order.delete_all_processed()\n self.reset_instance()\n\n def _delete_all_marks(self):\n for i in range(len(self.marks)):\n self._delete_last_mark()\n\n def _create_visual_mark(self, p, r, color):\n mark = Mark(p, r=r, color=color)\n mark.screen_ref = self.add_mark_to_screen(mark)\n return mark\n\n def complete_workspace(self):\n self.hide_all_visual_marks()\n self.add_angles()\n self.readd_all_visual_marks()\n self.add_additional_marks()\n\n def hide_all_visual_marks(self):\n for mark in self.marks:\n self.screen.delete(mark.screen_ref)\n mark.screen_ref = None\n\n def readd_all_visual_marks(self):\n for mark in self.marks:\n mark.screen_ref = self.add_mark_to_screen(mark)\n\n def _delete_last_mark(self):\n if len(self.marks) == 0:\n return\n m = self.marks.pop()\n self.screen.delete(m.screen_ref)\n\n def add_angles(self):\n pass\n\n def process_point_return_if_completed(self, point):\n completed_now = self.assign_point_to_face_pos_and_return_if_completed(point)\n if completed_now:\n self.process_full_patient()\n return completed_now\n\n def assign_point_to_face_pos_and_return_if_completed(self):\n pass\n\n def process_full_patient(self):\n patient = self.patient\n patient.values.calculate_additional()\n patient.values.angles.calculate(patient.values)\n self.complete_workspace()\n\n def reset_instance(self):\n pass\n\n def add_additional_marks(self):\n pass\n\n def remove_additional_marks(self):\n pass\n\n\nclass FrontalWorkspace(Workspace):\n\n def __init__(self):\n super().__init__()\n self.interocular_lines = []\n self.malar_lines = []\n self.chin_lines = []\n self.lines = []\n self.order = FrontalOrder\n self.show_malar_angles = True\n self.show_chin_angles = True\n self.show_interocular_angles = True\n\n def assign_point_to_face_pos_and_return_if_completed(self, p):\n if self.order.is_completed():\n return False\n next_point = self.order.get_next()\n if self.in_box(p) and next_point and not self._check_for_too_close_neighbors(p):\n self._set_mark_to_facial_value(p, next_point)\n FrontalOrder.add_to_processed(next_point)\n return self.order.is_completed()\n\n def _set_mark_to_facial_value(self, p, x):\n color = cs.GREEN\n if x == FrontalOrder.CHIN:\n self.patient.values.chin = p\n elif x == FrontalOrder.FOREHEAD:\n self.patient.values.middle = p\n elif x == FrontalOrder.EYE_OUTER_LEFT:\n self.patient.values.outer_eye.left = p\n elif x == FrontalOrder.EYE_OUTER_RIGHT:\n self.patient.values.outer_eye.right = p\n elif x == FrontalOrder.EYE_INNER_LEFT:\n self.patient.values.inner_eye.left = p\n elif x == FrontalOrder.EYE_INNER_RIGHT:\n self.patient.values.inner_eye.right = p\n elif x == FrontalOrder.CHEEKBONE_LEFT:\n self.patient.values.cheekbone.left = p\n elif x == FrontalOrder.CHEEKBONE_RIGHT:\n self.patient.values.cheekbone.right = p\n elif x == FrontalOrder.NOSE_LEFT:\n self.patient.values.nose.left = p\n elif x == FrontalOrder.NOSE_CENTER:\n self.patient.values.nose_center = p\n elif x == FrontalOrder.NOSE_RIGHT:\n self.patient.values.nose.right = p\n elif x == FrontalOrder.MOUTH_LEFT:\n self.patient.values.mouth.left = p\n elif x == FrontalOrder.MOUTH_RIGHT:\n self.patient.values.mouth.right = p\n elif x == FrontalOrder.CHEEK_LEFT:\n self.patient.values.cheek.left = p\n elif x == FrontalOrder.CHEEK_RIGHT:\n self.patient.values.cheek.right = p\n self.marks.append(self._create_visual_mark(p, r=4, color=color))\n\n def add_additional_marks(self):\n face = self.patient.values\n self.additional_marks.append(self._create_visual_mark(face.malar.left, r=4, color=cs.RED))\n self.additional_marks.append(self._create_visual_mark(face.malar.right, r=4, color=cs.RED))\n self.additional_marks.append(self._create_visual_mark(face.middle, r=4, color=cs.RED))\n\n def remove_additional_marks(self):\n for additonal_mark in self.additional_marks:\n self.screen.delete(additonal_mark.screen_ref)\n self.additional_marks.clear()\n\n def toggle_malar_angles(self):\n for line in self.malar_lines:\n if not self.show_malar_angles:\n self.screen.itemconfig(line.screen_ref, state=\"hidden\")\n else:\n self.screen.itemconfig(line.screen_ref, state=\"normal\")\n\n def toggle_chin_angles(self):\n for line in self.chin_lines:\n if not self.show_chin_angles:\n self.screen.itemconfig(line.screen_ref, state=\"hidden\")\n else:\n self.screen.itemconfig(line.screen_ref, state=\"normal\")\n\n def toggle_interocular_angles(self):\n for line in self.interocular_lines:\n if not self.show_interocular_angles:\n self.screen.itemconfig(line.screen_ref, state=\"hidden\")\n else:\n self.screen.itemconfig(line.screen_ref, state=\"normal\")\n\n def complete_workspace(self):\n self.hide_all_visual_marks()\n self.add_angles()\n self.readd_all_visual_marks()\n self.add_additional_marks()\n self.toggle_malar_angles()\n self.toggle_interocular_angles()\n self.toggle_chin_angles()\n\n def add_angles(self):\n self.lines = self.get_collection_of_lines()\n for line in self.lines:\n line.screen_ref = self.create_line(line)\n\n def get_collection_of_lines(self):\n self.malar_lines = []\n self.interocular_lines = []\n self.chin_lines = []\n face = self.patient.values\n width = 4\n self.create_guideline(face)\n self.interocular_lines += face.angles.outer_eye_middle.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.inner_eye_middle.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.nose_eye_outer.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.nose_eye_inner.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.nose_middle.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.cheekbone_middle.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.nose_nose_point.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.cheek_middle.get_predominance_lines(width=width)\n self.interocular_lines += face.angles.mouth_middle.get_predominance_lines(width=width)\n\n self.malar_lines += face.angles.malar_internal_cant.get_predominance_lines(width=width)\n self.malar_lines += face.angles.malar_nose.get_predominance_lines(width=width)\n self.malar_lines += face.angles.malar_middle.get_predominance_lines(width=width)\n self.malar_lines += face.angles.malar_nose_point.get_predominance_lines(width=width)\n\n self.chin_lines += face.angles.cheek_chin.get_predominance_lines(width=width)\n self.chin_lines += face.angles.mouth_chin.get_predominance_lines(width=width)\n self.chin_lines += face.angles.cheekbone_chin.get_predominance_lines(width=width)\n return self.chin_lines + self.interocular_lines + self.malar_lines\n\n def create_guideline(self, face):\n width = 4\n vertical_line = Line(face.middle, face.chin)\n start_line = Line(Point(-10000, 0), Point(10000, 0))\n top_point = intersects(vertical_line, start_line)\n if top_point is not None:\n self.guideline = Line(top_point, face.chin, color=cs.GREEN, w=width, dash=(4, 4))\n else:\n self.guideline = Line(face.middle, face.chin, color=cs.GREEN, w=width, dash=(4, 4))\n self.guideline.screen_ref = self.create_line(self.guideline)\n\n def process_move(self, p):\n x = self.order.get_next()\n if x == FrontalOrder.HORIZONTAL_LINE:\n self.remove_guideline()\n self.create_guideline(p)\n\n def reset_instance(self):\n self.patient.values = FrontalFace()\n\n\nclass AxialWorkspace(Workspace):\n\n def __init__(self):\n super().__init__()\n self.lines = []\n self.order = AxialOrder\n\n def assign_point_to_face_pos_and_return_if_completed(self, p):\n if self.order.is_completed():\n return False\n x = self.order.get_next()\n if self.in_box(p) and x and not self._check_for_too_close_neighbors(p):\n color = cs.GREEN\n if x == AxialOrder.CENTRAL_POINT:\n self.patient.values.central_point = p\n self.create_guideline(p)\n elif x == AxialOrder.POINT_NOSE:\n self.patient.values.point_nose = p\n color = cs.YELLOW\n elif x == AxialOrder.BREAK_POINT:\n self.patient.values.break_point = p\n color = cs.BLUE\n elif x == AxialOrder.WALL_LEFT:\n self.patient.values.wall.left = p\n elif x == AxialOrder.WALL_RIGHT:\n self.patient.values.wall.right = p\n elif x == AxialOrder.MAXILAR_LEFT:\n self.patient.values.maxilar.left = p\n color = cs.YELLOW\n elif x == AxialOrder.MAXILAR_RIGHT:\n self.patient.values.maxilar.right = p\n color = cs.YELLOW\n AxialOrder.add_to_processed(x)\n if not AxialOrder.is_empty():\n self.marks.append(self._create_visual_mark(p, r=4, color=color))\n return self.order.is_completed()\n\n def add_additional_marks(self):\n pass\n\n def create_guideline(self, p):\n self.guideline = Line(Point(p.x, self.rect.top), Point(p.x, self.rect.bottom), color=cs.YELLOW, w=3, dash=(4, 4))\n self.guideline.screen_ref = self.create_line(self.guideline)\n\n def add_angles(self):\n self.lines = self.get_collection_of_lines()\n for line in self.lines:\n line.screen_ref = self.create_line(line)\n\n def get_collection_of_lines(self):\n lines = []\n axial = self.patient.values\n width = 4\n top = Point(axial.central_point.x, 0)\n vertical_line = Line(axial.central_point, top, color=cs.RED, w=width, dash=(4, 4))\n lines.append(vertical_line)\n break_line = Line(axial.break_point, axial.point_nose, color=cs.ORANGE, w=width)\n lines.append(break_line)\n lines += axial.angles.central_point_wall.get_lines(cs.BLUE, width)\n lines += axial.angles.nose_point_wall.get_lines(cs.GREEN, width)\n lines += axial.angles.nose_point_maxilar.get_lines(cs.YELLOW, width)\n return lines\n\n def reset_instance(self):\n self.patient.values = AxialFace()\n", "sub_path": "workAreas/workspace.py", "file_name": "workspace.py", "file_ext": "py", "file_size_in_byte": 14288, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "workAreas.state_manager.get_patient", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 29, "usage_type": "name"}, {"api_name": "geometry.Rect", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 48, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 49, "usage_type": "call"}, {"api_name": "geometry.distance", "line_number": 71, "usage_type": "call"}, {"api_name": "geometry.Mark", "line_number": 115, "usage_type": "call"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 176, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.add_to_processed", "line_number": 187, "usage_type": "call"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 187, "usage_type": "name"}, {"api_name": "utils.colors.GREEN", "line_number": 191, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 191, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.CHIN", "line_number": 192, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 192, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.FOREHEAD", "line_number": 194, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 194, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.EYE_OUTER_LEFT", "line_number": 196, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 196, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.EYE_OUTER_RIGHT", "line_number": 198, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 198, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.EYE_INNER_LEFT", "line_number": 200, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 200, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.EYE_INNER_RIGHT", "line_number": 202, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 202, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.CHEEKBONE_LEFT", "line_number": 204, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 204, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.CHEEKBONE_RIGHT", "line_number": 206, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 206, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.NOSE_LEFT", "line_number": 208, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 208, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.NOSE_CENTER", "line_number": 210, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 210, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.NOSE_RIGHT", "line_number": 212, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 212, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.MOUTH_LEFT", "line_number": 214, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 214, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.MOUTH_RIGHT", "line_number": 216, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 216, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.CHEEK_LEFT", "line_number": 218, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 218, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.CHEEK_RIGHT", "line_number": 220, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 220, "usage_type": "name"}, {"api_name": "utils.colors.RED", "line_number": 226, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 226, "usage_type": "name"}, {"api_name": "utils.colors.RED", "line_number": 227, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 227, "usage_type": "name"}, {"api_name": "utils.colors.RED", "line_number": 228, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 228, "usage_type": "name"}, {"api_name": "geometry.Line", "line_number": 299, "usage_type": "call"}, {"api_name": "geometry.Line", "line_number": 300, "usage_type": "call"}, {"api_name": "geometry.Point", "line_number": 300, "usage_type": "call"}, {"api_name": "geometry.intersects", "line_number": 301, "usage_type": "call"}, {"api_name": "geometry.Line", "line_number": 303, "usage_type": "call"}, {"api_name": "utils.colors.GREEN", "line_number": 303, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 303, "usage_type": "name"}, {"api_name": "geometry.Line", "line_number": 305, "usage_type": "call"}, {"api_name": "utils.colors.GREEN", "line_number": 305, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 305, "usage_type": "name"}, {"api_name": "facial_measures.order.FrontalOrder.HORIZONTAL_LINE", "line_number": 310, "usage_type": "attribute"}, {"api_name": "facial_measures.order.FrontalOrder", "line_number": 310, "usage_type": "name"}, {"api_name": "facial_measures.FrontalFace", "line_number": 315, "usage_type": "call"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 323, "usage_type": "name"}, {"api_name": "utils.colors.GREEN", "line_number": 330, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 330, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.CENTRAL_POINT", "line_number": 331, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 331, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.POINT_NOSE", "line_number": 334, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 334, "usage_type": "name"}, {"api_name": "utils.colors.YELLOW", "line_number": 336, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 336, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.BREAK_POINT", "line_number": 337, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 337, "usage_type": "name"}, {"api_name": "utils.colors.BLUE", "line_number": 339, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 339, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.WALL_LEFT", "line_number": 340, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 340, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.WALL_RIGHT", "line_number": 342, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 342, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.MAXILAR_LEFT", "line_number": 344, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 344, "usage_type": "name"}, {"api_name": "utils.colors.YELLOW", "line_number": 346, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 346, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.MAXILAR_RIGHT", "line_number": 347, "usage_type": "attribute"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 347, "usage_type": "name"}, {"api_name": "utils.colors.YELLOW", "line_number": 349, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 349, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.add_to_processed", "line_number": 350, "usage_type": "call"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 350, "usage_type": "name"}, {"api_name": "facial_measures.order.AxialOrder.is_empty", "line_number": 351, "usage_type": "call"}, {"api_name": "facial_measures.order.AxialOrder", "line_number": 351, "usage_type": "name"}, {"api_name": "geometry.Line", "line_number": 359, "usage_type": "call"}, {"api_name": "geometry.Point", "line_number": 359, "usage_type": "call"}, {"api_name": "utils.colors.YELLOW", "line_number": 359, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 359, "usage_type": "name"}, {"api_name": "geometry.Point", "line_number": 371, "usage_type": "call"}, {"api_name": "geometry.Line", "line_number": 372, "usage_type": "call"}, {"api_name": "utils.colors.RED", "line_number": 372, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 372, "usage_type": "name"}, {"api_name": "geometry.Line", "line_number": 374, "usage_type": "call"}, {"api_name": "utils.colors.ORANGE", "line_number": 374, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 374, "usage_type": "name"}, {"api_name": "utils.colors.BLUE", "line_number": 376, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 376, "usage_type": "name"}, {"api_name": "utils.colors.GREEN", "line_number": 377, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 377, "usage_type": "name"}, {"api_name": "utils.colors.YELLOW", "line_number": 378, "usage_type": "attribute"}, {"api_name": "utils.colors", "line_number": 378, "usage_type": "name"}, {"api_name": "facial_measures.AxialFace", "line_number": 382, "usage_type": "call"}]} +{"seq_id": "573484185", "text": "import sys\nsys.path.insert(0, sys.path[0]+'\\\\classes')\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nimport twitter\nfrom classes.keys_class import Keys\nfrom classes.twitter_connector import Twitter_Connector\nfrom classes.twitterGUI import Ui_MainWindow\nfrom classes.choosing_account import UI_ChoosingAccount\nfrom classes.connection_error import UI_ConnectionError\nimport urllib\nimport requests\nimport time\nimport os.path\nimport re\n\nclass TwitterGUI_APP():\n\n\tdef __init__(self):\n\t\ttry:\n\t\t\t#setting twitter connection\n\t\t\tTconnector = Twitter_Connector()\n\n\t\t\t#setting window\n\t\t\tapp = QtWidgets.QApplication(sys.argv)\n\t\t\tmainWindow = QtWidgets.QMainWindow()\n\t\t\tself.ex = UI_ChoosingAccount()\n\t\t\tself.ex.setupUi(mainWindow)\n\n\t\t\t#setting tweets\n\t\t\ttweets = Tconnector.get_tweets()\n\t\t\tmyTimeLine = Tconnector.get_my_timeline()\n\t\t\tmessagesTM = Tconnector.get_messagesTM()\n\t\t\tmessagesFM = Tconnector.get_messagesFM()\n\t\t\tfavourites = Tconnector.get_favourites()\n\t\t\tfollowing = Tconnector.get_friends()\n\t\t\tfollowers = Tconnector.get_followers()\n\n\t\t\t#setting account\n\t\t\tdata = urllib.request.urlopen(Tconnector.get_user_image_url()).read()\n\t\t\tself.pixmap = QtGui.QPixmap()\n\t\t\tself.pixmap.loadFromData(data)\n\t\t\tself.ex.awatarLabel.setPixmap(QtGui.QPixmap(self.pixmap))\n\n\t\t\tself.ex.profileNameLabel.setText(\"@\"+Tconnector.get_user_screen_name())\n\n\t\t\t#start application\n\t\t\tself.ex.submitButton.clicked.connect(lambda: self.start_application(tweets, myTimeLine, messagesTM, messagesFM, favourites, followers, following, mainWindow, Tconnector))\n\n\t\t\tapp.aboutToQuit.connect(self.closeEvent)\n\n\t\t\tsys.exit(app.exec_())\n\n\t\texcept:\n\t\t\t#making error window\n\t\t\tapp = QtWidgets.QApplication(sys.argv)\n\t\t\tex = UI_ConnectionError()\n\t\t\tmainWindow = QtWidgets.QMainWindow()\n\t\t\tex.setupUi(mainWindow)\n\t\t\tsys.exit(app.exec_())\n\n\n\tdef start_application(self, tweets, myTimeLine, messagesTM, messagesFM, favourites, followers, following, mainWindow, Tconnector):\n\t\t#setting static htmls\n\t\tself.htmlMyTweets, self.nMyTweets = self.htmlTweetsSetting(myTimeLine)\n\t\tself.htmlMessagesFM, self.nmessagesFM = self.htmlMessagesSetting(messagesFM)\n\t\tself.htmlMessagesTM, nmessagesTM = self.htmlMessagesSetting(messagesTM)\n\n\t\t#setting htmls\n\t\thtmlTweets, ntweets = self.htmlTweetsSetting(tweets)\n\t\thtmlPhoto, nphoto = self.htmlPhotosSetting(myTimeLine)\n\t\thtmlFavourites, nfavourites = self.htmlTweetsSetting(favourites)\n\t\thtmlFollowing, nfollowing = self.htmlFollowingSetting(following)\n\t\thtmlFollowers, nfollowers = self.htmlFollowingSetting(followers)\n\t\t\n\t\t#closing previous window\n\t\tself.ex.MainWindow.close()\n\t\tself.ex.__del__()\n\n\t\t#setting new window\n\t\tself.ex = Ui_MainWindow()\n\t\tself.ex.setupUi(mainWindow)\n\n\t\t#setting name from twitter\n\t\tself.ex.profileNameLabel.setText(Tconnector.get_user_name())\n\n\t\t#setting screen name from twitter\n\t\tself.ex.profileHandleLabel.setText(\"@\"+Tconnector.get_user_screen_name())\n\n\t\t#setting awatar\n\t\tself.ex.awatarLabel.setPixmap(QtGui.QPixmap(self.pixmap))\n\n\t\t#setting description\n\t\tself.ex.descriptionLabel.setText(\"Description: {}\".format(Tconnector.get_user_description()))\n\t\t#setting location\n\t\tself.ex.locationLabel.setText(\"\\nLocation: {}\".format(Tconnector.get_user_location()))\n\t\t#setting website\n\t\tself.ex.websiteLabel.setText(\"Website: {}\".format(Tconnector.get_user_website()))\n\n\t\tself.ex.dataBrowser.setHtml(htmlTweets)\n\n\t\t#setting tweets number\n\t\tself.ex.tweetsNumberLabel.setText(str(self.nMyTweets))\n\t\t#setting photos number\n\t\tself.ex.photosNumberLabel.setText(str(nphoto))\n\t\t#setting messages from me number\n\t\tself.ex.messagesNumberLabel.setText(str(self.nmessagesFM))\n\t\t#setting favourites number\n\t\tself.ex.favouritesNumberLabel.setText(str(nfavourites))\n\t\t#setting following number\n\t\tself.ex.followingNumberLabel.setText(str(nfollowing))\n\t\t#setting followers number\n\t\tself.ex.followersNumberLabel.setText(str(nfollowers))\n\n\t\t#setting buttons actions\n\t\tself.ex.nextButton.clicked.connect(lambda: self.buttonClicked(mainWindow,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlTweets = htmlTweets,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlPhoto = htmlPhoto,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlFavourites = htmlFavourites,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlFollowers = htmlFollowers,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlFollowing = htmlFollowing))\n\t\tself.ex.backButton.clicked.connect(lambda: self.buttonClicked(mainWindow,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlTweets = htmlTweets,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlPhoto = htmlPhoto, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlFavourites=htmlFavourites,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlFollowers = htmlFollowers,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t htmlFollowing = htmlFollowing))\n\n\t\tself.ex.postingNewTweetButton.clicked.connect(lambda: self.postTweet(Tconnector))\n\n\t\tself.ex.sendingMessageButton.clicked.connect(lambda: self.sendMessage(Tconnector))\n\n\n\t#setting html variable which containts tweets and their numbers\n\t#if everything is ok -> number of tweets is 20\n\tdef htmlTweetsSetting(self,tweets):\n\t\tntweets = 0\n\t\thtml = [\"
\"]\n\t\tfor item in tweets:\n\t\t\tdate = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(item.created_at,'%a %b %d %H:%M:%S +0000 %Y'))\n\t\t\ttext = item.text\n\t\t\turls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\n\t\t\tif len(urls) == 1:\n\t\t\t\ttext = text.replace(urls[0], \"\")\n\t\t\tif item.media is not None:\n\t\t\t\tif item.media[0].type == \"photo\":\n\t\t\t\t\tntweets += 1\n\t\t\t\t\timage = item.media[0].media_url\n\t\t\t\t\tif not (os.path.exists(\"images/{}.png\".format(image[27:]))):\n\t\t\t\t\t\turllib.request.urlretrieve(image, \"images/{}\".format(image[27:]))\n\t\t\t\t\tif item.media[0].sizes[\"medium\"][\"w\"] > 400:\n\t\t\t\t\t\thtml.append(\"
{}


Date: {}
\".format(text,image[27:],date))\n\t\t\t\t\telse:\n\t\t\t\t\t\thtml.append(\"
{}


Date: {}
\".format(text,image[27:],date))\n\t\t\t\telif item.media[0].type == \"animated_gif\":\n\t\t\t\t\tntweets += 1\n\t\t\t\t\timage = item.media[0].media_url\n\t\t\t\t\tif not (os.path.exists(\"images/{}.png\".format(image[39:]))):\n\t\t\t\t\t\turllib.request.urlretrieve(image, \"images/{}\".format(image[39:]))\n\t\t\t\t\tif item.media[0].sizes[\"medium\"][\"w\"] > 400:\n\t\t\t\t\t\thtml.append(\"
{}


Date: {}
\".format(text,image[39:],date))\n\t\t\t\t\telse:\n\t\t\t\t\t\thtml.append(\"
{}


Date: {}
\".format(text,image[39:],date))\n\t\t\telse:\n\t\t\t\tntweets += 1\n\t\t\t\thtml.append(\"
{}
Date: {}
\".format(text,date))\n\t\treturn \t\"\".join(html), ntweets\n\n\n\t#setting html variable which contains an information about my photos and number of these\n\tdef htmlPhotosSetting(self,myTimeLine):\n\t\tnphoto = 0\n\t\thtml = [\"
\"]\n\t\tfor item in myTimeLine:\n\t\t\tdate = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(item.created_at,'%a %b %d %H:%M:%S +0000 %Y'))\n\t\t\tif item.media is not None:\n\t\t\t\timage = item.media[0].media_url\n\t\t\t\tif item.media[0].type == \"photo\":\n\t\t\t\t\tnphoto += 1\n\t\t\t\t\tif not (os.path.exists(\"images/{}.png\".format(image[27:]))):\n\t\t\t\t\t\turllib.request.urlretrieve(image, \"images/{}\".format(image[27:]))\n\t\t\t\t\tif item.media[0].sizes[\"medium\"][\"w\"] > 400:\n\t\t\t\t\t\thtml.append(\"
#{}

Date: {}
\".format(nphoto, image[27:],date))\n\t\t\t\t\telse:\n\t\t\t\t\t\thtml.append(\"
#{}

Date: {}
\".format(nphoto, image[27:],date))\n\t\t\t\telif item.media[0].type == \"animated_gif\":\n\t\t\t\t\tnphoto += 1\n\t\t\t\t\tif not (os.path.exists(\"images/{}.png\".format(image[39:]))):\n\t\t\t\t\t\turllib.request.urlretrieve(image, \"images/{}\".format(image[39:]))\n\t\t\t\t\tif item.media[0].sizes[\"medium\"][\"w\"] > 400:\n\t\t\t\t\t\thtml.append(\"
#{}

Date: {}
\".format(nphoto, image[39:],date))\n\t\t\t\t\telse:\n\t\t\t\t\t\thtml.append(\"
#{}

Date: {}
\".format(nphoto, image[39:],date))\n\t\treturn \"\".join(html), nphoto\n\n\t#setting html messages\n\tdef htmlMessagesSetting(self, messages):\n\t\tnmessages = 0\n\t\thtml = [\"body{background-color:#141d26;color: white;} b{font-size: 30px;}
\"]\n\t\tfor message in messages:\n\t\t\tnmessages += 1\n\t\t\tsender_name = message.sender_screen_name\n\t\t\trecipient_name = message.recipient_screen_name\n\t\t\ttext = \"\".join([\" \",\"\\\"\",message.text,\"\\\"\",\" \"])\n\t\t\tdate = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(message.created_at,'%a %b %d %H:%M:%S +0000 %Y'))\n\t\t\thtml.append(\"To:{}
From: {}
{}

Date: {}
\".format( recipient_name, sender_name, text, date))\n\t\treturn \t\"\".join(html), nmessages\n\n\n\t#setting html variable which contains an information about acounts which I am following and number of these\n\tdef htmlFollowingSetting(self,following):\n\t\tnfollowing = 0\n\t\thtml = [\"
\"]\n\t\tfor item in following:\n\t\t\tnfollowing += 1\n\t\t\tname = item.name\n\t\t\timage = item.profile_image_url\n\t\t\tdescription = item.description\n\t\t\tif not (os.path.exists(\"images/{}.png\".format(image.split(\"/\")[5]))):\n\t\t\t\turllib.request.urlretrieve(image, \"images/{}\".format(image.split(\"/\")[5]))\n\t\t\thtml.append(\"

{}
{}
\".format(image.split(\"/\")[5],name, description))\n\t\treturn \"\".join(html), nfollowing\n\n\n\t#posting tweet and updating interface\n\tdef postTweet(self, Tconnector):\n\t\ttry:\n\t\t\ttweetText = self.ex.tweetTextEdit.toPlainText()\n\t\t\tTconnector.post_tweet(tweetText)\n\n\t\t\tmyTimeLine = Tconnector.get_my_timeline()\n\t\t\tself.htmlMyTweets, self.nMyTweets = self.htmlTweetsSetting(myTimeLine)\n\t\t\tself.ex.dataBrowser.setHtml(self.htmlMyTweets)\n\t\t\tself.ex.tweetsNumberLabel.setText(str(self.nMyTweets))\n\n\t\t\tself.ex.dataThemeLabel.setText(\"My Tweets\")\n\t\t\tself.ex.nextButton.setEnabled(True)\n\t\t\tself.ex.backButton.setEnabled(True)\n\t\t\tself.ex.backButton.setText(\"Tweets\")\n\t\t\tself.ex.nextButton.setText(\"Photos\")\n\n\t\t\tself.ex.tweetTextEdit.setPlainText(\"\")\n\n\t\texcept:\n\t\t\tprint(\"Posting tweet failed!\")\n\t\t\tself.ex.tweetTextEdit.setPlainText(\"\")\n\n\n\t#sending message and updating interface\n\tdef sendMessage(self, Tconnector):\n\t\ttry:\n\t\t\treceiver = self.ex.receiverLineEdit.text()\n\t\t\tmessage = self.ex.messageTextEdit.toPlainText()\n\t\t\tTconnector.send_message(message, receiver)\n\n\t\t\tmessagesFM = Tconnector.get_messagesFM()\n\t\t\tmessagesTM = Tconnector.get_messagesTM()\n\t\t\tself.htmlMessagesFM, self.nmessagesFM = self.htmlMessagesSetting(messagesFM)\n\t\t\tself.htmlMessagesTM, nmessagesTM = self.htmlMessagesSetting(messagesTM)\n\n\t\t\tself.ex.dataBrowser.setHtml(self.htmlMessagesFM)\n\t\t\tself.ex.messagesNumberLabel.setText(str(self.nmessagesFM))\n\n\t\t\tself.ex.dataThemeLabel.setText(\"Messages from Me\")\n\t\t\tself.ex.backButton.setEnabled(True)\n\t\t\tself.ex.backButton.setText(\"Messages to Me\")\n\t\t\tself.ex.nextButton.setEnabled(True)\n\t\t\tself.ex.nextButton.setText(\"Following\")\n\n\t\t\tself.ex.receiverLineEdit.setText(\"\")\n\t\t\tself.ex.messageTextEdit.setPlainText(\"\")\n\n\t\texcept:\n\t\t\tprint(\"Message sending failed!\")\n\t\t\tself.ex.receiverLineEdit.setText(\"\")\n\t\t\tself.ex.messageTextEdit.setPlainText(\"\")\n\n\n\t#button clicked action\n\tdef buttonClicked(self, mainWindow,\n\t\t\t\t\t\t htmlTweets = None, \n htmlPhoto = None,\n htmlMessagesTM = None,\n htmlFavourites = None,\n htmlFollowers = None,\n htmlFollowing = None):\n\t\tsender = mainWindow.sender()\n\t\tif sender.text() == \"Tweets\":\n\t\t\tself.ex.dataBrowser.setHtml(htmlTweets)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setEnabled(False)\n\t\t\tself.ex.backButton.setText(\"\")\n\t\t\tself.ex.nextButton.setText(\"My Tweets\")\n\t\telif sender.text() == \"My Tweets\":\n\t\t\tself.ex.dataBrowser.setHtml(self.htmlMyTweets)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setEnabled(True)\n\t\t\tself.ex.nextButton.setEnabled(True)\n\t\t\tself.ex.backButton.setText(\"Tweets\")\n\t\t\tself.ex.nextButton.setText(\"Photos\")\n\t\telif sender.text() == \"Photos\":\n\t\t\tself.ex.dataBrowser.setHtml(htmlPhoto)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setText(\"My Tweets\")\n\t\t\tself.ex.nextButton.setText(\"Messages to Me\")\n\t\telif sender.text() == \"Messages to Me\":\n\t\t\tself.ex.dataBrowser.setHtml(self.htmlMessagesTM)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setText(\"Photos\")\n\t\t\tself.ex.nextButton.setText(\"Messages from Me\")\n\t\telif sender.text() == \"Messages from Me\":\n\t\t\tself.ex.dataBrowser.setHtml(self.htmlMessagesFM)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setEnabled(True)\n\t\t\tself.ex.nextButton.setEnabled(True)\n\t\t\tself.ex.backButton.setText(\"Messages to Me\")\n\t\t\tself.ex.nextButton.setText(\"Following\")\n\t\telif sender.text() == \"Following\":\n\t\t\tself.ex.dataBrowser.setHtml(htmlFollowing)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setText(\"Messages from Me\")\n\t\t\tself.ex.nextButton.setText(\"Followers\")\n\t\telif sender.text() == \"Followers\":\n\t\t\tself.ex.dataBrowser.setHtml(htmlFollowers)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setText(\"Following\")\n\t\t\tself.ex.nextButton.setText(\"Favourites\")\n\t\telif sender.text() == \"Favourites\":\n\t\t\tself.ex.dataBrowser.setHtml(htmlFavourites)\n\t\t\tself.ex.dataThemeLabel.setText(sender.text())\n\t\t\tself.ex.backButton.setText(\"Followers\")\n\t\t\tself.ex.nextButton.setEnabled(False)\n\t\t\tself.ex.nextButton.setText(\"\")\n\n\t#destructor \n\tdef __del__(self):\n\t\tdel self\n\n\t#event for closing window without error\n\tdef closeEvent(self):\n\t\tsys.exit(0)\n\n\ndef main():\n\tTwitterGUI_APP()\n\n\nif __name__ == \"__main__\":\n\tmain()", "sub_path": "__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 13960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.path.insert", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "classes.twitter_connector.Twitter_Connector", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 26, "usage_type": "name"}, {"api_name": "classes.choosing_account.UI_ChoosingAccount", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 43, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 56, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "classes.connection_error.UI_ConnectionError", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}, {"api_name": "classes.twitterGUI.Ui_MainWindow", "line_number": 81, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 91, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 91, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 140, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 140, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 149, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 150, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 158, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 159, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 159, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 175, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 180, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 181, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 188, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 189, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 189, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 205, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 219, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 220, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 220, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 341, "usage_type": "call"}]} +{"seq_id": "318363145", "text": "from django.shortcuts import render, get_object_or_404,redirect\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.core.paginator import Paginator,EmptyPage,PageNotAnInteger\nfrom .models import Post, Bugs\nfrom django.contrib import messages\nfrom .forms import PostForm\nfrom .classification import Image_identify\nimport os\nimport cv2\nimport tflearn\nimport numpy as np\nimport tensorflow as tf\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.estimator import regression\nimport io\nfrom google.cloud import vision\nfrom google.cloud.vision import types\nfrom google.oauth2 import service_account\n\ndef post_home(request):\n # query_set_list = Post.objects.all()#.order_by(\"-timestamp\")\n context = {\n # 'title': 'HOME PAGE',\n # 'Post_query': query_set,\n }\n return render(request, 'list_all.html', context)\n\n\ndef post_update(request,id=None):\n instance = get_object_or_404(Post, id=id)\n form = PostForm(request.POST or None,request.FILES or None,instance = instance)\n if form.is_valid():\n instance = form.save(commit=False)\n instance.save()\n messages.success(request, \"successfully update\")\n return HttpResponseRedirect(instance.get_absolute_url())\n context = {\n \"title\": \"Detail\",\n \"instance\": instance,\n \"form\":form\n }\n return render(request, \"forms.html\", context)\n\n\ndef post_create(request):\n form = PostForm(request.POST or None,request.FILES or None)\n try:\n if form.is_valid():\n instance = form.save(commit=False)\n instance.save()\n messages.success(request,\"successfully upload\")\n return HttpResponseRedirect(instance.get_absolute_url())\n except():\n return render(request, 'forms.html', {\n \"form\": form,\n 'error_message': \"You did not select a chioce.\"})\n # else:\n # messages.error(request,\"Not successfully upload yet\")\n context = {\n \"form\": form\n }\n return render(request, \"forms.html\", context)\n\n\ndef post_detail(request, id=None):\n instance = get_object_or_404(Post, id=id)\n if (request.GET.get('mybtn')):\n return HttpResponseRedirect(instance.get_absolute_url_result())\n context = {\n \"title\": \"Detail\",\n \"instance\": instance\n }\n return render(request, \"detail.html\", context)\n\n\ndef post_delete(request, id=None):\n instance = get_object_or_404(Post, id=id)\n instance.delete()\n messages.success(request, \"successfully delete\")\n return redirect('posts:list')\n\n\n# def classify_image(request, id=None):\n# instance = get_object_or_404(Post, id=id)\n# test_data = \"./media\"\n# str_label=Image_identify.process_test_data(test_data)\n# context={\n# \"instance\": instance,\n# \"str_label\": str_label\n# }\n# return render(request, \"result.html\", context)\n\n\n# def test(request, id=None):\n# IMG_SIZE = 50\n# LR = 1e-3\n# instance = get_object_or_404(Post, id=id)\n# test_data = \"./media\"\n# testing_data = []\n# for img in tqdm(os.listdir(test_data)):\n# if img != '.DS_Store':\n# path = os.path.join(test_data, img)\n# img_num = img.split('.')[-1]\n# img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n# img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n# testing_data.append([np.array(img), img_num])\n# shuffle(testing_data)\n# np.save('test_data.npy', testing_data)\n#\n# test_data = testing_data\n#\n# for num, data in enumerate(test_data):\n# img_data = data[0]\n# orig = img_data\n# data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)\n#\n# tf.reset_default_graph()\n#\n# convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n#\n# convnet = conv_2d(convnet, 32, 5, activation='relu')\n# convnet = max_pool_2d(convnet, 5)\n#\n# convnet = conv_2d(convnet, 64, 5, activation='relu')\n# convnet = max_pool_2d(convnet, 5)\n#\n# convnet = conv_2d(convnet, 128, 5, activation='relu')\n# convnet = max_pool_2d(convnet, 5)\n#\n# convnet = conv_2d(convnet, 64, 5, activation='relu')\n# convnet = max_pool_2d(convnet, 5)\n#\n# convnet = conv_2d(convnet, 32, 5, activation='relu')\n# convnet = max_pool_2d(convnet, 5)\n#\n# convnet = fully_connected(convnet, 1024, activation='relu')\n# convnet = dropout(convnet, 0.8)\n#\n# convnet = fully_connected(convnet, 2, activation='softmax')\n# convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy',\n# name='targets')\n#\n# model_import = tflearn.DNN(convnet, tensorboard_dir='log')\n#\n# model_import.load(\"./MODEL_NAME\", weights_only=False)\n#\n# model_out = model_import.predict([data])[0]\n# if np.argmax(model_out) == 1:\n# str_label = 'disease'\n# else:\n# str_label = 'healthy'\n#\n#\n# context={\n# \"instance\": instance,\n# \"str_label\": str_label\n# }\n# return render(request, \"result.html\", context)\n\n\ndef search_bug_category(request):\n\n return render(request, \"search_bugs_by_category.html\", {})\n\n\n\ndef search_bug_name(request):\n return render(request, \"search_bugs_by_name.html\", {})\n\n\ndef managebugs(request):\n Bug = Bugs.objects.all()\n query = request.GET.get('commonname')\n if query:\n Bug = Bug.filter(commonname__icontains=query)\n else:\n pass\n context = {\n \"Bug\": Bug\n }\n return render(request,'search_bugs_by_category.html',context)\n\n\n\ndef test(request,id=None):\n IMG_SIZE = 50\n LR = 1e-3\n MODEL_NAME = 'dwij28leafdiseasedetection-{}-{}.model'.format(LR, '2conv-basic')\n tf.logging.set_verbosity(tf.logging.ERROR)\n os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n verifying_data = []\n instance = get_object_or_404(Post, id=id)\n filepath = instance.image.url\n filepath = '.'+filepath\n img_name = filepath.split('.')[:2]\n img = cv2.imread(filepath, cv2.IMREAD_COLOR)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n\n verifying_data = [np.array(img), img_name]\n\n np.save('verify_data.npy', verifying_data)\n verify_data = verifying_data\n\n str_label = \"Cannot make a prediction.\"\n status = \"Error\"\n\n tf.reset_default_graph()\n\n convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')\n\n convnet = conv_2d(convnet, 32, 3, activation='relu')\n convnet = max_pool_2d(convnet, 3)\n\n convnet = conv_2d(convnet, 64, 3, activation='relu')\n convnet = max_pool_2d(convnet, 3)\n\n convnet = conv_2d(convnet, 128, 3, activation='relu')\n convnet = max_pool_2d(convnet, 3)\n\n convnet = conv_2d(convnet, 32, 3, activation='relu')\n convnet = max_pool_2d(convnet, 3)\n\n convnet = conv_2d(convnet, 64, 3, activation='relu')\n convnet = max_pool_2d(convnet, 3)\n\n convnet = fully_connected(convnet, 1024, activation='relu')\n convnet = dropout(convnet, 0.8)\n\n convnet = fully_connected(convnet, 4, activation='softmax')\n convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n\n model = tflearn.DNN(convnet, tensorboard_dir='log')\n\n if os.path.exists('{}.meta'.format(MODEL_NAME)):\n model.load(MODEL_NAME)\n #print ('Model loaded successfully.')\n else:\n #print ('Error: Create a model using neural_network.py first.')\n pass\n img_data, img_name = verify_data[0], verify_data[1]\n orig = img_data\n data = img_data.reshape(IMG_SIZE, IMG_SIZE, 3)\n\n model_out = model.predict([data])[0]\n if np.argmax(model_out) == 0: str_label = 'Healthy'\n elif np.argmax(model_out) == 1: str_label = 'Bacterial'\n elif np.argmax(model_out) == 2: str_label = 'Viral'\n elif np.argmax(model_out) == 3: str_label = 'Lateblight'\n\n if str_label =='Healthy': status = 'Healthy'\n else: status = 'Unhealthy'\n\n result = 'Status: ' + status + '.'\n\n if (str_label != 'Healthy'): result += '\\nDisease: ' + str_label + '.'\n\n\n credentials = service_account.Credentials.from_service_account_file('./posts/apikey.json')\n vision_client = vision.ImageAnnotatorClient(credentials=credentials)\n file_name = filepath\n with io.open(file_name, 'rb') as image_file:\n content = image_file.read()\n image = types.Image(content=content)\n\n response = vision_client.label_detection(image=image)\n labels = response.label_annotations\n leaf_identify=''\n for label in labels:\n if label.description == 'leaf':\n leaf_identify = 'It is a leaf'\n break\n if leaf_identify != 'It is a leaf':\n leaf_identify = 'It seems not a leaf picture, If you want to challenge me, COME ON ^_^'\n context = {\n \"leaf_indentify\": leaf_identify,\n \"instance\": instance,\n }\n if leaf_identify == 'It is a leaf':\n context = {\n \"leaf_indentify\":leaf_identify,\n \"instance\": instance,\n \"result\": result,\n\n }\n return render(request, \"result.html\", context)\n", "sub_path": "trydjango/posts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 31, "usage_type": "argument"}, {"api_name": "forms.PostForm", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 36, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 52, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 67, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 67, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 69, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 78, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 78, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 80, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 80, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 169, "usage_type": "call"}, {"api_name": "models.Bugs.objects.all", "line_number": 173, "usage_type": "call"}, {"api_name": "models.Bugs.objects", "line_number": 173, "usage_type": "attribute"}, {"api_name": "models.Bugs", "line_number": 173, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.logging.set_verbosity", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 191, "usage_type": "attribute"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 193, "usage_type": "argument"}, {"api_name": "cv2.imread", "line_number": 197, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 197, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 208, "usage_type": "call"}, {"api_name": "tflearn.layers.core.input_data", "line_number": 210, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.conv_2d", "line_number": 212, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.max_pool_2d", "line_number": 213, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.conv_2d", "line_number": 215, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.max_pool_2d", "line_number": 216, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.conv_2d", "line_number": 218, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.max_pool_2d", "line_number": 219, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.conv_2d", "line_number": 221, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.max_pool_2d", "line_number": 222, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.conv_2d", "line_number": 224, "usage_type": "call"}, {"api_name": "tflearn.layers.conv.max_pool_2d", "line_number": 225, "usage_type": "call"}, {"api_name": "tflearn.layers.core.fully_connected", "line_number": 227, "usage_type": "call"}, {"api_name": "tflearn.layers.core.dropout", "line_number": 228, "usage_type": "call"}, {"api_name": "tflearn.layers.core.fully_connected", "line_number": 230, "usage_type": "call"}, {"api_name": "tflearn.layers.estimator.regression", "line_number": 231, "usage_type": "call"}, {"api_name": "tflearn.DNN", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path", "line_number": 235, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 249, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 259, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 259, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account", "line_number": 259, "usage_type": "name"}, {"api_name": "google.cloud.vision.ImageAnnotatorClient", "line_number": 260, "usage_type": "call"}, {"api_name": "google.cloud.vision", "line_number": 260, "usage_type": "name"}, {"api_name": "io.open", "line_number": 262, "usage_type": "call"}, {"api_name": "google.cloud.vision.types.Image", "line_number": 264, "usage_type": "call"}, {"api_name": "google.cloud.vision.types", "line_number": 264, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "495387041", "text": "from app import app\nfrom flask import render_template\n\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n user = { 'eric': 'wang' } # fake user\n return render_template(\"index.html\",\n title = 'flask_app',\n user = user)\n\nif __name__ == '__main__':\n app.run(debug=True,host='0.0.0.0')\n", "sub_path": "flask_app/app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 307, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "flask.render_template", "line_number": 10, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 6, "usage_type": "call"}, {"api_name": "app.app", "line_number": 6, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 7, "usage_type": "call"}, {"api_name": "app.app", "line_number": 7, "usage_type": "name"}, {"api_name": "app.app.run", "line_number": 15, "usage_type": "call"}, {"api_name": "app.app", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "242814184", "text": "import numpy as np\nimport cv2\nimport os\nimport re\n\n\ndef imread(path, color=True):\n if not os.path.exists(path):\n raise FileNotFoundError(\"No such file: '\" + path + \"'.\")\n if color:\n return cv2.imread(path)[:, :, ::-1] # BGR -> RGB\n else:\n return cv2.imread(path, cv2.IMREAD_GRAYSCALE)[:, :, None]\n\n\ndef imwrite(path, image):\n image = image.reshape((*image.shape[:2], -1)) # Make at least 3D.\n return cv2.imwrite(path, image[:, :, ::-1]) # RGB -> BGR\n\n\ndef read_images(path, rexp=r'.*\\.png', sort=sorted, filter=None, color=True):\n files = [os.path.join(path, f)\n for f in os.listdir(path)\n if re.match(rexp, f)]\n if filter is not None:\n files = filter(files)\n if sort is not None:\n files = sort(files)\n if files == []:\n return []\n image0 = imread(files[0], color)\n images = np.empty((len(files), *image0.shape), dtype=image0.dtype)\n images[0] = image0\n for i, path in enumerate(files[1:], 1):\n images[i] = imread(path, color)\n return images\n\n\ndef write_images(directory, images, format=None):\n images = np.asarray(images)\n shape = images.shape[:-3]\n if format is None:\n format = \"image\" + (\"_{}\" * len(shape)) + \".png\"\n path = os.path.join(directory, format)\n for idx in np.ndindex(shape):\n filepath = path.format(*idx)\n directory, _ = os.path.split(filepath)\n os.makedirs(directory, exist_ok=True)\n imwrite(filepath, images[idx])\n", "sub_path": "io.py", "file_name": "io.py", "file_ext": "py", "file_size_in_byte": 1499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.path.exists", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "re.match", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 40, "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": "numpy.ndindex", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "234412262", "text": "\"\"\"\nTesting for transistor models\n\"\"\"\nimport unittest\nfrom SemiPy.Physics.Modeling.TwoDFETs.Stanford2D import Stanford2DSModel\nfrom SemiPy.Devices.Materials.TwoDMaterials.TMD import MoS2\nfrom SemiPy.Devices.Materials.Oxides.MetalOxides import SiO2\nfrom SemiPy.Devices.Materials.Semiconductors.BulkSemiconductors import Silicon\nfrom SemiPy.Devices.Devices.FET.ThinFilmFET import TFT\nfrom physics.value import Value, ureg\nfrom SemiPy.Physics.Modeling.BaseModel import ModelInput\nimport matplotlib.pyplot as plt\n\n\nclass Test2DFETModels(unittest.TestCase):\n\n def test_stanford2dsmodel(self):\n\n gate_oxide = SiO2(thickness=Value(1.0, ureg.nanometer))\n channel = MoS2(layer_number=1, thickness=Value(0.6, ureg.nanometer))\n substrate = Silicon()\n\n fet = TFT(channel=channel, gate_oxide=gate_oxide, length=Value(50, ureg.nanometer),\n width=Value(0.05, ureg.micrometer), substrate=substrate)\n\n fet.Vt_avg.set(Value(0.19, ureg.volt))\n\n fet.Rc.set(Value(100.0, ureg.ohm * ureg.micrometer),\n input_values={'n': Value(1e13, ureg.centimeter**-2)})\n\n fet.max_mobility.set(Value(40, ureg.centimeter**2 / (ureg.volt * ureg.second)),\n input_values={'Vd': Value(1, ureg.volt), 'Vg': Value(1, ureg.volt)})\n\n fet.mobility_temperature_exponent.set(Value(1.15, ureg.dimensionless))\n\n S2DModel = Stanford2DSModel(FET=fet)\n\n Vds = ModelInput(0.0, 1.0, num=70, unit=ureg.volt)\n\n Vgs = ModelInput(0.2, 1.0, num=3, unit=ureg.volt)\n\n temps = [290, 400]\n linestyles = ['-', '--']\n\n # temps = [310]\n for temp, linestyle in zip(temps, linestyles):\n S2DModel.model_output(Vds, Vgs, heating=True, vsat=True,\n ambient_temperature=Value(temp, ureg.kelvin), linestyle=linestyle)\n\n plt.savefig('IdVd_plot_at')\n plt.show()\n\n\n# if __name__ == '__main__':\n# test = TestFETExtractors()\n# test.test_fetextraction()\n\n", "sub_path": "SemiPy/Physics/Modeling/TwoDFETs/test_Stanford2D.py", "file_name": "test_Stanford2D.py", "file_ext": "py", "file_size_in_byte": 2010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 15, "usage_type": "attribute"}, {"api_name": "SemiPy.Devices.Materials.Oxides.MetalOxides.SiO2", "line_number": 19, "usage_type": "call"}, {"api_name": "physics.value.Value", "line_number": 19, "usage_type": "call"}, {"api_name": "physics.value.ureg.nanometer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 19, "usage_type": "name"}, {"api_name": "SemiPy.Devices.Materials.TwoDMaterials.TMD.MoS2", "line_number": 20, "usage_type": "call"}, {"api_name": "physics.value.Value", "line_number": 20, "usage_type": "call"}, {"api_name": "physics.value.ureg.nanometer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 20, "usage_type": "name"}, {"api_name": "SemiPy.Devices.Materials.Semiconductors.BulkSemiconductors.Silicon", "line_number": 21, "usage_type": "call"}, {"api_name": "SemiPy.Devices.Devices.FET.ThinFilmFET.TFT", "line_number": 23, "usage_type": "call"}, {"api_name": "physics.value.Value", "line_number": 23, "usage_type": "call"}, {"api_name": "physics.value.ureg.nanometer", "line_number": 23, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 23, "usage_type": "name"}, {"api_name": "physics.value.Value", "line_number": 24, "usage_type": "call"}, {"api_name": "physics.value.ureg.micrometer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 24, "usage_type": "name"}, {"api_name": "physics.value.Value", "line_number": 26, "usage_type": "call"}, {"api_name": "physics.value.ureg.volt", "line_number": 26, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 26, "usage_type": "name"}, {"api_name": "physics.value.Value", "line_number": 28, "usage_type": "call"}, {"api_name": "physics.value.ureg.ohm", "line_number": 28, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 28, "usage_type": "name"}, {"api_name": "physics.value.ureg.micrometer", "line_number": 28, "usage_type": "attribute"}, {"api_name": "physics.value.Value", "line_number": 29, "usage_type": "call"}, {"api_name": "physics.value.ureg.centimeter", "line_number": 29, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 29, "usage_type": "name"}, {"api_name": "physics.value.Value", "line_number": 31, "usage_type": "call"}, {"api_name": "physics.value.ureg.centimeter", "line_number": 31, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 31, "usage_type": "name"}, {"api_name": "physics.value.ureg.volt", "line_number": 31, "usage_type": "attribute"}, {"api_name": "physics.value.ureg.second", "line_number": 31, "usage_type": "attribute"}, {"api_name": "physics.value.Value", "line_number": 32, "usage_type": "call"}, {"api_name": "physics.value.ureg.volt", "line_number": 32, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 32, "usage_type": "name"}, {"api_name": "physics.value.Value", "line_number": 34, "usage_type": "call"}, {"api_name": "physics.value.ureg.dimensionless", "line_number": 34, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 34, "usage_type": "name"}, {"api_name": "SemiPy.Physics.Modeling.TwoDFETs.Stanford2D.Stanford2DSModel", "line_number": 36, "usage_type": "call"}, {"api_name": "SemiPy.Physics.Modeling.BaseModel.ModelInput", "line_number": 38, "usage_type": "call"}, {"api_name": "physics.value.ureg.volt", "line_number": 38, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 38, "usage_type": "name"}, {"api_name": "SemiPy.Physics.Modeling.BaseModel.ModelInput", "line_number": 40, "usage_type": "call"}, {"api_name": "physics.value.ureg.volt", "line_number": 40, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 40, "usage_type": "name"}, {"api_name": "physics.value.Value", "line_number": 48, "usage_type": "call"}, {"api_name": "physics.value.ureg.kelvin", "line_number": 48, "usage_type": "attribute"}, {"api_name": "physics.value.ureg", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "648790059", "text": "import sys\nimport pymongo\nfrom datetime import datetime\nfrom secret import *\n\nif len(sys.argv) < 3:\n print(\"Input FROM and TO\")\n exit()\nelse:\n FROM = int(sys.argv[1])\n TO = int(sys.argv[2])\n if (TO < FROM):\n print(\"TO must greater than FROM\")\n exit()\n\nSIZE_LIMIT = 10000\n\nclient = pymongo.MongoClient('mongodb://%s:%d/' % (MONGODB_HOST, MONGODB_PORT))\ndb = client[MONGODB_DATABASE]\ndb.authenticate(MONGODB_USERNAME, MONGODB_PASSWORD)\n\nusers_ = db.user.find({'friends': {'$exists': True}, 'screen_name': {'$exists': True}}, no_cursor_timeout=True).limit(SIZE_LIMIT).sort('_id', pymongo.ASCENDING)\nprint(\"Total:\", users_.count(), \"users, Size limit:\", SIZE_LIMIT)\nprint(\"Loading data...\")\nusers = list(users_)\nusers_.close()\n\nif FROM < 0:\n print(\"FROM must not be negative\")\n exit(1)\nif TO >= len(users):\n print(\"TO is too much (number of user is %d, max of TO is %d)\" % (len(users), len(users)-1))\n exit(1)\n\ndef now():\n now_ = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n return \"[\"+ now_ +\"]\"\n\nfor i_, u in enumerate(users[FROM: TO+1]):\n i = i_ + FROM\n\n if bool(db.friend_coeff.find_one({'_id': i})):\n print(now(), \"Duplicate key %d, skipped.\" % i)\n continue\n\n coeff = []\n for v in users:\n if u == v:\n coeff.append(-1)\n else:\n intersect_count = len(set(u['friends']).intersection(set(v['friends'])))\n union_count = len(set(u['friends']).union(set(v['friends'])))\n if (union_count == 0):\n coeff.append(0)\n else:\n coeff.append(intersect_count/union_count)\n obj = {\n '_id': i,\n 'user_id': u['_id'],\n 'screen_name': u['screen_name'],\n 'friend_coeff': coeff\n }\n try:\n db.friend_coeff.insert(obj)\n except pymongo.errors.DuplicateKeyError:\n print(now(), \"Duplicate key %d, skipped.\" % i)\n continue\n except Exception as e:\n print(e)\n raise\n else:\n print(now(), \"Calculate friend_coeff for User#%d\" % i)\n", "sub_path": "friend_coeff.py", "file_name": "friend_coeff.py", "file_ext": "py", "file_size_in_byte": 2056, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "sys.argv", "line_number": 6, "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": "pymongo.MongoClient", "line_number": 18, "usage_type": "call"}, {"api_name": "pymongo.ASCENDING", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "pymongo.errors", "line_number": 65, "usage_type": "attribute"}]} +{"seq_id": "458534263", "text": "\"\"\"\n==========================================\n\nAuthor: Lei Zhang \n\nFinancial analysis, Investment and Trading Software\n\nCopyright (c) 2017 by Alphavertex Inc\n\nNot Licensed for public use. This is proprietary code\n\nAll Rights Reserved\n\n==========================================\n\"\"\"\n#############################################\n# Import Libraries and modules\n#############################################\nfrom av_helpers.db_helpers import get_df_from_sql\nfrom av_helpers.file_helpers import load_json_file\nfrom av_helpers.g_cloud_storage import save_variable_to_gcloud, load_variable_from_gcloud, \\\n get_files_in_gs_folder, delete_gcloud_file\nfrom multiprocessing import cpu_count, Pool\nimport pandas_market_calendars as mcal\nfrom functools import partial\nfrom pandas.tseries.offsets import BDay\nimport quandl\nfrom typing import Union, Optional\nimport hashlib\nimport os\nimport numpy as np\nimport pandas as pd\n\n#############################################\n# Logging for Module\n#############################################\nimport logging\n\nlogger_name = \"{}_logger\".format(str(__file__).replace(\"//\", \"/\").split(\"/\")[-1].replace(\".py\", \"\"))\nFORMAT = 'apimethod''%(asctime)s - %(name)s - %(levelname)s - %(message)s'\nlogging.basicConfig(format=FORMAT)\nlogger = logging.getLogger(logger_name)\nlogger.setLevel(logging.DEBUG)\n\n\n#############################################\n# Global Variables\n#############################################\n\n\n#############################################\n# Function Definitions\n#############################################\n\nclass DataCenter(object):\n \"\"\"\n Feature:\n 1. stock price data from av database, quandl\n 2. precog preds from av database\n \"\"\"\n # get API key for quandl\n dir_path = os.path.dirname(os.path.realpath(__file__))\n credentials = load_json_file(f\"{dir_path}/../credentials/api_credentials.json\")\n quandl_api_key = credentials['QUANDL_API_KEY']\n\n cntry_symbol_map = {\n 'US': 'UNITED STATES', 'TQ': 'UNITED KINGDOM', 'S1': 'UNITED KINGDOM', 'PZ': 'UNITED KINGDOM',\n 'LN': 'UNITED KINGDOM', 'LI': 'UNITED KINGDOM', 'IX': 'UNITED KINGDOM', 'UH': 'UNITED ARAB EMIRATES',\n 'DU': 'UNITED ARAB EMIRATES', 'TI': 'TURKEY', 'TB': 'THAILAND', 'TT': 'TAIWAN', 'VX': 'SWITZERLAND',\n 'SW': 'SWITZERLAND', 'SS': 'SWEDEN', 'SL': 'SRI LANKA', 'SM': 'SPAIN', 'KS': 'SOUTH KOREA',\n 'SJ': 'SOUTH AFRICA', 'SP': 'SINGAPORE', 'AB': 'SAUDI ARABIA', 'RU': 'RUSSIA', 'QD': 'QATAR', 'PL': 'PORTUGAL',\n 'PW': 'POLAND', 'PM': 'PHILIPPINES', 'PE': 'PERU', 'NO': 'NORWAY', 'NL': 'NIGERIA', 'NZ': 'NEW ZEALAND',\n 'NA': 'NETHERLANDS', 'MM': 'MEXICO', 'MK': 'MALAYSIA', 'LX': 'LUXEMBOURG', 'JP': 'JAPAN', 'IM': 'ITALY',\n 'IT': 'ISRAEL', 'ID': 'IRELAND', 'IJ': 'INDONESIA', 'IN': 'INDIA', 'HB': 'HUNGARY', 'HK': 'HONG KONG',\n 'GA': 'GREECE', 'GR': 'GERMANY', 'FP': 'FRANCE', 'FH': 'FINLAND', 'EY': 'EGYPT', 'DC': 'DENMARK',\n 'CP': 'CZECH REPUBLIC', 'CB': 'COLOMBIA', 'CH': 'CHINA', 'CI': 'CHILE', 'CN': 'CANADA', 'BZ': 'BRAZIL',\n 'BB': 'BELGIUM', 'AV': 'AUSTRIA', 'AU': 'AUSTRALIA', 'AR': 'ARGENTINA',\n }\n\n # map of country to exchange for market calendar\n exchange_country_map = {\n 'UNITED STATES': 'NYSE',\n 'BRAZIL': 'BMF',\n 'UNITED KINGDOM': 'LSE',\n 'CANADA': 'TSX',\n 'JAPAN': 'JPX',\n }\n\n # raw price database\n _equity_px_schema = 'raw_prices'\n _equity_px_table = 'raw_prices'\n _best_source = ['eod', 'zep', 'edi', 'gfin', 'yfin', 'wiki']\n\n # precog raw preds\n _precog_schema = 'api'\n # by default\n _precog_table = ['time_series_pred_all_4_0']\n\n # sp500 components list\n _sp500_comp_schema = 'reference_data'\n _precog500_table = 'sp500_members'\n\n def __init__(self, start_date=None, end_date=None, gcloud_cache_path=None, use_cache=False):\n \"\"\"\n init the save path for the caching\n :param start_date:\n :param end_date:\n :param gcloud_cache_path:\n :param use_cache:\n \"\"\"\n if start_date is None:\n start_date = '2016-01-01'\n if end_date is None:\n end_date = 'today'\n\n self._start_date = pd.to_datetime(start_date)\n self._end_date = pd.to_datetime(end_date)\n self._gcloud_cache_path = gcloud_cache_path\n self._use_cache = use_cache\n self._gcloud_bucket = None\n self._gcloud_folder = None\n\n self.check_gcloud_cache_path()\n\n @property\n def start_date(self):\n return self._start_date\n\n @start_date.setter\n def start_date(self, start_date):\n self._start_date = pd.to_datetime(start_date)\n\n @property\n def end_date(self):\n return self._end_date\n\n @end_date.setter\n def end_date(self, end_date):\n self._end_date = pd.to_datetime(end_date)\n\n @property\n def use_cache(self):\n return self._use_cache\n\n @use_cache.setter\n def use_cache(self, use_cache):\n self._use_cache = use_cache\n self.check_gcloud_cache_path()\n\n @property\n def gcloud_cache_path(self):\n return self._gcloud_cache_path\n\n @gcloud_cache_path.setter\n def gcloud_cache_path(self, gcloud_cache_path):\n self._gcloud_cache_path = gcloud_cache_path\n self.check_gcloud_cache_path()\n\n @property\n def precog_table(self):\n return self._precog_table\n\n @precog_table.setter\n def precog_table(self, precog_table):\n self._precog_table = precog_table\n\n # ----------------------------------------------------------------\n # | |\n # | Equity Price |\n # | |\n # ----------------------------------------------------------------\n def get_equity_price_data_from_quandl(self, cntry_prim_ticker: str) -> Optional[pd.DataFrame]:\n \"\"\"\n get single stock price data from quandl api\n :param cntry_prim_ticker:\n :return:\n \"\"\"\n start_date = self._start_date\n end_date = self._end_date\n\n cntry_prim_ticker_old = cntry_prim_ticker\n # handle ticker symbol change mismatch\n cntry_prim_ticker = self.ticker_symbol_adjustment(cntry_prim_ticker, source='quandl')\n\n # transform cntry_prim_ticker to quandl ticker\n ticker_token = cntry_prim_ticker.split()\n ticker = ticker_token[0]\n\n # deal with special char\n ticker = ticker.replace('/', '_')\n\n if ticker_token[1] == 'US':\n prefix = 'EOD'\n elif ticker_token[1] == 'JP':\n prefix = 'TSE'\n else:\n raise NotImplementedError\n\n # use quandl API to get the price data\n try:\n data_df = quandl.get(f\"{prefix}/{ticker}\", authtoken=self.quandl_api_key,\n start_date=start_date.strftime('%Y-%m-%d'),\n end_date=end_date.strftime('%Y-%m-%d'))\n except Exception as e:\n logger.error(str(e))\n logger.warning(f\"{prefix}/{ticker} not founded.\")\n return None\n\n data_df = data_df.reset_index(drop=False)\n\n if data_df is not None and len(data_df) > 0:\n\n # rename the data_df columns to reconcile to the database\n data_df = data_df.rename(columns={'Date': 'record_date', 'Open': 'open', 'High': 'high', 'Low': 'low',\n 'Close': 'close', 'Volume': 'volume', 'Dividend': 'dividend',\n 'Adj_Open': 'px_open', 'Adj_High': 'px_high', 'Split': 'split_ratio',\n 'Adj_Low': 'px_low', 'Adj_Close': 'px_close', 'Adj_Volume': 'px_volume'})\n data_df['record_date'] = pd.to_datetime(data_df['record_date'])\n data_df.insert(loc=1, column='cntry_prim_ticker', value=cntry_prim_ticker_old)\n\n logger.info(f\"{cntry_prim_ticker} price data from Quandl: Success! \")\n return data_df\n\n else:\n logger.info(f\"{cntry_prim_ticker} price data from Quandl: Failed! ***********\")\n return None\n\n def get_equity_price_data_from_database(self, cntry_prim_ticker: str) -> Optional[pd.DataFrame]:\n \"\"\"\n get single stock price data and other ref data from database\n :param cntry_prim_ticker:\n :return:\n \"\"\"\n start_date = self._start_date\n end_date = self._end_date\n\n # handle ticker name change mismatch\n cntry_prim_ticker = self.ticker_symbol_adjustment(cntry_prim_ticker, source='database')\n\n px_schema = self._equity_px_schema\n px_table = self._equity_px_table\n best_source = self._best_source\n\n sql = f\" SELECT record_date, cntry_prim_ticker, open, high, low, close, volume, dividend,\" \\\n f\" split_ratio, px_open, px_high, px_low, px_close, px_volume, data_source \" \\\n f\" FROM {px_schema}.{px_table} WHERE cntry_prim_ticker = '{cntry_prim_ticker}' \" \\\n f\" AND record_date >='{start_date.strftime('%Y-%m-%d')}' \" \\\n f\" AND record_date <='{end_date.strftime('%Y-%m-%d')}' \" \\\n f\" AND data_source IN ({str(best_source)[1:-1]}) \" \\\n f\" ORDER BY record_date \"\n\n # get raw price data from database with mixed data_source\n data_df = get_df_from_sql(sql=sql, schema=px_schema)\n data_df['record_date'] = pd.to_datetime(data_df['record_date'])\n\n # check the data quality of the price data and select the best data source\n if data_df is not None and len(data_df) > 0:\n data_df = self._db_price_data_quality_check(data_df)\n\n # Drop the time_stamp column if it exists. This causes errors when assembling training data\n data_df = data_df.drop(labels=['time_stamp'], errors='ignore', axis=1)\n data_df = data_df.drop(labels=['data_source'], errors='ignore', axis=1)\n\n logger.info(f\"{cntry_prim_ticker} price data from database: Success!\")\n return data_df\n\n else:\n logger.info(f\"{cntry_prim_ticker} price data from database: Failed!***********\")\n return None\n\n def get_equity_price_data_from_gcloud(self, cntry_prim_ticker: str) -> Optional[pd.DataFrame]:\n \"\"\"\n get single stock price data from gcloud cache generated by this class\n :param cntry_prim_ticker:\n :return:\n \"\"\"\n if self._gcloud_cache_path is None:\n logger.warning(\"No gcloud cache path provided, return None\")\n return None\n\n start_date = self._start_date\n end_date = self._end_date\n gcloud_folder = f\"{self._gcloud_folder}/{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}\"\n filename = self.ensure_filename(f\"{cntry_prim_ticker}.csv\")\n data_df = load_variable_from_gcloud(filename, gcloud_folder, self._gcloud_bucket)\n\n if data_df is not None and len(data_df) > 0:\n logger.info(f\"{cntry_prim_ticker} price data from gcloud cache: Success!\")\n data_df['record_date'] = pd.to_datetime(data_df['record_date'])\n data_df = data_df.drop(columns='Unnamed: 0', errors='ignore')\n return data_df\n\n else:\n logger.info(f\"{cntry_prim_ticker} price data from gcloud cache: Failed!***********\")\n return None\n\n def get_equity_price_data(self, cntry_prim_ticker: str, source: str = 'database') -> Optional[pd.DataFrame]:\n \"\"\"\n wrapper of all get_equity_price_data methods\n :param cntry_prim_ticker:\n :param source: support 'database', 'quandl'. Default: 'database'\n :return:\n \"\"\"\n if self._use_cache:\n data_df = self.get_equity_price_data_from_gcloud(cntry_prim_ticker)\n if data_df is not None:\n logger.info(\"Using cached data...\")\n return data_df\n\n if source == 'database':\n return self.get_equity_price_data_from_database(cntry_prim_ticker)\n\n elif source == 'quandl':\n return self.get_equity_price_data_from_quandl(cntry_prim_ticker)\n\n else:\n raise NotImplementedError\n\n def cache_equity_price_data(self, cntry_prim_ticker: str, source: str = 'database'):\n \"\"\"\n cache the price data onto gcloud storage\n :param cntry_prim_ticker:\n :param source:\n :return:\n \"\"\"\n if self._gcloud_cache_path is None:\n logger.error(\"No gcloud cache path provided.\")\n raise ValueError\n\n start_date = self._start_date\n end_date = self._end_date\n gcloud_folder = f\"{self._gcloud_folder}/{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}\"\n filename = self.ensure_filename(f\"{cntry_prim_ticker}.csv\")\n\n if self._use_cache:\n px_df = self.get_equity_price_data_from_gcloud(cntry_prim_ticker)\n if px_df is not None:\n logger.info(f\"{cntry_prim_ticker} already cached.\")\n return None\n\n px_df = self.get_equity_price_data(cntry_prim_ticker, source)\n\n if px_df is not None:\n save_variable_to_gcloud(px_df, filename, gcloud_folder, self._gcloud_bucket)\n logger.info(f\"{cntry_prim_ticker} gcloud cache: Success!\")\n else:\n logger.info(f\"{cntry_prim_ticker} gcloud cache: Failed!***********\")\n\n def cache_equity_price_data_batch(self, ticker_list: Union[str, list], source='database'):\n \"\"\"\n multiprocessing cache the price data for a list of ticker to local/gcloud\n :param ticker_list:\n :param source: support 'database', 'quandl', default 'database'\n :return:\n \"\"\"\n if self._gcloud_cache_path is None:\n logger.error(\"No gcloud cache path provided, stop caching...\")\n return\n\n if isinstance(ticker_list, str):\n ticker_list = [ticker_list]\n else:\n assert isinstance(ticker_list, list), 'TypeError: ticker_list should be str or list of str'\n\n func = partial(self.cache_equity_price_data, source=source)\n\n pool = Pool(processes=cpu_count() - 1)\n pool.map(func, ticker_list)\n pool.close()\n pool.join()\n\n def get_equity_price_data_append(self, ticker_list: Union[str, list], source='database'):\n \"\"\"\n multiprocessing append the price data for a list of ticker to local/gcloud\n :param ticker_list:\n :param source: support 'database', 'quandl', default 'database'\n :return:\n \"\"\"\n start_date = self._start_date\n end_date = self._end_date\n if isinstance(ticker_list, str):\n ticker_list = [ticker_list]\n else:\n assert isinstance(ticker_list, list), 'TypeError: ticker_list'\n\n iid = hashlib.md5(\"{}\".format(ticker_list).encode(\"utf-8\")).hexdigest()\n filename = f\"price_data_append_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}_{iid}.csv\"\n\n if self._use_cache:\n append_df = load_variable_from_gcloud(filename, self._gcloud_folder, self._gcloud_bucket)\n if append_df is not None and len(append_df) > 0:\n logger.info(\"Loaded append df from gcloud cache.\")\n return append_df\n\n func = partial(self.get_equity_price_data, source=source)\n pool = Pool(processes=cpu_count() - 1)\n px_df_list = pool.map(func, ticker_list)\n pool.close()\n pool.join()\n\n append_df = pd.concat(px_df_list, axis=0)\n\n if append_df is not None and len(append_df) > 0 and self._gcloud_cache_path is not None:\n save_variable_to_gcloud(append_df, filename, self._gcloud_folder, self._gcloud_bucket)\n logger.info(f\"Cached Append df to gcloud cache! {filename}\")\n else:\n logger.info(f\"Append df gcloud cache: Failed!***********\")\n\n return append_df\n\n # ----------------------------------------------------------------\n # | |\n # | Precog Preds |\n # | |\n # ----------------------------------------------------------------\n def get_sp500_components(self):\n start_date = pd.to_datetime(self._start_date)\n end_date = pd.to_datetime(self._end_date)\n filename = f\"sp500_components_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}.csv\"\n\n if self._use_cache:\n sp500_df = load_variable_from_gcloud(filename, self._gcloud_folder, self._gcloud_bucket)\n if sp500_df is not None:\n logger.info(\"Use cached sp500 components df...\")\n return sp500_df\n\n sql = f\" select * from {self._sp500_comp_schema}.{self._precog500_table} \" \\\n f\" where end_date >= '{start_date.strftime('%Y-%m-%d')}' \"\n\n sp500_df = get_df_from_sql(sql, schema=self._precog_schema)\n\n if sp500_df is not None:\n sp500_df['start_date'] = pd.to_datetime(sp500_df['start_date'])\n sp500_df['end_date'] = pd.to_datetime(sp500_df['end_date'])\n\n if self._gcloud_cache_path is not None:\n save_variable_to_gcloud(sp500_df, filename, self._gcloud_folder, self._gcloud_bucket)\n\n logger.info(f\"Retrieved sp500 components: {len(sp500_df)} tickers.\")\n return sp500_df\n\n else:\n logger.info(f\"Failed to get sp500 components.***********\")\n return None\n\n def get_precog_pred(self, ticker_list=None, precog_table=None, pred_cols=None, pred_horizon=None, suffix_id=None):\n \"\"\"\n get and cache precog preds from database\n :param ticker_list: if None, use the universe (10000+) will be super slow\n :param precog_table\n :param pred_cols\n :param pred_horizon\n :param suffix_id:\n :return:\n \"\"\"\n if precog_table is not None:\n self._precog_table = precog_table\n\n if pred_cols is None:\n pred_cols = ['model_h', 'model_h2', 'omp', 'gradboost', 'bayesian', 'randomforest']\n\n if pred_horizon is None:\n pred_horizon = 5\n\n if isinstance(self._precog_table, str):\n self._precog_table = [self._precog_table]\n\n if isinstance(ticker_list, str):\n ticker_list = [ticker_list]\n iid = hashlib.md5(\"{}\".format(ticker_list).encode(\"utf-8\")).hexdigest()\n\n start_date = pd.to_datetime(self._start_date)\n end_date = pd.to_datetime(self._end_date)\n if suffix_id is None:\n filename = f\"precog_preds_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}\" \\\n f\"_h{pred_horizon}_{iid}.csv\"\n else:\n filename = f\"precog_preds_{start_date.strftime('%Y-%m-%d')}_\" \\\n f\"{end_date.strftime('%Y-%m-%d')}_h{pred_horizon}_{iid}_{suffix_id}.csv\"\n\n if self._use_cache:\n precog_df = load_variable_from_gcloud(filename, self._gcloud_folder, self._gcloud_bucket)\n if precog_df is not None:\n logger.info(\"Using cached precog preds...\")\n return precog_df\n\n precog_master_df = pd.DataFrame()\n for tb in self._precog_table:\n if ticker_list is None: # get all the preds for the universe\n sql = f\" select * from {self._precog_schema}.{tb} \" \\\n f\" where record_date >= '{start_date.strftime('%Y-%m-%d')}' \" \\\n f\" and record_date <= '{end_date.strftime('%Y-%m-%d')}' \" \\\n f\" and prediction_horizon = {pred_horizon} \"\n else:\n sql = f\" select * from {self._precog_schema}.{tb} \" \\\n f\" where record_date >= '{start_date.strftime('%Y-%m-%d')}' \" \\\n f\" and record_date <= '{end_date.strftime('%Y-%m-%d')}' \" \\\n f\" and cntry_prim_ticker in ({str(ticker_list)[1:-1]}) \" \\\n f\" and prediction_horizon = {pred_horizon} \"\n\n precog_df = get_df_from_sql(sql=sql, schema=self._precog_schema)\n if precog_df is not None and len(precog_df) > 0:\n logger.info(f\"Load the precog_preds table {tb}\")\n precog_df['record_date'] = pd.to_datetime(precog_df['record_date'])\n # remove weekend and holiday preds\n precog_df = self._remove_weekend_and_holiday(precog_df)\n precog_df = precog_df.sort_values(by=['record_date', 'cntry_prim_ticker'])\n precog_master_df = precog_master_df.append(precog_df)\n else:\n logger.error(f\"Cannot get precog_preds table {tb}\")\n raise FileNotFoundError\n\n # ensemble all the precog preds model_h by taking the average\n groupby_cols = ['record_date', 'cntry_prim_ticker']\n keep_cols = ['prediction_horizon', 'prediction_date', 'exch_region', 'exch_country', 'sector', 'industry']\n select_df = precog_master_df.groupby(by=groupby_cols)[pred_cols].mean()\n keep_df = precog_master_df.groupby(by=groupby_cols)[keep_cols].apply(lambda df: df.iloc[0, :])\n precog_master_df = pd.concat([select_df, keep_df], axis=1)\n precog_master_df = precog_master_df.reset_index(drop=False)\n\n if self._gcloud_cache_path is not None:\n save_variable_to_gcloud(precog_master_df, filename, self._gcloud_folder, self._gcloud_bucket)\n logger.info(f\"Cached precog preds to gcloud! {filename}\")\n\n return precog_master_df\n\n def get_precog_pred_2(self, ticker_list=None, suffix=None):\n \"\"\"\n Depreciated.... for 1_21 na and old version precog preds\n get and cache precog preds from database\n :param ticker_list: if None, use the universe (10000+) will be super slow\n :param suffix:\n :return:\n \"\"\"\n if isinstance(self._precog_table, str):\n self._precog_table = [self._precog_table]\n\n if isinstance(ticker_list, str):\n ticker_list = [ticker_list]\n iid = hashlib.md5(\"{}\".format(ticker_list).encode(\"utf-8\")).hexdigest()\n\n start_date = pd.to_datetime(self._start_date)\n end_date = pd.to_datetime(self._end_date)\n if suffix is None:\n filename = f\"precog_preds_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}_{iid}.csv\"\n else:\n filename = f\"precog_preds_{start_date.strftime('%Y-%m-%d')}_\" \\\n f\"{end_date.strftime('%Y-%m-%d')}_{iid}_{suffix}.csv\"\n\n if self._use_cache:\n precog_df = load_variable_from_gcloud(filename, self._gcloud_folder, self._gcloud_bucket)\n if precog_df is not None:\n logger.info(\"Using cached precog preds...\")\n return precog_df\n\n precog_master_df = pd.DataFrame()\n for tb in self._precog_table:\n if ticker_list is None: # get all the preds for the universe\n sql = f\" select * from {self._precog_schema}.{tb} \" \\\n f\" where date_of_prediction >= '{start_date.strftime('%Y-%m-%d')}' \" \\\n f\" and date_of_prediction <= '{end_date.strftime('%Y-%m-%d')}' \"\n else:\n sql = f\" select * from {self._precog_schema}.{tb} \" \\\n f\" where date_of_prediction >= '{start_date.strftime('%Y-%m-%d')}' \" \\\n f\" and date_of_prediction <= '{end_date.strftime('%Y-%m-%d')}' \" \\\n f\" and bbg_ticker in ({str(ticker_list)[1:-1]}) \"\n\n precog_df = get_df_from_sql(sql=sql, schema=self._precog_schema)\n\n precog_df = precog_df.rename(columns={\n 'date_of_prediction': 'record_date',\n 'bbg_ticker': 'cntry_prim_ticker',\n 'model_h_log_ret': 'model_h',\n 'model_h2_log_ret': 'model_h2',\n\n })\n\n if precog_df is not None and len(precog_df) > 0:\n logger.info(f\"Load the precog_preds table {tb}\")\n precog_df['record_date'] = pd.to_datetime(precog_df['record_date'])\n # remove weekend and holiday preds\n precog_df = self._remove_weekend_and_holiday(precog_df)\n precog_df = precog_df.reset_index(drop=True).sort_values(by=['record_date', 'cntry_prim_ticker'])\n precog_master_df = precog_master_df.append(precog_df)\n else:\n logger.error(f\"Cannot get precog_preds table {tb}\")\n raise FileNotFoundError\n\n # ensemble all the precog preds model_h by taking the average\n groupby_cols = ['record_date', 'cntry_prim_ticker']\n select_cols = ['model_h', 'model_h2']\n keep_cols = ['prediction_horizon', 'prediction_date', 'exch_region', 'exch_country']\n select_df = precog_master_df.groupby(by=groupby_cols)[select_cols].mean()\n keep_df = precog_master_df.groupby(by=groupby_cols)[keep_cols].apply(lambda df: df.iloc[0, :])\n precog_master_df = pd.concat([select_df, keep_df], axis=1)\n precog_master_df = precog_master_df.reset_index(drop=False)\n\n if self._gcloud_cache_path is not None:\n save_variable_to_gcloud(precog_master_df, filename, self._gcloud_folder, self._gcloud_bucket)\n logger.info(f\"Cached precog preds to gcloud! {filename}\")\n\n return precog_master_df\n\n def get_precog_pred_sp500(self, precog_table=None, pred_cols=None, pred_horizon=None, suffix_id=None):\n \"\"\"\n get exact precog preds for sp500 components considering the change of components adjustment for sp500, of which\n the liquidity for the underlying stocks should be reasonable well.\n :return:\n \"\"\"\n if precog_table is not None:\n self._precog_table = precog_table\n\n if pred_horizon is None:\n pred_horizon = 5\n\n if pred_cols is None:\n pred_cols = ['model_h', 'model_h2', 'omp', 'gradboost', 'bayesian', 'randomforest']\n\n start_date = pd.to_datetime(self._start_date)\n end_date = pd.to_datetime(self._end_date)\n\n if suffix_id is None:\n filename = f\"precog_preds_sp500_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}\" \\\n f\"_h{pred_horizon}.csv\"\n else:\n filename = f\"precog_preds_sp500_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}\" \\\n f\"_h{pred_horizon}_{suffix_id}.csv\"\n\n if self._use_cache:\n precog_df = load_variable_from_gcloud(filename, self._gcloud_folder, self._gcloud_bucket)\n if precog_df is not None:\n logger.info(\"Using cached precog preds...\")\n return precog_df\n\n sp500_df = self.get_sp500_components()\n\n if sp500_df is None:\n logger.error(\"Cannot get SP500 components df.\")\n raise FileNotFoundError\n\n ticker_list = sp500_df['cntry_prim_ticker'].unique().tolist()\n precog_df = self.get_precog_pred(ticker_list, precog_table, pred_cols, pred_horizon, suffix_id)\n\n if precog_df is None:\n logger.error(\"Cannot get precog preds.\")\n raise FileNotFoundError\n\n precog_df['record_date'] = pd.to_datetime(precog_df['record_date'])\n sp500_df['start_date'] = pd.to_datetime(sp500_df['start_date'])\n sp500_df['end_date'] = pd.to_datetime(sp500_df['end_date'])\n\n precog_df = precog_df.merge(sp500_df, on='cntry_prim_ticker')\n precog_df = precog_df[(precog_df['record_date'] >= precog_df['start_date']) &\n (precog_df['record_date'] <= precog_df['end_date'] - BDay(5))]\n\n keep_cols = ['record_date', 'cntry_prim_ticker'] + pred_cols + \\\n ['prediction_horizon', 'prediction_date', 'exch_region', 'exch_country', 'sector', 'industry']\n precog_df = precog_df[keep_cols]\n\n if self._gcloud_cache_path is not None:\n save_variable_to_gcloud(precog_df, filename, self._gcloud_folder, self._gcloud_bucket)\n logger.info(f\"Cached precog preds for SP500 to gcloud! {filename}\")\n\n return precog_df\n\n def get_precog_pred_sp500_2(self, suffix=None):\n \"\"\"\n Depreciated.... for 1_21 na and old version precog preds\n get exact precog preds for sp500 components considering the change of components adjustment for sp500, of which\n the liquidity for the underlying stocks should be reasonable well.\n :return:\n \"\"\"\n start_date = pd.to_datetime(self._start_date)\n end_date = pd.to_datetime(self._end_date)\n if suffix is None:\n filename = f\"precog_preds_sp500_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}.csv\"\n else:\n filename = f\"precog_preds_sp500_{start_date.strftime('%Y-%m-%d')}_{end_date.strftime('%Y-%m-%d')}_\" \\\n f\"{suffix}.csv\"\n\n if self._use_cache:\n precog_df = load_variable_from_gcloud(filename, self._gcloud_folder, self._gcloud_bucket)\n if precog_df is not None:\n logger.info(\"Using cached precog preds...\")\n return precog_df\n\n sp500_df = self.get_sp500_components()\n\n if sp500_df is None:\n logger.error(\"Cannot get SP500 components df.\")\n raise FileNotFoundError\n\n ticker_list = sp500_df['cntry_prim_ticker'].unique().tolist()\n precog_df = self.get_precog_pred_2(ticker_list, suffix)\n\n if precog_df is None:\n logger.error(\"Cannot get precog preds.\")\n raise FileNotFoundError\n\n precog_df['record_date'] = pd.to_datetime(precog_df['record_date'])\n sp500_df['start_date'] = pd.to_datetime(sp500_df['start_date'])\n sp500_df['end_date'] = pd.to_datetime(sp500_df['end_date'])\n\n precog_df = precog_df.merge(sp500_df, on='cntry_prim_ticker')\n precog_df = precog_df[(precog_df['record_date'] >= precog_df['start_date']) &\n (precog_df['record_date'] <= precog_df['end_date'] - BDay(5))]\n\n keep_cols = ['record_date', 'cntry_prim_ticker', 'model_h', 'model_h2',\n 'prediction_horizon', 'prediction_date', 'exch_region', 'exch_country']\n precog_df = precog_df[keep_cols]\n\n if self._gcloud_cache_path is not None:\n save_variable_to_gcloud(precog_df, filename, self._gcloud_folder, self._gcloud_bucket)\n logger.info(f\"Cached precog preds for SP500 to gcloud! {filename}\")\n\n return precog_df\n\n # ----------------------------------------------------------------\n # | |\n # | Helpers |\n # | |\n # ----------------------------------------------------------------\n def check_gcloud_cache_path(self):\n if self._use_cache and self._gcloud_cache_path is None:\n logger.error(\"Require gcloud path to enable use_cache.\")\n raise ValueError\n\n if self._gcloud_cache_path is not None:\n try:\n self._gcloud_bucket, self._gcloud_folder = self._gcloud_cache_path.split(':')\n except ValueError:\n logger.info(\"Please check gcloud path format. Should be like [bucket_name]:[sub_folder_name]\")\n raise ValueError\n\n @staticmethod\n def ticker_symbol_adjustment(ticker, source=None):\n \"\"\"\n handle the out-of-date mismatch problem of av database ticker symbol\n :param ticker\n :param source:\n :return:\n \"\"\"\n ticker_quandl = {\n 'YHOO US EQUITY': 'AABA US EQUITY',\n }\n\n if source == 'quandl':\n ticker_new = ticker_quandl.get(ticker)\n if ticker_new is not None:\n return ticker_new\n else:\n return ticker\n else:\n return ticker\n\n def delete_gcloud_folder(self):\n file_list = get_files_in_gs_folder(sub_folder=self._gcloud_folder, bucket_name=self._gcloud_bucket)\n logger.info(f\"Found {len(file_list)} to be deleted in {self._gcloud_cache_path}.\")\n yn = input(\"Confirm: Y / N ?\")\n go_on = 1\n while go_on:\n if yn.upper() == 'Y':\n logger.info(\"Deleting...\")\n for file in file_list:\n delete_gcloud_file(file, self._gcloud_bucket)\n logger.info(f\"Deleted all files in {self._gcloud_cache_path}\")\n return 1\n elif yn.upper() == 'N':\n logger.info(\"Canceled.\")\n return 0\n else:\n yn = input(\"Confirm: Y / N ?\")\n\n @staticmethod\n def ensure_filename(filename):\n \"\"\"\n ensure no system reserved special characters in the filename\n :param filename:\n :return:\n \"\"\"\n char_map = {\n '\\\\': '_',\n '/': '_',\n ':': '!',\n '*': '#',\n '?': '%',\n '\"': '^',\n '>': ']',\n '<': '[',\n }\n for char in char_map:\n filename = filename.replace(char, char_map[char])\n\n return filename\n\n def _db_price_data_quality_check(self, px_df: pd.DataFrame):\n \"\"\"\n pick the best data source and clean the bad data points\n :param px_df:\n :return:\n \"\"\"\n best_source = self._best_source\n\n # replace -100000 (default value for missing data) with NAs\n px_df = px_df.replace(-1e6, np.nan)\n\n # clean the record_date for weekends and holidays\n px_df = self._remove_weekend_and_holiday(px_df)\n\n # deal with the bad rows\n px_cols = ['px_close', 'px_open', 'px_high', 'px_low']\n px_df.loc[(px_df[px_cols] <= 0).any(axis=1), px_cols] = np.nan\n px_df.loc[(px_df[px_cols].isnull()).any(axis=1), px_cols] = np.nan\n px_df.loc[(px_df['px_high'] == px_df['px_low']), px_cols] = np.nan\n px_df = px_df.dropna(axis=0, how='all', subset=px_cols)\n\n # choose the best data source\n # pick at most top 3 sources according to the number of data points\n source_candidate = px_df.groupby(by='data_source').apply(len).sort_values(ascending=False)[:3].index\n # from the candidates pick the best one according to best_source\n source_selected = source_candidate[0]\n for bs in best_source:\n if bs in source_candidate:\n source_selected = bs\n break\n px_df = px_df[px_df['data_source'] == source_selected]\n\n # fill partial missing data and drop NAs\n px_df = px_df.fillna(method='ffill', limit=2)\n px_df = px_df.dropna(axis=0, subset=px_cols, how='any')\n\n px_df = px_df.drop_duplicates(['record_date', 'cntry_prim_ticker'])\n px_df = px_df.reset_index(drop=True)\n\n return px_df\n\n def _remove_weekend_and_holiday(self, px_df: pd.DataFrame):\n assert 'record_date' in px_df.columns, \"record_date required for removing bad date index.\"\n # drop the weekend data\n weekday = px_df['record_date'].dt.weekday\n px_df = px_df[(weekday != 5) & (weekday != 6)]\n\n px_df['cntry_symbol'] = px_df['cntry_prim_ticker'].apply(lambda x: x.split()[1])\n\n def _remove_holiday(df):\n cntry_symbol = df['cntry_symbol'].iloc[0]\n exch_country = self.cntry_symbol_map.get(cntry_symbol)\n\n if exch_country is not None:\n # drop the holiday data\n exch_symbol = self.exchange_country_map.get(exch_country)\n\n if exch_symbol is not None:\n exch = mcal.get_calendar(exch_symbol)\n holidays = pd.to_datetime(exch.holidays().holidays)\n df = df.loc[~df['record_date'].isin(holidays)]\n else:\n logger.warning(f'Missing/Not implemented exchange market calendar for {exch_country}. '\n f'Pass trading date filtering.')\n else:\n logger.warning(f'Missing/Not implemented country symbol for {cntry_symbol}. '\n f'Pass trading date filtering.')\n return df\n\n px_df = px_df.groupby('cntry_symbol').apply(_remove_holiday)\n px_df = px_df.reset_index(drop=True)\n px_df = px_df.drop(columns=['cntry_symbol'], errors='ignore')\n\n return px_df\n\n\n# ----------------------------------- end of DataCenter class ---------------------------------------------\n\n\n#############################################\n# Helper Functions\n#############################################\n\n\n#############################################\n# Main\n#############################################\ndef main_get_price():\n start_date = '2016-12-01'\n end_date = 'today'\n\n # get price data\n dc = DataCenter(start_date, end_date, \"av-projects:precog_trading/raw_price_data\", use_cache=False)\n\n # sp500_df = dc.get_sp500_components()\n # ticker500 = sp500_df['cntry_prim_ticker'].unique().tolist()\n # dc.get_equity_price_data_append(ticker500, source='quandl')\n\n ticker_df = pd.read_csv(\"T:/projects/bluecrest/liquidity_filtered_list_75000000.csv\")\n ticker_list = ticker_df['cntry_prim_ticker'].unique().tolist()\n dc.get_equity_price_data_append(ticker_list, source='quandl')\n\n\ndef main_get_precog_4_2_gb5():\n start_date = '2017-01-01'\n end_date = '2018-5-25'\n\n dc = DataCenter(start_date, end_date, \"av-projects:precog_trading/precog_data\", use_cache=False)\n pred_horizon = 3\n pred_cols = ['model_h', 'model_h2', 'omp', 'randomforest',\n 'gradboost1', 'gradboost2', 'gradboost3', 'gradboost4', 'gradboost5']\n precog_table = ['time_series_pred_all_4_2_gb5']\n\n # dc.get_precog_pred_sp500(precog_table, pred_cols, pred_horizon, suffix_id='4_2_gb5')\n\n ticker_df = pd.read_csv(\"T:/projects/bluecrest/liquidity_filtered_list_75000000.csv\")\n ticker_list = ticker_df['cntry_prim_ticker'].unique().tolist()\n dc.get_precog_pred(ticker_list, precog_table, pred_cols, pred_horizon, suffix_id='4_2_gb5')\n\n\ndef main_get_precog_4_2():\n start_date = '2018-01-01'\n end_date = '2018-5-25'\n\n dc = DataCenter(start_date, end_date, \"av-projects:precog_trading/precog_data\", use_cache=False)\n pred_horizon = 3\n pred_cols = ['model_h', 'model_h2', 'omp', 'randomforest', 'gradboost']\n precog_table = ['time_series_pred_all_4_2']\n\n # dc.get_precog_pred_sp500(precog_table, pred_cols, pred_horizon, suffix_id='4_2_gb5')\n\n ticker_df = pd.read_csv(\"T:/projects/bluecrest/liquidity_filtered_list_75000000.csv\")\n ticker_list = ticker_df['cntry_prim_ticker'].unique().tolist()\n dc.get_precog_pred(ticker_list, precog_table, pred_cols, pred_horizon, suffix_id='4_2')\n\n\ndef main_nuke_gcloud_cache():\n dc = DataCenter()\n\n dc.gcloud_cache_path = 'av-projects:precog_trading/raw_price_data'\n dc.delete_gcloud_folder()\n\n dc.gcloud_cache_path = \"av-projects:precog_trading/precog_data\"\n dc.delete_gcloud_folder()\n\n\n#############################################\n# Execution Guard\n#############################################\nif __name__ == '__main__':\n # main_get_price()\n # main_nuke_gcloud_cache()\n\n # main_get_precog_shawn_test()\n main_get_precog_4_2_gb5()\n # main_get_precog_4_2()\n", "sub_path": "precog_trading/data_center.py", "file_name": "data_center.py", "file_ext": "py", "file_size_in_byte": 39664, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "logging.basicConfig", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 62, "usage_type": "call"}, {"api_name": "av_helpers.file_helpers.load_json_file", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 116, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 139, "usage_type": "call"}, {"api_name": "quandl.get", "line_number": 201, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 218, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 172, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 172, "usage_type": "attribute"}, {"api_name": "av_helpers.db_helpers.get_df_from_sql", "line_number": 253, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 254, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 228, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 228, "usage_type": "attribute"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 285, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 289, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 271, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 271, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 297, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 297, "usage_type": "attribute"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 344, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 349, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 365, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 367, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 367, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 372, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 386, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 390, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 395, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 396, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 396, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 401, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 404, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 417, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 418, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 422, "usage_type": "call"}, {"api_name": "av_helpers.db_helpers.get_df_from_sql", "line_number": 430, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 433, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 434, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 437, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 470, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 472, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 473, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 482, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 487, "usage_type": "call"}, {"api_name": "av_helpers.db_helpers.get_df_from_sql", "line_number": 501, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 504, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 518, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 522, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 540, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 542, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 543, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 551, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 556, "usage_type": "call"}, {"api_name": "av_helpers.db_helpers.get_df_from_sql", "line_number": 568, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 580, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 595, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 599, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 619, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 620, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 630, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 648, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 649, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 650, "usage_type": "call"}, {"api_name": "pandas.tseries.offsets.BDay", "line_number": 654, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 661, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 673, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 674, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.load_variable_from_gcloud", "line_number": 682, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 700, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 701, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 702, "usage_type": "call"}, {"api_name": "pandas.tseries.offsets.BDay", "line_number": 706, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.save_variable_to_gcloud", "line_number": 713, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.get_files_in_gs_folder", "line_number": 757, "usage_type": "call"}, {"api_name": "av_helpers.g_cloud_storage.delete_gcloud_file", "line_number": 765, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 796, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 805, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 812, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 813, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 814, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 837, "usage_type": "attribute"}, {"api_name": "pandas_market_calendars.get_calendar", "line_number": 854, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 855, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 894, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 911, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 927, "usage_type": "call"}]} +{"seq_id": "416238694", "text": "import json\nimport sys\nimport time\nfrom datetime import datetime\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nclass V2EX(object):\n def __init__(self, f):\n self.f = f\n timezone = time.strftime(\"%z\", time.gmtime())\n timezone = timezone[:3] + ':' + timezone[3:]\n self.f.write('执行脚本:' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + \" \" + timezone + \" \\n\")\n self.urls = {}\n self.urls['home'] = 'https://v2ex.com'\n self.urls['signin'] = self.urls['home'] + '/signin'\n self.urls['daily'] = self.urls['home'] + '/mission/daily'\n self.urls['balance'] = self.urls['home'] + '/balance'\n self.headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36',\n 'Origin': 'http://www.v2ex.com',\n 'Referer': 'http://www.v2ex.com/signin',\n 'Host': 'www.v2ex.com',\n }\n self.data = {}\n\n def getUnAndPw(self):\n # self.f.write('Begin get username and password')\n\n with open('experiment.json') as f:\n uap = json.load(f)\n self.username = uap['v2ex']['username']\n self.password = uap['v2ex']['password']\n\n # self.f.write('Finished getUnAndPw')\n\n def login(self):\n # self.f.write('Begin login')\n\n # 获取账号密码\n self.getUnAndPw()\n\n # 登录账号\n self.session = requests.Session()\n self.session.headers = self.headers\n loginhtm = self.session.get(self.urls['signin']).content\n soup = BeautifulSoup(loginhtm, \"html.parser\")\n\n # 找到username和passwd输入框的name\n usernameform = soup.find(placeholder=\"用户名或电子邮箱地址\")\n passwdform = soup.find(type=\"password\")\n onceform = soup.find_all(type=\"hidden\")[0]\n\n # 构造data\n self.data[usernameform['name']] = self.username\n self.data[passwdform['name']] = self.password\n self.data['once'] = onceform['value']\n self.data['next'] = '/'\n\n # 登录\n loginp = self.session.post(self.urls['signin'], data=self.data)\n home = self.session.get(self.urls['home']).content\n soup2 = BeautifulSoup(home, 'html.parser')\n if 'bajnok33' != soup2.find_all('span', attrs={'class': 'bigger'})[0].string:\n self.f.write(\"Can't login!\\n\")\n sys.exit()\n\n # self.f.write('Finished login')\n\n def daily(self):\n # self.f.write('Begin daily')\n\n # 进行签到\n signpage = self.session.get(self.urls['daily']).content\n soup2 = BeautifulSoup(signpage, \"html.parser\")\n\n # 获取签到地址\n href = soup2.find(type=\"button\")['onclick']\n index = href.find(\"'\")\n qiandao = href[index + 1:-2]\n\n # 签到\n self.session.get(self.urls['home'] + qiandao)\n # self.f.write('Finished daily')\n\n def balance(self):\n # self.f.write('Begin balance')\n\n # 查询余额\n balancehtm = self.session.get(self.urls['balance']).content\n soup3 = BeautifulSoup(balancehtm, 'html.parser')\n\n # 找到存放金币信息的table\n table = soup3.find_all('table')[7]\n tr = table.find_all('tr')[1]\n tds = tr.find_all('td')\n\n self.f.write(\"签到时间:\" + tds[0].string + \"\\n\")\n self.f.write(\"获得金钱:\" + tds[2].string + \"\\n\")\n self.f.write(\"余额数量:\" + tds[3].string + \"\\n\")\n\n # self.f.write('Finished balance')\n", "sub_path": "v2ex.py", "file_name": "v2ex.py", "file_ext": "py", "file_size_in_byte": 3538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "time.strftime", "line_number": 13, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "json.load", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 46, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 77, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "624817949", "text": "from abc import ABC\n\nfrom scrapy.exceptions import DropItem\n\n\nclass BaseParser(ABC):\n\n def _extract_if_exists(self, values_list, default=None, position=0, raise_exception=False):\n \"\"\"\n Extract the element in position of the list if exists\n :param values_list: the list of values\n :param default: the default value to return\n :param position: the position where to extract\n :param raise_exception: raise\n :return: extracted value\n \"\"\"\n value = default\n try:\n value = values_list[position].extract()\n except (IndexError, AttributeError):\n if raise_exception:\n raise DropItem('Missing key values')\n return value\n\n\nclass CarDetailsViewParser(BaseParser):\n # query to obtain basic specs from car details view\n SPECS_XPATH_QUERY = \"//li[contains(@class, 'specs-item')]\"\n # query to obtains the name and value of the spec\n SPEC_DETAILS_CSS_QUERY = 'strong::text,span::text'\n # query to obtain the price currency\n CURRENCY_CSS_QUERY = \"span[class='price-tag-symbol']::text\"\n # query to obtain the car price\n PRICE_CSS_QUERY = \"span[class='price-tag-fraction']::text\"\n # query to obtain secondary specs list\n SECONDARY_SPECS_XPATH_QUERY = \"//ul[contains(@class, 'attribute-list')]/li/text()\"\n\n def __init__(self, response):\n self.response = response\n\n def __extract_specs(self):\n extracted_data = {}\n specs = self.response.xpath(self.SPECS_XPATH_QUERY)\n for spec in specs:\n # extract spec details\n spec_details = spec.css(self.SPEC_DETAILS_CSS_QUERY)\n spec_details = spec_details.extract()\n if spec_details:\n # expected format [name, value]\n extracted_data[spec_details[0]] = spec_details[1]\n return extracted_data\n\n def __extract_price(self):\n price = self.response.css(self.PRICE_CSS_QUERY)\n return self._extract_if_exists(price)\n\n def __extract_currency(self):\n # get data from response\n currency = self.response.css(self.CURRENCY_CSS_QUERY)\n # parse results and return data\n return self._extract_if_exists(currency)\n\n def __get_clean_spec_labeled_value(self, selector):\n value = \"\"\n try:\n value = selector.get().split(\":\")[0].strip()\n except IndexError:\n pass\n return value\n\n def __extract_secondary_specs(self):\n # initialize data containers\n features = []\n extracted_data = {'features': features}\n\n # parse data and store them in results containers\n secondary_specs = self.response.xpath(self.SECONDARY_SPECS_XPATH_QUERY)\n for spec in secondary_specs:\n # meaning if is a tag, value kind of spec\n if len(spec.get().split(':')) > 1:\n tag = self.__get_clean_spec_labeled_value(spec)\n value = spec.css('span::text').get()\n extracted_data[tag] = value\n else:\n # if not then its just a tag value\n # we will add it as a feature to the list\n features.append(spec.get().strip())\n return extracted_data\n\n def extract_data(self):\n extracted_data = {\n 'price': self.__extract_price(),\n 'specs': self.__extract_specs(),\n 'currency': self.__extract_currency(),\n 'secondary_specs': self.__extract_secondary_specs(),\n }\n return extracted_data\n", "sub_path": "ml-scrapy/spiders/parsers.py", "file_name": "parsers.py", "file_ext": "py", "file_size_in_byte": 3537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "abc.ABC", "line_number": 6, "usage_type": "name"}, {"api_name": "scrapy.exceptions.DropItem", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "513046205", "text": "#!/usr/bin/python3\n\"\"\" Unittest for BaseModel \"\"\"\nfrom models.base_model import BaseModel\nimport unittest\nimport datetime\nfrom uuid import UUID\nimport json\nimport os\nimport pep8\nimport unittest\nimport os\nfrom os import getenv\nfrom models import storage\nfrom models.state import State\nimport MySQLdb\n\n\nclass test_basemodel(unittest.TestCase):\n \"\"\" Test for BaseModel \"\"\"\n\n def test_pep8_BaseModel(self):\n \"\"\"Testing for pep8\"\"\"\n style = pep8.StyleGuide(quiet=True)\n p = style.check_files(['models/base_model.py'])\n self.assertEqual(p.total_errors, 0, \"fix pep8\")\n\n def test_module_docstring(self):\n \"\"\"Test for the existence of module docstring\"\"\"\n self.assertIsNot(module_doc, None,\n \"base_model.py needs a docstring\")\n self.assertTrue(len(module_doc) > 1,\n \"base_model.py needs a docstring\")\n\n def test_class_docstring(self):\n \"\"\"Test for the BaseModel class docstring\"\"\"\n self.assertIsNot(BaseModel.__doc__, None,\n \"BaseModel class needs a docstring\")\n self.assertTrue(len(BaseModel.__doc__) >= 1,\n \"BaseModel class needs a docstring\")\n\n def __init__(self, *args, **kwargs):\n \"\"\" Test for BaseModel \"\"\"\n super().__init__(*args, **kwargs)\n self.name = 'BaseModel'\n self.value = BaseModel\n\n def setUp(self):\n \"\"\" Test for BaseModel \"\"\"\n pass\n\n def tearDown(self):\n try:\n os.remove('file.json')\n except:\n pass\n\n def test_default(self):\n \"\"\" Test for BaseModel \"\"\"\n i = self.value()\n self.assertEqual(type(i), self.value)\n\n def test_kwargs(self):\n \"\"\" Test for BaseModel \"\"\"\n i = self.value()\n copy = i.to_dict()\n new = BaseModel(**copy)\n self.assertFalse(new is i)\n\n def test_kwargs_int(self):\n \"\"\" Test for BaseModel \"\"\"\n i = self.value()\n copy = i.to_dict()\n copy.update({1: 2})\n with self.assertRaises(TypeError):\n new = BaseModel(**copy)\n\n def test_save(self):\n \"\"\" Testing save \"\"\"\n i = self.value()\n i.save()\n key = self.name + \".\" + i.id\n with open('file.json', 'r') as f:\n j = json.load(f)\n self.assertEqual(j[key], i.to_dict())\n\n def test_str(self):\n \"\"\" Test for BaseModel \"\"\"\n i = self.value()\n self.assertEqual(str(i), '[{}] ({}) {}'.format(self.name, i.id,\n i.__dict__))\n\n def test_todict(self):\n \"\"\" Test for BaseModel \"\"\"\n i = self.value()\n n = i.to_dict()\n self.assertEqual(i.to_dict(), n)\n\n def test_kwargs_none(self):\n \"\"\" Test for BaseModel \"\"\"\n n = {None: None}\n with self.assertRaises(TypeError):\n new = self.value(**n)\n\n def test_id(self):\n \"\"\" Test for BaseModel \"\"\"\n new = self.value()\n self.assertEqual(type(new.id), str)\n\n def test_created_at(self):\n \"\"\" Test for BaseModel \"\"\"\n new = self.value()\n self.assertEqual(type(new.created_at), datetime.datetime)\n\n def test_updated_at(self):\n \"\"\" Test for BaseModel \"\"\"\n new = self.value()\n self.assertEqual(type(new.updated_at), datetime.datetime)\n n = new.to_dict()\n new = BaseModel(**n)\n self.assertFalse(new.created_at == new.updated_at)\n", "sub_path": "tests/test_models/test_base_model.py", "file_name": "test_base_model.py", "file_ext": "py", "file_size_in_byte": 3452, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pep8.StyleGuide", "line_number": 23, "usage_type": "call"}, {"api_name": "models.base_model.BaseModel.__doc__", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.base_model.BaseModel", "line_number": 36, "usage_type": "name"}, {"api_name": "models.base_model.BaseModel.__doc__", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.base_model.BaseModel", "line_number": 38, "usage_type": "name"}, {"api_name": "models.base_model.BaseModel", "line_number": 45, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "models.base_model.BaseModel", "line_number": 66, "usage_type": "call"}, {"api_name": "models.base_model.BaseModel", "line_number": 75, "usage_type": "call"}, {"api_name": "json.load", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 112, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.base_model.BaseModel", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "47482158", "text": "import cv2\nimport numpy as np\nimport os\nimport glob\nimport shutil\nos.mkdir(\"keyframeAccident\")\n\n\nfor name in glob.glob(\"/home/aman/Desktop/Mini-Project/Frame*\"):\n num=len([f for f in os.listdir(name)])\n a=[0]*num\n b=[0]*num\n count=0\n mean=0\n deviation=0\n i=0;\n while i int(th):\n path=name+\"/frame\"+str(i+1)+'.jpg'\n img = cv2.imread(path,cv2.IMREAD_COLOR)\n cv2.imwrite(\"/home/aman/Desktop/Mini-Project/keyframeAccident/frame{0}.jpg\".format(j), img)\n j = j + 1;\n #print(j)\n i = i + 1\n shutil.rmtree(name)\n\n\t\n\t\n\t\n\n\n", "sub_path": "KeyFrameExtract.py", "file_name": "KeyFrameExtract.py", "file_ext": "py", "file_size_in_byte": 1304, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "os.mkdir", "line_number": 6, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 9, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 45, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "643067134", "text": "#https://docs.python.org/3/howto/argparse.html\n\nimport sys\nimport regex #need to get from pypi - from command line: pip install regex\nimport os\nfrom xml.dom import minidom\nimport xml.etree.ElementTree as ET\n\nclass tokens():\n #define regular expression patterns\n def __init__(self):\n #find comments in the Jack language\n # the .*? matches as few characters as possible - try to find other matches\n self.multilineCommentStart = r\"(?P\" + regex.escape(r\"/*\") + r\".*$)\"\n self.multilineCommentEnd = r\"(?P^.*?\" + regex.escape(r\"*/\") + r\")\"\n self.endOfLineComment = r\"(?P(\" + regex.escape(r\"/\") * 2 + \".*|\" + regex.escape(r\"/*\") + \".*?\" + regex.escape(\"*/\") + r\")$)\"\n\n \"\"\"\n the symbol regular expression matches the following symbols:\n { } ( ) [ ] . , ; + - * / & | < > = ~\n \"\"\"\n self.symbol = (r\"(?P\" + \"|\".join(map(regex.escape, r\"{}()[].,;+-*/&|<>=~\")) + \")\")\n\n #match any of the keywords in the Jack language\n #must be followed by a space, end of line, or a symbol\n self.keyword = (r\"(?P(class)|(constructor)|(function)|(method)|(field)|(static)|(var)\" +\n r\"|(int)|(char)|(boolean)|(void)|(true)|(false)|(null)|(this)|(let)|(do)|(if)|(else)|(while)|(return))(?=\\s|$|\" + \"|\".join(map(regex.escape, r\"{}()[].,;+-*/&|<>=~\")) + \")\")\n\n \"\"\"\n the integerConstant regular expression matches any number in the range 0..32767. I have allowed any number\n of preceeding zeros to match an integer constant. Following the regular expression left to right, it will\n match:\n 0* = any number of preceeding zeros\n \\d{1,4} = any 1 to 4 digit number\n [0-2]\\d{4} = any 5 digit number starting with 0 through 2\n 3[0-1]\\d{3} = any 5 digit number starting with 3 then 0 through 1\n 32[0-6]\\d{2} = any 5 digit number starting with 32 then 0 through 6\n 327[0-5]\\d = any 5 digit number starting with 327 then 0 through 5\n 3276[0-7] any 5 digit number starting with 3276 and ending with 0 through 7\n (?=\\s): must be followed by whitespace or the end of the string\n \"\"\"\n self.integerConstant = r\"(?P0*(\\d{1,4}|[0-2]\\d{4}|3[0-1]\\d{3}|32[0-6]\\d{2}|327[0-5]\\d|3276[0-7])(?=\\s|$|\" + \"|\".join(map(regex.escape, r\"{}()[].,;+-*/&|<>=~\")) + \"))\"\n\n #matches anything between two double quotes\n self.stringConstant = r'\"(?P.*)\"(?=\\s|$|' + \"|\".join(map(regex.escape, r\"{}()[].,;+-*/&|<>=~\")) + ')'\n\n self.identifier = r\"(?P[a-zA-Z_][\\w_]*(?=\\s|$|\" + \"|\".join(map(regex.escape, r\"{}()[].,;+-*/&|<>=~\")) + \"))\"\n\n self.LexicalElement = r\"(\" + r\"|\".join([self.symbol, self.keyword, self.integerConstant, self.stringConstant, self.identifier]) + r\")\"\n\n #the double question mark makes a regex not match if possible\n #endOfLineComment must come before multilineCommentStart so it matches first if possible - otherwise multilineCommentStarts will always match,\n #even if they end on the same line.\n #self.line = (r\"^\" + self.multilineCommentEnd + \"??\\s*(\" + self.endOfLineComment + \"|(\\s*(\" + self.LexicalElement + \"\\s*)*(\" +\n # self.endOfLineComment + \"|\" + self.multilineCommentStart +\")?))$\")\n #since endofLineComment and multilineCommentStart match until the end of the line, it's safe to include them in an or statement with LexicalElement\n #that matches any number of times - Lexical elements will be found until an end of line comment or multilinecommentstart is found.\n self.line = (r\"^(\\s*\" + self.endOfLineComment + \")|(\" + self.multilineCommentEnd + \"?\\s*(\" + self.endOfLineComment + \"|\" + self.multilineCommentStart + \"|\" + self.LexicalElement + \"\\s*)*)$\")\n\nif __debug__:\n d = tokens()\n # test to see if the token regexes are working\n\n #check comments\n endOfLineComments = [\"//This file is part of ...\", \"/* test 123 */\", \"/** test 345 */\"]\n for i in endOfLineComments:\n assert regex.match(d.endOfLineComment, i)\n assert regex.match(d.line, i).group(\"endOfLineComment\")\n m = regex.search(d.line, i)\n assert not (m.group(\"symbol\") or m.group(\"keyword\") or m.group(\"integerConstant\") or m.group(\"stringConstant\") or m.group(\"identifier\"))\n assert regex.match(d.multilineCommentStart, \"/*foo bar doo wad 123\")\n assert regex.match(d.multilineCommentEnd, \"foo wad boo */\")\n\n normalLine = \" static boolean test; // Added for testing -- there is no static keyword\"\n assert regex.match(d.line, normalLine)\n assert regex.match(d.line, normalLine).group(\"endOfLineComment\")\n\n #check keywords\n keywords = (\"class|constructor|function|method|field|static|var\" +\n \"|int|char|boolean|void|true|false|null|this|let|do|if|else|while|return\").split(\"|\")\n for i in keywords:\n assert regex.match(d.keyword, i)\n assert regex.match(d.LexicalElement, i)\n\n #check symbols\n for i in r\"{}()[].,;+-*/&|<>=~\":\n assert regex.match(d.symbol, i)\n assert regex.match(d.LexicalElement, i)\n\n #check integer constants\n for i in \"0|01|10 - completed|0123|123|01234|1234|12345|22345|31345|32345|32745|32765|032765|32767\".split(\"|\"):\n assert regex.match(d.integerConstant, i)\n assert regex.match(d.LexicalElement, i)\n for i in r\"-1|32768|32771|32811|33111|40000|032768|032771|032811|033111|040000\".split(\"|\"):\n assert not regex.match(d.integerConstant, i)\n\n #check string constants\n import itertools #need to chain two rainges together\n for i in itertools.chain(range(10), range(11, 32768)): #ignore newline character - Jack language doesn't support it\n assert regex.match(d.stringConstant, '\"' + regex.escape(chr(i)) + '\"')\n assert regex.match(d.LexicalElement, '\"' + regex.escape(chr(i)) + '\"')\n assert regex.match(d.stringConstant, '\"\"')\n assert regex.match(d.LexicalElement, '\"\"')\n assert not regex.match(d.stringConstant, 'abc')\n assert not regex.match(d.stringConstant, '')\n\n #check identifiers\n for i in [\"foo\", \" i \", \"abc123\", \"a_1\", \"_123\"]:\n assert regex.search(d.identifier, i)\n\n #check lines\n for line in [r' class foo 12345 + 23456 12345', r'( //this is a comment', r'\"howdy\" if 00000012 let \"let\" ; ( ) /* another comment*/', \" function void main( ) {\\n\"]:\n x = regex.match(d.line, line)\n assert regex.match(d.line, line)\n\nclass tokenizer():\n def __init__(self, fname):\n self.iname = fname\n self.tokens = tokens()\n self.regExLine = regex.compile(self.tokens.line)\n self.tokenList = self.getTokenList()\n\n def tokenizeLine(self, line):\n matches = self.regExLine.match(line)\n #pull out the capture dictionary. This is a dict of the form the group name: match list\n matchDict = matches.capturesdict()\n lookupDict = dict()\n for groupName in matchDict:\n #for each group in the dictionary, get the starts list. This list is sorted first to last in terms of\n #capture order, as are the match lists in matchDict\n starts = matches.starts(groupName)\n for i, startIndex in enumerate(starts):\n lookupDict[startIndex] = (groupName, matchDict[groupName][i])\n tokenList = (lookupDict[startIndex] for startIndex in sorted(lookupDict))\n return tokenList\n\n def getTokenList(self):\n tokenList = []\n with open(self.iname, \"r\") as inputFile:\n nextline = inputFile.readline()\n while nextline:\n tokenMatch = regex.match(self.regExLine, nextline)\n if not tokenMatch:\n raise Exception(\"TokenizerError\", \"unparsable line found:\\n\" + nextline)\n if tokenMatch.group(\"multilineCommentEnd\"):\n raise Exception(\"TokenizerError\", \"comment end prior to comment start:\\n\" + nextline + \"\\n\" + str(list(self.tokenizeLine(nextline))))\n\n tokenList += [i for i in self.tokenizeLine(nextline) if i[0].find(\"Comment\") < 0]\n\n if tokenMatch.group(\"multilineCommentStart\"):\n foundCommentEnd = False\n lineForErrorMessage = nextline #save this for error handling later\n nextline = inputFile.readline()\n while nextline:\n tokenMatch = regex.match(self.regExLine, nextline)\n \"\"\"\n if tokenMatch:\n x = list(self.tokenizeLine(nextline))\n if x:\n tokenMatch = regex.match(self.regExLine, nextline)\n \"\"\"\n if tokenMatch and tokenMatch.group(\"multilineCommentEnd\"):\n tokenList += [i for i in self.tokenizeLine(nextline) if i[0].find(\"Comment\") < 0]\n nextline = inputFile.readline()\n foundCommentEnd = True\n break\n nextline = inputFile.readline()\n #check to see if we've read to the end of the file without finding a comment end mark\n if not foundCommentEnd:\n raise Exception(\"TokenizerError\", \"multilineCommentStart without multilineCommentEnd:\\nfilename:\" + self.iname + \"\\nline:\\n\" + lineForErrorMessage)\n else:\n nextline = inputFile.readline()\n return tokenList\n\n def printToXML(self, oname):\n if os.path.isfile(oname):\n return False\n\n tree = ET.Element(\"tokens\")\n\n for token in self.tokenList:\n t = ET.SubElement(tree, token[0])\n t.text = \" \" + token[1] + \" \"\n\n with open(oname, \"w\") as ofile:\n # ofile.write(ET.tostring(tree, \"unicode\"))\n ofile.write(prettify(tree))\n\nclass ParseException(Exception):\n def __init__(self, errMessage, token, tokenList, rootXMLElement, fileName):\n Exception.__init__(self, errMessage)\n self.token = token\n self.tokenList = tokenList\n self.rootXMLElement = rootXMLElement\n self.fileName = fileName\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n baseStr = Exception.__str__(self)\n if self.tokenList:\n #tokens = \"\\n\".join(str(i[1]) for i in self.tokenList[:self.token[0] + 1])\n tokens = \"\\n\".join(str(i[1]) for i in self.tokenList)\n else:\n tokens = \"no tokens found\"\n if not self.token:\n self.token = \"no token\"\n if not self.rootXMLElement:\n XML = \"no xml\"\n else:\n XML = prettify(self.rootXMLElement)\n return baseStr + \"\\nfilename: \" + self.fileName + \"\\nthe tokens are:\\n\" + tokens + \"\\nthe xml is:\\n\" + XML + \"\\ntoken that caused an issue:\\n\" + str(self.token) + \"\\n\"+ baseStr\n \nclass Parser():\n \"\"\"\n In each parse subroutine, we will assume the next element has already been selected and verified for the subroutine\n \"\"\"\n def __init__(self, tokenizer):\n self.tokenizer = tokenizer\n self.tokenList = tokenizer.tokenList\n self.tokenIterable = enumerate(self.tokenList) #could be just an iterator, but enumerating it gives the token position for error handling\n self.rootXMLElement = None\n self.nextToken = [None, None] #tokenIndex, token, where token is of the form [tokenType, tokenValue]\n\n def Parse(self):\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][1] != \"class\":\n raise ParseException(\"Parse error: the first token must be the keyword 'class'\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n self.rootXMLElement = ET.Element(\"class\")\n self.compileClass(self.rootXMLElement)\n\n def compileClass(self, parent):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][0] != \"identifier\":\n raise ParseException(\"compileClass error: the second token in a class must be an identifier\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][1] != \"{\":\n raise ParseException(\"compileClass error: the third token must be an opening bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n variableSet = set() #need to have access to variables local to the class\n\n while (self.nextToken[1][1] in [\"static\", \"field\"]):\n subEl = ET.SubElement(parent, \"classVarDec\")\n subEl.text = \"\\n\"\n variableSet = variableSet.union(self.compileVariabledeclaration(subEl)) #must call next element before exiting\n\n while self.nextToken[1][1] in [\"constructor\", \"function\", \"method\"]:\n subEl = ET.SubElement(parent, \"subroutineDec\")\n subEl.text = \"\\n\"\n #we're going to copy the variableList so it isn't modified by the compileFunction subroutine so we create a union with an empty list\n self.compileFunction(subEl, variableSet.union([])) #must call next element before exiting\n\n if self.nextToken[1][1] != \"}\":\n raise ParseException(\"compileClass error: the final token in a class must be a closing bracket.\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n try:\n self.nextToken = next(self.tokenIterable)\n raise ParseException(\"compileClass error: the class must end with the closing bracket after all subroutine declarations.\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n except StopIteration:\n pass\n\n def compileVariabledeclaration(self, parent):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if not (self.nextToken[1][0] == \"identifier\" or self.nextToken[1][1] in [\"int\", \"char\", \"boolean\"]):\n raise ParseException(\"compileVariabledeclaration error: the first token after a var statement must be a type name\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n variableList = []\n\n commaFound = True #this should really be false but we need it to be True to make the loop execute at least once\n\n while commaFound:\n if self.nextToken[1][0] != \"identifier\":\n raise ParseException(\"compileVariabledeclaration error: expected an identifier\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n variableList.append(self.nextToken[1][1]) #record the variable name to be passed back to the caller\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][1] not in [\",\", \";\"]:\n raise ParseException(\"compileVariabledeclaration error: the token after a variable name must be a comma or semicolon\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n commaFound = (self.nextToken[1][1] == \",\") #keep looping until a semicolon is found\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n return variableList #all variables declared in this statement\n\n \"\"\"\n variableSet must be a set containing all the class variables so that the function can know what variables are in its scope\n \"\"\"\n def compileFunction(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if not (self.nextToken[1][0] == \"identifier\" or self.nextToken[1][1] in [\"void\", \"int\", \"char\", \"boolean\"]):\n raise ParseException(\"compileFunction error: the token after a function declaration must be 'void' or a type declaration\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][0] != \"identifier\":\n raise ParseException(\"compileFunction error: the token after a function type declaration must be an identifier\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][1] != \"(\":\n raise ParseException(\"compileFunction error: the token after the subroutine name must be an opening parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n subEl = ET.SubElement(parent, \"parameterList\")\n subEl.text = \"\\n\"\n self.compileParameterList(subEl) #must call next element before exiting\n\n if self.nextToken[1][1] != \")\":\n raise ParseException(\"compileFunction error: the token after the expression list must be a closing parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][1] != \"{\":\n raise ParseException(\"compileFunction error: the token after a function name must be a forward bracket '{'\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, \"subroutineBody\")\n subEl.text = \"\\n\"\n self.compileSubroutineBody(subEl, variableSet) #must call next element before exiting\n\n def compileParameterList(self, parent):\n if not (self.nextToken[1][0] == \"identifier\" or self.nextToken[1][1] in [\"int\", \"char\", \"boolean\", \")\"]):\n raise ParseException(\"compileParameterList error: invalid token found. Expected type declaration or closing parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n continueBool = (self.nextToken[1][1] != \")\") #could be an empty parameterlist\n\n while continueBool:\n if not (self.nextToken[1][0] == \"identifier\" or self.nextToken[1][1] in [\"int\", \"char\", \"boolean\"]):\n raise ParseException(\"compileParameterList error: invalid token found. Expected type declaration\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][0] != \"identifier\":\n raise ParseException(\"compileParameterList error: each variable name must be an identifier\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if not (self.nextToken[1][1] in [\",\", \")\"]):\n raise ParseException(\"compileParameterList error: the parameterList must end with a parenthesis or be separated by commas.\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n continueBool = self.nextToken[1][1] == \",\"\n if continueBool: #if continue, you must first process the next token\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n \"\"\"\n variableSet must be a set containing all the class variables so that the function can know what variables are in its scope\n \"\"\"\n def compileSubroutineBody(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n while self.nextToken[1][1] == \"var\":\n subEl = ET.SubElement(parent, \"varDec\")\n subEl.text = \"\\n\"\n variableSet = variableSet.union(self.compileVariabledeclaration(subEl)) #must call next element before exiting\n\n subEl = ET.SubElement(parent, \"statements\")\n subEl.text = \"\\n\"\n self.compileStatements(subEl, variableSet) #must call next element before exiting\n\n if self.nextToken[1][1] != \"}\":\n raise ParseException(\"compileSubroutineBody error: the final token in a function must be a closing bracket.\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n \"\"\"\n variableSet must be a set containing all the class variables so that the statements can know what variables are in their scope\n \"\"\"\n def compileStatements(self, parent, variableSet):\n while self.nextToken[1][1] in [\"let\", \"if\", \"while\", \"do\", \"return\"]:\n if self.nextToken[1][1] == \"let\":\n subEl = ET.SubElement(parent, \"letStatement\")\n subEl.text = \"\\n\"\n self.compileLetStatement(subEl, variableSet) #must call next element before exiting\n elif self.nextToken[1][1] == \"if\":\n subEl = ET.SubElement(parent, \"ifStatement\")\n subEl.text = \"\\n\"\n self.compileIfStatement(subEl, variableSet) #must call next element before exiting\n elif self.nextToken[1][1] == \"while\":\n subEl = ET.SubElement(parent, \"whileStatement\")\n subEl.text = \"\\n\"\n self.compileWhileStatement(subEl, variableSet) #must call next element before exiting\n elif self.nextToken[1][1] == \"do\":\n subEl = ET.SubElement(parent, \"doStatement\")\n subEl.text = \"\\n\"\n self.compileDoStatement(subEl, variableSet) #must call next element before exiting\n elif self.nextToken[1][1] == \"return\":\n subEl = ET.SubElement(parent, \"returnStatement\")\n subEl.text = \"\\n\"\n self.compileReturnStatement(subEl, variableSet) #must call next element before exiting\n\n def compileLetStatement(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][0] != \"identifier\":\n raise ParseException(\"compileLetStatement error: the second token in a let statement must be an identifier\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n if not self.nextToken[1][1] in variableSet:\n raise ParseException(\"compileLetStatement error: variable not in scope. Could be a misspelling.\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n #handle an indexed let statement\n if self.nextToken[1][1] == \"[\":\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n subEl = ET.SubElement(parent, \"expression\")\n subEl.text = \"\\n\"\n self.compileExpression(subEl, variableSet) #must call next element before exiting\n\n if self.nextToken[1][1] != \"]\":\n raise ParseException(\"compileLetStatement error: an indexed let statement must have a closing bracket after the enclosed expression\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][1] != \"=\":\n raise ParseException(\"compileLetStatement error: a let statement must have an equals sign after the identifier (and optional index)\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n subEl = ET.SubElement(parent, \"expression\")\n subEl.text = \"\\n\"\n self.compileExpression(subEl, variableSet) # must call next element before exiting\n\n if self.nextToken[1][1] != \";\":\n raise ParseException(\"compileLetStatement error: a let statement must end in a semicolon\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n def compileIfStatement(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][1] != \"(\":\n raise ParseException(\"compileIfStatement error: the second token in an if statement must be a forward parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n subEl = ET.SubElement(parent, \"expression\")\n subEl.text = \"\\n\"\n self.compileExpression(subEl, variableSet) # must call next element before exiting\n\n\n if self.nextToken[1][1] != \")\":\n raise ParseException(\"compileIfStatement error: token following an expression in an if statement must be a closing parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n if self.nextToken[1][1] != \"{\":\n raise ParseException(\"compileIfStatement error: the token following the conditional in an if statement must be a forward bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n subEl = ET.SubElement(parent, \"statements\")\n subEl.text = \"\\n\"\n self.compileStatements(subEl, variableSet) # must call next element before exiting\n\n if self.nextToken[1][1] != \"}\":\n raise ParseException(\"compileIfStatement error: the token following the statements in an if statement must be a closing bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n #handle else statement\n if self.nextToken[1][1] == \"else\":\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][1] != \"{\":\n raise ParseException(\"compileIfStatement error: the token following the else keyword in an if statement must be a forward bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n subEl = ET.SubElement(parent, \"statements\")\n subEl.text = \"\\n\"\n self.compileStatements(subEl, variableSet) # must call next element before exiting\n\n if self.nextToken[1][1] != \"}\":\n raise ParseException(\"compileIfStatement error: the token following the statements in an if statement must be a closing bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n def compileWhileStatement(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][1] != \"(\":\n raise ParseException(\"compileWhileStatement error: the second token in a while statement must be a forward parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n subEl = ET.SubElement(parent, \"expression\")\n subEl.text = \"\\n\"\n self.compileExpression(subEl, variableSet) # must call next element before exiting\n\n\n if self.nextToken[1][1] != \")\":\n raise ParseException(\"compileWhileStatement error: token following an expression in a while statement must be a closing parenthesis\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][1] != \"{\":\n raise ParseException(\"compileWhileStatement error: the token following the conditional in a while statement must be a forward bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n subEl = ET.SubElement(parent, \"statements\")\n subEl.text = \"\\n\"\n self.compileStatements(subEl, variableSet) # must call next element before exiting\n\n if self.nextToken[1][1] != \"}\":\n raise ParseException(\"compileWhileStatement error: the token following the statements in a while statement must be a closing bracket\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n def compileDoStatement(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n self.compileSubroutineCall(parent, variableSet) #must call next element before exiting\n\n if self.nextToken[1][1] != \";\":\n raise ParseException(\"compileDoStatement error: a do statement must end in a semicolon\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n def compileReturnStatement(self, parent, variableSet):\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n if self.nextToken[1][1] != \";\":\n subEl = ET.SubElement(parent, \"expression\")\n subEl.text = \"\\n\"\n self.compileExpression(subEl, variableSet) # must call next element before exiting\n\n if self.nextToken[1][1] != \";\":\n raise ParseException(\"compileReturnStatement error: a return statement must end in a semicolon\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n\n def compileExpression(self, parent, variableSet):\n subEl = ET.SubElement(parent, \"term\")\n subEl.text = \"\\n\"\n self.compileTerm(subEl, variableSet) #must call next element before exiting\n\n while self.nextToken[1][1] in [\"+\", \"-\", \"*\", \"/\", \"&\", \"|\", \"<\", \">\", \"=\"]:\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n subEl = ET.SubElement(parent, \"term\")\n subEl.text = \"\\n\"\n self.compileTerm(subEl, variableSet) #must call next element before exiting\n\n \"\"\"\n compileTerm is the only subroutine that will do its own validation internally. It is only called from the compileExpression function\n so this isn't too big of a deal\n \"\"\"\n def compileTerm(self, parent, variableSet):\n tokenType = self.nextToken[1][0]\n tokenValue = self.nextToken[1][1]\n if not (tokenType in [\"integerConstant\", \"stringConstant\", \"identifier\"] or tokenValue in [\"(\", \"-\", \"~\", \"true\", \"false\", \"null\", \"this\"]):\n raise ParseException(\"compileTerm error: the first token in a term must be one of the following: integer, string, keywordConstant, variable name, \" +\n \"subroutine call, opening parenthesis, or unary operation\", self.nextToken, self.tokenList, self.rootXMLElement, self.tokenizer.iname)\n\n #first handle the terms that are a single token\n if tokenType in [\"integerConstant\", \"stringConstant\"]:\n subEl = ET.SubElement(parent, tokenType)\n subEl.text = tokenValue\n\n self.nextToken = next(self.tokenIterable)\n elif tokenValue in [\"true\", \"false\", \"null\", \"this\"]:\n subEl = ET.SubElement(parent, \"keyword\")\n subEl.text = tokenValue\n\n self.nextToken = next(self.tokenIterable)\n #next handle the terms that are more complicated\n elif tokenValue in [\"-\", \"~\"]:\n #unaryOp term\n subEl = ET.SubElement(parent, self.nextToken[1][0])\n subEl.text = self.nextToken[1][1]\n\n self.nextToken = next(self.tokenIterable)\n subEl = ET.SubElement(parent, \"term\")\n self.compileTerm(subEl, variableSet) #must call next element before exiting\n elif tokenType == \"identifier\":\n #could be a standalone term, or it could be an indexed term, or it could be a subroutine call\n self.nextToken = next(self.tokenIterable)\n if not self.nextToken[1][1] in [\"[\", \"(\", \".\"]:\n #must have been a variable name\n subEl = ET.SubElement(parent, \"identifier\")\n subEl.text = tokenValue\n #we don't iterate the tokenIterable, since we already did that before\n #self.nextToken = next(self.tokenIterable) < 1:\n fname = sys.argv[1]\n else: #for debugging\n fname = os.path.dirname(os.path.realpath(__file__)) + r\"\\arraytest\"\n\n if os.path.isdir(fname):\n for fileName in os.listdir(fname):\n fullName = os.path.join(fname, fileName)\n if os.path.isfile(fullName):\n extens = os.path.basename(fileName).rsplit(\".\", 1)[1]\n if extens == \"jack\":\n createXML(fullName)\n else:\n createXML(fname)\n\nif __name__ == '__main__':\n main()", "sub_path": "10 - completed/Tokenizer-Parser.py", "file_name": "Tokenizer-Parser.py", "file_ext": "py", "file_size_in_byte": 44151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "regex.escape", "line_number": 14, "usage_type": "call"}, {"api_name": "regex.escape", "line_number": 15, "usage_type": "call"}, {"api_name": "regex.escape", "line_number": 16, "usage_type": "call"}, {"api_name": "regex.escape", "line_number": 22, "usage_type": "attribute"}, {"api_name": "regex.escape", "line_number": 27, "usage_type": "attribute"}, {"api_name": "regex.escape", "line_number": 42, "usage_type": "attribute"}, {"api_name": "regex.escape", "line_number": 45, "usage_type": "attribute"}, {"api_name": "regex.escape", "line_number": 47, "usage_type": "attribute"}, {"api_name": "regex.match", "line_number": 67, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 68, "usage_type": "call"}, {"api_name": "regex.search", "line_number": 69, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 71, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 72, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 75, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 76, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 82, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 83, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 87, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 88, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 92, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 93, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 95, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 99, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 100, "usage_type": "call"}, {"api_name": "regex.escape", "line_number": 100, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 101, "usage_type": "call"}, {"api_name": "regex.escape", "line_number": 101, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 102, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 103, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 104, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 105, "usage_type": "call"}, {"api_name": "regex.search", "line_number": 109, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 113, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 114, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 120, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 142, "usage_type": "call"}, {"api_name": "regex.match", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 179, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 179, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 182, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 182, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 230, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 230, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 234, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 234, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 240, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 240, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 246, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 246, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 254, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 254, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 259, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 259, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 266, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 266, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 276, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 276, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 283, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 283, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 294, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 294, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 303, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 303, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 314, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 314, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 320, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 320, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 326, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 326, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 333, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 333, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 337, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 337, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 343, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 343, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 350, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 350, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 362, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 362, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 369, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 369, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 377, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 377, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 386, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 386, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 392, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 392, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 396, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 396, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 403, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 403, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 414, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 414, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 418, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 418, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 422, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 422, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 426, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 426, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 430, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 430, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 435, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 435, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 444, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 444, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 451, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 451, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 455, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 455, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 461, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 461, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 467, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 467, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 472, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 472, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 478, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 478, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 484, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 484, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 492, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 492, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 497, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 497, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 505, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 505, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 512, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 512, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 517, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 517, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 524, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 524, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 531, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 531, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 539, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 539, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 544, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 544, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 551, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 551, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 557, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 557, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 565, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 565, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 570, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 570, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 578, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 578, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 586, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 586, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 591, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 591, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 598, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 598, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 604, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 604, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 613, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 613, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 619, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 619, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 625, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 625, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 632, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 632, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 638, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 638, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 643, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 643, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 647, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 647, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 664, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 664, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 669, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 669, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 676, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 676, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 680, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 680, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 687, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 687, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 693, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 693, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 695, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 695, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 699, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 699, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 704, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 704, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 713, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 713, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 716, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 716, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 722, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 722, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 753, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 753, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 756, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 756, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 762, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 762, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 766, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 766, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 772, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 772, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 777, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 777, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 783, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 783, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 790, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 790, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 794, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 794, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 799, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 799, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 808, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 808, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 809, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 809, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 813, "usage_type": "call"}, {"api_name": "os.path", "line_number": 813, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 816, "usage_type": "call"}, {"api_name": "os.path", "line_number": 816, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 817, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 836, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 837, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 839, "usage_type": "call"}, {"api_name": "os.path", "line_number": 839, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 839, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 841, "usage_type": "call"}, {"api_name": "os.path", "line_number": 841, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 842, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 843, "usage_type": "call"}, {"api_name": "os.path", "line_number": 843, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 844, "usage_type": "call"}, {"api_name": "os.path", "line_number": 844, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 845, "usage_type": "call"}, {"api_name": "os.path", "line_number": 845, "usage_type": "attribute"}]} +{"seq_id": "278577049", "text": "from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ec\nfrom selenium.webdriver.common.by import By\nfrom time import time\nimport json\nimport os.path\nimport selenium\nimport logging\nfrom selectors import init_selectors\nfrom Bot import Bot\n\n\nclass FindEnemiesBot(Bot):\n \"\"\"\n This class should be used to process, change and create good enemy_list\n \"\"\"\n def __init__(self, browser, character_name, level_treshold):\n super(FindEnemiesBot, self).__init__(browser, character_name)\n self.ranking_site = 'https://g.arenamody.pl/ranking.php'\n self.potential_enemies = set()\n self.checked_enemies = self.initialize_checked_enemies()\n self.level_treshold = level_treshold\n\n def save_checked_enemies(self):\n \"\"\"\n saving checked_enemies to file\n \"\"\"\n filename = self.character_name + \"_checked_enemies.txt\"\n with open(filename, 'w') as file:\n json.dump(self.checked_enemies, file)\n\n def initialize_checked_enemies(self):\n \"\"\"\n loading checked_enemies from file\n \"\"\"\n filename = self.character_name + \"_checked_enemies.txt\"\n if os.path.isfile(filename):\n with open(filename) as file:\n checked_enemies = json.load(file)\n else:\n checked_enemies = list()\n return checked_enemies\n\n def gather_potential_enemies_from(self, tab):\n \"\"\"\n the function takes enemies from ranking\n :param tab: the tab in the ranking, from which I want to gather potential enemies\n \"\"\"\n if self.browser.current_url != self.ranking_site:\n self.browser.get(self.ranking_site)\n self.browser.execute_script(\"getRanking(currentType, '\" + tab + \"')\")\n for i in range(1, 21):\n self.browser.execute_script(\"getRanking('daily','\" + tab + \"' ,\" + str(i) + \")\")\n try:\n WebDriverWait(self.browser, 10).until(ec.element_to_be_clickable((By.ID, \"N\" + str(20 * i - 19))))\n except selenium.common.exceptions.StaleElementReferenceException:\n pass\n for attempt in range(10):\n succeed = False\n try:\n ids = self.browser.find_elements_by_css_selector('td>a.player-name')\n ids = list(map(lambda x: x.get_attribute('href'), ids))\n ids = set(map(lambda x: x.split('=')[1], ids))\n succeed = True\n break\n except selenium.common.exceptions.StaleElementReferenceException:\n pass\n if succeed:\n self.potential_enemies = self.potential_enemies | ids\n logging.debug('Succeed in attempt ' + str(attempt))\n else:\n raise Exception('selenium.common.exceptions.StaleElementReferenceException')\n # print(list(self.potential_enemies))\n\n def gather_potential_enemies(self):\n \"\"\"\n this function gathers processes potential enemies to avoid checking the same person twice\n :return:\n \"\"\"\n self.gather_potential_enemies_from('exp')\n self.gather_potential_enemies_from('duels_won')\n self.gather_potential_enemies_from('duels_money_won')\n to_del = []\n for potential_enemy in self.potential_enemies:\n if potential_enemy in self.enemy_list.keys() or potential_enemy in self.checked_enemies:\n to_del.append(potential_enemy)\n for todel in to_del:\n self.potential_enemies.remove(todel)\n\n def check_player(self, player_id):\n \"\"\"\n checks if I have higher stats than enemy\n :param player_id: string\n :return: True if I can attack this person and win else False\n \"\"\"\n self.browser.get(self.profile_start_site + str(player_id))\n enemy_stats = {\n 'level': int(self.browser.find_element_by_class_name(self.classes['enemyLevel']).text.split(' ')[1]),\n 'style': int(self.browser.find_element_by_css_selector(self.selectors['enemyStyle']).text),\n 'creativity': int(self.browser.find_element_by_css_selector(self.selectors['enemyCreativity']).text),\n 'devotion': int(self.browser.find_element_by_css_selector(self.selectors['enemyDevotion']).text),\n 'beauty': int(self.browser.find_element_by_css_selector(self.selectors['enemyBeauty']).text),\n 'generosity': int(self.browser.find_element_by_css_selector(self.selectors['enemyGenerosity']).text),\n 'loyalty': int(self.browser.find_element_by_css_selector(self.selectors['enemyLoyalty']).text)\n }\n logging.debug('enemylevel ' + self.browser.find_element_by_class_name(self.classes['enemyLevel']).text)\n logging.debug(self.stats['level'])\n logging.debug(enemy_stats['level'])\n if (self.stats['level'] > enemy_stats['level'] >= self.stats['level'] - self.level_treshold and\n self.stats['style'] > enemy_stats['style'] and\n self.stats['creativity'] > enemy_stats['creativity'] and\n self.stats['devotion'] > enemy_stats['devotion'] and\n self.stats['beauty'] > enemy_stats['beauty'] and\n self.stats['generosity'] > enemy_stats['generosity']):\n return True\n else:\n return False\n def check_potential_players(self):\n \"\"\"\n checks every person from previously gathered list of potential players\n if the function check_player() returns True\n if yes, then append this person to enemy_list\n \"\"\"\n for enemy_id in self.potential_enemies:\n if self.check_player(enemy_id):\n self.enemy_list[enemy_id] = [0,0]\n self.save_enemy_list()\n else:\n self.checked_enemies.append(enemy_id)\n self.save_checked_enemies()\n\n def recheck_people_from_enemy_list(self):\n \"\"\"\n This function deletes every enemy with level lower than my_level - level_treshold and\n bigger than my_level\n \"\"\"\n to_del = []\n for enemy_id in self.enemy_list:\n if self.check_player(enemy_id):\n pass\n else:\n to_del.append(enemy_id)\n self.checked_enemies.append(enemy_id)\n for _ in to_del:\n del self.enemy_list[_]\n self.save_enemy_list()\n self.save_checked_enemies()\n def recheck_people_from_checked_list(self):\n \"\"\"\n this function rechecks every character from checked_list if he satisfies the condition\n my_level > enemy_level > my_level - self.treshold\n \"\"\"\n to_del = []\n for enemy_id in self.checked_enemies:\n if self.check_player(enemy_id):\n to_del.append(enemy_id)\n for _ in to_del:\n self.enemy_list[_] = [0, 0]\n self.checked_enemies.remove(_)\n self.save_checked_enemies()\n self.save_enemy_list()\n", "sub_path": "FindEnemiesBot.py", "file_name": "FindEnemiesBot.py", "file_ext": "py", "file_size_in_byte": 7099, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "Bot.Bot", "line_number": 14, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "json.load", "line_number": 40, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 56, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 56, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 56, "usage_type": "name"}, {"api_name": "selenium.common", "line_number": 57, "usage_type": "attribute"}, {"api_name": "selenium.common", "line_number": 67, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 107, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 108, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "118713164", "text": "#!/usr/bin/env/ python\n#-*- coding:utf-8-*-\n\nimport tensorflow as tf\nimport keras\nfrom keras.applications import InceptionV3\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.optimizers import Adam\n\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport sys, os, copy, random, argparse, gc, glob, re\n\nfrom scipy.stats import truncnorm \nfrom numpy.random import choice \nfrom logging import getLogger, DEBUG, INFO, StreamHandler, basicConfig, FileHandler, Formatter\n\n# InceptionV3の畳み込み層数\nLAYER_NUM = 94\n\n# SEED値の固定\nnp.random.seed(seed=0)\ntf.set_random_seed(0)\n\ndef main(args):\n\n pre_model=InceptionV3(weights='imagenet',include_top=True)\n\n image_shape=(args.image_shape,args.image_shape,3)\n\n # ImageDataGenerator インスタンスを作成\n train_datagen = ImageDataGenerator(\n featurewise_center=False, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=False, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n zca_whitening=False, # apply ZCA whitening\n rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)\n width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\n height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\n horizontal_flip=True, # randomly flip images\n vertical_flip=False) #\n\n test_datagen=ImageDataGenerator()\n\n train_generator = train_datagen.flow_from_directory(\n os.path.join(args.dataset_dir, 'train'), \n target_size=image_shape[:2], \n batch_size=args.batch_size, \n class_mode='categorical')\n\n test_generator = test_datagen.flow_from_directory(\n os.path.join(args.dataset_dir, 'test'), \n target_size=image_shape[:2], \n batch_size=args.batch_size, \n class_mode='categorical')\n\n\n \n # 畳み込み層の層番号\n convolutional_layer_index = [i for i in range(LAYER_NUM)]\n\n for generation in range(args.generation_num):\n logger.info('{}th generation START'.format(generation))\n generation_start_time = datetime.datetime.now()\n\n # 第１世代の時は全て初期化した遺伝型を用いる\n if generation == 0:\n # 学習可能層の番号を重複なしで選ぶ\n adjustable_layer_index = random.sample(convolutional_layer_index,args.init_gene_num)\n survived_genes = initialize_genes(adjustable_layer_index)\n logger.info('Inititalized genes') \n\n # どの遺伝型を表す、学習可能層の番号を格納\n gene_number = []\n # 各遺伝型での適応率を格納\n gene_scores = []\n # 学習履歴を保存\n histories = []\n\n for gene in survived_genes:\n # 学習可能層の番号 \n adjustable_layer = [i+1 for i, x in enumerate(gene) if x == 1]\n gene_number.append(adjustable_layer)\n\n model = gene2model(pre_model, args.n_classes, gene)\n logger.info('{} layer adjustable gene model created'.format(adjustable_layer))\n model.compile(loss='categorical_crossentropy',\n optimizer=Adam(),\n metrics=['accuracy'])\n\n # 学習を実行 \n logger.info('{} layer trainable model training START'.format(adjustable_layer))\n # 学習開始時間 \n train_start_time = datetime.datetime.now()\n\n history=model.fit_generator(\n train_generator,\n steps_per_epoch=args.num_images_train//args.batch_size,\n epochs=args.epochs,\n validation_data=test_generator,\n validation_steps=args.num_images_test//args.batch_size,\n use_multiprocessing=args.use_multiprocessing,\n workers=1,\n verbose=1)\n\n # 学習終了時間\n train_elapsed_time = datetime.datetime.now()\n logger.info('{} layer trainable model training END time: {}'.format(adjustable_layer, train_elapsed_time - train_start_time))\n \n logger.info('{} layer adjustable model train acc: {}, test acc: {}'.format(adjustable_layer,\n history.history['acc'][-1],\n history.history['val_acc'][-1]))\n gene_scores.append(history.history['acc'][-1])\n \n # 最終的な正解率と損失関数の値を配列にする\n history=np.array([adjustable_layer,\n history.history['loss'][-1],\n history.history['acc'][-1],\n history.history['val_loss'][-1],\n history.history['val_acc'][-1]])\n\n histories.append(history)\n\n # 世代ごとに遺伝方の学習結果を保存\n history_save_path = os.path.join(SAVE_DIR, '{}th_generation_result.csv'.format(generation))\n result = pd.DataFrame(histories, columns=['adjsutable_layer','loss', 'acc', 'val_loss', 'val_acc'])\n result.to_csv(history_save_path,index=False)\n\n # エリート遺伝型のみをとりだす\n elite_idx = np.argsort(gene_scores)[::-1][:args.init_gene_num//2]\n elite_gene_numbers = [gene_number[i] for i in elite_idx]\n logger.info('elite genes {}'.format(elite_gene_numbers))\n\n # 交叉\n child_genes = cross_over_elite_select(elite_gene_numbers)\n child_gene_numbers = []\n for gene in child_genes:\n adjustable_layer = [i+1 for i, x in enumerate(gene) if x == 1]\n child_gene_numbers.append(adjustable_layer)\n logger.info('child genes {}'.format(child_gene_numbers))\n\n # 次の世代に残すエリート遺伝型\n top_elite_genes = [survived_genes[i] for i in elite_idx][:len(child_genes)//2]\n\n # 新しく遺伝型を初期化\n new_adjustable_layer_index = random.sample(convolutional_layer_index,len(top_elite_genes))\n new_init_genes = initialize_genes(new_adjustable_layer_index)\n\n new_gene_numbers = []\n for gene in new_init_genes:\n adjustable_layer = [i+1 for i, x in enumerate(gene) if x == 1]\n new_gene_numbers.append(adjustable_layer)\n logger.info('new init genes {}'.format(new_gene_numbers))\n\n # 次の世代の遺伝型を作成\n next_generation_genes = []\n next_generation_genes.extend(child_genes)\n next_generation_genes.extend(top_elite_genes)\n next_generation_genes.extend(new_init_genes)\n\n logger.info('chile gene num:{}'.format(len(child_genes)))\n logger.info('top elite gene num:{}'.format(len(top_elite_genes)))\n logger.info('new init gene num:{}'.format(len(new_init_genes)))\n\n next_generation_genes = mutation(next_generation_genes, args.mutation_ratio)\n\n # 世代の移り変わり\n survived_genes = next_generation_genes.copy()\n\n generation_elapsed_time = datetime.datetime.now()\n logger.info('{}th generation END time: {}'.format(generation, generation_elapsed_time - generation_start_time))\n\n if generation == args.generation_num - 1:\n ensemble(pre_model, train_generator, test_generator, top_elite_genes)\n logger.info('{} experiment FINISH'.format(args.exp_num))\n\n\ndef ensemble(pre_model, train_generator, test_generator, top_elite_genes):\n\n logger.info('{} model ensemble START'.format(len(top_elite_genes)))\n\n # 各遺伝型でのテストデータへの予測確率を求める\n test_pred_datagen = ImageDataGenerator()\n\n # batch_size=1にすることで１枚ずつの予測を行う。Shuffle=Falseでクラス１から順番に生成される。同じ順番で画像が生成される。\n test_pred_generator = test_pred_datagen.flow_from_directory(\n os.path.join(args.dataset_dir, 'test'), \n target_size=(args.image_shape, args.image_shape), \n batch_size=1, \n class_mode='categorical',\n shuffle=False)\n\n # テストデータの正解ラベルを格納\n y_labels = []\n img_num = 0\n for _ , y in test_pred_generator:\n if img_num >= args.num_images_test: \n break\n label = list(y[0]).index([1])\n y_labels.append(label)\n img_num += 1\n\n # 遺伝型ごとに訓練\n for num, gene in enumerate(top_elite_genes):\n\n adjustable_layer = [i+1 for i, x in enumerate(gene) if x == 1]\n\n model = gene2model(pre_model, args.n_classes, gene) \n model.compile(loss='categorical_crossentropy',\n optimizer=Adam(),\n metrics=['accuracy'])\n \n # 学習を実行 \n logger.info('{} layer trainable model training START'.format(adjustable_layer))\n\n # 学習開始時間 \n train_start_time = datetime.datetime.now()\n\n # generator の初期化\n train_generator.reset(), test_generator.reset()\n\n history=model.fit_generator(\n train_generator,\n steps_per_epoch=args.num_images_train//args.batch_size,\n epochs=args.ensemble_epochs,\n validation_data=test_generator,\n validation_steps=args.num_images_test//args.batch_size,\n use_multiprocessing=args.use_multiprocessing,\n workers=1,\n verbose=1)\n\n # 学習終了時間\n train_elapsed_time = datetime.datetime.now()\n logger.info('No.{} gene, {} trainable model training END time: {}'.format(num+1,\n adjustable_layer,\n train_elapsed_time - train_start_time))\n \n logger.info('No.{} gene, {} layer adjustable model train acc: {}, test acc: {}'.format(num+1, \n adjustable_layer,\n history.history['acc'][-1],\n history.history['val_acc'][-1]))\n\n # 遺伝型ごとの結果を保存\n result = pd.DataFrame(history.history)\n history_save_path = os.path.join(SAVE_DIR, 'No{}_{}_adjustable_{}epoch_history.csv'.format(num+1, adjustable_layer, args.ensemble_epochs))\n result.to_csv(history_save_path)\n logger.info('No{}_{}_adjustable_{}epoch_history.csv SAVED'.format(num+1, adjustable_layer, args.ensemble_epochs))\n\n model_save_path = os.path.join(SAVE_DIR, 'No{}_{}_adjustable_{}epoch_model.h5'.format(num+1, adjustable_layer, args.ensemble_epochs))\n model.save(model_save_path)\n logger.info('No{}_{}_adjustable_{}epoch_model.h5 SAVED'.format(num+1, adjustable_layer, args.ensemble_epochs))\n\n # generatorの初期化\n test_pred_generator.reset()\n\n logger.info('No.{} gene, {} layer adjustable model prediction START'.format(num+1, adjustable_layer))\n\n # テストデータ１画像ずつ予測確率を求める。stepsにテストデータ数全体を入れる。 \n predict = model.predict_generator(test_pred_generator, steps=args.num_images_test)\n logger.info('No.{} gene, {} layer adjustable model prediction END'.format(num+1, adjustable_layer))\n\n prob = pd.DataFrame(predict)\n prob['label'] = y_labels\n predict_save_path = os.path.join(SAVE_DIR, 'No{}_{}_adjustable_{}epoch_test_predict.csv'.format(num+1, adjustable_layer, args.ensemble_epochs))\n prob.to_csv(predict_save_path, index=False)\n \n del model\n gc.collect()\n\n predictions_path = os.path.join(SAVE_DIR, '*test_predict.csv')\n prediction_files = glob.glob(predictions_path)\n\n if args.voting == 'hard':\n accuracy = hard_voting(prediction_files, y_labels)\n\n elif args.voting == 'soft':\n accuracy = soft_voting(prediction_files, y_labels)\n\n else:\n raise ValueError('invalid voting method')\n\n logger.info('{} models ensemble test accuracy: {}'.format(len(prediction_files), accuracy))\n logger.info('{} model ensemble END'.format(len(top_elite_genes)))\n\n\n# hard voting アンサンブル\ndef hard_voting(prediction_files, y_labels):\n\n logger.info('{} models hard voting START'.format(len(prediction_files)))\n total = pd.DataFrame()\n for i, pred_file in enumerate(prediction_files):\n df = pd.read_csv(pred_file)\n total['num'+str(i+1)] = df.iloc[:, :-1].idxmax(axis=1).astype(int).values\n \n # 最頻値を予測ラベルとする \n hard_voting_pred = total.mode(axis=1)[0]\n accuracy = sum(hard_voting_pred == y_labels) / args.num_images_test\n\n logger.info('{} models hard voting END'.format(len(prediction_files)))\n return accuracy\n \n# soft voting アンサンブル\ndef soft_voting(prediction_files, y_labels):\n\n logger.info('{} models soft voting START'.format(len(prediction_files)))\n\n total = pd.DataFrame(np.zeros((args.num_images_test, args.n_classes))) \n total.columns = [str(i) for i in range(args.n_classes)]\n\n for pred_file in prediction_files:\n df = pd.read_csv(pred_file)\n total = total + df.iloc[:,:-1]\n \n # 各モデルの予測確率の平均値から予測ラベルを決定する\n total = total/len(prediction_files)\n soft_voting_pred = total.idxmax(axis=1).values.astype(int)\n accuracy = sum(soft_voting_pred == y_labels) / args.num_images_test\n\n logger.info('{} models soft voting END'.format(len(prediction_files)))\n return accuracy\n\n# 突然変異\ndef mutation(genes, mutation_ratio):\n mutated_genes = []\n for gene in genes:\n if random.random() < mutation_ratio:\n logger.info('mutation!!!')\n\n # ランダムに一部分を値を反転させる\n mutation_idx = random.choice([i for i in range(len(gene)-1)])\n gene[mutation_idx] = 1 if gene[mutation_idx]==0 else 0\n mutated_genes.append(gene)\n\n return mutated_genes\n\n# 交叉\ndef cross_over_elite_select(elite_gene_numbers):\n child_genes = []\n for i in range(-1,len(elite_gene_numbers)-1):\n new_gene = [1 if (layer+1) in elite_gene_numbers[i] or (layer+1) in elite_gene_numbers[i+1] else 0 \n for layer in range(LAYER_NUM)] \n new_gene.extend([1]) \n child_genes.append(new_gene)\n return child_genes\n\n# 遺伝型の初期化\ndef initialize_genes(adjustable_layer_index):\n genes_list = []\n for idx in adjustable_layer_index:\n gene = [1 if i==idx else 0 for i in range(LAYER_NUM)]\n gene.extend([1])\n genes_list.append(gene)\n return genes_list\n\ndef gene2model(pre_model, n_classes, gene):\n\n #tmp_li_is_weighted はbool型のリストをもつ。重みがない層はTrueを入れる\n tmp_li_is_weighted = [is_weighted(l) for l in pre_model.layers]\n \n #i_last_weightedには数字が入る. 最後の重みのある層 全結合層のindex番号を取得\n i_last_weighted = -1 - tmp_li_is_weighted[::-1].index(True)\n\n tmp_li_is_weighted = tmp_li_is_weighted[:i_last_weighted]\n \n x = pre_model.layers[i_last_weighted-1].output\n\n #出力層のユニット数を分類クラス数、活性化関数をソフトマックス関数にする\n predictions = keras.layers.Dense(n_classes, activation='softmax', name='dense_top')(x)\n\n #学習済みモデルの出力層以外と転移学習用の出力層を結合'\n tmp_model = keras.models.Model(inputs=pre_model.input, outputs=predictions)\n \n li_is_weighted = [is_weighted(l) for l in tmp_model.layers]\n\n # 遺伝型の長さは94 + 1 層\n i_gene = 0\n for i_layer in range(len(tmp_model.layers)):\n # 畳み込み層 or 全結合層の場合に操作を行う \n if li_is_weighted[i_layer]:\n if gene[i_gene] == 0:\n tmp_model.layers[i_layer].trainable=False\n\n elif gene[i_gene] == 1:\n tmp_model.layers[i_layer].trainable=True\n \n if i_gene == len(gene) - 1:\n logger.info('output layer trainable')\n \n else:\n logger.info('{} convolutional layer trainable'.format(i_gene + 1))\n else:\n sys.exit('invalid gene value %s' % gene)\n \n # geneのイテレータを増やす\n i_gene += 1\n\n return tmp_model\n\n\n# 重みのあるレイヤーがTrueになるリストを返す\ndef is_weighted(layer):\n\n not_weighted = len(layer.weights) == 0 or len(layer.weights) == 4 or len(layer.weights) == 3\n #重みがある場合Trueを返す \n return not(not_weighted)\n\n\nif __name__ == '__main__':\n\n # parserの設定\n parser = argparse.ArgumentParser(description='elite_selection_beta')\n \n parser.add_argument('dataset_dir')\n parser.add_argument('n_classes', type=int, help='target_task_classes')\n parser.add_argument('image_shape', type=int, default=299, help='pre_trainded model input shape') \n parser.add_argument('num_images_test',type=int, default=10000)\n parser.add_argument('exp_num', type=int,default=1, help='experiment number')\n parser.add_argument('--init_gene_num', type=int, default=20, help='initial gene number')\n parser.add_argument('--num_images_train', type=int, default=50000, help='number of images for training')\n parser.add_argument('--batch_size', type=int, default=16)\n parser.add_argument('--epochs', type=int, default=1, help='epoch number for train')\n parser.add_argument('--ensemble_epochs', type=int, default=5, help='ensemble epoch number for train')\n parser.add_argument('--use_multiprocessing',action='store_true')\n parser.add_argument('--mutation_ratio', type=int, default=0.01)\n parser.add_argument('--generation_num', type=int, default=5)\n parser.add_argument('--voting', type=str, default='soft', help='ensemble method, hard or soft')\n\n args = parser.parse_args()\n\n # 実行時の現在時刻を取得\n EXECUTION_TIME = datetime.datetime.now().strftime(\"%Y-%m-%d-%H:%M:%S\")\n\n namespace = str(args)\n arguments = re.findall(r'\\(.+?\\)', namespace)[-1]\n arguments = arguments.strip('()')[:-1]\n\n # ファイルの出力先フォルダを作成\n SAVE_DIR = os.path.join('elite_selection', 'exp'+str(args.exp_num))\n if not os.path.exists(SAVE_DIR):\n os.mkdir(SAVE_DIR)\n \n # 実験条件ファイルを作成\n description_file = os.path.join(SAVE_DIR, 'experiment_description')\n with open(description_file, 'a+') as f:\n f.write('execution time: %s \\n' % EXECUTION_TIME)\n f.write('======arguments======\\n')\n for argument in arguments.split(','):\n f.write(argument + '\\n')\n\n # loggerの設定\n logger = getLogger(__name__)\n logger.setLevel(DEBUG)\n\n # Format\n formatter = '%(levelname)s : %(asctime)s : %(message)s'\n format = Formatter(formatter)\n\n # FileHandler\n log_file = EXECUTION_TIME + '-' + os.path.basename(__file__) + '.log'\n log_path = os.path.join(SAVE_DIR, log_file)\n fh = FileHandler(log_path)\n fh.setFormatter(format)\n logger.addHandler(fh)\n\n # StreamHandler\n sh = StreamHandler()\n sh.setLevel(DEBUG)\n sh.setFormatter(format)\n logger.addHandler(sh)\n\n main(args) \n\n\n", "sub_path": "GA_layer_selection/ensemble.py", "file_name": "ensemble.py", "file_ext": "py", "file_size_in_byte": 19807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "numpy.random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.set_random_seed", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.applications.InceptionV3", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 96, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 132, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 172, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 172, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adam", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 219, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 219, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 235, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 235, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 246, "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": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 273, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 292, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 294, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 309, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 313, "usage_type": "call"}, {"api_name": "random.random", "line_number": 328, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 332, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 370, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 370, "usage_type": "attribute"}, {"api_name": "keras.models.Model", "line_number": 373, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 373, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 394, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 413, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 433, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 433, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 436, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 440, "usage_type": "call"}, {"api_name": "os.path", "line_number": 440, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 441, "usage_type": "call"}, {"api_name": "os.path", "line_number": 441, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 442, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path", "line_number": 445, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 453, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 454, "usage_type": "argument"}, {"api_name": "logging.Formatter", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path", "line_number": 461, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 462, "usage_type": "call"}, {"api_name": "os.path", "line_number": 462, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 463, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 468, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 469, "usage_type": "argument"}]} +{"seq_id": "19062242", "text": "from django.contrib.auth.models import User\nfrom django.core.management.base import BaseCommand\nfrom django.urls import reverse\nfrom django.core.files import File\nfrom mainapp.models import Menu, Post, Article, PostPhoto, Tag, Category\nfrom mainapp.models import Contact, Document\nfrom django.conf import settings\nfrom mixer.backend.django import mixer\nimport random\nfrom django.utils import timezone\n# from model_mommy.recipe import Recipe, foreign_key, seq\n\ntry:\n popov_user = User.objects.get(username='popov')\nexcept:\n popov_user=User.objects.create(username='popov', email='popov@naks.ru', password='2011')\n\nimages = [\n 'media/01.JPG',\n 'media/02.JPG',\n 'media/03.JPG',\n 'media/04.JPG',\n 'media/05.JPG',\n 'media/06.JPG',\n]\n\nnews_titles = [\n 'Конференция НАКС',\n 'Общее собрание',\n 'Семинар НАКС',\n 'Вебинар НАКС',\n]\n\ndocuments = [\n 'media/document1.doc',\n 'media/document2.doc',\n 'media/document3.doc',\n 'media/document4.doc',\n 'media/document5.doc',\n]\n\nmenu_urls = [\n 'ABOUT_US', 'ASSP', 'ASSV', 'ATTSP', 'ATTST', 'COK', 'CONTACT', 'DOKZAYAV',\n 'INFO', 'OBLD', 'OBLDATT', 'PROFST', 'REGISTRY', 'RKNK', 'SPECSVAR', 'VSENOVOSTI', 'ZAYAV', 'SOSTAV_KOMISS'\n]\nmenu_urls_titles = [\n 'О центре', 'Аттестация сварщиков и специалистов', 'Аттестация сварщиков', \n 'Аттестация специалистов', 'Аттестация сварочных технологий', 'Центр оценки квалификации',\n 'Контакты', 'Документы и заявки', 'Информация для заявителей', 'Область деятельности', \n 'Область аттестации', 'Профессиональные стандарты', 'Реестры', 'Разрушающий и неразрушающий контроль', \n 'Спецподготовка сварщиков', 'Все новости', 'Заявки', 'Состав комиссии',\n]\n\nprofstandards = [\n {'title': 'Сварщик' , 'ps_code': '40.002', 'reg_number': '14', 'mintrud_reg': 'Приказ Минтруда России № 701н от 28.11.2013 г., зарегистрирован Минюстом России 13.02.2014г., рег. № 31301'},\n {'title': 'Специалист сварочного производства' , 'ps_code': '40.002', 'reg_number': '15', 'mintrud_reg': 'Приказ Минтруда России № 701н от 28.11.2013 г., зарегистрирован Минюстом России 13.02.2014г., рег. № 31301'},\n {'title': 'Контролер сварочных работ', 'ps_code': '40.002', 'reg_number': '16', 'mintrud_reg': 'Приказ Минтруда России № 701н от 28.11.2013 г., зарегистрирован Минюстом России 13.02.2014г., рег. № 31301'},\n {'title': 'Специалист неразрушающего контроля', 'ps_code': '40.002', 'reg_number': '16', 'mintrud_reg': 'Приказ Минтруда России № 701н от 28.11.2013 г., зарегистрирован Минюстом России 13.02.2014г., рег. № 31301'},\n {'title': 'Термист', 'ps_code': '40.002', 'reg_number': '16', 'mintrud_reg': 'Приказ Минтруда России № 701н от 28.11.2013 г., зарегистрирован Минюстом России 13.02.2014г., рег. № 31301'}\n]\n\ndocument_titles = [\n 'Постановление Госгортехнадзора России №36 от 25.06.2002г. Об утверждении \\\n новой редакции \"Технологического регламента проведения аттестации \\\n сварщиков и специалистов сварочного производства\"',\n 'ПБ-03-273-99 \"Правила аттестации сварщиков и специалистов \\\n сварочного производства',\n 'Положение о порядке продления срока действия аттестационных удостоверений \\\n сварщиков и специалистов сварочного производства',\n 'Перечень групп технических устройств опасных производственных объектов',\n 'Инструкция по оформлению заявок на аттестацию заявителей - физических лиц',\n]\n\nclass Command(BaseCommand):\n def handle(self, *args, **options):\n #delete all Posts, Articles, Menus and other\n Tag.objects.all().delete()\n Category.objects.all().delete()\n Menu.objects.all().delete()\n Post.objects.all().delete()\n Article.objects.all().delete()\n PostPhoto.objects.all().delete()\n Document.objects.all().delete()\n Contact.objects.all().delete()\n\n #make PostPhotos\n for i in range(0, len(images)):\n #make Tags\n mixer.blend(Tag),\n #make Categories\n mixer.blend(Category),\n #make Posts without pictures\n mixer.blend(Post, title=random.choice(news_titles), published_date=timezone.now())\n mixer.blend(PostPhoto,\n image=File(open(images[i], 'rb')))\n #make Articles\n mixer.blend(Article, author=popov_user),\n mixer.blend(Contact)\n # for i in range(0, len(profstandards)):\n # mixer.blend(Profstandard, title=profstandards[i]['title'],\n # reg_number=profstandards[i]['reg_number'],\n # mintrud_reg=profstandards[i]['mintrud_reg'],\n # ps_code=profstandards[i]['ps_code'],\n # document=documents[i])\n\n for i in range(0, len(documents)):\n mixer.blend(Document, document=File(open(documents[i], 'rb')),\n title=document_titles[i], post=random.choice(Post.objects.all()))\n\n #make Menus\n for i in range(0, len(menu_urls)):\n mixer.blend(Menu, url_code=menu_urls[i], url=reverse(\n 'detailview', kwargs={'content': 'post', 'pk': Post.objects.first().pk}),\n title=menu_urls_titles[i])\n # for i in range(0, len(images)):\n # PostPhoto.objects.create(\n # title ='image{}'.format(i),\n # image=File(open(images[i], 'rb')))\n print('*********fill_db_complete************')", "sub_path": "mainapp/management/commands/fill_db.py", "file_name": "fill_db.py", "file_ext": "py", "file_size_in_byte": 6667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 74, "usage_type": "name"}, {"api_name": "mainapp.models.Tag.objects.all", "line_number": 77, "usage_type": "call"}, {"api_name": "mainapp.models.Tag.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "mainapp.models.Tag", "line_number": 77, "usage_type": "name"}, {"api_name": "mainapp.models.Category.objects.all", "line_number": 78, "usage_type": "call"}, {"api_name": "mainapp.models.Category.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "mainapp.models.Category", "line_number": 78, "usage_type": "name"}, {"api_name": "mainapp.models.Menu.objects.all", "line_number": 79, "usage_type": "call"}, {"api_name": "mainapp.models.Menu.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "mainapp.models.Menu", "line_number": 79, "usage_type": "name"}, {"api_name": "mainapp.models.Post.objects.all", "line_number": 80, "usage_type": "call"}, {"api_name": "mainapp.models.Post.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "mainapp.models.Post", "line_number": 80, "usage_type": "name"}, {"api_name": "mainapp.models.Article.objects.all", "line_number": 81, "usage_type": "call"}, {"api_name": "mainapp.models.Article.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "mainapp.models.Article", "line_number": 81, "usage_type": "name"}, {"api_name": "mainapp.models.PostPhoto.objects.all", "line_number": 82, "usage_type": "call"}, {"api_name": "mainapp.models.PostPhoto.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "mainapp.models.PostPhoto", "line_number": 82, "usage_type": "name"}, {"api_name": "mainapp.models.Document.objects.all", "line_number": 83, "usage_type": "call"}, {"api_name": "mainapp.models.Document.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "mainapp.models.Document", "line_number": 83, "usage_type": "name"}, {"api_name": "mainapp.models.Contact.objects.all", "line_number": 84, "usage_type": "call"}, {"api_name": "mainapp.models.Contact.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "mainapp.models.Contact", "line_number": 84, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 89, "usage_type": "call"}, {"api_name": "mainapp.models.Tag", "line_number": 89, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 89, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 91, "usage_type": "call"}, {"api_name": "mainapp.models.Category", "line_number": 91, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 91, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 93, "usage_type": "call"}, {"api_name": "mainapp.models.Post", "line_number": 93, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 93, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 93, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 93, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 93, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 94, "usage_type": "call"}, {"api_name": "mainapp.models.PostPhoto", "line_number": 94, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 94, "usage_type": "name"}, {"api_name": "django.core.files.File", "line_number": 95, "usage_type": "call"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 97, "usage_type": "call"}, {"api_name": "mainapp.models.Article", "line_number": 97, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 97, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 98, "usage_type": "call"}, {"api_name": "mainapp.models.Contact", "line_number": 98, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 98, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 107, "usage_type": "call"}, {"api_name": "mainapp.models.Document", "line_number": 107, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 107, "usage_type": "name"}, {"api_name": "django.core.files.File", "line_number": 107, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 108, "usage_type": "call"}, {"api_name": "mainapp.models.Post.objects.all", "line_number": 108, "usage_type": "call"}, {"api_name": "mainapp.models.Post.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "mainapp.models.Post", "line_number": 108, "usage_type": "name"}, {"api_name": "mixer.backend.django.mixer.blend", "line_number": 112, "usage_type": "call"}, {"api_name": "mainapp.models.Menu", "line_number": 112, "usage_type": "argument"}, {"api_name": "mixer.backend.django.mixer", "line_number": 112, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 112, "usage_type": "call"}, {"api_name": "mainapp.models.Post.objects.first", "line_number": 113, "usage_type": "call"}, {"api_name": "mainapp.models.Post.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "mainapp.models.Post", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "515220292", "text": "#!/usr/bin/env python\n# coding:utf-8\nimport os\nimport sys\nimport time\nimport signal\n\nimport cv2\nimport numpy as np\n\nfrom cyber_py3 import cyber\n\nfrom modules.planning.proto.planning_pb2 import Trajectory\nfrom modules.planning.proto.planning_pb2 import Point\n\nfrom modules.localization.proto.localization_pb2 import localization\nfrom modules.localization.proto.localization_pb2 import pos\n\npoint_xy = Point()\n\nmaps = cv2.imread(\"maps1.jpeg\", cv2.IMREAD_GRAYSCALE) # 读取地图图像，灰度读入。灰度为0表示障碍物\nmaps_size = np.array(maps) # 获取图像行和列大小\nhight = maps_size.shape[0] # 行数->y\nwidth = maps_size.shape[1] # 列数->x\nscale = 144.9\n\n\nclass planning(object):\n def __init__(self, node):\n self.node = node\n self.start_x = 0\n self.start_y = 0\n self.goal_x = 0\n self.goal_y = 0\n self.node.create_reader(\"/geek/uwb/localization\", pos,\n self.localizationcallback)\n self.node.create_reader(\"/planning/mission_point\", Point,\n self.missioncallback)\n self.writer = self.node.create_writer(\"/planning/global_trajectory\",\n Trajectory)\n\n signal.signal(signal.SIGINT, self.sigint_handler)\n signal.signal(signal.SIGHUP, self.sigint_handler)\n signal.signal(signal.SIGTERM, self.sigint_handler)\n self.is_sigint_up = False\n while True:\n time.sleep(0.05)\n if self.is_sigint_up:\n print(\"Exit!\")\n self.is_sigint_up = False\n sys.exit()\n\n def sigint_handler(self, signum, frame):\n self.is_sigint_up = True\n print(\"catch interrupt signal!\")\n\n def localizationcallback(self, pos):\n self.start_x = int(pos.x * scale)\n self.start_y = int(pos.y * scale)\n\n def missioncallback(self, Point):\n self.goal_x = int(Point.x)\n self.goal_y = int(Point.y)\n\n pathList = self.start(self.start_x, self.start_y, self.goal_x,\n self.goal_y)\n\n self.planning_path = Trajectory()\n if not pathList:\n print(\"Failed to find a path\")\n else:\n for path_point in pathList:\n point_xy.x = path_point[0]\n point_xy.y = path_point[1]\n\n self.planning_path.point.append(point_xy)\n\n if not cyber.is_shutdown() and self.planning_path:\n self.writer.write(self.planning_path)\n\n def start(self, start_x, start_y, goal_x, goal_y):\n\n if not os.path.exists('global.txt'):\n f = open(\"global.txt\", 'w')\n f.close()\n\n plan_path = []\n maps_size = np.array(maps) # 获取图像行和列大小\n height = maps_size.shape[0] # 行数->y\n width = maps_size.shape[1] # 列数->x\n\n star = {\n 'position': (start_x, start_y),\n 'cost': 700,\n 'parent': (start_x, start_y)\n } # 起点\n end = {\n 'position': (goal_x, goal_y),\n 'cost': 0,\n 'parent': (goal_x, goal_y)\n } # 终点\n\n print('start_point:', [start_x, start_y])\n print('end_point:', [goal_x, goal_y])\n\n if maps[start_y, start_x] == 255:\n print(\"error: 非法起点\")\n return []\n\n if maps[goal_y, goal_x] == 255:\n print(\"error: 非法终点\")\n return []\n\n openlist = [] # open列表，存储可能路径\n closelist = [star] # close列表，已走过路径\n step_size = 10 # 搜索步长\n step_size_scan = 1\n safe_size = step_size\n # 步长太小，搜索速度就太慢。步长太大，可能直接跳过障碍，得到错误的路径\n # 步长大小要大于图像中最小障碍物宽度\n time_start = time.time()\n\n while 1:\n s_point = closelist[-1]['position'] # 获取close列表最后一个点位置，S点\n\n add = ([0, step_size], [0, -step_size], [step_size, 0],\n [-step_size, 0], [-step_size,\n step_size], [step_size, -step_size],\n [step_size, step_size], [-step_size,\n -step_size]) # 可能运动的四个方向增量\n\n add_scan = ([0, step_size_scan], [0, -step_size_scan],\n [step_size_scan,\n 0], [-step_size_scan,\n 0], [-step_size_scan, step_size_scan\n ], [step_size_scan, -step_size_scan],\n [step_size_scan,\n step_size_scan], [-step_size_scan, -step_size_scan])\n\n lane_point = []\n current_point = [s_point[0], s_point[1]]\n\n n = 1\n\n if maps[s_point[1], s_point[0]] >= 120 and maps[s_point[1],\n s_point[0]] <= 140:\n lane_point = [s_point[0], s_point[1]]\n\n while lane_point == []:\n for i in range(len(add_scan)):\n x = current_point[0] + n * add_scan[i][0] # 检索超出图像大小范围则跳过\n if x < 0 or x >= width:\n continue\n y = current_point[1] + n * add_scan[i][1]\n if y < 0 or y >= height: # 检索超出图像大小范围则跳过\n continue\n\n if maps[y, x] >= 120 and maps[y, x] <= 140:\n lane_point = [x, y]\n break\n\n n += 1\n\n if lane_point != []:\n break\n\n for i in range(len(add)):\n x = s_point[0] + add[i][0] # 检索超出图像大小范围则跳过\n if x < 0 or x >= width:\n continue\n y = s_point[1] + add[i][1]\n if y < 0 or y >= height: # 检索超出图像大小范围则跳过\n continue\n\n G = abs(x - star['position'][0]) + abs(\n y - star['position'][1]) # 计算代价\n H = abs(x - end['position'][0]) + abs(\n y - end['position'][1]) # 计算代价\n\n I = abs(x - lane_point[0]) + abs(y - lane_point[1])\n\n if (maps[y, x] >= 120 and maps[y, x] <= 140) or (\n (x - end['position'][0])**2 +\n (y - end['position'][1])**2)**0.5 < safe_size:\n G_I = 0\n else:\n G_I = I * 30\n\n F = G + H + G_I\n\n if ((x - end['position'][0])**2 + (y - end['position'][1])**\n 2)**0.5 <= step_size: # 当逐渐靠近终点时，搜索的步长变小\n step_size = 1\n\n addpoint = {\n 'position': (x, y),\n 'cost': F,\n 'parent': s_point\n } # 更新位置\n count = 0\n for i in openlist:\n if i['position'] == addpoint['position']:\n count += 1\n for i in closelist:\n if i['position'] == addpoint['position']:\n count += 1\n if count == 0: # 新增点不在open和close列表中\n if maps[int(y), int(x)] != 255: # 非障碍物\n openlist.append(addpoint)\n t_point = {'position': (50, 50), 'cost': 10000, 'parent': (50, 50)}\n for j in range(len(openlist)): # 寻找代价最小点\n if openlist[j]['cost'] < t_point['cost']:\n t_point = openlist[j]\n for j in range(len(openlist)): # 在open列表中删除t点\n if t_point == openlist[j]:\n openlist.pop(j)\n break\n closelist.append(t_point) # 在close列表中加入t点\n\n # cv2.circle(informap,t_point['位置'],1,(200,0,0),-1)\n if t_point['position'] == end['position']: # 找到终点！！\n print(\"find dest\")\n break\n\n if len(closelist) > ((1300 * 80) / step_size):\n print(\"Error: can not find the goal!\")\n break\n\n # 逆向搜索找到路径\n road = []\n road.append(closelist[-1])\n point = road[-1]\n k = 0\n\n while 1:\n print(\"3\")\n for i in closelist:\n if i['position'] == point['parent']: # 找到父节点\n point = i\n road.append(point)\n if point == star:\n print(\"search okay\")\n break\n\n for i in road: # 画出规划路径\n plan_path.append(i['position'])\n\n time_end = time.time()\n\n print('totally cost', time_end - time_start)\n\n f = open('global.txt', 'w')\n f.write(str(plan_path))\n f.close()\n\n return plan_path\n\n\nif __name__ == '__main__':\n\n cyber.init()\n cyber_node = cyber.Node(\"planning\")\n exercise = planning(cyber_node)\n\n cyber_node.spin()\n cyber.shutdown()\n", "sub_path": "exercise7-control/example/planning_a_star_exercise6_example.py", "file_name": "planning_a_star_exercise6_example.py", "file_ext": "py", "file_size_in_byte": 9280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "modules.planning.proto.planning_pb2.Point", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "modules.localization.proto.localization_pb2.pos", "line_number": 35, "usage_type": "argument"}, {"api_name": "modules.planning.proto.planning_pb2.Point", "line_number": 37, "usage_type": "argument"}, {"api_name": "modules.planning.proto.planning_pb2.Trajectory", "line_number": 40, "usage_type": "argument"}, {"api_name": "signal.signal", "line_number": 42, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 43, "usage_type": "call"}, {"api_name": "signal.SIGHUP", "line_number": 43, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 44, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 44, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 51, "usage_type": "call"}, {"api_name": "modules.localization.proto.localization_pb2.pos.x", "line_number": 58, "usage_type": "attribute"}, {"api_name": "modules.localization.proto.localization_pb2.pos", "line_number": 58, "usage_type": "name"}, {"api_name": "modules.localization.proto.localization_pb2.pos.y", "line_number": 59, "usage_type": "attribute"}, {"api_name": "modules.localization.proto.localization_pb2.pos", "line_number": 59, "usage_type": "name"}, {"api_name": "modules.planning.proto.planning_pb2.Point.x", "line_number": 62, "usage_type": "attribute"}, {"api_name": "modules.planning.proto.planning_pb2.Point", "line_number": 62, "usage_type": "name"}, {"api_name": "modules.planning.proto.planning_pb2.Point.y", "line_number": 63, "usage_type": "attribute"}, {"api_name": "modules.planning.proto.planning_pb2.Point", "line_number": 63, "usage_type": "name"}, {"api_name": "modules.planning.proto.planning_pb2.Trajectory", "line_number": 68, "usage_type": "call"}, {"api_name": "cyber_py3.cyber.is_shutdown", "line_number": 78, "usage_type": "call"}, {"api_name": "cyber_py3.cyber", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 121, "usage_type": "call"}, {"api_name": "time.time", "line_number": 248, "usage_type": "call"}, {"api_name": "cyber_py3.cyber.init", "line_number": 261, "usage_type": "call"}, {"api_name": "cyber_py3.cyber", "line_number": 261, "usage_type": "name"}, {"api_name": "cyber_py3.cyber.Node", "line_number": 262, "usage_type": "call"}, {"api_name": "cyber_py3.cyber", "line_number": 262, "usage_type": "name"}, {"api_name": "cyber_py3.cyber.shutdown", "line_number": 266, "usage_type": "call"}, {"api_name": "cyber_py3.cyber", "line_number": 266, "usage_type": "name"}]} +{"seq_id": "563868724", "text": "import pygame\nfrom util import *\nfrom animationController import *\nfrom enum import Enum\n\n\nclass Direction(Enum):\n\tNone_ = 0\n\tUp = 1\n\tDown = 2\n\tLeft = 3\n\tRight = 4\n\n\tdef state(self, moving):\n\t\tif moving:\n\t\t\tif self.name == 'None_':\n\t\t\t\treturn 'walk_back'\n\t\t\telif self.name == 'Up':\n\t\t\t \treturn\"walk_back\"\n\t\t\telif self.name == 'Down': \n\t\t\t\treturn \"walk_forward\"\n\t\t\telif self.name == \"Left\": \n\t\t\t\treturn \"walk_left\"\n\t\t\telif self.name == 'Right': \n\t\t\t\treturn \"walk_right\"\n\t\telse:\n\t\t\tif self.name == 'Up':\n\t\t\t\treturn \"stand_back\"\n\t\t\telif self.name == \"Down\":\n\t\t\t\treturn \"stand_forward\"\n\t\t\telif self.name == \"Left\":\n\t\t\t\treturn \"stand_left\"\n\t\t\telif self.name == 'Right':\n\t\t\t\treturn 'stand_right'\n\n\n\n\nclass Entity(object):\n\t\"\"\"docstring for Entity\"\"\"\n\tdef __init__(self, x, y):\n\t\tsuper(Entity, self).__init__()\n\t\tself.pos = Point(x,y)\n\t\tself.direction = Direction.None_\n\t\tself.animController = AnimationController()\n\t\tself.moving = False\n\n\tdef __move(self, direction, speed, timeStep, game):\n\t\tnewPos = Point(self.pos.x,self.pos.y)\n\t\tif self.direction == Direction.Up:\n\t\t\tnewPos.y-= speed * timeStep\n\t\telif self.direction == Direction.Down:\n\t\t\tnewPos.y+= speed * timeStep\n\t\telif self.direction == Direction.Left:\n\t\t\tnewPos.x-= speed * timeStep\n\t\telif self.direction == Direction.Right:\n\t\t\tnewPos.x+= speed * timeStep\n\n\t\tself.direction = direction\n\t\tself.moving = True\n\t\ttile = Point(newPos.x//game.map.tileSize, newPos.y//game.map.tileSize).int()\n\t\t\n\t\tif game.map.tileAt(tile[0],tile[1]).type != 1:\n\t\t\tself.pos = newPos\n\n\n\tdef moveUp(self, game, speed):\n\t\tself.__move(Direction.Up, speed, game.deltaTime, game)\n\t\t\n\tdef moveDown(self, game, speed):\n\t\tself.__move(Direction.Down, speed, game.deltaTime, game)\n\t\n\tdef moveLeft(self, game, speed):\n\t\tself.__move(Direction.Left, speed, game.deltaTime, game)\n\t\n\n\tdef moveRight(self, game, speed):\n\t\tself.__move(Direction.Right, speed, game.deltaTime, game)\n\t\n\n\tdef draw(self,game): \n\t\tdrawPos = game.getCameraPoint(self.pos)\n\t\t#pygame.draw.circle(game.screen,(0,0,255),drawPos,5)\n\n\t\tgame.screen.blit(self.animController.getFrame(),(int(drawPos[0]-64),int(drawPos[1]-128)))\n\nclass Player(Entity):\n\t\n\tdef __init__(self, x, y):\n\t\tsuper().__init__(x,y)\n\t\tself.moveSpeed = 4\n\t\t#self.direction = 0\n\t\tself.walkAnimationSpeed = 0.15\n\t\t#setting up player AnimationController\n\n\t\tframes = sliceTilemap(pygame.image.load(\"textures/PlayerTilemap.png\"), 32,(128,128))\n\n\t\tself.animController.addAnimationState(\"stand_forward\", [frames[16]],0)\n\t\tself.animController.addAnimationState(\"stand_back\", [frames[17]],0)\n\t\tself.animController.addAnimationState(\"stand_right\", [frames[18]],0)\n\t\tself.animController.addAnimationState(\"stand_left\", [frames[19]],0)\n\t\tself.animController.addAnimationState(\"walk_forward\", frames[0:4], self.walkAnimationSpeed, True)\n\t\tself.animController.addAnimationState(\"walk_back\", frames[4:8], self.walkAnimationSpeed, True)\n\t\tself.animController.addAnimationState(\"walk_right\", frames[8:12], self.walkAnimationSpeed, True)\n\t\tself.animController.addAnimationState(\"walk_left\", frames[12:16], self.walkAnimationSpeed, True)\n\t\t\n\t\n\t\t\n\tdef move(self,game):\n\t\t\n\t\t\n\t\tcurrentRoom = game.map.roomAt(self.pos.x//game.map.tileSize, self.pos.y//game.map.tileSize)\n\n\t\tcurrentRoom.setVisibility(visible=True)\n\n\n\n\n\t\tpressedKeys = pygame.key.get_pressed()\n\n\n\t\tif pressedKeys[pygame.K_SPACE]:\n\t\t\tself.moveSpeed = 8\n\t\t\tself.walkAnimationSpeed = 0.20\n\t\telse:\n\t\t\tself.moveSpeed = 4\n\t\t\tself.walkAnimationSpeed = 0.15\n\t\t#for running (space to run)\n\n\t\t\n\t\tself.moving = False\n\t\tif pressedKeys[pygame.K_w]:\n\t\t\tself.moveUp(game, self.moveSpeed)\n\t\tif pressedKeys[pygame.K_s]:\n\t\t\tself.moveDown(game, self.moveSpeed)\n\t\tif pressedKeys[pygame.K_a]:\n\t\t\tself.moveLeft(game, self.moveSpeed)\n\t\tif pressedKeys[pygame.K_d]:\n\t\t\tself.moveRight(game, self.moveSpeed)\n\n\t\tself.animController.setState(self.direction.state(self.moving))\n", "sub_path": "player.py", "file_name": "player.py", "file_ext": "py", "file_size_in_byte": 3844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "enum.Enum", "line_number": 7, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 138, "usage_type": "attribute"}]} +{"seq_id": "47010790", "text": "import glob\nimport os\nimport cv2\nimport json\nimport torch\nimport numpy as np\n\npaths = glob.glob(os.path.join('*', '*', '*.jpg'))\nprint(len(paths))\n\ndef get_mean_std(paths):\n means = torch.Tensor([0, 0, 0])\n stdevs = torch.Tensor([0, 0, 0])\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n means = means.to(device)\n stdevs = stdevs.to(device)\n print(device)\n for path in paths:\n img = cv2.imread(path)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n # img = img.astype(np.float32) / 255.\n img = img.astype(np.float32)\n img = img.transpose(2, 0, 1) #hwc 2 chw\n img = torch.from_numpy(img)\n img = img.to(device)\n\n\n for j in range(3):\n means[j] += img[j, :, :].mean()\n stdevs[j] += img[j, :, :].std()\n\n means = means.tolist()\n stdevs = stdevs.tolist()\n means = np.asarray(means) / len(paths)\n stdevs = np.asarray(stdevs) / len(paths)\n print(\"{} : normMean = {}\".format(type, means))\n print(\"{} : normstdevs = {}\".format(type, stdevs))\n \n with open('mean_std.txt', 'w') as f:\n json.dump({'means': list(means), 'stdevs': list(stdevs)}, f)\n\nget_mean_std(paths)", "sub_path": "lh_cal_mean_std.py", "file_name": "lh_cal_mean_std.py", "file_ext": "py", "file_size_in_byte": 1206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "glob.glob", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "453596838", "text": "import speech_recognition as sr\nfrom google.cloud import language\nfrom google.cloud.language import enums\nfrom google.cloud.language import types\nfrom google.cloud import texttospeech\nimport os\nimport serial\nimport time\nimport re\nimport pyrebase\nimport datetime\nimport firebase_admin\nfrom firebase_admin import credentials\n\nser = serial.Serial('/dev/cu.usbmodem1421', 9600, timeout = 1)\n\n# Enter your own info :)\nconfig = {\n \"apiKey\": \"\",\n \"authDomain\": \"\",\n \"databaseURL\": \"\",\n \"projectId\": \"\",\n \"storageBucket\": \"\",\n \"messagingSenderId\": \"\"\n}\n\nfirebase = pyrebase.initialize_app(config)\n# Instantiates a text-to-speech client\nclient = texttospeech.TextToSpeechClient()\n\n# Build the voice request, select the language code (\"en-US\") and the ssml\n# voice gender (\"neutral\")\nvoice = texttospeech.types.VoiceSelectionParams(\n language_code='en-US',\n ssml_gender=texttospeech.enums.SsmlVoiceGender.NEUTRAL)\n\n# Select the type of audio file you want returned\naudio_config = texttospeech.types.AudioConfig(\n audio_encoding=texttospeech.enums.AudioEncoding.MP3,\n pitch=5)\n\ndef saveToFireBase(text, sentimentScore):\n\n # Get a reference to the auth service\n auth = firebase.auth()\n\n cred = credentials.Certificate(\"baymax2-ac673-firebase-adminsdk-58hu4-2143252c1a.json\")\n firebase_admin.initialize_app(cred)\n\n # Get a reference to the database service\n db = firebase.database()\n\n # data to save\n data = {\n \"text\": text,\n \"sentimentScore\": sentimentScore,\n \"date\": datetime.datetime.now()\n }\n\n # Pass the user's idToken to the push method\n results = db.push(data, user['idToken'])\n\ndef outputAudio(text):\n # Set the text input to be synthesized\n synthesis_input = texttospeech.types.SynthesisInput(text=text)\n\n # Perform the text-to-speech request on the text input with the selected\n # voice parameters and audio file type\n response = client.synthesize_speech(synthesis_input, voice, audio_config)\n\n # The response's audio_content is binary.\n with open('output.mp3', 'wb') as out:\n # Write the response to the output file.\n out.write(response.audio_content)\n\n os.system('afplay output.mp3')\n\ndef sendSerial(value):\n ser.write(str(value).encode())\n time.sleep(1)\n ser.flush()\n\ndef checkScore(sentimentScore):\n # Send 0 if negative sentiment and 1 if positive\n if (sentimentScore < 0):\n outputAudio(\"Oh no, here's a hug...\")\n sendSerial(0)\n else:\n outputAudio(\"Yay! I'm happy for you!\") \n sendSerial(1)\n\ndef analyzeSentiment(text):\n \"\"\"Run a sentiment analysis request on text within a passed filename.\"\"\"\n language_client = language.LanguageServiceClient()\n\n # Instantiates a plain text document.\n document = types.Document(\n content=text,\n type=enums.Document.Type.PLAIN_TEXT)\n \n sentiment = language_client.analyze_sentiment(document=document).document_sentiment\n print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))\n checkScore(sentiment.score)\n #saveToFireBase(text, sentiment.score)\n\ndef analyze(text):\n if re.search('lifestyle change', text):\n outputAudio(\"Here's some strategies recommended by BC First Responders' Mental Health. You may want to set goals in lifestyle areas such as: sleep, diet, use of alcohol and grugs, and exercise.\")\n elif re.search('reduce stigma', text):\n outputAudio(\"You can contribute to anti-stigma campaigns. One example is Real Warriors. You can check out Real Warriors Dot Net.\")\n elif re.search('thank you', text):\n outputAudio(\"You're welcome!\")\n else:\n analyzeSentiment(text)\n\nif __name__ == '__main__': \n count = 0\n while True: \n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.adjust_for_ambient_noise(source)\n print ('Say Something!')\n audio = r.listen(source, timeout=1)\n print ('Done!')\n try:\n text = r.recognize_google(audio)\n print (text)\n analyze(text)\n except: \n pass", "sub_path": "baymax.py", "file_name": "baymax.py", "file_ext": "py", "file_size_in_byte": 4120, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "serial.Serial", "line_number": 15, "usage_type": "call"}, {"api_name": "pyrebase.initialize_app", "line_number": 27, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech.TextToSpeechClient", "line_number": 29, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech", "line_number": 29, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.types.VoiceSelectionParams", "line_number": 33, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech.types", "line_number": 33, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 33, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.enums", "line_number": 35, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 35, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.types.AudioConfig", "line_number": 38, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech.types", "line_number": 38, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 38, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.enums", "line_number": 39, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 39, "usage_type": "name"}, {"api_name": "firebase_admin.credentials.Certificate", "line_number": 47, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 47, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech.types.SynthesisInput", "line_number": 65, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech.types", "line_number": 65, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 65, "usage_type": "name"}, {"api_name": "os.system", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 80, "usage_type": "call"}, {"api_name": "google.cloud.language.LanguageServiceClient", "line_number": 94, "usage_type": "call"}, {"api_name": "google.cloud.language", "line_number": 94, "usage_type": "name"}, {"api_name": "google.cloud.language.types.Document", "line_number": 97, "usage_type": "call"}, {"api_name": "google.cloud.language.types", "line_number": 97, "usage_type": "name"}, {"api_name": "google.cloud.language.enums.Document", "line_number": 99, "usage_type": "attribute"}, {"api_name": "google.cloud.language.enums", "line_number": 99, "usage_type": "name"}, {"api_name": "re.search", "line_number": 107, "usage_type": "call"}, {"api_name": "re.search", "line_number": 109, "usage_type": "call"}, {"api_name": "re.search", "line_number": 111, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 119, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "579996236", "text": "\"\"\"*PyTest* configuration and general purpose fixtures.\"\"\"\n# pylint: disable=W0621\nimport pytest\nimport numpy as np\nfrom sdnet.networks import random_network\nfrom sdnet.utils import euclidean_dist, make_dist_matrix\n\n\ndef pytest_addoption(parser):\n \"\"\"Custom `pytest` command-line options.\"\"\"\n parser.addoption(\n '--benchmarks', action='store_true', default=False,\n help=\"Run benchmarks (instead of tests).\"\n )\n parser.addoption(\n '--slow', action='store_true', default=False,\n help=\"Run slow tests / benchmarks.\"\"\"\n )\n\ndef pytest_collection_modifyitems(config, items):\n \"\"\"Modify test runner behaviour based on `pytest` settings.\"\"\"\n run_benchmarks = config.getoption('--benchmarks')\n run_slow = config.getoption('--slow')\n if run_benchmarks:\n skip_test = \\\n pytest.mark.skip(reason=\"Only benchmarks are run with --benchmarks\")\n for item in items:\n if 'benchmark' not in item.keywords:\n item.add_marker(skip_test)\n else:\n skip_benchmark = \\\n pytest.mark.skip(reason=\"Benchmarks are run only with --run-benchmark\")\n for item in items:\n if 'benchmark' in item.keywords:\n item.add_marker(skip_benchmark)\n if not run_slow:\n skip_slow = pytest.mark.skip(reason=\"Slow tests are run only with --slow\")\n for item in items:\n if 'slow' in item.keywords:\n item.add_marker(skip_slow)\n\n\n# Fixtures --------------------------------------------------------------------\n\nK = 30\nN_NODES = 100\nRANDOM_SEED = 423423\n\n@pytest.fixture(scope='session')\ndef d2_uniform():\n np.random.seed(RANDOM_SEED)\n X = np.random.uniform(0, 1, (N_NODES, 2))\n return X\n\n@pytest.fixture(scope='session')\ndef d2_lognormal():\n np.random.seed(RANDOM_SEED)\n X = np.random.lognormal(10, 2, (N_NODES, 1))\n return X\n\n@pytest.fixture(scope='session')\ndef dist_matrix(d2_uniform):\n X = d2_uniform\n return make_dist_matrix(X, euclidean_dist, symmetric=True)\n\n@pytest.fixture(scope='function')\ndef adj_matrix():\n np.random.seed(RANDOM_SEED)\n A = random_network(N_NODES, k=K, directed=False)\n return A\n", "sub_path": "test/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2187, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "1", "api": [{"api_name": "pytest.mark.skip", "line_number": 26, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pytest.mark.skip", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pytest.mark.skip", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.random.lognormal", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 55, "usage_type": "call"}, {"api_name": "sdnet.utils.make_dist_matrix", "line_number": 64, "usage_type": "call"}, {"api_name": "sdnet.utils.euclidean_dist", "line_number": 64, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 61, "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": "sdnet.networks.random_network", "line_number": 69, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "581081699", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport os\nimport re\nimport openpyxl\nfrom openpyxl.styles import Font\nfrom openpyxl.styles import Alignment\nfrom openpyxl.utils import get_column_letter, column_index_from_string\n\n# input_filename = 'touch_log.txt'\ninput_filename = sys.argv[1]\noutput_filename = os.path.splitext(sys.argv[1])[0] + \".xlsx\"\n\ntest_pattern = re.compile(\n '''\n \\[\\s*\n (?P