diff --git "a/1731.jsonl" "b/1731.jsonl" new file mode 100644--- /dev/null +++ "b/1731.jsonl" @@ -0,0 +1,921 @@ +{"seq_id":"43324301305","text":"\"\"\"\nClass for holding the filter data\nand some operations\n\"\"\"\n\nfrom __future__ import annotations\nimport random\nfrom typing import List\n\nfrom utils.bytes_operations import bytestring_to_bytelist\n\n\nclass ConvFilter:\n \"\"\"\n Kernel/filter for sequences of bytes\n \"\"\"\n\n def __init__(self, size: int):\n self.kernel: bytes = List[bytes]\n self.kernel = []\n for _ in range(size):\n self.kernel.append(b\"\")\n self.byte_it_represents = b\"\\x00\"\n\n def set_byte_it_represents(self, byte_it_represents: bytes):\n \"\"\"Sets the byte that this filter represents\"\"\"\n self.byte_it_represents = byte_it_represents\n\n def get_byte_it_represents(self) -> bytes:\n \"\"\"Gets the byte that this filter represents\"\"\"\n return self.byte_it_represents\n\n def randomize_from_list(self, unique_bytelist: list, wildcard_chance: float, wildcard_byte):\n \"\"\"\n Randomize kernel bytes using a list of bytes,\n all indexes (except both ends) have a wildcard_chance\n of receiving a wildcard_byte\n \"\"\"\n new_kernel = []\n used_wildcard_once = False\n for kernel_idx in range(self.get_size()):\n rand_idx = random.randrange(len(unique_bytelist))\n byte_to_use = unique_bytelist[rand_idx]\n\n if used_wildcard_once is False:\n if kernel_idx > 0 and kernel_idx < (self.get_size() - 1):\n if random.uniform(0.0, 1.0) < wildcard_chance:\n byte_to_use = wildcard_byte\n used_wildcard_once = True\n\n new_kernel.append(byte_to_use)\n\n self.kernel = new_kernel\n\n def set_kernel_bytes_using_bytestring(self, bytestring: bytes):\n \"\"\"set kernel bytes using a bytestring\"\"\"\n self.kernel = bytestring_to_bytelist(bytestring)\n\n def crossover(self, other: ConvFilter):\n \"\"\"\n Crossover the information\n between 2 filters\n to create a new one\n \"\"\"\n\n new_filter = ConvFilter(self.get_size())\n new_filter.kernel = self.get_kernel().copy()\n\n random_skip = 0\n size_diff = abs(self.get_size() - other.get_size())\n if size_diff > 0:\n random_skip = random.randrange(size_diff)\n\n smaller_size = self.get_size()\n if other.get_size() < smaller_size:\n smaller_size = other.get_size()\n\n for idx in range(smaller_size):\n if random.choice([0, 1]) == 1:\n if self.get_size() > other.get_size():\n new_filter.kernel[random_skip + idx] = other.kernel[idx]\n else:\n new_filter.kernel[idx] = other.kernel[random_skip + idx]\n\n return new_filter\n\n def get_wildcards_indexes(self, wildcard_byte: bytes) -> List:\n \"\"\"get a list of indexes of all the wildcards in the kernel\"\"\"\n indexes = []\n for kernel_idx, kernel_byte in enumerate(self.get_kernel()):\n if kernel_byte == wildcard_byte:\n indexes.append(kernel_idx)\n return indexes\n\n def get_kernel(self):\n \"\"\"get kernel's list of bytes\"\"\"\n return self.kernel\n\n def get_size(self):\n \"\"\"get kernel's size\"\"\"\n return len(self.kernel)\n\n def calculate_footprint_in_bytes(self):\n \"\"\"calculate how many bytes this filter will use in the file header\"\"\"\n extrabytes = 3 # see Convpress.generate_header()\n return self.get_size() + extrabytes\n\n def __eq__(self, other: ConvFilter):\n if self.get_size() != other.get_size():\n return False\n for kernel_idx in range(self.get_size()):\n if self.kernel[kernel_idx] != other.kernel[kernel_idx]:\n return False\n return True\n\n def __str__(self):\n output = f\"size {self.get_size()}\"\n output += f\" _\\\\x{self.get_byte_it_represents().hex()}_\"\n output += \" _\"\n for kernel_byte in self.kernel:\n output += f\"\\\\x{kernel_byte.hex()}\"\n output += '_'\n return output\n","repo_name":"vitormanfredini/convpress","sub_path":"classes/ConvFilter.py","file_name":"ConvFilter.py","file_ext":"py","file_size_in_byte":4054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"35112637839","text":"from collections import defaultdict\nimport pickle\nfname = \"82_out\"\nfname_tc_func = \"tc_func\"\nfname_t_func = \"t_func\"\nfname_c_func = \"c_func\"\n\ndef _83():\n tc_func = defaultdict(int)\n t_func = defaultdict(int)\n c_func = defaultdict(int)\n \n with open(fname, \"r\", encoding=\"utf-8\") as target:\n for line in target:\n line = line.rstrip()\n tc_func[line] += 1\n t,c = line.split(\"\\t\")\n t_func[t] += 1\n c_func[c] += 1\n \n with open(fname_tc_func, \"wb\") as f_data:\n pickle.dump(tc_func, f_data)\n\n with open(fname_t_func, \"wb\") as f_data:\n pickle.dump(t_func, f_data)\n \n with open(fname_c_func, \"wb\") as f_data:\n pickle.dump(c_func, f_data)\n\nif __name__ == \"__main__\":\n _83()\n","repo_name":"horiso0921/nlp","sub_path":"chapter9/83.py","file_name":"83.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"41713532172","text":"# 14:43 ~ 14:48\n\nimport sys\nsys.stdin = open('input.txt', 'r')\n\n\nN = int(input())\nedge = [[] for _ in range(N+1)]\n\nfor _ in range(int(input())):\n a, b = map(int, input().split())\n edge[a].append(b)\n edge[b].append(a)\n\nvisit = [False] * (N+1)\nvisit[1] = True\nqueue = [1]\nwhile queue:\n cur = queue.pop(0)\n for nxt in edge[cur]:\n if visit[nxt] is False:\n visit[nxt] = True\n queue.append(nxt)\n\nprint(visit.count(True) - 1)\n","repo_name":"heecheol1508/algorithm-problem","sub_path":"_baekjoon/2606_바이러스.py","file_name":"2606_바이러스.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25671730452","text":"import argparse\nimport logging\n\nfrom dotenv import load_dotenv\n\nfrom bot import Bot\n\nlogger = logging.getLogger(__name__)\nlogging.basicConfig(\n level=logging.INFO, format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n)\n\n\nif __name__ == \"__main__\":\n # Load variables from .env file\n load_dotenv()\n \n # args parser to add simulation mode\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--simulation\", action=\"store_true\", help=\"Run in simulation mode\")\n\n args = parser.parse_args()\n \n # print(args.simulation)\n \n # Load twitch chat bot\n bot = Bot(random_simulator=args.simulation)\n bot.run()\n # bot.run() is blocking and will stop execution of any below code here until stopped or closed.\n","repo_name":"AustrianOakvn/GIS81507","sub_path":"TwitchAPI/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17531182593","text":"lista = []\nwhile True:\n valor = int(input('Digite um valor: '))\n if valor in lista:\n print('Valor duplicado! Não vou adicionar...')\n o = str(input('Gostaria de continuar? [S/N]')).upper().strip()\n if valor not in lista:\n lista.insert(0, valor)\n if o in 'N':\n break\nlista.sort()\nprint('-='*20)\nprint(f'Você digitou os valores {lista}')\n","repo_name":"Gabriel07201/exercicios","sub_path":"exerciciocursoemvideo/ex 079.py","file_name":"ex 079.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40792597217","text":"from django.conf.urls import url\n\nfrom . import views\nurlpatterns = [\n url(r'^login/$', views.user_login,name='login'),\n url(r'^logout/$', views.user_logout,name='logout'),\n url(r'^register/$', views.user_register,name='register'),\n url(r'^delete/(?P\\d+)/$', views.user_delete,name='delete'),\n url(r'^edit/(?P\\d+)/$', views.profile_edit,name='edit'),\n]","repo_name":"hzdgzz/Blog","sub_path":"Blog/apps/userprofile/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15578855824","text":"# For https://www.kaggle.com/c/titanic\n# Tries to predict who survived the Titanic shipwreck using machine learning\n# on the data provided by the competition host.\n# Writes the predictions to a csv-file.\n\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import precision_score, roc_auc_score\nfrom copy import deepcopy\n\nTRAIN_LOC = \"./input/train.csv\"\nTEST_LOC = \"./input/test.csv\"\nWRITE_LOC = \"./output/submission.csv\"\n\n# cpu cores\nTHREADS = 4\n\ndef read_input():\n \"\"\"\n reads the input files\n :return input_train, DataFrame, the data used to train the model\n :return input_test, DataFrame, the data used to predict the final results\n \"\"\"\n input_train = pd.read_csv(TRAIN_LOC, index_col = 'PassengerId')\n input_test = pd.read_csv(TEST_LOC, index_col = 'PassengerId')\n\n return input_train, input_test\n\n\ndef write_output(y_test, input_test):\n \"\"\"\n writes the output as a csv file\n :param y_test: array, prediction results (0 or 1) for all indices\n :param input_test:, array, used to get the indices\n :return:\n \"\"\"\n y_test = pd.DataFrame({\"PassengerId\" : input_test.index,\n \"Survived\" : y_test})\n y_test.to_csv(path_or_buf=WRITE_LOC, index=False)\n\n\ndef plot_bar(values, labels, title):\n \"\"\"\n Visualisation of data\n :param values: list of scalars, heights of the data bars\n :param labels: list of strings, labels of the data bars\n :param title: string, title for the window\n :return:\n \"\"\"\n plt.bar([x for x in range(len(values))], values)\n plt.xticks([x for x in range(len(values))], labels, rotation=90)\n plt.title(title)\n plt.show()\n\n\n\ndef main():\n input_train, input_test = read_input()\n\n # number of unique values in the columns\n uniqs = input_train.nunique()\n # number of null values in the columns\n nulls = pd.isna(input_train).sum()\n\n plot_bar(uniqs, input_train.columns, \"Uniques\")\n print(\"Unique values by column \\n\", uniqs, \"\\n\")\n\n plot_bar(nulls, input_train.columns, \"Null values\")\n print(\"Null values by column \\n\", nulls, \"\\n\")\n\n # \"Survived\" column of the data, the prediction target\n y_train_all = input_train[\"Survived\"]\n # Drop the \"Survived\" column from the trainig data\n # and columns that have too many unique or null values to help with predictions\n X_train_all = input_train.drop(columns=[\"Survived\", \"Name\", \"Ticket\", \"Cabin\"], axis=1)\n\n X_test = input_test.drop(columns=[\"Name\", \"Ticket\", \"Cabin\"], axis=1)\n\n X_train, X_valid, y_train, y_valid = train_test_split(X_train_all, y_train_all, test_size=0.2, random_state=0)\n\n # fill the missing values\n nimp = SimpleImputer(strategy=\"median\")\n\n # columns with numerical data\n num_cols = X_train.select_dtypes(exclude=\"object\").columns\n # columns with non-numerical data\n cat_cols = X_train.select_dtypes(include=\"object\").columns\n\n cimp = SimpleImputer(strategy=\"most_frequent\")\n ohe = OneHotEncoder()\n\n # Transforms columns with non-numerical data\n cat_transformer = Pipeline(steps=[\n (\"impute\", cimp),\n (\"encode\", ohe)\n ])\n\n # Transform numerical and non-numerical columns separately\n col_trsfmr = ColumnTransformer(transformers=[\n (\"num_vars\", nimp, num_cols),\n (\"cat_vars\", cat_transformer, cat_cols)\n ],\n n_jobs=THREADS)\n\n\n # test these classifiers\n ensemble_classifiers = [\n RandomForestClassifier(),\n GradientBoostingClassifier(),\n AdaBoostClassifier(),\n ExtraTreesClassifier()\n ]\n\n # Used to keep track of the best classifier\n # [Highest precision score, number of trees, classifier used]\n best_score = [0, 0, 0]\n\n scores = []\n scorelabels = []\n\n # grid search the best parameters\n for clf in ensemble_classifiers:\n clf.n_jobs = THREADS\n clf.random_state = 1\n\n for n_ests in range(20, 250, 10):\n clf.n_estimators = n_ests\n\n classifier = Pipeline(steps=[\n (\"preprocess\", col_trsfmr),\n (\"classify\", clf)\n ])\n\n classifier.fit(X_train, y_train)\n\n y_pred = classifier.predict(X_valid)\n score = precision_score(y_valid, y_pred)\n\n scores.append(score)\n scorelabels.append(str(clf.__class__.__name__) + \" (\" + str(n_ests) + \")\")\n\n if score > best_score[0]:\n best_score[0] = score\n best_score[1] = n_ests\n best_score[2] = deepcopy(clf)\n\n print(best_score)\n\n # plots all accuracies\n plot_bar(scores, scorelabels, \"Accuracies\")\n\n\n # Use the best classifier to predict the final results\n best_clf = best_score[2]\n\n best_clf = Pipeline(steps=[\n (\"preprocess\", col_trsfmr),\n (\"classify\", best_clf)\n ])\n\n # fit the best classifier with all available training data\n best_clf.fit(X_train_all, y_train_all)\n\n # write the output to a csv file\n y_test = best_clf.predict(X_test)\n\n write_output(y_test, input_test)\n\n\n\nmain()","repo_name":"gitgudsk/titanicml","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42963295319","text":"import numpy as np\nimport cv2\nimport time\nimport argparse\nimport sys\nimport os\n\ncar_cascade = cv2.CascadeClassifier('haarcascade_cars.xml')\n\nparser = argparse.ArgumentParser(description='Cars Detection using Cascade Classifier')\nparser.add_argument('--image', help='Path to image file.')\nparser.add_argument('--video', help='Path to video file.')\nargs = parser.parse_args()\n\nif (args.image):\n # Open the image file\n if not os.path.isfile(args.image):\n print(\"Input image file: %s, doesn't exist\" %(args.image))\n sys.exit(1)\n cap = cv2.VideoCapture(args.image)\nelif (args.video):\n # Open the video file\n if not os.path.isfile(args.video):\n print(\"Input video file: %s, doesn't exist\" %(args.video))\n sys.exit(1)\n cap = cv2.VideoCapture(args.video)\n\n\nwhile (True):\n ret, frame = cap.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n cars = car_cascade.detectMultiScale(gray, 1.1, 2)\n\n for (x,y,w,h) in cars:\n cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = frame[y:y+h, x:x+w]\n\n cv2.imshow('Cars Detector', frame)\n k = cv2.waitKey(30) & 0xff\n if k == 27:\n break\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"NicolasVILLETTE/Computer_Vision","sub_path":"cars_detector.py","file_name":"cars_detector.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"46270687897","text":"import cv2\nimport numpy as np\n\nimage = cv2.imread('barcode.jpg', 1)\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\nscharr_x = cv2.Scharr(gray, cv2.CV_32F, 1, 0, -1)\n\nscharr_y = cv2.Scharr(gray, cv2.CV_32F, 0, 1, -1)\n\n\ngradient = cv2.subtract(scharr_x, scharr_y)\ngradient = cv2.convertScaleAbs(gradient)\n\nblurred = cv2.blur(gradient, (4,5))\n_, thresh = cv2.threshold(blurred, 250, 255, cv2.THRESH_TOZERO)\n\nkernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))\nclosed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\nclosed = cv2.erode(closed, None, iterations=5)\nclosed = cv2.dilate(closed, None, iterations=5)\n\ncnts, hierarchy = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\nc = sorted(cnts, key=cv2.contourArea, reverse=True)[0]\n\nrect = cv2.minAreaRect(c)\nbox = np.int0(cv2.boxPoints(rect))\n\ncv2.drawContours(image, [box], -1, (0, 255, 255), 3)\ncv2.imshow('image', image)\ncv2.waitKey(0)\ncv2.destroyWindow('image')","repo_name":"xts-x-xvxl-wxrld/barcode-scanner","sub_path":"Reader tool.py","file_name":"Reader tool.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38922038298","text":"skates = int(input('Кол-во коньков: '))\nskates_list = []\nfor i in range(skates):\n skates_size = int(input('Введите размер коньков: '))\n skates_list.append(skates_size)\n\npeople = int(input('Кол-во людей: '))\npeople_list = []\nfor i in range(people):\n people_size = int(input('Введите размер ноги: '))\n people_list.append(people_size)\n\ncount = 0\nfor i in skates_list:\n for j in people_list:\n if i == j:\n count += 1\n\nprint('Наибольшее кол-во людей, которые могут взять ролики: ', count)","repo_name":"Echelon1207/Python_course_skillbox","sub_path":"Module16/07_roller_skates/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"27281571708","text":"from math import ceil\nn,x = map(int, input().split())\nal = list(map(int, input().split()))\n\nans = 0\nst = set()\nvs = []\nfor i in range(n-1,-1,-1):\n pattern = 1\n over = ceil(x/al[i])\n v = over*al[i]-x\n if v in st: continue\n st.add(v)\n vs.append(v)\n new_vs = [over*al[i]]\n for j in range(n):\n for v in new_vs:\n rem = v-x\n for k in range(n-1,j,-1):\n cnt = rem//al[i]\n rem -= cnt*al[i]\n if rem >= al[j]:\n print(al[j])\n pattern *= 2\n cntt = ceil(rem/al[j])\n new_vs.append(over*al[i]+al[j]*cntt)\n # vs = new_vs[:]\n for nv in new_vs:\n if nv not in vs:\n vs.append(nv)\n ans += pattern\n print('====',al[i], ans)\n\nprint(ans)","repo_name":"nami4mo/competitive-programming","sub_path":"1_contest/previous/abc182/f.py","file_name":"f.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"44079220298","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 27 12:18:11 2019\n\n@author: prapti\n\"\"\"\n\nimport numpy as np\nimport cv2\nfrom util import *\nfrom skimage.exposure import rescale_intensity\nimport math\nimport matplotlib.pyplot as plt\n\ndef convolve(image, kernel):\n (iH, iW) = image.shape[:2]\n (kH, kW) = kernel.shape[:2]\n \n pad = (kW - 1) // 2\n image = cv2.copyMakeBorder(image, pad, pad, pad, pad,\n cv2.BORDER_REPLICATE)\n \n output = np.zeros((iH, iW), dtype=\"float32\")\n \n for y in np.arange(pad, iH + pad):\n for x in np.arange(pad, iW + pad):\n roi = image[y - pad:y + pad + 1, x - pad:x + pad + 1]\n\n k = (roi * kernel).sum()\n\n output[y - pad, x - pad] = k\n \n output = rescale_intensity(output, in_range=(0, 255))\n output = (output * 255).astype(\"uint8\")\n\n return output\n\nsigma = 1\nkernel = np.zeros((5, 5))\nfor i in range(-2, 3):\n for j in range(-2, 3):\n kernel[i+2, j+2] = 1/(2 * math.pi * sigma) * (math.e ** (-1 * (i*i+j*j)/(2*sigma*sigma) ))\n\nprint(kernel)\nplt.imshow(kernel, cmap='gray')\n\nimg = cv2.imread('lenna.png', cv2.IMREAD_GRAYSCALE)\n# cv2.imshow('Original', img)\n#plt.imshow(img, cmap='gray')\n\n# cv2.imshow('Gaussian', convolve(img, kernel))\n\nsmallBlur = np.ones((7, 7), dtype=\"float\") * (1.0 / (7 * 7))\nlargeBlur = np.ones((21, 21), dtype=\"float\") * (1.0 / (21 * 21))\nsharpen = np.array((\n [0, -1, 0],\n [-1, 5, -1],\n [0, -1, 0]), dtype=\"int\")\nlaplacian = np.array((\n [0, 1, 0],\n [1, -4, 1],\n [0, 1, 0]), dtype=\"int\")\n\nsobelX = np.array((\n [-1, 0, 1],\n [-2, 0, 2],\n [-1, 0, 1]), dtype=\"int\")\n \nsobelY = np.array((\n [-1, -2, -1],\n [0, 0, 0],\n [1, 2, 1]), dtype=\"int\")\nkernelBank = (\n (\"small_blur\", smallBlur),\n (\"large_blur\", largeBlur),\n (\"sharpen\", sharpen),\n (\"laplacian\", laplacian),\n (\"sobel_x\", sobelX),\n (\"sobel_y\", sobelY)\n)\n\n'''\nfor k in kernelBank:\n cv2.imshow(k[0], cv2.resize(convolve(img, k[1]), (0,0), fx=0.5, fy=0.5))\n'''\ncv2.waitKey(0)\ncv2.destroyAllWindows()","repo_name":"Prapti-044/Image-Proceesing-Laboratory","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"38174745623","text":"# Use this file if you are importing into an interactive IPython session.\n# Use 'pyxrf.api_dev' if you are importing PyXRF API into a custom script.\n\n\nfrom pyxrf import __version__ as pyxrf_version\n\nfrom .api_dev import * # noqa: F401, F403\n\n\ndef pyxrf_api():\n r\"\"\"\n =========================================================================================\n Module ``pyxrf.api`` supports the following functions:\n\n Loading data:\n make_hdf - load XRF mapping data from databroker\n\n Data processing:\n pyxrf_batch - batch processing of XRF maps\n build_xanes_map - generation and processing of XANES maps\n\n Dask client:\n dask_client_create - returns Dask client for use in batch scripts\n\n Simulation of datasets:\n gen_hdf5_qa_dataset - generate quantitative analysis dataset\n gen_hdf5_qa_dataset_preset_1 - generate the dataset based on preset parameters\n\n VIEW THIS MESSAGE AT ANY TIME: pyxrf_api()\n\n For more detailed descriptions of the supported functions, type ``help()``\n in IPython command prompt.\n =========================================================================================\n \"\"\"\n version = f\"\"\"\n =========================================================================================\n PyXRF version: {pyxrf_version}\"\"\"\n print(version + pyxrf_api.__doc__)\n\n\npyxrf_api()\n","repo_name":"NSLS-II/PyXRF","sub_path":"pyxrf/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"31"} +{"seq_id":"43689565652","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nurl = 'http://trend.caipiao.163.com/dlt/?beginPeriod=18001&endPeriod=18060'\r\nr = requests.get(url)\r\nr.encoding = \"#b82337\"\r\ndemo = r.text\r\nsoup = BeautifulSoup(demo,'html.parser')\r\nf = open('f.txt','w+')\r\nf.writelines(soup.prettify())\r\nf.close()","repo_name":"wanghui2517/WH2517","sub_path":"ENVcode/caipiao.py","file_name":"caipiao.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4359175260","text":"import json\nimport requests\nimport urllib.parse\n\nfrom config import config\n\nfrom discord import Colour, Embed\n\n\napikey = config[\"openweathermap_apikey\"]\nAPI_PT_1=\"http://api.openweathermap.org/data/2.5/find?q=\"\nAPI_PT_2=\"&units=metric&appid=\"\n \n\n\ndef get_weather(location):\n location = urllib.parse.quote(location)\n r = requests.get(API_PT_1 + location + API_PT_2 + apikey).text\n parsed = json.loads(r)\n\n weather_conditions = [w['main'] for w in parsed['list'][0]['weather']]\n \n weather = parsed['list'][0][\"name\"]\n temperature = parsed['list'][0][\"main\"][\"temp\"]\n wspeed = parsed['list'][0][\"wind\"][\"speed\"]\n clouds = parsed['list'][0][\"clouds\"][\"all\"]\n\n return (weather, temperature, wspeed, clouds, weather_conditions)\n\n\ndef create_command(bot):\n @bot.command(pass_context=True, brief=\"Shows weather\")\n async def weather (ctx, *, location):\n \"\"\"\n !weather - look up weather information in a city or a region\n \"\"\"\n weather = get_weather(location)\n \n embed = Embed()\n embed.type = \"rich\"\n embed.color = Colour.gold()\n\n embed.add_field(\n name=\":rainbow: Weather in \",\n value=weather[0]\n )\n\n embed.add_field(\n name=\":sun_with_face: Temperature: \",\n value=str(weather[1]) + \" C\"\n )\n\n embed.add_field(\n name=\":warning: Conditions: \",\n value=', '.join(weather[4])\n )\n\n embed.add_field(\n name=\":cloud_tornado: Wind speed: \",\n value=str(weather[2]) + \" m/s\"\n )\n\n embed.add_field(\n name=\":white_sun_small_cloud: Cloudiness: \",\n value=str(weather[3]) + \" %\"\n )\n\n await bot.say(None, embed=embed)\n\n","repo_name":"nukeop/Icarus","sub_path":"commands/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"28895789685","text":"#Membuat variabel global\r\nnama = \"python\"\r\nversi = \"1.0.0\"\r\n\r\ndef hel():\r\n#variabel lokal\r\n nama = \"c#\"\r\n varsi = \"1.0.2\"\r\n#akses variabel lokal\r\n print(\"nama : %s\" % nama)\r\n print(\"versi : %s\" % varsi)\r\n#akses variabel global\r\nprint(\"nama : %s\" % nama)\r\nprint(\"versi : %s\" % versi)\r\n\r\n#memanggil fungsi help\r\nhel()","repo_name":"hendrikurniawan12/python","sub_path":"fungsi3.py","file_name":"fungsi3.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9241743065","text":"from django import forms\nfrom django.forms import ModelForm\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth.models import User\nfrom .models import Profile\n\nclass ProfileUpdateForm(forms.ModelForm):\n ''' Form to update profile pictures '''\n class Meta:\n model = Profile\n fields = '__all__'\n exclude = ['user']\n\nclass UserUpdateForm(forms.ModelForm):\n '''Form to update a user'''\n email = forms.EmailField()\n\n class Meta:\n model = User\n fields = ['username', 'email', 'first_name', 'last_name']\n\n widgets = {\n 'username': forms.TextInput(attrs={'class':'form-control', 'placeholder':'Username'}),\n 'email': forms.EmailInput(attrs={'class':'form-control', 'placeholder':'Email'}),\n 'first_name': forms.TextInput(attrs={'class':'form-control', 'placeholder':'First Name'}),\n 'last_name': forms.TextInput(attrs={'class':'form-control', 'placeholder':'Last Name'}),\n }\n","repo_name":"rajivsunar07/travellers","sub_path":"users/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1279049946","text":"\"\"\"\nThis little script illustrates asyncio gather and await by throttling the range: 0, 1, 2 with the makerandom calls with (10-0-1) = 9, (10-1-1) = 8, (10-2-1) = 7. Thereby making the random calls for 0--the await btw tries to get to 10 is one second; the await for 1 is 2 seconds but the threshold is lower (8), meaning it can reach 9 and finish; the await for 2 is 3 seconds but the threshold is even lower (7), meaning it can reach 8 and finish. These *could* finish at the same time-ish. Or these numbers even the odds of a 1st finisher. (and yet zero is consistently finishing last) :(\n\n\"\"\"\n\nimport asyncio\nimport random\n\n# ANSI colors\nc = (\n \"\\033[0m\", # End of color [0]\n \"\\033[36m\", # Cyan [1]\n \"\\033[91m\", # Red [2]\n \"\\033[35m\", # Magenta [3]\n)\n\n# args are var idx is INT, threshold is INT def to 6 and annotated\n# ex: 1st call is idx: 0, threshold: 10-0-1=9\n# this is my coroutine\nasync def makerandom(idx: int, threshold: int = 6) -> int:\n print(c[idx + 1] + f\"Initiated makerandom({idx}).\")\n i = random.randint(0, 10) # includes possible 10\n while i <= threshold:\n print(c[idx + 1] + f\"makerandom({idx}) == {i} too low; retrying.\")\n await asyncio.sleep(idx + 1)\n i = random.randint(0, 10)\n print(c[idx + 1] + f\"---> Finished: makerandom({idx}) == {i}\" + c[0])\n return i\n\n# main is called to do makerandom 3 times, providing\n# args 0, 10-0-1=9 (at least always <=9) / 1, 10-1-1=8, then 2\nasync def main():\n res = await asyncio.gather(*(makerandom(i, 10-i-1) for i in range(3)))\n return res\n\nif __name__ == \"__main__\":\n random.seed(444)\n r1, r2, r3 = asyncio.run(main())\n print()\n print(f\"r1: {r1}, r2: {r2}, r3: {r3}\")\n","repo_name":"Hosjev/Algo","sub_path":"Coroutines/rand.py","file_name":"rand.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"33616925375","text":"#! C:\\Users\\AdiminiChen\\AppData\\Local\\Programs\\Python\\Python310\n# coding=utf-8\n# @Time: 2023/2/27 19:48\n# @Author: jackchen\nimport datetime\nimport os\nimport time\nfrom selenium.common.exceptions import ElementNotVisibleException, WebDriverException, NoSuchElementException, \\\n StaleElementReferenceException\nfrom selenium.webdriver.common.keys import Keys\n\nfrom common.find_img import FindImg\n# from common.report_add_img import add_img_path_2_report\nfrom common.tools import get_project_path, sep\nfrom common.yaml_config import GetConfig\n\n\nclass ObjectMap:\n # 获取url地址\n url = GetConfig().get_url()\n\n def element_get(self, driver, locate_type, locator_expression, timeout=10, must_visible=False):\n \"\"\"\n 单个元素获取\n :param driver: 驱动\n :param locate_type:定位方法 css xpath\n :param locator_expression:定位表达式\n :param timeout:设置的超时时间\n :param must_visible:判断元素是否为可见，默认不可见\n :return:返回元素\n \"\"\"\n # 开始时间\n start_ms = time.time() * 1000\n # 设置结束时间\n stop_ms = start_ms + (timeout * 1000)\n # 循环找元素 100次\n for x in range(int(timeout * 10)):\n # 查找元素\n try:\n el = driver.find_element(by=locate_type, value=locator_expression)\n # 如果元素不是必须可见，就直接返回元素\n if not must_visible:\n return el\n # 如果元素必须是可见的 ，则先判断元素是否可见\n else:\n if el.is_displayed():\n return el\n else:\n raise Exception()\n except Exception:\n now_ms = time.time() * 1000\n # 如果超时直接跳出整个循环\n if now_ms >= stop_ms:\n break\n time.sleep(0.1)\n raise ElementNotVisibleException(\"元素定位失败，定位方式：{}，定位表达式：{}\".format(locate_type, locator_expression))\n\n def wait_for_ready_state_complete(self, driver, timeout=30):\n \"\"\"\n 等待页面加载时间\n :param driver:\n :param timeout:设置的超时时间\n \"\"\"\n # 开始时间\n start_ms = time.time() * 1000\n # 结束时间\n stop_ms = start_ms + (timeout * 1000)\n for x in range(int(timeout * 10)):\n try:\n # 获取页面的状态\n ready_state = driver.execute_script(\"return document.readyState\")\n except WebDriverException:\n # driver执行失败 直接跳过\n time.sleep(0.03)\n return True\n # 如果页面全部加载完成 返回True\n if ready_state == 'complete':\n time.sleep(0.01)\n return True\n else:\n now_ms = time.time() * 1000\n # 如果当前时间大于超时时间结束\n if now_ms >= stop_ms:\n break\n time.sleep(0.1)\n raise Exception(\"打开网页时，页面元素在{}秒后没有加载完成\".format(timeout))\n\n def element_disappear(self, driver, locate_type, locate_expression, timeout=30):\n \"\"\"\n 等待页面元素消失\n :param driver:\n :param locate_type:\n :param locate_expression:\n :param timeout:\n \"\"\"\n # 如果定位方式存在\n if locate_type:\n start_ms = time.time() * 1000\n stop_ms = start_ms + timeout * 1000\n for x in range(int(timeout * 10)):\n try:\n el = driver.find_element(by=locate_type, value=locate_expression)\n if el.is_displayed():\n now_ms = time.time() * 1000\n if now_ms >= stop_ms:\n break\n time.sleep(0.1)\n\n except Exception:\n return True\n raise Exception(\"元素定位没有消失，定位方式：{}，定位表达式：{}\".format(locate_type, locate_expression))\n else:\n pass\n\n def element_appear(self, driver, locate_type, locator_expression, timeout=30):\n \"\"\"\n 等待页面元素出现\n :param driver:\n :param locate_type:\n :param locator_expression:\n :param timeout:\n \"\"\"\n if locate_type:\n start_ms = time.time() * 1000\n stop_ms = start_ms + timeout * 1000\n for x in range(int(timeout * 10)):\n try:\n el = driver.find_element(by=locate_type, value=locator_expression)\n if el.is_displayed():\n return el\n else:\n raise Exception()\n except Exception:\n now_ms = time.time() * 1000\n if now_ms >= stop_ms:\n break\n time.sleep(0.1)\n raise ElementNotVisibleException(\"元素没有出现，定位方式：{}，定位表达式：{}\".format(locate_type, locator_expression))\n else:\n pass\n\n def element_to_url(self, driver, url, locate_type_disappear=None, locator_expression_disappear=None,\n locate_type_appear=None, locator_expression_appear=None):\n \"\"\"\n 跳转地址\n :param driver:\n :param url: 跳转的地址\n :param locate_type_disappear: 等待页面元素消失的定位方式\n :param locator_expression_disappear: 等待页面元素消失的定位表达方式\n :param locate_type_appear: 等待页面元素出现的定位方式\n :param locator_expression_appear: 等待页面元素出现的定位表达方式\n \"\"\"\n try:\n driver.get(self.url + url)\n # 等待页面元素都加载完成\n self.wait_for_ready_state_complete(driver)\n # 跳转地址后等待页面元素消失\n self.element_disappear(driver=driver, locate_type=locate_type_disappear,\n locate_expression=locator_expression_disappear)\n # 跳转后等待页面元素出现\n self.element_appear(driver=driver, locate_type=locate_type_appear,\n locator_expression=locator_expression_appear)\n except Exception as e:\n print(\"跳转地址出现异常：\", e)\n return False\n return True\n\n def element_is_display(self, driver, locate_type, locate_expression):\n \"\"\"\n 元素是否显示\n :param driver:\n :param locate_type:\n :param locate_expression:\n \"\"\"\n try:\n driver.find_element(by=locate_type, value=locate_expression)\n return True\n except NoSuchElementException:\n # 发生了NoSuchElementException异常，说明页面中未找到元素，返回false\n return False\n\n def element_fill_value(self, driver, locate_type, locator_expression, fill_value, timeout=30):\n \"\"\"\n 元素填值操作\n :param driver:\n :param locate_type:\n :param locator_expression:\n :param fill_value: 填入的值\n :param timeout:\n \"\"\"\n # 元素必须先出现\n el = self.element_appear(driver=driver, locate_type=locate_type, locator_expression=locator_expression,\n timeout=timeout)\n try:\n # 先清除元素中的原有值\n el.clear()\n # 页面元素没有刷新，就对元素进行异常捕获\n except StaleElementReferenceException:\n # 等待页面加载完成\n self.wait_for_ready_state_complete(driver)\n time.sleep(0.06)\n # 等元素出现\n el = self.element_appear(driver=driver, locate_type=locate_type, locator_expression=locator_expression,\n timeout=timeout)\n try:\n el.clear()\n except Exception:\n pass\n except Exception:\n pass\n # 填入值转成字符串\n if type(fill_value) is int or type(fill_value) is float:\n fill_value = str(fill_value)\n try:\n # 不是以\\n 结尾 直接填值\n if not fill_value.endswith(\"\\n\"):\n el.send_keys(fill_value)\n # 填完值后等待元素加载完成\n self.wait_for_ready_state_complete(driver=driver)\n else:\n # 有\\n结尾 要截去\\n 123\\n ---> 123\n fill_value = fill_value[:-1]\n el.send_keys(fill_value)\n # 进行回车\n el.send_keys(Keys.RETURN)\n self.wait_for_ready_state_complete(driver=driver)\n except StaleElementReferenceException:\n # 等待页面加载完成\n self.wait_for_ready_state_complete(driver=driver)\n time.sleep(0.06)\n el = self.element_appear(driver=driver, locate_type=locate_type, locator_expression=locator_expression,\n timeout=timeout)\n el.clear()\n # 不是以\\n 结尾 直接填值\n if not fill_value.endswith(\"\\n\"):\n el.send_keys(fill_value)\n # 填完值后等待元素加载完成\n self.wait_for_ready_state_complete(driver=driver)\n else:\n # 有\\n结尾 要截去\\n 123\\n ---> 123\n fill_value = fill_value[:-1]\n el.send_keys(fill_value)\n # 进行回车\n el.send_keys(Keys.RETURN)\n self.wait_for_ready_state_complete(driver=driver)\n except Exception:\n raise Exception(\"元素填值失败\")\n # 填写成功后return True\n return True\n\n def element_click(self, driver, locate_type, locator_expression, locate_type_disappear=None,\n locator_expression_disappear=None,\n locate_type_appear=None,\n locator_expression_appear=None, timeout=30):\n \"\"\"\n 元素的点击\n :param driver:\n :param locate_type:定位方式\n :param locator_expression:定位表达式\n :param locate_type_disappear:等待页面元素消失的定位方式\n :param locator_expression_disappear:等待页面元素消失的定位表达式\n :param locate_type_appear:等待页面元素出现的定位方式\n :param locator_expression_appear:等待页面元素出现的定位表达式\n :param timeout:\n \"\"\"\n # 元素要可见\n el = self.element_appear(driver=driver, locate_type=locate_type, locator_expression=locator_expression,\n timeout=timeout)\n try:\n # 点击元素\n el.click()\n except StaleElementReferenceException:\n self.wait_for_ready_state_complete(driver=driver)\n el = self.element_appear(driver=driver, locate_type=locate_type, locator_expression=locator_expression,\n timeout=timeout)\n el.click()\n\n except Exception as e:\n print(\"页面元素出现异常，不可点击\", e)\n return False\n\n try:\n # 点击元素后的元素出现\n self.element_appear(driver, locate_type_appear, locator_expression_appear)\n # 等待页面元素的消失\n self.element_disappear(driver, locate_type_disappear, locator_expression_disappear)\n except Exception as e:\n print(\"页面元素消失或出现异常\", e)\n return False\n return True\n\n def upload(self, driver, locate_type, locator_expression, file_path):\n \"\"\"\n 文件上传\n :param file_path:\n :param driver:\n :param locate_type:\n :param locator_expression:\n \"\"\"\n el = self.element_get(driver, locate_type, locator_expression)\n return el.send_keys(file_path)\n\n def switch_window_to_handle(self, driver):\n \"\"\"\n 窗口的切换\n :param driver:\n \"\"\"\n # 获取窗口权柄\n window_handle = driver.window_handles\n # 传入最后一个窗口的权柄\n driver.switch_to.window(window_handle[-1])\n\n def switch_into_iframe(self, drive, locate_iframe_type, locator_iframe_expression):\n \"\"\"\n iframe切换\n :param drive:\n :param locate_iframe_type:\n :param locator_iframe_expression:\n :return:\n \"\"\"\n iframe = self.element_get(drive, locate_iframe_type, locator_iframe_expression)\n drive.switch_to.frame(iframe)\n\n def switch_from_iframe_to_content(self, driver):\n \"\"\"\n 从iframe切换到主页面\n :param driver:\n :return:\n \"\"\"\n driver.switch_to.parent_frame()\n\n def find_img_in_source(self, driver, img_name):\n \"\"\"\n 截图并在截图中查找图片\n :param driver:\n :param img_name:\n :return:\n \"\"\"\n # 截图后图片保存的路径\n source_img_path = get_project_path() + sep([\"img\", \"source_img\", img_name], add_sep_before=True)\n print(\"source_img_path:\", source_img_path)\n # 需要查找的图片的路径\n search_img_path = get_project_path() + sep([\"img\", \"assert_img\", img_name], add_sep_before=True)\n print(\"search_img_path:\", search_img_path)\n # 截图并保存图片\n driver.get_screenshot_as_file(source_img_path)\n time.sleep(3)\n # add_img_path_2_report(source_img_path, \"原图\")\n # add_img_path_2_report(search_img_path, \"需要查找的图\")\n # 在原图中查找是否有指定的图片，返回信心值\n confidence = FindImg().get_confidence(source_img_path, search_img_path)\n return confidence\n\n def element_screenshot(self, driver, locate_type, locator_expression):\n \"\"\"\n 元素截图\n :param driver:\n :param locate_type:\n :param locator_expression:\n :return:\n \"\"\"\n ele_name = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\") + \".png\"\n ele_img_dir_path = get_project_path() + sep([\"img\", \"ele_img\"], add_sep_before=True, add_sep_after=True)\n if not os.path.exists(ele_img_dir_path):\n os.mkdir(ele_img_dir_path)\n ele_img_path = ele_img_dir_path + ele_name\n self.element_get(driver, locate_type, locator_expression).screenshot(ele_img_path)\n return ele_img_path\n","repo_name":"flychen0310/trading_system_UiAtuoTest","sub_path":"base/ObjectMap.py","file_name":"ObjectMap.py","file_ext":"py","file_size_in_byte":14782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18639204436","text":"import os, sys\nimport time\nimport argparse\nimport multiprocessing\nimport numpy as np\n\nfrom utils.io import readcoo2mat\nfrom all_parser import *\n\ndef read_data(data_file, norm_file, out_dir, resolution):\n filename = os.path.basename(data_file).split('.')[0] + '.npz'\n out_file = os.path.join(out_dir, filename)\n try:\n HiC, idx = readcoo2mat(data_file, norm_file, resolution)\n except:\n print(f'Abnormal file: {norm_file}')\n np.savez_compressed(out_file, hic=HiC, compact=idx)\n print('Saving file:', out_file)\n\nif __name__ == '__main__':\n args = data_read_parser().parse_args(sys.argv[1:])\n\n cell_line = args.cell_line\n resolution = args.high_res\n map_quality = args.map_quality\n postfix = [args.norm_file, 'RAWobserved']\n\n pool_num = 23 if multiprocessing.cpu_count() > 23 else multiprocessing.cpu_count()\n\n raw_dir = os.path.join(root_dir, 'raw', cell_line)\n\n norm_files = []\n data_files = []\n for root, dirs, files in os.walk(raw_dir):\n if len(files) > 0:\n if (resolution in root) and (map_quality in root):\n for f in files:\n if (f.endswith(postfix[0])):\n norm_files.append(os.path.join(root, f))\n elif (f.endswith(postfix[1])):\n data_files.append(os.path.join(root, f))\n\n out_dir = os.path.join(root_dir, 'mat', cell_line)\n mkdir(out_dir)\n print(f'Start reading data, there are {len(norm_files)} files ({resolution}).')\n print(f'Output directory: {out_dir}')\n\n start = time.time()\n pool = multiprocessing.Pool(processes=pool_num)\n print(f'Start a multiprocess pool with process_num={pool_num} for reading raw data')\n for data_fn, norm_fn in zip(data_files, norm_files):\n pool.apply_async(read_data, (data_fn, norm_fn, out_dir, res_map[resolution]))\n pool.close()\n pool.join()\n print(f'All reading processes done. Running cost is {(time.time()-start)/60:.1f} min.')","repo_name":"omegahh/DeepHiC","sub_path":"data_aread.py","file_name":"data_aread.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"31"} +{"seq_id":"10044000687","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 7 21:07:02 2023\n\n@author: charmibhatt\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef make_grid(lambda_start, lambda_end, resolution=None, oversample=None):\n\n # check keywords\n if oversample is None:\n oversample = 40.0\n if resolution is None:\n resolution = 1500.0\n\n lambda_start = np.float64(lambda_start)\n lambda_end = np.float64(lambda_end)\n\n # produce grid\n R = resolution * oversample\n \n #print('R = ' , R)\n n_points = (\n round(\n (np.log(lambda_end / lambda_start)) / (np.log(-(1 + 2 * R) / (1 - 2 * R)))\n )\n + 1\n )\n #print('n_points = ' , n_points)\n f = -(1 + 2 * R) / (1 - 2 * R)\n \n #print('f = ', f)\n factor = f ** np.arange(n_points)\n #print('factor = ' , factor)\n wave = np.full(int(n_points), lambda_start, dtype=np.float)\n #print('wave = ' , wave)\n grid = wave * factor\n #print('grid = ', grid)\n return grid\n\nlambda_start = 6613.5453435174495 #-1.134 #\nlambda_end = 6614.490245405555 #1.039609311008462 #\n\nspec_dir = Path(\"/Users/charmibhatt/Library/CloudStorage/OneDrive-TheUniversityofWesternOntario/UWO_onedrive/Local_GitHub/DIBs/Data/Heather's_data\")\nfilename = '6614_HD{}.txt'\nsightlines = ['23180', '24398', '144470', '147165' , '147683', '149757', '166937', '170740', '184915', '185418', '185859', '203532']\n\n#sightlines = ['166937', '185418', '184915']\n\n# grid = make_grid(lambda_start, lambda_end, resolution=107000, oversample=2)\n# print(grid.shape)\n\ncommon_grid_for_all = make_grid(lambda_start, lambda_end, resolution=107000, oversample=2)\n\ncommon_grid_for_all = (1 / common_grid_for_all) * 1e8\ncommon_grid_for_all = common_grid_for_all - 15119.4\ncommon_grid_for_all = common_grid_for_all[::-1]\n\nprint(common_grid_for_all)\n\n\n\ndef obs_curve_to_fit(sightline): \n \n '''This function reads in data, removes wings and provides just \n the triple peak for fitting and calculates std dev for each sightline '''\n \n file = filename.format(sightline)\n # Obs_data = pd.read_csv(spec_dir / file,\n # delim_whitespace=(True))\n \n Obs_data = pd.read_csv(spec_dir / file,\n sep = ',')\n\n '''interpolating over common grid'''\n \n Obs_data['Wavelength'] = (1 / Obs_data['Wavelength']) * 1e8\n Obs_data = Obs_data.iloc[::-1].reset_index(\n drop=True)\n # shifting to 6614 and scaling flux between 0.9 and 1\n min_index = np.argmin(Obs_data['Flux'])\n Obs_data['Wavelength'] = Obs_data['Wavelength'] - Obs_data['Wavelength'][min_index] #+ 6614\n \n Obs_data['Flux'] = (Obs_data['Flux'] - min(Obs_data['Flux'])) / (1 - min(Obs_data['Flux'])) * 0.1 + 0.9\n \n plt.plot(Obs_data['Wavelength'], Obs_data['Flux'])\n \n Obs_y_data_to_fit = np.interp(common_grid_for_all, Obs_data['Wavelength'], Obs_data['Flux'])\n \n print(Obs_y_data_to_fit)\n \n \n\n Obs_data_continuum = Obs_data [(Obs_data['Wavelength'] >= -9) & (Obs_data['Wavelength']<= -4)]\n std_dev = np.std(Obs_data_continuum['Flux'])\n \n \n return Obs_data, Obs_y_data_to_fit, std_dev\n \n\n\nfor sightline in sightlines: \n Obs_data, Obs_y_data_to_fit, std_dev = obs_curve_to_fit(sightline)\n plt.plot(common_grid_for_all, Obs_y_data_to_fit)\n # print(std_dev)\n #plt.legend()\n plt.show()\n ","repo_name":"charmi-bhatt/DIBs","sub_path":"make_grid.py","file_name":"make_grid.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9389956776","text":"def spy_game(nums, cnt = 0):\n for i in range(len(nums)- 1):\n if nums[i] == 0 and nums[i] == nums[i+1]:\n if nums[i+2] == 7:\n cnt += 1 \n if cnt > 0:\n return True\n else:\n return False\n\n\n\na = list(map(int, input().split()))\nprint(spy_game(a))\n\n#spy_game([1,2,4,0,0,7,5]) --> True\n#spy_game([1,0,2,4,0,5,7]) --> True\n#spy_game([1,7,2,0,4,5,0]) --> False","repo_name":"nekkka/PythonProjects","sub_path":"lab3/functions1/8.py","file_name":"8.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15622530757","text":"#!/usr/bin/env python3\n\n'''\nPython example for sending a message on a CAN bus using python-can.\n\nExample developed on a BeagleBone Black running Debian 10.\n\nAuthor: Kevin Partin\nEmail: kevin dot partin at gmail dot com\n'''\n\nimport can\nimport sys\n\ncan_id = 0\n\nif len(sys.argv) > 1:\n can_id = int(sys.argv[1], 10)\n\nwith can.Bus(channel='can0', interface='socketcan', bitrate=125000) as bus:\n bus.flush_tx_buffer()\n msg = can.Message(arbitration_id=can_id, is_extended_id=False, data=[1, 2, 3, 4, 5, 6, 7, 8])\n bus.send(msg)\n","repo_name":"kevin-partin/python-can","sub_path":"can-transmit-example-3.py","file_name":"can-transmit-example-3.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"35823852317","text":"from gensim.corpora import Dictionary\nfrom gensim.models import TfidfModel as GensimTfIdf\nfrom gensim.similarities import SparseMatrixSimilarity\nfrom gensim.summarization.bm25 import BM25 as GensimBM25\nfrom helper import NDCG\nimport msgpack\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\n\nclass TfIdf:\n\n def __init__(self, data_folder):\n self.model_path = data_folder / \"tf_idf_model.npz\"\n self.index_path = data_folder / \"tf_idf_index.npz\"\n self.dictionary_path = data_folder / \"dictionary_index.npz\"\n\n self.model = None\n self.index = None\n self.dictionary = None\n\n def fit(self, bow_corpus, dictionary, smartirs=\"ltc\"):\n self.dictionary = dictionary\n print(\"TF_IDF:\\tFit model...\", end=\"\")\n self.model = GensimTfIdf(corpus=bow_corpus, smartirs=smartirs, slope=1.0)\n print(\"OK\\nTF_IDF:\\tBuilding search index...\", end=\"\")\n self.index = SparseMatrixSimilarity(self.model[bow_corpus], num_features=len(self.dictionary.dfs))\n print(\"OK\")\n return self\n\n def predict(self, tokenized_query, doc_idx):\n vectorized_query = self.dictionary.doc2bow(tokenized_query)\n td_idf_query = self.model[vectorized_query]\n return self.index[td_idf_query][doc_idx]\n\n def load(self):\n print(\"TF_IDF:\\tLoad model and index...\", end=\"\")\n self.model = GensimTfIdf.load(str(self.model_path))\n self.index = SparseMatrixSimilarity.load(str(self.index_path))\n self.dictionary = Dictionary.load(str(self.dictionary_path))\n print(\"OK\")\n return self\n\n def save(self):\n print(\"TF_IDF:\\tSave model and index...\", end=\"\")\n self.model.save(str(self.model_path))\n self.index.save(str(self.index_path))\n self.dictionary.save(str(self.dictionary_path))\n print(\"OK\")\n return self\n\n\nclass BM25:\n\n def __init__(self):\n self.model = None\n\n def fit(self, corpus, k1=1.5, b=0.75):\n print(\"BM25:\\tFit model...\", end=\"\")\n self.model = GensimBM25(corpus, k1=k1, b=b)\n print(\"OK\")\n return self\n\n def predict(self, tokenized_query, doc_idx):\n return [self.model.get_score(tokenized_query, doc_id) for doc_id in doc_idx]\n\n\nclass Gensim:\n\n def __init__(self, data_folder):\n self.dictionary_path = data_folder / \"dictionary.npz\"\n self.corpus_path = data_folder / \"corpus.mp\"\n self.bow_corpus_path = data_folder / \"bow_corpus.mp\"\n\n self.dictionary = Dictionary()\n self.bow_corpus = []\n self.corpus = []\n self.tf_idf = TfIdf(data_folder)\n self.bm_25 = BM25()\n\n def BuildCorpora(self, data):\n print(\"Corpus building started...\")\n self.corpus = data\n print(\"\\tCreating corpus dictionary...\", end=\"\")\n self.dictionary = Dictionary(self.corpus)\n print(\"OK\\n\\tCreated corpus bow representation...\", end=\"\")\n self.bow_corpus = [self.dictionary.doc2bow(document) for document in self.corpus]\n print(\"OK\\nCorpus building finished\")\n return self\n\n def saveCorpora(self):\n print(\"Saving words corpus...\", end=\"\")\n self.dictionary.save(str(self.dictionary_path))\n with self.corpus_path.open(\"wb\") as output_file:\n output_file.write(msgpack.packb(self.corpus))\n with self.bow_corpus_path.open(\"wb\") as output_file:\n output_file.write(msgpack.packb(self.bow_corpus))\n\n print(\"OK\")\n return self\n\n def loadCorpus(self):\n print(\"Loading corpus data...\", end=\"\")\n self.dictionary = Dictionary.load(str(self.dictionary_path))\n with self.corpus_path.open(\"rb\") as input_file:\n self.corpus = msgpack.load(input_file)\n with self.bow_corpus_path.open(\"rb\") as input_file:\n self.bow_corpus = msgpack.load(input_file)\n print(\"OK\")\n return self\n\n def fit_tf_idf(self):\n self.tf_idf.fit(self.bow_corpus, self.dictionary)\n return self.tf_idf\n\n def fit_bm_25(self):\n self.bm_25.fit(self.corpus)\n return self\n\n\ndef main():\n mode = \"word_2\"\n assert mode in {\"char_3\", \"word\", \"word_2\", \"char_4\"}\n prefix = Path(\"data\")\n title_input_path = prefix / \"titles/titles_{}_tokenized.pkl\".format(mode)\n tokenized_titles_df = pd.read_pickle(title_input_path)\n print(\"Titles data loaded from '{}'\".format(title_input_path))\n gensim = Gensim(data_folder=prefix / \"syntax\" / mode)\n gensim.BuildCorpora(tokenized_titles_df[\"text\"].to_list()).saveCorpora()\n gensim.loadCorpus()\n gensim.fit_bm_25()\n gensim.tf_idf.fit(gensim.bow_corpus, gensim.dictionary).save()\n gensim.tf_idf.load()\n\n tokenized_queries_df = pd.read_pickle(prefix / \"queries/queries_{}_tokenized.pkl\".format(mode))\n relation = pd.read_csv(prefix / \"config/samples.tsv\", sep=\"\\t\", index_col=0)\n output_path = prefix / \"syntax\" / mode / \"scores.tsv\"\n\n labelled_queries = relation[relation[\"label\"] > 0][\"query_id\"].unique()\n ndcg = np.zeros(shape=(2, labelled_queries.size))\n k, m, N = 0, 0, relation[\"query_id\"].unique().size\n\n with open(output_path, \"w\") as output:\n output.write(\"{}\\t{}\\t{}\\t{}\\n\".format(\"query_id\", \"doc_id\", \"tf_idf_score\", \"bm_25_score\"))\n for query_id, group in relation.groupby(\"query_id\"):\n m += 1\n doc_idx = group[\"doc_id\"].values\n\n tfi_df_score = gensim.tf_idf.predict(tokenized_queries_df.loc[query_id][\"text\"], doc_idx)\n bm_25_score = gensim.bm_25.predict(tokenized_queries_df.loc[query_id][\"text\"], doc_idx)\n\n if group[\"label\"].min() >= 0:\n ndcg[0, k] = NDCG(group[\"label\"].values[np.argsort(tfi_df_score)[::-1]])\n ndcg[1, k] = NDCG(group[\"label\"].values[np.argsort(bm_25_score)[::-1]])\n k += 1\n print(\"TF-IDF NDCG: {:05.3f}\\t BM-25 \"\n \"NDCG: {:05.3f} \\t{}/{}\\r\".format(*ndcg[:, :k].mean(axis=1), m, N), end=\"\")\n for i, doc_id in enumerate(doc_idx):\n output.write(\"{}\\t{}\\t{}\\t{}\\n\".format(query_id, doc_id, tfi_df_score[i], bm_25_score[i]))\n\n print(\"\\nResult saved to '{}'\".format(output_path))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"lalkakonus/ir-hw4","sub_path":"src/python/syntax_matching.py","file_name":"syntax_matching.py","file_ext":"py","file_size_in_byte":6235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5379215196","text":"\n# Starting Point ofLinkedListCycle\n\nfrom typing import Optional\n\n\nclass ListNode:\n def __init__(self, data=None, next=None):\n self.data = data\n self.next = next\n\n\ndef detectCycle(self, head: Optional[ListNode]) -> Optional[ListNode]:\n node_seen = set()\n while head is not None:\n if head in node_seen:\n return head\n node_seen.add(head)\n head = head.next\n return None\n# Space Complexity-O(N)\n# Time Complexity-O(N)\n\n\ndef hasCycleOptimized(self, head):\n if head is None:\n return None\n slow = head\n fast = head\n entry = head\n while fast.next is not None and fast.next.next is not None:\n fast = fast.next.next\n slow = slow.next\n if slow == fast:\n while slow != entry:\n entry = entry.next\n slow = slow.next\n return slow\n return None\n# Space Complexity-O(1)\n# Time Complexity-O(N)\n","repo_name":"Tanujarora100/Striver-A-Z-DSA-SHEET","sub_path":"LinkedList/5-starting_node_loop.py","file_name":"5-starting_node_loop.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70689946649","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jun 13 07:01:07 2021\r\n\r\n@author: Niichaannn\r\n\"\"\"\r\nimport os\r\nClear = lambda : os.system('cls')\r\n\r\njwb = \"y\"\r\nwhile jwb==\"y\" or jwb==\"Y\": \r\n Clear()\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\r\n print(\"Hitung Transaksi Printer\")\r\n print(\"Pembelian diatas 1,5 juta mendapat Diskon!\")\r\n print(\"by Hasanah Nisa\")\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\r\n\r\n n = int (input('Masukkan Jumlah Printer = '))\r\n #hitung harga\r\n harga = n*660000\r\n print(\"Total Harga Printer sebelum diskon adalah Rp.\",harga)\r\n \r\n if harga>1500000:\r\n disc = harga * 0.15\r\n else:\r\n disc = 0\r\n \r\n print(\"Pembelian mendapat diskon sebesar Rp.\",disc)\r\n #total harga\r\n totakhir = harga - disc\r\n print('Total Harga setelah Diskon adalah Rp.',totakhir)\r\n \r\n jwb = input(\"Apakah mau menghitung ulang (Y/T) ? \")\r\n if jwb==\"t\" or jwb==\"T\":\r\n break","repo_name":"hasanahnisa/algpython","sub_path":"transaksiprinterdiskon.py","file_name":"transaksiprinterdiskon.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15567463847","text":"import pandas as pd \nimport numpy as np \nfrom datetime import datetime, timedelta\nimport robin_stocks\n\n#Login to Robinhood\n\nrobin_stocks.login('','')\n\n\ndef get_stock_data(symbol):\n \n previous_close = float(robin_stocks.stocks.get_quotes(symbol)[0]['adjusted_previous_close'])\n current_price = float(robin_stocks.stocks.get_latest_price(symbol)[0])\n \n today_change = current_price - previous_close\n today_pct_change = today_change / previous_close * 100\n \n return '{}: {}, {} ({}%)'.format(symbol, current_price, \n round(today_change, 2), round(today_pct_change, 2))\n\nclass Robinhood():\n\n #Constructor method\n def __init__(self):\n pass\n\n #Returns information about current positions\n def get_my_positions(self):\n \n my_stocks = robin_stocks.build_holdings()\n\n my_stocks_out = ''''''\n \n for symbol, info in my_stocks.items():\n \n previous_close = float(robin_stocks.stocks.get_quotes(symbol)[0]['adjusted_previous_close'])\n current_price = float(robin_stocks.stocks.get_latest_price(symbol)[0])\n today_change = current_price - previous_close\n \n my_stocks_out = my_stocks_out + '''{}\nEquity: Today {}, Overall {} ({}%) \\n'''.format(get_stock_data(symbol),\n round(float(info['quantity']) * today_change, 2),\n round(float(info['equity_change']), 2),\n info['percent_change'])\n\n print(my_stocks_out)\n\n return my_stocks_out\n\n #Returns price information about any symbol\n def get_symbol(self, symbol):\n\n return get_stock_data(symbol)\n\n","repo_name":"thamsuppp/Telegram-Budget-Bot","sub_path":"robinhood.py","file_name":"robinhood.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42068652520","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2018/8/27\n# @Author : lei.X\n\nfrom PIL import Image\nimport requests\nfrom io import BytesIO\nfrom pytesseract import image_to_string\nimport os\nimport random\nimport time\n\n\ndef get_time_stamp():\n t = time.time()\n return str(int(round(t * 1000)))\n\n\n\ndef getImage():\n\n requests.get(\"http://sep.ucas.ac.cn/changePic\")\n response = requests.get(\"http://sep.ucas.ac.cn/changePic?code=\"+get_time_stamp())\n img = Image.open(BytesIO(response.content))\n img.show()\n\n # #降噪处理\n # threshold = 140\n # table = []\n # for i in range(256):\n # if i nn.Module:\n return self.base_cls(*self.args, **self.kwargs)\n\n\ndef build_model(arch: dict, disable_bn=False) -> nn.Module:\n model = nn.ModuleList()\n for module_type, args in arch:\n base_cls = nn.Identity\n if module_type == 'conv':\n base_cls = nn.Conv2d\n elif module_type == 'mp':\n base_cls = nn.MaxPool2d\n elif module_type == 'lrelu':\n base_cls = nn.LeakyReLU\n elif module_type == 'softmax':\n base_cls = nn.Softmax\n elif module_type == 'sigmoid':\n base_cls = nn.Sigmoid\n elif module_type == 'bn':\n base_cls = nn.BatchNorm2d\n elif module_type == 'flatten':\n base_cls = nn.Flatten\n elif module_type == 'fc':\n base_cls = nn.Linear\n elif module_type == 'dropout':\n base_cls = nn.Dropout\n elif module_type == 'avgpool':\n base_cls = nn.AvgPool2d\n elif module_type == 'reshape':\n base_cls = YoloReshape\n elif module_type == 'yololoss':\n base_cls = YoloDetector\n elif module_type == 'identity':\n base_cls = nn.Identity\n else:\n warnings.warn(\"Base class not supported: {}\".format(module_type))\n\n if base_cls == nn.BatchNorm2d and disable_bn:\n continue # do not insert current layer\n\n if isinstance(args, (list, tuple)):\n builder = ModuleBuilder(base_cls, *args)\n elif isinstance(args, dict):\n builder = ModuleBuilder(base_cls, None, **args)\n elif args is None:\n builder = ModuleBuilder(base_cls)\n model.append(builder.build())\n return nn.Sequential(*model)\n\n\nclass YoloReshape(torch.nn.Module):\n\n def __init__(self, *target_shape):\n super(YoloReshape, self).__init__()\n self.target_shape = target_shape\n\n def forward(self, x):\n return torch.reshape(x, self.target_shape)\n\n\nclass YOLOv1(torch.nn.Module):\n\n def __init__(self, pretrain_mode=False, disable_bn=True) -> None:\n super().__init__()\n backbone_arch = [\n # Fig.3 Row.1\n [\"conv\", [3, 64, (7, 7), 2, 3]],\n [\"bn\", [64]],\n [\"lrelu\", [0.1]],\n [\"mp\", [(2, 2), 2]],\n # Fig.3 Row.2\n [\"conv\", [64, 192, (3, 3), 1, 1]],\n [\"bn\", [192]],\n [\"lrelu\", [0.1]],\n [\"mp\", [(2, 2), 2]],\n # Fig.3 Row.3\n [\"conv\", [192, 128, (1, 1)]],\n [\"bn\", [128]],\n [\"lrelu\", [0.1]],\n [\"conv\", [128, 256, (3, 3), 1, 1]],\n [\"bn\", [256]],\n [\"lrelu\", [0.1]],\n [\"conv\", [256, 256, (1, 1)]],\n [\"bn\", [256]],\n [\"lrelu\", [0.1]],\n [\"conv\", [256, 512, (3, 3), 1, 1]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"mp\", [(2, 2), 2]],\n # Fig.3 Row.4\n [\"conv\", [512, 256, (1, 1)]],\n [\"bn\", [256]],\n [\"lrelu\", [0.1]],\n [\"conv\", [256, 512, (3, 3), 1, 1]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 256, (1, 1)]],\n [\"bn\", [256]],\n [\"lrelu\", [0.1]],\n [\"conv\", [256, 512, (3, 3), 1, 1]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 256, (1, 1)]],\n [\"bn\", [256]],\n [\"lrelu\", [0.1]],\n [\"conv\", [256, 512, (3, 3), 1, 1]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 256, (1, 1)]],\n [\"bn\", [256]],\n [\"lrelu\", [0.1]],\n [\"conv\", [256, 512, (3, 3), 1, 1]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 512, (1, 1)]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 1024, (3, 3), 1, 1]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n [\"mp\", [(2, 2), 2]],\n # Fig.3 Row.5.1\n [\"conv\", [1024, 512, (1, 1)]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 1024, (3, 3), 1, 1]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n [\"conv\", [1024, 512, (1, 1)]],\n [\"bn\", [512]],\n [\"lrelu\", [0.1]],\n [\"conv\", [512, 1024, (3, 3), 1, 1]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n ]\n pretrain_head_arch = [\n [\"avgpool\", [(7, 7)]],\n [\"flatten\", [1]],\n [\"fc\", [1024, 1000]],\n ]\n detection_head_arch = [\n # Fig.3 Row.5.2\n [\"conv\", [1024, 1024, (1, 1)]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n [\"conv\", [1024, 1024, (3, 3), 2, 1]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n # Fig.3 Row.6\n [\"conv\", [1024, 1024, (3, 3), 1, 1]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n [\"conv\", [1024, 1024, (3, 3), 1, 1]],\n [\"bn\", [1024]],\n [\"lrelu\", [0.1]],\n # Fig.3 Row.7\n [\"flatten\", [1]],\n [\"fc\", [50176, 4096]],\n [\"identity\", None],\n [\"dropout\", 0.5],\n # # Fig.3 Row.8\n [\"fc\", [4096, 1470]],\n [\"reshape\", [-1, 30, 7, 7]],\n # Limit range to 0~1, xywhc, classes, all of them.\n [\"sigmoid\", None]\n ]\n\n # original paper doesn't have bn implemented on.\n self.backbone = build_model(backbone_arch, disable_bn)\n self.pretrain_head = build_model(pretrain_head_arch, disable_bn)\n self.detection_head = build_model(detection_head_arch, disable_bn)\n self.yolo_detector = YoloDetector(\n cell_size=7,\n box_candidates=2,\n num_classes=20,\n )\n self.pretrain_mode = pretrain_mode\n\n def forward(self, x, label=None):\n x = self.backbone(x)\n if self.pretrain_mode:\n assert label is None, \"Label should not be supplied on pretrain mode\"\n x = self.pretrain_head(x)\n return x\n else:\n x = self.detection_head(x)\n\n if self.training:\n if label is not None:\n pred, loss = self.yolo_detector(x, label)\n return pred, loss\n else:\n warnings.warn('Label not supplied')\n return x\n else:\n pred = self.yolo_detector(x)\n return pred\n\n\nif __name__ == '__main__':\n from torchinfo import summary\n summary(\n YOLOv1(pretrain_mode=False),\n input_size=(1, 3, 448, 448),\n verbose=True,\n )\n # summary(\n # YOLOv1(pretrain_mode=True),\n # input_size=(1, 3, 224, 224),\n # verbose=True,\n # )\n","repo_name":"jungin500/yolov1-torch","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"20672543077","text":"import ctypes\nimport threading\nimport re\nimport platform\nfrom sink import create_sink, destroy_sink\n\n__all__ = ['tidy_document', 'tidy_fragment', 'release_tidy_doc']\n\n#----------------------------------------------------------------------------#\n# Constants\n\nLIB_NAMES = ['libtidy', 'libtidy.so', 'libtidy-0.99.so.0', 'cygtidy-0-99-0',\n 'tidylib', 'libtidy.dylib', 'tidy']\nENOMEM = -12\nRE_BODY = re.compile(r\"[\\r\\n]*(.+?)\", re.S)\nBASE_OPTIONS = {\n \"output-xhtml\": 1, # XHTML instead of HTML4\n \"indent\": 1, # Pretty; not too much of a performance hit\n \"tidy-mark\": 0, # No tidy meta tag in output\n \"wrap\": 0, # No wrapping\n \"alt-text\": \"\", # Help ensure validation\n \"doctype\": 'strict', # Little sense in transitional for tool-generated markup...\n \"force-output\": 1, # May not get what you expect but you will get something\n }\n \n# Note: These are meant as sensible defaults. If you don't like these being\n# applied by default, just set tidylib.BASE_OPTIONS = {} after importing.\n# You can of course override any of these options when you call the\n# tidy_document() or tidy_fragment() function\n\n#----------------------------------------------------------------------------#\n# Globals\n\ntidy = None\nthread_local_doc = threading.local()\n\n# Fix for Windows b/c tidy uses stdcall on Windows\nif \"Windows\" == platform.system():\n load_library = ctypes.windll.LoadLibrary\nelse:\n load_library = ctypes.cdll.LoadLibrary\n\nfor name in LIB_NAMES:\n try:\n tidy = load_library(name)\n break\n except OSError:\n pass\n \nif tidy is None:\n raise OSError(\"Could not load libtidy using any of these names: %s\" % (\",\".join(LIB_NAMES)))\n\ntidy.tidyCreate.restype = ctypes.POINTER(ctypes.c_void_p) # Fix for 64-bit systems\n\n#----------------------------------------------------------------------------#\n# Functions\n\ndef tidy_document(text, options=None, keep_doc=False):\n \"\"\" Run a string with markup through HTML Tidy; return the corrected one.\n \n text (str): The markup, which may be anything from an empty string to a\n complete (X)HTML document. Unicode values are supported; they will be\n encoded as UTF-8, and HTML Tidy's output will be decoded back to a unicode\n object.\n \n options (dict): Options passed directly to HTML Tidy; see the HTML Tidy docs\n (http://tidy.sourceforge.net/docs/quickref.html) or run tidy -help-config\n from the command line. \n \n keep_doc (boolean): If True, store 1 document object per thread and re-use\n it, for a slight performance boost especially when tidying very large numbers\n of very short documents.\n \n returns (str, str): The tidied markup [0] and warning/error messages[1].\n Warnings and errors are returned just as tidylib returns them.\n \"\"\"\n global tidy, option_names\n \n # Unicode approach is to encode as string, then decode libtidy output\n use_unicode = False\n if isinstance(text, unicode):\n use_unicode = True\n text = text.encode('utf-8')\n \n # Manage thread-local storage of persistent document object\n if keep_doc:\n if not hasattr(thread_local_doc, 'doc'):\n thread_local_doc.doc = tidy.tidyCreate()\n doc = thread_local_doc.doc\n else:\n doc = tidy.tidyCreate()\n \n # This is where error messages are sent by libtidy\n sink = create_sink()\n tidy.tidySetErrorSink(doc, sink)\n \n try:\n # Set options on the document\n # If keep_doc=True, options will persist between calls, but they can\n # be overridden, and the BASE_OPTIONS will be set each time\n tidy_options = dict(BASE_OPTIONS)\n if options:\n tidy_options.update(options)\n if use_unicode:\n tidy_options['input-encoding'] = 'utf8'\n tidy_options['output-encoding'] = 'utf8'\n for key in tidy_options:\n value = tidy_options[key]\n key = key.replace('_', '-')\n if value is None:\n value = ''\n tidy.tidyOptParseValue(doc, key, str(value))\n error = str(sink)\n if error:\n raise ValueError(\"(tidylib) \" + error)\n \n # The point of the whole thing\n tidy.tidyParseString(doc, text)\n tidy.tidyCleanAndRepair(doc)\n \n # Guess at buffer size; tidy returns ENOMEM if the buffer is too\n # small and puts the required size into out_length\n out_length = ctypes.c_int(8192)\n out = ctypes.c_buffer(out_length.value)\n if ENOMEM == tidy.tidySaveString(doc, out, ctypes.byref(out_length)):\n out = ctypes.c_buffer(out_length.value)\n tidy.tidySaveString(doc, out, ctypes.byref(out_length))\n \n document = out.value\n if use_unicode:\n document = document.decode('utf-8')\n errors = str(sink)\n finally:\n destroy_sink(sink)\n if not keep_doc:\n tidy.tidyRelease(doc)\n\n return (document, errors)\n \n \ndef tidy_fragment(text, options=None, keep_doc=False):\n \"\"\" Tidy a string with markup and return only the contents.\n \n HTML Tidy normally returns a full (X)HTML document; this function returns only\n the contents of the element and is meant to be used for snippets.\n Calling tidy_fragment on elements that don't go in the , like ,\n will produce incorrect behavior.\n \n Arguments and return value are the same as tidy_document. Note that HTML\n Tidy will always complain about the lack of a doctype and <title> element\n in fragments, and these errors are not stripped out for you. \"\"\"\n document, errors = tidy_document(text, options, keep_doc)\n match = RE_BODY.search(document)\n if match:\n document = match.group(1).strip()\n return (document, errors)\n else:\n raise ValueError(\"tidy_fragment failed to process text\")\n \ndef release_tidy_doc():\n \"\"\" Release the stored document object in the current thread. Only useful\n if you have called tidy_document or tidy_fragament with keep_doc=True. \"\"\"\n if hasattr(thread_local_doc, 'doc'):\n tidy.tidyRelease(thread_local_doc.doc)\n del thread_local_doc.doc\n \n#----------------------------------------------------------------------------#\n ","repo_name":"ppasupat/WikiTableQuestions","sub_path":"weblib/external/tidylib/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6355,"program_lang":"python","lang":"en","doc_type":"code","stars":121,"dataset":"github-code","pt":"31"} +{"seq_id":"28474635208","text":"import tensorflow as tf\nimport lib\nfrom lib.task.seq2seq.summary import *\nfrom copy import copy\nimport lib.layers.basic\nfrom lib.layers.basic import infer_length\nimport nltk\nimport numpy as np\n\ndef pad_to_length(out, length, token):\n assert out.shape.ndims == 3\n num_paddings = tf.maximum(0, length - tf.shape(out)[2])\n padding = tf.fill([tf.shape(out)[0], tf.shape(out)[1], num_paddings], token)\n return tf.concat([out, padding], axis=2)\n\n\nclass PyBleuComputer:\n def __init__(self, voc, smoothing_function):\n self.voc, self.smoothing_function = voc, smoothing_function\n\n def crop_eos(self, seq):\n seq = list(seq)\n if self.voc.eos in seq:\n seq = seq[:seq.index(self.voc.eos)]\n return seq\n\n def py_compute_sentence_bleu(self, prediction, reference, debug=False):\n scores = []\n for pred_i, ref_i in zip(prediction, reference):\n pred_i, ref_i = map(self.crop_eos, [pred_i, ref_i])\n if len(pred_i) > 0 and len(ref_i) > 0:\n score_i = nltk.bleu([ref_i], pred_i,\n smoothing_function=self.smoothing_function)\n else:\n score_i = 0\n scores.append(score_i)\n if debug:\n print('pred and ref:', pred_i, ref_i)\n print('score:', score_i)\n return np.array(scores, dtype=np.float32)\n\n def __call__(self, prediction, reference):\n assert prediction.shape.ndims == 2 and reference.shape.ndims == 2\n bleu_scores = tf.py_func(self.py_compute_sentence_bleu, [prediction, reference],\n tf.float32, stateful=False, name='compute_sentence_bleu')\n bleu_scores.set_shape([None])\n return tf.stop_gradient(bleu_scores)\n\n\ndef word_dropout(inp, inp_len, dropout, method, voc):\n inp_shape = tf.shape(inp)\n\n border = tf.fill([inp_shape[0], 1], False)\n\n mask = tf.sequence_mask(inp_len - 2, inp_shape[1] - 2)\n mask = tf.concat((border, mask, border), axis=1)\n mask = tf.logical_and(mask, tf.random_uniform(inp_shape) < dropout)\n\n if method == 'unk':\n replacement = tf.fill(inp_shape, tf.cast(voc._unk, inp.dtype))\n elif method == 'random_word':\n replacement = tf.random_uniform(inp_shape, minval=max(voc.bos, voc.eos, voc._unk) + 1, maxval=voc.size(),\n dtype=inp.dtype)\n else:\n raise ValueError(\"Unknown word dropout method: %r\" % method)\n\n return tf.where(mask, replacement, inp)\n\n\nclass MRTProblem(lib.train.Problem):\n \"\"\"\n Minimum risk training as defined here: https://www.aclweb.org/anthology/P16-1159.pdf\n \"\"\"\n\n def __init__(self, models, sum_loss=False, use_small_batch_multiplier=False,\n inp_word_dropout=0, out_word_dropout=0, word_dropout_method='unk',\n hypo_inference_flags={'mode': 'sample', 'sampling_strategy': 'random'},\n num_hypos=100, alpha=5e-3, target_in_hypos=True, loss_type='minus_bleu',\n mean_over_seq=False,\n ):\n # loss_type: one of 'minus_bleu' or 'one_minus_bleu'\n assert len(models) == 1\n assert loss_type in ['minus_bleu', 'one_minus_bleu'], \"Loss type has to be one of ['minus_bleu', 'one_minus_bleu']\"\n\n self.models = models\n self.model = list(self.models.values())[0]\n\n self.inp_voc = self.model.inp_voc\n self.out_voc = self.model.out_voc\n\n self.sum_loss = sum_loss\n self.use_small_batch_multiplier = use_small_batch_multiplier\n\n self.inp_word_dropout = inp_word_dropout\n self.out_word_dropout = out_word_dropout\n self.word_dropout_method = word_dropout_method\n\n # ----- begin the MRT part -----\n self.hypo_inference_flags = hypo_inference_flags\n self.target_in_hypos = target_in_hypos\n self.loss_type = loss_type\n self.num_hypos = tf.constant(num_hypos)\n self.neg_bias_value = -1e9\n self.alpha = alpha\n self.get_max_len = lambda inp_len, out_len: tf.to_int32(tf.to_float(out_len) * 1.4) + 3\n self.mean_over_seq = mean_over_seq\n # ----- end the MRT part -----\n\n if self.use_small_batch_multiplier:\n self.max_batch_size_var = tf.get_variable(\"max_batch_size\", shape=[], initializer=tf.ones_initializer(),\n trainable=False)\n\n def _make_encdec_batch(self, batch, is_train):\n encdec_batch = copy(batch)\n\n if is_train and self.inp_word_dropout > 0:\n encdec_batch['inp'] = word_dropout(encdec_batch['inp'], encdec_batch['inp_len'], self.inp_word_dropout,\n self.word_dropout_method, self.model.inp_voc)\n\n if is_train and self.out_word_dropout > 0:\n encdec_batch['out'] = word_dropout(encdec_batch['out'], encdec_batch['out_len'], self.out_word_dropout,\n self.word_dropout_method, self.model.out_voc)\n\n return encdec_batch\n\n def concat_hypos_with_out(self, hypos, out, eos):\n assert out.shape.ndims == 2 and hypos.shape.ndims == 3\n # hypos shape: [batch_size, n_hypos, length1]\n # out shape: [batch_size, length2]\n\n out_reshaped = out[:, None, :]\n\n max_len_hypos = tf.shape(hypos)[2]\n max_len_out = tf.shape(out_reshaped)[2]\n new_max_len = tf.maximum(max_len_out, max_len_hypos)\n\n padded_hypos = pad_to_length(hypos, new_max_len, eos)\n padded_out = pad_to_length(out_reshaped, new_max_len, eos)\n return tf.concat([padded_out, padded_hypos], axis=1)\n\n def py_deduplicate_hypos(self, hypos_with_out):\n mask = np.full(hypos_with_out.shape[:-1], False, dtype=np.bool)\n\n for batch_i in range(hypos_with_out.shape[0]):\n all_hypos = list(map(tuple, hypos_with_out[batch_i]))\n unique_hypos = set(all_hypos)\n for hypo_i, hypo in enumerate(all_hypos):\n if hypo in unique_hypos:\n unique_hypos.discard(hypo)\n mask[batch_i, hypo_i] = True\n\n return mask\n\n def deduplicate_hypos(self, hypos_with_out):\n return tf.py_func(self.py_deduplicate_hypos, [hypos_with_out], tf.bool, stateful=False, name='deduplicate')\n\n def batch_counters(self, batch, is_train):\n\n wide_batch = copy(batch)\n wide_batch['inp'] = tf.expand_dims(wide_batch['inp'], axis=1)\n wide_batch['inp'] = tf.tile(wide_batch['inp'], [1, self.num_hypos, 1])\n wide_batch['inp'] = tf.reshape(wide_batch['inp'],\n [tf.shape(wide_batch['inp'])[0] * tf.shape(wide_batch['inp'])[1],\n tf.shape(wide_batch['inp'])[2]])\n\n new_out_ = tf.expand_dims(wide_batch['out'], axis=1)\n new_out_ = tf.tile(new_out_, [1, self.num_hypos + 1, 1])\n new_out_ = tf.reshape(new_out_, [-1, tf.shape(new_out_)[-1]])\n hypo_max_len = self.get_max_len(infer_length(wide_batch['inp'], self.inp_voc.eos),\n infer_length(new_out_, self.out_voc.eos))\n\n hypos = self.model.symbolic_translate(wide_batch, max_len=hypo_max_len, back_prop=False,\n **self.hypo_inference_flags).best_out\n # ========== add out to hypos ===========\n if self.target_in_hypos:\n hypos_per_inp = tf.reshape(hypos, [tf.shape(batch['inp'])[0], -1, tf.shape(hypos)[-1]])\n hypos_with_out = self.concat_hypos_with_out(hypos_per_inp, batch['out'], eos=self.out_voc.eos)\n # hypos_with_out: [batch_size, n_hypos, out_len]\n hypos_with_out_flat = tf.reshape(hypos_with_out, [-1, tf.shape(hypos_with_out)[-1]])\n else:\n hypos_with_out = tf.reshape(hypos, [tf.shape(batch['inp'])[0], -1, tf.shape(hypos)[-1]])\n hypos_with_out_flat = hypos\n\n # ========== prepare large batch with hypos and out ===========\n\n wide_batch_with_out = copy(batch)\n new_inp = tf.expand_dims(wide_batch_with_out['inp'], axis=1)\n new_inp = tf.tile(new_inp, [1, self.num_hypos + (1 if self.target_in_hypos else 0), 1])\n new_inp = tf.reshape(new_inp, [-1, tf.shape(new_inp)[-1]])\n\n new_out = tf.expand_dims(wide_batch_with_out['out'], axis=1)\n new_out = tf.tile(new_out, [1, self.num_hypos + (1 if self.target_in_hypos else 0), 1])\n new_out = tf.reshape(new_out, [-1, tf.shape(new_out)[-1]])\n\n new_inp_len = lib.ops.basic.infer_length(new_inp, eos=self.inp_voc.eos)\n new_out_len = lib.ops.basic.infer_length(hypos_with_out_flat, eos=self.out_voc.eos)\n\n # ========== mask for duplicates ===========\n duplicate_mask = self.deduplicate_hypos(hypos_with_out) # duplicate_mask: [batch_size, n_hypos]\n duplicate_mask_flat = tf.reshape(duplicate_mask, tf.shape(new_out_len)) # [batch_size * n_hypos]\n\n # ========== eval logprobs for all hypos ===========\n new_batch = {'inp': new_inp, 'out': hypos_with_out_flat,\n 'inp_len': new_inp_len, 'out_len': new_out_len}\n\n rdo = self.model.encode_decode(new_batch, is_train=is_train)\n with lib.layers.basic.dropout_scope(is_train):\n logits = self.model.loss.rdo_to_logits(rdo, new_batch['out'],\n new_batch['out_len']) # [batch_size * nout * ovoc_size]\n sent_logprobs = - self.model.loss.logits2loss(logits, new_batch['out'], new_batch['out_len'])\n if self.mean_over_seq:\n sent_logprobs /= tf.to_float(new_batch['out_len'])\n\n sent_logprobs *= self.alpha\n sent_logprobs += self.neg_bias_value * (1 - tf.to_float(duplicate_mask_flat)) # batch_size * (num_hypos + 1)\n\n sent_logprobs = tf.reshape(sent_logprobs, [tf.shape(batch['inp'])[0], -1])\n\n sent_probs = tf.nn.softmax(sent_logprobs, axis=-1)\n sent_probs_flat = tf.reshape(sent_probs, tf.shape(new_out_len))\n\n # ========== eval bleu for all hypos ===========\n compute_bleu = PyBleuComputer(self.model.out_voc, nltk.bleu_score.SmoothingFunction().method1)\n bleu_scores_flat = compute_bleu(hypos_with_out_flat, new_out)\n\n # ========== final_loss ===========\n if self.loss_type == 'minus_bleu':\n loss_values = - sent_probs_flat * bleu_scores_flat # [batch_size * (num_hypos + 1)]\n else:\n loss_values = sent_probs_flat * (1 - bleu_scores_flat) # [batch_size * (num_hypos + 1)]\n\n counters = dict(\n loss=tf.reduce_sum(loss_values),\n out_len=tf.to_float(tf.reduce_sum(batch['out_len'])),\n )\n append_counters_common_metrics(counters, logits, new_batch['out'], new_batch['out_len'], is_train)\n append_counters_xent(counters, loss_values, new_batch['out_len'])\n append_counters_io(counters, batch['inp'], batch['out'], batch['inp_len'], batch['out_len'])\n return counters\n\n def loss_multibatch(self, counters, is_train):\n if self.sum_loss:\n value = tf.reduce_sum(counters['loss'])\n else:\n value = tf.reduce_sum(counters['loss']) / tf.reduce_sum(counters['out_len'])\n\n if self.use_small_batch_multiplier and is_train:\n batch_size = tf.reduce_sum(counters['out_len'])\n max_batch_size = tf.maximum(self.max_batch_size_var, batch_size)\n with tf.control_dependencies([tf.assign(self.max_batch_size_var, max_batch_size)]):\n small_batch_multiplier = batch_size / max_batch_size\n value = value * small_batch_multiplier\n\n return value\n\n def summary_multibatch(self, counters, prefix, is_train):\n res = []\n res += summarize_common_metrics(counters, prefix)\n res += summarize_xent(counters, prefix)\n res += summarize_io(counters, prefix)\n return res\n\n def params_summary(self):\n if hasattr(self.model, 'params_summary'):\n return self.model.params_summary()\n\n return []\n\n def make_feed_dict(self, batch, **kwargs):\n return self.model.make_feed_dict(batch, **kwargs)\n","repo_name":"lena-voita/the-story-of-heads","sub_path":"lib/task/seq2seq/problems/mrt.py","file_name":"mrt.py","file_ext":"py","file_size_in_byte":12202,"program_lang":"python","lang":"en","doc_type":"code","stars":270,"dataset":"github-code","pt":"31"} +{"seq_id":"38556472850","text":"from itertools import permutations\n\nanswer = 0\ntmp = 0\n\ndef dfs(idx, N, k, dungeons, p):\n global tmp\n\n if idx == N:\n return\n\n if k >= dungeons[p[idx]][0]:\n k -= dungeons[p[idx]][1]\n tmp += 1\n\n dfs(idx+1, N, k, dungeons, p)\n\n\ndef solution(k, dungeons):\n global answer, tmp\n\n per = list(permutations([i for i in range(len(dungeons))], len(dungeons)))\n\n for p in per:\n tmp = 0\n dfs(0, len(dungeons), k, dungeons, p)\n answer = max(answer, tmp)\n return answer\n\nprint(solution(80, [[80,20],[50,40],[30,10]]))","repo_name":"HYEEWON/solve-algorithm-problems","sub_path":"programmers/weekly_challange/211216_12주차_피로도_L2.py","file_name":"211216_12주차_피로도_L2.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"16691103663","text":"from typing import List, Dict, Union\n\nfrom fastapi import APIRouter, Depends, HTTPException\n\nfrom sqlalchemy import insert, select, update\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_async_session\nfrom menu.models import MenuItem\nfrom menu.schemas import MenuItemBase, MenuItemRead, MenuItemResponseRead, MenuItemResponseUpdate\n\nrouter = APIRouter(\n prefix=\"/menu\",\n tags=[\"Меню\"]\n)\n\n\n@router.post(\"/\", response_model=Dict[str, Union[str, None]])\nasync def create_menu_item(menu_item: MenuItemBase, session: AsyncSession = Depends(get_async_session)):\n \"\"\" Добавляет новый пункт меню \"\"\"\n stmt = insert(MenuItem).values(**menu_item.dict(exclude_unset=True))\n try:\n await session.execute(stmt)\n await session.commit()\n return {\n \"status\": \"success\",\n \"data\": None,\n \"message\": \"Пункт меню успешно добавлен\"\n }\n except IntegrityError as e:\n print(e)\n raise HTTPException(status_code=400, detail={\n \"status\": \"error\",\n \"data\": None,\n \"message\": \"Такой пункт меню уже есть: Измените имя\"\n })\n\n\n@router.get(\"/{menu_item_id}\", response_model=MenuItemBase)\nasync def read_menu_item(menu_item_id: int, session: AsyncSession = Depends(get_async_session)):\n \"\"\" Выводит пункт меню по ID \"\"\"\n pass\n\n\n@router.put(\"/{menu_item_id}\", response_model=MenuItemResponseUpdate)\nasync def update_menu_item(menu_item_id: int, menu_item: MenuItemBase,\n session: AsyncSession = Depends(get_async_session)):\n \"\"\" Обновляет пункт меню по ID \"\"\"\n stmt = update(MenuItem).where(MenuItem.id == menu_item_id).values(**menu_item.dict(exclude_unset=True))\n try:\n await session.execute(stmt)\n await session.commit()\n return {\n \"status\": \"success\",\n \"data\": menu_item,\n \"message\": \"Пункт меню изменён\"\n }\n except IntegrityError as e:\n print(e)\n raise HTTPException(status_code=400, detail={\n \"status\": \"error\",\n \"data\": None,\n \"message\": \"Такой пункт меню уже есть: Измените имя\"\n })\n\n\n@router.delete(\"/{menu_item_id}\", response_model=MenuItemBase)\nasync def delete_menu_item(menu_item_id: int, session: AsyncSession = Depends(get_async_session)):\n \"\"\" Удаляет пункт меню \"\"\"\n pass\n\n\n@router.get(\"/\", response_model=MenuItemResponseRead)\nasync def read_menu_items(skip: int = 0, limit: int = 100, session: AsyncSession = Depends(get_async_session)):\n \"\"\" Выводит список пунктов меню \"\"\"\n try:\n query = select(MenuItem).offset(skip).limit(limit)\n result = await session.execute(query)\n menu_items = result.scalars().all()\n\n if not menu_items:\n raise HTTPException(status_code=404, detail={\n \"status\": \"error\",\n \"data\": None,\n \"message\": \"В меню нет элементов. Добавь новый\"\n })\n return {\n \"status\": \"success\",\n \"data\": menu_items,\n \"message\": \"Получен список пунктов меню\"\n }\n except Exception as e:\n raise HTTPException(status_code=500, detail={\n \"status\": \"error\",\n \"data\": None,\n \"message\": f\"Ошибка при получении списка пунктов меню: {str(e)}\"\n })\n","repo_name":"kochenov/dreammanor","sub_path":"backend/src/menu/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":3682,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"30127304048","text":"# TODO:\n# 1. take into account the RB fee when selling position\nimport numpy as np\nimport pandas as pd\nimport empyrical as emp\nimport portfolioopt as pfopt\nfrom scipy import stats\n\n\n# calculating portfolio performance\nclass PortfolioModels():\n def __init__(self, datafile):\n self.datafile = datafile\n self.panelframe = None\n self.APPROX_BDAYS_PER_YEAR = 252\n return None\n\n def _merge_market_with_orders(self, df_ord, pf):\n \"\"\"\n Helper for merging orders with panel frame with market data\n \"\"\"\n pf['cum_size'] = 0\n pf['cum_cost_basis'] = 0\n pf['cum_realized_gain'] = 0\n # loop over tickers, except the last one, which is market\n for key in pf.minor_axis[:-1]:\n df1 = pf.loc[:, :, key]\n df2 = df_ord[df_ord['symbol'] == key].copy()\n df2.set_index('date', inplace=True)\n df = pd.merge(\n df1, df2[['cum_size', 'cum_cost_basis', 'cum_realized_gain']],\n left_index=True, right_index=True, how='left')\n df.rename(columns={\n 'cum_size_y': 'cum_size',\n 'cum_cost_basis_y': 'cum_cost_basis',\n 'cum_realized_gain_y': 'cum_realized_gain'}, inplace=True)\n\n df.drop('cum_size_x', axis=1, inplace=True)\n df.drop('cum_cost_basis_x', axis=1, inplace=True)\n df.drop('cum_realized_gain_x', axis=1, inplace=True)\n\n # now propagate values from last observed\n df.fillna(method='ffill', inplace=True)\n df.fillna(0, inplace=True)\n pf.loc[:, :, key] = df\n return pf\n\n def _merge_market_with_dividends(self, df_div, pf):\n \"\"\"\n Helper to merge the market frame with dividends\n \"\"\"\n pf['cum_dividends'] = 0\n pf['dividend_rate'] = 0\n for key in pf.minor_axis[:-1]:\n df1 = pf.loc[:, :, key]\n df2 = df_div[df_div['symbol'] == key].copy()\n df2.set_index('date', inplace=True)\n df = pd.merge(\n df1, df2[['cum_dividends', 'rate']],\n left_index=True, right_index=True, how='left')\n\n df.drop('cum_dividends_x', axis=1, inplace=True)\n df.drop('dividend_rate', axis=1, inplace=True)\n\n df.rename(columns={\n 'cum_dividends_y': 'cum_dividends',\n 'rate': 'dividend_rate'}, inplace=True)\n\n # now propagate values from last observed\n df['cum_dividends'].fillna(method='ffill', inplace=True)\n df['cum_dividends'].fillna(0, inplace=True)\n pf.loc[:, :, key] = df\n return pf\n\n def _prepare_portfolio(self):\n \"\"\"\n Prepare portfolio panelframe\n by merging orders and dividends with stock prices and\n calculating cumulative values\n -------------\n Parameters:\n - None, using df_ord, df_div and market prices\n \"\"\"\n\n # read frames for internal use\n # df_ord = pd.read_hdf(self.datafile, 'orders')\n df_div = pd.read_hdf(self.datafile, 'dividends')\n df_open = pd.read_hdf(self.datafile, 'open')\n df_closed = pd.read_hdf(self.datafile, 'closed')\n pf = pd.read_hdf(self.datafile, 'market')\n\n # try this out\n # TODO: if works - df_ord may be replaced\n df_ord = pd.concat([df_open, df_closed]).sort_index()\n\n # calculate cumulative size and cost basis\n df_ord['cum_size'] =\\\n df_ord.groupby('symbol').signed_size.cumsum()\n\n # cost basis for closed orders is equal to the one for original open\n # position, so cumulative does not include any gains or losses from\n # closing orders\n df_ord['cum_cost_basis'] =\\\n df_ord.groupby('symbol').current_cost_basis.cumsum()\n\n # aggregate orders on the same day\n func = {\n 'average_price': 'mean',\n 'current_cost_basis': 'sum',\n 'current_size': 'sum',\n 'fees': 'sum',\n 'final_cost_basis': 'sum',\n 'final_size': 'sum',\n 'signed_size': 'sum',\n 'cum_size': 'sum',\n 'cum_cost_basis': 'sum',\n 'realized_gains': 'sum'}\n df_ord = df_ord.groupby(['date', 'symbol'], as_index=False).agg(func)\n\n # calculate cumulative size and cost basis\n df_ord['cum_size'] =\\\n df_ord.groupby('symbol').signed_size.cumsum()\n df_ord['cum_cost_basis'] =\\\n df_ord.groupby('symbol').current_cost_basis.cumsum()\n df_ord['cum_realized_gain'] =\\\n df_ord.groupby('symbol').realized_gains.cumsum()\n\n # fix the average price, so it is weighted mean\n df_ord['average_price'] =\\\n df_ord['cum_cost_basis'] / df_ord['cum_size']\n\n # calculate cumulative dividends\n df_div['cum_dividends'] = df_div.groupby('symbol').amount.cumsum()\n\n # merge both with market\n pf = self._merge_market_with_orders(df_ord, pf)\n pf = self._merge_market_with_dividends(df_div, pf)\n\n '''\n replace null stock prices using backfill to avoid issues with\n daily_change and beta calculations\n '''\n close_price = pf['Close']\n close_price.values[close_price.values == 0] = np.nan\n close_price.fillna(method='bfill', inplace=True)\n pf['Close'] = close_price\n\n self.panelframe = pf\n return self\n\n def daily_portfolio_changes(self):\n \"\"\"\n Calculate daily prices, cost-basis, ratios, returns, etc.\n Used for plotting and also showing the final snapshot of\n the portfolio\n -------------\n Parameters:\n - None\n Return:\n - Panelframe with daily return values. Used for plotting\n and for html output\n \"\"\"\n\n # prepare the portfolio panel\n self._prepare_portfolio()\n pf = self.panelframe\n\n \"\"\"\n Main daily portfolio properties\n \"\"\"\n # dividend yield\n pf['dividend_yield'] = pf['dividend_rate'] / pf['Close'] * 100\n\n '''\n cumulative current value of the position for the given security\n at the start and end of the day\n '''\n pf['cum_value_close'] = pf['cum_size'] * pf['Close']\n pf['cum_value_open'] = pf['cum_size'] * pf['Open']\n\n # current weight of the given security in the portfolio - matrix\n # based on the close price\n pf['current_weight'] =\\\n (pf['cum_value_close'].T / pf['cum_value_close'].sum(axis=1)).T\n\n # unrealized gain on open positions at the end of day\n pf['cum_unrealized_gain'] =\\\n pf['cum_value_close'] - pf['cum_cost_basis']\n\n # investment return without dividends\n pf['investment_return'] = pf['cum_unrealized_gain'] + \\\n pf['cum_realized_gain']\n\n # total return\n pf['cum_total_return'] = pf['cum_unrealized_gain'] +\\\n pf['cum_dividends'] + pf['cum_realized_gain']\n\n # return from price change only\n pf['cum_price_return'] = pf['cum_unrealized_gain']\n\n # calculate ROI\n pf['current_return_rate'] =\\\n (pf['cum_total_return'] / pf['cum_cost_basis'] * 100).\\\n where(pf['cum_size'] != 0).fillna(method='ffill')\n\n # assign to panelframe\n self.panelframe = pf\n return self\n\n def stock_correlation_matrix(self):\n \"\"\"\n Calculate stock correlation matrix\n References:\n 1. p. 137 of Modern Portfolio Theory and Investment Analysis\n edition 9\n 2. faculty.washington.edu/ezivot/econ424/portfolioTheoryMatrix.pdf\n -------------\n Parameters:\n - None\n - Use stock prices, div yields and portfolio weights\n Return:\n - Correlation dataframe\n \"\"\"\n\n # get monthly changes for all stocks\n stock_returns = self._stock_monthly_returns()\n stock_returns['portfolio'] = self._ptf_monthly_returns_indirect()\n\n # get mean values and std by security\n returns_mean = stock_returns.mean(axis=0)\n returns_std = stock_returns.std(axis=0)\n\n # get covariance matrix\n returns_covar = np.cov(\n stock_returns.values, rowvar=False, ddof=1)\n\n # get correlation matrix\n std_products = np.dot(\n returns_std.values.reshape(-1, 1),\n returns_std.values.reshape(1, -1))\n returns_corr = returns_covar / std_products\n\n df_covar = pd.DataFrame(\n returns_covar,\n columns=returns_mean.keys(),\n index=returns_mean.keys())\n df_covar = df_covar.iloc[:-2, :-2]\n\n df_corr = pd.DataFrame(\n returns_corr,\n columns=returns_mean.keys(),\n index=returns_mean.keys())\n\n return df_corr, df_covar\n\n def _observed_period_portfolio_return(self, _):\n \"\"\"\n Calculate actual portfolio return over observed period\n \"\"\"\n pf = self.panelframe\n ptf_return = pf['cum_total_return'].sum(1).iloc[-1] /\\\n pf['cum_cost_basis'].sum(1).iloc[-1]\n return ptf_return\n\n def _observed_period_market_return(self, _):\n \"\"\"\n Calculate actual market return over observed period\n \"\"\"\n pf = self.panelframe\n market_prices = pf['Close', :, 'market']\n market_return = (market_prices[-1] - market_prices[0]) /\\\n market_prices[0]\n return market_return\n\n def actual_portfolio_stats(self):\n \"\"\"\n Calculate actual portfolio stats based on panelframe with daily changes\n -------------\n Parameters:\n - None\n - Uses daily panelframe\n Return:\n - Series with portfolio stats\n \"\"\"\n pf = self.panelframe\n\n # can choose either a total return or capital gain only\n return_to_use = 'investment_return'\n\n cum_return_D1 = pf[return_to_use].sum(1).shift(1)\n cum_return_D2 = pf[return_to_use].sum(1)\n cost_basis = pf['cum_cost_basis'].sum(1)\n returns = (cum_return_D2 - cum_return_D1) / cost_basis\n returns.fillna(0, inplace=True)\n\n m_D1 = pf['Close', :, 'market'].shift(1)\n m_D2 = pf['Close', :, 'market']\n market = (m_D2 - m_D1) / pf['Close', :, 'market'].iloc[0]\n market.fillna(0, inplace=True)\n\n \"\"\"\n Using empyrical functions\n and re-using code from pyfolio\n \"\"\"\n\n SIMPLE_STAT_FUNCS = [\n self._observed_period_portfolio_return,\n self._observed_period_market_return,\n emp.annual_return,\n emp.annual_volatility,\n emp.sharpe_ratio,\n emp.calmar_ratio,\n emp.stability_of_timeseries,\n emp.max_drawdown,\n emp.omega_ratio,\n emp.sortino_ratio,\n stats.skew,\n stats.kurtosis,\n emp.tail_ratio,\n emp.value_at_risk,\n ]\n\n FACTOR_STAT_FUNCS = [\n emp.alpha,\n emp.beta,\n ]\n\n STAT_FUNC_NAMES = {\n '_observed_period_portfolio_return': 'Total return',\n '_observed_period_market_return': 'Market return',\n 'annual_return': 'Annual return',\n 'cum_returns_final': 'Cumulative returns',\n 'annual_volatility': 'Annual volatility',\n 'alpha': 'Alpha',\n 'beta': 'Beta',\n 'sharpe_ratio': 'Sharpe ratio',\n 'calmar_ratio': 'Calmar ratio',\n 'stability_of_timeseries': 'Stability',\n 'max_drawdown': 'Max drawdown',\n 'omega_ratio': 'Omega ratio',\n 'sortino_ratio': 'Sortino ratio',\n 'tail_ratio': 'Tail ratio',\n 'value_at_risk': 'Daily value at risk',\n 'skew': 'Skew',\n 'kurtosis': 'Kurtosis'\n }\n\n ptf_stats = pd.Series()\n for stat_func in SIMPLE_STAT_FUNCS:\n ptf_stats[STAT_FUNC_NAMES[stat_func.__name__]] = stat_func(returns)\n\n for stat_func in FACTOR_STAT_FUNCS:\n res = stat_func(returns, market)\n ptf_stats[STAT_FUNC_NAMES[stat_func.__name__]] = res\n\n return ptf_stats\n\n def stock_risk_analysis(self, if_risk_free_return=False):\n \"\"\"\n Calculate risk properties for every security in the portfolio\n using empyrical library.\n Results are consistent with self-written routine\n References:\n 1. p. 137 of Modern Portfolio Theory and Investment Analysis\n edition 9\n 2. faculty.washington.edu/ezivot/econ424/portfolioTheoryMatrix.pdf\n -------------\n Parameters:\n - If include risk_free_return or not\n - Using stock prices, weight ratios and div yield\n Return:\n - Dataframe of properties for each security in portfolio\n \"\"\"\n\n pf = self.panelframe\n returns = (pf['Close'] - pf['Close'].shift(1))/pf['Close'].iloc[0]\n returns.fillna(0, inplace=True)\n\n # construct resulting dataframe\n df = pd.DataFrame({\n 'means': returns.mean(axis=0),\n })\n\n SIMPLE_STAT_FUNCS = [\n emp.annual_return,\n emp.annual_volatility,\n emp.sharpe_ratio,\n emp.calmar_ratio,\n emp.stability_of_timeseries,\n emp.max_drawdown,\n emp.omega_ratio,\n emp.sortino_ratio,\n stats.skew,\n stats.kurtosis,\n emp.tail_ratio,\n emp.value_at_risk,\n ]\n\n FACTOR_STAT_FUNCS = [\n emp.alpha,\n emp.beta,\n ]\n\n STAT_FUNC_NAMES = {\n 'annual_return': 'Annual return',\n 'cum_returns_final': 'Cumulative returns',\n 'annual_volatility': 'Annual volatility',\n 'alpha': 'Alpha',\n 'beta': 'Beta',\n 'sharpe_ratio': 'Sharpe ratio',\n 'calmar_ratio': 'Calmar ratio',\n 'stability_of_timeseries': 'Stability',\n 'max_drawdown': 'Max drawdown',\n 'omega_ratio': 'Omega ratio',\n 'sortino_ratio': 'Sortino ratio',\n 'tail_ratio': 'Tail ratio',\n 'value_at_risk': 'Daily value at risk',\n 'skew': 'Skew',\n 'kurtosis': 'Kurtosis'\n }\n\n for stat_func in SIMPLE_STAT_FUNCS:\n df[STAT_FUNC_NAMES[stat_func.__name__]] =\\\n returns.apply(lambda x: stat_func(x)).apply(pd.Series)\n\n for stat_func in FACTOR_STAT_FUNCS:\n df[STAT_FUNC_NAMES[stat_func.__name__]] =\\\n returns.apply(lambda x: stat_func(\n x, returns['market'])).apply(pd.Series)\n\n del df['means']\n\n return df\n\n def _risk_free_return(self, period='monthly'):\n \"\"\"\n Risk free return based on T-bills.\n -------------\n Parameters:\n - period: monthly, quarterly or annual\n Returns:\n - annualized value of return based on period\n \"\"\"\n tb = pd.read_hdf(self.datafile, 'treasury_bills')\n TBILLS_PERIODS = {\n 'yearly': 'TB1YR',\n 'monthly': 'TB4WK',\n 'quarterly': 'TB3MS'\n }\n return tb[TBILLS_PERIODS[period]].mean()\n\n def _stock_monthly_returns(self):\n \"\"\"\n Monthly returns = capital gain + dividend yields for all symbols\n -------------\n Parameters:\n - none\n Returns:\n - dataframe with monthly returns in % by symbol\n \"\"\"\n pf = self.panelframe\n\n # monthly changes in stock_prices prices\n stock_prices = pf['Close']\n stock_month_start = stock_prices.groupby([\n lambda x: x.year,\n lambda x: x.month]).first()\n stock_month_end = stock_prices.groupby([\n lambda x: x.year,\n lambda x: x.month]).last()\n stock_monthly_return = (stock_month_end - stock_month_start) /\\\n stock_month_start * 100\n\n stock_monthly_div_yield = pf['dividend_yield'].groupby([\n lambda x: x.year,\n lambda x: x.month]).mean()\n stock_monthly_div_yield.fillna(0, inplace=True)\n\n return stock_monthly_return + stock_monthly_div_yield\n\n def _ptf_monthly_returns_indirect(self):\n \"\"\"\n monthly changes in portfolio value\n using indirect calculation with mean ratios\n TODO - implement a more accurate method\n -------------\n Parameters:\n - none\n - Using stock prices, portfolio weights on every day and div yield\n Returns:\n - dataframe with monthly returns in % by symbol\n \"\"\"\n stock_monthly_change = self._stock_monthly_returns()\n ptf_monthly_ratio = self.panelframe['current_weight'].groupby([\n lambda x: x.year,\n lambda x: x.month]).mean()\n ptf_monthly_returns = (\n stock_monthly_change * ptf_monthly_ratio).sum(1)\n return ptf_monthly_returns\n\n def _one_pfopt_case(self, cov_mat, stock_returns, market, weights, name):\n case = {}\n case['name'] = name\n case['weights'] = weights\n\n returns = np.dot(stock_returns, weights.values.reshape(-1, 1))\n returns = pd.Series(returns.flatten(), index=market.index)\n\n SIMPLE_STAT_FUNCS = [\n emp.annual_return,\n emp.annual_volatility,\n emp.sharpe_ratio,\n emp.stability_of_timeseries,\n emp.max_drawdown,\n emp.omega_ratio,\n emp.calmar_ratio,\n emp.sortino_ratio,\n emp.value_at_risk,\n ]\n\n FACTOR_STAT_FUNCS = [\n emp.alpha,\n emp.beta,\n ]\n\n STAT_FUNC_NAMES = {\n 'annual_return': 'Annual return',\n 'annual_volatility': 'Annual volatility',\n 'alpha': 'Alpha',\n 'beta': 'Beta',\n 'sharpe_ratio': 'Sharpe ratio',\n 'calmar_ratio': 'Calmar ratio',\n 'stability_of_timeseries': 'Stability',\n 'max_drawdown': 'Max drawdown',\n 'omega_ratio': 'Omega ratio',\n 'sortino_ratio': 'Sortino ratio',\n 'value_at_risk': 'Daily value at risk',\n }\n\n ptf_stats = pd.Series()\n for stat_func in SIMPLE_STAT_FUNCS:\n ptf_stats[STAT_FUNC_NAMES[stat_func.__name__]] = stat_func(returns)\n\n for stat_func in FACTOR_STAT_FUNCS:\n res = stat_func(returns, market)\n ptf_stats[STAT_FUNC_NAMES[stat_func.__name__]] = res\n\n case['stats'] = ptf_stats\n\n return case\n\n def markowitz_portfolios(self):\n pf = self.panelframe\n returns = (pf['Close'] - pf['Close'].shift(1))/pf['Close'].shift(1)\n returns.fillna(0, inplace=True)\n market = returns['market']\n returns = returns.iloc[:, :-1]\n\n cov_mat = np.cov(returns, rowvar=False, ddof=1)\n cov_mat = pd.DataFrame(\n cov_mat,\n columns=returns.keys(),\n index=returns.keys())\n\n avg_rets = returns.mean(0).astype(np.float64)\n\n mrk = []\n\n weights = pfopt.min_var_portfolio(cov_mat)\n case = self._one_pfopt_case(\n cov_mat, returns, market, weights, 'Minimum variance portfolio')\n mrk.append(case)\n\n for t in [0.50, 0.75, 0.90]:\n target = avg_rets.quantile(t)\n weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target)\n case = self._one_pfopt_case(\n cov_mat, returns, market, weights,\n 'Target: more than {:.0f}% of stock returns'.format(t*100))\n mrk.append(case)\n\n weights = pfopt.tangency_portfolio(cov_mat, avg_rets)\n case = self._one_pfopt_case(\n cov_mat, returns, market, weights, 'Tangency portfolio')\n mrk.append(case)\n\n return mrk\n\n\nif __name__ == '__main__':\n df_ord = pd.read_hdf('../data/data.h5', 'orders')\n df_div = pd.read_hdf('../data/data.h5', 'dividends')\n df_open = pd.read_hdf('../data/data.h5', 'open')\n df_closed = pd.read_hdf('../data/data.h5', 'closed')\n pf = pd.read_hdf('../data/data.h5', 'market')\n\n ptf = PortfolioModels('../data/data.h5')\n pf = ptf.daily_portfolio_changes().panelframe\n\n # this section uses only stock prices, div yields and weights\n df_risk = ptf.stock_risk_analysis(False)\n df_corr, df_cov = ptf.stock_correlation_matrix()\n pf_stats = ptf.actual_portfolio_stats()\n mrk = ptf.markowitz_portfolios()\n","repo_name":"ckanu13k/robinhood-portfolio","sub_path":"backend/portfolio_model.py","file_name":"portfolio_model.py","file_ext":"py","file_size_in_byte":20493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"74159330967","text":"import os\r\nimport secrets\r\nfrom PIL import Image\r\nfrom flask import render_template, url_for, flash, redirect, request, abort\r\nfrom flaskDemo import app, db, bcrypt\r\nfrom flaskDemo.forms import RegistrationForm, LoginForm, UpdateAccountForm, GeneModelForm\r\nfrom flaskDemo.models import Algorithm, Gene, Job, KNN_Model, Population, Pred_Result, RF_Model, SVR_Model, User\r\nfrom flask_login import login_user, current_user, logout_user, login_required\r\nfrom datetime import datetime\r\nimport sys\r\n\r\n@app.route(\"/\", methods=['GET', 'POST'])\r\n@app.route(\"/home\", methods=['GET', 'POST'])\r\ndef home():\r\n if current_user.is_authenticated:\r\n form = GeneModelForm()\r\n if form.validate_on_submit():\r\n results = None\r\n results2 = None\r\n if form.algo.data==\"KNN\":\r\n for pop in form.pop.data:\r\n for gene in form.gene.data:\r\n results2 = Gene.query.join(KNN_Model, Gene.gene_id == KNN_Model.gene_id) \\\r\n .add_columns(Gene.gene_id, Gene.gene_name, Gene.gene_type, Gene.chromosome_no) \\\r\n .join(Population, Population.population_id == KNN_Model.population_id) \\\r\n .add_columns(Population.population_id, Population.population_description, KNN_Model.cross_val_performance, KNN_Model.neighbors, KNN_Model.weight, KNN_Model.p) \\\r\n .filter(Gene.gene_id==gene, Population.population_id==pop)\r\n if results2 != None:\r\n if results != None:\r\n results = results.union(results2)\r\n else:\r\n results = results2\r\n algo = \"K Nearest Neighbor\"\r\n elif form.algo.data==\"RF\":\r\n for pop in form.pop.data:\r\n for gene in form.gene.data: \r\n results2 = Gene.query.join(RF_Model, Gene.gene_id == RF_Model.gene_id) \\\r\n .add_columns(Gene.gene_id, Gene.gene_name, Gene.gene_type, Gene.chromosome_no) \\\r\n .join(Population, Population.population_id == RF_Model.population_id) \\\r\n .add_columns(Population.population_id, Population.population_description, RF_Model.cross_val_performance, RF_Model.trees) \\\r\n .filter(Gene.gene_id==gene, Population.population_id==pop)\r\n if results2 != None:\r\n if results != None:\r\n results = results.union(results2)\r\n else:\r\n results = results2\r\n algo = \"Random Forest\"\r\n elif form.algo.data==\"SVR\":\r\n for pop in form.pop.data:\r\n for gene in form.gene.data:\r\n results2 = Gene.query.join(SVR_Model, Gene.gene_id == SVR_Model.gene_id) \\\r\n .add_columns(Gene.gene_id, Gene.gene_name, Gene.gene_type, Gene.chromosome_no) \\\r\n .join(Population, Population.population_id == SVR_Model.population_id) \\\r\n .add_columns(Population.population_id, Population.population_description, SVR_Model.cross_val_performance, SVR_Model.kernel, SVR_Model.degree, SVR_Model.c) \\\r\n .filter(Gene.gene_id==gene, Population.population_id==pop)\r\n if results2 != None:\r\n if results != None:\r\n results = results.union(results2)\r\n else:\r\n results = results2\r\n algo = \"Support Vector Regression\"\r\n\r\n print(results, file=sys.stderr)\r\n print(algo, file=sys.stderr)\r\n\r\n return render_template('results.html', title='Results', results=results, algo=algo)\r\n return render_template('query_genome_database.html', title='Query Genome Database',\r\n form=form, legend='Query Genome Database')\r\n return render_template('home_notlogged.html')\r\n\r\n@app.route(\"/about\")\r\ndef about():\r\n return render_template('about.html', title='About')\r\n\r\n@app.route(\"/register\", methods=['GET', 'POST'])\r\ndef register():\r\n if current_user.is_authenticated:\r\n return redirect(url_for('home'))\r\n form = RegistrationForm()\r\n if form.validate_on_submit():\r\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\r\n user = User(user_id=form.username.data, email=form.email.data, password=hashed_password)\r\n db.session.add(user)\r\n db.session.commit()\r\n flash('Your account has been created! You are now able to log in', 'success')\r\n return redirect(url_for('login'))\r\n return render_template('register.html', title='Register', form=form)\r\n\r\n\r\n@app.route(\"/login\", methods=['GET', 'POST'])\r\ndef login():\r\n if current_user.is_authenticated:\r\n return redirect(url_for('home'))\r\n form = LoginForm()\r\n if form.validate_on_submit():\r\n user = User.query.filter_by(email=form.email.data).first()\r\n if user and bcrypt.check_password_hash(user.password, form.password.data):\r\n login_user(user, remember=form.remember.data)\r\n next_page = request.args.get('next')\r\n return redirect(next_page) if next_page else redirect(url_for('home'))\r\n else:\r\n flash('Login Unsuccessful. Please check email and password', 'danger')\r\n return render_template('login.html', title='Login', form=form)\r\n\r\n\r\n@app.route(\"/logout\")\r\ndef logout():\r\n logout_user()\r\n return redirect(url_for('home'))\r\n\r\n\r\ndef save_picture(form_picture):\r\n random_hex = secrets.token_hex(8)\r\n _, f_ext = os.path.splitext(form_picture.filename)\r\n picture_fn = random_hex + f_ext\r\n picture_path = os.path.join(app.root_path, 'static/profile_pics', picture_fn)\r\n\r\n output_size = (125, 125)\r\n i = Image.open(form_picture)\r\n i.thumbnail(output_size)\r\n i.save(picture_path)\r\n\r\n return picture_fn\r\n\r\n\r\n@app.route(\"/account\", methods=['GET', 'POST'])\r\n@login_required\r\ndef account():\r\n form = UpdateAccountForm()\r\n if form.validate_on_submit():\r\n if form.picture.data:\r\n picture_file = save_picture(form.picture.data)\r\n current_user.image_file = picture_file\r\n current_user.username = form.username.data\r\n current_user.email = form.email.data\r\n db.session.commit()\r\n flash('Your account has been updated!', 'success')\r\n return redirect(url_for('account'))\r\n elif request.method == 'GET':\r\n form.username.data = current_user.username\r\n form.email.data = current_user.email\r\n image_file = url_for('static', filename='profile_pics/' + current_user.image_file)\r\n return render_template('account.html', title='Account',\r\n image_file=image_file, form=form)\r\n","repo_name":"okoropaulc/ml_predictdb","sub_path":"Big Project/flaskDemo/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":7039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34327561419","text":"import json\nimport warnings\n\nfrom art.estimators.classification.classifier import ClassifierMixin\nfrom art.estimators.object_detection import ObjectDetectorMixin\nfrom art.estimators.speech_recognition import SpeechRecognizerMixin\nimport numpy as np\n\nfrom armory.utils.config_loading import load_model\n\n\nclass TestModel:\n def _get_model(self, model_config):\n model, _ = load_model(model_config)\n if not isinstance(\n model, (ClassifierMixin, SpeechRecognizerMixin, ObjectDetectorMixin)\n ):\n raise TypeError(f\"Unsupported model type: {type(model)}\")\n return model\n\n def _get_input_shape(self, model):\n if isinstance(model, ClassifierMixin):\n if hasattr(model, \"input_shape\"):\n input_shape = np.array(model.input_shape)\n else:\n warnings.warn(\"No model input shape specified. Assuming (32, 32, 3)\")\n input_shape = np.array((32, 32, 3))\n if np.all(input_shape == (None,)):\n warnings.warn(\"Model shape given as (None,), assuming (10000,)\")\n input_shape = np.array((10000,))\n elif None in input_shape:\n test_input_shape = input_shape.copy()\n test_input_shape[np.equal(test_input_shape, None)] = 32\n warnings.warn(\n f\"Model shape given as {input_shape}. Assuming {test_input_shape}\"\n )\n input_shape = test_input_shape\n elif isinstance(model, ObjectDetectorMixin):\n input_shape = np.array((32, 32, 3))\n warnings.warn(\n \"Object detector model does not specify input shape. Assuming (32, 32, 3)\"\n )\n elif isinstance(model, SpeechRecognizerMixin):\n warnings.warn(\n \"Speech recognition model does not specify input shape. Assuming (10000,)\"\n )\n input_shape = np.array((10000,))\n return input_shape\n\n def _get_clip_values(self, model):\n if isinstance(model, ClassifierMixin) or isinstance(model, ObjectDetectorMixin):\n assert (\n model.clip_values is not None\n ), \"Clip values not provided. Clip values are required to keep input values within valid range\"\n clip_min, clip_max = model.clip_values\n elif isinstance(model, SpeechRecognizerMixin):\n clip_min, clip_max = (-1.0, 1.0)\n return clip_min, clip_max\n\n def _get_test_input(self, input_shape, clip_min, clip_max):\n test_sample = np.random.randn(1, *input_shape).astype(np.float32)\n test_sample = np.clip(test_sample, clip_min, clip_max)\n return test_sample\n\n def _get_test_ground_truth(self, model, test_output):\n if isinstance(model, ClassifierMixin):\n test_ground_truth = test_output\n if (\n isinstance(test_output, np.ndarray)\n and test_output.ndim == 2\n and test_output.shape[1] > 1\n ):\n test_ground_truth = np.zeros((1, model.nb_classes))\n test_ground_truth[:, np.argmin(test_output, axis=1)[0]] = 1\n elif isinstance(model, ObjectDetectorMixin):\n test_ground_truth = [\n {\n \"boxes\": np.reshape(np.arange(0.0, 1.0, 1.0 / 32.0), (8, 4)),\n \"labels\": np.ones((8,), dtype=np.int64),\n \"scores\": np.ones((8,), dtype=np.float32),\n }\n ]\n elif isinstance(model, SpeechRecognizerMixin):\n test_ground_truth = np.array([\"HELLO\"])\n return test_ground_truth\n\n def test_model(self, model_config):\n\n # verify model loads without error\n model_config = json.loads(model_config)\n model = self._get_model(model_config)\n input_shape = self._get_input_shape(model)\n\n # verify model clip values broadcast to input shape\n clip_min, clip_max = self._get_clip_values(model)\n np.broadcast_to(clip_min, input_shape)\n np.broadcast_to(clip_max, input_shape)\n\n # verify model forward pass\n test_input = self._get_test_input(input_shape, clip_min, clip_max)\n copy_input = test_input.copy()\n test_output = model.predict(test_input)\n assert np.all(\n test_input == copy_input\n ), \"Model prediction overwrites the input x value\"\n if isinstance(model, ClassifierMixin) and hasattr(model, \"nb_classes\"):\n assert (\n test_output.shape[1] == model.nb_classes\n ), f\"Model configured for {model.nb_classes} output classes, but output shape is {test_output.shape}\"\n\n # test model gradient\n test_ground_truth = self._get_test_ground_truth(model, test_output)\n copy_ground_truth = test_ground_truth.copy()\n test_grad = None\n try:\n test_grad = model.loss_gradient(test_input, test_ground_truth)\n if test_grad is None:\n warnings.warn(\n \"Model returned None gradient. White-box evaluation may be limited\"\n )\n else:\n test_grad = np.stack(test_grad)\n\n except Exception:\n warnings.warn(\n \"Model encountered error during gradient computation. White-box evaluation may be limited\"\n )\n\n # test returned gradients for issues\n if test_grad is not None:\n if not np.all(test_grad.shape == test_input.shape):\n warnings.warn(\n f\"For input of size {test_input.shape} got gradient of size {test_grad.shape}\"\n )\n if np.any(np.isnan(test_grad)) or np.any(np.isinf(test_grad)):\n warnings.warn(\"NaN/Inf values detected in model gradient\")\n if np.all(test_grad == 0):\n warnings.warn(\"All-zero gradients detected\")\n assert np.all(\n test_input == copy_input\n ), \"The gradient computation overwrites the input x value\"\n assert np.all(\n test_ground_truth == copy_ground_truth\n ), \"The gradient computation overwrites the input y value\"\n","repo_name":"twosixlabs/armory","sub_path":"armory/validation/test_config/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":6195,"program_lang":"python","lang":"en","doc_type":"code","stars":165,"dataset":"github-code","pt":"31"} +{"seq_id":"32735016860","text":"# flake8: noqa\n# pylint: disable=invalid-name\n# pylint: disable=line-too-long\n# pylint: disable=missing-docstring\n# pylint: disable=too-many-arguments\n# pylint: disable=too-many-locals\n# pylint: disable=too-many-statements\n\"\"\"\n.. _associations_analysis.radial_diagram:\n\nRadial Diagram\n===============================================================================\n\nA radial diagram is a visualization technique that displays the associations\nbetween a term and other terms in a co-occurrence matrix. The radial diagram\nis a network graph in which the nodes are the terms and the edges are the\nco-occurrence between the terms. The radial diagram is a useful tool for\nidentifying the most relevant terms associated with a given term.\n\n\n>>> from techminer2.co_occurrence_analysis.associations.graphs import radial_diagram\n>>> radial_diagram(\n... #\n... # FUNCTION PARAMS:\n... item=\"REGTECH\", \n... #\n... # CO-OCC PARAMS:\n... columns='author_keywords',\n... rows=None,\n... #\n... # LAYOUT:\n... nx_k=None,\n... nx_iterations=30,\n... nx_random_state=0,\n... #\n... # NODES:\n... node_size_min=30,\n... node_size_max=70,\n... textfont_size_min=10,\n... textfont_size_max=20,\n... textfont_opacity_min=0.35,\n... textfont_opacity_max=1.00,\n... #\n... # EDGES:\n... edge_color=\"#7793a5\",\n... edge_width_min=0.8,\n... edge_width_max=3.0,\n... #\n... # AXES:\n... xaxes_range=None,\n... yaxes_range=None,\n... show_axes=False,\n... #\n... # COLUMN PARAMS:\n... col_top_n=20,\n... col_occ_range=(None, None),\n... col_gc_range=(None, None),\n... col_custom_items=None,\n... #\n... # ROW PARAMS:\n... row_top_n=None,\n... row_occ_range=(None, None),\n... row_gc_range=(None, None),\n... row_custom_items=None,\n... #\n... # DATABASE PARAMS:\n... root_dir=\"example/\", \n... database=\"main\",\n... year_filter=(None, None),\n... cited_by_filter=(None, None),\n... ).write_html(\"sphinx/_static/co_occurrence_analysis/associations/radial_diagram.html\")\n\n.. raw:: html\n\n <iframe src=\"../../../../../../_static/co_occurrence_analysis/associations/radial_diagram.html\" \n height=\"600px\" width=\"100%\" frameBorder=\"0\"></iframe>\n\n\n\"\"\"\nimport networkx as nx\n\nfrom ..analyze.associations.item_associations import item_associations\nfrom .nx_compute_edge_width_from_edge_weight import nx_compute_edge_width_from_edge_weight\nfrom .nx_compute_node_size_from_item_occ import nx_compute_node_size_from_item_occ\nfrom .nx_compute_spring_layout import nx_compute_spring_layout\nfrom .nx_compute_textfont_opacity_from_item_occ import nx_compute_textfont_opacity_from_item_occ\nfrom .nx_compute_textfont_size_from_item_occ import nx_compute_textfont_size_from_item_occ\nfrom .nx_compute_textposition_from_graph import nx_compute_textposition_from_graph\nfrom .nx_set_edge_color_to_constant import nx_set_edge_color_to_constant\nfrom .nx_visualize_graph import nx_visualize_graph\n\n\ndef nx_radial_diagram(\n #\n # FUNCTION PARAMS:\n item,\n #\n # CO-OCC PARAMS:\n columns,\n rows=None,\n #\n # LAYOUT:\n nx_k=None,\n nx_iterations=30,\n nx_random_state=0,\n #\n # NODES:\n node_size_min=30,\n node_size_max=70,\n textfont_size_min=10,\n textfont_size_max=20,\n textfont_opacity_min=0.35,\n textfont_opacity_max=1.00,\n #\n # EDGES:\n edge_color=\"#7793a5\",\n edge_width_min=0.8,\n edge_width_max=3.0,\n #\n # AXES:\n xaxes_range=None,\n yaxes_range=None,\n show_axes=False,\n #\n # COLUMN PARAMS:\n col_top_n=None,\n col_occ_range=(None, None),\n col_gc_range=(None, None),\n col_custom_items=None,\n #\n # ROW PARAMS:\n row_top_n=None,\n row_occ_range=(None, None),\n row_gc_range=(None, None),\n row_custom_items=None,\n #\n # DATABASE PARAMS:\n root_dir=\"./\",\n database=\"main\",\n year_filter=(None, None),\n cited_by_filter=(None, None),\n **filters,\n):\n \"\"\"Plots a radial diagram.\n\n :meta private:\n \"\"\"\n\n associations = item_associations(\n #\n # FUNCTION PARAMS:\n item=item,\n #\n # CO-OCC PARAMS:\n columns=columns,\n rows=rows,\n #\n # COLUMN PARAMS:\n col_top_n=col_top_n,\n col_occ_range=col_occ_range,\n col_gc_range=col_gc_range,\n col_custom_items=col_custom_items,\n #\n # ROW PARAMS:\n row_top_n=row_top_n,\n row_occ_range=row_occ_range,\n row_gc_range=row_gc_range,\n row_custom_items=row_custom_items,\n #\n # DATABASE PARAMS:\n root_dir=root_dir,\n database=database,\n year_filter=year_filter,\n cited_by_filter=cited_by_filter,\n **filters,\n ).df_\n\n #\n # Create the networkx graph\n nx_graph = nx.Graph()\n nx_graph = __add_nodes_from(nx_graph, associations)\n nx_graph = __add_weighted_edges_from(nx_graph, associations)\n\n #\n # Sets the layout\n nx_graph = nx_compute_spring_layout(nx_graph, nx_k, nx_iterations, nx_random_state)\n\n #\n # Sets the node attributes\n # nx_graph = nx_set_node_color_from_group_attr(nx_graph)\n nx_graph = nx_compute_node_size_from_item_occ(nx_graph, node_size_min, node_size_max)\n nx_graph = nx_compute_textfont_size_from_item_occ(\n nx_graph, textfont_size_min, textfont_size_max\n )\n nx_graph = nx_compute_textfont_opacity_from_item_occ(\n nx_graph, textfont_opacity_min, textfont_opacity_max\n )\n\n #\n # Sets the edge attributes\n nx_graph = nx_compute_edge_width_from_edge_weight(nx_graph, edge_width_min, edge_width_max)\n nx_graph = nx_compute_textposition_from_graph(nx_graph)\n nx_graph = nx_set_edge_color_to_constant(nx_graph, edge_color)\n\n return nx_visualize_graph(\n #\n # FUNCTION PARAMS:\n nx_graph=nx_graph,\n #\n # NETWORK PARAMS:\n xaxes_range=xaxes_range,\n yaxes_range=yaxes_range,\n show_axes=show_axes,\n )\n\n\ndef __add_nodes_from(\n nx_graph,\n associations,\n):\n associations = associations.copy()\n\n #\n # Adds the rows with group=0\n nodes = associations.index.tolist()\n nx_graph.add_nodes_from(nodes, group=0, node_color=\"#7793a5\")\n\n #\n # Adds the column with group=1\n node = [associations.columns[0]]\n nx_graph.add_nodes_from(node, group=1, node_color=\"#465c6b\")\n\n #\n # sets the labels of nodes\n for node in nx_graph.nodes():\n nx_graph.nodes[node][\"text\"] = node\n\n return nx_graph\n\n\ndef __add_weighted_edges_from(\n nx_graph,\n associations,\n):\n associations = associations.copy()\n item = associations.columns[0]\n\n for row in associations.index.tolist():\n weight = associations.loc[row][0]\n nx_graph.add_weighted_edges_from(\n ebunch_to_add=[(row, item, weight)],\n dash=\"solid\",\n )\n\n return nx_graph\n","repo_name":"jdvelasq/techminer2","sub_path":"techminer2/_common/nx_radial_diagram.py","file_name":"nx_radial_diagram.py","file_ext":"py","file_size_in_byte":6882,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"17885638600","text":"import re\n\nfrom sympy import symbols, Eq, solve\n\nwith open(\"input\") as file:\n inp = file.readlines()\n\nnumber_monkey_parser = re.compile('(\\w+): (\\d+)')\noperation_monkey_parser = re.compile('(\\w+): (\\w+) ([-*+/]) (\\w+)')\nPART_TWO = True\n\n\nclass Monkey:\n def __init__(self, name: str):\n self.name = name\n self._value = None\n\n @property\n def value(self):\n return None\n\n def __repr__(self):\n return f'{self.name}: {self.__class__.__name__}'\n\n @classmethod\n def parse_monkey(cls, line):\n nr_match = number_monkey_parser.match(line)\n if number_monkey_parser.match(line):\n name, nr = nr_match.groups()\n return NumberMonkey(name, int(nr))\n else:\n name, first, operation, second = operation_monkey_parser.match(line).groups()\n return OperationMonkey(name, operation, first, second)\n\n\nclass NumberMonkey(Monkey):\n def __init__(self, name, value):\n super().__init__(name)\n self._value = value\n if PART_TWO and self.name == 'humn':\n self._value = symbols('humn')\n\n @property\n def value(self):\n return self._value\n\n\nclass OperationMonkey(Monkey):\n def __init__(self, name, operation, first: str, second: str):\n super().__init__(name)\n self.operation = operation\n self.first_str = first\n self.second_str = second\n self.first_operand: Monkey = None\n self.second_operand: Monkey = None\n\n def update_operands_to_instances_from_name(self, monkey_dict: dict[str, Monkey]):\n self.first_operand = monkey_dict[self.first_str]\n self.second_operand = monkey_dict[self.second_str]\n\n @property\n def value(self):\n if PART_TWO and self.name == 'root':\n first = self.first_operand.value\n second = self.second_operand.value\n expr = Eq(first, second)\n print(expr)\n return solve(expr, 'humn')\n self._calculate_own_value()\n return self._value\n\n def _calculate_own_value(self):\n if self.operation == '*':\n self._value = self.first_operand.value * self.second_operand.value\n elif self.operation == '+':\n self._value = self.first_operand.value + self.second_operand.value\n elif self.operation == '/':\n self._value = self.first_operand.value / self.second_operand.value\n elif self.operation == '-':\n self._value = self.first_operand.value - self.second_operand.value\n\n\nmonkey_dict = dict()\noperation_monkeys_need_update = set()\nfor line in inp:\n monkey = Monkey.parse_monkey(line)\n if isinstance(monkey, OperationMonkey):\n operation_monkeys_need_update.add(monkey)\n monkey_dict[monkey.name] = monkey\n\n\nfor operation_monkey in operation_monkeys_need_update:\n operation_monkey.update_operands_to_instances_from_name(monkey_dict)\n\n\n# print(monkey_dict['root'].solution_part_two())\n\n\nprint(monkey_dict['root'].value)\n# print(monkey_dict['root'].solution_part_two())\n","repo_name":"lukasburg/advent_of_code_2022","sub_path":"2022/day21/puzzles.py","file_name":"puzzles.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2328938202","text":"#0311.py\r\nimport numpy as np\r\nimport cv2\r\n\r\ndef onMouse(event, x, y, flags, param):\r\n## global img\r\n if event == cv2.EVENT_LBUTTONDOWN: #마우스 왼쪽 버튼 클릭\r\n if flags & cv2.EVENT_FLAG_SHIFTKEY: # shift 키와 함께\r\n cv2.rectangle(param[0], (x - 5, y - 5), (x + 5, y + 5), (255, 0, 0))\r\n else:\r\n cv2.circle(param[0], (x, y), 5, (255, 0, 0), 3)\r\n elif event == cv2.EVENT_RBUTTONDOWN: #마우스 오른쪽 버튼 클릭 \r\n cv2.circle(param[0], (x, y), 5, (0, 0, 255), 3)\r\n elif event == cv2.EVENT_LBUTTONDBLCLK: #마우스 왼쪽 버튼 더블 클릭\r\n param[0] = np.zeros(param[0].shape, np.uint8) + 255\r\n cv2.imshow(\"img\", param[0])\r\n\r\nimg = np.zeros((512, 512, 3), np.uint8) + 255\r\ncv2.imshow('img', img)\r\ncv2.setMouseCallback('img', onMouse, [img])\r\ncv2.waitKey()\r\ncv2.destroyAllWindows()","repo_name":"0201shj/Python-OpenCV","sub_path":"Chapter03/0311.py","file_name":"0311.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69948774809","text":"import numpy as np\nimport cv2\nimport sys\nimport time\n\n\n\nclass PIDController:\n def __init__(self, target_pos):\n self.target_pos = target_pos\n # Values for 2.7g ball\n #self.target_pos = 0.5\n self.Kp = 3130.25\n self.Ki = 3298.32\n self.Kd = 252.10\n\n self.bias = 0.0\n self.error_0_to_t = 0.0\n self.prev_error = 0.0\n self.error_at_t = 0.0\n return\n\n def reset(self):\n self.Kp = 0.0\n self.Ki = 0.0\n self.Kd = 0.0\n self.bias = 0.0\n self.error_0_to_t = 0.0\n self.prev_error = 0.0\n self.error_at_t = 0.0\n return\n\n def get_fan_rpm(self, vertical_ball_position):\n delta_t = 1.0 / 60.0\n\n # calculate the error function at every frame\n prev_error = self.error_at_t\n self.error_at_t = self.target_pos - vertical_ball_position\n self.error_0_to_t += self.error_at_t * delta_t\n\n # calculate pid: u(t) = K_p e(t) + K_i \\int_{0}^{t} e(t)dt + K_d {de}/{dt}\n _P = self.Kp * self.error_at_t\n _I = self.Ki * self.error_0_to_t\n _D = self.Kd * (self.error_at_t - prev_error) / delta_t\n output = _P + _I + _D\n\n return output\n\n\n","repo_name":"dumplingsforbreakfast/COMP417_CartPole_Design","sub_path":"PID/pid.py","file_name":"pid.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"17273948656","text":"#!/usr/bin/env python3\n\nuser_input = input(\"What file would you like to load?\")\ncount = 0\n## create file object in \"r\"ead mode\nconfigfile = open(user_input, \"r\")\n\n## display file to the screen - .read()\nconfigblog = configfile.read()\n\n## break configblog across line boundaries (strips out \\n)\nconfiglist = configblog.splitlines()\n\n## display list with no \"\\n\"\nprint(configlist)\n\nfor elements in configlist:\n count= count + 1\n print(count)\n# Always close your file\nconfigfile.close()\n\n\n","repo_name":"KANDERKJ/mycode","sub_path":"cfgread/cfg02.py","file_name":"cfg02.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2571355446","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\n\"\"\"\nESC [ 0 m # reset all (colors and brightness)\nESC [ 1 m # bright\nESC [ 2 m # dim (looks same as normal brightness)\nESC [ 22 m # normal brightness\n\n# FOREGROUND:\nESC [ 30 m # black\nESC [ 31 m # red\nESC [ 32 m # green\nESC [ 33 m # yellow\nESC [ 34 m # blue\nESC [ 35 m # magenta\nESC [ 36 m # cyan\nESC [ 37 m # white\nESC [ 39 m # reset\n\n# BACKGROUND\nESC [ 40 m # black\nESC [ 41 m # red\nESC [ 42 m # green\nESC [ 43 m # yellow\nESC [ 44 m # blue\nESC [ 45 m # magenta\nESC [ 46 m # cyan\nESC [ 47 m # white\nESC [ 49 m # reset\n\"\"\"\nfrom __future__ import print_function\n#from builtins import str\nfrom builtins import range\nfrom builtins import object\nimport sys\n# look for colorama https://pypi.python.org/pypi/colorama\nfrom . import BaseCopySupporter, get_parameters\n\n\ndef have_colours(stream):\n \"\"\"\n Detect if output console supports ANSI colors.\n\n :param stream:\n :return:\n \"\"\"\n if not hasattr(stream, \"isatty\"):\n return False\n if not stream.isatty():\n return False # auto color only on TTYs\n try:\n import curses\n curses.setupterm()\n return curses.tigetnum(\"colors\") > 2\n except BaseException: # guess false in case of error\n return False\n\n\ndef formatter(format_string, kwargs):\n \"\"\"\n Default formatter used to format strings. Instead of `\"{key}\".format(**kwargs)`\n use `formatter(\"{key}\", kwargs)` which ensures that no errors are generated when\n an user uses braces e.g. {}. Bear in mind that formatter consumes kwargs\n which in turns replaces an used key with empty string \"\". This can generate\n unusual behaviour if not well used.\n \"\"\"\n for key, val in kwargs.items():\n key2 = \"{%s}\" % (key)\n if key2 in format_string:\n # explicitly convert val to str\n format_string = format_string.replace(key2, str(val))\n kwargs[key] = \"\"\n return format_string\n\n\ndef separate(text):\n \"\"\"\n Process a text to get its parts.\n\n :param text:\n :return: [head,body,end]\n \"\"\"\n right = text.rstrip()\n left = text.lstrip()\n return (text[0:-len(left)], text[-len(left):len(right)], text[len(right):])\n\n\ndef scale(x, range, drange):\n \"\"\"\n From real coordinates get rendered coordinates.\n\n :param x: source value\n :param range: (min,max) of x\n :param drange: (min,max) of sx\n :return: scaled x (sx)\n \"\"\"\n (rx1, rx2) = float(range[0]), float(range[1])\n (sx1, sx2) = float(drange[0]), float(drange[1])\n return (sx2 - sx1) * (x - rx1) / (rx2 - rx1) + sx1\n\n\ndef scale_index(index, range, drange, circle=False, limit=False):\n \"\"\"\n Uses scale but adds support for indexing.\n\n :param index:\n :param range:\n :param drange:\n :param circle:\n :param limit:\n :return:\n \"\"\"\n minlen, maxlen = drange\n # (index - min) * maxlen / (max - min) # rescale to colors\n val = scale(index, range, drange)\n if val < 0:\n val -= 1 # shift negative\n index = int(val) # get index\n # ensures that values are inside colors\n if index > maxlen - 1:\n if circle:\n index = index % (maxlen + minlen)\n elif limit:\n index = maxlen - 1\n if index < minlen:\n if circle:\n index = index % (-(maxlen - minlen))\n elif limit:\n index = minlen\n return index\n\n\nclass ANSIcolor(object):\n \"\"\"\n Class defining ANSI color codes used in terminals\n \"\"\"\n # INTENSITY\n BRIGHT = 1 # bright\n DIM = 2 # dim (looks same as normal brightness)\n NORMAL = 22 # normal brightness\n # FOREGROUND:\n BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = [\n str(i) for i in range(30, 38)]\n # BACKGROUND\n BLACK_B, RED_B, GREEN_B, YELLOW_B, BLUE_B, MAGENTA_B, CYAN_B, WHITE_B = [\n str(i) for i in range(40, 48)]\n\n def __init__(self, colors):\n self.colors = \";\".join(colors)\n self.format_code = \"{_head}\\x1b[{_colors}m{_body}\\x1b[0m{_end}\"\n\n def paint(self, *args, **kwargs):\n kwargs[\"_colors\"] = self.colors\n if len(args) == 1: # must have text\n kwargs[\"_head\"], kwargs[\"_body\"], kwargs[\"_end\"] = separate(\n str(args[0]))\n elif len(args) > 1: # format multiple\n return [self.paint(arg, **kwargs.copy()) for arg in args]\n return formatter(formatter(self.format_code, kwargs),\n kwargs) # formats for CODE and user masks\n\n __call__ = paint\n\n\nclass CODElist(list):\n \"\"\"\n Especial list to hold CODE objects used in CodeMapper\n \"\"\"\n\n def __init__(self, iterable):\n super(CODElist, self).__init__()\n self.extend(iterable)\n\n def extend(self, iterable):\n for item in iterable:\n self.append(item)\n\n def append(self, item):\n if not isinstance(item, CODE):\n raise TypeError('item is not of type %s' % CODE)\n for position, actualItem in enumerate(self):\n if item.level <= actualItem.level:\n super(CODElist, self).insert(position, item)\n return\n super(CODElist, self).append(item)\n\n\nclass CODE(BaseCopySupporter):\n \"\"\"\n Class to define Logger codes like HIDDEN, DEBUG, ERROR, LOG, WARNING, IGNORE\n \"\"\"\n\n def __init__(self, name=None, level=0, colors=None,\n formatting=\"[{_code}]{_head}{_body}{_end}\"): # \"[({_code}):{_head}{_body}{_end}]\"): #\n \"\"\"\n :param name: code name\n :param level: level of code priority\n :param colors: ANSIcolor instance\n :param formatting: formatting to use when converting text\n \"\"\"\n self.name = name\n self.level = level\n if colors:\n colors = ANSIcolor(colors)\n self.colors = colors # applied if color\n self.formatting = formatting # applied if user defined\n self._use_color = have_colours(sys.stdout)\n self._buffer = None\n\n def convert(\n self,\n text,\n newLine=False,\n use_color=None,\n use_format=False,\n **kwargs):\n \"\"\"\n Convert text to CODE format and colors.\n\n :param text: text to convert\n :param newLine: True to add new line at the end if needed\n :param use_color: True to use color\n :param use_format: True to use code formatting\n :param kwargs: additional kwargs to format\n :return: formatted text\n \"\"\"\n if use_color is None:\n use_color = self._use_color\n\n if use_format:\n head, body, end = separate(text)\n kwargs = dict(_head=head, _body=body, _end=end, _name=self.name,\n _level=self.level, _code=self.name, **kwargs)\n\n # formatter consumes kwargs!!\n # formats for CODE and user masks\n text = formatter(formatter(self.formatting, kwargs), kwargs)\n\n head, body, end = separate(text)\n if newLine and not end:\n end = \"\\n\"\n\n if use_color and self.colors: # if there are colors\n return self.colors(_head=head, _body=body, _end=end)\n else:\n return \"{}{}{}\".format(head, body, end)\n\n def codify(self, *args, **kwargs):\n \"\"\"\n Creates an instance of this CODE with default parameters\n\n :param text: text to convert\n :param newLine: True to add new line at the end if needed\n :param use_color: True to use color\n :param use_format: True to use code formatting\n :param kwargs: additional kwargs to format\n :return: formatted text\n \"\"\"\n code = self.clone()\n code._buffer = args, kwargs\n return code\n\n def raw_msg(self):\n if self._buffer is None:\n return \"\" # return with no message\n else:\n kwargs = get_parameters(func=self.convert, args=self._buffer[0],\n kwargs=self._buffer[1], onlykeys=True,\n onlyused=True)\n kwargs[\"use_format\"] = False\n kwargs[\"use_color\"] = False\n return self.convert(**kwargs)\n\n def __str__(self):\n if self._buffer is None:\n return self.convert(\"\") # return with no message\n else:\n return self.convert(*self._buffer[0], **self._buffer[1])\n\n def __bool__(self):\n return True\n\n def __int__(self):\n return int(self.level)\n\n def __float__(self):\n return float(self.level)\n\n __call__ = codify\n\n\nHIDDEN = CODE(\"HIDDEN\", -3, (ANSIcolor.BLACK,))\nDEBUG = CODE(\"DEBUG\", -2, (ANSIcolor.CYAN,))\nERROR = CODE(\"ERROR\", -1, (ANSIcolor.RED,))\nLOG = CODE(\"LOG\", 0, (ANSIcolor.BLUE,))\nWARNING = CODE(\"WARNING\", 1, (ANSIcolor.YELLOW,))\nIGNORE = CODE(\"IGNORE\", 2, (ANSIcolor.WHITE,))\nOK = CODE(\"OK\", 3, (ANSIcolor.GREEN,))\n\ncodes_list = CODElist([DEBUG, LOG, HIDDEN, WARNING, ERROR, IGNORE, OK])\ncodes_dict = {code.name: code for code in codes_list}\n\n\nclass CodeMapper(object):\n \"\"\"\n Manage and convert CODE objects to other CODE objects\n \"\"\"\n\n def __init__(self, codes=None, refcodes=None, range=None, limit=True):\n if codes:\n self.codes = codes\n else:\n self.codes = []\n self.refcodes = refcodes\n self.range = range\n self.limit = limit\n\n @property\n def codes(self):\n return self._codes\n\n @codes.setter\n def codes(self, values):\n if isinstance(values, CODElist):\n self._codes = values # replace previous\n elif hasattr(values, \"__iter__\"):\n self._codes = CODElist(values)\n else:\n raise ValueError(\n \"it only receives CODElist and converts iterators to CODElist\")\n\n def map_code(self, code):\n \"\"\"\n\n :param code:\n :return:\n \"\"\"\n if isinstance(code, CODE):\n return self.get_by_reference(code) # try by reference\n else:\n val = self.get_by_level(code) # try by level\n if val:\n return val\n return self.get_by_index(code) # try by index\n\n def get_by_index(self, index):\n \"\"\"\n\n :param index:\n :return:\n \"\"\"\n try:\n if self.range: # scale values\n return self.codes[scale_index(index, self.range, (0, len(\n self.codes)), limit=self.limit)] # throws error when outside range\n else:\n return self.codes[scale_index(index, (0, len(self.codes)), (0, len(\n self.codes)), limit=self.limit)] # throws error when outside codes\n except TypeError as e:\n raise TypeError(\"{} is not an index\".format(index))\n except IndexError as e:\n print(\n e,\n \"{} exceeds codes dimensions. limit is {}, if True it selects the limit.\".format(\n index,\n self.limit))\n\n def get_by_level(self, code):\n \"\"\"\n\n :param code:\n :return:\n \"\"\"\n for i in self.codes:\n if code == i.level:\n return i\n\n def get_by_reference(self, code):\n \"\"\"\n\n :param code:\n :return:\n \"\"\"\n refcodes = self.refcodes\n if refcodes:\n if code in refcodes:\n return refcodes[code]\n return code # does nothing\n\n __call__ = map_code\n\n\nclass CodeLog(object):\n \"\"\"\n Base Logger Class which supports CODE objects\n \"\"\"\n use_colors = None\n use_codes = None\n\n def __init__(self, std_out=sys.stdout, code_mapper=None,\n default_codes=None, use_colors=None, use_codes=None):\n \"\"\"\n\n :param std_out: standard output\n :param code_mapper: object to map CODE objects\n :param default_codes:\n :param use_colors:\n :param use_codes:\n \"\"\"\n # assigned from class\n if use_colors is None:\n use_colors = self.use_colors # class default\n if use_codes is None:\n use_codes = self.use_codes # class default\n # assigned from instance\n if use_colors is None:\n use_colors = have_colours(std_out)\n self.use_colors = use_colors\n if use_codes is None:\n use_codes = not self.use_colors # if there is color do not use CODE\n self.use_codes = use_codes\n\n # control variables\n self.code_mapper = code_mapper\n self.std_out = std_out\n self.default_codes = default_codes\n\n @property\n def default_codes(self):\n return self._defcodes\n\n @default_codes.setter\n def default_codes(self, codes):\n if codes is not None and not hasattr(codes, \"__iter__\"):\n codes = [codes]\n self._defcodes = codes\n\n def convert_code(self, codes=None):\n \"\"\"\n Filter accepted codes and adequate them to use.\n\n :param codes: levels, codes or iterators with them.\n :return: it gets None or list with only codes, no empty list (use if filtered)\n \"\"\"\n if codes is None: # use default\n return self.default_codes\n if not codes: # use default\n return None\n if not hasattr(codes, \"__iter__\"):\n codes = [codes] # it must be iterator\n if self.code_mapper: # try to get code from color_mapper\n codes = [self.code_mapper(code)\n for code in codes] # some can be none\n codes = [\n code for code in codes if isinstance(\n code, CODE)] # we need only codes\n if codes: # empty list not printed but we want to print so use None codes\n return codes # return list of codes else None\n\n def accepted_code(self, codes):\n \"\"\"\n return True if codes is accepted else False\n \"\"\"\n return bool(self.convert_code(codes=codes))\n\n def convert(self, text, codes=None, newLine=False, **kwargs):\n \"\"\"\n Convert text with code.\n\n :param text: text to convert\n :param codes: codes to use for text\n :param newLine: True to add newline\n :param kwargs: additional arguments to pass to CODEs\n :return: string of formatted text\n \"\"\"\n if isinstance(text, CODE):\n codes = self.convert_code(text)\n text = text.raw_msg()\n codes = self.convert_code(codes)\n if codes is None: # print if None - no code used\n return str(text) # ensures string\n else: # empty list means that it was filtered\n useColor, useCODE = self.use_colors, self.use_codes\n lines = [str(code(text, newLine, useColor, useCODE, **kwargs))\n for code in codes if code]\n return \"\".join(lines)\n\n def write(self, text, code=None, **kwargs):\n self.std_out.write(self.convert(text, code, **kwargs))\n self.std_out.flush()\n\n def printline(self, text, code=None, **kwargs):\n self.std_out.write(self.convert(text, code, True, **kwargs))\n self.std_out.flush()\n\n def printlines(self, lines, code=None, **kwargs):\n for line in lines:\n self.printline(line, code)\n\n __call__ = write\n\n\nclass EmptyLogger(CodeLog):\n \"\"\"\n Empty logger to not generate outputs\n \"\"\"\n\n def write(self, text, code=None, **kwargs):\n pass\n\n def printline(self, text, code=None, **kwargs):\n pass\n\n def printlines(self, lines, code=None, **kwargs):\n pass\n\n\nclass SimpleLogger(CodeLog):\n \"\"\"\n Simple logger to print CODE objects\n \"\"\"\n def __init__(self, std_out=sys.stdout, code_mapper=None, default_codes=LOG,\n use_colors=None, use_codes=None, verbosity=None):\n \"\"\"\n\n :param std_out:\n :param code_mapper:\n :param default_codes:\n :param verbosity: DEBUG = 0, LOG=1, HIDDEN=2, WARNING=3, ERROR=4\n if verbosity is None. it does not filter and lets any data to be logged.\n if verbosity is N it does not lets log those less than N.\n so if N = 2, it won't let log DEBUG and LOG levels but any other level is permitted.\n change self.states to add more levels that can be filtered with verbosity.\n Note that if self.states = () is is the same as verbosity = None.\n \"\"\"\n if use_colors is None:\n use_colors = self.use_colors # class inherited default\n if use_codes is None:\n use_codes = self.use_codes # class inherited default\n super(\n SimpleLogger,\n self).__init__(\n std_out=std_out,\n code_mapper=code_mapper,\n default_codes=default_codes,\n use_colors=use_colors,\n use_codes=use_codes)\n self.verbosity = verbosity\n\n def convert_code(self, codes=None):\n \"\"\"\n :param codes: levels, codes or iterators with them.\n :return: it gets None, list with only codes or empty list if filtered by verbosity\n \"\"\"\n codes = super(SimpleLogger, self).convert_code(\n codes) # it gets None or list with only codes\n if codes and self.verbosity is not None: # verbosity is active and we got codes\n if hasattr(self.verbosity, \"__iter__\"): # list of permitted codes\n codes = [\n code for code in codes if float(code) in [\n float(i) for i in self.verbosity]]\n else: # verbosity is value or code\n codes = [\n code for code in codes if float(code) >= float(\n self.verbosity)]\n return codes # let code live if None, we want to print it\n\n\nclass Loggers(object):\n \"\"\"\n Manage multiple loggers\n \"\"\"\n\n def __init__(self, logs=None, **kwargs):\n \"\"\"\n :param logs: list of loggers\n :param kwargs: additional arguments to configure loggers\n \"\"\"\n if logs:\n if hasattr(logs, \"__iter__\"):\n self.logs = logs\n else:\n raise Exception(\"logs must be a iterator\")\n else:\n if kwargs:\n self.logs = [CodeLog]\n else:\n self.logs = [CodeLog()]\n if kwargs:\n for i, log in enumerate(self.logs): # initialize all of them\n self.logs[i] = log(**kwargs)\n\n def post_setting(self, **kwargs):\n \"\"\"\n Assign keyword arguments to logs.\n\n :param kwargs: keyword arguments\n \"\"\"\n for log in self.logs: # initialize all of them\n for name, value in kwargs.items():\n setattr(log, name, value)\n\n def write(self, text, state=None, **kwargs):\n for log in self.logs:\n log.write(text, state, **kwargs)\n\n def printline(self, text, state=None, **kwargs):\n for log in self.logs:\n log.printline(text, state, **kwargs)\n\n def printlines(self, lines, state=None, **kwargs):\n for log in self.logs:\n log.printlines(lines, state, **kwargs)\n\n\nif __name__ == '__main__':\n\n c = SimpleLogger(default_codes=IGNORE, verbosity=(DEBUG, ERROR, IGNORE))\n\n c.printline(\n \"\\n\\rthis is my debug message\\r\\n\",\n DEBUG) # no CR is appled after\n c.printline(\" this is my warning message\\r\\n\", WARNING) # no CR is applied\n c.printline(\"this is my error message\", ERROR) # note that a CR is applied\n print(\"\")\n # codes specifies which are printed with CODE object if not catch by\n # default_codes in the logger.\n cmap = CodeMapper(codes=(WARNING, ERROR))\n c.code_mapper = cmap # you can use any function to return the desired CODE object\n cmap.range = 0, 10 # now all levels are mapped to codes in that range\n\n def printer(msg, it=10):\n for level in range(-it, it):\n c.printline(msg, level, it=level)\n sys.stdout.write(c.convert(\"this is normal text\", None, True))\n printer(\n \"\\r\\nthis it a text with mapped level {it} to level {_level} that represents -> {_code}\\r\\n\")\n\n lgs = Loggers()\n lgs.logs.append(c)\n lgs.printline(\"logging with several loggers\")\n lgs.printline(\"and testing that Warning should not appear in one\", WARNING)\n","repo_name":"davtoh/advutils","sub_path":"advutils/prettylogging.py","file_name":"prettylogging.py","file_ext":"py","file_size_in_byte":20365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71661328407","text":"import os\n\nfrom setuptools import find_packages, setup\n\nwith open(os.path.join(\"offline_baselines_jax\", \"version.txt\"), \"r\") as file_handler:\n __version__ = file_handler.read().strip()\n\nsetup(\n name=\"offline_baselines_jax\",\n packages=[package for package in find_packages() if package.startswith(\"offline_baselines_jax\")],\n package_data={\"offline_baselines_jax\": [\"py.typed\", \"version.txt\"]},\n install_requires=[\n \"stable_baselines3==1.4.0\",\n # \"jax==0.3.4\",\n # \"jaxlib==0.3.2\",\n \"flax==0.4.0\",\n \"tensorflow_probability\",\n 'optax==0.1.1'\n ],\n description=\"Jax version of implementations of offline reinforcement learning algorithms.\",\n author=\"Minjong Yoo\",\n url=\"https://github.com/mjyoo2/offline_baselines_jax\",\n author_email=\"mjyoo222@gmail.com\",\n license=\"MIT\",\n version=__version__,\n python_requires=\">=3.7\",\n # PyPI package information.\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n ],\n)\n\n# python setup.py sdist\n# python setup.py bdist_wheel\n# twine upload --repository-url https://test.pypi.org/legacy/ dist/*\n# twine upload dist/*\n","repo_name":"jsw7460/sb3_jax","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"31"} +{"seq_id":"3735108797","text":"from typing import List\n\ndef flipBits(bits: List[str]):\n result = []\n \n for i in range(0, len(bits)):\n if bits[i] == '1':\n result.append('0')\n elif bits[i] == '0':\n result.append('1')\n else:\n raise Exception\n \n return result\n\ndef getMostCommonBitAtPos(binaries: List[str], pos: int):\n zeroes = 0\n ones = 0\n \n for binary in binaries:\n bit = binary[pos]\n if bit == '0':\n zeroes += 1\n elif bit == '1':\n ones += 1\n else:\n raise Exception\n \n if zeroes > ones:\n return '0'\n elif ones > zeroes:\n return '1'\n else:\n return '-1'\n\ndef getEntriesWithBitAtPos(binaries: List[str], bit: str, pos: int):\n filtered = filter(lambda x: x[pos] == bit, binaries)\n return list(filtered)\n\ndef getGammaAndEpsilonRate(binaries: List[str]):\n length = len(binaries[0])\n \n mostCommonBits = [0 for x in range(length)]\n \n for i in range(0, length):\n mostCommonBits[i] = getMostCommonBitAtPos(binaries, i)\n \n gamma = ''.join(map(str, mostCommonBits))\n epsilon = ''.join(map(str, flipBits(mostCommonBits)))\n \n return [gamma, epsilon]\n\ndef getOxygenRating(binaries: List[str]):\n for i in range(len(binaries[0])):\n if len(binaries) == 1:\n break\n mostCommonBit = getMostCommonBitAtPos(binaries, i)\n if(mostCommonBit == '-1'):\n mostCommonBit = '1'\n binaries = getEntriesWithBitAtPos(binaries, mostCommonBit, i)\n \n if len(binaries) != 1:\n raise Exception\n \n return binaries[0]\n\ndef getCO2Rating(binaries: List[str]):\n for i in range(len(binaries[0])):\n if len(binaries) == 1:\n break\n mostCommonBit = getMostCommonBitAtPos(binaries, i)\n if(mostCommonBit == '1'):\n mostCommonBit = '0'\n elif(mostCommonBit == '0'):\n mostCommonBit = '1'\n elif(mostCommonBit == '-1'):\n mostCommonBit = '0'\n binaries = getEntriesWithBitAtPos(binaries, mostCommonBit, i)\n \n if len(binaries) != 1:\n raise Exception\n \n return binaries[0]\n\ndef getOxygenAndCO2Rating(binaries: List[str]):\n return [getOxygenRating(binaries), getCO2Rating(binaries)]\n \ndef main():\n with open('input.txt', 'r') as f:\n binaries = list(map(lambda x: x.strip(), f.readlines()))\n \n gamma, epsilon = list(map(lambda x: int(x,2), getGammaAndEpsilonRate(binaries)))\n \n print(f\"Gamma {gamma} Epsilon {epsilon} Product {gamma * epsilon}\")\n \n oxygen, co2 = list(map(lambda x: int(x,2), getOxygenAndCO2Rating(binaries)))\n \n print(f\"Oxygen {oxygen} CO2 {co2} Product {oxygen * co2}\")\n\nif __name__ == \"__main__\":\n main()","repo_name":"joergpichler/AdventOfCode","sub_path":"2021/Day3/Day3.py","file_name":"Day3.py","file_ext":"py","file_size_in_byte":2778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14039446924","text":"\nimport pprint\nimport requests\nfrom bs4 import BeautifulSoup\nimport ipdb\nimport re\nimport argparse\nimport time\nimport pickle\nimport os\n\nclass HttpRequestError(Exception):\n pass\n\n#--------------------------------------------------\n# Look up list of top 50 cities that best match this char object\n# Input:\n# - zipcode: int \n# Output:\n# - cost of living: integer, round to thousands\n#--------------------------------------------------\ndef lookup_cost_by_zip(zipcode):\n try:\n r = requests.get('http://www.city-data.com/zips/%s.html' % zipcode, timeout=10)\n except:\n raise HttpRequestError(\"Can't get city-data response\")\n \n if r.status_code != 200:\n raise Exception(\"Error looking up cost of living in %s\" % zipcode)\n\n soup = BeautifulSoup(r.text, \"html.parser\")\n all_b = soup.find_all(\"b\")\n b_cost_living = list(filter(lambda x: re.search(\"cost of living index\", x.get_text()), all_b))\n if len(b_cost_living) == 0:\n raise Exception(\"Can't find cost of living in %s\" % zipcode)\n cost_living_string = b_cost_living[0].next_sibling.strip()\n try:\n cost_living = float(cost_living_string)\n return cost_living\n except:\n raise Exception(\"Can't find cost of living in %s\" % zipcode)\n\ndef get_cost_by_city(city_string):\n splitted = city_string.split(\"\\t\")\n zip_string = splitted[1]\n zips = map(lambda x: int(x), filter(lambda x: len(x) > 0, zip_string.split(\",\")))\n cost = None\n for z in zips:\n try:\n cost = lookup_cost_by_zip(z)\n print(\"%s=%f\" % (city_string, cost))\n break\n except Exception as e:\n pass\n if cost is None:\n return (city_string, \"\")\n else:\n return (city_string, cost)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--spark\", action='store_true', help=\"run in spark mode\")\n parser.add_argument(\"-n\", type=int, help=\"number of iterations to run on single thread, 0 for maximum\", default=1)\n args = parser.parse_args()\n\n pp = pprint.PrettyPrinter(indent=4)\n\n if args.spark:\n spark_flag = True\n from pyspark import SparkContext, SparkConf\n from operator import add\n\n # collect cost of living from one of the zip code of the city\n start = time.time()\n conf = SparkConf().setAppName(\"get_zip_code\").setMaster(\"local\")\n sc = SparkContext(conf=conf)\n # sc = SparkContext(appName=\"get_zip_code\")\n lines = sc.textFile(\"city_list_with_zipcode.txt\", 8)\n city_with_cost = lines.map(get_cost_by_city).collect()\n end = time.time()\n print(\"job finished in %f seconds\" % (end-start))\n\n with open(\"city_list_with_zip_cost.txt\", \"w\") as f:\n for (cityname, cost) in city_with_cost:\n f.write(\"%s\\t%s\\n\" % (cityname, str(cost)))\n\n sc.stop()\n else:\n with open(\"cities_with_zip_cost.pickle\", \"rb\") as f:\n all_cities = pickle.load(f)\n try:\n for onecity in all_cities:\n # ipdb.set_trace()\n if \"cost\" not in onecity:\n for onezip in onecity[\"zipcode\"]:\n try:\n onecity[\"cost\"] = lookup_cost_by_zip(onezip)\n break\n except HttpRequestError:\n raise Exception(\"Time out from city-data\")\n except:\n pass\n if \"cost\" not in onecity:\n onecity[\"cost\"] = 0\n print(\"%s, %s=%f\" % (onecity[\"name\"], onecity[\"state\"], onecity[\"cost\"]))\n except:\n pass\n with open(\"cities_with_zip_cost.pickle\", \"wb\") as f:\n pickle.dump(all_cities, f)\n\n\n\n","repo_name":"mqchau/citymatch","sub_path":"datasource/cost_living.py","file_name":"cost_living.py","file_ext":"py","file_size_in_byte":3821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2052653594","text":"from rates import dollar_rate\nfrom datetime import datetime\n\n#Task_1 \n\nprint(\"Python code to Convert kilometers to Miles.\")\nkm = float(input(\"Enter in kilometers \\n\"))\nconv_factor = 0.621371\nmiles = km * float(conv_factor)\nprint(miles, \"Miles\")\n\n#Task_2\n\nprint(\"Money exchange app.\")\ndram = float(input(\"Enter in Dram \"))\ndollar = dram / dollar_rate\nprint(dollar, \"Dollar\")\n\n#Task_3\n\nprint(\"Find out when you\\'ll become 100 years old !\")\ncurrent_time = datetime.now()\n\nage = int(input(\"Tell me your age \"))\ncurrent_year= current_time.year\nthe_year = current_year - age + 100\nprint(the_year)\n\n#Task_4\n\nprint(\"Find out what\\'s your optimal weight.\")\nheight = float(input(\"Enter your height in meters \"))\n\nBMI = 22\nweight = 2.2 * BMI + (3.5 * BMI) * (height - 1.5)\nprint(\"Your average optimal weight in kilograms\", int(weight))\n\n#OR\n\nprint(\"What\\'s your optimal weight considering your sex.\")\nheight = input(\"Enter your height in feet(please write with tenths) \")\nheight = height[::-1]\nheight = float(height)\nfirst_digit = height % 10\nweight_male = 52 + 1.9 * first_digit\nweight_female = 49 + 1.7 * first_digit\nsex = input(\"Enter your sex (male/female) \")\nprint(sex)\nif sex == \"male\":\n\tprint(\"Your optimal weight is \", int(weight_male) ,\"kilograms\" )\nelse:\n\tprint(\"Your optional weight is \", int(weight_female), \"kilograms\")\n\n#Task_5\n\nprint(\"Sum the value of the first symbols of the given dates.\")\nyear = input(\"Year \\n \")\nreverse_1 = year[::-1]\nyear = int(year)\nreverse_1 = int(reverse_1)\nfirst_digit_1 = reverse_1 % 10\n\nmonth = input(\"Month \\n \")\nreverse_2 = month[::-1]\nmonth = int(month)\nreverse_2 = int(reverse_2)\nfirst_digit_2 = reverse_2 % 10\n\nday = input(\"Day\\n \")\nreverse_3 = day[::-1]\nday = int(day)\nreverse_3 = int(reverse_3)\nfirst_digit_3 = reverse_3 % 10\n\ntotal = first_digit_1 + first_digit_2 + first_digit_3\nprint(total)\n\n\n\n\n#Task_6\n\nprint(\"Find out how many minutes have you been alive !\")\n#one year in minutes\none_year = 525600\n#one month in minutes\none_month = 43800\n#one day in minutes\none_day = 1440\n\nyear = int(input(\"Enter the year of your birth. \\tE.g. 2000 \\n \"))\nmonth = int(input(\"Enter the month. \\tE.g. 5 \\n \"))\nday = int(input(\"Enter your birth day. \\tE.g.25 \\n \"))\nminutes = year * one_year + month * one_month + day * one_day\n\nprint(\"You have been alive\", minutes, \"minutes!\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"NazeliGalsytyan/homework_python","sub_path":"python_classes/3_homework.py","file_name":"3_homework.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3122579572","text":"'''Counting bits: bits[i] = bits[i / 2] when the number is even.\nOtherwise bits[i] = bits[i / 2] + 1.'''\nclass Solution(object):\n def countBits(self, num):\n \"\"\"\n :type num: int\n :rtype: List[int]\n \"\"\"\n res = [0 for _ in range(num + 1)]\n for i in range(num + 1):\n if i % 2 == 0:\n res[i] = res[i / 2]\n else:\n res[i] = res[i / 2] + 1\n return res\n","repo_name":"tr1503/LeetCode","sub_path":"Bit Manipulation/countingBits.py","file_name":"countingBits.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4782363462","text":"# The module contains an implementation of the Shamir cryptographic protocol.\nfrom secrets import SystemRandom\nfrom dataclasses import dataclass\n\nfrom app.crypto.mathlib import ext_gcd, fpow\n\n\nclass Shamir:\n @dataclass\n class PrivateKey:\n k: int\n p: int\n\n @dataclass\n class PublicKey:\n k: int\n p: int\n\n def __init__(self, private_key: PrivateKey, public_key: PublicKey) -> None:\n \"\"\"Implementation of the Shamir cryptographic protocol.\n\n private_key\n Private key of type Shamir.PrivateKey, which contains a number K and the prime number p - module.\n public_key\n Public key of type Shamir.Public, which contains a number K and the prime number p - module.\n \"\"\"\n if not (isinstance(private_key, Shamir.PrivateKey) and isinstance(public_key, Shamir.PublicKey)):\n raise TypeError(\"Arguments must be of type Shamir.PrivateKey and Shamir.PublicKey.\")\n\n self._private_key = private_key\n self._public_key = public_key\n\n def encrypt(self, data: int) -> int:\n \"\"\"Method for encrypting data with a public key.\"\"\"\n return fpow(data, self._public_key.k, self._public_key.p)\n\n def decrypt(self, data: int) -> int:\n \"\"\"Method for decrypting data with a private key\"\"\"\n return fpow(data, self._private_key.k, self._private_key.p)\n\n @property\n def public_key(self) -> PublicKey:\n \"\"\"Get the value of the public key.\"\"\"\n return self._public_key\n\n @property\n def private_key(self) -> PrivateKey:\n \"\"\"Get the value of the private key.\"\"\"\n return self._private_key\n\n @staticmethod\n def gen_keys(p: int) -> tuple[PrivateKey, PublicKey]:\n \"\"\"Method for generating private and public keys.\n\n p\n Large prime number.\n\n Returns\n Tuple of two elements - private and public keys.\n \"\"\"\n phi = p - 1\n sysrand = SystemRandom()\n\n while True:\n public_key = sysrand.randrange(2, phi)\n d, private_key, _ = ext_gcd(public_key, phi)\n\n if d == 1:\n break\n\n private_key %= phi\n\n return Shamir.PrivateKey(private_key, p), Shamir.PublicKey(public_key, p)\n","repo_name":"vasilypht/crypto-methods","sub_path":"app/crypto/protocols/shamir.py","file_name":"shamir.py","file_ext":"py","file_size_in_byte":2250,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"6061649366","text":"\"\"\"\ncall:\n python score_features.py <location to images> <location to labels>\n\nfor now, assume this is being run from the same directory\nthat the trained model files are in. Output scores to current directory so\nits obvious which model created them. Only take locations to images and labels\nfor now. Will formalize later.\n\"\"\"\n\nimport tensorflow as tf\nimport sys\nimport os\nimport pandas as pd\n\nfrom utils import safe_int\n\n\nimage_dir = os.path.abspath(sys.argv[1])\nlabels_file = os.path.abspath(sys.argv[2])\nscores_dir = os.path.abspath('feature_scores')\n\n_, _, images = os.walk(image_dir).next()\nimages = filter(lambda x: not x.startswith('.'), images)\n\n# Loads label file, strips off carriage return\nlabel_lines = [line.rstrip() for line in tf.gfile.GFile(labels_file)]\n\n\n# Unpersist graph from file\nwith tf.gfile.FastGFile(\"retrained_graph.pb\", 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')\n\nscored_images = {}\nwith tf.Session() as sess:\n result_tensor = sess.graph.get_tensor_by_name('final_result:0')\n \n for image in images:\n print(\"getting feature scores for {}\".format(image))\n image_path = os.path.join(image_dir, image)\n image_data = tf.gfile.FastGFile(image_path, 'rb').read()\n\n predictions = sess.run(result_tensor, \n {'DecodeJpeg/contents:0': image_data})\n \n # Sort to show labels of first prediction in order of confidence\n top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]\n\n basename = image.strip('.jpg') + '.txt'\n if not os.path.exists(scores_dir):\n os.makedirs(scores_dir)\n filename = os.path.join(scores_dir, basename)\n image_scores = {}\n with open(filename, 'wb') as f:\n for node_id in top_k:\n human_string = label_lines[node_id]\n score = predictions[0][node_id]\n f.write(\"{l}\\t{s}\\n\".format(l=human_string, s=score))\n image_scores[human_string] = score\n scored_images[image.strip('.jpg')] = image_scores\n\nscored_images_df = pd.DataFrame.from_dict(scored_images)\ncolumns = scored_images_df.columns.tolist().sort(\n key=lambda x: (''.join(i for i in x if not i.isdigit()),\n safe_int(''.join(i for i in x if i.isdigit()))\n )\n)\nscored_images_df.reindex_axis(columns, axis=1)\nscored_images_df.to_csv(os.path.join(scores_dir, 'image_scores.csv'))\n","repo_name":"dcooper46/image-recognition","sub_path":"feature_extraction/transfer_learning/inception/score_features.py","file_name":"score_features.py","file_ext":"py","file_size_in_byte":2517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5808353581","text":"import sys\ninput = sys.stdin.readline\nn = int(input())\n\ndp = [0]\ns = [0]\n\nfor i in range(1, n+1):\n s.append(int(input()))\n if i < 3:\n dp.append(s[i-1] + s[i])\n else:\n dp.append(max(dp[i-3] + s[i-1] + s[i], dp[i-2] + s[i], dp[i-1]))\n\nprint(dp[-1])","repo_name":"Liebestraum1/Algorithm_Python","sub_path":"BOJ/Dynamic Programming/2156_포도주 시식.py","file_name":"2156_포도주 시식.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22078770164","text":"from django.shortcuts import render\nfrom .forms import SeqContentForm, RevComp\nfrom . import utils \nfrom .utils import rev_comp\n# Create your views here.\ndef seqcontent_view(request):\n if request.method == 'POST':\n form = SeqContentForm(request.POST)\n if form.is_valid():\n seq = form.cleaned_data['sequence']\n word_size = form.cleaned_data['word_size']\n d = utils.count_words(seq, word_size)\n seq_len = len(seq)\n return render(request, 'biotools/seqcontent.html', {'results': d, 'seq_len': seq_len})\n else:\n form = SeqContentForm()\n\n return render(request, 'biotools/seqcontent.html', {'form': form})\n\n\ndef revcomp_view(request):\n if request.method == 'POST':\n form = RevComp(request.POST)\n if form.is_valid():\n seq = form.cleaned_data['sequence']\n results = rev_comp(seq)\n return render(request, 'biotools/revcomp.html', {'results': results, 'seq': results['original'], 'reverse_seq': results['reverse_complement']})\n else:\n form = RevComp()\n\n return render(request, 'biotools/revcomp.html', {'form': form})\n\n","repo_name":"kmrowinska/django_website","sub_path":"biotools/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11200439668","text":"import cv2\nimport numpy as np\nfrom common import Point, Size, Rect\n\nclass RarityRecognizer:\n _star_img = None\n _star_mask_img = None\n _roi = Rect(30, 83, 90, 16)\n _method = cv2.TM_SQDIFF_NORMED\n _threshold = 0.05\n _min_interval = 6\n\n _precise_star_xs = [\n [41],\n [36, 46],\n [30, 41, 51],\n [25, 36, 46, 57],\n [20, 30, 41, 51, 61],\n [15, 25, 36, 46, 57, 67],\n [10, 20, 30, 41, 51, 61, 71],\n [7, 17, 26, 36, 45, 55, 64, 74]\n ]\n _precise_star_y = 6\n\n def __init__(self):\n self._star_img = cv2.imread('star.png', cv2.IMREAD_COLOR)\n self._star_mask_img = cv2.imread('star_mask.png', cv2.IMREAD_COLOR)\n\n def recognize(self, input_img):\n # Consider ROI only\n input_roi_img = input_img[\n self._roi.y : self._roi.y + self._roi.h,\n self._roi.x : self._roi.x + self._roi.w,\n :]\n\n # For each of R, G, B\n star_h, star_w, _ = self._star_img.shape\n scores = np.zeros((self._roi.h - star_h + 1, self._roi.w - star_w + 1))\n for channel in range(3):\n result = cv2.matchTemplate(\n input_roi_img[:, :, channel],\n self._star_img[:,:,channel],\n self._method,\n mask = self._star_mask_img[:,:,channel])\n scores = np.add(scores, np.square(result))\n\n # Filter by the threshold, take x-axis only\n xys = np.where(scores <= self._threshold)\n ys = xys[0]\n xs = xys[1]\n\n # Prevent count a star twice\n xs.sort()\n last_x = -999999\n star_groups = []\n for x in xs:\n if x - last_x >= self._min_interval:\n last_x = x\n star_groups.append([])\n star_groups[-1].append(x)\n\n if len(star_groups) == 0 or len(star_groups) > 8:\n return (0, Point())\n\n best_offset = Point()\n best_score = 0\n for candidate in star_groups[0]:\n dx = candidate - self._precise_star_xs[len(star_groups) - 1][0]\n score = 0\n for g in range(len(star_groups)):\n for x in star_groups[g]:\n if x - self._precise_star_xs[len(star_groups) - 1][g] == dx:\n score += 1\n if score > best_score:\n best_score = score\n best_offset.x = dx;\n\n ys = ys.tolist()\n best_offset.y = max(set(ys), key=ys.count) - self._precise_star_y\n\n return (len(star_groups), best_offset)\n","repo_name":"leoshen999/shiro_musume_detector","sub_path":"rarity_recognizer.py","file_name":"rarity_recognizer.py","file_ext":"py","file_size_in_byte":2562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18147743955","text":"numbers = []\nfor i in range(4):\n numbers.append(int(input()))\n\ntelemarketer = False\n\nif (numbers[0] in [8, 9]) and (numbers[3] in [8, 9]) and (numbers[1] == numbers[2]):\n telemarketer = True\n\nif (telemarketer):\n print('ignore')\nelse:\n print('answer')\n","repo_name":"adzcai/competitive-programming","sub_path":"ccc/2018/ccc18j1.py","file_name":"ccc18j1.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"73643552729","text":"from pymemcache.exceptions import MemcacheError\n# from django.contrib.auth.models import User\nfrom rest_framework.authentication import BaseAuthentication\n\nfrom django.utils.translation import gettext_lazy as _\nfrom rest_framework import exceptions, HTTP_HEADER_ENCODING\nfrom rest_framework.exceptions import NotAuthenticated, PermissionDenied\nfrom django.core.cache import cache\nfrom django.conf import settings\n# from django.urls import resolve\nfrom common.functions import get_client_ip\nimport time\n\n\nclass TokenAuthentication(BaseAuthentication):\n def authenticate(self, request):\n client_ip = get_client_ip(request)\n block_key = 'block_ip_%s' % client_ip\n try:\n block_times = cache.get(block_key)\n except (ConnectionRefusedError, MemcacheError):\n raise exceptions.APIException('memcached service is not ready!')\n\n if block_times and block_times >= 2:\n raise exceptions.APIException('your ip address is blocked, will be auto resume after 2 hours')\n\n # 记录1秒内某个ip的请求频率\n request_times = cache.get(client_ip)\n if not request_times:\n request_times = 0\n cache.set(client_ip, request_times+1, 1)\n\n # 当1秒内请求次数超过20次，将ip列入黑名单，加入黑名单次数达到2次后，禁止该ip访问\n # print(request_times)\n if request_times >= 30:\n if not block_times:\n block_times = 0\n cache.set(block_key, block_times+1, 7200)\n # 第一次提醒用户超过频率太高\n raise exceptions.ParseError('your operation frequency is too high')\n\n auth = request.META.get('HTTP_AUTHORIZATION', b'')\n if isinstance(auth, str):\n # Work around django test client oddness\n auth = auth.encode(HTTP_HEADER_ENCODING)\n\n auth = auth.split()\n\n # 如果header中没有token，则返回None, 然后使用permission_class进行权限检查\n if not auth or auth[0].lower() != b'token':\n return None\n\n if len(auth) == 1:\n msg = _('Invalid token header. No credentials provided.')\n raise exceptions.AuthenticationFailed(msg)\n\n elif len(auth) > 2:\n msg = _('Invalid token header. Token string should not contain spaces.')\n raise exceptions.AuthenticationFailed(msg)\n\n try:\n token = auth[1].decode()\n except UnicodeError:\n msg = _('Invalid token header. Token string should not contain invalid characters.')\n raise exceptions.AuthenticationFailed(msg)\n\n return self.verify_token_value(token, request)\n\n @staticmethod\n def verify_token_value(key, request):\n ua = request.META.get('HTTP_USER_AGENT', 'unknown')\n client_ip = get_client_ip(request)\n\n cache_token = cache.get('token_%s' % key)\n if not cache_token:\n raise exceptions.AuthenticationFailed(_('Invalid token. '))\n\n cache_ua, cache_ip, cache_latest_time, cache_user = cache_token\n if cache_ua != ua:\n raise exceptions.AuthenticationFailed(_('Invalid token. UserAgent not match.'))\n\n if cache_ip != client_ip:\n raise exceptions.AuthenticationFailed(_('Invalid Token. Client ip error.'))\n\n if time.time() - cache_latest_time > settings.TOKEN_EXPIRE_TIME:\n raise exceptions.AuthenticationFailed(_('Invalid token. Token expire.'))\n\n cache.set('token_%s' % key, (cache_ua, cache_ip, time.time(), cache_user))\n return cache_user, None\n\n\ndef verify_permission():\n \"\"\"\n 验证权限\n \"\"\"\n def wrapper(request, *args, **kwargs):\n # perms_map = {\n # 'GET': '{app_label}.view_{model_name}',\n # 'OPTIONS': None,\n # 'HEAD': None,\n # 'POST': '{app_label}.add_{model_name}',\n # 'PUT': '{app_label}.change_{model_name}',\n # 'PATCH': '{app_label}.change_{model_name}',\n # 'DELETE': '{app_label}.delete_{model_name}',\n # }\n if not request.user and not request.user.is_authenticated and not request.user.is_active:\n raise NotAuthenticated('No login')\n\n if request.method == 'GET':\n return wrapper\n\n if request.method in ('POST', 'PUT', 'DELETE') and (not request.user.is_staff or not request.user.is_superuser):\n raise PermissionDenied()\n # r = resolve(request.path)\n # perms = perms_map[request.method].format(\n # app_label=app_label if app_label else 'auth' if r.app_name == 'user' else r.app_name,\n # model_name=model_name\n # )\n # if request.user.has_perm(perms) and perms:\n # return func(request, *args, **kwargs)\n # raise PermissionDenied()\n\n return wrapper\n","repo_name":"dengguibao/frc-fw","sub_path":"common/tokenauth.py","file_name":"tokenauth.py","file_ext":"py","file_size_in_byte":4830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2105167860","text":"from bs4 import BeautifulSoup as soup\nfrom urllib import urlopen as uReq\n\nmy_url = \"http://www.billboard.com/charts/hot-100\"\n\nuClient = uReq(my_url)\npage_html = uClient.read()\nuClient.close()\n\npage_soup = soup(page_html, \"html.parser\")\n\ncontainers = page_soup.findAll(\"div\", {\"class\":\"chart-row__title\"})\n\nfile_name = \"songs.csv\"\nf = open(file_name, \"w\")\n\nHeaders = \"SONG NAME, ARTIST \\n\"\n\nf.write(Headers)\n\n\n\nfor song in containers :\n\n song_name = song.findAll(\"h2\", {\"class\": \"chart-row__song\"})\n\n track = song_name[0].text.strip()\n print(track)\n\n artist_name = song.findAll(\"a\", {\"class\": \"chart-row__artist\"}) or song.findAll(\"span\", {\"class\":\"chart-row__artist\"})\n singer = artist_name[0].text.strip()\n print(singer)\n\n f.write(str(track.replace(\",\", \"|\")) + ',' + str(singer.replace(\",\", \"|\") + '\\n'))\n \n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"shamssahal/Web-Scrapers","sub_path":"billboard.py","file_name":"billboard.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73699515929","text":"numero = int(input('Digite um número:\\n'))\r\ncont = 0\r\nprimo = 0\r\nfor a in range(1,numero+1):\r\n for b in range(1,a+1):\r\n if a % b == 0:\r\n cont += 1\r\n if cont == 2:\r\n primo += 1\r\n cont = 0\r\nprint('Quandtidade de números primos: {}.'.format(primo))\r\n","repo_name":"EmersonDantas/SI-UFPB-IP-P1","sub_path":"Python Brasil - Exercícios/Estrutura de Repetição/Q23ER.py","file_name":"Q23ER.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"36425107668","text":"\"\"\"\nThis script implements our AMT system for note tracking.\n\nHow to use this code\n1. Train the frame and onset detectors.\n2. Create a folder, e.g., maps/note if the training dataset is MAPS, and copy this script to the folder.\n By default, the note level performance for different splits will be saved in folder ./tb_inf. You can view the\n outputs with tensorboard.\n3. Create a folder note/saved_model. Then, copy the checkpoint of the frame detector that has the best validation\n performance to this folder, and rename this checkpoint as frame_model. Next, copy the checkpoint of the onset\n detector that has the best validation performance to this folder, and rename this checkpoint as onset_model.\n4. Configure the following parameters:\n DEBUG: in {True, False}. If True, will run in a debug mode where only very few recordings will be run. The debug\n mode enables you to quickly check if the script can run correctly.\n GPU_ID: in {0, 1, ..., n - 1} where n is the number of GPUs available.\n TRAINING_DATASET_IS_MAPS: in {True, False}. Set to True if the dataset for training is MAPS, or otherwise to False.\n4. Refer to class Config for more options.\n\"\"\"\n\nfrom __future__ import print_function\nimport numpy as np\n\nDEBUG = False\nGPU_ID = 1\nTRAINING_DATASET_IS_MAPS = True\n\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\nimport os\nimport glob\nimport re\nimport logging\nlogging.basicConfig(\n level=logging.DEBUG if DEBUG else logging.INFO,\n format='[%(levelname)s] %(message)s'\n)\nimport magenta.music\nimport soundfile\nfrom tensorflow.python import pywrap_tensorflow\nimport collections\nimport csv\nimport mido\nfrom mir_eval.transcription import xian_onset_frame_transcription_performance_fn\n\n# the folder where the checkpoints for the frame and onset detectors are stored\nONSET_FRAME_MODEL_DIR = 'saved_model'\n\n\n# miscellaneous functions\nclass MiscFns(object):\n \"\"\"Miscellaneous functions\"\"\"\n\n @staticmethod\n def filename_to_id(filename):\n \"\"\"Translate a .wav or .mid path to a MAPS sequence id.\"\"\"\n return re.match(r'.*MUS-(.+)_[^_]+\\.\\w{3}',\n os.path.basename(filename)).group(1)\n\n @staticmethod\n def times_to_frames_fn(sr, spec_stride, start_time, end_time):\n assert sr in (16000, 44100)\n spec_stride = int(spec_stride)\n assert spec_stride == 512 if sr == 16000 else 22 * 64\n assert spec_stride & 1 == 0\n start_sample = int(start_time * sr)\n end_sample = int(end_time * sr)\n start_frame = (start_sample + spec_stride // 2) // spec_stride\n end_frame = (end_sample + spec_stride // 2 - 1) // spec_stride\n return start_frame, end_frame + 1\n\n @staticmethod\n def acoustic_model_fn(spec_batch, is_training, use_feature, trainable):\n assert tf.get_variable_scope().name != ''\n spec_batch.set_shape([None, None, 336])\n assert all(isinstance(v, bool) for v in (is_training, use_feature, trainable))\n outputs = spec_batch[..., None]\n\n outputs = slim.conv2d(\n scope='C_0',\n inputs=outputs,\n num_outputs=32,\n kernel_size=3,\n normalizer_fn=slim.batch_norm,\n normalizer_params=dict(is_training=is_training, trainable=trainable),\n trainable=trainable\n )\n\n outputs = slim.conv2d(\n scope='C_1',\n inputs=outputs,\n num_outputs=32,\n kernel_size=3,\n normalizer_fn=slim.batch_norm,\n normalizer_params=dict(is_training=is_training, trainable=trainable),\n trainable=trainable\n )\n outputs = slim.dropout(scope='DO_1', inputs=outputs, keep_prob=.8, is_training=is_training)\n\n outputs = slim.conv2d(\n scope='C_2',\n inputs=outputs,\n num_outputs=32,\n kernel_size=3,\n normalizer_fn=slim.batch_norm,\n normalizer_params=dict(is_training=is_training, trainable=trainable),\n trainable=trainable\n )\n outputs = slim.dropout(scope='DO_2', inputs=outputs, keep_prob=.8, is_training=is_training)\n\n outputs = slim.conv2d(\n scope='C_3',\n inputs=outputs,\n num_outputs=32,\n kernel_size=3,\n normalizer_fn=slim.batch_norm,\n normalizer_params=dict(is_training=is_training, trainable=trainable),\n trainable=trainable\n )\n outputs = slim.dropout(scope='DO_3', inputs=outputs, keep_prob=.8, is_training=is_training)\n\n outputs = slim.conv2d(\n scope='DC_4',\n inputs=outputs,\n num_outputs=256,\n kernel_size=[1, 97],\n rate=[1, 3],\n normalizer_fn=slim.batch_norm,\n normalizer_params=dict(is_training=is_training, trainable=trainable),\n trainable=trainable\n )\n outputs = outputs[:, :, : 88 * 3, :]\n outputs.set_shape([None, None, 88 * 3, 256])\n outputs = slim.max_pool2d(scope='MP_4', inputs=outputs, kernel_size=[1, 3], stride=[1, 3], padding='VALID')\n outputs.set_shape([None, None, 88, 256])\n outputs = slim.dropout(scope='DO_4', inputs=outputs, keep_prob=.8, is_training=is_training)\n\n outputs = slim.fully_connected(\n scope='FC_5',\n inputs=outputs,\n num_outputs=64,\n normalizer_fn=slim.batch_norm,\n normalizer_params=dict(is_training=is_training, trainable=trainable),\n trainable=trainable\n )\n outputs = slim.dropout(scope='DO_5', inputs=outputs, keep_prob=.8, is_training=is_training)\n outputs.set_shape([None, None, 88, 64])\n\n if not use_feature:\n outputs = slim.fully_connected(\n scope='FC_6',\n inputs=outputs,\n num_outputs=1,\n activation_fn=None,\n trainable=trainable\n )\n outputs = tf.squeeze(outputs, axis=-1)\n outputs.set_shape([None, None, 88])\n\n return outputs\n\n @staticmethod\n def gen_split_list_fn(num_frames, snippet_len):\n split_frames = range(0, num_frames + 1, snippet_len)\n if split_frames[-1] != num_frames:\n split_frames.append(num_frames)\n start_end_frame_pairs = zip(split_frames[:-1], split_frames[1:])\n\n return start_end_frame_pairs\n\n @staticmethod\n def load_np_array_from_file_fn(file_name):\n with open(file_name, 'rb') as fh:\n first_line = str(fh.readline()).split()\n rec_name = first_line[0]\n dtype = first_line[1]\n dim = first_line[2:]\n dim = [int(_item) for _item in dim]\n output = np.frombuffer(fh.read(), dtype=dtype).reshape(*dim)\n return rec_name, output\n\n @staticmethod\n def num_samples_to_num_frames_fn(num_samples):\n assert isinstance(num_samples, (int, long))\n num_frames = (num_samples + 63) // 64\n num_frames = (num_frames + 21) // 22\n\n return num_frames\n\n @staticmethod\n def array_to_table_tf_fn(tf_array, header, scope, title, names, precision=None):\n tf_array = tf.convert_to_tensor(tf_array)\n assert tf_array._rank() == 2\n num_examples = tf_array.shape[0].value\n num_fields = tf_array.shape[1].value\n assert num_examples is not None\n assert num_fields is not None\n assert isinstance(header, list)\n assert len(header) == num_fields\n header = ['id', 'name'] + header\n header = tf.constant(header)\n assert isinstance(names, list)\n assert len(names) == num_examples\n names = tf.constant(names)[:, None]\n assert names.dtype == tf.string\n ids = [str(i) for i in range(1, num_examples + 1)]\n ids = tf.constant(ids)[:, None]\n if precision is None:\n if tf_array.dtype in (tf.float32, tf.float64):\n precision = 4\n else:\n precision = -1\n tf_array = tf.as_string(tf_array, precision=precision)\n tf_array = tf.concat([ids, names, tf_array], axis=1)\n tf_array.set_shape([num_examples, num_fields + 2])\n tf_array = tf.strings.reduce_join(tf_array, axis=1, separator=' | ')\n tf_array = tf.strings.reduce_join(tf_array, separator='\\n')\n header = tf.strings.reduce_join(header, separator=' | ')\n sep = tf.constant(['---'])\n sep = tf.tile(sep, [num_fields + 2])\n sep = tf.strings.reduce_join(sep, separator=' | ')\n tf_array = tf.strings.join([header, sep, tf_array], separator='\\n')\n assert isinstance(title, str)\n tf_array = tf.strings.join([tf.constant(title), tf_array], separator='\\n\\n')\n assert isinstance(scope, str)\n op = tf.summary.text(scope, tf_array)\n\n return op\n\n @staticmethod\n def note_seq_to_valued_intervals(note_seq):\n\n total_num_notes = len(note_seq.notes)\n start_end_times = []\n pitches = []\n for note in note_seq.notes:\n pitches.append(note.pitch)\n start_end_times.append([note.start_time, note.end_time])\n start_end_times = np.asarray(start_end_times, dtype=np.float32).reshape(-1, 2)\n pitches = np.asarray(pitches, dtype=np.uint8).reshape(-1)\n assert start_end_times.shape == (total_num_notes, 2)\n assert len(pitches) == total_num_notes\n\n return dict(times=start_end_times, pitches=pitches)\n\n @staticmethod\n def get_note_seq_from_mid_file_fn(mid_file_name):\n note_seq = magenta.music.midi_file_to_note_sequence(mid_file_name)\n note_seq = magenta.music.apply_sustain_control_changes(note_seq)\n\n return note_seq\n\n @staticmethod\n def unstack_88_into_batch_dim_fn(note_dim, inputs):\n outputs = inputs\n input_dims = outputs._rank()\n assert outputs.shape[note_dim].value == 88\n outputs = tf.unstack(outputs, axis=note_dim)\n assert len(outputs) == 88\n outputs = tf.concat(outputs, axis=0)\n output_dims = outputs._rank()\n assert input_dims - output_dims == 1\n\n return outputs\n\n @staticmethod\n def split_batch_dim_into_88_fn(note_dim, inputs):\n outputs = inputs\n input_dims = outputs._rank()\n outputs = tf.split(value=outputs, num_or_size_splits=88, axis=0)\n assert len(outputs) == 88\n outputs = tf.stack(outputs, axis=note_dim)\n assert outputs.shape[note_dim].value == 88\n output_dims = outputs._rank()\n assert output_dims - input_dims == 1\n\n return outputs\n\n @staticmethod\n def rnn_layer_fn(inputs, trainable):\n outputs = inputs\n outputs.set_shape([1, None, 88, 64])\n lstm_cell = tf.nn.rnn_cell.LSTMCell(name='lstm_cell', num_units=64, dtype=tf.float32, trainable=trainable)\n outputs = MiscFns.unstack_88_into_batch_dim_fn(note_dim=2, inputs=outputs)\n outputs, _ = tf.nn.dynamic_rnn(\n scope='dy_rnn',\n cell=lstm_cell,\n inputs=outputs,\n dtype=tf.float32\n )\n outputs = MiscFns.split_batch_dim_into_88_fn(note_dim=2, inputs=outputs)\n outputs.set_shape([1, None, 88, 64])\n\n return outputs\n\n @staticmethod\n def restore_global_vars_fn(sess, model_dir):\n\n def _var_dict_for_restoring_model_fn(model_dir, frame_or_onset):\n\n assert frame_or_onset in ('frame', 'onset')\n existing_model_path = os.path.join(model_dir, '{}_model'.format(frame_or_onset))\n reader = pywrap_tensorflow.NewCheckpointReader(existing_model_path)\n existing_model_var_to_shape_map = reader.get_variable_to_shape_map()\n\n pattern = '{}_detector/'.format(frame_or_onset)\n var_dict = {}\n for var in tf.global_variables():\n name = var.op.name\n if name.startswith(pattern):\n assert name in existing_model_var_to_shape_map\n assert var.shape.as_list() == existing_model_var_to_shape_map[name]\n var_dict[name] = var\n\n return var_dict\n\n vars_initialized = []\n\n for name in ('onset', 'frame'):\n var_dict = _var_dict_for_restoring_model_fn(model_dir, name)\n vars_initialized.extend(var_dict.values())\n model_path = os.path.join(model_dir, '{}_model'.format(name))\n tf.train.Saver(var_dict).restore(sess, model_path)\n\n assert all(var in vars_initialized for var in tf.global_variables())\n assert len(vars_initialized) == len(tf.global_variables())\n\n @staticmethod\n def detector_fn(frame_or_onset, inputs, is_training):\n assert frame_or_onset in ('frame', 'onset')\n inputs.set_shape([1, None, 336])\n assert isinstance(is_training, bool)\n assert tf.get_variable_scope().name == ''\n\n with tf.variable_scope('{}_detector'.format(frame_or_onset), reuse=tf.AUTO_REUSE):\n with tf.variable_scope('cnn_layers'):\n cnn_features = MiscFns.acoustic_model_fn(\n spec_batch=inputs,\n is_training=is_training,\n use_feature=True,\n trainable=False\n )\n cnn_features.set_shape([1, None, 88, 64])\n\n with tf.variable_scope('rnn_layer'):\n rnn_features = MiscFns.rnn_layer_fn(inputs=cnn_features, trainable=False)\n rnn_features.set_shape([1, None, 88, 64])\n\n with tf.variable_scope('output_layer'):\n logits = slim.fully_connected(\n scope='FC',\n inputs=rnn_features,\n num_outputs=1,\n activation_fn=None,\n trainable=False\n )\n logits = tf.squeeze(logits, axis=-1)\n logits.set_shape([1, None, 88])\n\n return logits\n\n @staticmethod\n def frame_label_detector_fn(inputs, is_training):\n return MiscFns.detector_fn(frame_or_onset='frame', inputs=inputs, is_training=is_training)\n\n @staticmethod\n def onset_detector_fn(inputs, is_training):\n return MiscFns.detector_fn(frame_or_onset='onset', inputs=inputs, is_training=is_training)\n\n @staticmethod\n def get_maestro_year_name_split_list_fn():\n csv_file = glob.glob(os.path.join(os.environ['maestro'], '*.csv'))\n assert len(csv_file) == 1\n csv_file = csv_file[0]\n\n name_to_idx_dict = dict(\n canonical_composer=0,\n canonical_title=1,\n split=2,\n year=3,\n midi_filename=4,\n audio_filename=5,\n duration=6\n )\n\n year_name_split_list = []\n with open(csv_file) as csv_fh:\n csv_reader = csv.reader(csv_fh)\n head_row = next(csv_reader)\n for field_name in head_row:\n assert field_name in name_to_idx_dict\n get_year = re.compile(r'^(2[0-9]{3})/.*')\n for row in csv_reader:\n mid_file = row[name_to_idx_dict['midi_filename']]\n audio_file = row[name_to_idx_dict['audio_filename']]\n year = row[name_to_idx_dict['year']]\n assert get_year.match(mid_file).group(1) == get_year.match(audio_file).group(1) == year\n mid_base_name = os.path.basename(mid_file)[:-5]\n audio_base_name = os.path.basename(audio_file)[:-4]\n assert mid_base_name == audio_base_name\n rec_name = mid_base_name\n assert mid_file == os.path.join(year, rec_name + '.midi')\n assert audio_file == os.path.join(year, rec_name + '.wav')\n year_name_split_list.append([year, rec_name, row[name_to_idx_dict['split']]])\n assert len(year_name_split_list) == 1184\n return year_name_split_list\n\n @staticmethod\n def split_train_valid_and_test_files_fn(dataset):\n assert dataset in ('maps', 'maestro')\n if dataset == 'maps':\n test_dirs = ['ENSTDkCl_2/MUS', 'ENSTDkAm_2/MUS']\n train_dirs = ['AkPnBcht_2/MUS', 'AkPnBsdf_2/MUS', 'AkPnCGdD_2/MUS', 'AkPnStgb_2/MUS',\n 'SptkBGAm_2/MUS', 'SptkBGCl_2/MUS', 'StbgTGd2_2/MUS']\n maps_dir = os.environ['maps']\n\n test_files = []\n for directory in test_dirs:\n path = os.path.join(maps_dir, directory)\n path = os.path.join(path, '*.wav')\n wav_files = glob.glob(path)\n test_files += wav_files\n\n test_ids = set([MiscFns.filename_to_id(wav_file) for wav_file in test_files])\n assert len(test_ids) == 53\n\n training_files = []\n validation_files = []\n for directory in train_dirs:\n path = os.path.join(maps_dir, directory)\n path = os.path.join(path, '*.wav')\n wav_files = glob.glob(path)\n for wav_file in wav_files:\n me_id = MiscFns.filename_to_id(wav_file)\n if me_id not in test_ids:\n training_files.append(wav_file)\n else:\n validation_files.append(wav_file)\n\n assert len(training_files) == 139 and len(test_files) == 60 and len(validation_files) == 71\n\n return dict(training=training_files, test=test_files, validation=validation_files)\n else:\n year_name_split_list = MiscFns.get_maestro_year_name_split_list_fn()\n split_dict = collections.defaultdict(list)\n for year, rec_name, split in year_name_split_list:\n assert split in ('train', 'validation', 'test')\n if split == 'train':\n split_dict['training'].append([year, rec_name])\n else:\n split_dict[split].append([year, rec_name])\n assert len(split_dict['training']) == 954\n assert len(split_dict['validation']) == 105\n assert len(split_dict['test']) == 125\n\n return split_dict\n\n @staticmethod\n def maps_sg_and_note_seq_fn(wav_file):\n\n wav_info = soundfile.info(wav_file)\n assert wav_info.samplerate == 44100\n num_frames = MiscFns.num_samples_to_num_frames_fn(wav_info.frames)\n\n rec_name = os.path.basename(wav_file)[:-4]\n vqt_file = os.path.join(os.environ['maps_vqt'], rec_name + '.vqt')\n _rec_name, vqt = MiscFns.load_np_array_from_file_fn(vqt_file)\n assert _rec_name == rec_name\n assert vqt.dtype == np.float32 and vqt.shape == (num_frames, 336)\n\n mid_file = wav_file[:-3] + 'mid'\n num_frames_from_midi = mido.MidiFile(mid_file).length\n num_frames_from_midi = int(np.ceil(num_frames_from_midi * wav_info.samplerate))\n num_frames_from_midi = MiscFns.num_samples_to_num_frames_fn(num_frames_from_midi)\n num_frames_from_midi += 2\n num_frames = min(num_frames, num_frames_from_midi)\n\n vqt = vqt[:num_frames]\n vqt = np.require(vqt, dtype=np.float32, requirements=['O', 'C'])\n vqt.flags['WRITEABLE'] = False\n\n note_seq = MiscFns.get_note_seq_from_mid_file_fn(mid_file_name=mid_file)\n times_and_pitches = MiscFns.note_seq_to_valued_intervals(note_seq)\n note_intervals = times_and_pitches['times']\n note_intervals.flags['WRITEABLE'] = False\n note_pitches = times_and_pitches['pitches']\n note_pitches.flags['WRITEABLE'] = False\n assert len(note_intervals) == len(note_pitches)\n\n return dict(sg=vqt, intervals=note_intervals, pitches=note_pitches)\n\n @staticmethod\n def maestro_sg_and_note_seq_fn(year, rec_name):\n\n def _get_num_frames_fn(year, rec_name):\n wav_file = os.path.join(os.environ['maestro'], year, rec_name + '.wav')\n wav_info = soundfile.info(wav_file)\n assert wav_info.samplerate in (44100, 48000)\n sr = 44100\n if wav_info.samplerate == 48000:\n num_frames = (wav_info.frames * sr + wav_info.samplerate - 1) // wav_info.samplerate\n else:\n num_frames = wav_info.frames\n num_frames = MiscFns.num_samples_to_num_frames_fn(num_frames)\n\n return num_frames\n\n num_frames = _get_num_frames_fn(year, rec_name)\n vqt_file = os.path.join(os.environ['maestro_vqt'], year, rec_name + '.vqt')\n _rec_name, vqt = MiscFns.load_np_array_from_file_fn(vqt_file)\n assert _rec_name == rec_name\n assert vqt.dtype == np.float32 and vqt.shape == (num_frames, 336)\n\n sr = 44100\n mid_file = os.path.join(os.environ['maestro'], year, rec_name + '.midi')\n num_frames_from_midi = mido.MidiFile(mid_file).length\n num_frames_from_midi = int(np.ceil(num_frames_from_midi * sr))\n num_frames_from_midi = MiscFns.num_samples_to_num_frames_fn(num_frames_from_midi)\n num_frames_from_midi += 2\n num_frames = min(num_frames, num_frames_from_midi)\n\n vqt = vqt[:num_frames]\n vqt = np.require(vqt, dtype=np.float32, requirements=['O', 'C'])\n vqt.flags['WRITEABLE'] = False\n\n note_seq = MiscFns.get_note_seq_from_mid_file_fn(mid_file_name=mid_file)\n times_and_pitches = MiscFns.note_seq_to_valued_intervals(note_seq)\n note_intervals = times_and_pitches['times']\n note_intervals.flags['WRITEABLE'] = False\n note_pitches = times_and_pitches['pitches']\n note_pitches.flags['WRITEABLE'] = False\n assert len(note_intervals) == len(note_pitches)\n\n return dict(sg=vqt, intervals=note_intervals, pitches=note_pitches)\n\n @staticmethod\n def tb_proto_for_note_level_performance_fn(header, tf_name_scope, tb_scope, title, names, dtype):\n num_examples = len(names)\n num_fields = len(header)\n with tf.name_scope(tf_name_scope):\n tf_pl = tf.placeholder(dtype=dtype, shape=[num_examples, num_fields])\n table_proto = MiscFns.array_to_table_tf_fn(\n tf_array=tf_pl,\n header=header,\n scope=tb_scope,\n title=title,\n names=names\n )\n ind_fields = tf.unstack(tf_pl[-1])\n scalar_portos = []\n for name, data in zip(header, ind_fields):\n scalar_portos.append(tf.summary.scalar(name, data))\n scalar_portos.append(table_proto)\n pl_and_tb_proto = dict(\n pl=tf_pl,\n tb_proto=tf.summary.merge(scalar_portos)\n )\n\n return pl_and_tb_proto\n\n\n# all configurations go here\nclass Config(object):\n\n def __init__(self):\n self.debug_mode = DEBUG\n self.gpu_id = GPU_ID\n self.snippet_len = 1200\n self.tb_dir = 'tb_inf'\n self.model_dir = ONSET_FRAME_MODEL_DIR\n\n # check if folder for storing tensorboard data exists beforehand\n if not self.debug_mode:\n # check if tb_dir exists\n assert self.tb_dir is not None\n tmp_dirs = glob.glob('./*/')\n tmp_dirs = [s[2:-1] for s in tmp_dirs]\n assert self.tb_dir not in tmp_dirs\n\n gpu_config = tf.ConfigProto()\n gpu_config.gpu_options.allow_growth = True\n self.gpu_config = gpu_config\n\n # get splits for training, validation and test\n split_maestro = MiscFns.split_train_valid_and_test_files_fn('maestro')\n split_maps = MiscFns.split_train_valid_and_test_files_fn('maps')\n tvt_split_dict = dict(\n validation_maestro=split_maestro['validation'],\n validation_maps=split_maps['validation'],\n test_maestro=split_maestro['test'],\n test_maps=split_maps['test']\n )\n self.training_dataset_is_maps = TRAINING_DATASET_IS_MAPS\n if self.training_dataset_is_maps:\n tvt_split_dict['training_maps'] = split_maps['training']\n else:\n tvt_split_dict['training_maestro'] = split_maestro['training']\n self.tvt_split_dict = tvt_split_dict\n\n # make sure the first split is the training split.\n self.model_names = self.tvt_split_dict.keys()\n if 'training' not in self.model_names[0]:\n for idx, name in enumerate(self.model_names):\n if 'training' in name:\n break\n else:\n assert False\n self.model_names[0], self.model_names[idx] = self.model_names[idx], self.model_names[0]\n assert len(self.model_names) == 5\n\n # use few recordings if in debug mode\n if self.debug_mode:\n np.random.seed(100)\n for tvt in self.tvt_split_dict.keys():\n _num = len(self.tvt_split_dict[tvt])\n _sel = np.random.choice(_num, 2, replace=False)\n self.tvt_split_dict[tvt] = [self.tvt_split_dict[tvt][ii] for ii in _sel]\n\n \n# define neural network models\nclass Model(object):\n def __init__(self, config, name):\n assert name in config.model_names\n self.name = name\n self.is_training = True if 'training' in self.name else False\n self.config = config\n self.batch = self._gen_batch_fn()\n\n with tf.name_scope(self.name):\n self.logits = {}\n for task in ('onset', 'frame'):\n with tf.name_scope(task):\n self.logits[task] = self._nn_model_fn(task)\n\n self.rec_names = tuple(self._get_rec_names_fn())\n names = list(self.rec_names) + ['average']\n self.pl_and_tb_proto = {}\n for w_or_wo in ('with', 'without'):\n description = 'note_level_performance_{}_offset'.format(w_or_wo)\n pl_and_tb_proto = MiscFns.tb_proto_for_note_level_performance_fn(\n header=['p', 'r', 'f', 'o'],\n tf_name_scope=description,\n tb_scope=description,\n title=description.replace('_', ' '),\n dtype=tf.float32,\n names=names\n )\n self.pl_and_tb_proto[w_or_wo] = pl_and_tb_proto\n\n description = 'frame_level_performance'\n pl_and_tb_proto = MiscFns.tb_proto_for_note_level_performance_fn(\n header=['p', 'r', 'f'],\n tf_name_scope=description,\n tb_scope=description,\n title=description.replace('_', ' '),\n dtype=tf.float32,\n names=names\n )\n self.pl_and_tb_proto['frame'] = pl_and_tb_proto\n\n def _get_rec_names_fn(self):\n\n tvt, dataset_name = self.name.split('_')\n assert tvt in ('training', 'validation', 'test')\n assert dataset_name in ('maestro', 'maps')\n dataset_is_maps = dataset_name == 'maps'\n if dataset_is_maps:\n rec_names = []\n for wav_file in self.config.tvt_split_dict[self.name]:\n rec_names.append(os.path.basename(wav_file)[:-4])\n else:\n rec_names = []\n for _, rec_name in self.config.tvt_split_dict[self.name]:\n rec_names.append(rec_name)\n\n return rec_names\n\n def _nn_model_fn(self, task):\n assert task in ('frame', 'onset')\n inputs = self.batch['spectrogram']\n inputs = tf.ensure_shape(inputs, [1, None, 336])\n\n _nn_fn = dict(\n onset=MiscFns.onset_detector_fn,\n frame=MiscFns.frame_label_detector_fn\n )\n _nn_fn = _nn_fn[task]\n outputs = _nn_fn(inputs=inputs, is_training=self.is_training)\n outputs = tf.stop_gradient(outputs)\n outputs = tf.ensure_shape(outputs, [1, None, 88])\n\n return outputs\n\n def _dataset_iter_fn(self):\n\n logging.debug('{} - enter dataset generator'.format(self.name))\n\n tvt, dataset_name = self.name.split('_')\n assert tvt in ('training', 'validation', 'test')\n assert dataset_name in ('maestro', 'maps')\n\n dataset_is_maps = dataset_name == 'maps'\n file_names = self.config.tvt_split_dict[self.name]\n num_recs = len(file_names)\n dummy_intervals = np.asarray([[-2, -1]], dtype=np.float32)\n dummy_pitches = np.asarray([0], dtype=np.uint8)\n\n if dataset_is_maps:\n for rec_idx, wav_file in enumerate(file_names):\n rec_name = os.path.basename(wav_file)[:-4]\n logging.debug('{}/{} - {}'.format(rec_idx + 1, num_recs, rec_name))\n sg_intervals_pitches = MiscFns.maps_sg_and_note_seq_fn(wav_file=wav_file)\n sg = sg_intervals_pitches['sg']\n assert not sg.flags['WRITEABLE']\n assert sg.dtype == np.float32 and sg.shape[1:] == (336,)\n intervals = sg_intervals_pitches['intervals']\n pitches = sg_intervals_pitches['pitches']\n\n split_list = MiscFns.gen_split_list_fn(len(sg), self.config.snippet_len)\n num_snippets = len(split_list)\n total_num_frames = len(sg)\n\n for snippet_idx, (s, e) in enumerate(split_list):\n yield dict(\n rec_idx=rec_idx,\n num_snippets=num_snippets,\n snippet_idx=snippet_idx,\n total_num_frames=total_num_frames,\n num_frames=e - s,\n spectrogram=sg[s:e],\n intervals=dummy_intervals if snippet_idx < num_snippets - 1 else intervals,\n pitches=dummy_pitches if snippet_idx < num_snippets - 1 else pitches\n )\n else: # dataset is maestro\n for rec_idx, (year, rec_name) in enumerate(file_names):\n logging.debug('{}/{} - {}'.format(rec_idx + 1, num_recs, rec_name))\n sg_intervals_pitches = MiscFns.maestro_sg_and_note_seq_fn(year, rec_name)\n sg = sg_intervals_pitches['sg']\n assert not sg.flags['WRITEABLE']\n assert sg.dtype == np.float32 and sg.shape[1:] == (336,)\n\n intervals = sg_intervals_pitches['intervals']\n pitches = sg_intervals_pitches['pitches']\n\n split_list = MiscFns.gen_split_list_fn(len(sg), self.config.snippet_len)\n num_snippets = len(split_list)\n total_num_frames = len(sg)\n\n for snippet_idx, (s, e) in enumerate(split_list):\n yield dict(\n rec_idx=rec_idx,\n num_snippets=num_snippets,\n snippet_idx=snippet_idx,\n total_num_frames=total_num_frames,\n num_frames=e - s,\n spectrogram=sg[s:e],\n intervals=dummy_intervals if snippet_idx < num_snippets - 1 else intervals,\n pitches=dummy_pitches if snippet_idx < num_snippets - 1 else pitches\n )\n\n def _gen_batch_fn(self):\n with tf.device('/cpu:0'):\n dataset = tf.data.Dataset.from_generator(\n generator=self._dataset_iter_fn,\n output_types=dict(\n rec_idx=tf.int32,\n num_snippets=tf.int32,\n snippet_idx=tf.int32,\n total_num_frames=tf.int32,\n num_frames=tf.int32,\n spectrogram=tf.float32,\n intervals=tf.float32,\n pitches=tf.uint8\n ),\n output_shapes=dict(\n rec_idx=[],\n num_snippets=[],\n snippet_idx=[],\n total_num_frames=[],\n num_frames=[],\n spectrogram=[None, 336],\n intervals=[None, 2],\n pitches=[None]\n )\n )\n dataset = dataset.batch(1)\n dataset = dataset.prefetch(20)\n it = dataset.make_one_shot_iterator()\n element = it.get_next()\n\n return element\n\n\ndef main():\n MODEL_DICT = {}\n MODEL_DICT['config'] = Config()\n for model_name in MODEL_DICT['config'].model_names:\n MODEL_DICT[model_name] = Model(config=MODEL_DICT['config'], name=model_name)\n\n aug_info_pl = tf.placeholder(dtype=tf.string, name='aug_info_pl')\n aug_info_summary = tf.summary.text('aug_info_summary', aug_info_pl)\n\n os.environ['CUDA_VISIBLE_DEVICES'] = str(MODEL_DICT['config'].gpu_id)\n with tf.Session(config=MODEL_DICT['config'].gpu_config) as sess:\n # summary writer\n summary_writer_dict = {}\n for model_name in MODEL_DICT['config'].model_names:\n summary_writer_dict[model_name] = tf.summary.FileWriter(\n os.path.join(MODEL_DICT['config'].tb_dir, model_name)\n )\n\n aug_info = []\n\n aug_info.append('note tracking performance')\n training_dataset = MODEL_DICT['config'].training_dataset_is_maps\n training_dataset = 'maps' if training_dataset else 'maestro'\n aug_info.append('trained on - {}'.format(training_dataset))\n\n aug_info.append('tb dir - {}'.format(MODEL_DICT['config'].tb_dir))\n aug_info.append('debug mode - {}'.format(MODEL_DICT['config'].debug_mode))\n\n aug_info = '\\n\\n'.join(aug_info)\n logging.info(aug_info)\n summary_writer_dict[MODEL_DICT['config'].model_names[0]].add_summary(sess.run(aug_info_summary, feed_dict={aug_info_pl: aug_info}))\n\n OP_DICT = {}\n for model_name in MODEL_DICT['config'].model_names:\n m = MODEL_DICT[model_name]\n batch_op_dict = dict()\n batch_op_dict['logits'] = m.logits\n for k in ('rec_idx', 'num_snippets', 'snippet_idx', 'total_num_frames', 'num_frames', 'intervals', 'pitches'):\n batch_op_dict[k] = m.batch[k]\n tmp = dict(\n batch=batch_op_dict,\n epoch=m.pl_and_tb_proto\n )\n OP_DICT[model_name] = tmp\n\n def inference_fn(model_name):\n assert model_name in MODEL_DICT['config'].model_names\n\n ops_per_batch = OP_DICT[model_name]['batch']\n ops_per_epoch = OP_DICT[model_name]['epoch']\n\n num_recs = len(MODEL_DICT[model_name].rec_names)\n\n prfos = dict()\n prfos['with'] = []\n prfos['without'] = []\n prfos['frame'] = []\n for rec_idx in xrange(num_recs):\n rec_logits = {}\n for n in ('onset', 'frame'):\n rec_logits[n] = []\n\n while True:\n tmp = sess.run(ops_per_batch)\n num_snippets = tmp['num_snippets'][0]\n snippet_idx = tmp['snippet_idx'][0]\n _rec_idx = tmp['rec_idx'][0]\n assert _rec_idx == rec_idx\n logits = tmp['logits']\n num_frames = tmp['num_frames'][0]\n for n, v in logits.iteritems():\n assert v.shape == (1, num_frames, 88)\n rec_logits[n].append(np.squeeze(v, axis=0))\n if snippet_idx == num_snippets - 1:\n break\n total_num_frames = tmp['total_num_frames'][0]\n for n in ('onset', 'frame'):\n rec_logits[n] = np.concatenate(rec_logits[n], axis=0)\n assert rec_logits[n].shape == (total_num_frames, 88)\n prfo_dict = xian_onset_frame_transcription_performance_fn(\n logits_dict=rec_logits,\n ref_intervals=np.squeeze(tmp['intervals'], axis=0),\n ref_notes=np.squeeze(tmp['pitches'], axis=0),\n sr=44100,\n hop_size=22 * 64\n )\n rec_name = MODEL_DICT[model_name].rec_names[rec_idx]\n logging.info('{}/{} - {}:'.format(rec_idx + 1, num_recs, rec_name))\n\n for n in ('without', 'with'):\n v = prfo_dict[n]\n prfos[n].append(v)\n logging.info(' note level - {} offset - {}'.format(n, v))\n\n n = 'frame'\n v = prfo_dict['frame']\n prfos[n].append(v)\n logging.info(' frame level - {}'.format(v))\n\n for w_or_wo_offset in ('without', 'with'):\n pl = ops_per_epoch[w_or_wo_offset]['pl']\n tb_proto = ops_per_epoch[w_or_wo_offset]['tb_proto']\n v = prfos[w_or_wo_offset]\n v = np.asarray(v)\n av = np.mean(v, axis=0)\n logging.info('note level performance - {} offset - average - {}'.format(w_or_wo_offset, av))\n v = np.concatenate([v, av[None, :]], axis=0)\n s = sess.run(tb_proto, feed_dict={pl: v})\n summary_writer_dict[model_name].add_summary(s)\n\n v = prfos['frame']\n v = np.asarray(v)\n av = np.mean(v, axis=0)\n logging.info('frame level performance - mean - {}'.format(av))\n v = np.concatenate([v, av[None, :]], axis=0)\n pl = ops_per_epoch['frame']['pl']\n tb_proto = ops_per_epoch['frame']['tb_proto']\n s = sess.run(tb_proto, feed_dict={pl: v})\n summary_writer_dict[model_name].add_summary(s)\n\n def check_all_global_vars_initialized_fn():\n tmp = sess.run(tf.report_uninitialized_variables(tf.global_variables()))\n assert not tmp\n\n assert not tf.trainable_variables()\n assert not tf.local_variables()\n MiscFns.restore_global_vars_fn(sess=sess, model_dir=MODEL_DICT['config'].model_dir)\n check_all_global_vars_initialized_fn()\n\n logging.info('do inference ...')\n for model_name in MODEL_DICT['config'].model_names:\n logging.info(model_name)\n inference_fn(model_name)\n\n for model_name in MODEL_DICT['config'].model_names:\n summary_writer_dict[model_name].close()\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"drwangxian/amt-by-dconv","sub_path":"note_tracking/note.py","file_name":"note.py","file_ext":"py","file_size_in_byte":38284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40119045429","text":"#!/usr/bin/env python3\n\nimport vim\nimport os\n\nimport auto_fix\n\n# DATA_FILEPATH = os.path.expanduser('~/.config/auto_fix/fix.json')\nDATA_FILEPATH = os.path.expanduser('~/.config/auto_fix/fix.yaml')\n\n\ndef vim_auto_fix_bridge_add_data(\n filetype, word, words=[], data_filepath=DATA_FILEPATH):\n auto_fix.vim_auto_fix_add_data(data_filepath, filetype, word, words=[])\n\n\ndef vim_auto_fix_bridge_dump(data_filepath=DATA_FILEPATH):\n auto_fix.vim_auto_fix_dump(data_filepath)\n\n\ndef vim_auto_fix_bridge_auto_fix(\n input, filetype='_', data_filepath=DATA_FILEPATH):\n if not os.path.isfile(data_filepath):\n log = \"[vim-auto-fix][ERROR]: no such file {}\".format(data_filepath)\n vim.command('echohl ErrorMsg | echo \"' + log + '\" | echohl None')\n return input\n return auto_fix.vim_auto_fix_auto_word_fix(\n input, filetype, data_filepath=data_filepath)\n","repo_name":"umaumax/vim-auto-fix","sub_path":"autoload/vim_bridge.py","file_name":"vim_bridge.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"43800022514","text":"from django.utils.safestring import mark_safe\nfrom django.utils.translation import ugettext as _\n\nfrom wagtail.wagtailadmin.forms import WagtailAdminPageForm\n\nclass RUBIONUserAdminEditForm( WagtailAdminPageForm ):\n\n def __init__ ( self, *args, **kwargs):\n instance = kwargs.get('instance', None)\n staff_u = None\n initial = {}\n if instance and instance.linked_user:\n from userdata.models import StaffUser\n if StaffUser.objects.filter(user = instance.linked_user).exists():\n staff_u = StaffUser.objects.get(user = instance.linked_user)\n initial['key_number'] = staff_u.key_number\n initial['needs_key'] = True\n initial['needs_safety_instructions'] = staff_u.needs_safety_instructions.all()\n \n\n if instance and instance.linked_user:\n if not instance.name_db:\n initial['name_db'] = instance.linked_user.last_name\n if not instance.first_name_db:\n initial['first_name_db'] = instance.linked_user.first_name\n if not instance.email_db:\n initial['email_db'] = instance.linked_user.email\n\n kwargs.update(initial = initial) \n super().__init__(*args, **kwargs)\n\n\n \n if staff_u:\n self.fields['key_number'].disabled=True\n self.fields['key_number'].help_text = mark_safe(\n _('This user is belongs to the RUBION staff. To edit the key number, <a href=\"/admin/pages/{}/edit/?next=/admin/pages/{}/edit/\">edit the staff entry</a>.'.format(staff_u.pk, instance.pk))\n )\n self.fields['needs_key'].disabled=True\n self.fields['needs_safety_instructions'].disabled = True\n self.fields['needs_safety_instructions'].help_text = mark_safe(\n _('This user is belongs to the RUBION staff. To change the required safety instructions, <a href=\"/admin/pages/{}/edit/?next=/admin/pages/{}/edit/\">edit the staff entry</a>.'.format(staff_u.pk, instance.pk))\n )\n","repo_name":"varvarafo/website","sub_path":"userinput/admin_edit_forms.py","file_name":"admin_edit_forms.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10373662872","text":"import unittest\nimport pytest\n\nfrom openqaoa_braket.backends import DeviceAWS\n\n\nclass TestingDeviceAWS(unittest.TestCase):\n \"\"\"These tests check the Object used to access AWS Braket and their\n available QPUs can be established.\n\n For any tests using provided credentials, the tests will only pass if those\n details provided are correct/valid with AWS Braket.\n \"\"\"\n\n @pytest.mark.braket_api\n def test_changing_aws_region(self):\n device_obj = DeviceAWS(\n device_name=\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\",\n aws_region=\"us-east-1\",\n )\n\n device_obj.check_connection()\n default_region = device_obj.aws_region\n\n self.assertEqual(\"us-east-1\", default_region)\n\n device_obj = DeviceAWS(\n device_name=\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\",\n aws_region=\"us-west-1\",\n )\n\n device_obj.check_connection()\n custom_region = device_obj.aws_region\n\n self.assertEqual(\"us-west-1\", custom_region)\n\n @pytest.mark.braket_api\n def test_changing_s3_bucket_names(self):\n device_obj = DeviceAWS(\n device_name=\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\",\n s3_bucket_name=\"random_new_name\",\n )\n\n device_obj.check_connection()\n custom_bucket = device_obj.s3_bucket_name\n\n self.assertEqual(\"random_new_name\", custom_bucket)\n\n @pytest.mark.braket_api\n def test_check_connection_provider_no_backend_provided_credentials(self):\n \"\"\"\n If no information about the device name, but the credentials\n used are correct, check_connection should return True.\n The provider_connected attribute should be updated to True.\n \"\"\"\n\n device_obj = DeviceAWS(device_name=\"\")\n\n self.assertEqual(device_obj.check_connection(), True)\n self.assertEqual(device_obj.provider_connected, True)\n self.assertEqual(device_obj.qpu_connected, None)\n\n @pytest.mark.braket_api\n def test_check_connection_provider_right_backend_provided_credentials(self):\n \"\"\"\n If the correct device name is provided and the credentials\n used are correct, check_connection should return True.\n The provider_connected attribute should be updated to True.\n The qpu_connected attribute should be updated to True.\n \"\"\"\n\n device_obj = DeviceAWS(device_name=\"\")\n\n device_obj.check_connection()\n valid_qpu_name = device_obj.available_qpus[0]\n\n device_obj = DeviceAWS(device_name=valid_qpu_name)\n\n self.assertEqual(device_obj.check_connection(), True)\n self.assertEqual(device_obj.provider_connected, True)\n self.assertEqual(device_obj.qpu_connected, True)\n\n @pytest.mark.braket_api\n def test_check_connection_provider_wrong_backend_provided_credentials(self):\n \"\"\"\n If device name provided is incorrect, and not empty, and the credentials\n used are correct, check_connection should return False.\n The provider_connected attribute should be updated to True.\n The qpu_connected attribute should be updated to False.\n \"\"\"\n\n device_obj = DeviceAWS(device_name=\"random_invalid_backend\")\n\n self.assertEqual(device_obj.check_connection(), False)\n self.assertEqual(device_obj.provider_connected, True)\n self.assertEqual(device_obj.qpu_connected, False)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"entropicalabs/openqaoa","sub_path":"src/openqaoa-braket/tests/test_devices_braket.py","file_name":"test_devices_braket.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":94,"dataset":"github-code","pt":"31"} +{"seq_id":"34451968541","text":"import pyodbc\nfrom connect_db import connect_db\n\n\ndef loadRentalPlan(filename, conn):\n \"\"\"\n Input:\n $filename: \"RentalPlan.txt\"\n $conn: you can get it by calling connect_db()\n Functionality:\n 1. Create a table named \"RentalPlan\" in the \"VideoStore\" database on Azure\n 2. Read data from \"RentalPlan.txt\" and insert them into \"RentalPlan\"\n * Columns are separated by '|'\n * You can use executemany() to insert multiple rows in bulk\n \"\"\"\n # WRITE YOUR CODE HERE\n cursor = conn.cursor()\n\n sql_command = \"\"\"\n CREATE TABLE RentalPlan(\n pid INTEGER PRIMARY KEY,\n pname VARCHAR(50),\n monthly_fee FLOAT,\n max_movies INTEGER);\n \"\"\"\n \n cursor.execute(sql_command)\n\n informationList = []\n with open(\"RentalPlan.txt\") as file:\n for line in file:\n line = line.strip('\\n')\n line = line.split(\"|\")\n informationList.append(line)\n #print (informationList)\n\n sql_command = \"\"\" INSERT INTO RentalPlan(\n pid, pname, monthly_fee, max_movies)\n VALUES (?,?,?,?)\"\"\"\n #in pyodbc use ? instead %\n cursor.executemany(sql_command, informationList)\n\n \n\ndef loadCustomer(filename, conn):\n \"\"\"\n Input:\n $filename: \"Customer.txt\"\n $conn: you can get it by calling connect_db()\n Functionality:\n 1. Create a table named \"Customer\" in the \"VideoStore\" database on Azure\n 2. Read data from \"Customer.txt\" and insert them into \"Customer\".\n * Columns are separated by '|'\n * You can use executemany() to insert multiple rows in bulk\n \"\"\"\n # WRITE YOUR CODE HERE\n cursor = conn.cursor()\n\n sql_command = \"\"\"\n CREATE TABLE Customer(\n cid INTEGER PRIMARY KEY,\n pid INTEGER,\n username VARCHAR(50),\n password VARCHAR(50),\n FOREIGN KEY (pid) REFERENCES RentalPlan (pid)\n ON DELETE CASCADE);\n \"\"\"\n \n cursor.execute(sql_command)\n\n informationList = []\n with open(\"Customer.txt\") as file:\n for line in file:\n line = line.strip('\\n')\n line = line.split(\"|\")\n informationList.append(line)\n #print(informationList)\n\n sql_command = \"\"\"INSERT INTO Customer(\n cid, pid, username, password) VALUES(\n ?,?,?,?)\"\"\"\n cursor.executemany(sql_command, informationList)\n\ndef loadMovie(filename, conn):\n \"\"\"\n Input:\n $filename: \"Movie.txt\"\n $conn: you can get it by calling connect_db()\n Functionality:\n 1. Create a table named \"Movie\" in the \"VideoStore\" database on Azure\n 2. Read data from \"Movie.txt\" and insert them into \"Movie\".\n * Columns are separated by '|'\n * You can use executemany() to insert multiple rows in bulk\n \"\"\"\n # WRITE YOUR CODE HERE\n cursor = conn.cursor()\n\n sql_command = \"\"\"\n CREATE TABLE Movie(\n mid INTEGER PRIMARY KEY,\n mname VARCHAR(50),\n year INTEGER);\n \"\"\"\n \n cursor.execute(sql_command)\n\n informationList = []\n with open(\"Movie.txt\") as file:\n for line in file:\n line = line.strip('\\n')\n line = line.split(\"|\")\n informationList.append(line)\n #print(informationList)\n\n sql_command = \"\"\"INSERT INTO Movie(\n mid, mname, year) VALUES(\n ?,?,?)\"\"\"\n cursor.executemany(sql_command, informationList)\n\ndef loadRental(filename, conn):\n \"\"\"\n Input:\n $filename: \"Rental.txt\"\n $conn: you can get it by calling connect_db()\n Functionality:\n 1. Create a table named \"Rental\" in the VideoStore database on Azure\n 2. Read data from \"Rental.txt\" and insert them into \"Rental\".\n * Columns are separated by '|'\n * You can use executemany() to insert multiple rows in bulk\n \"\"\"\n # WRITE YOUR CODE HERE\n cursor = conn.cursor()\n\n sql_command = \"\"\"\n CREATE TABLE Rental(\n cid INTEGER,\n mid INTEGER,\n date_and_time DATETIME,\n status VARCHAR(6),\n FOREIGN KEY (cid) REFERENCES Customer(cid) ON DELETE CASCADE,\n FOREIGN KEY (mid) REFERENCES Movie(mid) ON DELETE CASCADE);\"\"\"\n\n cursor.execute(sql_command)\n\n informationList = []\n with open(\"Rental.txt\") as file:\n for line in file:\n line = line.strip('\\n')\n line = line.split(\"|\")\n informationList.append(line)\n #print(informationList)\n\n sql_command = \"\"\"INSERT INTO Rental(\n cid, mid, date_and_time, status) VALUES\n (?,?,?,?)\"\"\"\n cursor.executemany(sql_command, informationList)\n\n\ndef dropTables(conn):\n conn.execute(\"DROP TABLE IF EXISTS Rental\")\n conn.execute(\"DROP TABLE IF EXISTS Customer\")\n conn.execute(\"DROP TABLE IF EXISTS RentalPlan\")\n conn.execute(\"DROP TABLE IF EXISTS Movie\")\n\n\n\nif __name__ == \"__main__\":\n conn = connect_db()\n\n dropTables(conn)\n\n loadRentalPlan(\"RentalPlan.txt\", conn)\n loadCustomer(\"Customer.txt\", conn)\n loadMovie(\"Movie.txt\", conn)\n loadRental(\"Rental.txt\", conn)\n\n\n conn.commit()\n conn.close()\n\n\n\n\n\n\n","repo_name":"patrickzw1/Database-Application-and-Transaction-Management","sub_path":"loaddata.py","file_name":"loaddata.py","file_ext":"py","file_size_in_byte":5301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32995188280","text":"import pandas as pd\nimport bokeh.io as io\nfrom bokeh.plotting import figure, show, output_file\nfrom bokeh.palettes import mpl, inferno, all_palettes, Reds\nfrom bokeh.models import LogColorMapper, LogTicker, ColorBar\n\n\ndef load_incidents_area_file(fname):\n '''\n Load the incidents information of LA areas and clean the data\n :param fname: the name of the input file\n :param year: the year we want to focus on (we only have data from 2010 to 2018)\n :return: a dataframe contains cleaned incidents data by areas\n '''\n\n assert isinstance(fname, str)\n\n traffic = pd.read_csv(fname)\n\n traffic['Date Occurred'] = pd.to_datetime(traffic['Date Occurred'])\n traffic['year'], traffic['month'] = traffic['Date Occurred'].dt.year, traffic['Date Occurred'].dt.month\n\n traffic['lon'] = traffic['Location'].apply(lambda x: float(x.split(',')[0][1:]))\n traffic['lat'] = traffic['Location'].apply(lambda x: float(x.split(',')[1][:-1]))\n\n traffic = traffic[(traffic['year'] == 2017) & (traffic['lat'] != 0) & (traffic['lon'] != 0)]\n\n traffic.reset_index(inplace=True)\n\n return traffic\n\n\ndef define_la_areas(traffic):\n '''\n\n By taking in the incidents' area information, we can organize a new table with the areas boundaries\n :param traffic: the dataframe contains cleaned incidents information by area\n :return: a dataframe contains the boundary of Los Angeles areas\n '''\n\n assert isinstance(traffic, pd.DataFrame)\n\n areas = dict(traffic.groupby('Area Name')['Area Name'].count())\n area_name = list(areas.keys())\n\n area_name_rows = []\n for i in area_name:\n area_row = []\n area_row.append(i)\n lats = list(traffic[traffic['Area Name'] == i]['lat'])\n lons = list(traffic[traffic['Area Name'] == i]['lon'])\n area_row.append(min(lats))\n area_row.append(max(lats))\n\n area_row.append(min(lons))\n area_row.append(max(lons))\n\n area_name_rows.append(area_row)\n\n area_name_table = pd.DataFrame(area_name_rows, columns=['Area_Name', 'min_lat', 'max_lat', 'min_lon', 'max_lon'])\n\n new = area_name_table.drop([0, 7, 11, 13, 15, 17, 20], axis=0)\n\n # 1 & 9\n new.at[1, 'min_lat'] = min(new.loc[[1, 9], 'min_lat'])\n new.at[1, 'max_lat'] = max(new.loc[[1, 9], 'max_lat'])\n new.at[1, 'min_lon'] = min(new.loc[[1, 9], 'min_lon'])\n new.at[1, 'max_lon'] = max(new.loc[[1, 9], 'max_lon'])\n new = new.drop(9, axis=0)\n\n # 6 & 8\n new.at[6, 'min_lat'] = min(new.loc[[6, 8], 'min_lat'])\n new.at[6, 'max_lat'] = max(new.loc[[6, 8], 'max_lat'])\n new.at[6, 'min_lon'] = min(new.loc[[6, 8], 'min_lon'])\n new.at[6, 'max_lon'] = max(new.loc[[6, 8], 'max_lon'])\n new = new.drop(8, axis=0)\n\n # 10\n new.at[10, 'min_lat'] = new.loc[10, 'min_lat'] + 0.025\n new.at[10, 'max_lat'] = new.loc[10, 'max_lat'] + 0.025\n new.at[10, 'min_lon'] = new.loc[10, 'min_lon'] + 0.045\n new.at[10, 'max_lon'] = new.loc[10, 'max_lon'] + 0.045\n\n # 14\n new.at[14, 'min_lat'] = new.loc[14, 'min_lat'] + 0.015\n new.at[14, 'max_lat'] = new.loc[14, 'max_lat'] + 0.015\n\n # 18\n new.at[18, 'min_lat'] = new.loc[18, 'min_lat'] - 0.055\n new.at[18, 'max_lat'] = new.loc[18, 'max_lat'] - 0.055\n\n # 19\n new.at[19, 'max_lat'] = new.loc[19, 'max_lat'] - 0.01\n\n # 3\n new.at[3, 'min_lon'] = new.loc[3, 'min_lon'] + 0.025\n new.at[3, 'max_lon'] = new.loc[3, 'max_lon'] + 0.025\n\n # 2\n new.at[2, 'min_lon'] = new.loc[2, 'min_lon'] + 0.018\n new.at[2, 'max_lon'] = new.loc[2, 'max_lon'] + 0.018\n\n new.reset_index(inplace=True)\n return new\n\n\ndef draw_blocks_area(new):\n '''\n By taking in the incidents' area information, we create a plot with the areas boundaries\n :param new: the dataframe contains cleaned incidents information by area\n :param fname: the output filename\n :return: a html file contains the blocks of Los Angeles areas\n '''\n\n assert isinstance(new, pd.DataFrame)\n\n io.output_notebook()\n\n p = figure(plot_width=450, plot_height=400)\n # num_square = 21\n p = figure(title='LA Areas')\n p.quad(top=list(new['max_lon']), bottom=list(new['min_lon']),\n left=list(new['min_lat']), right=list(new['max_lat']), fill_alpha=0)\n\n p.xaxis.axis_label = 'Latitude'\n p.yaxis.axis_label = 'Longitude'\n return show(p)\n\n\ndef draw_blocks_incidents(new, traffic):\n '''\n By taking in the incidents' area information, we create a plot with the areas boundaries.\n The colors filled is based on the number of incidents happened in the area.\n If there are more incidents, the color will be darker.\n :param new: the dataframe contains cleaned incidents information by area\n :param traffic: the dataframe will full traffic incidents information\n :param fname: the output filename\n :return: a html file contains the blocks of Los Angeles areas\n '''\n\n assert isinstance(new, pd.DataFrame)\n assert isinstance(traffic, pd.DataFrame)\n\n new_area = list(dict(new.groupby('Area_Name')['Area_Name'].count()))\n incidents_counter = [0] * len(new_area)\n\n for i in range(traffic.shape[0]):\n for j in range(len(new_area)):\n if (traffic['lon'][i] >= new['min_lon'][j]) & (traffic['lon'][i] <= new['max_lon'][j]) & (\n traffic['lat'][i] >= new['min_lat'][j]) & (traffic['lat'][i] <= new['max_lat'][j]):\n incidents_counter[j] += 1\n\n area_dict = dict(zip(new_area, incidents_counter))\n area_dict['Southeast'] += 3187\n area_dict['West LA'] += 1361\n sorted_by_value = sorted(area_dict.items(), key=lambda kv: kv[1], reverse=True)\n\n checklist = []\n for i in sorted_by_value:\n checklist.append(i[0])\n\n max_lons = []\n min_lons = []\n max_lats = []\n min_lats = []\n\n for i in checklist:\n max_lons.append(float(new[new['Area_Name'] == i]['max_lon']))\n min_lons.append(float(new[new['Area_Name'] == i]['min_lon']))\n max_lats.append(float(new[new['Area_Name'] == i]['max_lat']))\n min_lats.append(float(new[new['Area_Name'] == i]['min_lat']))\n\n io.output_notebook()\n\n palettes = inferno(120)\n final_palettes = []\n count = 0\n for i in palettes:\n count += 1\n if count % 10 == 0:\n final_palettes.append(i)\n\n #all_palettes['Reds'][9]\n\n p = figure(plot_width=450, plot_height=400)\n p.quad(top=max_lons, bottom=min_lons, left=min_lats, right=max_lats, color=final_palettes, fill_alpha=0.8)\n\n color_mapper = LogColorMapper(palette=final_palettes)\n color_bar = ColorBar(color_mapper=color_mapper, location=(0, 0))\n\n p.add_layout(color_bar, 'left')\n\n return show(p)\n","repo_name":"bananannn/ECE-143-Group-2","sub_path":"incidents_areas.py","file_name":"incidents_areas.py","file_ext":"py","file_size_in_byte":6631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"13975473261","text":"from __future__ import absolute_import\nimport fabric.api as fab\nfrom .base import BaseCommandUtil\nfrom .utils import legacy_wrap\nimport warnings\n\n\nclass Django(BaseCommandUtil):\n manage_path = None\n\n def __init__(self, virtualenv, **kwargs):\n self.virtualenv = virtualenv\n super(Django, self).__init__(**kwargs)\n if self.manage_path is None:\n raise RuntimeError('No manage_path specified (class or constructor)')\n\n def run(self, command, *options):\n self._run(\"%s %s %s %s\" % (\n self.virtualenv.python_bin(),\n self._abs_path(self.manage_path),\n command,\n ' '.join([o for o in options if not o is None]),\n ))\n\n def collectstatic(self, clear=False):\n self.run(\n 'collectstatic',\n '--noinput',\n '-c' if clear else None,\n )\n\n def syncdb(self, migrate=False, database=None):\n self.run(\n 'syncdb',\n '--noinput',\n '--database=\"%s\"' % database if database else None,\n '--migrate' if migrate else None,\n )\n\n def migrate(self, app=None, migration=None, database=None, fake=False, merge=False):\n self.run(\n 'migrate',\n '--noinput',\n '--merge' if merge else None,\n '--database=\"%s\"' % database if database else None,\n '--fake' if fake else None,\n app if app else None,\n migration if migration else None,\n )\n\n\n# BACKWARDS COMPATIBILITY\n\n\ndef _legacy_django():\n from .virtualenv import _legacy_virtualenv\n\n warnings.warn('You are using the legacy function, please switch to class based version', PendingDeprecationWarning)\n\n fab.require('deploy_manage_path')\n virtualenv = _legacy_virtualenv()\n return Django(virtualenv,\n manage_path=fab.env.deploy_manage_path)\n\n\nrun_command = legacy_wrap(_legacy_django, 'run')\ncollectstatic = legacy_wrap(_legacy_django, 'collectstatic')\nsyncdb = legacy_wrap(_legacy_django, 'syncdb')\nmigrate = legacy_wrap(_legacy_django, 'migrate')\n","repo_name":"team23/fabdeploit","sub_path":"fabdeploit/django.py","file_name":"django.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"31"} +{"seq_id":"1234562808","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport gzip\nimport argparse\n\nparser = argparse.ArgumentParser(\n description='Extract plasmid info to table')\nparser.add_argument('infile', nargs='?', type=argparse.FileType('r'),\n default=sys.stdin,\n help='plasmid list')\nparser.add_argument('taxfile', type=str,\n help='taxonomy lineage file')\nparser.add_argument('datadir', type=str,\n help='dirctory with plasmid sequence files')\nparser.add_argument('outfile', nargs='?', type=argparse.FileType('w'),\n default=sys.stdout,\n help='output file')\nparser.add_argument('--version', action='version', version='%(prog)s 1.0')\nargs = parser.parse_args()\n\ntax2lin={}\nwith gzip.open(args.taxfile, 'rt') as fh_tax:\n for line in fh_tax:\n if line.strip() == '':\n continue\n taxid, lineage = line.split('\\t', 1)\n tax2lin[taxid] = lineage.strip()\n\nheader = [\n 'Caption', 'SourceDB', 'AccessionVersion', 'MoleculeType', 'SeqName', 'Size',\n 'Topology', 'UpdateDate', 'Title', 'TaxId', 'TaxName', 'TaxCategory',\n 'TaxDivision', 'TaxLineage', 'TaxSuperkingdom', 'TaxPhylum', 'TaxClass', 'TaxOrder',\n 'TaxFamily', 'TaxGenus', 'TaxSpecies', 'OrganismName', 'StrainName', 'IsolateName'\n ]\n\nseq_info_fields = [\n 'caption', 'source_db', 'refseq_sequence_acc', 'molecule_type',\n 'sequence_name', 'size', 'topology', 'update_date', 'title', 'pubmed',\n 'taxid', 'organism_name', 'strain_name', 'isolate_name'\n ]\n\nargs.outfile.write('\\t'.join(header)+'\\n')\n\nfor line in args.infile:\n accver = line.strip()\n if accver == '':\n continue\n\n with open(os.path.join(args.datadir, accver+'.fna')) as fh_fna:\n title = fh_fna.readline().strip().split(' ', 1)[1]\n\n with open(os.path.join(args.datadir, accver+'.gbff')) as fh_gbff:\n line = fh_gbff.readline()\n pubmed = []\n size = '-'\n topology = '-'\n organism_name = '-'\n strain_name = '-'\n isolate_name = '-'\n sequence_name = '-'\n taxid = '-'\n while True:\n if line == '':\n break\n if line == '\\n':\n continue\n if line[0:6] == 'LOCUS ':\n size = line[29:40].strip()\n topology = line[55:63].strip()\n update_date = line[68:79].strip()\n if line.find('/organism=\"') != -1:\n organism_name = line.split('=')[1].strip().strip('\"')\n elif line.find('/strain=\"') != -1:\n strain_name = line.split('=')[1].strip().strip('\"')\n elif line.find('/isolate=\"') != -1:\n isolate_name = line.split('=')[1].strip().strip('\"')\n elif line.find('/plasmid=\"') != -1:\n sequence_name = line.split('=')[1].strip().strip('\"')\n elif line.find('/db_xref=\"taxon:') != -1:\n taxid = line.split(':')[1].strip().strip('\"')\n if line[0:6] == 'CONTIG':\n break\n if line[0:6] == 'ORIGIN':\n break\n if line[0:2] == '//':\n break\n line = fh_gbff.readline()\n\n seq_info = []\n seq_info.append(accver.split('.')[0])\n seq_info.append('refseq')\n seq_info.append(accver)\n #seq_info.append(insd_accver)\n seq_info.append('Plasmid')\n seq_info.append(sequence_name)\n seq_info.append(size)\n seq_info.append(topology)\n seq_info.append(update_date)\n seq_info.append(title)\n seq_info.append(taxid)\n seq_info.append(tax2lin[taxid])\n seq_info.append(organism_name)\n seq_info.append(strain_name)\n seq_info.append(isolate_name)\n args.outfile.write('\\t'.join(seq_info)+'\\n')\n","repo_name":"santirdnd/PTU_paper","sub_path":"extract_plasmid_info.py","file_name":"extract_plasmid_info.py","file_ext":"py","file_size_in_byte":3762,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"3589638807","text":"import requests\nimport re\nimport hashlib\n\nurl = \"<your-URL\"\n\n# Session for Cookie\nsession = requests.Session()\n\n#get needed Stuff\nresponse = session.get(url)\nif response.status_code != 200:\n print(f\"Failed to fetch the website: {response.status_code}\")\n exit()\n\n#Regex to find needed string after h3\npattern = r'<h3 align=\\'center\\'>(.*?)</h3>'\nmatch = re.search(pattern, response.text)\nif not match:\n print(\"String not found in the source code.\")\n exit()\n\n# Match the right group\nspecific_string = match.group(1)\n\n# generate hash\nmd5_hash = hashlib.md5(specific_string.encode()).hexdigest()\n\n# Post data\npost_data = {\n \"hash\": md5_hash \n} \n \n# Cookie with session for post \nresponse = session.post(url, data=post_data)\n\nif response.status_code == 200:\n print(f\"POST request successful. Response: {response.text}\")\nelse:\n print(f\"Failed to send the POST request: {response.status_code}\")\n","repo_name":"snuxs/String-to-MD5-Post","sub_path":"MD5HashGen.py","file_name":"MD5HashGen.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"8259053957","text":"from typing import Dict\nimport re\n\nfrom molecular.monitoring.log import get_logger\n\nlogger = get_logger()\n\n\nclass MoleculeParser:\n \"\"\"\n This class allows to parse molecules, and get the number of atoms that compose it.\n\n It is mainly string operations.\n See documentation used : https://docs.python.org/3/library/stdtypes.html\n \"\"\"\n\n def parse_molecule(self, molecule: str) -> Dict[str, int]:\n \"\"\"\n This method returns the dictionary of atoms for a specified molecule.\n :param molecule: the molecule to parse\n :return: the dictionary of atoms, containing the amount for each atom\n \"\"\"\n logger.info(f\"Molecule : '{molecule}'\")\n\n # check if the molecule has a valid format for parenthesis\n if not self._is_valid_parenthesis(molecule=molecule):\n logger.info(f\"Molecule '{molecule}' has not a valid format.\\n\")\n return None\n\n # get list of molecule elements\n list_molecules_elements = self.get_list_molecules_elements(molecule=molecule)\n logger.info(f'list_molecules_elements = {list_molecules_elements}')\n\n # recursively parse each molecule element, counting them, using parenthesis_multiplier\n atoms_dict = self.parse_group(list_molecules_elements=reversed(list_molecules_elements), atoms_dict={})\n logger.info(f\"Dictionary of atoms for '{molecule}' : {atoms_dict}\\n\")\n\n return atoms_dict\n\n def _is_valid_parenthesis(self, molecule: str) -> bool:\n \"\"\"\n This method ensures the validity of the number of parenthesis opening / closing.\n For the second part of the function, it was inspired by :\n https://www.w3resource.com/python-exercises/class-exercises/python-class-exercise-3.php\n https://docs.python.org/3/tutorial/datastructures.html\n :param molecule: the molecule to analyse\n :return: True if the parenthesis are valid (opened / closed for each one, on the good order)\n \"\"\"\n only_parenthesis = ''\n parenthesis_char = {\"(\", \")\", \"{\", \"}\", \"[\", \"]\"}\n for character in molecule:\n if character in parenthesis_char:\n only_parenthesis += character\n\n stack = []\n parenthesis_opposites = {\"(\": \")\", \"{\": \"}\", \"[\": \"]\"}\n for parenthese in only_parenthesis:\n if parenthese in parenthesis_opposites:\n stack.append(parenthese)\n elif len(stack) == 0 or parenthesis_opposites[stack.pop()] != parenthese:\n return False\n return len(stack) == 0\n\n def get_list_molecules_elements(self, molecule: str) -> Dict[str, int]:\n \"\"\"\n This method returns the list of molecule elements.\n A molecule element can be :\n - atom_name : an atom can be composed of 1 letter uppercase, or 1 letter uppercase + 1 letter lowercase\n - atom_multiplier : the multiplier linked to the atom. For example : 'H(S4[SO]3)2', atom multipler of S = 4.\n - parenthesis : can be one of the following : \"(\", \")\", \"[\", \"]\", \"{\", \"}\"\n - parenthesis_multiplier : multiplified amount of all current surrounding parenthesis\n :param molecule: the molecule to analyse\n :return: the list of molecule elements, composing the molecule\n \"\"\"\n return re.findall(r'[A-Z][a-z]?|[()\\[\\]{}]|[0-9]', molecule)\n\n def parse_group(self, list_molecules_elements, atoms_dict, parenthesis_multiplier=1):\n \"\"\"\n This method returns the updated dictionary of atoms for a specified molecule.\n Here we handle the mulitplier of atoms, defined by the parenthesis.\n The recursiveness of this method allows to multiply the parenthesis_multiplier at each parenthesis.\n We 'read' the molecule from right to left, so that we catch the multiplier first. Easier fot calculation.\n :param list_molecules_elements: the molecule, parsed by molecule elements\n :param atoms_dict: the dictionary of atoms to construct / return\n :param parenthesis_multiplier: the parenthesis_multiplier, which evolve at each opened/closed parenthesis\n :return: the dictionary of atoms, containing the amount for each atom\n \"\"\"\n _parenthesis_multiplier = parenthesis_multiplier\n starting_parenthesis_char = {\"(\", \"{\", \"[\"}\n closing_parenthesis_char = {\")\", \"}\", \"]\"}\n for molecule_element in list_molecules_elements:\n\n # entering a parenthesis : we use the parenthesis_multiplier\n if molecule_element in closing_parenthesis_char:\n self.parse_group(\n list_molecules_elements=list_molecules_elements,\n atoms_dict=atoms_dict,\n parenthesis_multiplier=_parenthesis_multiplier)\n # exiting a parenthesis : we do not use the parenthesis_multiplier anymore\n elif molecule_element in starting_parenthesis_char:\n break\n elif molecule_element.isdecimal():\n _parenthesis_multiplier = parenthesis_multiplier * int(molecule_element)\n continue\n elif molecule_element.isalpha():\n atoms_dict[molecule_element] = atoms_dict.get(molecule_element, 0) + _parenthesis_multiplier\n _parenthesis_multiplier = parenthesis_multiplier\n return atoms_dict\n\n\nif __name__ == '__main__':\n molecule_parser = MoleculeParser()\n\n wrong_format_molecule = '(Mg3[T)]'\n atoms_dict = molecule_parser.parse_molecule(molecule=wrong_format_molecule)\n\n water = 'H2O'\n atoms_dict = molecule_parser.parse_molecule(molecule=water)\n\n magnesium_hydroxide = 'Mg(OH)2'\n atoms_dict = molecule_parser.parse_molecule(molecule=magnesium_hydroxide)\n\n fremy_salt = 'K4[ON(SO3)2]2'\n atoms_dict = molecule_parser.parse_molecule(molecule=fremy_salt)\n","repo_name":"PaulJardel/molecule_parser","sub_path":"molecular/molecule_parser.py","file_name":"molecule_parser.py","file_ext":"py","file_size_in_byte":5829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31731186870","text":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport tensorflow as tf\n\n\ndef preprocess_input(x: tf.Tensor, swap_channel:bool=True) -> tf.Tensor:\n '''\n Preprocess the input image.\n '''\n assert x.shape[-1] == 3, f'The shape of x must be (batch_size, height, width, channel). But got {x.shape}.'\n\n \n # Swap the channel of the image\n if swap_channel:\n x = tf.reverse(x, axis=[-1])\n x = x - tf.constant([103.939, 116.779, 123.68])\n return x\n else:\n x = x - tf.constant([123.68, 116.779, 103.939])\n return x\n\ndef process_output(x:tf.Tensor, swap_channel:bool = True):\n '''\n Process output \n '''\n if swap_channel:\n x = x + tf.constant([103.939, 116.779, 123.68])\n x = tf.reverse(x, axis=[-1])\n return x\n else:\n x = x + tf.constant([123.68, 116.779, 103.939])\n return x","repo_name":"Thehunk1206/StyleTransferNet","sub_path":"utils/preprocess_io.py","file_name":"preprocess_io.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22489586962","text":"\n\n\n# Find maximum subarray having given sum\n# NOT YET SOLVED\n\narr = [5, 6, -5, 5, 3, 5, 3, -2, 0]\n\ndef max_len_subarray(arr, given_num=8):\n print(\"Source array: \", arr)\n if not arr:\n return False\n\n sk = [0]\n for el in arr:\n sk.append(sk[-1] + el)\n\n ss =dict()\n for i in range(len(sk)):\n ss.update({given_num + sk[i]: (sk[i], i)})\n\n for i in range(len(sk)):\n val = ss.get(sk[i], None)\n if val:\n print(val, i)\n if i >= val[-1]:\n return [val[-1], i-1]\n\n return None\n\n\n\nprint(max_len_subarray(arr))\n\n\n\n\n\n","repo_name":"scidam/algos","sub_path":"algorithms/intervieweing.io/max_subarray_given_sum.py","file_name":"max_subarray_given_sum.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"703459798","text":"import datetime\nimport logging\nimport time\nimport os\n\nimport torch\nfrom tqdm import tqdm\n\nfrom image_captioning.utils.miscellaneous import decode_sequence\nfrom image_captioning.data.datasets.evaluation import coco_eval\n\n\ndef compute_on_dataset(\n model, criterion, data_loader, vocab, beam_size, device, logger,\n):\n model.eval()\n cpu_device = torch.device(\"cpu\")\n val_loss_sum = 0.\n val_loss_count = 0\n seq_per_img = data_loader.dataset.seq_per_img\n predictions = []\n done_ids = dict()\n with torch.no_grad():\n for i, data in enumerate(tqdm(data_loader, ncols=100, ascii=True, desc=\"decoding\")):\n fc_features = data['fc_features'].to(device)\n att_fatures = data['att_features'].to(device)\n captions = data['captions'].to(device)\n cap_lens = data['cap_lens'].to(device)\n cocoids = data['cocoids']\n outputs, weights = model(fc_features, att_fatures, captions)\n loss = criterion(outputs, captions[:, 1:], cap_lens+1)\n val_loss = loss.item()\n val_loss_count += 1\n val_loss_sum += val_loss\n seqs, seq_log_probs, weights = model.decode_search(\n fc_features, att_fatures, beam_size=beam_size\n )\n sents = decode_sequence(vocab, seqs)\n for k, sent in enumerate(sents):\n entry = {'image_id': cocoids[k], 'caption': sent}\n if cocoids[k] not in done_ids:\n done_ids[cocoids[k]] = 0\n predictions.append(entry)\n return predictions, val_loss_sum / val_loss_count\n\n\ndef inference(\n model,\n criterion,\n data_loader,\n dataset_name,\n vocab,\n beam_size,\n device='cpu',\n):\n device = torch.device(device)\n logger = logging.getLogger(\"image_captioning.inference\")\n dataset = data_loader.dataset\n logger.info(\"Start evaluation on {} dataset({} images)\".format(dataset_name, len(dataset)))\n start_time = time.time()\n predictions, loss = compute_on_dataset(\n model, criterion, data_loader, vocab, beam_size, device, logger,\n )\n\n metrics_score = coco_eval(predictions, dataset_name)\n\n return loss, predictions, metrics_score\n\n\n","repo_name":"congve1/image_captioning","sub_path":"image_captioning/engine/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"9989132836","text":"# coding=utf-8\n# @Author : zpchcbd HG team\n# @Blog : https://www.cnblogs.com/zpchcbd/\n# @Time : 2020-11-23 20:45\n\nfrom core.setting import HTTP_PROXY\nfrom core.exception.github import GithubPrivilegeError\nfrom core.public import *\nfrom spider import BaseSpider\nfrom spider.common import config\n\n\nclass GithubSpider(BaseSpider):\n def __init__(self, domain):\n super().__init__()\n self.source = 'GithubSpider' #\n self.domain = domain\n self.per_page = 50\n self.githubApi = config.githubApi\n self.addr = 'https://api.github.com/search/code?s=indexed&type=Code&o=desc&q=\"{}\"&page={}&per_page={}'\n # self.keyword = ['password', 'smtp', 'passwd', 'mail', 'mysql']\n\n # 这个writeFile保存函数其实是可以进行优化的，后面写完了继续改下，要不然每个Spider保存数据的时候都需要去加上这将近10行的代码\n # 主要的还是对这个要写入Dict中的字段进行遍历解析即可\n def writeFile(self, web_lists, page):\n # 这里的writeFile用于到时候github中敏感信息的保存函数\n pass\n\n # def process_pages(self, _contents, _keyword):\n # def get_data(github_file):\n # if not github_file.last_modified:\n # try:\n # github_file.update()\n # except UnknownObjectException:\n # pass\n # repo = github_file.repository\n # return {\n # 'keyword': _keyword,\n # 'sha': github_file.sha,\n # 'fragment': format_fragments(github_file.text_matches),\n # 'html_url': github_file.html_url,\n # 'last_modified': dateutil.parser.parse(\n # github_file.last_modified) if github_file.last_modified else None,\n # 'file_name': github_file.name,\n # 'repo_name': repo.name,\n # 'repo_url': repo.html_url,\n # 'user_avatar': repo.owner.avatar_url,\n # 'user_name': repo.owner.login,\n # 'user_url': repo.owner.html_url\n # }\n #\n # def format_fragments(_text_matches):\n # return ''.join([f['fragment'] for f in _text_matches])\n #\n # for _file in _contents:\n # print(_file)\n # data = get_data(_file)\n # # 这里对数据进行存储\n # self.resList.append(data)\n # # get_data(1)\n\n async def githubSearch(self, session, page):\n # @ske\n # type=c搜索代码，s=indexed排序的类型，o=desc排序方式，page第几页，\n # q=搜索的关键字，q=signtool+sign+pfx+language:Batchfile 指定语言在q参数里，使用language参数\n # extension:pfx 指定后缀在q参数里，使用extension参数\n headers = {\"Authorization\": 'token {}'.format(self.githubApi)}\n async with session.get(url=self.addr.format(self.domain, page, self.per_page, proxy=HTTP_PROXY),\n headers=headers, timeout=self.reqTimeout, verify_ssl=False) as response:\n text = await response.json()\n await asyncio.sleep(2)\n if 'API rate limit exceeded' in text:\n print('[-] check your github api rate limit')\n raise GithubPrivilegeError from None\n return text\n\n async def getSubdomains(self, session, url):\n async with session.get(url=url, headers=self.headers, timeout=self.reqTimeout, verify_ssl=False, proxy=HTTP_PROXY) as response:\n text = await response.text('utf-8', 'ignore')\n subdomains = self.matchSubdomain(self.domain, text)\n self.resList.extend(subdomains)\n\n async def getSensitiveInfor(self):\n pass\n\n async def spider(self):\n # # 获取github请求会话\n # session = Github(login_or_token=self.githubApi, per_page=self.page)\n # # 获取请求页数\n # while True:\n # try:\n # response = session.search_code('password zjhu.edu.cn', sort='indexed', order='desc', highlight=True)\n #\n # # github api支持最多搜索1000条记录\n # total = min(response.totalCount, 1000)\n # break\n # except GithubException as e:\n # if 'rate limit' in e.data.get('message', ''):\n # print('[-] Github token error, {}'.format(e.__str__()))\n # return\n # except ReadTimeoutError:\n # continue\n # # 防止由于网络原因导致的获取失败\n # except Exception as e:\n # print('[-] Github search error, error is {}'.format(e.__str__()))\n # return\n taskList = []\n taskRawHtmlList = []\n # get first page\n try:\n async with aiohttp.ClientSession() as session:\n text1 = await self.githubSearch(session, 1)\n total_count = text1['total_count']\n # E.G. total = 50，max_page = 1; total = 51, max_page = 2\n # 需要搜索的页数为max_page和task.page中最小的值\n max_page = (total_count // self.per_page) if (not total_count % self.per_page) else (\n total_count // self.per_page + 1)\n pages = min(max_page, 300)\n print('[+] github get pages is {}.'.format(pages))\n for page in range(1, pages): # pages page 20 is test something\n json_text = await self.githubSearch(session, page)\n if json_text and 'items' in json_text.keys():\n items = json_text['items']\n for item in items:\n raw_url = item['html_url'].replace('https://github.com/', 'https://raw.githubusercontent.com/').replace('/blob/', '/')\n taskRawHtmlList.append(raw_url)\n\n taskRawHtmlList = list(set(taskRawHtmlList))\n for _ in taskRawHtmlList:\n taskList.append(asyncio.create_task(self.getSubdomains(session, _)))\n\n # 这里则进行异步操作对每个构造好的raw.githubusercontent.com中的内容进行匹配\n await asyncio.gather(*taskList) # [[{}],[{}]]\n except GithubPrivilegeError:\n print('[-] curl github.com error, please check your API Limit.'.format(self.source))\n return []\n except aiohttp.ClientHttpProxyError:\n print('[-] curl github.com need outer proxy.'.format(self.source))\n return []\n except Exception as e:\n print('[-] [{}] curl api.github.com error, the erorr is {}'.format(self.source, e.args))\n return []\n\n self.resList = list(set(self.resList))\n print('[+] [{}] [{}] {}'.format(self.source, len(self.resList), self.resList))\n return self.resList\n # # 搜索代码\n # page = 0\n # # 获取每页的内容\n # while page < max_page:\n # try:\n # page_content = response.get_page(page)\n # page += 1\n # except GithubException as e:\n # print('[-] GithubException, error is {}'.format(e.__str__()))\n # continue\n # # 防止由于网络原因导致的获取失败\n # except Exception as e:\n # print('[-] Github search error, error is {}'.format(e.__str__()))\n # return\n # print(page_content)\n # self.process_pages(page_content, self.keyword)\n\n # 这里进行打印即可\n # self.resList = list(set(self.resList))\n # print(self.resList)\n # print('[+] [{}] [{}] {}'.format(self.source, len(self.resList), self.resList))\n\n async def main(self):\n return await self.spider()\n\n\nif __name__ == '__main__':\n github = GithubSpider('zjhu.edu.cn')\n loop = asyncio.get_event_loop()\n res = loop.run_until_complete(github.main())\n","repo_name":"AkunWin/myscan","sub_path":"spider/GithubSpider.py","file_name":"GithubSpider.py","file_ext":"py","file_size_in_byte":8021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"2751109320","text":"from tkinter import *\r\nfrom tkinter import messagebox\r\n\r\n\r\ntomorse={'A':'.-','B':'-...','C':'-.-.','D':'-..','E':'.','F':'..-.','G':'--.','H':'....','I':'..','J':'.---','K':'-.-','L':'.-..','M':'--','N':'-.','O':'---','P':'.--.','Q':'--.-','R':'.-.','S':'...','T':'-','U':'..-','V':'...-','W':'.--','X':'-..-','Y':'-.--','Z':'--..','0':'-----','1':'.----','2':'..---','3':'...--','4':'....-','5':'.....','6':'-....','7':'--...','8':'---..','9':'----.','.':'.-.-.-',',':'--..--',':':'---...','?':'..--..',\"'\":'.----.','-':'-....-','/':'-..-.','(':'-.--.',')':'-.--.-','\"':'.-..-.','=':'-...-','+':'.-.-.','@':'.--.-.'}\r\nfrommorse={}\r\nfor key,value in tomorse.items():\r\n frommorse[value]=key\r\n\r\n\r\n#создание окна\r\nokno = Tk()\r\nokno.minsize(420,300)\r\nokno.geometry(\"620x300\")\r\nokno.title('Переводчик: Азбука морзе')\r\n\r\n#функция перевода\r\ndef perevod(kyda,text):\r\n if kyda==1: \r\n textboxout.delete(1.0,END)\r\n count=0\r\n res=''\r\n i=0\r\n while i!=len(text):\r\n if text[i]==' ':\r\n count+=1\r\n i+=1\r\n if count==7: \r\n ttext=text[:i]\r\n for j in ttext.split():\r\n symbol=frommorse.get(j)\r\n res+=symbol\r\n res+=' '\r\n text=text[i:]\r\n i=0\r\n count=0\r\n if text[i]!=' ':\r\n count=0\r\n i+=1\r\n if i==len(text):\r\n for j in text.split():\r\n symbol=frommorse.get(j)\r\n res+=symbol\r\n textboxout.insert(INSERT,res)\r\n else:\r\n textboxout.delete(1.0,END)\r\n res=''\r\n i=0\r\n while i!=len(text):\r\n if text[i]==' ':\r\n ttext=text[:i]\r\n for j in ttext:\r\n symbol=tomorse.get(j.upper())\r\n res=res+symbol+' '\r\n res+=' '\r\n text=text[i+1:]\r\n i=0\r\n if text[i]!=' ':\r\n i+=1\r\n if i==len(text):\r\n for j in text:\r\n symbol=tomorse.get(j.upper())\r\n res=res+symbol+' '\r\n textboxout.insert(INSERT,res[:len(res)-1])\r\n \r\ndef info():\r\n messagebox.showinfo(\"Пояснение\",\"-Тире равно трем точкам.\\n-Интервал между сигналами, образющими один символ, равен одной точке.\\n-Интервал между двумя символами равен трем точкам.\\n-Интервал между двумя словами равен семи точкам.\\n\")\r\n \r\n\r\n##########создание интерфейса\r\n \r\n#кнопка выбора языка\r\nyaz = IntVar()\r\nnaeng = Radiobutton(okno, text=\"Морзе -> Английский\",font=\"Verdana\", variable=yaz, value=1)\r\nnaeng.select()\r\nnamorse = Radiobutton(okno, text=\"Английский -> Морзе\",font=\"Verdana\", variable=yaz, value=2)\r\n\r\n#кнопки\r\nleave = Button(okno, text=\"Выход\",font=\"Verdana\",activebackground=\"red\",command=okno.destroy)\r\ntranslate = Button(okno, text=\"Перевести\",font=\"Verdana\",command=lambda:perevod(yaz.get(),textboxin.get(\"1.0\",'end-1c')))\r\ninfo = Button(okno, text=\"Информация\",font=\"Verdana\",activebackground=\"blue\",command=info)\r\n#окно ввода\r\ntextboxin = Text(okno,height=5,width=30)\r\n\r\n#окно вывода\r\ntextboxout = Text(okno,height=5,width=30)\r\n\r\n#заголовки\r\ntitle=Label(okno,text='Переводчик',font=\"Verdana, 20\")\r\ninp=Label(okno,text='Ввод',font=\"Verdana\")\r\nout=Label(okno,text='Вывод',font=\"Verdana\")\r\n\r\n#размещение обьектов в окне\r\ntitle.place(anchor=\"center\",relx=0.5,rely=0.1)\r\ninp.place(relx=0.05,rely=0.2)\r\nout.place(relx=0.6,rely=0.2)\r\ntextboxin.place(relx=0.05,rely=0.35,relwidth=0.35,relheight=0.3)\r\ntextboxout.place(relx=0.6,rely=0.35,relwidth=0.35,relheight=0.3)\r\nleave.place(relx=0.85,rely=0.85)\r\ninfo.place(x=10,y=10)\r\ntranslate.place(anchor=\"center\",relx=0.5,rely=0.85)\r\nnaeng.place(relx=0.05,rely=0.7)\r\nnamorse.place(relx=0.6,rely=0.7)\r\n","repo_name":"megadybina/Shirokoff-morse-translator","sub_path":"morse project(noimport).py","file_name":"morse project(noimport).py","file_ext":"py","file_size_in_byte":4340,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"23807834829","text":"from openerp.osv import osv, fields\n\nclass hrEmployee(osv.Model):\n _inherit = 'hr.employee'\n \n _defaults = {\n 'ins_exportable': True,\n 'ins_working_day': 'Tiempo completo',\n 'ins_paid_days': 30,\n 'ins_paid_hours': 240,\n }\n \n _columns = {\n 'ins_exportable': fields.boolean('Export?'),\n 'ins_id_type': fields.selection([\n ('CN','Cédula Nacional'),\n ('CR','Cédula Residencia'),\n ('NP', 'Número Pasaporte'),\n ('PT','Permiso Trabajo'),\n ('SD','Sin Documentos'),\n ], 'Id Type'),\n 'ins_name': fields.char('Name', size=128),\n 'ins_last_name1': fields.char('First Last Name', size=128),\n 'ins_last_name2': fields.char('Second Last Name', size=128),\n 'ins_working_day': fields.selection([('Tiempo completo','Tiempo completo'),\n ('Medio tiempo','Medio tiempo'),\n ('Ocasional','Ocasional'),\n ('Por jornales','Por jornales'),\n ],'Working Day'),\n 'ins_paid_days': fields.integer('Paid Days'),\n 'ins_paid_hours': fields.integer('Paid Hours'),\n 'ins_job_code': fields.char('Job Code'),\n }","repo_name":"ClearCorp-dev/odoo-costa-rica","sub_path":"l10n_cr_hr_ins_csv_generator/l10n_cr_hr_ins_csv_generator.py","file_name":"l10n_cr_hr_ins_csv_generator.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"705036135","text":"import numpy as np\nimport torch\n\ndef eval_voc(results, targets, iou_thresh, num_classes):\n for result in results:\n result[\"boxes\"] = result[\"boxes\"].cpu().clone().detach().numpy()\n result[\"labels\"] = result[\"labels\"].cpu().clone().detach().numpy()\n result[\"scores\"] = result[\"scores\"].cpu().clone().detach().numpy()\n for target in targets:\n target[\"boxes\"] = target[\"boxes\"].cpu().clone().detach().numpy()\n target[\"labels\"] = target[\"labels\"].cpu().clone().detach().numpy()\n \n ap_per_cls = []\n ap_sum = .0\n for cls in range(1, num_classes+1):\n cls_results = []\n cls_targets = []\n for img_result in results:\n labels = img_result['labels']\n img_cls_result = {\n 'boxes':[],\n 'confidence':[]\n }\n for idx, label in enumerate(labels):\n if label == cls:\n img_cls_result['boxes'].append(img_result['boxes'][idx])\n img_cls_result['confidence'].append(img_result['scores'][idx])\n cls_results.append(img_cls_result)\n for img_target in targets:\n labels = img_target['labels']\n img_cls_target = {\n 'boxes':[]\n }\n for idx, label in enumerate(labels):\n if label == cls:\n img_cls_target['boxes'].append(img_target['boxes'][idx])\n cls_targets.append(img_cls_target)\n ap = cal_ap(cls_results, cls_targets, iou_thresh)\n ap_per_cls.append(ap)\n ap_sum += ap\n mAP = ap_sum / num_classes\n print(\"mAP = {:.4f}\".format(mAP))\n return mAP, ap_per_cls\n \n \ndef cal_ap(results, targets, iou_thresh):\n gt_count = 0\n tp = np.array([],dtype=int)\n fp = np.array([],dtype=int)\n conf = np.array([])\n for result, target in zip(results, targets):\n gt_boxes = np.array(target['boxes'])\n pred_boxes = np.array(result['boxes'])\n\n gt_count += len(gt_boxes)\n if len(pred_boxes) == 0:\n continue\n img_tp = np.zeros(len(pred_boxes))\n img_fp = np.ones(len(pred_boxes))\n if len(gt_boxes) != 0:\n match_matrix = box_iou(gt_boxes, pred_boxes)\n maxidx = match_matrix.argmax(axis=1)\n for idx, match in zip(maxidx, match_matrix):\n maxIou = match[idx]\n if maxIou > iou_thresh:\n img_tp[idx] = 1\n img_fp[idx] = 0\n tp = np.concatenate((tp, img_tp))\n fp = np.concatenate((fp, img_fp))\n conf = np.concatenate((conf, result['confidence']))\n tp = np.cumsum(tp)\n fp = np.cumsum(fp)\n rec = tp / float(gt_count)\n prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)\n ap = cal_voc_ap(rec, prec)\n\n return ap\n\ndef cal_voc_ap(rec, prec):\n recall = np.concatenate(([0.], rec, [1.0]))\n precision = np.concatenate(([0.], prec, [0.]))\n \n for i in range(len(precision)-1, 0, -1):\n precision[i-1] = np.maximum(precision[i-1], precision[i])\n \n i = np.where(recall[1:] != recall[:-1])[0]\n \n ap = np.sum((recall[i+1] - recall[i]) * precision[i+1]) \n\n return ap\n\ndef box_area(boxes):\n return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])\n\ndef box_iou(boxes1, boxes2):\n \"\"\"\n @param boxes1: boxes one, shape: (N, 4)\n @param boxes2: boxes two, shape: (M, 4)\n @return:\n \"\"\"\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n lt = np.maximum(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = np.minimum(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n wh = rb - lt\n wh = np.clip(wh, 0, np.Inf) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n union = area1[:, None] + area2 - inter\n iou = inter / union\n return iou\n\n\nif __name__ == \"__main__\":\n pass","repo_name":"vlgdglv/faster-rcnn-simple","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":3867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36606532115","text":"def fill_pipes(p1):\n seen_twice = set()\n seen = set()\n for pipe in inp:\n x1, y1 = pipe[0][0], pipe[0][1]\n x2, y2 = pipe[1][0], pipe[1][1]\n steps = max(abs(x2 - x1), abs(y2 - y1))\n dx = (x2 - x1) // steps\n dy = (y2 - y1) // steps\n if p1 and (dx != 0 and dy != 0):\n continue\n for i in range(steps + 1):\n new_pos = (x1 + (i * dx), y1 + (i * dy))\n if new_pos in seen:\n seen_twice.add(new_pos)\n seen.add(new_pos)\n return len(seen_twice)\n\n\ninp = [[[int(x) for x in pos.split(\",\")] for pos in line.strip().split(\" -> \")] for line in open(\"input\").readlines()]\nprint(\"Part 1:\", fill_pipes(True))\nprint(\"Part 2:\", fill_pipes(False))\n","repo_name":"evanphoward/AdventOfCode","sub_path":"AOC_21/Day5/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"71291927128","text":"with open('input.txt') as f:\n lines = f.read().strip().splitlines()\n\nmax_y = 0\nunique_coors = set()\nfor line in lines: \n \n line_coordinates = []\n\n for coordinates in line.split(' -> '):\n x, y = map(int, coordinates.split(','))\n line_coordinates.append((x, y))\n \n\n for i in range(1, len(line_coordinates)):\n\n curr_x, curr_y = line_coordinates[i]\n prev_x, prev_y = line_coordinates[i - 1]\n\n if curr_y > max_y:\n max_y = curr_y\n\n if curr_x == prev_x:\n for i in range(min(curr_y, prev_y), max(curr_y, prev_y) + 1):\n unique_coors.add((curr_x, i))\n\n if curr_y == prev_y:\n for i in range(min(curr_x, prev_x), max(curr_x, prev_x) + 1):\n unique_coors.add((i, curr_y))\n \n \ndef drop_sand():\n x, y = 500, 0\n global unique_coors\n\n if (x, y) in unique_coors:\n return (x, y)\n\n\n while y < max_y + 1:\n \n if (x, y + 1) not in unique_coors:\n y += 1\n continue\n\n if (x - 1, y + 1) not in unique_coors:\n x -= 1\n y += 1\n continue\n\n if (x + 1, y + 1) not in unique_coors:\n x += 1\n y += 1\n continue\n\n break\n\n return (x, y)\n\ncounter = 0\nwhile True: \n x, y = drop_sand()\n unique_coors.add((x, y))\n counter += 1\n\n if (x, y) == (500, 0): \n break\n\nprint(counter)\n\n\n ","repo_name":"JopVerbeek/AOC_2022","sub_path":"day_14/aocD14p2.py","file_name":"aocD14p2.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"27452131242","text":"import sys\nimport re\nimport math\nimport fileinput\n\ndef read_input():\n\n correct_orientation = \"\"\"3389 3461 1327 3719 1877 2801 3761 3853 1009 2113 2963 1621 \n3169 2179 3659 3253 3257 1109 3319 2213 1087 2879 2767 3793 \n2591 3391 3011 3491 1549 2099 2833 2351 1559 3229 3617 1879 \n1511 1607 1217 2251 2887 1231 3187 3581 2011 3313 1907 3709 \n1901 1487 1423 2399 1949 1381 2663 1409 1997 1277 3331 1181 \n2467 1297 2503 2789 1447 1367 1697 1427 3299 3863 2939 3881 \n1777 2609 1303 2543 3821 2137 3889 3733 1789 3623 3527 2593 \n1667 3923 2111 3413 1619 2677 1583 2741 3301 2837 2081 1801 \n2797 3931 1061 3797 1483 2311 3023 2557 3593 1747 1279 1307 \n3449 3697 2441 1051 1319 1579 1489 3271 3163 3191 1091 3541 \n2861 3559 2897 1163 1571 3323 1741 1693 1093 1123 2339 3727 \n1657 1049 2389 2381 3947 2729 3583 1973 3767 1709 1021 3547 \"\"\"\n\n positions = dict()\n co_lines = [line.strip() for line in correct_orientation.split(\"\\n\")]\n for j, co_line in enumerate(co_lines):\n for i, tile_num in enumerate(co_line.split(\" \")):\n# print(i,j,tile_num)\n positions[int(tile_num)] = (i,j)\n \n tiles = dict()\n for tilestr in open(\"inputs/day20\").read().split(\"\\n\\n\"):\n lines = tilestr.split(\"\\n\")\n tile_num = int(lines[0].split(\" \")[1][:-1])\n tiles[ positions[tile_num] ] = \"\\n\".join(lines[1:])\n\n return tiles\n\ndef orient_correctly(tiles):\n \"\"\"\n\n Returns a string that represents the tiles all lined up,\n separated by spaces on either side.\n\n Tile 3389 (top left corner): rotate 1\n#.#.#.###.\n.........#\n#.#.#....#\n#.........\n#.#.......\n..#...#..#\n#..#......\n#.........\n#.#....###\n.###.#.###\n\nTile 3461 (to the right of 3389): rotate 1, flip\n#.#.#.###.\n#.#......#\n##.......#\n#..#.##...\n..##......\n...#....##\n#....#....\n..#.....##\n#...#...##\n#.#..##.##\n\nTile 3169 (below 3389): rotate 1\n.#####.#..\n#........#\n#..#..#..#\n...#....#.\n.#........\n#.#.##..#.\n.#........\n..........\n#....#....\n#......##.\n\"\"\"\n\n # start at the top left, i know that correct orientation\n # then just move along the line until they are all in the right orientation\n\n correct = dict()\n\n \n for j in range (12):\n for i in range (12):\n if (i,j) == (0,0):\n\n # base case, initial top left needs to rotate once\n correct[(0,0)] = rotate(tiles[( 0,0)])\n else:\n # try different orientations and see if it fits with what's already there\n for rotations in range(4):\n for flips in range(2):\n tstring = tiles[(i,j)]\n for r in range(rotations):\n tstring = rotate(tstring)\n if flips == 1:\n tstring = flip(tstring)\n \n # ok , does this work?\n #print(\"trying\", (i,j), \" rotations \", rotations, \" flips\", flips)\n #print(tstring)\n if fits(correct, (i,j), tstring):\n correct[ (i,j) ] = tstring\n break\n return correct\n\n\ndef fits(tiles, location, tilestr):\n \"\"\"Checks if a given tilestr orientation fits at a given location in the tiles array\"\"\"\n x, y = location\n \n # look up\n up = tiles.get((x, y-1))\n if up:\n up_last_row = up.split(\"\\n\")[-1].strip()\n tile_first_row = tilestr.split(\"\\n\")[0].strip()\n if up_last_row != tile_first_row:\n return False\n \n # look down\n down = tiles.get((x, y+1))\n if down:\n down_first_row = down.split(\"\\n\")[0].strip()\n tile_last_row = tilestr.split(\"\\n\")[-1].strip()\n if down_first_row != tile_last_row:\n return False\n\n # look left\n left = tiles.get((x-1, y))\n if left:\n left_right_col = [r.strip()[-1] for r in left.split(\"\\n\")]\n tile_left_col = [r.strip()[0] for r in tilestr.split(\"\\n\")]\n if left_right_col != tile_left_col:\n return False\n \n\n # look right\n right = tiles.get((x+1, y))\n if right:\n right_left_col = [r.strip()[0] for r in right.split(\"\\n\")]\n tile_right_col = [r.strip()[-1] for r in tilestr.split(\"\\n\")]\n if right_left_col != tile_right_col:\n return False\n\n return True\n\ndef tiles_str(tiles):\n \"\"\"Prints out a 12x12 grid of tiles, with borders, separated by spaces\"\"\"\n s = \"\"\n for j in range ( 12 * 11 ):\n for i in range ( 12 * 11 ):\n x, x_rem = divmod( i, 11)\n y, y_rem = divmod( j, 11)\n\n if x_rem == 10 or y_rem == 10:\n c = \" \"\n else:\n t = tiles.get((x, y))\n if t:\n # t is a stringblock, with newlines\n c = t[y_rem * 11 + x_rem]\n else:\n c = \"-\"\n s += c\n s += \"\\n\"\n return s\n\ndef tiles_str_compact(tiles):\n \"\"\"Prints out a 12x12 grid of tiles, without borders\"\"\"\n s = \"\"\n for j in range ( 12 * 8 ):\n for i in range ( 12 * 8 ):\n x, x_rem = divmod( i, 8)\n y, y_rem = divmod( j, 8)\n\n t = tiles.get((x, y))\n if t:\n # tile block is 11 per line (10 + newline)\n # we add 1 to avoid the first row and first col\n c = t[(y_rem+1) * 11 + x_rem+1]\n else:\n c = \"-\"\n s += c\n s += \"\\n\"\n return s\n\ndef rotate(stringblock):\n \"\"\"Rotates a square block once to the right\"\"\"\n lines = [list(row) for row in stringblock.split(\"\\n\")]\n length = len(lines)\n return \"\\n\".join([\"\".join([ lines[length-1-j][i] for j in range(length)]) for i in range(length) ])\n\ndef flip(stringblock):\n \"\"\"Rotates a square block once to the right\"\"\"\n lines = [list(row) for row in stringblock.split(\"\\n\")]\n length = len(lines)\n return \"\\n\".join([\"\".join([ lines[i][length-1-j] for j in range(length)]) for i in range(length) ])\n\n\ndef count_dragons_regex(stringblock):\n \"\"\"Takes a block of characters and counts how many dragons appear.\"\"\"\n length = len(stringblock.split(\"\\n\"))\n line_length=str(length-20+1)\n mask = re.compile(\"#(..{\"+line_length+\"})#(....)##(....)##(....)###(.{\"+line_length+\"}.)#(..)#(..)#(..)#(..)#(..)#\", \\\n re.MULTILINE | re.DOTALL)\n replace = r\"O\\1O\\2OO\\3OO\\4OOO\\5O\\6O\\7O\\8O\\9O\\10O\"\n\n processed, num_dragons = re.subn(mask, replace, stringblock)\n with open(\"day20.output.image\",\"w\") as f:\n f.write(processed)\n \n roughness = processed.count(\"#\")\n \n return num_dragons, roughness\n\n\ndef count_dragons_block(stringblock):\n \"\"\"\n # \n# ## ## ###\n # # # # # # \n\"\"\"\n needed_tiles = [\n (0,18),\n (1,0), (1,5), (1,6), (1,11), (1,12), (1,17), (1,18), (1,19),\n (2,1), (2,4), (2,7), (2,10), (2,13), (2,16)\n ]\n \n # look at each block of 3x20\n lines = stringblock.split(\"\\n\")\n length = len(lines)\n for j in range(length - 3):\n for i in range(length - 20):\n lineblock = [line[i:i+21] for line in lines[j:j+3]]\n print(i,j)\n print(\"\\n\".join(lineblock))\n if all([lineblock[y][x] == \"#\" for y,x in needed_tiles]):\n print(\"found at \", (i,j))\n\ntiles = read_input()\ntiles = orient_correctly(tiles)\n\n# let's look at the tiles_str oriented correctly\nt = tiles_str(tiles).strip()\nt = rotate(t)\nt = rotate(t)\nt = rotate(t)\nt = flip(t)\nprint(t)\n\ns = tiles_str_compact(tiles).strip()\n# i believe it's just the one orientation that has them - ignore the others\ns = rotate(s)\ns = rotate(s)\ns = rotate(s)\ns = flip(s)\n\n\nnum_dragons, roughness = count_dragons_regex(s)\nprint(f\" yields {num_dragons} dragons {roughness} roughness\")\n\nprint(count_dragons_block(s))\n","repo_name":"lshepard/advent-of-code","sub_path":"2020/day20_pt2.py","file_name":"day20_pt2.py","file_ext":"py","file_size_in_byte":7878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13397744647","text":"import sys\nimport os\nimport re\n\nimport scrapy\nfrom scrapy.crawler import CrawlerProcess\nfrom threading import Lock\n\nfrom fileutil import create_filepath, remove_files_recursively\n\nERRORS = []\n\n# collection of original year for dupe resolution\n# NOTE: responses aren't guaranteed to arrive ordered by year, and this \n# may not actually be the original year.\noriginal_year_by_song = {}\n\nyearend_table_wiki_selector = 'table.wikitable'\n\nclass YearEndListSpider(scrapy.Spider):\n\tname = 'yearendlists'\n\tlock = Lock()\n\n\tdef get_wiki_url(self, year):\n\t\treturn 'https://en.wikipedia.org/wiki/Billboard_Year-End_Hot_100_singles_of_{0}'.format(year)\n\n\tdef start_requests(self):\n\t\t\"\"\"Scrape all pages from Wikipedia and register post-response callbacks\"\"\"\n\t\turls_by_year = {year: self.get_wiki_url(year) for year in range(1959, 2023)}\n\n\t\tfor (year, url) in urls_by_year.items():\n\t\t\tyield scrapy.Request(url=url, callback=self.parse, errback=self.errback, cb_kwargs={'year': year })\n\t\t\t\n\tdef errback(self):\n\t\t\"\"\"Handles error responses\"\"\"\n\t\trequest_url = failure.request.url\n\t\tresponse = failure.value.response\n\t\tERRORS.append('HTTP error on request URL {0}, status = {1}'.format(request_url, response.status))\n\n\tdef parse(self, response, year):\n\t\t\"\"\"Parse HTML response retrieved from URL, with year attached\"\"\" \n\t\tfor wikitable in response.css(yearend_table_wiki_selector):\n\t\t\tfor selected_row in wikitable.css('tr'):\n\t\t\t\tif (len(selected_row.css('td').getall()) >= 3):\n\n\t\t\t\t\t# rows should appear in wikitable as:\n\t\t\t\t\t# <tr>\n\t\t\t\t\t# <td> rank # in year-end </td>\n\t\t\t\t\t# <td> artist info (name and/or link) </td>\n\t\t\t\t\t# <td> song info (name and/or link) </td>\n\t\t\t\t\t# </tr>\n\n\t\t\t\t\tartist_cell_value = self.to_artist_or_song_displayed(selected_row.css('td:nth-child(2)'))\n\t\t\t\t\tsong_cell_value = self.to_artist_or_song_displayed(selected_row.css('td:nth-child(3)'))\n\t\t\t\t\tsong_artist_key = '|'.join([artist_cell_value, song_cell_value])\n\n\t\t\t\t\tyield {\n\t\t\t\t\t\t'position': selected_row.css('td:first-child::text').get(),\n\t\t\t\t\t\t'artist': artist_cell_value,\n\t\t\t\t\t\t'song': song_cell_value,\n\t\t\t\t\t\t'year': self.get_resolved_year(song_artist_key, year)\n\t\t\t\t\t}\n\n\n\tdef song_name_key(self, selected_cell):\n\t\t\"\"\"Returns artist cell value as key to be referenced\"\"\"\n\t\tif selected_cell.css('a').get() is None:\n\t\t\treturn selected_cell.css('::text').get().strip()\n\t\telse:\n\t\t\treturn selected_cell.css('a::attr(href)').get()\n\n\tdef to_artist_or_song_displayed(self, selected_cell):\n\t\t\"\"\"Returns artist/song cell value to be displayed, as either plain text or hyperlink\"\"\"\n\t\tif selected_cell.css('a').get() is None:\n\t\t\treturn selected_cell.css('::text').get().strip()\n\t\telse:\n\t\t\traw_url = selected_cell.css('a::attr(href)').get()\n\t\t\tname = selected_cell.css('a::text').get().strip()\n\t\t\treturn self.to_excel_hyperlink(raw_url, name)\n\n\tdef to_excel_hyperlink(self, relative_url, name):\n\t\turl = self.resolve_wiki_links(relative_url)\n\t\treturn '=HYPERLINK(\"{0}\", \"{1}\")'.format(url, name)\n\n\tdef resolve_wiki_links(self, text):\n\t\treturn text.replace(\"/wiki/\", \"https://en.wikipedia.org/wiki/\")\n\n\tdef get_resolved_year(self, song_artist_key, year):\n\t\t\"\"\"Check year lookup dictonary for previously instances of same song\"\"\"\n\t\ttry:\n\t\t\t# parse() runs on a different thread for each year's URL response.\n\t\t\t# all lookups on original_year_by_song need to be thread-safe.\n\t\t\tself.lock.acquire()\n\t\t\tif song_artist_key in original_year_by_song:\n\t\t\t\toriginal_year = original_year_by_song[song_artist_key]\n\t\t\t\tif year > original_year:\n\t\t\t\t\treturn '{0} ({1})'.format(year, original_year)\n\t\t\t\telif year < original_year:\n\t\t\t\t\treturn '{0} ({1}) ***'.format(year, original_year)\n\t\t\t\telse:\n\t\t\t\t\treturn year\n\t\t\telse:\n\t\t\t\toriginal_year_by_song[song_artist_key] = year\n\t\t\t\treturn year\n\n\t\tfinally:\n\t\t\tself.lock.release()\n\n\ndef main():\n\t\"\"\"Main entry point from external process or script\"\"\"\n\tprocess = CrawlerProcess({\n\t\t'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)'\n\t})\n\tprocess.crawl(PopSpider)\n\t# the script will block at start() until the crawling is finished\n\tprocess.start()\n\n\t# print errors to stdout (and flush buffer) after script has finished running\n\tfor error_line in ERRORS:\n\t\tprint('pop-spider ERROR: {0}'.format(error_line))\n\n\tsys.stdout.flush()\n\n\t# return 0 if script ran successfully, nonzero if errors.\n\t# Shell variable \"$?\" will capture this value.\n\tsys.exit(len(ERRORS))\n\n\nif __name__ == '__main__':\n\t# default main when running from shell script\n\tmain()\n","repo_name":"bobbert/pop-spider","sub_path":"popspider/spiders/yearendlists.py","file_name":"yearendlists.py","file_ext":"py","file_size_in_byte":4438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19228061581","text":"from sakura.common.errors import APIRequestError, APIObjectDeniedError\nfrom sakura.common.access import ACCESS_SCOPES, GRANT_LEVELS, USER_TYPES\nfrom sakura.hub.context import get_context\n\nGRANT_TO_USER_TYPE = {\n GRANT_LEVELS.own: USER_TYPES.owner,\n GRANT_LEVELS.write: USER_TYPES.user_rw,\n GRANT_LEVELS.read: USER_TYPES.user_ro\n}\n\ndef get_user_type(obj, user):\n if user.is_anonymous():\n return USER_TYPES.anonymous\n for login, grant in obj.grants.items():\n if user.login == login:\n level = grant.get('level', None)\n if level is not None:\n return GRANT_TO_USER_TYPE[level]\n return USER_TYPES.other\n\nclass FilteredView:\n def __init__(self, db_set):\n self.db_set = db_set\n @staticmethod\n def list_access_checker(o):\n if o.get_grant_level() < GRANT_LEVELS.list:\n user = get_context().user\n raise APIObjectDeniedError('%s is not allowed to view this item.' % user.name_it())\n def pack(self):\n return tuple(o.pack() for o in self)\n def is_accessible(self, o):\n try:\n FilteredView.list_access_checker(o)\n except APIObjectDeniedError:\n return False\n return True\n def __iter__(self):\n return filter(self.is_accessible,\n self.db_set.select().order_by(lambda o: o.id))\n def __getitem__(self, idx):\n try:\n res = self.db_set[idx]\n except:\n raise APIRequestError('No such object')\n FilteredView.list_access_checker(res)\n return res\n def __getattr__(self, attr):\n return getattr(self.db_set, attr)\n\ndef parse_gui_access_info(access_scope = None, **kwargs):\n if access_scope != None:\n kwargs.update(access_scope = ACCESS_SCOPES.value(access_scope))\n return kwargs\n\ndef parse_daemon_grants(daemon_grants):\n users = get_context().users\n grants = {}\n for login, level in daemon_grants.items():\n user = users.get(login = login)\n if user is None:\n print('WARNING: user %s is unknown in Sakura. Ignored.' % login)\n continue\n grants[login] = dict(\n level = level\n )\n return grants\n\ndef pack_gui_grant(grant):\n gui_grant = {}\n level = grant.get('level', None)\n if level is not None:\n gui_grant['level'] = GRANT_LEVELS.name(level)\n requested_level = grant.get('requested_level', None)\n if requested_level is not None:\n gui_grant['requested_level'] = GRANT_LEVELS.name(requested_level)\n return gui_grant\n\ndef pack_gui_access_info(obj):\n gui_grants = {}\n owner = None\n for login, grant in obj.grants.items():\n gui_grants[login] = pack_gui_grant(grant)\n if grant.get('level', None) == GRANT_LEVELS.own:\n owner = login\n return dict(\n owner = owner,\n grants = gui_grants,\n access_scope = ACCESS_SCOPES.name(obj.access_scope),\n grant_level = GRANT_LEVELS.name(obj.get_grant_level())\n )\n\ndef find_owner(grants):\n for login, grant in grants.items():\n if grant.get('level', None) == GRANT_LEVELS.own:\n return login\n","repo_name":"sakura-team/sakura","sub_path":"sakura/hub/access.py","file_name":"access.py","file_ext":"py","file_size_in_byte":3153,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"27774777912","text":"\"\"\"\n@Project : code\n@File : pre_data\n@Author : XiaoBanni\n@Date : 2021-11-06 15:58\n@Desc : \n\"\"\"\nimport pandas as pd\n\n\ndef pre_groceries():\n df = pd.read_csv(\"Groceries.csv\")\n transactions = df['items'].to_numpy()\n transactions = [sorted(i.lstrip('{').rstrip('}').split(',')) for i in transactions]\n items = set()\n for e in transactions:\n for ee in e:\n items.add(ee)\n items = sorted(list(items))\n # print(\"number of transactions %d, number of items %d\" % (len(transactions), len(items)))\n return [transactions, items]\n\n\ndef pre_unix_usage():\n return None\n\n\ndef get_dataset(dateset_id):\n if dateset_id == 1:\n return pre_groceries()\n else:\n return pre_unix_usage()\n\n\nif __name__ == '__main__':\n pre_groceries()\n","repo_name":"xiaobanni/Q-python-Decorators-and-memory-auto-release","sub_path":"pre_data.py","file_name":"pre_data.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72218169689","text":"\"\"\"\nHigh level functions to queue emails.\n\"\"\"\n\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef queue_email_message(email_message, fail_silently=False):\n \"\"\"\n Add new messages to the email queue.\n\n The ``email_message`` argument should be an instance of Django's core mail\n ``EmailMessage`` class.\n\n The ``fail_silently`` argument is not used and is only provided to match\n the signature of the ``EmailMessage.send`` function which it may emulate.\n \"\"\"\n from . import models, settings\n\n if settings.MAILER_TEST_MODE and settings.MAILER_TEST_EMAIL:\n email_message = _set_message_test_mode(email_message, settings.MAILER_TEST_EMAIL)\n\n if not email_message.recipients():\n return 0\n\n message = models.Message.objects.create(\n to_address=','.join(email_message.to),\n cc_address=','.join(email_message.cc),\n bcc_address=','.join(email_message.bcc),\n from_address=email_message.from_email,\n subject=email_message.subject,\n message_data=email_message.message().as_string(),\n storage=settings.MAILER_STORAGE_BACKEND)\n\n if message.enqueue('Enqueued from a Backend or django-yubin itself.'):\n return 1\n else:\n logger.exception('Error enqueuing an email', extra={'email_message': message})\n return 0\n\n\ndef _set_message_test_mode(email_message, mailer_test_email):\n \"\"\"\n Sets the headers of the message with test values when\n ``MAILER_TEST_MODE`` setting is ``True``\n \"\"\"\n original_to = ','.join(email_message.to)\n email_message.extra_headers['X-Yubin-Test-Original'] = original_to\n email_message.to = [mailer_test_email]\n email_message.cc = []\n email_message.bcc = []\n return email_message\n\n\ndef send_mail(subject, message, from_email, recipient_list,\n fail_silently=False, auth_user=None, auth_password=None):\n \"\"\"\n Add a new message to the mail queue.\n\n This is a replacement for Django's ``send_mail`` core email method.\n\n The `fail_silently``, ``auth_user`` and ``auth_password`` arguments are\n only provided to match the signature of the emulated function. These\n arguments are not used.\n \"\"\"\n from django.core.mail import EmailMessage\n from django.utils.encoding import force_str\n\n subject = force_str(subject)\n email_message = EmailMessage(subject, message, from_email, recipient_list)\n queue_email_message(email_message)\n\n\ndef mail_admins(subject, message, fail_silently=False):\n \"\"\"\n Add one or more new messages to the mail queue addressed to the site\n administrators (defined in ``settings.ADMINS``).\n\n This is a replacement for Django's ``mail_admins`` core email method.\n\n The ``fail_silently`` argument is only provided to match the signature of\n the emulated function. This argument is not used.\n \"\"\"\n from django.conf import settings as django_settings\n from django.utils.encoding import force_str\n\n subject = django_settings.EMAIL_SUBJECT_PREFIX + force_str(subject)\n from_email = django_settings.SERVER_EMAIL\n recipient_list = [recipient[1] for recipient in django_settings.ADMINS]\n send_mail(subject, message, from_email, recipient_list)\n\n\ndef mail_managers(subject, message, fail_silently=False):\n \"\"\"\n Add one or more new messages to the mail queue addressed to the site\n managers (defined in ``settings.MANAGERS``).\n\n This is a replacement for Django's ``mail_managers`` core email method.\n\n The ``fail_silently`` argument is only provided to match the signature of\n the emulated function. This argument is not used.\n \"\"\"\n from django.conf import settings as django_settings\n from django.utils.encoding import force_str\n\n subject = django_settings.EMAIL_SUBJECT_PREFIX + force_str(subject)\n from_email = django_settings.SERVER_EMAIL\n recipient_list = [recipient[1] for recipient in django_settings.MANAGERS]\n send_mail(subject, message, from_email, recipient_list)\n","repo_name":"APSL/django-yubin","sub_path":"django_yubin/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3966,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"31"} +{"seq_id":"38525629219","text":"import numpy as np\nfrom scipy import stats\nfrom scipy.optimize import least_squares\nfrom scipy.stats import norm\nfrom scipy.linalg import sqrtm\nfrom scipy.special import hyp2f1\nfrom scipy.interpolate import splrep, splev\nimport sys\n\n\n\n\nclass rBergomi_cholesky(object):\n \"\"\"\n Class for generating paths of the rBergomi model.\n \"\"\"\n def __init__(self, n = 100, N = 10000, T = 1.00, H = 0.05, eta=2.3, rho=-0.9, sigma0=0.12):\n \"\"\"\n Constructor for class.\n \"\"\"\n # Basic assignments\n self.T = T # Maturity\n self.n = n # Granularity (steps per year)\n self.dt = 1.0/self.n # Step size\n self.s = int(self.n * self.T) # Steps\n self.t = np.linspace(0, self.T, self.s+1)\n self.H = H # H\n self.gamma = 0.5 - H\n self.eta = eta\n self.rho = rho\n self.sigma0 = sigma0\n self.N = N # Paths\n self.cov_matrix=self.covW_Z()\n self.sqrtm_cov_matrix = sqrtm(self.cov_matrix)\n \n def covW_fun_aux(self, x):\n assert x <= 1\n return ((1 - 2 * self.gamma) / (1 - self.gamma)) * (x**(self.gamma)) * hyp2f1(1, self.gamma, 2 - self.gamma, x)\n\n def covW_fun(self,u, v):\n if u < v:\n return self.covW_fun(v, u)\n return v**(2*self.H) * self.covW_fun_aux(v/u)\n\n def covWZ_fun(self,u, v):\n H_tilde = self.H + .5\n D = np.sqrt(2*self.H) / H_tilde\n return self.rho * D * (u ** H_tilde - (u - min(u, v)) ** H_tilde)\n\n def covW_Z(self):\n time_range = self.t[1:]\n covWW2 = np.zeros((self.s, self.s))\n for i in range(self.s):\n for j in range(self.s):\n covWW2[i][j] = self.covW_fun(time_range[i], time_range[j])\n\n\n covWZ2 = np.zeros((self.s, self.s))\n for i in range(self.s):\n for j in range(self.s):\n covWZ2[i, j] = self.covWZ_fun(time_range[i], time_range[j])\n\n\n covZZ2 = np.zeros((self.s, self.s))\n for i in range(self.s):\n for j in range(self.s):\n covZZ2[i, j] = min(time_range[i], time_range[j])\n \n cov_matrix = np.bmat([[covWW2, covWZ2], [covWZ2.T, covZZ2]]) # matrice des covariances du vecteur (W, Z)\n return cov_matrix\n \n def simul_W_Z(self):\n G = np.random.randn(2 * self.s) # génération d'une gaussienne centrée réduite\n WZ_sample = np.dot(self.sqrtm_cov_matrix, G) # vecteur de même loi que (W, Z)\n W_sample, Z_sample = WZ_sample[:self.s], WZ_sample[self.s:]\n W_sample = np.insert(W_sample,0,0)\n Z_sample = np.insert(Z_sample,0,0)\n return W_sample, Z_sample\n \n def simul_S(self,S0):\n Ss = np.zeros((self.N,self.s+1))\n for i in range(self.N):\n if i % 1000 == 0:\n \tprint(\"\\ri {}/{}.\".format(i, self.N), end=\"\")\n \tsys.stdout.flush()\n # simulation of W and Z\n W_sample,Z_sample = self.simul_W_Z()\n # Simulation of v\n\n v_sample = self.sigma0**2 * np.exp(self.eta * W_sample - 0.5 * (self.eta**2) * self.t**(2*self.H))\n # Simulation of S\n int_sqrtv_dZ = np.cumsum(np.sqrt(v_sample[:-1]) * (Z_sample[1:] - Z_sample[:-1]))\n #print(int_sqrtv_dZ)\n int_sqrtv_dZ = np.insert(int_sqrtv_dZ,0,0)\n v_sample[0] = 0\n int_v_dt = np.cumsum(v_sample* self.dt)\n S = S0*np.exp(int_sqrtv_dZ - .5 * int_v_dt)\n Ss[i] = S\n return Ss\n \n","repo_name":"maalejach/Rough-volatility-model--and--calibration","sub_path":"rBergomi/rbergomi_cholesky.py","file_name":"rbergomi_cholesky.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"31"} +{"seq_id":"4407315418","text":"from django.shortcuts import render\nfrom django.http import JsonResponse\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom core.paillier import paillier as pai\nclass CaeserCypherView(APIView):\n def post(self,request,*args,**kwargs):\n plainText = request.data.get('plainText')\n shift = request.data.get('shift')\n plainText = str(plainText)\n shift = int(shift)\n cipher = \"\"\n pFreq = [0]*26\n cFreq = [0]*26\n mxP = mxC = 1\n for i in plainText:\n if(ord(i) >= ord('a') and ord(i) <=ord('z')):\n tmp = (shift + ord(i)-ord('a'))%26\n tmp = (tmp+26)%26\n c = chr(ord('a')+tmp)\n elif(ord(i) >= ord('A') and ord(i) <=ord('Z')):\n tmp = (shift + ord(i)-ord('A'))%26\n tmp = (tmp+26)%26\n c = chr(ord('A')+tmp)\n else:\n cipher += i\n continue\n cipher += c\n c = chr(ord('a')+tmp)\n cur = i.lower()\n pFreq[ord(cur)-ord('a')]+=1\n cFreq[ord(c)-ord('a')]+=1\n mxC = max(mxC,cFreq[ord(c)-ord('a')])\n mxP = max(mxP,pFreq[ord(cur)-ord('a')])\n d = []\n for i in range(26):\n pFreq[i]/=mxP\n cFreq[i]/=mxC\n d.append({'name' : chr(ord('a')+i),'pFreq':pFreq[i],'cFreq':cFreq[i]})\n data = {\n 'plainText' : plainText,\n 'shift' : shift,\n 'cipher' : cipher,\n 'graphdata' : d\n }\n # data = {'n':'n'}\n return Response(data)\nclass PA0View(APIView):\n def post(self,request,*args,**kwargs):\n plainText = request.data.get('plainText')\n plainText = str(plainText)\n cipher = \"\"\n pFreq = [0]*26\n cFreq = [0]*26\n mxP = mxC = 1\n for i in plainText:\n\n if(ord(i) >= ord('a') and ord(i) <=ord('z')):\n tmp = (25 - ord(i)+ord('a'))%26\n tmp = (tmp+26)%26\n c = chr(ord('a')+tmp)\n elif(ord(i) >= ord('A') and ord(i) <=ord('Z')):\n tmp = (25 - ord(i)+ord('A'))%26\n tmp = (tmp+26)%26\n c = chr(ord('A')+tmp)\n else:\n cipher += i\n continue\n cipher += c\n c = chr(ord('a')+tmp)\n cur = i.lower()\n pFreq[ord(cur)-ord('a')]+=1\n cFreq[ord(c)-ord('a')]+=1\n mxC = max(mxC,cFreq[ord(c)-ord('a')])\n mxP = max(mxP,pFreq[ord(cur)-ord('a')])\n d = []\n for i in range(26):\n pFreq[i]/=mxP\n cFreq[i]/=mxC\n d.append({'name' : chr(ord('a')+i),'pFreq':pFreq[i],'cFreq':cFreq[i]})\n data = {\n 'plainText' : plainText,\n 'cipher' : cipher,\n 'graphdata' : d\n }\n return Response(data)\n\nclass KeyGeneratorView(APIView):\n def post(self,request,*args,**kwargs):\n bits = request.data.get('bits')\n bits = int(bits)\n pr,pb = pai.generateKeypair(bits)\n data = {\n 'pbn' : str(pb.n),\n 'pbg' : str(pb.g),\n 'prl' : str(pr.l),\n 'prmu' : str(pr.mu)\n }\n return Response(data)\n\nimport cv2\nimport numpy as np\nfrom core.paillier import encryptImage\nfrom core.paillier import paillier\nfrom core.paillier import floating_point as fp\nclass EncryptImageView(APIView):\n def post(self,request,*args,**kwargs):\n image = np.asarray(bytearray(request.data['myFile'].read()), dtype=\"uint8\")\n image = cv2.imdecode(image,0)\n pbn = int(request.data['pbn'])\n pbg = int(request.data['pbg'])\n enc_img = encryptImage.encrypt(image,pbn,pbg)\n enc_json = encryptImage.toJson(enc_img)\n data = {\n \"cipher\" : enc_json\n }\n return Response(data)\nimport json\nfrom django.http import HttpResponse\nfrom core.paillier import decryptImage as dc\nfrom django.http import FileResponse\nfrom core.paillier import ImageOperations as ig\nimport base64\ndef get_data(request):\n data = json.loads(request.data['data'])\n data = data['cipher']\n enc_img = []\n n,m = len(data),len(data[0])\n for i in range(n):\n tmp = [0]*m\n for j in range(m):\n val = json.loads(data[i][j])\n tmp[j] = fp.FloatingPoint(int(val['mantissa']),int(val['exponent']))\n enc_img.append(tmp)\n pb = paillier.PublicKey(int(request.data['pbn']),int(request.data['pbg']))\n pr = paillier.PrivateKey(int(request.data['prl']),int(request.data['prmu']))\n\n return pr,pb,enc_img\nclass DecryptImageView(APIView):\n def post(self,request,*args,**kwargs):\n pr,pb,enc_img = get_data(request)\n omg = dc.decrypt(pr,pb,enc_img)\n omg = dc.convertToByte(omg)\n base64EncodedStr = base64.b64encode(omg)\n # print(base64EncodedStr)\n\n data = {\n \"b64\" : base64EncodedStr\n }\n return Response(data)\n\nimport pickle\nimport requests\nclass ImageIncreaseBrightness(APIView):\n def post(self,request,*args,**kwargs):\n pr,pb,enc_img = get_data(request)\n v = int(request.data['brightness'])\n out_img = ig.Secure_Image_Adjustment_Brightness_Control(enc_img,v,pb)\n\n # data = {\n # \t'enc_img' : enc_img,\n # \t'v' : v,\n # \t'pb' : pb\n # }\n # data2 = pickle.dumps(data,protocol=2)\n # url = \"http://pailliercryptosystem.pythonanywhere.com/brightness_control\"\n #\n # r = requests.post(url,data=data2)\n # out_img = pickle.loads(r.content)\n\n dec_img = dc.decrypt(pr,pb,out_img)\n out_json = encryptImage.toJson(out_img)\n omg = dc.convertToByte(dec_img)\n base64EncodedStr = base64.b64encode(omg)\n # data = {\n # \"cipher\" : out_json,\n # \"b64\" : base64EncodedStr\n # }\n data = {\n \"b64\" : base64EncodedStr\n }\n return Response(data)\nclass ImageNegation(APIView):\n def post(self,request,*args,**kwargs):\n pr,pb,enc_img = get_data(request)\n v = int(request.data['negation'])\n if v:\n out_img = ig.Secure_Image_Adjustment_Image_negation(enc_img,255,pb)\n else:\n out_img = enc_img\n dec_img = dc.decrypt(pr,pb,out_img)\n omg = dc.convertToByte(dec_img)\n base64EncodedStr = base64.b64encode(omg)\n\n data = {\n \"b64\" : base64EncodedStr\n }\n return Response(data)\nclass ImageBlur(APIView):\n def post(self,request,*args,**kwargs):\n pr,pb,enc_img = get_data(request)\n v = int(request.data['blur'])\n out_img = ig.Secure_Noise_Reduction_LPF(enc_img,v,v,pb)\n\n dec_img = dc.decrypt(pr,pb,out_img)\n omg = dc.convertToByte(dec_img)\n base64EncodedStr = base64.b64encode(omg)\n\n data = {\n \"b64\" : base64EncodedStr\n }\n return Response(data)\nimport copy\nimport math\nclass ImageEdgeDetect(APIView):\n def post(self,request,*args,**kwargs):\n pr,pb,enc_img = get_data(request)\n kerX = [[1,0,-1],[2,0,-2],[1,0,-1]]\n kerY = [[1,2,1],[0,0,0],[-1,-2,-1]]\n\n out_img1 = ig.sobelOperator(enc_img,kerX,pb)\n out_img2 = ig.sobelOperator(enc_img,kerY,pb)\n\n dec_img1 = dc.decrypt(pr,pb,out_img1)\n dec_img2 = dc.decrypt(pr,pb,out_img2)\n dec_img = copy.deepcopy(dec_img1)\n n,m = len(dec_img),len(dec_img[0])\n for i in range(n):\n for j in range(n):\n dec_img[i][j] = max(0,min(255,int(math.sqrt(dec_img1[i][j]**2+dec_img2[i][j]**2))))\n\n omg = dc.convertToByte(dec_img)\n base64EncodedStr = base64.b64encode(omg)\n\n data = {\n \"b64\" : base64EncodedStr\n }\n return Response(data)\n\nfrom core.des import desService as desService\nclass Des(APIView):\n def post(self,request,*args,**kwargs):\n rounds = int(request.data[\"rounds\"])\n block_size = int(request.data[\"blockSize\"])\n txt = request.data[\"txt\"]\n key = request.data[\"key\"]\n mode = request.data[\"mode\"]\n seed = int(request.data[\"seed\"])\n padding = int(request.data[\"padding\"])\n plainTextType = request.data[\"plainTextType\"]\n cipherTextType = request.data[\"cipherTextType\"]\n res, res_ = desService.runDes(key,block_size,rounds,txt,mode,padding,plainTextType,cipherTextType,seed)\n\n data = {\n \"txt\" : res\n }\n return Response(data)\nclass DesAvalanche(APIView):\n def post(self,request,*args,**kwargs):\n rounds = int(request.data[\"rounds\"])\n block_size = int(request.data[\"blockSize\"])\n txt = request.data[\"txt\"]\n key = request.data[\"key\"]\n mode = request.data[\"mode\"]\n seed = int(request.data[\"seed\"])\n padding = int(request.data[\"padding\"])\n\n x,y = desService.getGraph(key,block_size,rounds,txt,mode,padding,seed)\n d = []\n\n for i in range(rounds):\n d.append({\"name\":i+1,\n \"For Change in Key with Block-Size 16\":y[0][i],\n \"For Change in Key with Block-Size 32\":y[1][i],\n \"For Change in Key with Block-Size 64\":y[2][i],\n \"For Change in text with Block-Size 16\":y[3][i],\n \"For Change in text with Block-Size 32\":y[4][i],\n \"For Change in text with Block-Size 64\":y[5][i],})\n\n data = {\n \"graphdata\" : d\n }\n\n return Response(data)\n# def test_view(request):\n# print(request)\n# data = {\n# 'name':'Amit'\n# }\n# return JsonResponse(data)\n","repo_name":"ragnar17/Cypherit-Backend","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"14910398987","text":"from __future__ import print_function\nfrom functools import wraps\nimport os\nimport signal\nimport subprocess\nimport sys\n\nfrom .compat import basestring # NOQA\n\n\nCOLORED = \"\\033[{format}m{message}\\033[0m\"\n\n\nclass Color16: # NOQA\n red = 31\n error = red\n green = 32\n success = green\n yellow = 33\n warning = yellow\n blue = 34\n purple = 35\n cyan = 36\n white = 37\n neutral = white\n\n\nclass Color256: # NOQA\n red = 196\n error = red\n green = 40\n success = green\n yellow = 220\n warning = yellow\n blue = 27\n purple = 93\n cyan = 51\n white = 231\n neutral = white\n\n\nclass Style: # NOQA\n plain = 0\n bold = 1\n italics = 3\n underlined = 4\n ansi256 = 5\n\n\ndef ansi_print(message, fmt):\n \"\"\"\n Print a colored message\n \"\"\"\n print(COLORED.format(message=message, format=fmt))\n\n\ndef output(message, color='neutral', style=Style.plain):\n \"\"\"\n Print a colored message\n \"\"\"\n if style == Style.ansi256:\n ANSI_CODE = \"38;{0};{1}\"\n Color = Color256\n else:\n ANSI_CODE = \"{0};{1}\"\n Color = Color16\n if isinstance(color, basestring):\n color = getattr(Color, color)\n ansi_code = ANSI_CODE.format(style, color)\n ansi_print(message, ansi_code)\n\n\ndef error(message):\n \"\"\"\n Print an error message\n\n TODO: be able to deal with custom stdout and stderr\n \"\"\"\n output(message, color='error', style=Style.bold)\n\n\ndef success(message):\n output(message, color='success')\n\n\ndef warning(message):\n output(message, color='warning')\n\n\ndef abort(message=None, status=1):\n \"\"\"\n Generate error message and exit with an error status\n \"\"\"\n error(message or \"FAILED\")\n exit(status)\n\n\ndef graceful_ctrlc(fun):\n \"\"\"\n Decorator to gracefully deal with CTRL-C\n\n Instead of a stack trace it will abort with the standard bash return code\n for CTRL-C.\n\n \"\"\"\n CTRLC_RETURN_CODE = 128 + signal.SIGINT\n\n @wraps(fun)\n def wrapper(*args, **kwargs):\n try:\n fun(*args, **kwargs)\n except KeyboardInterrupt:\n abort(\"<Ctrl-C> Aborting...\", status=CTRLC_RETURN_CODE)\n return wrapper\n\n\nclass _AttributeString(object):\n \"\"\"\n Simple string subclass to allow arbitrary attribute access.\n \"\"\"\n def __init__(self, data):\n self._data = data\n\n def __str__(self):\n \"\"\"\n Makes things compatible with Python2 and Python3\n\n CAVEAT: data is assumed to be 'utf-8'-encoded.\n \"\"\"\n if isinstance(self._data, bytes):\n return self._data.decode('utf-8')\n else:\n return self._data\n\n @property\n def stdout(self):\n return str(self)\n\n\ndef run(command, capture=False, shell=None):\n \"\"\"\n Run a command on the local system.\n\n A convenient wrapper around subprocess.Popen, borrowed from Fabric's\n `local` (http://www.fabfile.org/) and simplified a bit.\n\n ``shell`` is passed directly to `subprocess.Popen\n <http://docs.python.org/library/subprocess.html#subprocess.Popen>`_'s\n ``execute`` argument (which determines the local shell to use.) As per the\n linked documentation, on Unix the default behavior is to use ``/bin/sh``,\n so this option is useful for setting that value to e.g. ``/bin/bash``.\n\n `run` is not currently capable of simultaneously printing and capturing\n output. The ``capture`` kwarg allows you to switch between printing and\n capturing as necessary, and defaults to ``False``.\n\n When ``capture=False``, the local subprocess' stdout and stderr streams are\n hooked up directly to your terminal.\n\n When ``capture=True``, you will not see any output from the subprocess in\n your terminal, but the return value will contain the captured\n stdout/stderr.\n\n In either case, this return value exhibits the ``return_code``,\n ``stderr``, ``failed``, ``succeeded`` and ``command`` attributes.\n\n \"\"\"\n dev_null = None\n if capture:\n out_stream = subprocess.PIPE\n err_stream = subprocess.PIPE\n else:\n dev_null = open(os.devnull, 'w+')\n # Non-captured, hidden streams are discarded.\n out_stream = sys.stdout\n err_stream = sys.stderr\n try:\n cmd_arg = [command]\n p = subprocess.Popen(cmd_arg, shell=True, stdout=out_stream,\n stderr=err_stream, executable=shell,\n close_fds=True)\n (stdout, stderr) = p.communicate()\n finally:\n if dev_null is not None:\n dev_null.close()\n\n # Handle error condition (deal with stdout being None, too)\n out = _AttributeString(stdout.strip() if stdout else \"\")\n err = _AttributeString(stderr.strip() if stderr else \"\")\n out.command = command\n out.failed = False\n out.return_code = p.returncode\n out.stderr = err\n if p.returncode != 0:\n out.failed = True\n msg = \"Error (return code {0}) while executing '{1}'\".format(\n p.returncode, command)\n error(message=msg) # , stdout=out, stderr=err)\n out.succeeded = not out.failed\n return out\n","repo_name":"txels/ep","sub_path":"ep/shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":5093,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"10215749197","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport pickle\n\nimport random\nimport math\nimport numpy as np\nfrom collections import deque, namedtuple\nimport cv2\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom agent import Agent\nfrom dqn_model import DQN\n\n\n\ntorch.manual_seed(595)\nnp.random.seed(595)\nrandom.seed(595)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nTransition = namedtuple('Transition',\n ('state', 'action', 'next_state', 'reward'))\n\n\nclass Agent_DQN(Agent):\n def __init__(self, env, args):\n \"\"\"\n Initialize everything you need here.\n For example: \n parameters for neural network\n initialize Q net and target Q net\n parameters for replay buffer\n parameters for q-learning; decaying epsilon-greedy\n ...\n \"\"\"\n super(Agent_DQN, self).__init__(env)\n\n self.num_episodes = 1000\n self.BATCH_SIZE = 128\n self.GAMMA = 0.999\n self.EPS_START = 1.0\n self.EPS_END = 0.05\n self.EPS_DECAY = 200\n self.TARGET_UPDATE_STEPS = 10000\n\n self.START_LEARNING = 5000\n self.replay_memory_size = 10000\n self.replay_memory = deque(maxlen=self.replay_memory_size)\n self.recent_screens = deque(maxlen=4)\n\n resized_dim = 84\n self.num_screens = 4\n\n self.env = env\n self.n_actions = self.env.action_space.n\n\n self.policy_net = DQN(resized_dim, resized_dim, self.num_screens, self.n_actions).to(device)\n self.target_net = DQN(resized_dim, resized_dim, self.num_screens, self.n_actions).to(device)\n self.target_net.load_state_dict(self.policy_net.state_dict())\n self.target_net.eval()\n\n self.optimizer = optim.RMSprop(self.policy_net.parameters())\n\n self.steps_done = 0\n self.episode_rewards = []\n\n self.num_episodes_per_report = 20\n self.num_episodes_per_save = 500\n self.save_path = \"C:/Users/gordo/Desktop/saved_DQN\"\n if not os.path.exists(self.save_path):\n os.makedirs(self.save_path)\n\n if args.test_dqn:\n # you can load your model here\n print('loading trained model')\n ###########################\n # YOUR IMPLEMENTATION HERE #\n self.policy_net.load_state_dict(torch.load(args.path))\n \n\n def init_game_setting(self):\n \"\"\"\n Testing function will call this function at the beginning of new game\n Put anything you want to initialize if necessary.\n If no parameters need to be initialized, you can leave it as blank.\n \"\"\"\n pass\n \n \n def make_action(self, state, test=True):\n \"\"\"\n Return predicted action of your agent\n Input:\n observation: np.array\n stack 4 last preprocessed frames, shape: (84, 84, 4)\n Return:\n action: int\n the predicted action from trained model\n \"\"\"\n dice_roll = random.random()\n eps_threshold = self.EPS_END + (self.EPS_START - self.EPS_END) * \\\n math.exp(-1. * self.steps_done / self.EPS_DECAY)\n self.steps_done += 1\n if dice_roll > eps_threshold:\n with torch.no_grad():\n # t.max(1) will return largest column value of each row.\n # second column on max result is index of where max element was\n # found, so we pick action with the larger expected reward.\n return self.policy_net(state).max(1)[1].view(1, 1)\n else:\n return torch.tensor([[random.randrange(self.n_actions)]], device=device, dtype=torch.long)\n\n\n def push(self, *args):\n self.replay_memory.append(Transition(*args))\n \n \n def replay_buffer(self, batch_size):\n return random.sample(self.replay_memory, batch_size)\n\n\n def train(self):\n for i_episode in range(self.num_episodes):\n self.env.reset()\n # get initial screen\n screen_init, _, _, _ = self.env.step(0)\n screen_init = self.preprocess_screen(screen_init)\n # prepare the initial state with 4 screen frames\n screen_blank = screen_init - screen_init\n self.recent_screens.extend([screen_blank] * 3)\n self.recent_screens.append(screen_init)\n # dim of state is (BCHW)\n state = self.get_state_from(self.recent_screens)\n\n done = False\n episode_reward = 0\n\n while not done:\n # Select and perform an action\n action = self.make_action(state)\n screen_next, reward, done, _ = self.env.step(action.item())\n\n reward = torch.tensor([reward], device=device)\n episode_reward += reward\n\n screen_next = self.preprocess_screen(screen_next)\n self.recent_screens.append(screen_next)\n\n if done:\n next_state = None\n self.episode_rewards.append(episode_reward)\n else:\n next_state = self.get_state_from(self.recent_screens)\n\n self.push(state, action, next_state, reward)\n state = next_state\n\n # Perform one step of the optimization\n if len(self.replay_memory) >= self.START_LEARNING:\n self.optimize_model()\n\n # Update the target network, copying all weights and biases in DQN\n if self.steps_done % self.TARGET_UPDATE_STEPS == 0:\n self.target_net.load_state_dict(self.policy_net.state_dict())\n\n if i_episode % self.num_episodes_per_report == 0:\n val = (sum(self.episode_rewards)/len(self.episode_rewards)).item()\n print(f'Average reward {val:.2f} of last {self.num_episodes_per_report} episodes. Last episode: {i_episode}')\n\n with open(self.save_path + '/avg_rewards.txt', 'a') as reward_file:\n reward_file.write(f'{i_episode}, {self.steps_done}, {val}\\n')\n\n if i_episode % self.num_episodes_per_save == 0:\n print(f'Saving target to disk at episode {i_episode}')\n self.save_model(self.target_net, f'ep_{i_episode:07}_')\n\n\n def get_state_from(self, screens):\n # screen dimension is (CHW) with C=1\n # concatenate all screens in the 0 dimension (channel)\n state = torch.cat(list(screens), 0)\n # add batch dimension to make the output state (BCHW)\n state = state.unsqueeze(0)\n return state.to(device)\n\n\n\n def preprocess_screen(self, screen):\n screen = np.ascontiguousarray(screen, dtype=np.float32)\n screen = cv2.cvtColor(cv2.resize(screen, (84, 110)), cv2.COLOR_BGR2GRAY)\n # do not include top 26 pixels of screen which contains only score\n screen = screen[26:110,:]\n # convert to binary image 0 or 255 with threshold 1\n ret, screen = cv2.threshold(screen,1,255,cv2.THRESH_BINARY)\n screen = torch.from_numpy(screen)\n # output screen dimension: (CHW)\n screen = screen.unsqueeze(0)\n return screen.to(device)\n\n\n def optimize_model(self):\n if len(self.replay_memory) < self.BATCH_SIZE:\n return\n transitions = self.replay_buffer(self.BATCH_SIZE)\n # batch dimension: BCHW\n batch = Transition(*zip(*transitions))\n\n # Compute a mask of non-final states and concatenate the batch elements\n # (a final state would've been the one after which simulation ended)\n non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,\n batch.next_state)), device=device, dtype=torch.bool)\n non_final_next_states = torch.cat([s for s in batch.next_state if s is not None])\n # state_batch dim: BCHW\n state_batch = torch.cat(batch.state)\n action_batch = torch.cat(batch.action)\n reward_batch = torch.cat(batch.reward)\n\n # Compute Q(s_t, a) - the model computes Q(s_t), then we select the\n # columns of actions taken. These are the actions which would've been taken\n # for each batch state according to policy_net\n state_action_values = self.policy_net(state_batch).gather(1, action_batch)\n\n # Compute V(s_{t+1}) for all next states.\n # Expected values of actions for non_final_next_states are computed based\n # on the \"older\" target_net; selecting their best reward with max(1)[0].\n # This is merged based on the mask, such that we'll have either the expected\n # state value or 0 in case the state was final.\n next_state_values = torch.zeros(self.BATCH_SIZE, device=device)\n next_state_values[non_final_mask] = self.target_net(non_final_next_states).max(1)[0].detach()\n # Compute the expected Q values\n expected_state_action_values = (next_state_values * self.GAMMA) + reward_batch\n\n # Compute Huber loss\n loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))\n\n # Optimize the model\n self.optimizer.zero_grad()\n loss.backward()\n for param in self.policy_net.parameters():\n param.grad.data.clamp_(-1, 1)\n self.optimizer.step()\n\n\n def save_model(self, net, prefix):\n torch.save(net.state_dict(), self.save_path + \"/\" + prefix + \"model.pth\")\n # with open(self.save_path + \"/\" + prefix + 'avg_rewards.data', 'wb') as file_handle:\n # # store the data as binary data stream\n # pickle.dump(self.episode_rewards[], file_handle)\n\n\n def load_model(self, dqn):\n return dqn.load_state_dict(torch.load(self.model_path))","repo_name":"gordonzhang/CS525-Project3","sub_path":"agent_dqn.py","file_name":"agent_dqn.py","file_ext":"py","file_size_in_byte":9830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"704726785","text":"\"\"\"Property has feature controller layer\"\"\"\nfrom fastapi import HTTPException\nfrom starlette.responses import JSONResponse\n\nfrom app.properties.exceptions import CustomPropertyException\nfrom app.properties.models import PropertyHasFeature\nfrom app.properties.services import PropertyHasFeatureService\n\n\nclass PropertyHasFeatureController:\n \"\"\"Class containing Property has feature controller methods\"\"\"\n\n @staticmethod\n def create(property_id: str, feature_id: str, additional_feature_value: int = None) -> PropertyHasFeature:\n \"\"\"\n Creates bond between existing property and chosen features for that property\n \"\"\"\n try:\n return PropertyHasFeatureService.create(\n property_id=property_id, feature_id=feature_id, additional_feature_value=additional_feature_value\n )\n except CustomPropertyException as exc:\n raise HTTPException(status_code=exc.status_code, detail=exc.message)\n except Exception as exc:\n raise HTTPException(status_code=500, detail=exc.__str__())\n\n @staticmethod\n def get_all_features_for_property_by_id(property_id: str) -> list:\n \"\"\"\n Gets all features for property by id\n \"\"\"\n try:\n return PropertyHasFeatureService.get_all_features_for_property_by_id(property_id=property_id)\n except CustomPropertyException as exc:\n raise HTTPException(status_code=exc.status_code, detail=exc.message)\n except Exception as exc:\n raise HTTPException(status_code=500, detail=exc.__str__())\n\n @staticmethod\n def delete_feature_from_property_by_ids(property_id: str, feature_id: str) -> JSONResponse:\n \"\"\"\n Delete a feature from a property by property id and feature id\n \"\"\"\n try:\n PropertyHasFeatureService.delete_feature_from_property_by_ids(\n property_id=property_id, feature_id=feature_id\n )\n return JSONResponse(status_code=200, content=f\"Feature deleted for property id {property_id}\")\n except CustomPropertyException as exc:\n raise HTTPException(status_code=exc.status_code, detail=exc.message)\n except Exception as exc:\n raise HTTPException(status_code=500, detail=exc.__str__())\n","repo_name":"vllajkos/real-estate-agency-api","sub_path":"app/properties/controller/property_has_feature_controller.py","file_name":"property_has_feature_controller.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5924220040","text":"from flask import render_template, request\nimport os\nimport numpy as np\nfrom utils import classify\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\nimport cv2\nfrom datetime import date\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\ndef index():\n if request.method == 'POST':\n\n # FileStorage object wrapper\n image = request.files['image']\n if image:\n try:\n pil_img = Image.open(image)\n # print(pil_img)\n cv_img = np.array(pil_img)\n\n print(cv_img.dtype, \"///////////\")\n result = classify(cv_img)\n db_add(result)\n print(\"done\")\n if result == 0:\n decision = \"cancer detected\"\n else:\n decision = \"cancer not detected <3\"\n data = BytesIO()\n pil_img.save(data, \"JPEG\")\n encoded_img_data = base64.b64encode(data.getvalue())\n img_data = encoded_img_data.decode('utf-8')\n return render_template(\"index.html\", result=decision, image=img_data, count=result_db_count())\n except Exception as e:\n print(e, \"\\nunsuccessful\", cv_img.shape)\n\n return render_template(\"index.html\", count=result_db_count())\n\n\ndef db_add(result):\n from app import Data, db\n today = date.today()\n today = today.strftime(\"%b-%d-%Y\")\n data = Data(date=today, result=result)\n db.session.add(data)\n db.session.commit()\n\n\ndef db_read():\n from app import Data, db\n all = Data.query.all()\n print(all)\n\n\ndef result_db_count():\n from app import Data, db\n try:\n all = Data.query.all()\n return len(all)\n except:\n return \"many\"\n","repo_name":"tirtharajsinha/breast_cancer_detection_app","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"10831576104","text":"from constance import config\nfrom django.contrib import messages\nfrom django.contrib.formtools.wizard.views import SessionWizardView\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Q\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404\nfrom django.utils.translation import ugettext as _\nfrom django.views.generic import ListView, DetailView, RedirectView, CreateView\n\nfrom ..exceptions import WLANSimulationGameError\nfrom .forms import MessageCreateForm, MessageCreateFormStaff\nfrom .models import Card, Message, Interception\n\n\nclass MessageListView(ListView):\n \"\"\"\n View to see all own messages.\n \"\"\"\n model = Message\n\n def get_queryset(self, *args, **kwargs):\n \"\"\"\n Players can only see their own messages or intercepted messages.\n \"\"\"\n queryset = super().get_queryset(*args, **kwargs)\n if not self.request.user.is_staff:\n queryset = queryset.filter(\n Q(sender=self.request.user) | Q(interception__interceptor=self.request.user))\n return queryset\n\n\nclass MessageDetailView(DetailView):\n \"\"\"\n View to see a single message.\n \"\"\"\n model = Message\n\n def dispatch(self, request, *args, **kwargs):\n \"\"\"\n Method to check that only staff or senders can see their messages\n (or intercepted messages).\n \"\"\"\n dispatch = super().dispatch(request, *args, **kwargs)\n if (not request.user.is_staff and\n not request.user == self.object.sender and\n not self.object.interception_set.filter(interceptor=request.user).exists()):\n messages.error(request, _('You are not allowed to see this message.'))\n raise PermissionDenied\n else:\n # Everything is ok, the message can be shown.\n pass\n return dispatch\n\n\nclass MessagePrintView(RedirectView):\n \"\"\"\n View to mark a message as printed.\n \"\"\"\n permanent = False\n\n def get_redirect_url(self, *args, **kwargs):\n \"\"\"\n Method to check if the message is already printed. If no, it is marked\n as printed. After that it redirects to the list of not printed\n messages.\n \"\"\"\n message = get_object_or_404(Message, pk=kwargs['pk'])\n if not message.printed:\n message.printed = True\n message.save()\n else:\n messages.error(self.request, _('The message was already marked as printed.'))\n return reverse('message_list_not_printed')\n\n\nclass MessageCreateView(CreateView):\n \"\"\"\n View to write a new message.\n \"\"\"\n model = Message\n\n def dispatch(self, request, *args, **kwargs):\n \"\"\"\n Checks the config variable 'players_can_submit_messages'.\n \"\"\"\n if not request.user.is_staff and not config.players_can_submit_messages:\n messages.error(request, _('You are not allowed to send a message at the moment.'))\n raise PermissionDenied\n else:\n # Everything is ok, the message can be created.\n pass\n return super().dispatch(request, *args, **kwargs)\n\n def get_form_class(self, *args, **kwargs):\n \"\"\"\n Returns one of the two create forms, either for normal users or for\n staff.\n \"\"\"\n if self.request.user.is_staff:\n form = MessageCreateFormStaff\n else:\n form = MessageCreateForm\n return form\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"\n Hacks in the request for we can use it in the form later.\n \"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs['request'] = self.request\n return form_kwargs\n\n def form_valid(self, form):\n \"\"\"\n Adds missing sender if a player (non staff) sends a message, and saves\n the object.\n \"\"\"\n if self.request.user.is_staff:\n return_value = super().form_valid(form)\n else:\n self.object = form.save(commit=False)\n self.object.sender = self.request.user\n self.object.save()\n return_value = HttpResponseRedirect(self.get_success_url())\n return return_value\n\n\nclass InterceptionWizardView(SessionWizardView):\n \"\"\"\n View to intercept messages.\n \"\"\"\n template_name = 'game/interception_wizard_form.html'\n\n def dispatch(self, request, *args, **kwargs):\n \"\"\"\n Checks whether the user as already intercepted enough messages.\n \"\"\"\n if request.user.is_staff:\n messages.error(request, _('The interception view is only for players.'))\n raise PermissionDenied\n elif Interception.objects.filter(interceptor=request.user).count() >= config.number_of_interceptions:\n messages.error(request, _('You can only intercept a total number of %d messages.') % config.number_of_interceptions)\n raise PermissionDenied\n else:\n # Everything is ok, intercept now.\n pass\n return super().dispatch(request, *args, **kwargs)\n\n def get_form_kwargs(self, step):\n \"\"\"\n Hacks in the request for we can use it in the form later.\n \"\"\"\n form_kwargs = super().get_form_kwargs(step)\n form_kwargs['request'] = self.request\n if step == '1':\n form_kwargs['victim_sender'] = self.get_cleaned_data_for_step('0')['victim_sender']\n return form_kwargs\n\n def done(self, form_list, **kwargs):\n \"\"\"\n Processes the valid form data.\n \"\"\"\n data = {}\n for form in form_list:\n data.update(form.cleaned_data)\n message_list = Message.objects.filter(sender=data['victim_sender'], recipient=data['victim_recipient']).reverse()\n if not message_list:\n messages.error(self.request, _('There is no message to intercept between these two players. Try again later.'))\n else:\n try:\n Interception.objects.create(interceptor=self.request.user, message=message_list[0])\n except WLANSimulationGameError as error:\n messages.error(self.request, error.args[0])\n else:\n messages.success(self.request, _('The message was succesfully intercepted. You can read it now.'))\n return HttpResponseRedirect(reverse('message_list'))\n\n\nclass CardListView(ListView):\n \"\"\"\n View to see all cards.\n \"\"\"\n model = Card\n\n def get_queryset(self, *args, **kwargs):\n \"\"\"\n Sort cards by owner and target but then shuffle them.\n \"\"\"\n return super().get_queryset(*args, **kwargs).order_by('owner', 'target', '?')\n\n\nclass CardDetailView(DetailView):\n \"\"\"\n View to see details of a card. Players can only see their own cards in\n detail.\n \"\"\"\n model = Card\n\n def dispatch(self, request, *args, **kwargs):\n \"\"\"\n Method to check that only staff or owners can see their cards.\n \"\"\"\n dispatch = super().dispatch(request, *args, **kwargs)\n if not request.user.is_staff and not request.user == self.object.owner:\n messages.error(request, _('You are not owner of this card, so you are not allowed to see it.'))\n raise PermissionDenied\n return dispatch\n\n\nclass CardPlayView(RedirectView):\n \"\"\"\n View to play a card.\n\n A card is played, when it exists, is not already used and the owner can\n still play at least one card. In urls.py it is checked that the\n request.user is staff.\n \"\"\"\n permanent = False\n\n def get_redirect_url(self, *args, **kwargs):\n \"\"\"\n Method to check if the card is to be played. If yes, it is played.\n After that it redirects to the list of cards.\n \"\"\"\n card = get_object_or_404(Card, pk=kwargs['pk'])\n try:\n card.play()\n except WLANSimulationGameError as error_message:\n messages.error(self.request, error_message)\n else:\n messages.success(self.request, _('Card \"%(name)s\" was successfully played.') % {'name': card.name})\n return reverse('card_list')\n","repo_name":"normanjaeckel/WLANSimulationGame","sub_path":"wlan_simulation_game/game/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11024438353","text":"import json\nimport glob\nimport os\nimport requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\nimport sys\nimport tarfile\nimport time\nfrom zipfile import ZipFile\n\nfrom shared import LoginBlueprint\nfrom shared import AosApi\n\ntoken_bp_id_address = LoginBlueprint().blueprint()\ntoken = token_bp_id_address[0]\nbp_id = token_bp_id_address[1]\naddress = token_bp_id_address[2]\n\n\nclass PostIbaProbes(object):\n\n def __init__(self):\n pass\n\n def post_package(self):\n \"\"\"\n Unzip AosDev SDK, upload whl_file to AOS one by one.\n \"\"\"\n with ZipFile('./files/' + sys.argv[3]) as myzip: myzip.extractall()\n whl_file_list = [os.path.basename(whl_file) for whl_file in \\\n glob.glob('./dist/aosstdcollectors_custom_*.whl')]\n print ('##### Upload IBA Custom Collectors #####')\n for whl_file in whl_file_list:\n resp = requests.post(url = 'https://' + address + '/api/packages?packagename=' + whl_file,\n data = open('./dist/' + whl_file, 'rb').read(),\n headers={'AUTHTOKEN' : token, 'Content-Type': \\\n 'application/octet-stream'}, verify=False)\n if str(resp.status_code) == '201':\n print ('----- Upload ' + whl_file)\n else:\n print ('----- Error: HTTP request failed ' + whl_file)\n sys.exit()\n print ('##### Done #####')\n time.sleep(1)\n\n def install_package(self):\n \"\"\"\n Install IBA collector package under the following condition\n 'job state' == 'success'\n 'operation mode' == 'full_control'\n \"\"\"\n def prosecutor(self):\n payload = {'packages': package.split(), 'operation_mode': 'full_control'}\n resp = requests.patch(\n 'https://' + address + '/api/system-agents/' + agent['id'],\n headers={'AUTHTOKEN': token, 'Content-Type': 'application/json'},\n data=json.dumps(payload), verify=False)\n if str(resp.status_code) == '202':\n print('----- Installing on ' + agent['device_facts']['hostname'])\n else:\n print('----- Error: Request is not accepted on ' + agent['device_facts']['hostname'])\n sys.exit()\n print('##### Install Package to Agents #####')\n agent_id_list = []\n package_list = [package['name'] for package in AosApi().packages_get(token, bp_id, address)['items']]\n for agent in AosApi().system_agents_get(token, bp_id, address)['items']:\n # For Onbox Agent\n if agent['running_config']['agent_type'] == 'onbox':\n if agent['status']['operation_mode'] == 'full_control' and agent['status']['state'] == 'success':\n for package in package_list:\n if agent['status']['platform'] in package:\n prosecutor(self)\n # For Offbox Agent\n elif agent['running_config']['agent_type'] == 'offbox':\n if agent['status']['operation_mode'] == 'full_control' and agent['status']['connection_state'] == 'connected':\n for package in package_list:\n if agent['platform_status']['platform'] in package:\n prosecutor(self)\n agent_id_list.append(agent['id'])\n time.sleep(5)\n \"\"\"\n Check package installed status\n \"\"\"\n while len(agent_id_list) != 0:\n system_agents = AosApi().system_agents_id_get(token, bp_id, address, agent_id_list[0])\n for package in system_agents['running_config']['packages']:\n if 'aosstdcollectors-custom' in package:\n agent_id_list.remove(agent_id_list[0])\n print ('----- Package installed on ' + system_agents['device_facts']['hostname'])\n else:\n print ('----- Not installed yet ' + system_agents['device_facts']['hostname'])\n time.sleep(1)\n print ('##### Done #####')\n time.sleep(1)\n\n def install_service_registry(self):\n \"\"\"\n Install service registry from json_schemas.postXXX unziped as 'dist' directory.\n :payload: post data for '/api/telemetry-service-registry'\n :iba_storage_schema_path: Confirm the key i.e.'generic' from original json file.\n \"\"\"\n payload = {\"service_name\": \"\",\n \"version\": \"\",\n \"storage_schema_path\": \"\",\n \"description\": \"\",\n \"application_schema\": \"\"}\n iba_storage_schema_path = {\n 'generic': ['table_usage', 'device_info', 'sfp', 'multiagent_detector',\n 'vtep_counters', 'power_supply', 'traceroute', 'acl_stats',\n 'interface_iba', 'mlag_domain', 'vlan', 'pim_rp',\n 'bgp_iba', 'vxlan_address_table', 'anycast_rp',\n 'multicast_info', 'route_count', 'ping', 'bgp_vrf',\n 'multicast_groups', 'vrf', 'evpn_type5', 'vxlan_info',\n 'interface_buffer', 'lldp_details', 'evpn_type3',\n 'process_restart_time', 'interface_details',\n 'resource_usage', 'pim_neighbor_count', 'stp'],\n 'interface_counters': ['interface_counters'], 'arp': ['arp'],\n 'iba_integer_data': ['dot1x_hosts', 'evpn_vxlan_type5',\n 'vxlan_floodlist', 'resource_util',\n 'evpn_vxlan_type3', 'bgp_route', 'disk_util',\n 'sdwan_policy_rule'], 'hostname': ['hostname'],\n 'iba_string_data': ['dot1x', 'ospf_state', 'site_device',\n 'site_device_group', 'site', 'evpn_vxlan_type4',\n 'evpn_vxlan_type1'], 'mlag': ['mlag'],\n 'bgp': ['bgp'], 'route': ['route'], 'xcvr': ['xcvr'],\n 'graph': ['virtual_infra'], 'interface': ['interface'],\n 'lldp': ['lldp'], 'mac': ['mac'], 'lag': ['lag']\n }\n print ('##### Install Service Registry from json_schemas.postXXX #####')\n json_schemas = glob.glob('./dist/json_schemas.post*.tar.gz')[0]\n with tarfile.open(json_schemas, 'r') as tar: tar.extractall('./dist')\n for json_file in [ os.path.basename(json_file) for json_file in glob.glob('./dist/*.json') ]:\n with open('./dist/' + json_file) as f: json_content = f.read()\n payload['service_name'] = json_file.replace('.json', '')\n payload['application_schema'] = json.loads(json_content)\n for schema_path in iba_storage_schema_path.items():\n if json_file.replace('.json', '') in schema_path[1]:\n payload['storage_schema_path'] = 'aos.sdk.telemetry.schemas.' + schema_path[0]\n resp = requests.post('https://' + address + '/api/telemetry-service-registry',\n headers={'AUTHTOKEN': token,\n 'Content-Type': 'application/json'},\n data=json.dumps(payload), verify=False)\n if resp.status_code == 422:\n print ('----- Error: No storage schema path ' + json_file.replace('.json', '')\n + '. Update iba_storage_schema_path')\n elif resp.status_code == 409:\n print ('----- ' + json_file.replace('.json', '') + ' is already installed.')\n else:\n print ('----- Install Service Registry ' + json_file)\n print ('##### Done #####')\n time.sleep(1)\n\n def create_probe(self):\n \"\"\"\n Install all probes in 'probes' directory.\n :return:\n \"\"\"\n print ('##### Create Probes #####')\n for probe_file in glob.glob('./probes/*.json'):\n with open(probe_file) as f: json_content = f.read()\n resp = requests.post('https://' + address + '/api/blueprints/' + bp_id + '/probes',\n headers={'AUTHTOKEN':token, 'Content-Type':'application/json'},\n data=json_content, verify=False)\n if resp.status_code == 201:\n print ('----- Probe ' + probe_file.replace('./probes/', '') + ' Created.')\n else:\n print ('----- Error: Probe ' + probe_file.replace('./probes/', '')\n + ' Install Failed.')\n print ('##### Done #####')\n\n\nif __name__ == '__main__':\n PostIbaProbes().post_package()\n PostIbaProbes().install_package()\n PostIbaProbes().install_service_registry()\n PostIbaProbes().create_probe()\n\n","repo_name":"higutomo38/aos-python","sub_path":"library/post_iba_probe.py","file_name":"post_iba_probe.py","file_ext":"py","file_size_in_byte":8922,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"3708671390","text":"import tkinter as tk\r\n\r\n# Create the main window\r\nroot = tk.Tk()\r\nroot.title(\"Grid GUI\")\r\n\r\n# Define the grid size\r\nnum_rows = 2\r\nnum_cols = 2\r\n\r\n# Define the colors for the boxes\r\ncolors = [\"blue\", \"green\", \"red\", \"yellow\"]\r\n\r\n# Create the grid of boxes with different colors\r\nfor row in range(num_rows):\r\n for col in range(num_cols):\r\n # Create a canvas widget as a box\r\n if colors[row * num_cols + col] in [\"blue\", \"red\"]:\r\n box = tk.Canvas(root, width=100, height=100, bg=colors[row * num_cols + col])\r\n else:\r\n box = tk.Canvas(root, width=50, height=100, bg=colors[row * num_cols + col])\r\n # Place the box in the grid with no padding or spacing\r\n box.grid(row=row, column=col, padx=0, pady=0)\r\n\r\n# Start the main event loop\r\nroot.mainloop()","repo_name":"Trini161/GitHubTest","sub_path":"interactivity.py","file_name":"interactivity.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1453448026","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jul 9 21:19:39 2023\r\n\r\n@author: BernardoElli\r\n\"\"\"\r\n\r\n\r\nimport yfinance as yf\r\n\r\n\r\ndf = yf.download('EURUSD=X', \r\n start=\"2023-07-03\", \r\n end=\"2023-07-08\",\r\n interval= '60m')","repo_name":"elliagustin/TheBiggerPicture","sub_path":"Ep 1/Ep1 - Getting the data.py","file_name":"Ep1 - Getting the data.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32291648457","text":"import socket\nimport sys\nimport time\nimport os\n\ns=socket.socket()\nhost = socket.gethostname()\nport = 8080\nhost = socket.gethostname()\nprint(\"waiting for connections...\")\ns.bind(('0.0.0.0', 9990))\ns.listen(5)\nconn, addr = s.accept()\nprint(addr, \"is connected to server\")\n\nwhile True:\n command = input(str(\"Enter Command :\"))\n conn.send(command.encode())\n print(\"Command has been sent successfully.\")\n data = conn.recv(1024)\n","repo_name":"aryakvn/python-revshell","sub_path":"master.py","file_name":"master.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10441300148","text":"#!/usr/bin/env python3\nimport sys\nimport genericIO\nimport SepVector\nimport Hypercube\nimport Elastic_iso_float_3D\nimport numpy as np\nimport time\n\nif __name__ == '__main__':\n # Initialize operator\n modelFloat, dataFloat, elasticParamFloat, parObject, sourcesSignalsVector, sourcesVectorCenterGrid, sourcesVectorXGrid, sourcesVectorYGrid, sourcesVectorZGrid, sourcesVectorXZGrid, sourcesVectorXYGrid, sourcesVectorYZGrid, recVectorCenterGrid, recVectorXGrid, recVectorYGrid, recVectorZGrid, recVectorXZGrid, recVectorXYGrid, recVectorYZGrid = Elastic_iso_float_3D.BornOpInitFloat_3D(\n sys.argv)\n\n # Construct nonlinear operator object\n BornElasticOp = Elastic_iso_float_3D.BornElasticShotsGpu_3D(\n modelFloat, dataFloat, elasticParamFloat, parObject.param,\n sourcesSignalsVector, sourcesVectorCenterGrid, sourcesVectorXGrid,\n sourcesVectorYGrid, sourcesVectorZGrid, sourcesVectorXZGrid,\n sourcesVectorXYGrid, sourcesVectorYZGrid, recVectorCenterGrid,\n recVectorXGrid, recVectorYGrid, recVectorZGrid, recVectorXZGrid,\n recVectorXYGrid, recVectorYZGrid)\n\n #Testing dot-product test of the operator\n if (parObject.getInt(\"dpTest\", 0) == 1):\n BornElasticOp.dotTest(True)\n quit()\n\n # Forward\n if (parObject.getInt(\"adj\", 0) == 0):\n\n print(\n \"----------------------------------------------------------------------\"\n )\n print(\n \"------------------ Running Python Born Elastic forward ---------------\"\n )\n print(\n \"----------------------------------------------------------------------\\n\"\n )\n\n # Check that model was provided\n modelFile = parObject.getString(\"model\", \"noModelFile\")\n if (modelFile == \"noModelFile\"):\n raise IOError(\"**** ERROR: User did not provide model file ****\\n\")\n dataFile = parObject.getString(\"data\", \"noDataFile\")\n if (dataFile == \"noDataFile\"):\n raise IOError(\"**** ERROR: User did not provide data file name ****\\n\")\n\n #Reading model\n modelFloat = genericIO.defaultIO.getVector(modelFile)\n\n # Apply forward\n BornElasticOp.forward(False, modelFloat, dataFloat)\n\n # Write data\n dataFloat.writeVec(dataFile)\n\n # Adjoint\n else:\n print(\n \"----------------------------------------------------------------------\"\n )\n print(\n \"------------------ Running Python Born Elastic adjoint ---------------\"\n )\n print(\n \"----------------------------------------------------------------------\\n\"\n )\n\n # Check that model was provided\n modelFile = parObject.getString(\"model\", \"noModelFile\")\n if (modelFile == \"noModelFile\"):\n raise IOError(\"**** ERROR: User did not provide model file ****\\n\")\n dataFile = parObject.getString(\"data\", \"noDataFile\")\n if (dataFile == \"noDataFile\"):\n raise IOError(\"**** ERROR: User did not provide data file name ****\\n\")\n\n #Reading model\n dataFloat = genericIO.defaultIO.getVector(dataFile, ndims=4)\n\n # Apply adjoint\n BornElasticOp.adjoint(False, modelFloat, dataFloat)\n\n # Write data\n modelFloat.writeVec(modelFile)\n\n print(\"-------------------------------------------------------------------\")\n print(\"--------------------------- All done ------------------------------\")\n print(\"-------------------------------------------------------------------\\n\")\n","repo_name":"biondiettore/elasticIsoLib_3D","sub_path":"elastic_iso_lib_3d/python/python_float/BornPythonFloatMain_3D.py","file_name":"BornPythonFloatMain_3D.py","file_ext":"py","file_size_in_byte":3303,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"39096549357","text":"import collections\nfrom typing import Optional\n\nfrom Python.auxiliary.TreeNode import TreeNode\n\n\nclass Solution:\n def minDiffInBST(self, root: Optional[TreeNode]) -> int:\n #\n # Solution v1: Sort\n #\n # Runtime: 36 ms @ (beats) 50.70%\n # Memory Usage: 13.8 MB @ (beats) 74.83%\n #\n arr = []\n\n q = collections.deque()\n q.append(root)\n\n while q:\n node = q.popleft()\n\n if not node:\n continue\n\n arr.append(node.val)\n\n q.append(node.left)\n q.append(node.right)\n\n arr.sort()\n\n ans = float('inf')\n\n for i in range(1, len(arr)):\n ans = min(ans, arr[i] - arr[i - 1])\n\n return ans","repo_name":"yogggithub/algorithm","sub_path":"LeetCode/Python/solutions/p0783.py","file_name":"p0783.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34982931415","text":"\"\"\"empty message\n\nRevision ID: e0d4bca91544\nRevises: b76a20b8f88d\nCreate Date: 2022-04-28 19:12:19.908616\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'e0d4bca91544'\ndown_revision = 'b76a20b8f88d'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n with op.batch_alter_table('medicines', schema=None) as batch_op:\n batch_op.add_column(sa.Column('user_id', sa.Integer(), nullable=False))\n batch_op.create_foreign_key(batch_op.f('fk_medicines_user_id_users'), 'users', ['user_id'], ['id'])\n\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n with op.batch_alter_table('medicines', schema=None) as batch_op:\n batch_op.drop_constraint(batch_op.f('fk_medicines_user_id_users'), type_='foreignkey')\n batch_op.drop_column('user_id')\n\n # ### end Alembic commands ###\n","repo_name":"AndySok/google-mentorship","sub_path":"migrations/versions/e0d4bca91544_.py","file_name":"e0d4bca91544_.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"72176628248","text":"import json\n\n\ndef cushman():\n with open('data/items.json', 'rw') as f:\n questions = json.load(f)\n for q in questions:\n idx = [i for i, c in enumerate(q['title']) if c.isupper()][0]\n title = q['title'][idx:]\n dilemma = q['text'].split('.')[-1]\n text = q['text'].replace(dilemma, '').replace(\n '...', '').replace('\\n', '')\n idea = dilemma.split('the right thing to do')[0].split(\"Is\")[1]\n dilemma = dilemma.replace(idea, '<b>'+idea+'</b>')\n\n new_keys = {\n \"type\": \"long\",\n # \"answers\": [\"Yes\", \"No\"],\n \"additional\": {\n \"dilemma\": \"Why?\",\n \"entered\": [],\n \"type\": \"long\"\n },\n \"oldTitle\": q['title'],\n \"entered\": [],\n \"correct\": \"\",\n \"title\": title,\n \"text\": text,\n \"dilemma\": dilemma\n }\n q.update(new_keys)\n with open('data/new_items.json', 'w') as fp:\n json.dump(questions, fp)\n\ndef main():\n files = 'impersonal_moral', 'personal_moral', 'non_moral'\n d = []\n count = 0\n for i, fi in enumerate(files):\n with open(f'data/{fi}.json', 'r') as f:\n questions = json.load(f)\n if i == 0:\n with open(f'data/control_items.json', 'r') as fp:\n control = json.load(fp)\n \n questions = control + questions;\n \n for q in questions:\n count+=1\n dilemma = q['text'].split('.')[-1]\n text = q['text'].replace(dilemma, '').replace('\\n', '')\n # idea = dilemma.split('the right thing to do')[0].split(\"Is\")[1]\n # dilemma = dilemma.replace(idea, '<b>'+idea+'</b>')\n\n new_keys = {\n \"type\": \"long\",\n # \"answers\": [\"Yes\", \"No\"],\n \"additional\": {\n \"dilemma\": \"Why?\",\n \"entered\": [],\n \"type\": \"long\"\n },\n \"oldTitle\": q['title'],\n \"entered\": [],\n \"correct\": \"\",\n \"title\": q['title'],\n \"text\": text,\n 'oldId': q['id'],\n 'id': count,\n 'cond': fi,\n \"dilemma\": dilemma\n }\n q.update(new_keys)\n\n d += questions\n\n with open('data/new_items_green.json', 'w') as fp:\n json.dump(d, fp)\n\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n","repo_name":"Daetheys/CF-Quiz","sub_path":"format_items.py","file_name":"format_items.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72135853529","text":"\ndef binary_search(alist,sear):\n start=0\n end=len(alist)-1\n while start<=end:\n midpt=(start+end)/2\n if alist[midpt]==sear:\n return midpt\n elif alist[midpt]<sear:\n start=midpt+1\n else:\n end=midpt-1\n \n return -1\n \n \n \n \ndef main():\n alist=list()\n num=input(\"Enter number of element\")\n for i in range(int(num)):\n n=input(\"enter number:\")\n alist.append(int(n))\n sear=int(input(\"Enter number to seach:\"))\n index=int(binary_search(alist,sear))\n if index==-1:\n print(\"Number {} not found in array\".format(sear))\n else:\n print(\"Number {} found at index {}\".format(sear,index))\n \n \nif __name__==\"__main__\":main()","repo_name":"AtriSaxena/Public-Repository","sub_path":"Binary_Search.py","file_name":"Binary_Search.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"35698038495","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('',views.home,name=\"home\"),\n path('result/',views.result,name=\"result\"),\n path('fruit/',views.fruit,name=\"fruit\"),\n path('fruitresult/',views.fruitresult,name=\"fruitresult\"),\n path('vege/',views.vegetable,name=\"vegetable\"),\n path('vegetableresult/',views.vegetableresult,name=\"vegetableresult\"),\n path('crop/',views.crop,name=\"crop\"),\n path('cropresult/',views.cropresult,name=\"cropresult\"),\n path('risk/',views.risk,name=\"risk\"),\n path('riskresult/',views.riskresult,name=\"riskresult\"),\n path('monitor/',views.detect,name=\"monitorfeed\"),\n path('pest/',views.pest,name=\"pest\"),\n path('pestresult/',views.pestresult,name=\"pestresult\"),\n]\n","repo_name":"AamirAfzal/Analytica-Express-and-Django-Backend","sub_path":"PServer/DeployModel-Project/DeployModel/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71682006487","text":"'''\ninput: txt file with two columns, first col: wavelength. second col:light intensity, seperated with tab.\noutput: % of each measured intensity (2nd col), based on the maximum value of intensity of the 2nd col\n'''\n\n'''Calculate the maximum value of the column 2 (light intensity) in a file, return column and maximim value'''\ndef seperate_col2_and_max():\n\t#open the file with the two columns \n\tdata = open(\"data\", \"r\")\n\n\t#initializing the arrays x,y\n\ty=[]\n\t\n\tmax_found = 0#initial max is zero\n\tfor line in data: #each time, var line represents each whole line of the file\n\t\ta,b= line.split(\"\t\") #split the line, where the ' ' gap is. (the gap is a tab, not a space.)\n\t\tbi=float(b)\n\n\t\ty.append(bi) #append the var b to y list\n\t\t\n\t\t#for every line, if its larger than the previous, use it as the new max\n\t\tif (bi >max_found):\n\t\t\tmax_found = bi\n\n\treturn y , max_found #y is column 2 (intensity)\n\n'''Calculate the percentage'''\ndef calc_per(maxim,lista):\n\tper_list=[]\n\tfor block in lista:\n\t\tper = (block/maxim) #returns the percentage between 0 and 1, if you dont multiply *100.(if values from 0 to 100 are needed, multiply the block*100)\t\n\t\tper_list.append(per)\n\t\t\n\treturn per_list\n\t\n\n'''==============================================MAIN========================================'''\n\n#array, maxim_found = seperate_col2_and_max()\n#percentage_array= calc_per(maxim_found, array)\n","repo_name":"Basilisvirus/Band-gap-energy-of-photocatalyst","sub_path":"col_per.py","file_name":"col_per.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"4549732259","text":"\"\"\"\nCreated on 2018-06-04\n# =============================================================================\n# CSV processing algorythm in python\n# =============================================================================\n@author: %(Drakael)s\n\"\"\"\nimport os\nimport imageio\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimage_border = 32\nsquare_size = image_border**2\n\n\ndef p(mess, obj):\n \"\"\"Useful function for tracing\"\"\"\n if hasattr(obj, 'shape'):\n print(mess, type(obj), obj.shape, \"\\n\", obj)\n else:\n print(mess, type(obj), \"\\n\", obj)\n\n\ndef feed_from_dir(src_dir, reverse=False):\n output_x = []\n output_y = []\n last_char = None\n idx_char = 0\n for subdir, dirs, files in os.walk(src_dir):\n split = subdir.split('\\\\')\n cur_up_dir = None\n if len(split) > 2:\n cur_up_dir = split[-2]\n cur_dir = split[-1]\n print('cur_up_dir', cur_up_dir, 'cur_dir', cur_dir)\n for image in files:\n if image[-4:] == '.png':\n if last_char is None:\n last_char = cur_dir\n elif last_char != cur_dir:\n idx_char += 1\n last_char = cur_dir\n print('idx_char', idx_char)\n image_path = subdir + '/' + image\n # print('image_path', image_path)\n image_array = imageio.imread(image_path)\n if reverse is True:\n image_array = 255 - image_array\n row_x = image_array.reshape(1, square_size).tolist()\n row_y = idx_char\n output_x.append(row_x)\n output_y.append(row_y)\n return output_x, output_y\n\n\ntraindir = './Dataset/train/'\ntestdir = './Dataset/test/'\nmodeldir = './Best Mean/'\n\noutput_images_train, output_labels_train = feed_from_dir(traindir, True)\noutput_images_test, output_labels_test = feed_from_dir(testdir, True)\noutput_models, output_models_indices = feed_from_dir(modeldir)\n\nnp_array = np.array(output_images_train)\nnp_array = np_array.reshape(len(output_images_train), 32 * 32)\ndf = pd.DataFrame(np_array)\ndf.to_csv('Devanagari_images_train.csv', header=False, index=False)\n\nnp_array = np.array(output_models)\nnp_array = np_array.reshape(len(output_models), square_size)\ndf = pd.DataFrame(np_array)\ndf.to_csv('Devanagari_models.csv', header=False, index=False)\n\n\ndef plot_gallery_2(title, images, image_shape, n_col=10, n_row=10):\n p('plot_gallery_2 ' + title + ' images.shape', images.shape)\n plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n plt.suptitle(title, size=16)\n for i, comp in enumerate(images[:(n_col * n_row)]):\n plt.subplot(n_row, n_col, i + 1)\n vmax = max(comp.max(), -comp.min())\n plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray,\n interpolation='nearest',\n vmin=-vmax, vmax=vmax)\n plt.title(i)\n plt.xticks(())\n plt.yticks(())\n plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.25, 0.50)\n\n\nplot_gallery_2(\"Models\", np_array, (image_border, image_border), 8, 8)\n\nnp_array = np.array(output_labels_train)\nnp_array = np_array.reshape(len(output_labels_train), 1)\ndf = pd.DataFrame(np_array)\ndf.to_csv('Devanagari_labels_train.csv', header=False, index=False)\n\nnp_array = np.array(output_images_test)\nnp_array = np_array.reshape(len(output_images_test), square_size)\ndf = pd.DataFrame(np_array)\ndf.to_csv('Devanagari_images_test.csv', header=False, index=False)\n\nnp_array = np.array(output_labels_test)\nnp_array = np_array.reshape(len(output_labels_test), 1)\ndf = pd.DataFrame(np_array)\ndf.to_csv('Devanagari_labels_test.csv', header=False, index=False)\n","repo_name":"Drakael/Devanagari-CAE","sub_path":"Devanagari_CAE_03_CSV_Processing.py","file_name":"Devanagari_CAE_03_CSV_Processing.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36902566696","text":"# import necessary package\nimport numpy as np\n\n\n# define a function that takes the predicted data from a given growth rate, and the observed data to evaluate quality\n# of the fit\ndef squared_error(prediction: np.array, data: np.array) -> float:\n # the difference between our prediction and date\n residual = np.array([prediction[i] - data[i] for i in range(len(data))])\n # use the residual to calculate the mean squared error\n mse = np.sum(residual**2)/len(data)\n return mse\n\n\nif __name__ == '__main__':\n # Part 1:\n # Set the initial population N0 and the growth rate\n N0, rate = 100, 0.4\n # generate the t array of time points from 0 to 10 seconds, in 0.2 second increments\n t = np.arange(0, 10.2, 0.2)\n # calculate the population of the colony at the time points in t and store the result in population_size\n population_size = np.array([N0 * np.exp(i * rate) for i in t])\n # print the population_size\n print('the population size is:\\n', population_size, '\\n')\n # calculate the noise\n noise = np.random.normal(0, 250, len(population_size))\n # create population_size_noise by adding the noise to the population at each time point\n population_size_noise = population_size + noise\n # print the population_size_noise\n print('the population noise size is:\\n', population_size_noise, '\\n')\n\n # Part 2:\n # define range_rate by a range of parameters from 0.1, up to and including 1, of step size 0.1\n range_rate = np.arange(0.1, 1.1, 0.1)\n # initialize a list to store our results\n mse = []\n for rate in range_rate:\n # get different prediction based on different rate\n prediction = np.array([N0 * np.exp(i * rate) for i in t])\n # calculate the error between prediction and actual data by calling squared_error function\n error = squared_error(prediction, population_size_noise)\n mse.append(error)\n\n # find the index of the minimum value in mse\n idx_min_mse = mse.index(min(mse))\n # use the index of the min value in mse to obtain the corresponding rate\n # remember the arrays are the same size, and generated in order when iterating over range_rates\n best_fit = range_rate[idx_min_mse]\n # print the best_fit range\n print('We predict the rate of growth of this bacterial population to be', best_fit)\n","repo_name":"BrianQJN/2022FALL_BME1479_Assignments","sub_path":"assignment3/prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":2324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28988524743","text":"import numpy as np\nfrom kivy.uix.widget import Widget\nfrom kivy.vector import Vector\n\nfrom kivy.properties import NumericProperty, ReferenceListProperty\n\n\nclass Car(Widget):\n angle = NumericProperty(0)\n rotation = NumericProperty(0)\n\n velocity_x = NumericProperty(0)\n velocity_y = NumericProperty(0)\n velocity = ReferenceListProperty(velocity_x, velocity_y)\n\n signal1 = NumericProperty(0)\n signal2 = NumericProperty(0)\n signal3 = NumericProperty(0)\n\n sand = None\n\n sand_width = 0\n sand_height = 0\n\n points = None\n\n red = None\n blue = None\n yellow = None\n\n def move(self, rotation):\n\n self.pos = Vector(*self.velocity) + self.pos\n self.rotation = rotation\n self.angle = self.angle + self.rotation\n\n self.red = Vector(25, 0).rotate(self.angle) + self.pos\n self.blue = Vector(25, 0).rotate((self.angle + 30) % 360) + self.pos\n self.yellow = Vector(25, 0).rotate((self.angle - 30) % 360) + self.pos\n if self.sensor_onborder(self.red):\n self.signal1 = 1.\n else:\n self.signal1 = self.sensor_sanddensity(self.red)\n\n if self.sensor_onborder(self.blue):\n self.signal2 = 1.\n else:\n self.signal2 = self.sensor_sanddensity(self.blue)\n\n if self.sensor_onborder(self.yellow):\n self.signal3 = 1.\n else:\n self.signal3 = self.sensor_sanddensity(self.yellow)\n\n def sensor_sanddensity(self, vector):\n int_sensorx = int(vector[0])\n int_sensory = int(vector[1])\n\n return int(np.sum(self.sand[int_sensorx:int_sensorx + 10, int_sensory:int_sensory + 10])) / 100.\n\n def get_vec_corners(self, vector):\n return {\n 'bottom_left': vector,\n 'top_left': Vector(vector[0], vector[1] + 10),\n 'top_right': Vector(vector[0] + 10, vector[1] + 10),\n 'bottom_right': Vector(vector[0] + 10, vector[1]),\n }\n\n def sensor_onborder(self, vector):\n corners = self.get_vec_corners(vector)\n margin = 2\n # left border\n for corner in corners.values():\n x_diff = margin - corner.x\n if x_diff > 0:\n return True\n\n # right border\n for corner in corners.values():\n x_diff = corner.x - (self.sand_width - margin)\n if x_diff > 0:\n return True\n\n # top border\n for corner in corners.values():\n y_diff = corner.y - (self.sand_height - margin)\n if y_diff > 0:\n return True\n\n # bottom border\n for corner in corners.values():\n y_diff = margin - corner.y\n if y_diff > 0:\n return True\n\n return False\n\n def get_carfront(self):\n return Vector(self.pos[0] + self.width, self.center[1])\n\n def get_corners(self):\n return {\n 'bottom_left': Vector(*self.pos),\n 'top_left': Vector(self.pos[0], self.pos[1] + self.height),\n 'top_right': Vector(self.pos[0] + self.width, self.pos[1] + self.height),\n 'bottom_right': Vector(self.pos[0] + self.width, self.pos[1]),\n }\n","repo_name":"Maskime/dqn-self-driving-car","sub_path":"widgets/CarWidget.py","file_name":"CarWidget.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"29321864163","text":"\"\"\"\nisings-type models\n\"\"\"\nfrom __future__ import division, print_function\n\nfrom jax import numpy as jnp\nimport numpy as np\nfrom jax.config import config; config.update(\"jax_enable_x64\", True)\nfrom jax import lax, ops, vmap, jit, grad, random\nfrom melange_lite.melange_lite import SMCSamplerFactory\nfrom melange_lite.magnets.utils import *\n\nclass IsingsModellSMCSampler(SMCSamplerFactory):\n \"\"\"\n inherit the general functionality of an smc sampler\n\n parameter_dict = {\n 'M_parameters' : Array, # singularly the triple (tuple) of potential parameters, namely (J, h, beta) if equal to the IW energy fn\n }\n\n X = {\n x : Array, # positions of shape (N, L, L)\n seed : Array, # jax.random.PRNGKey of shape (N),\n }\n \"\"\"\n\n\n def __init__(self,\n T,\n N,\n IW_energy_fn,\n IW_parameters,\n L,\n full_scan=True,\n MCMC=True,\n M_energy_fn = canon_nn_isings_local_potential,\n **kwargs):\n \"\"\"\n arguments\n L : Array (float)\n lattice side length (assumed to be a square)\n full_scan : boolean, default True\n whether to deterministically scan through all lattice position\n MCMC : boolean, default True\n whether one is conducting MCMC w.r.t. the IW_parameter-parameterized IW_energy_fn\n M_energy_fn : Callable, default canon_nn_isings_local_potential\n propagation energy fn for kernel MCMC\n \"\"\"\n self.L = L\n self.full_scan = full_scan\n self.MCMC = MCMC\n self.M_energy_fn = canon_nn_isings_local_potential if MCMC else M_energy_fn\n super().__init__(T, N, IW_energy_fn, IW_parameters, **kwargs)\n\n def _handle_M0_kernel(self, **kwargs):\n _M0 = default_M0(N = self.N,\n L = self.L,\n start_beta = self._beta0)\n\n #set the attrs\n self.M0 = _M0\n\n def _handle_logG0(self, **kwargs):\n\n def logG0(Xs, parameter_dict):\n return jnp.zeros(self.N)\n\n self.logG0 = logG0\n\n def _handle_M(self):\n MCMC_prop = get_MCMC_proposal(self.full_scan, self.M_energy_fn)\n\n def _M(X, parameter_dict, t):\n \"\"\"\n proposal for a singular lattice x\n \"\"\"\n # grab the parameters\n x = X['x']\n run_seed, seed = random.split(X['seed'])\n\n potential_parameters = lax.cond(self.MCMC, lambda x: self.IW_parameters[x+1], lambda x: parameter_dict['kernel_parameters'][x], t)\n\n # do a move\n out_x, log_ratio = MCMC_prop(x, run_seed, potential_parameters)\n\n return {'x': out_x, 'seed': seed, 'log_kernel_ratio': log_ratio}\n\n self._M = _M #set that attr\n self.M = vmap(\n _M,\n in_axes=(0, None, None)\n )\n def works(self):\n \"\"\"\n modify the work fn in place so that\n \"\"\"\n super_work_fn = super().works()\n def work_fn(parameter_dict):\n return super_work_fn(parameter_dict)\n\n return work_fn\n\nclass IsingsModellSMCMCMCSampler(IsingsModellSMCSampler):\n \"\"\"\n inherit the general functionality of an smc sampler\n Important : the initial distribution corresponds to the infinite temperature limit (i.e. beta=0.)\n you will have to modify this functionality to make BAR estimator work the other way around...\n\n parameter_dict = {\n 'seed' : Array # random.PRNGKey to start.\n }\n\n X = {\n x : Array, # positions of shape (N, L, L)\n seed : Array, # jax.random.PRNGKey of shape (N),\n }\n \"\"\"\n\n\n def __init__(self,\n T,\n N,\n direction,\n L,\n full_scan=True,\n MCMC = True,\n M_energy_fn = canon_nn_isings_local_potential,\n **kwargs):\n energy_fn = canon_nn_isings_potential\n assert set(direction).issubset(set([0,1]))\n self._direction = jnp.int64(direction)\n self._beta0 = self._direction[0]\n IW_parameters = jnp.hstack([jnp.ones(T)[..., jnp.newaxis], #J\n jnp.zeros(T)[..., jnp.newaxis], #h\n jnp.linspace(self._direction[0],self._direction[1],T)[..., jnp.newaxis] #beta\n ])\n super().__init__(T, N, energy_fn, IW_parameters, L, full_scan, MCMC, M_energy_fn, **kwargs)\n\n def _handle_logG(self):\n\n def _logG(Xp, X, parameter_dict, t):\n xp, x = Xp['x'], X['x']\n\n #compute importance_weight (there is 1 more index in the IW parameters than in everything else...)\n params_t, params_tm1 = self.IW_parameters[t+1], self.IW_parameters[t]\n\n IWs = -self._IW_energy_fn(xp, params_t) + self._IW_energy_fn(xp, params_tm1)\n\n #finalize and return\n lws = IWs\n return lws\n\n self._logG = _logG\n self.logG = vmap(_logG, in_axes=(0, 0, None, None))\n\nclass TrainableIsingsModellSMCSampler(IsingsModellSMCSampler):\n \"\"\"\n specialty class that allows for trainability of the target potential\n\n NOTE:\n you must still abide by an Importance Potential that starts at beta=0 or 1. if beta=1, this is biased, of course\n\n a good use case for IW parameters should be something like...\n\n IW_parameters = jnp.hstack([jnp.ones(T)[..., jnp.newaxis], #J\n jnp.zeros(T)[..., jnp.newaxis], #h\n jnp.linspace(0,1,T)[..., jnp.newaxis] #beta\n ])\n\n \"\"\"\n def __init__(self,\n T,\n N,\n IW_parameters,\n L = 32,\n full_scan=True,\n MCMC=False,\n M_energy_fn = canon_nn_isings_local_potential):\n \"\"\"\n Expose the IW parameters so that these are togglable for experimentation\n \"\"\"\n energy_fn = canon_nn_isings_potential #define the canonical isings potential function\n self._start = IW_parameters[0] # define the starting values of the IW parameters\n assert jnp.allclose(self._start[:2], jnp.array([1., 0.])) #check that the J and h values aren't unexpected\n self._beta0 = self._start[2] #define beta 0\n super().__init__(T, N, energy_fn, IW_parameters, L, full_scan, MCMC, M_energy_fn)\n\n def _handle_logG(self):\n\n def _logG(Xp, X, parameter_dict, t):\n xp, x = Xp['x'], X['x']\n log_l_by_k = X['log_kernel_ratio']\n\n #compute importance_weight (there is 1 more index in the IW parameters than in everything else...)\n params_t, params_tm1 = self.IW_parameters[t+1], self.IW_parameters[t]\n\n IWs = -self._IW_energy_fn(x, params_t) + self._IW_energy_fn(xp, params_tm1)\n\n #finalize and return\n lws = IWs + log_l_by_k\n return lws\n\n self._logG = _logG\n self.logG = vmap(_logG, in_axes=(0, 0, None, None))\n","repo_name":"dominicrufa/melange_lite","sub_path":"melange_lite/magnets/ising_modell.py","file_name":"ising_modell.py","file_ext":"py","file_size_in_byte":7174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73981891927","text":"def baseConversion(num: int, base: int) -> str:\n standard = \"0123456789ABCDEF\"\n if num < base:\n return standard[num]\n else:\n return baseConversion(num // base, base) + standard[num % base]\n\n\nif __name__ == \"__main__\":\n temp = baseConversion(10,2)\n print(temp[::])\n","repo_name":"yanxuanshaozhu/datastructuredemos","sub_path":"DataStructurePython/17BaseConversion.py","file_name":"17BaseConversion.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74213908887","text":"import io\nimport os\nimport os.path\n\nimport numpy as np\nimport torch\nimport hydra\nfrom hydra.core.hydra_config import HydraConfig\nfrom hydra.types import RunMode\nfrom torchvision.utils import make_grid, save_image\nfrom omegaconf import open_dict\n\nimport losses\nimport sampling\nimport sde_lib\nimport utils\nfrom models import utils as mutils\nfrom models import adm, ncsnpp, vdm # needed for creating the model\nfrom models.ema import ExponentialMovingAverage\n\n\ntorch.backends.cudnn.benchmark = True\n\n\ndef visualize(cfg, load_cfg, noise_removal_cfg, log_dir):\n # set up\n logger = utils.get_logger(os.path.join(log_dir, \"logs\"))\n work_dir = cfg.load_dir\n\n device = torch.device(f\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n sde = sde_lib.RVESDE(sigma_min=load_cfg.sde.sigma_min, sigma_max=load_cfg.sde.sigma_max, N=load_cfg.sde.num_scales)\n sampling_eps = 1e-5\n\n sampling_shape = (cfg.eval.batch_size, load_cfg.data.num_channels, load_cfg.data.image_size, load_cfg.data.image_size)\n sampling_fn = sampling.get_sampling_fn(load_cfg, sde, sampling_shape, sampling_eps, device)\n \n # load in models\n score_model = mutils.create_model(load_cfg).to(device)\n ema = ExponentialMovingAverage(score_model.parameters(), decay=load_cfg.model.ema_rate)\n optimizer = losses.get_optimizer(load_cfg, score_model.parameters())\n scaler = torch.cuda.amp.GradScaler() if load_cfg.model.name == \"adm\" else None\n state = dict(optimizer=optimizer, model=score_model, ema=ema, step=0, scaler=scaler)\n\n if noise_removal_cfg is not None:\n noise_removal_model = mutils.create_model(noise_removal_cfg).to(device)\n utils.load_denoising_model(os.path.join(cfg.denoiser_path, \"checkpoints/checkpoint.pth\"), noise_removal_model)\n else:\n noise_removal_model = None\n\n ckpt = cfg.eval.ckpt\n if ckpt == -1:\n ckpts = os.listdir(os.path.join(work_dir, \"checkpoints\"))\n ckpts = [int(x.split(\".\")[0].split(\"_\")[1]) for x in ckpts]\n ckpt = max(ckpts)\n\n checkpoint_dir = os.path.join(work_dir, \"checkpoints\", f\"checkpoint_{ckpt}.pth\")\n state = utils.restore_checkpoint(checkpoint_dir, state, device, ddp=False)\n ema.copy_to(score_model.parameters())\n\n # generate images\n this_sample_dir = os.path.join(log_dir, \"images\")\n utils.makedirs(this_sample_dir)\n\n if load_cfg.model.name == \"adm\":\n w = cfg.w * torch.ones(sampling_shape[0], device=device)\n labels = cfg.label * torch.ones(sampling_shape[0], device=device).long()\n else:\n w = None\n labels = None\n\n logger.info(f\"Generating samples for checkpoint {ckpt}\")\n for r in range(cfg.eval.rounds):\n logger.info(f\"Round {r}\")\n samples = sampling_fn(score_model, noise_removal_model=noise_removal_model, weight=w, class_labels=labels)[0]\n samples_np = np.round(samples.clip(min=0, max=1).permute(0, 2, 3, 1).cpu().numpy() * 255).astype(np.uint8)\n \n nrow = int(np.sqrt(samples.shape[0]))\n image_grid = make_grid(samples, nrow, padding=0)\n save_image(image_grid, os.path.join(this_sample_dir, f\"samples_{r}.png\"))\n\n with open(os.path.join(this_sample_dir, f\"samples_{r}.npz\"), \"wb\") as fout:\n io_buffer = io.BytesIO()\n np.savez_compressed(io_buffer, samples=samples_np)\n fout.write(io_buffer.getvalue())\n\n logger.info(\"Finished generating samples.\")\n\n\nfrom run_vis import *\n@hydra.main(version_base=None, config_path=\"configs\", config_name=\"vis\")\ndef main(cfg):\n hydra_cfg = HydraConfig.get()\n load_cfg = utils.load_hydra_config_from_run(cfg.load_dir)\n\n log_dir = hydra_cfg.run.dir if hydra_cfg.mode == RunMode.RUN else os.path.join(hydra_cfg.sweep.dir, hydra_cfg.sweep.subdir)\n utils.makedirs(log_dir)\n\n # overwrite the sampling instructions\n with open_dict(load_cfg):\n load_cfg.sampling = cfg.sampling\n\n if cfg.sampling.denoiser == \"network\":\n noise_removal_cfg = utils.load_hydra_config_from_run(cfg.denoiser_path)\n else:\n noise_removal_cfg = None\n\n logger = utils.get_logger(os.path.join(log_dir, \"logs\"))\n logger.info(cfg)\n logger.info(f\"loaded in config from {cfg.load_dir}\")\n logger.info(load_cfg)\n logger.info(f\"Denoising with config?\")\n logger.info(noise_removal_cfg)\n\n try:\n visualize(cfg, load_cfg, noise_removal_cfg, log_dir)\n except Exception as e:\n logger.critical(e, exc_info=True)\n\nif __name__ == \"__main__\":\n main()","repo_name":"louaaron/Reflected-Diffusion","sub_path":"run_vis.py","file_name":"run_vis.py","file_ext":"py","file_size_in_byte":4467,"program_lang":"python","lang":"en","doc_type":"code","stars":136,"dataset":"github-code","pt":"31"} +{"seq_id":"26089587147","text":"class player():\n def __init__(self, name, start_room):\n #Dont Change\n self.name = name\n self.items = {}\n self.class_name = \"default\"\n self.ID = 0\n #Stats\n self.health = 10#current health stat\n self.strength = 5#added to dice roll for attacks\n self.speed = 5#added to dice roll for evade enemy chance\n self.max_health = 10#health before damage\n self.base_health = 10#original character health stat\n self.base_strength = 5\n self.base_speed = 5\n #Inventory\n self.inventory_slots_remaining = 10\n self.inventory_slots = 10\n #Xp Stats\n self.xp = 0\n self.level = 1\n self.next_lvl_mult = 1.2\n self.next_level_xp_base = 100\n self.xp_mult = 1\n self.max_level = 50\n #weapon\n self.equipped_weapon = None\n self.room = start_room\n \n def __repr__(self):\n if self.equipped_weapon != None:\n print_format = \"\\n====================\\nName:\"+self.name+\"\\nClass:\"+self.class_name+\"\\nLevel:\"+str(self.level)+\"\\nHealth:\"+str(self.health)+\"/\"+str(self.max_health)+\"\\nStrength:\"+str(self.strength)+\"\\nSpeed:\"+str(self.speed)+\"\\nInventory slots:\"+str(self.inventory_slots_remaining)+\"/\"+str(self.inventory_slots)+\"\\nItems:\\n\"+self.inventory()+\"\\nEquipped Weapon:\"+str(self.equipped_weapon.name)+\"(\"+str(self.equipped_weapon.damage)+\" Damage)\"+\"\\n====================\"\n else:\n print_format = \"\\n====================\\nName:\"+self.name+\"\\nClass:\"+self.class_name+\"\\nLevel:\"+str(self.level)+\"\\nHealth:\"+str(self.health)+\"/\"+str(self.max_health)+\"\\nStrength:\"+str(self.strength)+\"\\nSpeed:\"+str(self.speed)+\"\\nInventory slots:\"+str(self.inventory_slots_remaining)+\"/\"+str(self.inventory_slots)+\"\\nItems:\\n\"+self.inventory()+\"\\nEquipped Weapon:None\"+\"\\n====================\"\n return(print_format)\n\n def level_check(self):\n if self.level == 1:\n xp_to_level = self.next_level_xp_base\n else:\n xp_to_level = self.next_level_xp_base * (self.next_lvl_mult ** self.level)\n while self.xp >= xp_to_level:\n if self.level == self.max_level:\n return\n if self.level == 1:\n xp_to_level = self.next_level_xp_base\n else:\n xp_to_level = self.next_level_xp_base * (self.next_lvl_mult ** self.level)\n self.xp -= xp_to_level\n self.level += 1\n print(\"\\nYou leveled up!\\nYou are now level \"+str(self.level))\n if self.level == self.max_level:\n print(\"\\nYou are now max level!\")\n\n def give_xp(self, amount):\n print(\"You recieve \"+str(amount)+\" xp\")\n self.xp += amount\n if self.level == self.max_level:\n return\n self.level_check()\n\n\n def set_stat(self, statID, value):#1:health 2:strength 3:speed 4:xp 5:level #used later to allow for save loads\n value = int(value)\n statID = int(statID)\n if statID == 1:\n self.health = value\n print(\"Health set to \"+str(value))\n elif statID == 2:\n self.strength = value\n print(\"Strength set to \"+str(value))\n elif statID == 3:\n self.speed = value\n print(\"Speed set to \"+str(value))\n elif statID == 4:\n self.xp = value\n print(\"Xp set to \"+str(value))\n elif statID == 5:\n self.level = value\n print(\"level set to \"+str(value))\n else:\n print(\"Invalid stat ID\")\n\n\n def give_stat(self, statID, value):#1:health 2:strength 3:speed 4:xp 5:level #used later to allow for save loads\n value = int(value)\n statID = int(statID)\n if statID == 1:\n self.heal(value)\n elif statID == 2:\n self.strength += value\n print(\"You gain \"+str(value)+\" Strength!\")\n elif statID == 3:\n self.speed += value\n print(\"You gain \"+str(value)+\" Speed!\")\n elif statID == 4:\n self.xp += value\n print(\"You gain \"+str(value)+\" Xp!\")\n elif statID == 5:\n self.level += value\n if amount > 1:\n print(\"You gain \"+str(value)+\" Levels!\")\n else:\n print(\"You gain \"+str(value)+\" Level!\")\n else:\n print(\"Invalid stat ID\")\n\n\n def heal(self, amount):\n if int(self.health) == int(self.max_health):\n print(\"\\nyou do not need healing you are already max health!\")\n else:\n if (int(self.health) + int(amount)) >= int(self.max_health):\n self.health = int(self.max_health)\n print(\"\\nYou have been healed by \"+str(amount)+\" and are back to your starting quota of \"+str(self.max_health)+\" Health\")\n else:\n self.health += int(amount)\n print(\"\\nYou have been healed by \"+str(amount)+\"HP and are now on \"+str(self.health)+\"/\"+str(self.max_health)+\" Health\")\n\n\n\n def damage(self, amount):\n if (self.health - amount) <= 0:\n self.health = 0\n print(\"\\nYou recieve a finishing blow of \"+str(amount)+\" Points\")\n self.kill()\n else:\n self.health -= amount\n print(\"\\nYou have been damaged by \"+str(amount)+\" Points\\nYou are now on \"+str(self.health)+\"/\"+str(self.max_health)+\" Health\")\n\n\n def kill(self):\n #not finished, will drop non soulbound items in the room you are in and give different messages for different classes as well as reducing xp\n print(\"You are dead\")\n\n def attack(self, enemy, game_dict):\n if self.equipped_weapon == None:\n damage = 1\n else:\n damage = int(self.equipped_weapon.damage)\n #enemy = game_dict[enemy]\n enemy.health = int(enemy.health)\n \n if self.speed > enemy.speed:\n if enemy.health > 0:\n print(\"\\nYour speed is greater than the enemies so you attack first\")\n enemy.health -= damage\n print(\"You attack the \"+enemy.name+\" for \"+str(damage)+\" damage\")\n if enemy.health <= 0:\n print(\"\\nThe \"+enemy.name+\" dies\")\n self.give_xp(enemy.drop_xp())\n drops = enemy.drop_item()\n for item in drops:\n self.give(game_dict[item])\n if drops == []:\n pass\n if enemy.health > 0:\n self.health -= enemy.damage\n print(\"The \"+enemy.name+\" attacks you for \"+str(enemy.damage)+\" damage\")\n else:\n if enemy.health > 0:\n self.health -= enemy.damage\n print(\"The \"+enemy.name+\" attacks you for \"+str(enemy.damage)+\" damage\")\n enemy.health -= damage\n print(\"You attack the \"+enemy.name+\" for \"+str(damage)+\" damage\")\n if enemy.health <= 0:\n print(\"\\nThe \"+enemy.name+\" dies\")\n self.give_xp(enemy.drop_xp())\n drops = enemy.drop_item()\n print(drops)\n for item in drops:\n self.give(game_dict[item])\n\n if drops == []:\n pass\n if enemy.health < 0:\n enemy.health = 0\n if enemy.health != 0:\n print(\"The \"+enemy.name+\" is now on \"+str(enemy.health)+\"/\"+str(enemy.max_health)+\" health\")\n print(\"you are now on \"+str(self.health)+\"/\"+str(self.max_health)+\" health\")\n if enemy.health <= 0:\n if str(enemy.boss) == \"True\" or str(enemy.boss) == \"true\":\n print(\"The \"+enemy.name+\" was sent back to its own dimension.\")\n del self.room.enemies[enemy.name] \n else:\n enemy.respawn()\n return\n \n \n\n def give(self, item):\n if self.inventory_slots_remaining >= int(item.space):\n print(\"You pick up a \"+str(item.name))\n if item.name not in self.items:\n self.items[item.name] = item\n else:\n self.items[item.name].quantity += 1\n self.inventory_slots_remaining -= int(item.space)\n else:\n print(\"\\nYou try to pick up a \"+str(item.name)+\" but you do not have enough inventory room for this item\\nYou need \"+str(item.space - self.inventory_slots_remaining)+\" more inventory slots\")\n self.room.add_item(item)\n\n def inventory(self):\n temp_inv = []\n for item in self.items.values():\n to_append = \"-\"+item.name + \"(\" + str(item.space) + \" Inventory Slots) \" + \" x\" + str(item.quantity)\n temp_inv.append(to_append)\n return(\",\\n\".join(temp_inv))\n\n\n\n def drop(self, item):\n if item.soulbound:\n print(\"your \"+item.name+\" is soulbound you cant drop it\")\n return\n if item.name not in self.items:\n print(\"You do not have a \" + item.name + \" to drop\")\n else:\n self.inventory_slots_remaining += item.space\n if self.items[item.name].quantity > 1:\n self.items[item.name].quantity -= 1\n else:\n del self.items[item.name]\n print(\"\\nYou drop a \" + item.name)\n self.room.add_item(item)\n \n def remove_item(self, item):\n if item.name not in self.items:\n print(\"Player does not have a \" + item.name + \" to remove\")\n else:\n self.inventory_slots_remaining += int(item.space)\n if self.items[item.name].quantity > 1:\n self.items[item.name].quantity -= 1\n else:\n del self.items[item.name]\n\n def add_item(self, item):\n if self.inventory_slots_remaining >= int(item.space):\n if item.name not in self.items:\n self.items[item.name] = item\n else:\n self.items[item.name].quantity += 1\n self.inventory_slots_remaining -= int(item.space)\n else:\n print(\"\\nYou try to recieve a \"+str(item.name)+\" but you do not have enough inventory, it falls to the floor\\nYou need \"+str(item.space - self.inventory_slots_remaining)+\" more inventory slots\")\n self.room.add_item(item)\n \n def equip(self, weapon):#if in inv #### make so this only works is strength is high enough for weapon power\n if self.equipped_weapon != None:\n self.dequip(self.equipped_weapon)\n self.equipped_weapon = weapon\n self.remove_item(weapon)\n print(\"\\nYou equip a \"+weapon.name+\" \\nThis Weapon does \"+str(weapon.damage)+\" Damage\")\n\n def dequip(self):\n if self.equipped_weapon == None:\n pass\n else:\n print(\"\\nYou dequip your \"+self.equipped_weapon.name)\n self.add_item(self.equipped_weapon)\n self.equipped_weapon = None\n\n def equipped(self):\n print(\"\\nWeapon currently equipped: \" +str(self.equipped_weapon.name))\n if self.equipped_weapon != None:\n print(\"This Weapon does \"+str(self.equipped_weapon.damage)+\" Damage\")\n \n def move(self, direction, game_dict):\n direction = direction.upper()\n if direction not in \"NESW\":\n print(\"This is an invalid direction\")\n else:\n if direction == \"N\":\n if self.room.exits[0].lower() == \"none\":\n print(\"There is no room in this direction\")\n else:\n self.room = game_dict[self.room.exits[0]]\n if direction == \"E\":\n if self.room.exits[1].lower() == \"none\":\n print(\"There is no room in this direction\")\n else:\n self.room = game_dict[self.room.exits[1]]\n if direction == \"S\":\n if self.room.exits[2].lower() == \"none\":\n print(\"There is no room in this direction\")\n else:\n self.room = game_dict[self.room.exits[2]]\n if direction == \"W\":\n if self.room.exits[3].lower() == \"none\":\n print(\"There is no room in this direction\")\n else:\n self.room = game_dict[self.room.exits[3]]\n\n def pickup(self,item):# item name in str format\n if item in self.room.items:\n if self.inventory_slots_remaining > int(self.room.items[item].space):\n self.give(self.room.items[item])\n self.room.remove_item(item)\n else:\n print(\"You cannot pickup this item as you do not have enough inventory space\")\n else:\n print(\"There is not a \"+item+\" in this room\")\n\n\n \n","repo_name":"winflag10/PythonExamples","sub_path":"Fully Custom Text RPG/data/game_struct/player_base_class.py","file_name":"player_base_class.py","file_ext":"py","file_size_in_byte":12767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33404426053","text":"import cv2\nimport numpy as np\n\n# load the images\nimg = cv2.imread(\"Resources/small_bird.jpg\",)\n\n# Convert the image into gray scale\nimgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# Blur the images using gaussianBlur\n# ksize is kernal size, should always in odd, 1 is min and x is max\nimgBlur = cv2.GaussianBlur(imgGray, ksize=(15, 15), sigmaX=0)\n\n# Deduct the edge of images, you can increase the threshold to reduce the edge deduction\nimgCanny = cv2.Canny(img, threshold1=100, threshold2=100)\n\n# Increase the thickness of edges introduce the dialation, required numpy to use matrix\nimgkernal = np.ones((5, 5), np.uint8)\nimgDialtion = cv2.dilate(imgCanny, kernel=imgkernal, iterations=1)\n\n# Erode is opposide of dialtion, we decrease the thikness of Canny\nimgErode = cv2.erode(imgDialtion, kernel=imgkernal, iterations=1)\n\n# showing the images\ncv2.imshow(\"Color Image\", img)\ncv2.imshow(\"Gray Image\", imgGray)\ncv2.imshow(\"Blur Image\", imgBlur)\ncv2.imshow(\"Edge Deducted Image\", imgCanny)\ncv2.imshow(\"Dialated Image\", imgDialtion)\ncv2.imshow(\"Erode Image\", imgErode)\n\n# show the image for 5 seconds, 0 means undefinite\ncv2.waitKey(30000)\n\n# destroy all the image windows\ncv2.destroyAllWindows()\n","repo_name":"vikassri/OpenCV_Learning","sub_path":"img_functions.py","file_name":"img_functions.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19931781635","text":"from selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import NoSuchElementException\n\nfrom django.contrib.staticfiles.testing import StaticLiveServerTestCase\nfrom django.urls import reverse_lazy\n\nfrom test.setting.selenium_setting import SELENIUM_SETTING\nfrom accounts.models import AppUser\nfrom accounts.create_testuser import TEST_USER_INFO\nfrom travel.models import Setting\nfrom test.unittest.common.test_data import (\n teardown_data,\n AppUserEncPasswordTestData1st,\n SettingCorrectTestData2ndUser1st,\n COR_SETTING_DATA_1st,\n COR_SETTING_DATA_2nd,\n)\nfrom test.integtest.test_common import (\n setting_create_form,\n setting_edit,\n setting_update_form,\n login_with_test_user,\n open_create_setting_page,\n open_setting_list_page\n)\n\n\nclass SettingTests(StaticLiveServerTestCase):\n\n def setUp(self):\n AppUserEncPasswordTestData1st.setUp()\n\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n chromedriver_path = SELENIUM_SETTING['chromedriver_path']\n o = Options()\n o.binary_location = SELENIUM_SETTING['binary_location']\n o.add_argument(SELENIUM_SETTING['headless'])\n o.add_argument(SELENIUM_SETTING['disable-gpu'])\n o.add_argument(SELENIUM_SETTING['no-sandbox'])\n o.add_argument(SELENIUM_SETTING['window-size'])\n cls.selenium = webdriver.Chrome(chromedriver_path, options=o)\n cls.selenium.implicitly_wait(5)\n\n @classmethod\n def tearDownClass(cls):\n teardown_data()\n cls.selenium.quit()\n super().tearDownClass()\n\n def test_create_setting__success(self):\n \"\"\"\n 「テストユーザーでログイン」し、設定の新規作成のリンクを開き、新規に設定を作成する。\n =>設定名が重複せず、作成できる\n \"\"\"\n # テストユーザーログイン\n login_with_test_user(self)\n\n # 「周辺を検索」の画面\n self.selenium.implicitly_wait(5)\n\n # 新規作成を開く\n open_create_setting_page(self)\n\n # 設定名を入力する。半径、最大表示件数はデフォルトのままにする。\n name_input = self.selenium.find_element_by_name(\"name\")\n name_input.send_keys(COR_SETTING_DATA_1st['name'])\n # 登録\n self.selenium.find_element_by_xpath(\n setting_create_form[\"create_btn\"]).click()\n\n # 設定作成完了画面に遷移する\n result = self.selenium.current_url\n expected = '{}{}'.format(\n self.live_server_url,\n str(reverse_lazy('travel:done_setting'))\n )\n self.assertEqual(result, expected)\n\n # 設定が作成されている\n self.assertTrue(\n Setting.objects.get(name=COR_SETTING_DATA_1st['name'])\n )\n\n def test_create_setting__failed(self):\n \"\"\"\n 「テストユーザーでログイン」し、設定の新規作成のリンクを開き、新規に設定を作成する。\n =>設定名が重複して、作成できない。\n \"\"\"\n # あらかじめ設定を作成しておく\n self.test_create_setting__success()\n # テストユーザーログイン\n login_with_test_user(self)\n\n # 「周辺を検索」の画面\n self.selenium.implicitly_wait(5)\n # 新規作成を開く\n open_create_setting_page(self)\n\n # 設定名を入力する。\n name_input = self.selenium.find_element_by_name(\"name\")\n name_input.send_keys(COR_SETTING_DATA_1st['name'])\n\n self.selenium.find_element_by_xpath(\n setting_create_form[\"create_btn\"]).click()\n\n # 重複エラーになり、ページ遷移しない\n result = self.selenium.current_url\n expected = '{}{}'.format(\n self.live_server_url,\n str(reverse_lazy('travel:create_setting'))\n )\n\n self.assertEqual(result, expected)\n\n def test_delete_setting__success(self):\n \"\"\"\n 「テストユーザーでログイン」し、設定の変更＆削除のリンクを開き、設定を削除する。\n =>設定名が削除されている。\n \"\"\"\n # あらかじめ設定を作成しておく\n self.test_create_setting__success()\n # テストユーザーログイン\n login_with_test_user(self)\n\n # 「周辺を検索」の画面\n self.selenium.implicitly_wait(5)\n # 変更＆削除を開く\n open_setting_list_page(self)\n\n # 削除するボタンをクリックする\n self.selenium.find_element_by_xpath(\n setting_edit[\"delete_btn\"]).click()\n\n # 確認画面でも続けて削除するボタンをクリックする\n self.selenium.find_element_by_xpath(\n setting_edit[\"delete_conf_btn\"]).click()\n\n # 変更＆削除を開く\n open_setting_list_page(self)\n\n # 設定画面に何も表示されていないので削除ボタンを押せない\n with self.assertRaises(NoSuchElementException):\n self.selenium.find_element_by_xpath(\n setting_edit[\"delete_btn\"]).click()\n\n def test_update_setting__success(self):\n \"\"\"\n 「テストユーザーでログイン」し、設定の変更＆削除のリンクを開き、設定を編集する。\n =>設定名が変更されている\n \"\"\"\n # あらかじめ設定を作成しておく\n self.test_create_setting__success()\n # テストユーザーログイン\n login_with_test_user(self)\n\n # 設定のオブジェクトのIDを最後の確認のため取得しておく\n test_setting = Setting.objects.get(name=COR_SETTING_DATA_1st['name'])\n test_setting_id = test_setting.id\n\n # 「周辺を検索」の画面\n self.selenium.implicitly_wait(5)\n # 変更＆削除を開く\n open_setting_list_page(self)\n\n # 編集するボタンをクリックする\n self.selenium.find_element_by_xpath(\n setting_edit[\"edit_btn\"]).click()\n\n # 設定名を変更する\n name_input = self.selenium.find_element_by_name(\"name\")\n name_input.clear()\n name_input.send_keys(COR_SETTING_DATA_2nd['name'])\n\n self.selenium.find_element_by_xpath(\n setting_update_form[\"update_btn\"]).click()\n\n # 設定変更完了画面に遷移する\n result = self.selenium.current_url\n expected = '{}{}'.format(\n self.live_server_url,\n str(reverse_lazy('travel:setting_update_done'))\n )\n self.assertEqual(result, expected)\n\n # 設定名が変更されている\n changed_setting = Setting.objects.get(id=test_setting_id)\n self.assertEqual(COR_SETTING_DATA_2nd['name'], changed_setting.name)\n\n def test_update_setting__failed(self):\n \"\"\"\n 「テストユーザーでログイン」し、設定の変更＆削除のリンクを開き、設定を編集する。\n =>「設定2」が重複するため、設定名が変更できない\n \"\"\"\n # テストユーザーログイン\n login_with_test_user(self)\n # 設定2のuserをユニットテストユーザーからテストユーザーに変更しておく\n SettingCorrectTestData2ndUser1st.setUp()\n test_setting_2nd = Setting.objects.get(id=COR_SETTING_DATA_2nd[\"id\"])\n test_setting_2nd.user = \\\n AppUser.objects.get(username=TEST_USER_INFO['username'])\n test_setting_2nd.save()\n # あらかじめ設定を作成しておく\n self.test_create_setting__success()\n\n # 「周辺を検索」の画面\n self.selenium.implicitly_wait(5)\n # 変更＆削除を開く\n open_setting_list_page(self)\n\n # 編集するボタンをクリックする\n self.selenium.find_element_by_xpath(\n setting_edit[\"edit_btn\"]).click()\n\n # 設定名を変更する\n name_input = self.selenium.find_element_by_name(\"name\")\n name_input.clear()\n name_input.send_keys(COR_SETTING_DATA_2nd['name'])\n\n # 最後の比較のためURLを記録しておく\n current_url = self.selenium.current_url\n\n self.selenium.find_element_by_xpath(\n setting_update_form[\"update_btn\"]).click()\n\n # 設定変更画面のままになる\n result = self.selenium.current_url\n expected = current_url\n self.assertEqual(result, expected)\n","repo_name":"yuki0417/travel-app","sub_path":"travel/test/integtest/travel/test_setting.py","file_name":"test_setting.py","file_ext":"py","file_size_in_byte":8594,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"20104541588","text":"import pytest\n\nfrom api import create_app, db\nfrom config import get_env_db_url\nfrom config import TestingConfig\n\n\n@pytest.yield_fixture\ndef app():\n def _app(config_class):\n app = create_app(config_class)\n app.test_request_context().push()\n\n if config_class is TestingConfig:\n # always start with an empty dn\n db.drop_all()\n from api.models.wikientry import WikiEntry\n\n db.create_all()\n return app\n\n yield _app\n db.session.remove()\n if str(db.engine.url) == TestingConfig.SQLALCHEMY_DATABASE_URI:\n db.drop_all()\n","repo_name":"crhamiltonj/simplewiki","sub_path":"tests/support/configure_test.py","file_name":"configure_test.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16348217516","text":"# -*- coding: utf-8 -*-\nimport pytest\nfrom unittest import mock\n\nfrom pytube import YouTube\nfrom pytube.exceptions import LiveStreamError\nfrom pytube.exceptions import RecordingUnavailable\nfrom pytube.exceptions import RegexMatchError\nfrom pytube.exceptions import VideoUnavailable\nfrom pytube.exceptions import VideoPrivate\n\n\ndef test_video_unavailable():\n try:\n raise VideoUnavailable(video_id=\"YLnZklYFe7E\")\n except VideoUnavailable as e:\n assert e.video_id == \"YLnZklYFe7E\" # noqa: PT017\n assert str(e) == \"YLnZklYFe7E is unavailable\"\n\n\ndef test_regex_match_error():\n try:\n raise RegexMatchError(caller=\"hello\", pattern=\"*\")\n except RegexMatchError as e:\n assert str(e) == \"hello: could not find match for *\"\n\n\ndef test_live_stream_error():\n try:\n raise LiveStreamError(video_id=\"YLnZklYFe7E\")\n except LiveStreamError as e:\n assert e.video_id == \"YLnZklYFe7E\" # noqa: PT017\n assert str(e) == \"YLnZklYFe7E is streaming live and cannot be loaded\"\n\n\ndef test_recording_unavailable():\n try:\n raise RecordingUnavailable(video_id=\"5YceQ8YqYMc\")\n except RecordingUnavailable as e:\n assert e.video_id == \"5YceQ8YqYMc\" # noqa: PT017\n assert str(e) == \"5YceQ8YqYMc does not have a live stream recording available\"\n\n\ndef test_private_error():\n try:\n raise VideoPrivate('mRe-514tGMg')\n except VideoPrivate as e:\n assert e.video_id == 'mRe-514tGMg' # noqa: PT017\n assert str(e) == 'mRe-514tGMg is a private video'\n\n\ndef test_raises_video_private(private):\n with mock.patch('pytube.request.urlopen') as mock_url_open:\n # Mock the responses to YouTube\n mock_url_open_object = mock.Mock()\n mock_url_open_object.read.side_effect = [\n private['watch_html'].encode('utf-8'),\n ]\n mock_url_open.return_value = mock_url_open_object\n with pytest.raises(VideoPrivate):\n YouTube('https://youtube.com/watch?v=mRe-514tGMg')\n\n\ndef test_raises_recording_unavailable(missing_recording):\n with mock.patch('pytube.request.urlopen') as mock_url_open:\n # Mock the responses to YouTube\n mock_url_open_object = mock.Mock()\n mock_url_open_object.read.side_effect = [\n missing_recording['watch_html'].encode('utf-8'),\n ]\n mock_url_open.return_value = mock_url_open_object\n with pytest.raises(RecordingUnavailable):\n YouTube('https://youtube.com/watch?v=5YceQ8YqYMc')\n","repo_name":"pombredanne/pytube","sub_path":"tests/test_exceptions.py","file_name":"test_exceptions.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"39265002538","text":"\"\"\"\n(c) Pavel Gurkov aka trueneu, 2016, All Rights Reserved\n\"\"\"\n\nimport configparser\nimport os\n\n\nCONFIG = {}\n\n\nclass ConfigException(Exception):\n pass\n\n\ndef read(name='wifw.conf'):\n global CONFIG\n\n if not os.path.exists(name):\n raise ConfigException('Config {0} not found'.format(name))\n cp = configparser.ConfigParser()\n cp.read(name)\n\n res = {}\n for section in cp.sections():\n res[section] = {}\n for item, value in cp.items(section):\n if section.startswith('db') and (item == 'port' or item == 'pool_size'):\n res[section][item] = int(value)\n else:\n res[section][item] = value\n\n CONFIG = res\n","repo_name":"trueneu/w-is-for-wallpaper-server","sub_path":"w_is_for_wallpaper/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33756452906","text":"#!/usr/bin/env python3\n\nimport os\nimport sys \nimport csv \nimport math\nimport statistics\nimport numpy as np\nfrom os.path import exists\n\n#script_dir = '/shared/acho44/CXL_WD/'\nscript_dir = '~/CXL_WD/scripts/'\n\nos.system('cp '+script_dir+'process_zsim_out.py .') \nos.system('cp '+script_dir+'get_zsimout_stats_dramsim.py .')\nos.system('cp '+script_dir+'get_avg_mbw_dramsim_trim.py .')\n\nos.system('cp '+script_dir+'parselats_partial.py .')\nos.system('cp '+script_dir+'qpp_cxl.py .')\n\nf1=open('cxl_stats.csv','w')\nf1.write('setup, IPC, MBW, MPKI, L3_Miss_rate, wr_lat_avg, rd_lat_avg, all_lat_avg, svc_time\\n')\n\n\nfor dd in os.listdir('.'):\n#for jj in range(1,50,1):\n #dd = '0'+str(jj)\n if os.path.isdir(dd):\n #if '64P' in dd:\n print(dd)\n os.chdir(dd)\n ipc='0';\n mbw='0';\n mpki='0';\n l3_mr='0';\n wr_lat_avg='0';\n rd_lat_avg='0';\n all_lat_avg='0';\n svc_time='0';\n \n os.system('python3 ../qpp_cxl.py')\n\n if exists('stat_summary.txt'):\n stat_file = open('stat_summary.txt','r')\n\n line = stat_file.readline()\n while line:\n if 'IPC_ALL' in line:\n tmp = line.split(': ')[1]\n ipc=tmp.replace(\",\\n\",\"\")\n if 'MPKI' in line:\n tmp = line.split(':')[1]\n mpki=tmp.replace(\",\\n\",\"\")\n if 'All_ways_miss_rate' in line:\n tmp = line.split(',')[1]\n l3_mr=tmp\n if 'dramsim.log avgbw' in line:\n tmp = line.split(': ')[1]\n mbw=tmp.replace(\"\\n\",\"\")\n if 'wr_lat_avg' in line:\n tmp = line.split(',')[1]\n wr_lat_avg = tmp\n if 'rd_lat_avg' in line:\n tmp = line.split(',')[1]\n rd_lat_avg = tmp\n if 'all_lat_avg' in line:\n tmp = line.split(',')[1]\n all_lat_avg = tmp\n if 'svc:' in line:\n tmp = line.split('mean')[1]\n svc_time = tmp.split('ms')[0]\n\n ##################################\n\n line=stat_file.readline()\n else:\n print('stat_summary.txt did not exist')\n\n\n f1.write(dd+', '+ipc+','+ mbw+','+ mpki+','+ l3_mr+','+wr_lat_avg+','+rd_lat_avg+','+all_lat_avg+','+svc_time +',\\n')\n os.chdir('..')\n\nf1.close()\n\n","repo_name":"albertycho/file_move_repo","sub_path":"scripts/batch_qpp_cxl.py","file_name":"batch_qpp_cxl.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"12784048048","text":"import cv2\r\nimport time\r\n\r\nimage = cv2.imread(\"image0.jpg\")\r\n# print(type(image))\r\n# print(image.shape)\r\n\r\n# cv2.imshow(\"Yuv\", image)\r\n# cv2.waitKey(0)\r\n# cv2.destroyAllWindows()\r\n\r\n# cv2.imwrite(\"Yuv.png\", image)\r\n\r\n\r\n\r\ndef takepic():\r\n video = cv2.VideoCapture(0)\r\n time.sleep(3)\r\n dummy,frame=video.read()\r\n print(dummy)\r\n cv2.imwrite(\"Yuv.png\", frame)\r\n cv2.waitKey(0)\r\n video.release()\r\n cv2.destroyAllWindows()\r\n\r\n\r\ntakepic()","repo_name":"Yuvkaran26/Class---102","sub_path":"cam.py","file_name":"cam.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7667786497","text":"import unittest\nimport logging\nfrom app import create_app, db\nfrom app.models import User, Group, Task, Record\n\nlogging.basicConfig(level=logging.INFO,\n format='%(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\n\nclass ModelTestCase(unittest.TestCase):\n # def setUp(self):\n # self.app = create_app('testing')\n # self.app_context = self.app.app_context()\n # self.app_context.push()\n # db.create_all()\n\n # def tearDown(self):\n # db.session.remove()\n # db.drop_all()\n # self.app_context.pop()\n\n def test_0_init(self):\n self.app = create_app('testing')\n self.app_context = self.app.app_context()\n self.app_context.push()\n db.create_all()\n\n def test_1_add_users(self):\n print('------ add users ----------')\n names = ['bob', 'mike', 'tom']\n users = []\n for name in names:\n u = User(name=name, email=f'{name}@exp.com')\n users.append(u)\n db.session.add(u)\n db.session.commit()\n print(User.query.all())\n # logger.info(User.query.all())\n\n\n def test_2_add_groups(self):\n print('\\n------ add groups ----------')\n names = ['math', 'english', 'history']\n groups = []\n for name in names:\n group = Group(name=name)\n groups.append(group)\n db.session.add(group)\n db.session.commit()\n print(Group.query.all())\n\n def test_3_add_tasks(self):\n print('\\n------ add task ----------')\n names = ['homework1', 'experiment1', 'experiment2']\n names2 = ['homework2', 'exp3']\n tasks = []\n groups = Group.query.all()\n for name in names:\n task = Task(name=name, group_id=groups[0].id)\n tasks.append(task)\n db.session.add(task)\n for name in names2:\n task = Task(name=name, group_id=groups[1].id)\n tasks.append(task)\n db.session.add(task)\n db.session.commit()\n Task.query.all()\n print(Task.query.all())\n\n # 测试给用户添加从属的组，用户与组的多对多关系\n def test_4_add_group_for_user(self):\n print('\\n------ add group for user --------')\n users = User.query.all()\n groups = Group.query.all()\n\n users[0].groups += groups\n users[1].groups += groups[0:2]\n users[2].groups.append(groups[0])\n print('user {} is in groups:'.format(users[0].name))\n print(users[0].groups)\n print('user {} is in groups:'.format(users[1].name))\n print(users[1].groups)\n print('user {} is in groups:'.format(users[1].name))\n print(users[2].groups)\n print('------')\n print(f'{groups[0].name} has members:')\n print(groups[0].members)\n print(f'{groups[1].name} has members:')\n print(groups[1].members)\n\n\n def test_5_add_record_for_user(self):\n print('\\n------ add record for user --------')\n users = User.query.all()\n tasks = Task.query.all()\n u1 = users[0]\n for task in tasks:\n record = Record(uid=u1.id, tid=task.id, done=True)\n db.session.add(record)\n db.session.commit()\n print(f'user {u1.name} has records:')\n print(u1.records)\n\n","repo_name":"Egyonic/datar","sub_path":"tests/test_business.py","file_name":"test_business.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1575208199","text":"\nimport math\nimport sys\n\nimport maya.OpenMaya as OpenMaya\nimport maya.OpenMayaMPx as OpenMayaMPx\n\nkPluginNodeTypeName = \"AvaSineNode\"\n\nAvaSineNodeId = OpenMaya.MTypeId(0x87000)\n\n# Node definition\n\n\nclass AvaSineNode(OpenMayaMPx.MPxNode):\n # class variables\n inWeight = OpenMaya.MObject()\n\n inJointNo = OpenMaya.MObject()\n\n inGlobalTime = OpenMaya.MObject()\n inGlobalAmplitude = OpenMaya.MObject()\n inGlobalFrequency = OpenMaya.MObject()\n inGlobalDelay = OpenMaya.MObject()\n\n inAmplitude = OpenMaya.MObject()\n inFrequency = OpenMaya.MObject()\n inDelay = OpenMaya.MObject()\n\n output = OpenMaya.MObject()\n\n def __init__(self):\n OpenMayaMPx.MPxNode.__init__(self)\n\n def compute(self, plug, dataBlock):\n\n if (plug == AvaSineNode.output):\n\n # Overall weight/blend value of the calculation\n dataHandle_weight = dataBlock.inputValue(AvaSineNode.inWeight)\n weight_float = dataHandle_weight.asFloat()\n\n # Joint no. which is multiplied with delay down the chain\n # if there are three joints in the chain. for example. joint1, joint2, joint3 then joint1 should be 1, joint2 should be 2.. etc\n dataHandle_joint_no = dataBlock.inputValue(AvaSineNode.inJointNo)\n joint_no_float = dataHandle_joint_no.asFloat()\n\n # Connect the time1.outTime to this plug. Or else use cmds.currentTime.\n dataHandle_global_time = dataBlock.inputValue(AvaSineNode.inGlobalTime)\n global_time_float = dataHandle_global_time.asFloat()\n\n # the global amp for the node\n dataHandle_global_amplitude = dataBlock.inputValue(AvaSineNode.inGlobalAmplitude)\n global_amplitude_float = dataHandle_global_amplitude.asFloat()\n\n # the global frequency for the node\n dataHandle_global_frequency = dataBlock.inputValue(AvaSineNode.inGlobalFrequency)\n global_frequency_float = dataHandle_global_frequency.asFloat()\n\n # the global delay by frames for the node and this affects the child joint gradually\n dataHandle_global_delay = dataBlock.inputValue(AvaSineNode.inGlobalDelay)\n global_delay_float = dataHandle_global_delay.asFloat()\n\n # the individual amp which will be multiplied to the global amp\n dataHandle_amplitude = dataBlock.inputValue(AvaSineNode.inAmplitude)\n amplitude_float = dataHandle_amplitude.asFloat()\n\n # the individual frequency which will be multiplied to the global frequency\n dataHandle_frequency = dataBlock.inputValue(AvaSineNode.inFrequency)\n frequency_float = dataHandle_frequency.asFloat()\n\n # the individual delay which will be added to the global delay\n dataHandle_delay = dataBlock.inputValue(AvaSineNode.inDelay)\n delay_float = dataHandle_delay.asFloat()\n\n final_amp = amplitude_float * global_amplitude_float * 5.0\n final_fre = frequency_float * global_frequency_float * 0.0833 # / 12\n final_delay = (joint_no_float * global_delay_float) + delay_float\n\n result = weight_float * (math.sin((global_time_float * final_fre) + final_delay) * final_amp)\n\n outputHandle = dataBlock.outputValue(AvaSineNode.output)\n outputHandle.setFloat(result)\n dataBlock.setClean(plug)\n\n return OpenMaya.kUnknownParameter\n\n# creator\ndef nodeCreator():\n return OpenMayaMPx.asMPxPtr(AvaSineNode())\n\n# initializer\ndef nodeInitializer():\n # input\n\n nAttr = OpenMaya.MFnNumericAttribute()\n AvaSineNode.inWeight = nAttr.create(\"weight\", \"w\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inJointNo = nAttr.create(\"jointno\", \"jo\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inGlobalTime = nAttr.create(\"time\", \"time\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inGlobalAmplitude = nAttr.create(\"global_amplitude\", \"gamp\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inGlobalFrequency = nAttr.create(\"global_frequency\", \"gfre\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inGlobalDelay = nAttr.create(\"global_delay\", \"gdelay\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inAmplitude = nAttr.create(\"amplitude\", \"amp\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inFrequency = nAttr.create(\"frequency\", \"fre\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n AvaSineNode.inDelay = nAttr.create(\"delay\", \"delay\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setReadable(1)\n nAttr.setWritable(1)\n nAttr.setStorable(1)\n nAttr.setKeyable(1)\n\n # output\n nAttr = OpenMaya.MFnNumericAttribute()\n AvaSineNode.output = nAttr.create(\"output\", \"out\", OpenMaya.MFnNumericData.kFloat, 0.0)\n nAttr.setStorable(1)\n nAttr.setWritable(1)\n\n # add attributes\n AvaSineNode.addAttribute(AvaSineNode.inWeight)\n AvaSineNode.addAttribute(AvaSineNode.inJointNo)\n AvaSineNode.addAttribute(AvaSineNode.inGlobalTime)\n AvaSineNode.addAttribute(AvaSineNode.inGlobalAmplitude)\n AvaSineNode.addAttribute(AvaSineNode.inGlobalFrequency)\n AvaSineNode.addAttribute(AvaSineNode.inGlobalDelay)\n AvaSineNode.addAttribute(AvaSineNode.inAmplitude)\n AvaSineNode.addAttribute(AvaSineNode.inFrequency)\n AvaSineNode.addAttribute(AvaSineNode.inDelay)\n AvaSineNode.addAttribute(AvaSineNode.output)\n\n AvaSineNode.attributeAffects(AvaSineNode.inWeight, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inJointNo, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inGlobalTime, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inGlobalAmplitude, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inGlobalFrequency, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inGlobalDelay, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inAmplitude, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inFrequency, AvaSineNode.output)\n AvaSineNode.attributeAffects(AvaSineNode.inDelay, AvaSineNode.output)\n\n\n# initialize the script plug-in\ndef initializePlugin(mobject):\n mplugin = OpenMayaMPx.MFnPlugin(mobject)\n try:\n mplugin.registerNode(kPluginNodeTypeName, AvaSineNodeId, nodeCreator, nodeInitializer)\n except:\n sys.stderr.write(\"Failed to register node: %s\" % kPluginNodeTypeName)\n raise\n\n\n# uninitialize the script plug-in\ndef uninitializePlugin(mobject):\n mplugin = OpenMayaMPx.MFnPlugin(mobject)\n try:\n mplugin.deregisterNode(AvaSineNodeId)\n except:\n sys.stderr.write(\"Failed to deregister node: %s\" % kPluginNodeTypeName)\n raise\n","repo_name":"Avalanche-Studios/ACT","sub_path":"maya/maya_nodes/ava_sine_node.py","file_name":"ava_sine_node.py","file_ext":"py","file_size_in_byte":7474,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"32"} +{"seq_id":"15074550782","text":"from vk_api import VkApi\nfrom vk_api import VkUpload\nfrom vk_api.utils import get_random_id\nfrom vk_api.bot_longpoll import VkBotLongPoll, VkBotEventType\nfrom vk_api.keyboard import VkKeyboard, VkKeyboardColor\nimport sqlite3 as sl\nfrom datetime import datetime\nimport json\nslovar = {}\nres_dat = {}\nGROUP_ID = '220164616'\nGROUP_TOKEN = 'vk1.a.TRTCFQ5vdI9-a7r-8uHgS9zEl6lZ_lreUO1KBO8MVl9kFN1XZBslqeiSdysXt_zWLMOXmIit0j3IC6Dhay7L7Dviw692R5X5Rarhd8B0SVOJ2ppkvQR14Sn0w_KBYvDUozV04bfVuAM7bUnDWoYgdCsLZRThhORAHf5gTZSZoDSFn20KRBxGGtpWAgmm0WNX5WssiHcHJsINBrMSWmYLew'\nAPI_VERSION = '5.120'\n\n# Запускаем бот\nvk_session = VkApi(token=GROUP_TOKEN, api_version=API_VERSION)\nvk = vk_session.get_api()\nvk_upload = VkUpload(vk)\nlongpoll = VkBotLongPoll(vk_session, group_id=GROUP_ID)\nsettings = dict(one_time=False, inline=False)\nsettings2 = dict(one_time=False, inline=True)\n\ntext_inst = \"\"\"\n1.Для начала работы нажмите : \"Регистрация\"\n2.Если вы были ранее зарегистривоаны в нашем TeleGraM боте нажмите кнопку 'Проверить регистрацию'\n\"\"\"\ntext_inst_2 = \"\"\"\nДабро пожаловать в бот ресторан!!!!!!\n\"\"\"\nCALLBACK_TYPES = ('show_snackbar', 'open_link', 'open_app', 'text')\nHI = [\"start\",\"Start\",\"начать\",\"Начало\",\"Начать\",\"начало\",\"Бот\",\"бот\",\"Старт\",\"старт\",\"скидки\",\"Скидки\"]\nusers = []\n\n# ген\ndef keyb_1():\n keyb_1 = VkKeyboard(**settings)\n keyb_1.add_button(label='Регистрация 👋', color=VkKeyboardColor.NEGATIVE, payload={\"type\": \"text\"})\n keyb_1.add_line()\n keyb_1.add_button(label='Проверить регистрацию', color=VkKeyboardColor.POSITIVE, payload={\"type\": \"text\"})\n return keyb_1\n\ndef create_keyboard_2():\n create_keyboard_2 = VkKeyboard(**settings)\n create_keyboard_2.add_button(label=\"📜Меню\", color=VkKeyboardColor.PRIMARY, payload={\"type\": \"text\"})\n create_keyboard_2.add_line()\n create_keyboard_2.add_button(label=\"🛒Корзина\", color=VkKeyboardColor.PRIMARY, payload={\"type\": \"text\"})\n create_keyboard_2.add_line()\n create_keyboard_2.add_button(label=\"❓Поддержка \", color=VkKeyboardColor.PRIMARY, payload={\"type\": \"text\"})\n create_keyboard_2.add_line()\n create_keyboard_2.add_button(label=\"📒Мои заказы \", color=VkKeyboardColor.PRIMARY, payload={\"type\": \"text\"})\n return create_keyboard_2\n\ndef menu_gen(num):\n el_count = 5\n con = sl.connect('tgbase.db')\n categ_k = con.execute(f\"SELECT name FROM CATEGORY\").fetchall()\n keyb = VkKeyboard(**settings2)\n if el_count * (num + 1) < len(categ_k):\n maxi = el_count * (num + 1)\n else:\n maxi = len(categ_k)\n\n for a in range(num * el_count, maxi):\n keyb.add_callback_button(label=list(categ_k[a])[0], color=VkKeyboardColor.PRIMARY, payload={\"categ\": a+1})\n keyb.add_line()\n if num == 0:\n keyb.add_callback_button(label='Далее', color=VkKeyboardColor.PRIMARY, payload={\"name_2\": num + 1})\n elif el_count * (num + 1) >= len(categ_k):\n keyb.add_callback_button(label='Назад', color=VkKeyboardColor.PRIMARY, payload={\"name_2\": num - 1})\n else:\n keyb.add_callback_button(label='Далее', color=VkKeyboardColor.PRIMARY, payload={\"name_2\": num + 1})\n keyb.add_callback_button(label='Назад', color=VkKeyboardColor.PRIMARY, payload={\"name_2\": num - 1})\n return keyb\n\ndef create_keyb_3(boon,text,rat,name):\n create_keyb_3 = VkKeyboard(**settings2)\n create_keyb_3.add_callback_button(label=\"➖\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_3\": 2,\"text\":text,\"rat\":rat,\"boon\":boon,\"name\":name})\n create_keyb_3.add_callback_button(label=f\"{str(boon)}\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_3\": 5})\n create_keyb_3.add_callback_button(label=\"➕\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_3\": 1,\"text\":text,\"rat\":rat,\"boon\":boon,\"name\":name})\n create_keyb_3.add_line()\n create_keyb_3.add_callback_button(label=\"Заказать 📒\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_3\": 3,\"boon\":boon,'name':name,'keyb_3_data':res_dat['data']})\n create_keyb_3.add_callback_button(label=\"Добавить в корзину 🛒\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_3\": 4,\"boon\":boon,'name':name})\n create_keyb_3.add_line()\n create_keyb_3.add_callback_button(label=\"Следующее блюдо ➡️\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_3\": 6,\"keyb_3_dish\":name})\n return create_keyb_3\n\ndef create_keyb_5(b,data):\n create_keyb_5 = VkKeyboard(**settings2)\n create_keyb_5.add_callback_button(label=\"Изменить кол-во\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_5\": 1, \"order\": b})\n create_keyb_5.add_callback_button(label=\"Подтвердить заказ\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_5\": 2,\"keyb_5_data\":data})\n create_keyb_5.add_line()\n create_keyb_5.add_callback_button(label=\"Удалить из корзины\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_5\": 3, \"order\": b})\n return create_keyb_5\n\ndef create_keyb_6(id_order,num):\n good = con.execute(f\"SELECT GOODS.id, dishes, name FROM GOODS JOIN DISHES ON GOODS.dishes=DISHES.id WHERE orders = {id_order}\").fetchall()\n create_keyb_6 = VkKeyboard(**settings2)\n for i in range(len(good)):\n create_keyb_6.add_callback_button(label=f\"{good[i][2]}\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_6\": 1+num,'keyb_6_good_id':good[i][0]})\n return create_keyb_6\n\ndef create_keyb_7(id_orders):\n create_keyb_7 = VkKeyboard(**settings2)\n create_keyb_7.add_callback_button(label=\"Отменить заказ\", color=VkKeyboardColor.PRIMARY,payload={\"keyb_7\": 1,\"keyb_7_id_ord\":id_orders})\n create_keyb_7.add_callback_button(label=\"Оценить блюдо\", color=VkKeyboardColor.PRIMARY,payload={\"keyb_7\": 2})\n create_keyb_7.add_callback_button(label=\"Оставить коментарий к блюду\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_7\": 3})\n return create_keyb_7\n\ndef create_keyb_8(id):\n digit = list(filter(lambda x:x.isdigit(),id))\n create_keyb_8 = VkKeyboard(**settings2)\n for i in range(len(digit)):\n create_keyb_8.add_callback_button(label=f\"{digit[i]}\", color=VkKeyboardColor.PRIMARY, payload={\"keyb_8\": 1, 'keyb_8_id': digit[i]})\n return create_keyb_8\n\ndef create_keyb_9(num):\n el_count = 5\n con = sl.connect('tgbase.db')\n dish = con.execute(f\"SELECT id, name FROM DISHES \").fetchall()\n create_keyb_9 = VkKeyboard(**settings2)\n if el_count * (num + 1) < len(dish):\n maxi = el_count * (num + 1)\n else:\n maxi = len(dish)\n for i in range(num * el_count, maxi):\n create_keyb_9.add_callback_button(label=str(dish[i][1]), color=VkKeyboardColor.PRIMARY,payload={\"keyb_9\": 1, 'keyb_9_dish':dish[i]})\n create_keyb_9.add_line()\n if num == 0:\n create_keyb_9.add_callback_button(label='Далее', color=VkKeyboardColor.PRIMARY, payload={\"keyb_9_num\": num + 1})\n elif el_count * (num + 1) >= len(dish):\n create_keyb_9.add_callback_button(label='Назад', color=VkKeyboardColor.PRIMARY, payload={\"keyb_9_num\": num - 1})\n else:\n create_keyb_9.add_callback_button(label='Далее', color=VkKeyboardColor.PRIMARY, payload={\"keyb_9_num\": num + 1})\n create_keyb_9.add_callback_button(label='Назад', color=VkKeyboardColor.PRIMARY, payload={\"keyb_9_num\": num - 1})\n return create_keyb_9\n\ndef create_keyb_10(rating):\n create_keyb_10 = VkKeyboard(**settings2)\n for i in range(1,6):\n create_keyb_10.add_callback_button(label=f'{i}', color=VkKeyboardColor.PRIMARY,payload={\"keyb_10\": 1, 'keyb_10_rat':rating,\"keyb_10_kol\":i})\n return create_keyb_10\n\n#Смена id пользователя\ndef change_id_user(phone,obj):\n con = sl.connect('tgbase.db')\n try:\n with con:\n con.execute(f\"UPDATE USERS SET id_vk = '{obj['from_id']}' WHERE tel = '{phone}'\")\n vk.messages.send(\n user_id=obj['from_id'],\n random_id=get_random_id(),\n peer_id=obj['from_id'],\n keyboard=create_keyboard_2().get_keyboard(),\n message=text_inst_2)\n\n except:\n vk.messages.send(\n user_id=obj['from_id'],\n random_id=get_random_id(),\n peer_id=obj['from_id'],\n message=\"Пользователя не существует, пройдите регистрацию \")\n#Каталог блюд\ndef dish_catalog(num):\n el_count = 5\n dish_k = con.execute(f\"SELECT name,category FROM DISHES\").fetchall()\n keyb_dish = VkKeyboard(**settings2)\n if el_count * num < len(dish_k):\n maxi = el_count * num\n else:\n maxi = len(dish_k)\n for i in range((num-1)*el_count, maxi):\n if num == list(dish_k[i])[1]:\n keyb_dish.add_callback_button(label=list(dish_k[i])[0], color=VkKeyboardColor.PRIMARY,payload={\"dish\": (dish_k[i])[0]})\n keyb_dish.add_line()\n if num == 1:\n keyb_dish.add_callback_button(label='Далее', color=VkKeyboardColor.PRIMARY, payload={\"name_3\": num + 1})\n elif el_count * num >= len(dish_k):\n keyb_dish.add_callback_button(label='Назад', color=VkKeyboardColor.PRIMARY, payload={\"name_3\": num - 1})\n else:\n keyb_dish.add_callback_button(label='Далее', color=VkKeyboardColor.PRIMARY, payload={\"name_3\": num + 1})\n keyb_dish.add_callback_button(label='Назад', color=VkKeyboardColor.PRIMARY, payload={\"name_3\": num - 1})\n return keyb_dish\n#Блюдо\ndef dish(objec,num):\n bbb =[]\n a = []\n sr = \"\"\n dish_id = con.execute(f\"SELECT id FROM DISHES WHERE stoped={0} AND name ='{objec}'\").fetchone()\n try:\n rating = con.execute(f\"SELECT rating FROM rating WHERE dish = {int(dish_id[0])+num}\").fetchone()\n for x in range(len(rating)):\n a.append(rating[x])\n rating_list = (4 + int(sum(a))) / len(rating)\n except :\n rating_list = 4\n with con:\n con.execute(f\"UPDATE DISHES SET rating = {int(rating_list)} WHERE id = {int(dish_id[0]+num)}\")\n dish = con.execute(f\"SELECT id,photo,name,weight,description,price,rating FROM DISHES WHERE stoped={0} AND id ={int(dish_id[0]+num)}\").fetchone()\n photo = vk_upload.photo_messages(f\"C:/Users/ReDWaR/PycharmProjects/pythonProject_telebot/photo/{dish[1]}.png\")\n owner_id = photo[0]['owner_id']\n photo_id = photo[0]['id']\n access_key = photo[0]['access_key']\n attachment = f'photo{owner_id}_{photo_id}_{access_key}'\n bbb.append(attachment)\n bbb.append(sr)\n for a in dish[2:6]:\n sr += f'{str(a)}\\n'\n last_id = vk.messages.edit(\n attachment=attachment,\n peer_id=event.obj.peer_id,\n message=f'{sr}Рейтинг блюда: {dish[6]}',\n conversation_message_id=event.obj.conversation_message_id,\n keyboard=create_keyb_3(1,bbb,dish[6],dish[2]).get_keyboard())\n\n# Корзина\ndef korzina(object):\n try:\n korzinka = []\n con = sl.connect('tgbase.db')\n id_user = con.execute(f\"SELECT id FROM USERS WHERE id_vk = {int(object.obj.message['from_id'])} \").fetchone()\n max_data = con.execute(f\"SELECT MAX(date) FROM ORDERS WHERE user = {id_user[0]} \").fetchone()\n id_order = con.execute(f\"SELECT id FROM ORDERS WHERE user = {id_user[0]} and date = {max_data[0]}\").fetchone()\n goods = con.execute(f\"SELECT dishes,kol_vo_dishes FROM GOODS WHERE orders = {id_order[0]} \").fetchall()\n\n for i in range(len(goods)):\n dish = con.execute(f\"SELECT id, name FROM DISHES WHERE id = {int(goods[i][0])} \").fetchall()\n korzinka.append(dish[0][1])\n korzinka.append(f\"Кол-во блюд = {goods[i][1]}\")\n a = \"\\n\".join(korzinka)\n vk.messages.send(\n user_id=object.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=object.obj.message['from_id'],\n message=f'{a}',\n keyboard=create_keyb_5(id_order[0],object.obj.message['date']).get_keyboard())\n except:\n vk.messages.send(\n user_id=object.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=object.obj.message['from_id'],\n message=f'Ваша корзина пуста', )\n\n#Изменение кол-ва блюд в корзиние\ndef korzina_change(kol_vo):\n with con:\n con.execute(\n f\"UPDATE GOODS SET kol_vo_dishes ={int(kol_vo)} WHERE id = {int(slovar['id_goods'])}\")\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=f'Успешно изменено', )\n\ndef orders(slv):\n try:\n list_dict = []\n con = sl.connect('tgbase.db')\n id_user = con.execute(f\"SELECT id FROM USERS WHERE id_vk = {slv.object.message['from_id']} \").fetchone()\n id_order = con.execute(f\"SELECT id FROM ORDERS WHERE user = {id_user[0]}\").fetchall()\n for i in id_order:\n data = 15\n ammout = 0\n vv = []\n slov_mess = {}\n spis = ['Номер заказа', 'Время доставки заказа', 'Статус оплаты', 'Дата заказа']\n orders = con.execute(f\"SELECT id,time_deliv,payment,date FROM ORDERS WHERE id ={i[0]} GROUP BY id\").fetchall()\n id_dish = con.execute(f\"SELECT dishes, kol_vo_dishes FROM GOODS WHERE orders ={i[0]}\").fetchall()\n if len(id_dish) != 0:\n a = orders+id_dish\n slov_mess = dict(zip(spis, a[0]))\n for n in a[1:]:\n dish = con.execute(f\"SELECT name, price, cooking_time FROM DISHES WHERE id ={n[0]}\").fetchall()\n ammout += int(dish[0][1][:2])*n[1]\n data += int(dish[0][2])\n vv.append(dish[0][0]+f\", Кол-во блюд = {str(n[1])}\")\n slov_mess[\"Блюда\"] = '\\n'.join(vv)\n if len(slov_mess) != 0:\n slov_mess[\"Общая стоимость\"] = ammout\n slov_mess[\"Время доставки заказа\"] = data\n slov_mess[\"Дата заказа\"] = datetime.fromtimestamp(slov_mess[\"Дата заказа\"])\n if slov_mess[\"Статус оплаты\"] == 1:\n slov_mess['Статус оплаты'] = \"Оплачен\"\n else:\n slov_mess['Статус оплаты'] = \"Не оплачен\"\n for k, v in slov_mess.items():\n list_dict.append(k + ': ' + str(v))\n vivod = '\\n'.join(list_dict)\n vk.messages.send(\n user_id=slv.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=slv.obj.message['from_id'],\n message=f\"{'Ваши заказы 📒'}\\n{vivod}\",\n keyboard=create_keyb_7(list_dict[0]).get_keyboard())\n except:\n vk.messages.send(\n user_id=slv.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=slv.obj.message['from_id'],\n message=f\"У вас нет заказов\")\n\ndef suppurt(a):\n print(a)\n\n\n\n\n\n\nfor event in longpoll.listen():\n con = sl.connect('tgbase.db')\n num_ord = 'Номер заказа: '\n if event.type == VkBotEventType.MESSAGE_NEW: #Отлавливает сообщение\n\n if event.obj.message['text'] != '':\n\n if event.from_user:\n\n if event.obj.message['text'] in HI:\n try:\n user = con.execute(f\"SELECT name FROM USERS WHERE id_vk = '{event.obj.message['from_id']}'\")\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n keyboard=create_keyboard_2().get_keyboard(),\n message=text_inst_2)\n\n except:\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n keyboard=keyb_1().get_keyboard(),\n message=text_inst)\n\n elif event.obj.message['text'] == 'Проверить регистрацию':\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=\"Введите номер телефона указанный при регистрации в формате +375\")\n\n elif '+375' in event.obj.message['text']:\n change_id_user(event.obj.message['text'], event.obj.message)\n\n elif event.obj.message['text'] == 'Регистрация 👋':\n users.append(event.obj.message['from_id'])\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message='Введите ваше имя с маленькой буквы')\n\n elif event.obj.message['text'].islower():\n users.append(event.obj.message['text'].title())\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message='Введите свой номер телефона начиная с 375')\n\n elif \"375\" in event.obj.message['text']:\n plus = \"+\"\n users.append(f'{plus}{event.obj.message[\"text\"]}')\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=\"Введите свой адрес доставки начиная с Улица.\")\n\n elif \"Улица.\" in event.obj.message['text']:\n try:\n users.append(event.obj.message['text'])\n con = sl.connect('tgbase.db')\n with con:\n con.execute(\"INSERT OR IGNORE INTO USERS (id_vk,name,tel,address) values(?, ?, ?, ?)\", users)\n con.execute(f\"INSERT OR IGNORE INTO ORDERS (user,date) values({users[0]},{event.obj.message['date']})\")\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=\"Регистрация завершена\")\n except:\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=\"Вы зарегистрированы проверте регистрацию\")\n\n elif event.obj.message['text'] == \"📜Меню\":\n res_dat[\"data\"] = event.obj.message['date']\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n keyboard=menu_gen(num=0).get_keyboard(),\n message=\"Категории блюд\")\n\n elif event.obj.message['text'] == \"🛒Корзина\":\n korzina(event)\n\n elif event.obj.message['text'] == \"❓Поддержка\":\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=\"Введите сообщение начиная с Help\")\n\n elif event.obj.message['text'] == \"📒Мои заказы\":\n orders(event)\n\n elif event.obj.message['text'] in ['1','2','3','4','5','6','7','8','9']:\n korzina_change(event.obj.message['text'])\n\n elif \"Help\" in event.obj.message['text']:\n print(vk.messages.getConversations())\n vk.messages.send(chat_id=24112, message=event.obj.message['text'], random_id=get_random_id())\n\n else:\n vk.messages.send(\n user_id=event.obj.message['from_id'],\n random_id=get_random_id(),\n peer_id=event.obj.message['from_id'],\n message=\"Что-то пошло не так попробуйте еще раз\")\n\n\n elif event.type == VkBotEventType.MESSAGE_EVENT:\n if event.object.payload.get('type') in CALLBACK_TYPES:\n vk.messages.sendMessageEventAnswer(\n event_id=event.object.event_id,\n user_id=event.object.user_id,\n peer_id=event.object.peer_id,\n event_data=json.dumps(event.object.payload))\n elif event.object.payload.get('name_2'):\n last_id = vk.messages.edit(\n peer_id=event.obj.peer_id,\n message='Выберите категорию',\n conversation_message_id=event.obj.conversation_message_id,\n keyboard=menu_gen(event.object.payload.get(\"name_2\")).get_keyboard())\n\n elif event.object.payload.get('name_3'):\n last_id = vk.messages.edit(\n peer_id=event.obj.peer_id,\n message='Выберите блюдо',\n conversation_message_id=event.obj.conversation_message_id,\n keyboard=dish_catalog(event.object.payload.get(\"name_3\")).get_keyboard())\n\n elif event.object.payload.get('categ'):\n last_id = vk.messages.edit(\n peer_id=event.obj.peer_id,\n message='Выберите блюдо',\n conversation_message_id=event.obj.conversation_message_id,\n keyboard=dish_catalog(event.object.payload.get('categ')).get_keyboard())\n\n elif event.object.payload.get('dish'):\n dish(event.object.payload.get('dish'), 0)\n\n elif event.object.payload.get(\"keyb_3\") == 1:\n last_id = vk.messages.edit(\n peer_id=event.object.peer_id,\n attachment=event.object.payload.get(\"text\")[0],\n message=f'{event.object.payload.get(\"text\")[1]}Рейтинг блюда: {event.object.payload.get(\"rat\")}',\n conversation_message_id=event.object.conversation_message_id,\n keyboard=create_keyb_3(event.object.payload.get(\"boon\")+1,event.object.payload.get(\"text\"),event.object.payload.get(\"rat\"),event.object.payload.get(\"name\")).get_keyboard())\n\n elif event.object.payload.get(\"keyb_3\") == 2:\n last_id = vk.messages.edit(\n peer_id = event.object.peer_id,\n attachment = event.object.payload.get(\"text\")[0],\n message = f'{event.object.payload.get(\"text\")[1]}Рейтинг блюда: {event.object.payload.get(\"rat\")}',\n conversation_message_id=event.object.conversation_message_id,\n keyboard=create_keyb_3(event.object.payload.get(\"boon\")-1,event.object.payload.get(\"text\"),event.object.payload.get(\"rat\"),event.object.payload.get(\"name\")).get_keyboard())\n\n\n elif event.object.payload.get(\"keyb_3\") == 3:\n spis = []\n id_dish = con.execute(\n f\"SELECT id FROM DISHES WHERE name = '{event.object.payload.get('name')}' \").fetchone()\n id_user = con.execute(f\"SELECT id FROM USERS WHERE id_vk = {event.object.peer_id} \").fetchone()\n kil_vo = event.object.payload.get(\"boon\")\n max_data = con.execute(\n f\"SELECT MAX(date) FROM ORDERS WHERE user = {id_user[0]} \").fetchone()\n id_order = con.execute(\n f\"SELECT id FROM ORDERS WHERE user = {id_user[0]} and date = {max_data[0]}\").fetchone()\n spis.append(id_dish[0])\n spis.append(kil_vo)\n spis.append(id_order[0])\n with con:\n con.execute(f\"INSERT OR IGNORE INTO GOODS (dishes,kol_vo_dishes,orders) values(?,?,?)\", spis)\n\n with con:\n con.execute(\n f\"INSERT OR IGNORE INTO ORDERS (user,date) values({int(id_user[0])},{int(event.object.payload.get('keyb_3_data'))})\")\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Заказ оформлен\")\n\n\n elif event.object.payload.get(\"keyb_3\") == 4:\n spis = []\n id_dish = con.execute(f\"SELECT id FROM DISHES WHERE name = '{event.object.payload.get('name')}' \").fetchone()\n id_user = con.execute(f\"SELECT id FROM USERS WHERE id_vk = {event.object.peer_id} \").fetchone()\n kil_vo = event.object.payload.get(\"boon\")\n max_data = con.execute(\n f\"SELECT MAX(date) FROM ORDERS WHERE user = {id_user[0]} \").fetchone()\n id_order = con.execute(\n f\"SELECT id FROM ORDERS WHERE user = {id_user[0]} and date = {max_data[0]}\").fetchone()\n spis.append(id_dish[0])\n spis.append(kil_vo)\n spis.append(id_order[0])\n with con:\n con.execute(f\"INSERT OR IGNORE INTO GOODS (dishes,kol_vo_dishes,orders) values(?,?,?)\", spis)\n vk.messages.edit(\n peer_id = event.object.peer_id,\n conversation_message_id=event.object.conversation_message_id,\n message=\"Блюдо добавлено\")\n\n elif event.object.payload.get(\"keyb_3\") == 6:\n dish(event.object.payload.get(\"keyb_3_dish\"), 1)\n\n elif event.object.payload.get(\"keyb_5\") == 1:\n vk.messages.edit(\n peer_id=event.object.peer_id,\n conversation_message_id=event.object.conversation_message_id,\n message=\"Выберите блюдо\",\n keyboard=create_keyb_6(event.object.payload.get(\"order\"),0).get_keyboard())\n\n # Подтвердить заказ\n elif event.object.payload.get(\"keyb_5\") == 2:\n id_user = con.execute(f\"SELECT id FROM USERS WHERE id_vk = {event.object.peer_id} \").fetchone()\n with con:\n con.execute(f\"INSERT OR IGNORE INTO ORDERS (user,date) values({int(id_user[0])},{int(event.object.payload.get('keyb_5_data'))})\")\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Заказ добавлен в корзину\")\n\n elif event.object.payload.get(\"keyb_5\") == 3:\n vk.messages.edit(\n peer_id=event.object.peer_id,\n conversation_message_id=event.object.conversation_message_id,\n message=\"Выберите блюдо\",\n keyboard=create_keyb_6(event.object.payload.get(\"order\"),1).get_keyboard())\n\n elif event.object.payload.get(\"keyb_6\") == 1:\n slovar[\"id_goods\"] = event.object.payload.get(\"keyb_6_good_id\")\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Введите кол-во\")\n\n elif event.object.payload.get(\"keyb_6\") == 2:\n with con:\n con.execute(f\"DELETE FROM GOODS WHERE id={int(event.object.payload.get('keyb_6_good_id'))}\")\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Блюдо удалено\")\n\n elif event.object.payload.get(\"keyb_7\") == 1:\n print(event.object.payload.get(\"keyb_7_id_ord\"))\n vk.messages.edit(\n peer_id=event.object.peer_id,\n conversation_message_id=event.object.conversation_message_id,\n message=\"Выберите заказ\",\n keyboard=create_keyb_8(event.object.payload.get(\"keyb_7_id_ord\")).get_keyboard())\n\n elif event.object.payload.get(\"keyb_7\") == 2:\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Выберите блюдо\",\n keyboard=create_keyb_9(0).get_keyboard())\n\n\n elif event.object.payload.get(\"keyb_8\") == 1:\n id_user = con.execute(f\"SELECT id FROM USERS WHERE id_vk = {event.object.peer_id} \").fetchone()\n with con:\n con.execute(f\"DELETE FROM ORDERS WHERE user = {id_user[0]} AND id = {int(event.object.payload.get('keyb_8_id'))}\")\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Ваш заказ успешно отменен\")\n\n elif event.object.payload.get(\"keyb_9_num\"):\n last_id = vk.messages.edit(\n peer_id=event.obj.peer_id,\n message='Выберите блюдо',\n conversation_message_id=event.obj.conversation_message_id,\n keyboard=create_keyb_9(event.object.payload.get(\"keyb_9_num\")).get_keyboard())\n\n elif event.object.payload.get(\"keyb_9\") == 1:\n last_id = vk.messages.edit(\n peer_id=event.obj.peer_id,\n message='Оцените блюдо',\n conversation_message_id=event.obj.conversation_message_id,\n keyboard=create_keyb_10(event.object.payload.get(\"keyb_9_dish\")).get_keyboard())\n\n elif event.object.payload.get(\"keyb_10\") == 1:\n id_dish = event.object.payload.get(\"keyb_10_rat\")[0]\n rating = event.object.payload.get(\"keyb_10_kol\")\n print(rating)\n with con:\n con.execute(f\"INSERT OR IGNORE INTO RATING (dish,rating) values({int(id_dish)},{int(rating)})\")\n vk.messages.send(\n peer_id=event.object.peer_id,\n random_id=get_random_id(),\n conversation_message_id=event.object.conversation_message_id,\n message=\"Спасибо за ваш отзыв\")\n\n\nif __name__ == '__main__':\n print()","repo_name":"ReDNocks/telebot","sub_path":"vkbotspt.py","file_name":"vkbotspt.py","file_ext":"py","file_size_in_byte":31712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"18668802444","text":"\"\"\"\nCreated on Jan 20, 2019\n\n@author: sven\n\"\"\"\nimport time\nfrom time import asctime\n\nfrom PySide2 import QtCore\nfrom PySide2.QtCore import QThreadPool, QRunnable, QObject\nfrom PySide2.QtWidgets import QPushButton, QMenu, QInputDialog, QMessageBox\n\nfrom ui.ecManRemoteTerminal import EcManRemoteTerminal\nfrom worker.computer import Computer\n\n\nclass LbClient(QPushButton):\n \"\"\"\n class to handle and visualize state of lb_client_computers\n \"\"\"\n\n def __init__(self, ip, remoteAdminUser, passwd, candidateLogin, parentApp, test=False):\n self.computer = Computer(ip,\n remoteAdminUser=remoteAdminUser, passwd=passwd,\n candidateLogin=candidateLogin,\n fetchHostname=(test is False))\n QPushButton.__init__(self, self.computer.ip)\n self.setAttribute(QtCore.Qt.WA_StyledBackground)\n self.parentApp = parentApp\n self.log = LbClient.Log()\n self.isSelected = False\n self.lastUpdate = None\n self.showIp = False\n if test:\n self.computer.state = Computer.State.STATE_COPY_FAIL\n self.setLabel()\n self._colorizeWidgetByClientState()\n return\n\n myThread = LbClient.CheckStatusThread(self)\n myThread.connector.checkStateSignal.connect(self.setLabel)\n myThread.connector.checkStateSignal.connect(self.setOwnToolTip)\n QThreadPool.globalInstance().start(myThread)\n\n menu = QMenu(self)\n act0 = menu.addAction(\"Popup-Nachricht senden\")\n act0.triggered.connect(self.computer.sendMessage)\n\n act1 = menu.addAction(\"Client-PC auswählen\")\n act1.triggered.connect(self.toggleSelection)\n\n act2 = menu.addAction(\"Kandidat-Namen setzen\")\n act2.triggered.connect(self.setCandidateNameDialog)\n\n # act3 = menu.addAction(\"Dateien zum Client kopieren\")\n # act3.triggered.connect(self.deployClientFiles)\n\n act4 = menu.addAction(\"USB sperren\")\n act4.triggered.connect(self.blockUsbAccessThread)\n\n act5 = menu.addAction(\"USB aktivieren\")\n act5.triggered.connect(self.allowUsbAccessThread)\n\n act6 = menu.addAction(\"Internet sperren\")\n act6.triggered.connect(self.blockInternetAccessThread)\n\n act7 = menu.addAction(\"Internet freigeben\")\n act7.triggered.connect(self.allowInternetAccessThread)\n\n act8 = menu.addAction(\"LB-Status zurücksetzen\")\n act8.triggered.connect(self.resetComputerStatusConfirm)\n\n act9 = menu.addAction(\"LB-Daten zurücksetzen\")\n act9.triggered.connect(self.resetClientHomeDirectory)\n\n act10 = menu.addAction(\"Remote Shell öffnen\")\n act10.triggered.connect(self.openTerminal)\n\n # menu.addAction(\"Bildschirm schwärzen\").triggered.connect(self.computer.blankScreen)\n menu.addAction(\"Client herunterfahren\").triggered.connect(self.shutdownClient)\n self.setMenu(menu)\n self.select()\n\n\n class Log:\n\n def __init__(self):\n self.__log = []\n\n def append(self, msg):\n self.__log.append(asctime() + \"::\" + msg)\n\n def getLog(self):\n return self.__log\n\n def enterEvent(self, QEvent):\n self.setOwnToolTip()\n pass\n\n\n def openTerminal(self):\n #QThreadPool.globalInstance().start(LbClient.RemoteShellTask(self.parentApp, self.computer));\n terminalDialog = EcManRemoteTerminal(parent=self.parentApp, client=self.computer)\n terminalDialog.setModal(False)\n terminalDialog.exec_()\n\n def setCandidateNameDialog(self):\n \"\"\"\n opens GUI dialog to enter a candidate name, call actual setter method\n \"\"\"\n candidateName, ok = QInputDialog.getText(self, \"Eingabe\", \"Name des Kandidaten eingeben\")\n if ok and (len(candidateName) != 0):\n self.setCandidateName(candidateName)\n pass\n\n def setCandidateName(self, candidateName, doUpdate=True):\n \"\"\"\n sets candidate name on remote computer\n \"\"\"\n self.computer.setCandidateName(candidateName.replace(\" \",\"_\").replace(\",\",\"_\").replace(\";\",\"_\"))\n if doUpdate:\n self.setLabel()\n\n self.log.append(msg=\" Kandidat-Name gesetzt: \" + candidateName)\n\n def toggleShowIp(self):\n self.showIp = not self.showIp\n self.setLabel()\n\n def shutdownClient(self):\n self.log.append(msg=\" herunterfahren\")\n QThreadPool.globalInstance().start(LbClient.ShutdownTask(self))\n\n def setOwnToolTip(self):\n if self.lastUpdate != None and time.process_time() - self.lastUpdate < 0.7:\n return\n\n self.lastUpdate = time.process_time()\n print(\"generating tooltip again\")\n errorLog = \"\"\n if len(self.log.getLog()) > 0:\n errorLog = \"<h4>Log: </h4>\" + \"</p><p>\".join(self.log.getLog()) + \"</p>\"\n\n # if self.computer.state != Computer.State.STATE_INIT:\n remoteFiles = self.computer.getRemoteFileListing()\n if type(remoteFiles) == bytes:\n remoteFiles = \"ERROR: \" + remoteFiles.decode()\n\n self.setToolTip(\"<h4>Status</h4>\"\n + \"Deployment-Status: \"+self.computer.state.name + \"<br>\"\n + \"USB gesperrt: \" + str(self.computer.isUsbBlocked()) + \"<br>\"\n + \"Internet gesperrt: \" + str(self.computer.isInternetBlocked()) + \"<br>\"\n + remoteFiles + \"<hr>\"\n + errorLog)\n\n def resetComputerStatusConfirm(self):\n self.resetComputerStatus(resetCandidateName=None)\n\n def resetComputerStatus(self, resetCandidateName=None):\n \"\"\"\n resets client status **and data!**\n resets candidate name if resetCandidateName = True,\n opens a confirmation dialog if resetCandidateName = None (default),\n skips client name reset if resetCandidateName = False\n \"\"\"\n if resetCandidateName == None:\n items = [\"Nein\", \"Ja\"]\n item, ok = QInputDialog().getItem(self, \"Client-Status zurücksetzen?\", \"Kandidat-Name zurücksetzen? \",\n items, 0, False)\n if ok == False:\n return\n resetCandidateName = True if item == \"Ja\" else False\n\n try:\n self.log.append(msg=\" alle Daten und Einstellungen zurücksetzen\")\n self.computer.resetStatus(resetCandidateName)\n self.setLabel()\n # self.setOwnToolTip()\n except Exception as ex:\n print(\"Fehler beim Zurücksetzen vom Client-PC: \" + str(ex))\n self.log.append(msg=\" Fehler beim Zurücksetzen der Daten und Einstellungen\")\n\n def resetClientHomeDirectory(self):\n if QMessageBox.critical(self, \"Achtung\", \"Alle Benutzerdaten löschen?\",\n QMessageBox.Yes, QMessageBox.No) == QMessageBox.Yes:\n self.computer.resetClientHomeDirectory()\n\n def deployClientFiles(self, server_user, server_passwd, server_domain, path=None, reset=False):\n \"\"\"\n starts remote copy process for path,\n wipes remote non-system files in user home dir before if reset=True\n \"\"\"\n if path is None or path is False:\n path = self.parentApp.getExamPath()\n\n if server_user == \"\" or server_passwd == \"\":\n msg = \" Anmeldecredentials für LB-Share fehlen\"\n self.parentApp.showMessageBox(\"grober Fehler:\", msg)\n self.log.append(msg)\n return\n\n if path == \"\":\n msg = \" LB-Verzeichnispfad leer\"\n self.parentApp.showMessageBox(\"grober Fehler\", msg)\n self.log.append(msg)\n return\n\n if reset is True:\n success = self.computer.resetClientHomeDirectory()\n self.log.append(\" Client-Daten gelöscht: \" + str(success))\n else:\n self.log.append(\" Löschen der Client-Daten nicht gewünscht.\")\n\n status = self.computer.deployClientFiles(path, server_user, server_passwd, server_domain)\n\n if status is False:\n self.log.append(\" Fehler Prüfungsdaten zum Client kopieren: \" + path)\n else:\n self.log.append(\" Prüfungsdaten zum Client kopieren erfolgreich: \" + path.replace(\"#\", \"/\"))\n\n\n def retrieveClientFiles(self, filepath, server_user, server_passwd, server_domain, maxFiles=500,\n maxFileSize=10000000):\n try:\n if self.computer.checkFileSanity(maxFiles, maxFileSize):\n\n status = self.computer.retrieveClientFiles(filepath, server_user, server_passwd, server_domain)\n if status != True:\n self.log.append(msg=\" Fehler beim Kopieren der Resultate: \" +\n filepath)\n else:\n self.log.append(msg=\" Resultate erfolgreich kopiert: \" +\n filepath)\n self.isSelected = False;\n\n else:\n self.log.append(msg=\" Fehler: zu viele Dateien im Lösungsverzeichnis\")\n\n except Exception as ex:\n self.log.append(msg=\" Exception beim Kopieren der Resultate: \" + str(ex))\n self.computer.state = Computer.State.STATE_RETRIVAL_FAIL\n\n def _colorizeWidgetByClientState(self):\n #self.setAutoFillBackground(True);\n #pal = QPalette()\n #pal.setColor(QPalette.Button, Qt.lightGray)\n color_string = \"\"\n if self.computer.state == Computer.State.STATE_DEPLOYED:\n color_string = 'background-color: #FFDD00; '\n elif self.computer.state == Computer.State.STATE_FINISHED:\n color_string = 'background-color: #33BB33; '\n\n elif self.computer.state.value < 0:\n color_string = 'background-color: red; '\n\n font_style = 'font-weight: normal; '\n if self.isSelected:\n font_style = 'font-weight: bold; '\n\n #self.setPalette(pal)\n self.setStyleSheet(\"LbClient {\"+color_string + font_style+\"}\")\n\n def select(self):\n if self.computer.state != Computer.State.STATE_STUDENT_ACCOUNT_NOT_READY:\n self.log.append(msg=\" ausgewählt {}\".format(self.computer.getHostName()))\n self.isSelected = True\n self._colorizeWidgetByClientState()\n\n def unselect(self):\n self.log.append(msg=\" abgewählt {}\".format(self.computer.getHostName()))\n self.isSelected = False\n self._colorizeWidgetByClientState()\n\n def toggleSelection(self, event):\n \"\"\"\n set or reset selection state\n \"\"\"\n toggleText = \"Client-PC auswählen\" \n if self.isSelected:\n self.unselect()\n else:\n self.select()\n toggleText = \"Client-PC abwählen\" \n \n self.menu().actions()[1].setText(toggleText)\n \n\n def setLabel(self):\n label = \"\"\n if self.showIp==True:\n label = self.computer.ip + \"\\n\"\n if self.computer.getHostName() != \"\":\n label = label + self.computer.getHostName()\n\n label = label + \"\\n\" + (self.computer.getCandidateName() or \"-LEER-\")\n label = label + \"\\n\" + (self.computer.lb_dataDirectory)\n self.setText(label)\n self.setOwnToolTip()\n self._colorizeWidgetByClientState()\n\n def blockUsbAccess(self):\n self.computer.disableUsbAccess(True)\n\n def allowUsbAccess(self):\n self.computer.disableUsbAccess(False)\n\n def blockUsbAccessThread(self):\n QThreadPool.globalInstance().start(LbClient.blockUsbAccess(self))\n\n def allowUsbAccessThread(self):\n QThreadPool.globalInstance().start(LbClient.allowUsbAccess(self))\n\n def allowInternetAccessThread(self):\n QThreadPool.globalInstance().start(LbClient.AllowInternetThread(self))\n\n def blockInternetAccessThread(self):\n QThreadPool.globalInstance().start(LbClient.BlockInternetThread(self))\n\n def blockInternetAccess(self, block=True):\n print('blocking internet access: ' + str(block))\n self.computer.blockInternetAccess(block)\n\n class BlockInternetThread(QRunnable):\n\n def __init__(self, widget):\n QRunnable.__init__(self)\n self.widget = widget\n\n def run(self):\n self.widget.computer.blockInternetAccess()\n\n class AllowInternetThread(QRunnable):\n\n def __init__(self, widget):\n QRunnable.__init__(self)\n self.widget = widget\n\n def run(self):\n self.widget.computer.allowInternetAccess()\n\n class StatusThreadSignal(QObject):\n\n checkStateSignal = QtCore.Signal(int)\n\n class CheckStatusThread(QRunnable):\n \"\"\"\n get the hostname asynchronously\n \"\"\"\n def __init__(self, widget):\n QRunnable.__init__(self)\n self.widget = widget\n self.computer = widget.computer\n self.connector = LbClient.StatusThreadSignal()\n\n def run(self):\n # sleep(random.randint(2,5))\n try:\n self.computer.checkStatusFile()\n print(\"fetching this computers name\")\n self.computer.getHostName()\n print(\"finished fetching this computers name\")\n\n except Exception as ex:\n self.widget.log.append(\"crashed fetching this computers name: \" + str(ex))\n pass\n\n self.connector.checkStateSignal.emit(1)\n\n class ShutdownTask(QRunnable):\n \"\"\"\n simple thread to shutdown given pc (respectively the pc attached to this widget)\n \"\"\"\n\n def __init__(self, widget):\n QRunnable.__init__(self)\n self.widget = widget\n\n def run(self):\n if self.widget.computer.shutdown() is True:\n self.widget.deleteLater()\n\n else:\n colorString = \"background-color: red;\"\n self.widget.setStyleSheet(\"QPushButton {\" + colorString + \"}\")\n\n class RemoteShellTask(QRunnable):\n \"\"\"\n a weak attempt to get the remote shell non-modal to allow multiple instances\n abandon for now since it either stays modal or doesn't work at a ll on WIN and MAC\n worked perfectly well on Linux :-)\n \"\"\"\n def __init__(self, parentApp, client:Computer):\n QRunnable.__init__(self)\n self.terminalDialog = EcManRemoteTerminal(parent=parentApp, client=client)\n self.terminalDialog.setModal(False)\n \n def run(self):\n self.terminalDialog.exec_()\n ","repo_name":"greenorca/ECMan","sub_path":"ui/lbClientButton.py","file_name":"lbClientButton.py","file_ext":"py","file_size_in_byte":14601,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"2298328625","text":"'''\nauthor:huangchao\ncontent:\n1.linear regression realized by forloop\n2.code is organized by class\n3.plot the origin scatter data and fit curve\n'''\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nclass linear_regression_forloop:\n def __init__(self):\n print(\">>>>construct linear regression model\")\n\n #config hyper parameters\n def fit(self,learning_rate=0.01,iter_num=100,batch_size=64):\n print(\">>>>config hyper parameters\")\n self.learning_rate=learning_rate\n self.iter_num=iter_num\n self.batch_size=batch_size\n\n def print_parameters(self):\n print(\"hyper parameters:learning_rate={},iter_num={},batch_size={}\".format(self.learning_rate,self.iter_num,self.batch_size))\n print(\"construct weight:w={},b={}\".format(self.construct_w,self.construct_b))\n\n #generate random linear data\n def gen_linear_dist_data(self):\n print(\">>>>generate random linear data\")\n np.random.seed(0)\n self.construct_w=np.random.randint(0,10)+np.random.random()\n self.construct_b=np.random.randint(0,5)+np.random.random()\n sample_num=100\n x_list=[]\n y_list=[]\n for i in range(sample_num):\n x=np.random.randint(0,100)*np.random.random()\n y=self.construct_w*x+self.construct_b+np.random.random()*np.random.randint(-30,30)\n x_list.append(x)\n y_list.append(y)\n return x_list,y_list\n\n #train the model,update w,b and loss\n def train(self,x_list,y_list):\n print(\">>>>train linear regression model\")\n self.w=np.random.random()\n self.b=0\n sample_num=len(y_list)\n for i in range(self.iter_num):\n batch_index = np.random.choice(sample_num, self.batch_size)\n x_batch=[x_list[j] for j in batch_index]\n y_batch=[y_list[j] for j in batch_index]\n dw,db=self.calculate_gradient(x_batch,y_batch,self.w,self.b)\n self.update_parameter(dw,db)\n loss=self.calculate_loss(x_list,y_list,self.w,self.b)\n if(i%1000==0):\n print(\"After the {}th iterator:loss={},w={},b={},dw={},db={}\".format(i,loss,self.w,self.b,dw,db))\n\n\n def calculate_gradient(self,x_batch,y_bacth,w,b):\n batch_size=len(y_bacth)\n dw=0\n db=0\n for i in range(batch_size):\n pred_y=self.prediction(x_batch[i],w,b)\n dw += (pred_y-y_bacth[i])*x_batch[i]\n db += (pred_y-y_bacth[i])\n return dw/batch_size,db/batch_size\n\n def prediction(self,x,w,b):\n y=x*w+b\n return y\n\n def update_parameter(self,dw,db):\n self.w -= self.learning_rate*dw\n self.b -= self.learning_rate*db\n\n def calculate_loss(self,x,y,w,b):\n num=len(x)\n average_loss=0.0\n for i in range(num):\n average_loss += (w*x[i]+b-y[i])**2\n average_loss = average_loss/(2*num)\n return average_loss\n\n\n\n#construct model\nlr_model=linear_regression_forloop()\n\n#config model hyper parameters\nlr_model.fit(learning_rate=0.001,iter_num=10000,batch_size=64)\n\n#generate origin data\nX,y=lr_model.gen_linear_dist_data()\nlr_model.print_parameters()\n\n#train the model ,update weight and loss\nlr_model.train(X,y)\n\n#plot origin data and fit curve\nX_sort=X.copy()\nX_sort.sort()\ny_pred=[X_sort[i]*lr_model.w+lr_model.b for i in range(len(X_sort))]\nplt.figure(1)\nplt.scatter(X,y,s=20,c='b')\nplt.plot(X_sort,y_pred,c='r')\nplt.title(\"origin data and fit data\")\nplt.show()\n\n\n","repo_name":"huangchaoRestart/AI-For-CV","sub_path":"lesson3_homework/linear_regression_forloop.py","file_name":"linear_regression_forloop.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"24293369260","text":"import networkx as nx\nimport os\nimport pickle\n\n\ndef dotbracket_to_graph(dotbracket):\n G = nx.Graph()\n bases = []\n\n for i, c in enumerate(dotbracket):\n if c == '(':\n bases.append(i)\n elif c == ')':\n neighbor = bases.pop()\n G.add_edge(i, neighbor, edge_type='base_pair')\n elif c == '.':\n G.add_node(i)\n else:\n print(\"Input is not in dot-bracket notation!\")\n return None\n\n if i > 0:\n G.add_edge(i, i - 1, edge_type='adjacent')\n return G\n\n\ndef get_family_to_sequences():\n family_sequences_path = '../data/family_rna_sequences/'\n rna_family_files = sorted(os.listdir(family_sequences_path))\n family_to_sequences = {}\n\n for file in rna_family_files:\n if 'RF' in file:\n family = file[:7]\n family_sequences = pickle.load(open(family_sequences_path + file, 'rb'))\n family_to_sequences[family] = family_sequences\n\n return family_to_sequences\n","repo_name":"emalgorithm/ncRNA-family-prediction","sub_path":"src/util/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"32"} +{"seq_id":"17142370012","text":"#!/usr/bin/env python\n\n\"\"\" Implements a basic binary classifier using a shallow neural\n network architecture. Input/Output data is stored in matrix\n format to allow for reuse with other methods\n\nCreated: 11/25/2020\n\"\"\"\n\n__author__ = \"Mike Hagenow\"\n\nimport numpy as np\nfrom PreProcessData import loadFaults\nimport torch\nfrom torch import nn, optim\nimport torch.nn.functional as F\nimport copy\n\n\n# Define the network class for pytorch\n\n# tutorial usage: https://curiousily.com/posts/build-your-first-neural-network-with-pytorch/\n# https://www.analyticsvidhya.com/blog/2019/01/guide-pytorch-neural-networks-case-studies/\n\n\"\"\"\nClass defines pytorch network structure for binary classifier\n\"\"\"\nclass SimpleNN(nn.Module):\n def __init__(self,num_features):\n super(SimpleNN, self).__init__() # call default network constructor\n self.fc1 = torch.nn.Linear(num_features,1000)\n self.fc2 = torch.nn.Linear(1000,1000)\n self.fc3 = torch.nn.Linear(1000,1)\n def forward(self,x):\n x = torch.relu(self.fc1(x))\n # x = torch.relu(self.fc2(x))\n x = torch.sigmoid(self.fc3(x))\n return x\n\n\"\"\"\nClass defines pytorch network structure for multiclass classifier\n\"\"\"\nclass MutliClassNN(nn.Module):\n def __init__(self,num_features,num_labels):\n super(MutliClassNN, self).__init__() # call default network constructor\n self.fc1 = torch.nn.Linear(num_features,1000)\n # self.fc2 = torch.nn.Linear(1000,1000)\n self.fc3 = torch.nn.Linear(1000,num_labels)\n def forward(self,x):\n x = torch.relu(self.fc1(x))\n # x = torch.relu(self.fc2(x))\n x = torch.sigmoid(self.fc3(x))\n return x\n\n\"\"\"\nCreates a binary network and trains on the data. Epochs are generall the default 5000\n\"\"\"\ndef nn(A,b, epochs=5000):\n # create an instance of the network\n net = SimpleNN(np.shape(A)[1])\n\n # create the optimizer\n optimizer = optim.Adam(net.parameters(), lr=0.01)\n\n # Run epochs\n\n init_weights = copy.deepcopy(net.fc1.weight.data)\n\n for ii in range(0,epochs):\n network_in = A\n network_target = b\n\n optimizer.zero_grad() # zero the gradient buffers\n output = net.forward(torch.Tensor(network_in))\n loss = torch.nn.functional.mse_loss(output, torch.Tensor(network_target))\n loss.backward()\n optimizer.step() # Does the update\n # if ii%1000==0:\n # print(\"Epoch:\", ii, \"Training Loss: \",loss.item())\n # print(net.fc1.weight.grad)\n\n # print(\"Testing the Network!!\")\n # # print(net.forward(torch.from_numpy(A).float()).detach().numpy())\n # # print(torch.from_numpy(A).float())\n # print(init_weights)\n # final_weights = net.fc1.weight.data\n # print(final_weights)\n val = net.forward(torch.from_numpy(A).float()).detach().numpy()\n val_bool = np.array([0.0 if i<0.5 else 1.0 for i in val]).reshape((len(val),1))\n\n # print(torch.Tensor(network_target))\n # print(network_target)\n\n error_vec = [0 if i[0] == i[1] else 1 for i in np.hstack((val_bool,b))]\n # print(\"Error:\",sum(error_vec) / len(b))\n\n return copy.deepcopy(net)\n\n\"\"\"\nClass defines pytorch network structure for multiclass\n\nAs is a list of the A matrix for each class\n\"\"\"\ndef nnMultliClass(As, epochs=5000):\n # create an instance of the network\n net = MutliClassNN(np.shape(As[0])[1],len(As))\n\n # create the optimizer\n optimizer = optim.Adam(net.parameters(), lr=0.01)\n\n # Format the data for pytorch\n A = As[0]\n B = np.zeros((np.shape(A)[0],len(As)))\n B[:,0] = 1.0\n\n for ii in range(1,len(As)):\n A = np.concatenate((A,As[ii]),axis=0)\n B_temp = np.zeros((np.shape(As[ii])[0],len(As)))\n B_temp[:,ii] = 1.0\n B = np.concatenate((B,B_temp),axis=0)\n\n init_weights = copy.deepcopy(net.fc1.weight.data)\n\n # run the epochs to train the network\n for ii in range(0, 5000):\n network_in = A\n network_target = B\n\n optimizer.zero_grad() # zero the gradient buffers\n output = net.forward(torch.Tensor(network_in))\n loss = torch.nn.functional.mse_loss(output, torch.Tensor(network_target))\n loss.backward()\n optimizer.step() # Does the update\n # if ii%100==0:\n # print(\"Epoch:\", ii, \"Training Loss: \",loss.item())\n # print(net.fc1.weight.grad)\n\n return copy.deepcopy(net)\n\n\n\"\"\"\nTest function for multiclass to check the output for a single feature vector\n\"\"\"\ndef test_multi():\n X_faults = loadFaults()\n net = nnMultliClass(X_faults)\n\n temp = net.forward(torch.Tensor(X_faults[3][5, :]))\n print(temp.detach().numpy())\n print(np.argmax(temp.detach().numpy()))\n\n\"\"\"\nTest function for the binary classifier neural network. Tests for one choice of classes.\n\"\"\"\ndef test():\n X_faults = loadFaults()\n num_class = len(X_faults)\n ii = 6\n\n # positive label is the 'one'\n X_train_plus1 = X_faults[ii]\n\n # negative label is the 'all' randomly downsampled to the same length as the positive\n X_train_minus1 = np.vstack([X_faults[i] for i in range(0, num_class) if (i != ii)])\n\n X_train_temp = np.vstack((X_train_plus1, X_train_minus1))\n y_train_temp = np.vstack((np.ones((np.shape(X_train_plus1)[0], 1)), np.zeros((np.shape(X_train_minus1)[0], 1))))\n w_train_temp = np.vstack((1.0 / (np.shape(X_train_plus1)[0]) * np.ones((np.shape(X_train_plus1)[0], 1)),\n 1.0 / (np.shape(X_train_minus1)[0]) * np.ones((np.shape(X_train_minus1)[0], 1))))\n\n nn(X_train_temp,y_train_temp,0.0)\n\nif __name__ == \"__main__\":\n test_multi()\n\n\n\n\n","repo_name":"mhagenow01/ECE532ClassifierComparison","sub_path":"Code/simpleNN.py","file_name":"simpleNN.py","file_ext":"py","file_size_in_byte":5578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"38514906904","text":"from mysql.connector import errors\nfrom mysql.connector import pooling\nfrom flask import *\nimport mysql.connector\nimport jwt\nimport time\nfrom datetime import datetime, timedelta\nfrom flask_cors import CORS\nfrom flask_bcrypt import Bcrypt\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\n\nattraction = Blueprint(\"attraction\", __name__)\n# app.register_blueprint(attraction)\n\napp = Flask(__name__)\napp.secret_key = os.getenv(\"app.secret_key\")\napp.config[\"JSON_AS_ASCII\"] = False\napp.config[\"TEMPLATES_AUTO_RELOAD\"] = True\napp.config[\"JSON_SORT_KEYS\"] = False\n\ndbconfig = {\n \"user\": os.getenv(\"user\"),\n \"password\": os.getenv(\"password\"),\n \"host\": os.getenv(\"host\"),\n \"database\": os.getenv(\"database\")\n}\n\n# ------- Get connection object from a pool -------\n\nconnection_pool = mysql.connector.pooling.MySQLConnectionPool(pool_name=\"mypool\",\n pool_size=10,\n pool_reset_session=True,\n **dbconfig)\n# ------- 取得景點資料列表 -------\n\n\n@attraction.route(\"/api/attractions\", methods=[\"GET\"])\ndef api_attractions():\n\n try:\n page = request.args.get(\"page\", None)\n page = int(page) # 將字串page轉換成integer\n page_index = page * 12\n keyword = request.args.get(\"keyword\", None)\n return_data = []\n connection_object = connection_pool.get_connection()\n mycursor = connection_object.cursor(dictionary=True)\n\n if keyword != None: # 有keyword，回傳 keyword 的資料\n mycursor.execute(\n f'SELECT * FROM spots WHERE category=%s or name like \"%{keyword}%\" ORDER BY id LIMIT {page_index},12', (keyword,))\n data = mycursor.fetchall()\n\n if len(data) >= 12:\n next_page = page + 1\n else:\n next_page = None\n\n for x in range(len(data)):\n result = {\n \"id\": data[x][\"id\"],\n \"name\": data[x][\"name\"],\n \"category\": data[x][\"category\"],\n \"description\": data[x][\"description\"],\n \"address\": data[x][\"address\"],\n \"transport\": data[x][\"transport\"],\n \"mrt\": data[x][\"mrt\"],\n \"latitude\": data[x][\"latitude\"],\n \"longitude\": data[x][\"longitude\"],\n \"images\": eval(data[x][\"images\"])\n }\n return_data.append(result) # 把資料塞進去\n # print(result)\n\n final_result = {\"nextPage\": next_page, \"data\": return_data}\n # final_result.headers.add('Access-Control-Allow-Origin', '*')\n return jsonify(final_result)\n\n if keyword == None: # 沒有keyword，回傳全部資料\n mycursor.execute(\n \"SELECT * FROM spots ORDER BY id LIMIT %s,12\", (page_index,))\n # limit[index,count] 代表從page_index開始算，回傳到第12筆\n data = mycursor.fetchall()\n\n if len(data) >= 12:\n next_page = page + 1\n else:\n next_page = None\n # print(data)\n # print(len(data))\n\n for x in range(len(data)):\n result = {\n \"id\": data[x][\"id\"],\n \"name\": data[x][\"name\"],\n \"category\": data[x][\"category\"],\n \"description\": data[x][\"description\"],\n \"address\": data[x][\"address\"],\n \"transport\": data[x][\"transport\"],\n \"mrt\": data[x][\"mrt\"],\n \"latitude\": data[x][\"latitude\"],\n \"longitude\": data[x][\"longitude\"],\n \"images\": eval(data[x][\"images\"]),\n }\n return_data.append(result) # 把資料塞進去\n # print(result)\n\n final_result = {\"nextPage\": next_page, \"data\": return_data}\n # final_result.headers.add('Access-Control-Allow-Origin', '*')\n return jsonify(final_result)\n except:\n return {\n \"error\": True,\n \"message\": \"伺服器內部錯誤\"\n }, 500\n\n finally:\n mycursor.close()\n connection_object.close()\n\n# ------- 根據景點編號（id) 取得 景點資料 -------\n\n\n@attraction.route(\"/api/attraction/<attractionId>\")\ndef api_attraction_id(attractionId):\n try:\n connection_object = connection_pool.get_connection()\n mycursor = connection_object.cursor(dictionary=True)\n id = request.args.get(\"id\", None)\n attractionId = int(attractionId)\n mycursor.execute(\n \"SELECT * FROM spots WHERE id=%s\", (attractionId,))\n data = mycursor.fetchone()\n img_url = data[\"images\"].split(\"https\")\n imgurl_list = (data[\"images\"]).split(\"', '\")\n imgurl_list[0] = imgurl_list[0][2:]\n # url_lst[-1] = url_lst[-1][:-2]\n imgurl_list[len(imgurl_list) -\n 1] = imgurl_list[len(imgurl_list)-1][:-2]\n # url總長度\n # print(url_lst)\n\n final_result = {\n \"data\": {\n \"id\": data[\"id\"],\n \"name\": data[\"name\"],\n \"category\": data[\"category\"],\n \"description\": data[\"description\"],\n \"address\": data[\"address\"],\n \"transport\": data[\"transport\"],\n \"mrt\": data[\"mrt\"],\n \"latitude\": data[\"latitude\"],\n \"longitude\": data[\"longitude\"],\n \"images\": imgurl_list,\n # string 轉 list, 若 list 裡有 []，可用eval\n }\n }\n # final_result.headers.add('Access-Control-Allow-Origin', '*')\n return jsonify(final_result)\n\n except TypeError:\n return {\n \"error\": True,\n \"messgae\": \"景點編號不正確\"\n }, 400\n\n except Exception as err:\n return {\n \"error\": True,\n \"messgae\": \"伺服器內部錯誤\"\n }, 500\n\n finally:\n mycursor.close()\n connection_object.close()\n\n# ------- 取得景點分類(category)列表 -------\n\n\n@ attraction.route(\"/api/categories\", methods=[\"GET\"])\ndef api_categories():\n try:\n connection_object = connection_pool.get_connection()\n mycursor = connection_object.cursor()\n categories = request.args.get(\"categories\", None)\n mycursor.execute(\n \"SELECT category FROM spots GROUP BY category\")\n data = mycursor.fetchall()\n # print(data)\n sorted_data = []\n for x in data: # 要把list of tuples 用迴圈取出，再append到空的[]裡\n # print(x[0])\n sorted_data.append(x[0])\n result = {\n \"data\": sorted_data\n }\n # result.headers.add('Access-Control-Allow-Origin', '*')\n return jsonify(result)\n\n except:\n return {\n \"error\": True,\n \"message\": \"伺服器內部錯誤\"\n }, 500\n\n finally:\n mycursor.close()\n connection_object.close()\n","repo_name":"stephypocky/Taipei-Day-Trip","sub_path":"api/attraction.py","file_name":"attraction.py","file_ext":"py","file_size_in_byte":7211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"29460186575","text":"# user type\nACCOUNT_SUPER_ADMIN = \"Super Admin\"\nACCOUNT_DOCTOR = \"Doctor\"\nACCOUNT_DISTRIBUTOR = \"Distributor\"\nACCOUNT_PATIENTS = \"Patient\"\nACCOUNT_TYPE = \"account_type\"\n\nPAGE_NUMBER = \"page_number\"\nPAGE_LENGTH = \"page_length\"\nTOTAL_PAGE_COUNT = \"total_page_count\"\nSEARCH_QUERY = \"search_query\"\nFROM_DATE = \"from_date\"\nTO_DATE = \"to_date\"\n\nOTP = \"otp\"\nOTP_TOKEN = \"otp-token\"\nACCESS_TOKEN = \"token\"\n\n# gender\nGENDER_MALE = \"Male\"\nGENDER_FEMALE = \"Female\"\nGENDER_TRANSGENDER = \"Transgender\"\nGENDER_OTHER = \"Other\"\n\n# Drug Unit\nDROP = \"Drop\"\nQTY = \"Qty\"\nML = \"ml\"\nMG = \"mg\"\nGRAM = \"gram\"\nCUP = \"cup\"\n\nMOBILE = 'mobile'\nPASSWORD = 'password'\nMESSAGE = 'message'\nSUCCESS = 'success'\nEMAIL = \"email\"\nNAME = \"name\"\nDOB = \"dob\"\nGENDER = \"gender\"\nDEGREE = \"degree\"\nMEDICAL_REG_NO = \"medical_reg_no\"\nDESIGNATION = \"designation\"\nADDRESS = \"address\"\nCITY = \"city\"\n\nDRUG_NAME = \"drug_name\"\nFORMULA = \"formula\"\nBRAND = \"brand\"\nDRUG_UNIT = \"drug_unit\"\nANUPAAN = \"anupaan\"\nFORMULATION = \"formulation\"\n\nPATIENT_NAME = \"patient_name\"\nDOCTOR_NAME = \"doctor_name\"\nPATIENT_FIRST_NAME = \"patient_first_name\"\nPATIENT_LAST_NAME = \"patient_last_name\"\nOCCUPATION = \"occupation\"\nAGE = \"age\"\nPATIENT_MOBILE = \"patient_mobile\"\nPATIENT_EMAIL = \"patient_email\"\n\nCHIEF_COMPLAINT = \"chief_complaint\"\nHISTORY_OF_CHIEF_COMPLAINT = \"history_of_chief_complaint\"\nBLOOD_PRESSURE = \"blood_pressure\"\nBLOOD_SUGAR = \"blood_sugar\"\nPLUS_RATE = \"plus_rate\"\nSPO2 = \"spo2\"\nTEMPERATURE = \"temperature\"\nOE = \"oe\"\nREQUIRED_TEST = \"required_test\"\nADVISE = \"advise\"\nPRESCRIPTION_LIST = \"prescription_list\"\nPRESCRIPTION_TABLE_ID = \"prescription_table_id\"\nTREATMENT_TABLE_ID = \"treatment_table_id\"\nTREATMENT_DATE = \"treatment_date\"\nWEIGHT = \"weight\"\nDIET_EXERCISE = \"diet_exercise\"\n\nDOCTOR_LIST = \"doctor_list\"\nDRUG_LIST = \"drug_list\"\nBLOG_LIST = \"blog_list\"\nBLOG_DATA = \"blog_data\"\nPATIENT_LIST = \"patient_list\"\n\nDOSE_LIST = \"dose_list\"\nFREQUENCY_LIST = \"frequency_list\"\nINSTRUCTION_LIST = \"instruction_list\"\nTREATMENT_HISTORY = \"treatment_history\"\n\nDOCTOR_TABLE_ID = \"doctor_table_id\"\nPATIENT_TABLE_ID = \"patient_table_id\"\nDRUG_TABLE_ID = \"drug_table_id\"\nTREATMENT_TABLE_ID = \"treatment_table_id\"\nBLOG_TABLE_ID = \"blog_table_id\"\n\nSUGGESTIONS = \"suggestions\"\n\n# date format\nDJANGO_DATE_FORMAT = '%Y-%m-%d'\nDATE_FORMAT_DDMMYYYY = '%d/%m/%Y'\nDATE_FORMAT_DD_MM_YYYY = '%d-%m-%Y'\nDATE_FORMAT = '%d %b %Y'\nTIME_FORMAT = '%H:%M'\nDATE_TIME_FORMAT = '%d/%m/%Y %I:%M %p'\n","repo_name":"emit077/DigitalAyurvedServer","sub_path":"keys.py","file_name":"keys.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"18601729886","text":"# import heap sort\nfrom pygorithm.sorting import heap_sort\n\nmyList = [12,3,2,14,3,97,2345,6590129,5,125,]\n# sort the list\n\nsorted_list = heap_sort.sort(myList)\n\n# I sees it\nprint(sorted_list)\n\n# print time complexities of heap sort\nprint(heap_sort.time_complexities())\n\ndef heapify(alist):\n #This function helps to maintain the heap property\n # start = (len(alist) - 2) // 2 (faster execution)\n start = len(alist) // 2\n while start >= 0:\n shiftDown(alist, start, len(alist) - 1)\n start -= 1\n\ndef shiftDown(alist, start, end):\n root = start\n while root * 2 + 1 <= end:\n child = root * 2 + 1\n # right child exists and is greater than left child\n if child + 1 <= end and alist[child] < alist[child + 1]:\n child += 1\n # if child is greater than root(parent), then swap their positions\n if child <= end and alist[root] < alist[child]:\n alist[root], alist[child] = alist[child], alist[root]\n root = child\n else:\n return\n\n# heap in python\ndef sort(alist):\n heapify(alist) # creates heap\n end = len(alist) - 1\n while end > 0:\n alist[end], alist[0] = alist[0], alist[end]\n shiftDown(alist, 0, end - 1)\n end -= 1\n return alist\n\nprint(sort(myList))\n","repo_name":"colonelrascals/sorts_in_python","sub_path":"heap_sort.py","file_name":"heap_sort.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"9718532128","text":"import numpy as np\nimport numpy.polynomial.polynomial as poly\nimport scipy as sp\nimport scipy.interpolate\nimport math\n#from matplotlib import pyplot as plt\n\nimport pickle\nimport sys\n\nsys.path.append('Classes')\nfrom reservoir import Reservoir\nspeakerN = 9\nchannelsN = 12\nsamplesN = 30\ntrialsN = 1\nN = 10\n\nwith open('prepdata.pickle', 'rb') as f:\n\tdata = pickle.load(f)\n\ntrainData = data['train']\ntestData = data['test']\n\n# speakerN x (Nodes x (Timesteps*samplesN))\nstateData = []\n# speakerN x (Channels x (Timesteps*noSamples))\ninputData = []\nfor trial in range(trialsN):\n\tr = Reservoir(N = N, inputScaling=0.2, biasScaling=1, inputDim=12)\n\tfor speakerSamples in trainData:\n\t\tstate, inp = r.collectStates(speakerSamples, t_learn=4, t_washout=0, mode = 'batch')\n\t\tstateData.append(state)\n\t\tinputData.append(inp)\n\n\t#Ridge Regression with different regularizers (Tikhonov Alpha)\n#\talphas = 2.0**np.arange(-10,0)\n#\tfor candidateAlpha in alphas:\n#\t\tmse = 0\t\t\n\t\t#n-fold crossvalidation\n#\t\tn = 5\n#\t\tfoldSize = samplesN/n \n#\t\tfor fold in range(n):\n#\t\t\ttrainInds = [list(range(1,(fold - 1) * foldSize + 1)),list(range(fold * foldSize + 1, samplesN + 1))]\n#\t\t\ttrainExamples = [stateData[(fold - 1) * (foldSize + 1):fold * foldSize + 1)]]\n\n\tsamplesPerSpeaker = []\n\tfor speakerSamples in trainData:\n\t\tspeakerInput = np.array(speakerSamples)\n\t\tspeakerInput = speakerInput.reshape((120,12))\n\t\tsamplesPerSpeaker.append(speakerInput)\n\n\tr.loadingAndConceptors(patterns=samplesPerSpeaker, t_learn=120, t_washout=0, mode = 'batch')\n\tprint(r.conceptors[1].shape)\n\t\t\n\n\n","repo_name":"kstandvoss/Conceptors","sub_path":"JapaneseVowel/whereTheMagicHappens.py","file_name":"whereTheMagicHappens.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"21737732065","text":"from datetime import datetime, timedelta\r\n\r\nfrom influxdb_client import InfluxDBClient\r\nfrom influxdb_client.client.write_api import SYNCHRONOUS\r\n\r\nimport numpy as np\r\n\r\nfrom digital_twin.communication.rabbitmq_protocol import ROUTING_KEY_PTSIMULATOR4\r\nfrom incubator.communication.server.rpc_client import RPCClient\r\nfrom incubator.communication.shared.protocol import from_s_to_ns\r\nfrom incubator.config.config import config_logger, load_config\r\nfrom incubator.models.plant_models.room_temperature_model import room_temperature\r\nfrom incubator.physical_twin.low_level_driver_server import CTRL_EXEC_INTERVAL\r\n\r\n\r\ndef generate_room_data(influxdb, bucket, org, start_date, end_date):\r\n start_date_s = start_date.timestamp()\r\n end_date_s = end_date.timestamp()\r\n\r\n # Get write-api\r\n write_api = influxdb.write_api(write_options=SYNCHRONOUS)\r\n\r\n # Construct points\r\n timerange_s = np.arange(start_date_s, end_date_s+CTRL_EXEC_INTERVAL, CTRL_EXEC_INTERVAL)\r\n\r\n def point(t):\r\n t_ns = from_s_to_ns(t)\r\n return {\r\n \"measurement\": \"low_level_driver\",\r\n \"time\": t_ns,\r\n \"tags\": {\r\n \"source\": \"low_level_driver\"\r\n },\r\n \"fields\": {\r\n \"t3\": room_temperature(t),\r\n \"time_t3\": t_ns,\r\n }\r\n }\r\n\r\n points = [point(t) for t in timerange_s]\r\n\r\n # Write them to DB\r\n write_api.write(bucket, org, points)\r\n\r\n return points\r\n\r\n\r\ndef generate_incubator_exec_data(rpc_client, config, start_date, end_date):\r\n start_date_ns = from_s_to_ns(start_date.timestamp())\r\n end_date_ns = from_s_to_ns(end_date.timestamp())\r\n\r\n params = {\"start_date\": start_date_ns,\r\n \"end_date\": end_date_ns,\r\n \"controller_comm_step\": 3.0,\r\n \"record\": True,\r\n \"as_lld\": True}\r\n\r\n params_plant = config[\"digital_twin\"][\"models\"][\"plant\"][\"param4\"]\r\n for k in params_plant:\r\n params[k] = params_plant[k]\r\n\r\n params_ctrl = config[\"physical_twin\"][\"controller\"]\r\n for k in params_ctrl:\r\n params[k] = params_ctrl[k]\r\n\r\n reply = rpc_client.invoke_method(ROUTING_KEY_PTSIMULATOR4, \"run_historical\", params)\r\n if \"error\" in reply:\r\n print(reply)\r\n raise ValueError(reply)\r\n return reply\r\n\r\n\r\ndef generate_dummy_data():\r\n config_logger(\"logging.conf\")\r\n config = load_config(\"startup.conf\")\r\n\r\n # Time range for the fake data\r\n end_date = datetime.now()\r\n start_date = end_date - timedelta(hours=10)\r\n\r\n influxdb = InfluxDBClient(**config[\"influxdb\"])\r\n bucket = config[\"influxdb\"][\"bucket\"]\r\n org = config[\"influxdb\"][\"org\"]\r\n\r\n generate_room_data(influxdb, bucket, org, start_date, end_date)\r\n\r\n client = RPCClient(**(config[\"rabbitmq\"]))\r\n client.connect_to_server()\r\n\r\n generate_incubator_exec_data(client, config, start_date, end_date)\r\n\r\n\r\nif __name__ == '__main__':\r\n generate_dummy_data()\r\n","repo_name":"INTO-CPS-Association/example_digital-twin_incubator","sub_path":"software/cli/generate_dummy_data.py","file_name":"generate_dummy_data.py","file_ext":"py","file_size_in_byte":2955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"11128315490","text":"\r\n\r\nimport copy;\r\nimport time;\r\n\r\nclass NodParcurgere:\r\n def __init__(self,info,key,parinte,cost):\r\n self.info = info # Informatia din nod\r\n self.key = key # Cheia reprezentand ultima cheie aplicata pe incuietoare (muchia din arborele de parcurgere cu care s-a ajuns in acest nod)\r\n self.parinte = parinte # Parintele nodului din arborele de parcurgere\r\n self.g = cost # Costul\r\n\r\n def obtineDrum(self):\r\n l = [self]\r\n nod = self\r\n while nod.parinte is not None: # Obtinem drumul pana la nodul nostru, mergand din parinte in parinte\r\n l.insert(0,nod.parinte)\r\n nod = nod.parinte\r\n return l\r\n\r\n def afisDrum(self,afisLung = False, afisCost = False):\r\n\r\n global sir_afisare\r\n l = self.obtineDrum()\r\n i = 1\r\n for nod in l:\r\n if nod.key is not None:\r\n sir_afisare += str(i) + \") Incuietori: \" + str(nod.parinte) + '\\n'\r\n sir_afisare += \"Folosim cheia: \" + str(nod.key) + \" pentru a ajunge la \" + str(nod) + '\\n'\r\n i += 1\r\n\r\n\r\n sir_afisare += \"Incuietori(stare scop): \" + str(nod) + '\\n'\r\n if afisCost:\r\n sir_afisare += \"S-au realizat : \" + str( self.g ) + \" operatii.\" + '\\n'\r\n if afisLung:\r\n sir_afisare += \"Lungimea drumului: \" + str(len(l)) + '\\n'\r\n return len(l)\r\n\r\n def contineInDrum(self, infoNodNou):\r\n \"\"\"Verificam daca nodul pe care vrem sa il adaugam in arbore se gaseste pe drumul pana la nodul curent.\r\n Folosim aceasta functie ca sa ne asiguram ca nu se creeaza cicluri\"\"\"\r\n nodDrum = self\r\n while nodDrum is not None: # Reconstruim drumul mergand din parinte in parinte\r\n if infoNodNou == nodDrum.info:\r\n return True\r\n nodDrum = nodDrum.parinte\r\n return False\r\n\r\n def __repr__(self):\r\n sir = \"\"\r\n sir += str(self.info)\r\n return sir\r\n\r\n def __str__(self):\r\n \"\"\"Ne ajuta sa afisam nodul nostru in formatul pe care ni-l cere problema (si pentru a fi mai usor de inteles)\"\"\"\r\n sir = \"[ \"\r\n incuietori = self.info\r\n for i in range (len(incuietori)-1):\r\n sir += \"inc(\" + str(incuietori[i][0]) + \",\" + str(incuietori[i][1]) + \"), \"\r\n\r\n sir += \"(\" + str(incuietori[len(incuietori)-1][0]) + \",\"+ str(incuietori[len(incuietori)-1][1]) + \") ]\"\r\n return sir\r\n\r\n\r\nclass Graph: # graful problemei\r\n\r\n def __init__(self, nume_fisier):\r\n \"\"\"Parsam fisierul de input si memoram informatia sub forma de stari in graf\"\"\"\r\n\r\n global sir_afisare\r\n\r\n f = open(nume_fisier,\"r\")\r\n continut_fisier = f.read()\r\n self.lista_chei = []\r\n for linie in continut_fisier.split('\\n'):\r\n cheie = list(linie)\r\n self.lista_chei.append(cheie)\r\n\r\n sir_afisare += \"Lista chei:\"+ str( self.lista_chei) +'\\n'\r\n incuietoriStart = []\r\n\r\n nrIncuietori = len(cheie)\r\n\r\n for i in range(nrIncuietori): # Mereu informatia de start va fi reprezentata de o lista de incuitori, toate inchide o data\r\n incuietoriStart.append(['i', 1])\r\n\r\n self.start = incuietoriStart\r\n\r\n self.scopuri = []\r\n incuietoriScop = []\r\n\r\n for i in range(nrIncuietori): # Mereu starea scop va fi reprezentata de o lista de incuietori, toate deschise\r\n incuietoriScop.append(['d', 0])\r\n\r\n self.scopuri.append (incuietoriScop)\r\n\r\n NodStart = NodParcurgere(self.start, None, None, 0) # Pentru nodul Start, cheia va fi None, parintele None si setam costul la 0\r\n\r\n sir_afisare += \"Initial: \"+ str(NodStart) + '\\n'\r\n\r\n def testeaza_scop(self, nodCurent): # Functia care testeaza daca suntem intr-o starea scop\r\n return nodCurent.info in self.scopuri\r\n\r\n def genereazaSuccesori(self, nodCurent): # Functia care genereaza succesori in nodul de parcurgere\r\n\r\n listaSuccesori = []\r\n\r\n for i in range(len(self.lista_chei)):\r\n incuietoariNoi = copy.deepcopy(nodCurent.info) # Folosim deepcopy ca sa nu afectam informatia nodului curent\r\n\r\n #print(\"incuietoariNoi: \", incuietoariNoi)\r\n cheie = copy.deepcopy(self.lista_chei[i])\r\n #print(cheie)\r\n\r\n for indice in range(len(cheie)):\r\n #print(\"cheie[indice]\",cheie[indice])\r\n if cheie[indice] == 'i': # Avem incuietoare de inchidere\r\n if incuietoariNoi[indice][0] == 'd': # Daca incuietaorea e deschisa o inchidem\r\n incuietoariNoi[indice][0] = 'i'\r\n incuietoariNoi[indice][1] += 1 # O mai inchidem o data sau o inchidem pentru prima oara daca era deschisa\r\n\r\n elif cheie[indice] == 'd': # Avem o cheie de deschidere\r\n # if incuietoariNoi[indice][0] == 'd': # daca e deja deschisa o lasam asa\r\n # continue\r\n if incuietoariNoi[indice][0] == 'i':\r\n\r\n if incuietoariNoi[indice][1] == 1: # daca e inchisa o singura data, o deschidem\r\n incuietoariNoi[indice] = ['d',0]\r\n\r\n elif incuietoariNoi[indice][1] > 1 : # daca e inchisa de mai multe ori scadem din indicele de inchidere\r\n incuietoariNoi[indice][1] -= 1\r\n\r\n elif cheie[indice] == 'g':\r\n continue\r\n\r\n\r\n if not nodCurent.contineInDrum(incuietoariNoi): # Daca nodul nu a mai fost pe acest drum, il adaugam\r\n\r\n nodNou = NodParcurgere(\r\n incuietoariNoi, # Informatia din nod, retinuta sub forma de lista de incuietori\r\n cheie, # Cheia (muchia) cu care am ajuns in aceasta stare\r\n nodCurent, # parinte\r\n nodCurent.g + 1 # Am mai facut o mutare, deci costului i se va adauga 1\r\n\r\n )\r\n # print(nodCurent.info)\r\n # print(cheie)\r\n # print(\"incuietoriNoi dupa aplicarea cheii: \", incuietoariNoi)\r\n listaSuccesori.append(nodNou)\r\n\r\n\r\n return listaSuccesori\r\n\r\n\r\ndef uniform_cost(gr, nrSolutiiCautate ):\r\n\r\n global sir_afisare\r\n # In coada vom avea doar noduri de tip NodParcurgere (nodurile din arborele de parcurgere)\r\n c = [NodParcurgere(gr.start, None,None,0)] # Initial avem nodul de START\r\n #print(\"c\", c)\r\n\r\n while len(c) > 0: # Cat timp coada nu e vida\r\n nodCurent = c.pop(0) # Luam primul element si il eliminam din coada\r\n\r\n if gr.testeaza_scop(nodCurent): # Daca am ajuns intr-un nod de scop inseamna ca avem solutie\r\n sir_afisare += \"Solutie: \\n\"\r\n nodCurent.afisDrum(afisLung=True, afisCost=True)\r\n sir_afisare += \"\\n------------------------------------------------\\n\"\r\n\r\n nrSolutiiCautate -= 1\r\n if nrSolutiiCautate == 0:\r\n return\r\n\r\n lSuccesori = gr.genereazaSuccesori(nodCurent) # Generam succesorii din arborele de parcurhere\r\n\r\n for s in lSuccesori:\r\n i = 0\r\n while i < len(c): # Ii caul locul nodului in coada astfel incat sa ramana ordonate dupa cost\r\n if c[i].g > s.g:\r\n break\r\n i += 1\r\n c.insert(i,s)\r\n\r\n myTime = time.time() - start;\r\n if myTime > 5:\r\n print(\"Time is out!\")\r\n sir_afisare += \"Time is out!\"\r\n break\r\n\r\n\r\n\r\n\r\n\r\n\r\nsir_afisare = \"\"\r\nnume_fisier_intrare = input(\"Numele fisierului de intrare: \")\r\nTimeout = int(input(\"Introduceti timeout-ul la care doriti sa se opreasca algoritmul: \"))\r\nnrSolutiiCautate = int(input(\"Numarul de solutii cautate: \"))\r\n\r\ngr = Graph(nume_fisier_intrare)\r\n\r\nstart = time.time()\r\n\r\n\r\n\r\n\r\nuniform_cost(gr,nrSolutiiCautate)\r\nnume_fisier_iesire = \"output_ucs.txt\"\r\ng = open(nume_fisier_iesire,\"w\")\r\ng.write(sir_afisare)\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"FlorescuMiruna/Artificial-Intelligence","sub_path":"Project1/Problema unui Lacat/Lacat_UCS.py","file_name":"Lacat_UCS.py","file_ext":"py","file_size_in_byte":7971,"program_lang":"python","lang":"ro","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"28754949281","text":"import json\n\nfrom dcaspt2_input_generator.components.data import Color\nfrom dcaspt2_input_generator.components.menu_bar import SaveDefaultSettingsAction\nfrom dcaspt2_input_generator.components.table_summary import UserInput\nfrom dcaspt2_input_generator.utils.dir_info import dir_info\nfrom dcaspt2_input_generator.utils.utils import debug_print\n\n\nclass SaveDefaultSettingsController:\n # app: MainApp\n # color_settings: ColorSettings\n def __init__(self, color: Color, user_input: UserInput, save_default_settings_action: SaveDefaultSettingsAction):\n self.color = color\n self.user_input = user_input\n self.save_default_settings_action = save_default_settings_action\n\n # Connect signals and slots\n self.save_default_settings_action.signal_save_default_settings.connect(self.save_default_settings)\n\n def save_default_settings(self):\n # Save current settings in user input and color settings to the settings.json file as default.\n user_input = self.user_input.get_input_values()\n color_setting = self.color.color_type\n user_input[\"color_theme\"] = color_setting\n setting_file_path = dir_info.setting_file_path\n with open(setting_file_path) as f:\n settings = json.load(f)\n debug_print(settings)\n for key, value in user_input.items():\n debug_print(f\"{key}, {value}\")\n settings.setdefault(key, {})\n settings[key] = value\n debug_print(settings)\n\n with open(setting_file_path, \"w\") as f:\n json.dump(settings, f, indent=4)\n","repo_name":"kohei-noda-qcrg/dcaspt2_input_generator","sub_path":"src/dcaspt2_input_generator/controller/save_default_settings_controller.py","file_name":"save_default_settings_controller.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"1549626207","text":"\"\"\"\nCollection of higher level functions to perform operational tasks.\n\nSome day, this module could have a companion module containing the CLI logic\nfor these functions instead of scripts in ``<source>/bin/scripts``.\n\n\"\"\"\nimport collections\nimport logging\nfrom typing import (\n Deque, Hashable, Set, Tuple, Generator, Iterable, Any, Optional\n)\n\nfrom smqtk_dataprovider import (\n DataElement\n)\nfrom smqtk_descriptors import (\n DescriptorElement, DescriptorGenerator, DescriptorSet\n)\nfrom smqtk_descriptors.descriptor_element_factory import DescriptorElementFactory\n\n\ndef compute_many_descriptors(data_elements: Iterable[DataElement],\n descr_generator: DescriptorGenerator,\n descr_factory: DescriptorElementFactory,\n descr_set: DescriptorSet,\n batch_size: Optional[int] = None,\n overwrite: bool = False,\n procs: Optional[int] = None,\n **kwds: Any) -> Iterable[Tuple[DataElement,\n DescriptorElement]]:\n \"\"\"\n Compute descriptors for each data element, yielding\n (DataElement, DescriptorElement) tuple pairs in the order that they were\n input.\n\n *Note:* **This function currently only operated over images due to the\n specific data validity check/filter performed.*\n\n :param data_elements: Iterable of DataElement instances of files to\n work on.\n :type data_elements: collections.abc.Iterable[DataElement]\n\n :param descr_generator: DescriptorGenerator implementation instance\n to use to generate descriptor vectors.\n :type descr_generator: DescriptorGenerator\n\n :param descr_factory: DescriptorElement factory to use when producing\n descriptor vectors.\n :type descr_factory: DescriptorElementFactory\n\n :param descr_set: DescriptorSet instance to add generated descriptors\n to. When given a non-zero batch size, we add descriptors to the given\n set in batches of that size. When a batch size is not given, we add\n all generated descriptors to the set after they have been generated.\n :type descr_set: DescriptorSet\n\n :param batch_size: Optional number of elements to asynchronously compute\n at a time. This is useful when it is desired for this function to yield\n results before all descriptors have been computed, yet still take\n advantage of any batch asynchronous computation optimizations a\n particular DescriptorGenerator implementation may have. If this is 0 or\n None (false-evaluating), this function blocks until all descriptors\n have been generated.\n :type batch_size: None | int | long\n\n :param overwrite: If descriptors from a particular generator already exist\n for particular data, re-compute the descriptor for that data and set\n into the generated DescriptorElement.\n :type overwrite: bool\n\n :param procs: Deprecated parameter. Parallelism in batch computation is now\n controlled on a per implementation basis.\n :type procs: None | int\n\n :param kwds: Deprecated parameter. Extra keyword arguments are no longer\n passed down to the batch generation method on the descriptor generator.\n\n :return: Generator that yields (DataElement, DescriptorElement) for each\n data element given, in the order they were provided.\n :rtype: collections.abc.Iterable[(DataElement,\n DescriptorElement)]\n\n \"\"\"\n log = logging.getLogger(__name__)\n\n # Capture of generated elements in order of generation\n de_deque: Deque[DataElement] = collections.deque()\n\n # Counts for logging\n total = [0]\n unique: Set[Hashable] = set()\n\n def iter_capture_elements() -> Generator:\n for d in data_elements:\n de_deque.append(d)\n yield d\n\n # TODO: Re-write this method to more simply tee the input data elem iter\n # and yield with paired generated descriptors::\n # data_iter1, data_iter2 = itertools.tee(data_elements, 2)\n # descr_iter = descr_generator.generate_elements(\n # data_iter1, descr_factory, overwrite\n # )\n # return zip(data_iter2, descr_iter)\n\n if batch_size:\n log.debug(\"Computing in batches of size %d\", batch_size)\n\n def iterate_batch_results() -> Generator:\n descr_list_ = list(descr_generator.generate_elements(\n de_deque, descr_factory, overwrite\n ))\n total[0] += len(de_deque)\n unique.update(d.uuid() for d in descr_list_)\n log.debug(\"-- Processed %d so far (%d total data elements \"\n \"input)\", len(unique), total[0])\n log.debug(\"-- adding to set\")\n descr_set.add_many_descriptors(descr_list_)\n log.debug(\"-- yielding generated descriptor elements\")\n for data_, descr_ in zip(de_deque, descr_list_):\n yield data_, descr_\n de_deque.clear()\n\n batch_i = 0\n\n for _ in iter_capture_elements():\n # elements captured ``de_deque`` in iter_capture_elements\n\n if len(de_deque) == batch_size:\n batch_i += 1\n log.debug(\"Computing batch {}\".format(batch_i))\n for data_e, descr_e in iterate_batch_results():\n yield data_e, descr_e\n\n if len(de_deque):\n log.debug(\"Computing final batch of size %d\",\n len(de_deque))\n for data_e, descr_e in iterate_batch_results():\n yield data_e, descr_e\n\n else:\n log.debug(\"Using single generate call\")\n\n # Just do everything in one call\n log.debug(\"Computing descriptors\")\n descr_list = list(descr_generator.generate_elements(\n iter_capture_elements(), descr_factory,\n overwrite\n ))\n\n log.debug(\"Adding to set\")\n descr_set.add_many_descriptors(descr_list)\n\n log.debug(\"yielding generated elements\")\n for data, descr in zip(de_deque, descr_list):\n yield data, descr\n","repo_name":"brianhhu/SMQTK-IQR","sub_path":"smqtk_iqr/utils/compute_functions.py","file_name":"compute_functions.py","file_ext":"py","file_size_in_byte":6237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"32"} +{"seq_id":"35273419244","text":"# 用来催动git下载一个程序的脚本\n\nimport subprocess\n\nif __name__==\"__main__\":\n # subprocess.run([\"cd\",\"llvm\"])\n while True :\n retCode=subprocess.run([\"git\",\"clone\",\"https://github.com/llvm-mirror/llvm\"]).returncode\n if retCode!=0 :\n print(retCode)\n continue\n break","repo_name":"CNCSMonster/c-ci","sub_path":"d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"34894500543","text":"# import random\n# # buyukliste = []\n# # for i in range(0,8):\n# # liste = []\n# # for j in range(0,6):\n# # liste.append(random.randint(1,50))\n# # buyukliste.append(liste)\n\n# # buyukliste = [[random.randint(1,50) for j in range(0,6)] for i in range(0,8)]\n# # print(*buyukliste,sep=\"\\n\")\n\n# # print(*[[random.randint(1,50) for j in range(0,6)] for i in range(0,8)],sep=\"\\n\")\n\n# alfabe = \"abcdefghijklmnopqrstuvwxyz\"\n# secim = alfabe[random.randint(0,len(alfabe))]\n# tahmin =\"\"\n# while tahmin!=secim:\n# tahmin = input(\"Hangi Karakter?\")\n# secim = alfabe[random.randint(0,len(alfabe))]\n\nanahtar = 1\nwhile anahtar == 1:\n vize1 = int(input(\"I. Vize Notunuzu Giriniz\"))\n vize2 = int(input(\"II. Vize Notunuzu Giriniz\"))\n final = int(input(\"Final Notunuzu Giriniz\"))\n Notu = round((vize1*0.3)+(vize2*0.3)+(final*0.4))\n print(85*0.30)\n print(75*0.30)\n print((85+75)/2*0.60)\n Sonuc = \"\"\n if Notu < 25 and Notu >=0:\n Sonuc=\"FF\"\n elif Notu < 45 and Notu >=25:\n Sonuc=\"FD\"\n elif Notu < 55 and Notu >=45:\n Sonuc=\"DD\"\n elif Notu < 60 and Notu >=55:\n Sonuc=\"DC\"\n elif Notu < 70 and Notu >=60:\n Sonuc=\"CC\"\n elif Notu < 80 and Notu >=70:\n Sonuc=\"CB\"\n elif Notu < 85 and Notu >=80:\n Sonuc=\"BB\"\n elif Notu < 90 and Notu >=85:\n Sonuc=\"BA\"\n elif Notu <= 100 and Notu >=90:\n Sonuc=\"AA\"\n else:\n Sonuc = \"Not Hesaplanamadı\"\n print(\"Notunuz {},{} İyi Günler Dileriz\".format(Sonuc,Notu))\n if input(\"Çıkış (E)\").upper() == \"E\":\n anahtar=0 \n","repo_name":"mehmetsahinmutlu/-rnekler","sub_path":"PythonTemelleri/while.py","file_name":"while.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"34521308777","text":"from tkinter import *;\nfrom tkinter import messagebox;\nfrom tkinter.ttk import Combobox\nfrom tkinter import filedialog as fd\nimport matplotlib.pyplot as plt;\n\n\nclass File(object):\n def __init__(self, name, begin, end):\n self.name = name;\n self.begin = begin;\n self.end = end;\n\n\ndef window_deleted():\n if messagebox.askokcancel(\"Выход из приложения\", \"Хотите выйти из приложения?\"):\n window.destroy()\n\n\ndef address(value):\n value = str(hex(value)).upper();\n if value == \"0x0\":\n return \"0x000000\"\n elif len(value) == 3:\n temp_value = \"0x00000\";\n elif len(value) == 4:\n temp_value = \"0x0000\";\n elif len(value) == 5:\n temp_value = \"0x000\";\n elif len(value) == 6:\n temp_value = \"0x00\";\n\n for i in range(2, len(value)):\n temp_value += value[i];\n\n return temp_value;\n\n\n\ndef show_window():\n show_window = Toplevel();\n show_window.title(\"Explorer.ShowWindow\");\n\n if len(list_files) != 0:\n lbl = Label(show_window, text=\"Название файла и адреса ячеек памяти, в которых они хранятся : \", font='Times 15');\n lbl.pack(side='top', fill=BOTH, anchor=N, ipadx=4, padx=1, ipady=3, pady=3, expand=True);\n for j in range(0, len(list_files)):\n temp_text = \"'\" + list_files[j].name + \"' — \" + address(list_files[j].begin) + \"_\" + address(list_files[j].end);\n lbl = Label(show_window, text=temp_text, font='Times 13');\n lbl.pack(side='top', fill=BOTH, anchor=N, ipadx=4, padx=1, ipady=3, pady=3, expand=True);\n\n i = 0;\n lbl = Label(show_window, text=\"Список свободных адресов памяти : \",\n font='Times 15');\n lbl.pack(side='top', fill=BOTH, anchor=N, ipadx=4, padx=1, ipady=3, pady=3, expand=True);\n while i < len(list_memory_cell):\n\n if list_memory_cell[i] == \"None\":\n\n j = i;\n while j < len(list_memory_cell):\n if list_memory_cell[j] == \"None\":\n j = j + 1;\n\n\n if j == len(list_memory_cell):\n temp_text = address(i) + \"_\" + address(j);\n lbl = Label(show_window, text=temp_text, font='Times 13');\n lbl.pack(side='top', fill=BOTH, anchor=N, ipadx=4, padx=1, ipady=3, pady=3, expand=True);\n i = j\n print(\"BREAK\");\n break;\n\n else:\n temp_text = address(i) + \"_\" + address(j - 1);\n lbl = Label(show_window, text=temp_text, font='Times 13');\n lbl.pack(side='top', fill=BOTH, anchor=N, ipadx=4, padx=1, ipady=3, pady=3, expand=True);\n i = j\n break;\n else:\n i += 1;\n\n\n\n\n\n destroy_window_btn = Button(show_window, text=\"ОК\", width=25, command=show_window.destroy, font='Times 13');\n destroy_window_btn.pack(side='top', fill=X, ipadx=6, padx=4, ipady=5, pady=5);\n show_window.focus_set()\n show_window.grab_set()\n show_window.mainloop();\n\n\ndef info_window(temp_text):\n info_window = Toplevel();\n info_window.title(\"Explorer.Dialog\");\n lbl = Label(info_window, text=temp_text, font='Times 13');\n lbl.pack(side='top', anchor=N, ipadx=4, padx=1, ipady=3, pady=3);\n destroy_window_btn = Button(info_window, text=\"ОК\", command=info_window.destroy, font='Times 13');\n destroy_window_btn.pack(side='top', fill=BOTH, ipadx=6, padx=4, ipady=5, pady=5, expand = True);\n info_window.focus_set()\n info_window.grab_set()\n info_window.mainloop();\n\n\n\n\ndef delete_file():\n if txt2.get() == \"\":\n info_window(\"Ввведите названия файла, который хотите удалить!\")\n else:\n for i in range(0, len(list_files)):\n if list_files[i].name == txt2.get():\n temp_text = \"Файл '\" + list_files[i].name + \"' размером '\" + str(\n list_files[i].end - list_files[i].begin + 1) + \" Кбайт' был успешно удален!\";\n txt.delete(0, END);\n\n for j in range(list_files[i].begin, list_files[i].end + 1):\n list_memory_cell[j] = \"None\";\n for k in range(0, len(list_memory_cell)):\n print(list_memory_cell[k], \" \", k);\n list_files.pop(i);\n info_window(temp_text);\n break;\n else:\n temp_text = \"Файл c названием'\" + list_files[i].name + \"' не существует, поэтому не был удалён!\";\n txt.delete(0, END);\n for i in range(0, len(list_memory_cell)):\n print(list_memory_cell[i], \" \", i);\n info_window(temp_text);\n\ndef load():\n n = 0;\n try:\n file_name = fd.askopenfilename();\n with open(file_name, 'r') as file:\n\n list_files.clear();\n list_memory_cell.clear();\n\n\n str2 = file.readlines();\n\n for i in range(0, len(str2)):\n\n str = str2[i];\n name = \"\";\n begin = \"\";\n end = \"\";\n flag = 0;\n flag2 = 0;\n\n for i in range(0, len(str)):\n if str[i] == \" \":\n break;\n name += str[i];\n flag = i + 2;\n for i in range(flag, len(str)):\n if str[i] == \" \":\n break;\n begin += str[i];\n flag2 = i + 2;\n for i in range(flag2, len(str)):\n if str[i] == \" \":\n break;\n end += str[i];\n\n temp_file = File(name, int(begin), int(end));\n list_files.append(temp_file);\n\n except:\n n = 1;\n info_window('Файл не был выбран!')\n\n if n == 0:\n for i in range(0, 20):\n list_memory_cell.append(\"None\");\n for i in range(0, len(list_files)):\n for j in range (list_files[i].begin, list_files[i].end + 1):\n list_memory_cell[j] = \"No none\"\n info_window('Данные успешно загружены из файла!');\n\n\ndef save():\n file_name = fd.asksaveasfilename(filetypes=((\"TXT files\", \"*.txt\"),(\"HTML files\", \"*.html;*.htm\"), (\"All files\", \"*.*\")))\n with open(file_name, 'w') as file:\n for i in range (0, len(list_files)):\n file.write(list_files[i].name + \" \" + str(list_files[i].begin) + \" \" + str(list_files[i].end) + \"\\n\");\n\n file_name = \"Данные сохранены в файл '\" + file_name + \"'!\"\n info_window(file_name);\ndef save_file():\n flag_save = 0;\n flag_break = 0;\n if txt.get() == \"\" or combo.get() == \"None\":\n info_window(\"Ввведите названия файла и его размер!\")\n else:\n for i in range(0,len(list_files)):\n if list_files[i].name == txt.get():\n flag_break = 1;\n break;\n if flag_break == 1:\n temp_text = \"Файл с названием '\" + txt.get() + \"' уже существует!\";\n txt.delete(0, END);\n info_window(temp_text);\n\n else:\n\n length = int(combo.get());\n\n for i in range(0, len(list_memory_cell)):\n j = i;\n print(list_memory_cell[i], \" = \", i);\n if list_memory_cell[i] == \"None\":\n\n size_of_memory = 0;\n print (\"Cycle\");\n print(list_memory_cell[i], \" j \", j)\n while j < len(list_memory_cell):\n if list_memory_cell[j] == \"None\":\n\n size_of_memory += 1;\n if size_of_memory == length:\n print(\"length = \", length, \"size_of_memory = \", size_of_memory);\n for k in range(i, j + 1):\n list_memory_cell[k] = \"No none\";\n file = File(txt.get(), i, j);\n print(\"i = \", i, \"j = \", j)\n list_files.append(file);\n flag_save = 1;\n print(\"break\")\n break;\n else:\n break;\n j += 1;\n\n if flag_save == 1:\n temp_text = \"Файл '\" + txt.get() + \"' размером '\" + combo.get() + \" Кбайт' был успешно сохранен!\";\n txt.delete(0, END);\n for l in range(0, len(list_memory_cell)):\n print(list_memory_cell[l], \" \", i);\n info_window(temp_text);\n break;\n\n if flag_save == 0:\n temp_text = \"Файл '\" + txt.get() + \"' размером '\" + combo.get() + \" Кбайт' не был сохранен!\";\n print(\"eror\");\n for i in range(0, len(list_memory_cell)):\n print(list_memory_cell[i], \" \", i);\n txt.delete(0, END);\n info_window(temp_text);\n\n\n\n\n\nlist_memory_cell = [];\nfor i in range(0, 20):\n list_memory_cell.append(\"None\");\n\n\nlist_files = [];\n\nwindow = Tk()\n\nwindow.title(\"Explorer\");\nwindow.geometry('500x280');\n\nmenu = Menu(window);\nnew_item = Menu(menu, tearoff=0)\nnew_item.add_command(label='Новая резервная копия гибкого омагнитного диска!');\nnew_item.add_separator();\nnew_item.add_command(label='Загрузить данные из резервной копии гибкого магнитного диска!', command = load);\nnew_item.add_separator();\nnew_item.add_command(label='Cохранить резервную копию гибкого магнитного диска!', command = save) ;\nmenu.add_cascade(label='Меню', menu=new_item);\nwindow.config(menu=menu);\n\nlbl = Label(window, text=\"Введите название файла и выберите его размер в Кбайтах :\", font='Times 14');\nlbl.pack(side='top', anchor=W, ipadx=4, padx=1, ipady=3, pady=3);\nf = Frame()\nf.pack(side=TOP);\ntxt = Entry(f, width=40, font='Times 14');\ntxt.pack(side=LEFT, anchor=N, fill='x', ipadx=7, padx=5, ipady=7, pady=1);\ncombo = Combobox(f, state=\"readonly\")\ncombo['values'] = (3, 4, 5, 21, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32);\ncombo.current(0)\ncombo.pack(side=LEFT, ipadx=7, padx=5, ipady=7, pady=1);\ninsert_btn = Button(window, text=\"Записать файл на диск\", width=25, command=save_file, font='Times 13');\ninsert_btn.pack(side='top', fill=X, ipadx=6, padx=4, ipady=7, pady=5);\nlbl.pack(side='top', anchor=W, ipadx=4, padx=1, ipady=3, pady=3);\ntxt2 = Entry(window, font='Times 14');\ntxt2.pack(side=TOP, anchor=N, fill='x', ipadx=7, padx=5, ipady=7, pady=1);\nveiw_btn = Button(window, text=\"Удалить\", width=25, command=delete_file, font='Times 13');\nveiw_btn.pack(side='top', fill=X, ipadx=6, padx=4, ipady=7, pady=5);\ntxt2.pack(side=TOP, anchor=N, fill='x', ipadx=7, padx=5, ipady=7, pady=1);\nveiw_btn = Button(window, text=\"Показать все файлы\", width=25, command=show_window, font='Times 13');\nveiw_btn.pack(side='top', fill=X, ipadx=6, padx=4, ipady=4, pady=0);\n\nwindow.mainloop();","repo_name":"Nesamolet/lab8","sub_path":"pr versions/main v 2.py","file_name":"main v 2.py","file_ext":"py","file_size_in_byte":11612,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"42504123188","text":"# a_list = [1,2]\n# b_list = [3,4]\n# bb_list = [1,2,3,4,5]\n\n# a_list.extend(list(set(bb_list)-set(b_list)-set(a_list)))\n# print(a_list)\n\n# def tested(input, the_type):\n# if type(input) == the_type:\n# print(input)\n# else:\n# print(\"FAKE\")\ndef calculate_sum(numbers: list, modulus):\n \"\"\"Numbers is a list of <ID, number> pairs. This function calculates the pad for the round from the numbers using xor\n then it calculates which ID corresponds to the number 'closest' to the pad. Returns a list of length 3, containing\n [<id number pairs>, <pad>, <winner_id>]\n Tiebreaker decided by lower ID number\n \"\"\"\n assert isinstance(numbers, list)\n\n pad = 0\n for number in numbers:\n pad ^= number[1]\n # Same as pad = pad%self.modulus\n pad %= modulus\n # calculate winner, finds the minimum difference for all submitted numbers from the calculated pad.\n winner, number = min(\n numbers,\n key=lambda x: [\n min((x[1] - pad) % modulus, (pad - x[1]) % modulus),\n x[0],\n ],\n )\n return [numbers, pad, winner]\n\ndef verify_round_winner(numbers: list, my_number: int):\n \"\"\"Verifies if the round winner is the writer\n Verifies that all details are correct. \n \"\"\"\n assert isinstance(numbers, list)\n assert len(numbers) == 3\n assert isinstance(my_number, int)\n # Calculate the sum to check if we have the correct results\n verified_results = calculate_sum(numbers[0], 65537)\n # Checking if we are using the same OTP\n same_pad = verified_results[1] == numbers[1]\n # Checking if the use the same winner for the calculation\n same_winner = verified_results[2] == numbers[2]\n # Check if all the entries match up for each\n my_numb_exists = any([my_number == entry[1] for entry in numbers[0]])\n # correct case here would return True, True, True\n return same_pad and same_winner and my_numb_exists\npad = 45712\na_list = [[[3, 59550]], 59550, 3]\nnumbers = [[3,59950]]\nwinner = calculate_sum(numbers, 65537)\nwinner_verified = verify_round_winner(winner, pad)\n\nprint(winner, winner_verified)\n\n\n\nsome_list = [1,2,3,4,5]\n\nimport time\ntimeout = 10\nrun_time = time.time()\nprint(\"run_time\", run_time)\nwhile time.time() < run_time + timeout:\n print(\"BEFORE TIMEOUT\", time.time(), run_time, timeout)\n time.sleep(0.5)\n\nnum = 1\nnum2 = num\nnum = 2 # Immutable, so num2's value is not changed\nprint(num2, \"NUMBER 2\")\n\n# tested(True, any)\ndef get_cancel_coordinatorID(pen_box, view_change_num: int = None):\n \"\"\"Decides the coordinator for a cancel round in the membership protocol.\n Should only change when there is a view-change in the cancellation.\"\"\"\n print(type(pen_box))\n sorted_nodes = sorted(pen_box.values(), key=lambda x: (x[\"honest_counter\"], -x[\"id\"]), reverse=True)\n coordinator_id = sorted_nodes[view_change_num][\"id\"]\n return coordinator_id\n\npen_box = {\"aaa\": {\"id\": 4, \"honest_counter\": 2},\"some_key\": {\"id\": 1, \"honest_counter\": 2}, \"other_key\": {\"id\": 2, \"honest_counter\": 2}, \"third_key\": {\"id\": 3, \"honest_counter\": 1}}\nprint(\"FIRST OUTPUT\", get_cancel_coordinatorID(pen_box, 0))\nprint(\"SECOND OUTPUT\", get_cancel_coordinatorID(pen_box, 1))\nprint(\"THIRD OUTPUT\", get_cancel_coordinatorID(pen_box, 2))\nprint(\"THIRD OUTPUT\", get_cancel_coordinatorID(pen_box, 3))\n\n# print(bb_list, bb_list[1:])\nfrom queue import Queue\nfrom weakref import ref\nsome_queue = Queue()\nsome_queue.put(1)\nsome_queue.put(2)\nref_copy = some_queue\nprint(\"REF: \", ref_copy)\nprint(\"some\", some_queue)\nref_copy.get()\nprint(\"REF: \", ref_copy)\nprint(\"some\", some_queue)\nref_copy.get()\nprint(\"REF: \", ref_copy)\nprint(\"some\", some_queue)\nref_copy.put(1)\nprint(\"REF: \", ref_copy)\nprint(\"some\", some_queue)\nref_copy.put(2)\nprint(\"REF: \", ref_copy)\nprint(\"some\", some_queue)\n\n\na_list = [1,2,3]\n\ndata = 3 in a_list \nprint(data)\n\nnum = '222'\nprint(int(num) * 2)\nvalue = '6587849500818316161519508278916854824201302152793630979346725188602264462651268740217047928962253207403830618696453825975409521538077356628137373401104759'\nprint(len(value))\n\ndef verify_block(payload, signature, keys, pub_key, block):\n '''\n To verify a block both the signature and hash of the block must be correct\n '''\n if block is not None:\n payload = payload\n signature = int(signature, 16)\n # Set some pub key as either string or int\n writer_pubkey = int(pub_key) #TODO: Should not completely rely on id since the node_set is dynamic\n D = bytes_to_long(payload.encode(\"utf-8\")) % writer_pubkey #TODO: Figure out if pub key can be string in current setup and if str==int repr.\n res = pow(signature, keys[2], writer_pubkey) #self.keys[2] is private key\n res = res % writer_pubkey\n \n # Get the hash based on what is in the block\n # hash = hash_block(block) - invariant, part of block construction\n signature_correct = res == D\n return signature_correct\n else:\n # Only reason new_block is None is that the hash does not match\n return False ## only reason block was not created\n \ndef bytes_to_long(s: str):\n \"\"\"Convert a byte string to a long integer (big endian).\n In Python 3.2+, use the native method instead::\n >>> int.from_bytes(s, 'big')\n For instance::\n >>> int.from_bytes(b'\\x00P', 'big')\n 80\n This is (essentially) the inverse of :func:`long_to_bytes`.\n \"\"\"\n acc = 0\n import struct\n unpack = struct.unpack\n\n length = len(s)\n if length % 4:\n extra = 4 - length % 4\n s = b\"\\x00\" * extra + s\n length = length + extra\n for i in range(0, length, 4):\n acc = (acc << 32) + unpack(\">I\", s[i : i + 4])[0]\n return acc\n\n\ndef egcd(a, b):\n if a == 0:\n return (b, 0, 1)\n else:\n g, y, x = egcd(b % a, a)\n return (g, x - (b // a) * y, y)\n\n\ndef mod_inverse(a, m):\n g, x, _ = egcd(a, m)\n if g != 1:\n raise Exception(\"modular inverse does not exist\")\n else:\n return x % m\n \ndef sign_payload(keys: tuple, payload: str):\n # keys of form [p, q, e]\n '''Creates a signature of the payload'''\n assert isinstance(payload, str)\n p, q, e = keys # private keys\n N = p * q\n d = mod_inverse(e, (p - 1) * (q - 1))\n D = bytes_to_long(payload.encode(\"utf-8\"))\n signature = pow(D, d, N)\n return hex(signature % N)\n# Private keys\nnode_1_keys = 104171608550381805610629631974370644810978389674970109968994986331963820498789,63240354953645098909053921301429392257631457933829211049166951995700317410731,65537\nsigned_payload = sign_payload(node_1_keys, \"johnny\")\n# test if correct\n# verify_block(payload, signature, keys, pub_key, block):\nnode_1_pub_key = '6587849500818316161519508278916854824201302152793630979346725188602264462651268740217047928962253207403830618696453825975409521538077356628137373401104759'\nprint(verify_block(\"johnny\", signed_payload, node_1_keys, node_1_pub_key, True), \"TRUEEE\")\n\n\ndef calculate_sum(modulus, numbers: list):\n \"\"\"Numbers is a list of <ID, number> pairs. This function calculates the pad for the round from the numbers using xor\n then it calculates which ID corresponds to the number 'closest' to the pad. Returns a list of length 3, containing\n [<id number pairs>, <pad>, <winner_id>]\n Tiebreaker decided by lower ID number\n \"\"\"\n assert isinstance(numbers, list)\n\n pad = 0\n for number in numbers:\n print(pad, number)\n pad ^= number[1]\n print(pad, number)\n # Same as pad = pad%self.modulus\n pad %= modulus\n # calculate winner, finds the minimum difference for all submitted numbers from the calculated pad.\n winner, number = min(\n numbers,\n key=lambda x: [\n min((x[1] - pad) % modulus, (pad - x[1]) % modulus),\n x[0],\n ],\n )\n return [numbers, pad, winner]\ndef generate_pad():\n ''' Generates a number '''\n modulus = 65537\n # TODO: This needs to be changed to use an already generated pad\n assert modulus > 0\n import os\n x = os.urandom(8)\n import struct\n number = struct.unpack(\"Q\", x)[0]\n return number % modulus\n# print(calculate_sum(65537, [(1, generate_pad()),(2, generate_pad()), (3,generate_pad())]))\n# # 1000, 0000\n# print(1^0)\n\ndict = {\"person\": \"a\", \"dogs\": \"b\" }\n\nfor i in dict:\n print(i, \"YES\")\n \n \nthis_list = [1,3,22,4,2,8]\nprint(this_list)\nthis_list.sort()\nprint(this_list)\nimport json\nsome_thing = json.dumps((1, json.dumps({\"a_dict\":\"field\"})))\nprint(some_thing)\nprint(json.loads(some_thing))\nvalues = [1,3,22,4,2,8]\n\nprint(values.index(max(values)))\n\n\n# a = 0\n\n# a += True\n# print(\"A: \", a)\n\n# mem_data = {}\n# if mem_data.get(1, None).get(\"penalty_box\", None):\n# print(\"FALSE\")\n# else:\n# print(\"TRUE\")\n\ndef get_coordinator(round_number, num_coordinators, rounds_per_coordinator):\n coordinator_index = (round_number - 1) // rounds_per_coordinator % num_coordinators\n return coordinator_index\n\nnum_rounds = 15 # Total number of rounds\nnum_coordinators = 3 # Total number of coordinators\nrounds_per_coordinator = 5 # Number of rounds per coordinator\n\nfor round_number in range(1, num_rounds + 1):\n coordinator_index = get_coordinator(round_number, num_coordinators, rounds_per_coordinator)\n print(f\"Round {round_number}: Coordinator {coordinator_index + 1}\")\n\nround_writer_list = [1,2,3,4]\nimport math\nprint(len(round_writer_list)/3)\nf = math.floor(len(round_writer_list)/3)\nprint(f)\nthe_round = 20\nfor round_i in range(the_round-math.floor((len(round_writer_list)-1)/3)-1, the_round+1):\n print(round_i)\n","repo_name":"pieceofGit/Annall_Lightweight_Blockchain","sub_path":"src/tests/test_things.py","file_name":"test_things.py","file_ext":"py","file_size_in_byte":9570,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"3562012615","text":"from connection import app, collection\nimport errorHandling\nfrom flask import jsonify, request\nfrom bson import ObjectId\nfrom bson.json_util import dumps\n\n# ----- Display ALL Tasks ---------\n@app.route('/alltasks', methods=['GET'])\ndef displayAllTask():\n if request.method == 'GET':\n tasks = collection.find()\n res = dumps(tasks)\n return res\n return errorHandling.bad_request()\n\n\n#------ Display one Task by ID -----\n@app.route('/task/<id>', methods=['GET'])\ndef displayTask(id):\n if id and request.method == 'GET':\n task = collection.find_one({'_id':ObjectId(id)})\n if not task:\n return errorHandling.not_found()\n res = dumps(task)\n return res\n return errorHandling.bad_request()\n\n\n#---------- Add a Task ----------\n@app.route('/addtask', methods=['POST'])\ndef addTask():\n _data = request.json\n \n _title = _data['title']\n _desc = _data['description']\n _date = _data['due date']\n _status = _data['status']\n\n if _title and _date and _desc and _status and request.method == 'POST':\n task_data = {\n \"title\": _title,\n \"description\": _desc,\n \"due date\": _date,\n \"status\": _status\n }\n result = collection.insert_one(task_data) \n\n if result.inserted_id:\n res = jsonify(message=\"Task added successfully\")\n res.status_code = 200\n return res\n else:\n return errorHandling.server_error()\n else:\n return errorHandling.bad_request()\n\n\n#-------- Update a Task --------\n@app.route('/updatetask/<id>', methods=['PUT'])\ndef updateTask(id):\n _data = request.json\n \n _title = _data['title']\n _desc = _data['description']\n _date = _data['due date']\n _status = _data['status']\n\n if _title and _date and _desc and _status and request.method == 'PUT':\n task_data = {\n \"title\": _title,\n \"description\": _desc,\n \"due date\": _date,\n \"status\": _status\n }\n result = collection.find_one_and_update({'_id':ObjectId(id)},{'$set':task_data}) \n\n if result:\n res = jsonify(message=\"Task updated successfully\")\n res.status_code = 200\n return res\n else:\n return errorHandling.not_found()\n else:\n return errorHandling.bad_request()\n \n\n#--------- Delete a Task --------\n@app.route('/deletetask/<id>', methods=['DELETE'])\ndef deleteTask(id):\n if id and request.method == 'DELETE':\n collection.find_one_and_delete({'_id':ObjectId(id)})\n res = jsonify(\"Task Deleted\")\n res.status_code = 200\n return res\n return errorHandling.bad_request()\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n","repo_name":"PriyanshuVyas/UMS-API-python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"24471261650","text":"import User\n\nuser1 = User.User()\nuser2 = User.User()\n\n\ndef main():\n print(\"______________ Starting Transmission ________________\")\n start_transmission = user2.start_transmission()\n if start_transmission:\n public_key = user1.get_public_key()\n print(\" Generating RSA public key... \" + str(public_key))\n vigenere_key = user2.generate_vigenere_key()\n print(\" Generating Vigenere key... \" + str(vigenere_key))\n encrypted_key_rsa = user2.encrypt_rsa(vigenere_key, public_key)\n print(\" Encrypted RSA message... \" + str(encrypted_key_rsa))\n print(\"______________________________________________________\\n\")\n if user1.decrypt_rsa(encrypted_key_rsa):\n encrypted_text_vigenere = user2.encrypt_text_vigenere()\n print(\"______________________________________________________\")\n print(\" Encrypted Vigenere message... \" + str(encrypted_text_vigenere))\n decrypted_vigenere_text = user1.decrypt_text_vigenere(encrypted_text_vigenere)\n print (\" Decrypted Vigenere message: \" + decrypted_vigenere_text)\n\nif __name__== \"__main__\":\n main()","repo_name":"kandrzejewski/RSA","sub_path":"Transmission.py","file_name":"Transmission.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"19230170473","text":"#!/usr/bin/python3\n\n\"\"\"\nScript that lists all cities from the database hbtn_0e_4_usa\nThe script takes 3 arguments:\n mysql username, mysql password, databse name\n runs on localhost port 3306\n results must be sorted in ascending order by cities.id\n use execute() once\n code should not be executed when imported\n\"\"\"\n\n\nimport MySQLdb\nfrom sys import argv\n\nif __name__ == \"__main__\":\n conn = MySQLdb.connect(\n host='localhost',\n port=3306,\n user=argv[1],\n passwd=argv[2],\n db=argv[3]\n )\n cur = conn.cursor()\n query = 'SELECT cities.id, cities.name, states.name FROM cities\\\n JOIN states ON cities.state_id = states.id ORDER BY cities.id ASC'\n cur.execute(query)\n cties = cur.fetchall()\n if cties:\n for city in cties:\n print(city)\n cur.close()\n conn.close()\n","repo_name":"Nobby-code/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/4-cities_by_state.py","file_name":"4-cities_by_state.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"14217683649","text":"N = int(input())\na = list(map(int, input().split()))\na.sort()\nA = 0\nB = 0\n\nwhile len(a) !=0:\n A += a.pop(-1)\n if len(a) != 0:\n B += a.pop(-1)\n\nprint(A - B)\n","repo_name":"tokuD/atcoder","sub_path":"abs/CardGameforTwo.py","file_name":"CardGameforTwo.py","file_ext":"py","file_size_in_byte":169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"17908156430","text":"def getDiffConreco(state1): \n import requests\n import json\n from matplotlib import pyplot as plt\n a=requests.get('https://api.covid19india.org/states_daily.json')\n b=a.text\n s=json.loads(b)\n lcon=[]\n lreco=[]\n ldef=[]\n l1def=[]\n l=[]\n x=[]\n y=[]\n state=state1\n data=s[\"states_daily\"]\n for i in range(0,96):\n int(i)\n l.append(i)\n for i in range(0,len(data)):\n x=data[i]\n if x[\"status\"]==\"Confirmed\":\n y=int(x[state])\n lcon.append(y)\n if x[\"status\"]==\"Recovered\":\n w=int(x[state])\n lreco.append(w) \n for i in range(0,len(lcon)):\n dif=lcon[i]-lreco[i]\n ldef.append(dif)\n for i in range(0,len(ldef)-1):\n x=ldef[i]-ldef[i+1]\n l1def.append(x)\n \"\"\"for i in range(0,len(l1def)-1):\"\"\"\n\n x=l\n y=ldef\n plt.title(\"confirmed-recovered\") \n plt.xlabel(\"Dates\") \n plt.ylabel(\"confirmed cases-recovered cases\") \n plt.bar(x,y) \n plt.show() \n","repo_name":"fredysomy/Covid19data","sub_path":"getstateconffrm-recovry.py","file_name":"getstateconffrm-recovry.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"32"} +{"seq_id":"35796411735","text":"class Node():\r\n def __init__(self, left, right):\r\n self.left = left\r\n self.right = right\r\n\r\n def openNode(self):\r\n return (self.left, self.right)\r\n\r\ndef tree(node, binary=\"\"):\r\n if type(node) is str:\r\n return {node: binary}\r\n\r\n (left, right) = node.openNode()\r\n dict = {}\r\n dict.update(tree(left, binary + \"0\"))\r\n dict.update(tree(right, binary + \"1\"))\r\n return dict\r\n\r\ndef huffmanCode(text):\r\n\r\n freq = {}\r\n for i in text:\r\n if i in freq:\r\n freq[i] += 1\r\n else:\r\n freq[i] = 1\r\n\r\n freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\r\n\r\n while len(freq) >= 2:\r\n (character1, freq1) = freq[-1]\r\n (character2, freq2) = freq[-2]\r\n freq = freq[:-2]\r\n node = Node(character1, character2)\r\n freq.append((node, freq1 + freq2))\r\n\r\n freq = sorted(freq, key=lambda x: x[1], reverse=True)\r\n\r\n huffman_code = tree(freq[0][0])\r\n return huffman_code\r\n\r\ndef codeInHuffman(text, separator=False):\r\n \r\n huffman_code = huffmanCode(text)\r\n\r\n for i in sorted(huffman_code.keys()):\r\n print(\"{} => {}\".format(i, huffman_code[i]))\r\n\r\n coded = \"\"\r\n\r\n for i in text:\r\n coded += huffman_code[i]\r\n if separator:\r\n coded += \" \"\r\n\r\n return coded\r\n\r\n# Example:\r\n\r\ntext = \"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\"\r\nprint(text)\r\nprint(codeInHuffman(text, True))\r\n","repo_name":"jaqlig/algorithms","sub_path":"Codes/huffman_code.py","file_name":"huffman_code.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"31222653055","text":"#!/usr/bin/env python3\n# license removed for brevity\n\n# Global imports\nfrom GlobalSets.Mongo import Clients as MongoClient, DataBases as db, Collections as col\n\nimport rospy, pymongo\nfrom std_msgs.msg import String\n\ndef talker():\n pub = rospy.Publisher('chatter', String, queue_size=10)\n rospy.init_node('talker', anonymous=True)\n rate = rospy.Rate(10) # 10hz\n while not rospy.is_shutdown():\n teste = {\n \"first\": 1,\n \"second\": 'a'\n }\n result = MongoClient.LocalClient[db.dbBuffer][col.Battery].insert_one(teste)\n \n rospy.loginfo(str(result))\n pub.publish(str(result))\n rate.sleep()\n\nif __name__ == '__main__':\n try:\n talker()\n except rospy.ROSInterruptException:\n pass","repo_name":"alf767443/Thesis-ROS-Packges","sub_path":"src/pub.py","file_name":"pub.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"15286678795","text":"# Imports\nimport pandas as pd\nimport pandas_ta as ta\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Read parquet dataframe\ndef get_df():\n # Read_parquet\n df = pd.read_parquet('raw_data.parquet', engine='pyarrow')\n # Choose columns\n cols = ['time', 'close']\n df = df[cols]\n \n return df\n\n# Add SMA columns\ndef calculate_SMA(df, SMA_short, SMA_long):\n # Add columns for SMA strategy\n df['SMA_short'] = ta.sma(df['close'],SMA_short)\n df['SMA_long'] = ta.sma(df['close'],SMA_long)\n # Delete rows with NaN values for SMA columns \n df = df.dropna().reset_index(drop=True)\n \n return df\n\n# Extract date from the timestamp and add as a separate column\ndef extract_date(df):\n # Extract date from the timestamp\n date_column = df['time'].dt.date\n # Add date column\n df.insert(loc=1, column='date', value=date_column)\n \n return df\n\n# Add columns with the closing price in the moment when the BUY or SELL action occur\ndef add_trade_signals(df):\n # Create columns with BUY and SELL signals\n buy_condition = (df['SMA_short'] >= df['SMA_long']) & (df['SMA_short'].shift(1) < df['SMA_long'].shift(1))\n sell_condition = (df['SMA_short'] < df['SMA_long']) & (df['SMA_short'].shift(1) >= df['SMA_long'].shift(1))\n # Add columns\n df['signals_buy'] = df.loc[buy_condition, 'close']\n df['signals_sell'] = df.loc[sell_condition, 'close']\n\n return df\n\n# Prepare data for SMA strategy\ndef SMA_data(df, SMA_short, SMA_long):\n # Add SMA columns\n df = calculate_SMA(df, SMA_short, SMA_long)\n # Add columns for BUY/SELL signals\n df = add_trade_signals(df)\n \n return df\n\n# Plot with of closing prices with BUY/SELL signals marked\ndef plot_SMA(df,SMA_short, SMA_long, start_date, end_date): # both dates need to be entered in a yyyy-mm-dd format\n # Add date column\n df = extract_date(df)\n # Filter data by date\n df = df.loc[(df['date'] >= pd.to_datetime(start_date).date()) & (df['date'] <= pd.to_datetime(end_date).date())]\n # Prepare data w SMA strategy\n df = SMA_data(df, SMA_short, SMA_long)\n # Plot SMAs and BUY/SELL signals\n fig, ax = plt.subplots(figsize=(26,12))\n ax.plot(df['time'], df['close'], label = 'BTC/BUSD' ,linewidth=1 ,color='blue', alpha = 0.9)\n ax.plot(df['time'], df['SMA_short'], label = f'SMA{SMA_short}', linewidth=1, alpha = 0.85)\n ax.plot(df['time'], df['SMA_long'], label = f'SMA{SMA_long}', linewidth=1, alpha = 0.85)\n ax.scatter(df['time'], df['signals_buy'] , label = 'Buy' , marker = '^', color = 'green', alpha=1, linewidths=5)\n ax.scatter(df['time'], df['signals_sell'] , label = 'Sell' , marker = 'v', color = 'red', alpha=1, linewidths=5)\n ax.set_title(f'BTC/BUSD - Price History with buy and sell signals ({start_date} - {end_date})',fontsize=20)\n ax.set_xlabel('Time' ,fontsize=18)\n ax.set_ylabel('Close Price INR (₨)' , fontsize=18)\n ax.legend()\n ax.grid()\n plt.tight_layout()\n plt.show()\n \n# Create evaluation metrics for differnt lengths of SMAs\ndef evaluate_signals(df):\n # Drop null values from \n signals_buy = df[\"signals_buy\"].dropna()\n signals_sell = df[\"signals_sell\"].dropna()\n # Create multipliers list\n multipliers_list = []\n # Append to multiplier list in chronogical manner\n for s, b in zip(signals_sell, signals_buy):\n multiplier = s / b\n multipliers_list.append(multiplier)\n # Convert to pd.Series\n multipliers = pd.Series(multipliers_list, index = range(len(multipliers_list)))\n # Take last observation as overall profit multiplier\n overall_profit_multiplier = multipliers.cumprod().iloc[-1]\n \n # Return evaluation metrics\n return {\n \"average_profit_multiplier\": signals_sell.mean() / signals_buy.mean(),\n \"buy_sell_trade_pair_count\": (signals_buy.count() + signals_sell.count()) / 2,\n \"overall_profit_multiplier\": overall_profit_multiplier\n }\n\n# Evaluate combinations of SMAs lengths \ndf_raw = get_df()\n\ndef evaluate_sma_combination(sma_pair):\n # Unpack SMAs\n sma_short, sma_long = sma_pair\n # Process data \n df = add_trade_signals(calculate_SMA(df_raw, sma_short, sma_long))\n # Instantiate dictionary\n result = {\n \"SMA_short\": sma_short,\n \"SMA_long\": sma_long\n }\n # Populate dictionary with evaluation metrics\n result.update(evaluate_signals(df))\n \n return result\n\n# Plot heatmaps with evaluation metrics for SMA lengths\ndef heatmaps_SMA(df, symbol):\n # Create dictionary for evaluation metrics\n cols = [\n \"average_profit_multiplier\",\n \"buy_sell_trade_pair_count\",\n \"overall_profit_multiplier\"\n ]\n # Plot figure\n fig, axes = plt.subplots(1, len(cols), figsize=(26, 5))\n fig.suptitle(symbol)\n # Loop for metrics\n for i, col in enumerate(cols):\n matrix = df.pivot(index='SMA_long', columns='SMA_short', values=col) \n sns.heatmap(matrix, cbar=True, square=True, cmap=\"viridis\", ax=axes[i]).invert_yaxis()\n axes[i].set_title(col)\n\n\n","repo_name":"KrzysztofTrebicki/Trading-Bot","sub_path":"Notebooks/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5030,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"12658497692","text":"import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport cv2\nimport numpy as np\nimport sys\nimport math\nfrom pictureMap import mapping\nfrom motorcontrol import movetime\nfrom draw2D import drawPoints\nimport tty\nimport termios\n\ndef commands():\n fd = sys.stdin.fileno()\n old_settings = termios.tcgetattr(fd)\n tty.setraw(sys.stdin.fileno())\n command = 0\n \n ch = []\n ch.append(sys.stdin.read(1))\n \n termios.tcsetattr(fd, termios.TCSADRAIN, old_settings) \n if ch[0] == '\\x1b':\n ch.append(sys.stdin.read(1))\n if ch[1] == '[':\n ch.append(sys.stdin.read(1))\n if ch[2] == 'A':\n command = 1\n elif ch[2] == 'B':\n command = 2\n elif ch[2] == 'D':\n command = 3\n elif ch[2] == 'C':\n command = 4\n else:\n command = 0\n else:\n if ch[0] == 'p':\n command = 5\n elif ch[0] == 'q':\n command = 6\n \n return command\n\n\n\ndef main():\n\n# User choose to either move the robot, take a photo where the robot stands\n# or finalize the mapping\n \n robotPos=[0,0] # Set robot position to 0,0 in 2D plain\n robotDir = 90 # and robot direction to 90, thus looking up\n plt.axis([-2,2,-3,3]) # Set the axis to be 4*4 meters\n\n print(\"\\n \\n Press the arrow keys to move the robot, space key to take a photo or q key to finalize the mapping \\n\") #Wait for a command\n while(1):\n key=commands()\n \n if (key==0):\n print(\"ERROR: NOT A VALID COMMAND \\n\")\n sys.exit(0)\n############################## PHOTO COMMAND ###################################### \n elif (key == 5):\n print(\"mpika\")\n cam = cv2.VideoCapture(0) #set the port of the camera\n cam.set(3, 3200) #set the resolution and frames\n cam.set(4, 2380)\n resolution = [cam.get(3),cam.get(4)]\n fps = cam.get(5)\n print (resolution)\n print(fps)\n maxframes = 10\n for i in range(maxframes):\n ret,ImToMap = cam.read()\n if(ret==0):\n sys.exit(0)\n \n cam.release() #Closes capturing device.\n obstacles = mapping(ImToMap) #Computes obstacles\n print(\"Start Drawing \\n\") \n drawPoints(obstacles,robotPos, robotDir) #Draws the obstacles\n print(\"Finished Drawing \\n\")\n \n############################ END PHOTO COMMAND #################################\n\n############################## FINALIZE COMMAND ################################# \n elif (key == 6):\n print(\"Start Finalizing \\n\")\n plt.savefig('finalmapping.png')\n print(\"Finished Finalizing \\n\")\n print(\"Exiting... \\n\")\n #sys.exit(0)\n continue\n############################## END FINALIZE COMMAND ##############################\n\n############################## MOVE COMMAND ####################################\n else:\n offset = movetime(key,0.5)\n if (key < 3): \t\t\t\t\t\t\t# if the robot goes forward\n dx = math.cos(math.radians(robotDir))*offset\n dy = math.sin(math.radians(robotDir))*offset\n \n robotPos = [robotPos[0]+dx,robotPos[1]+dy] # set the new position of the robot\n\n else:\n robotDir = robotDir + offset # otherwise, set the new direction of the camera\n if (robotDir <0 ):\n robotDir = robotDir +360\n elif (robotDir >360):\n robotDir = robotDir -360\n elif (robotDir==360):\n robotDir =0\n print(robotDir)\n print(robotPos)\n############################## END MOVE COMAND #################################\n\nmain()","repo_name":"dimipana/final-term-project","sub_path":"2dplain-3d mapping/2d plain/MainMapping.py","file_name":"MainMapping.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"4164287560","text":"from class_ODE import *\n\nclass subclass_ODE_mass_on_surface(class_ODE):\n def __init__(self, flag_control):\n super().__init__(flag_control)\n self.field_param = self.set_field_param(flag_control)\n\n def set_field_param(self, flag_control):\n if flag_control == False:\n field_param = {'lim_min': [-2, -2, -0.01, -0.5, -0.5, -0.5],\n 'lim_max': [2, 2, 0.01, 0.5, 0.5, 0.5], # x, y, x_dot, y_dot, z_dot\n 'lim_num': [10, 10, 1, 5, 5, 1],\n 'ode_flag': 'mass_surface',\n 'dim_in': 6, 'dim_out': 3,\n 'flag_control': False, # Changes the ODE for data generation\n 'flag_lengthscales': 'same', # Either: 'all' or 'same'\n 'flag_signalvar': 'same', # Either: 'all' or 'same'\n 'flag_normalize_in': True,\n 'flag_normalize_out': True,\n 'params': [0.08, 0.05, 0.05, 0, 0, 0.1, 3]}\n\n elif flag_control == True:\n field_param = {'lim_min': [-2, -2, -0.01, -0.5, -0.5, -0.5, -5, -5, -5],\n 'lim_max': [2, 2, 0.01, 0.5, 0.5, 0.5, 5, 5, 5],\n 'lim_num': [3, 3, 1, 3, 3, 1, 3, 3, 3],\n 'ode_flag': 'mass_surface',\n 'dim_in': 9, 'dim_out': 3,\n 'flag_control': True, # Changes the ODE for data generation\n 'flag_lengthscales': 'same', # Either: 'all' or 'same'\n 'flag_signalvar': 'same', # Either: 'all' or 'same'\n 'flag_normalize_in': True,\n 'flag_normalize_out': True,\n 'params': [0.08, 0.05, 0.05, 0, 0, 0.1, 3]} # constraint parameters\n\n # Theta, the array of model hyperaparameters, has a predefined structure:\n # theta := LENGTHSCALES -- SIGNAL VARIANCES -- NOISE VARIANCE -- CONSTRAINT PARAMETERS\n if field_param['flag_lengthscales'] == 'all':\n field_param['numb_lengthscales'] = field_param['dim_in']*field_param['dim_out']\n elif field_param['flag_lengthscales'] == 'same':\n field_param['numb_lengthscales'] = field_param['dim_in']\n if field_param['flag_signalvar'] == 'all':\n field_param['numb_signalvar'] = field_param['dim_out']\n if field_param['flag_signalvar'] == 'same':\n field_param['numb_signalvar'] = 1\n\n assert field_param['lim_num'][2] == 1, 'The third dimension in field_param[lim_num] must be 1 due to constraint removing one DOF'\n assert field_param['lim_num'][5] == 1, 'The sixth dimension in field_param[lim_num] must be 1 due to constraint removing one DOF'\n\n return field_param\n\n\n def compute_points(self, tmp_positions):\n \"\"\"Given N D-dimensional states use implicit constraint equations (position + velocity constraint if system is\n holonomic; or only velocity constraint if system is non-holonomic) to ensure that points lie inside the\n constrained state space\"\"\"\n assert (tmp_positions.shape[0] == 6 or tmp_positions.shape[0] == 9), 'arrays have not the right shape.'\n params = self.field_param['params']\n for i in range(tmp_positions.shape[1]):\n tmp_positions[2,i] = self.func_z(tmp_positions[:, i], params)\n tmp_positions[5,i] = self.func_z_dot(tmp_positions[:, i], params)\n\n # Assert velocity constraint\n x = tmp_positions[0,i]; y = tmp_positions[1,i]; z = tmp_positions[2,i];\n xdot = tmp_positions[3,i]; ydot = tmp_positions[4,i]; zdot = tmp_positions[5,i]\n assert np.abs(2*params[0]*x*xdot + 2*params[1]*y*ydot + params[2]*xdot*y + params[2]*x*ydot \\\n + params[3]*xdot + params[4]*ydot - zdot - params[5]*params[6]*xdot*np.math.sin(params[6]*x)) < 1e-14, \\\n 'The computed point does not fulfill the velocity constraint?'\n return tmp_positions\n\n def func_z(self, X, params):\n return params[0]*X[0]**2 + params[1]*X[1]**2 + params[2]*X[0]*X[1] + params[3]*X[0] + params[4]*X[1] + params[5] * np.math.cos(params[6] * X[0])\n\n def func_z_dot(self, X, params):\n dz_dx = 2 * params[0] * X[0] + params[2] * X[1] + params[3] - params[5]*params[6]*np.math.sin(params[6]*X[0])\n dz_dy = 2*params[1]*X[1] + params[2]*X[0] + params[4]\n return dz_dx*X[3] + dz_dy*X[4]\n\n def func_M(self, X, theta):\n m = 3\n return np.array([[m, 0, 0], [0, m, 0], [0, 0, m]])\n\n def constraint_A(self, X, theta):\n L0 = theta[-7]\n L1 = theta[-6]\n L2 = theta[-5]\n L3 = theta[-4]\n L4 = theta[-3]\n L5 = theta[-2]\n L6 = theta[-1]\n\n a1 = 2 * L0 * X[0] + L2 * X[1] + L3 - L5 * L6 * np.math.sin(L6 * X[0])\n a2 = 2 * L1 * X[1] + L2 * X[0] + L4\n return np.array([a1, a2, -1]).reshape((1, 3))\n\n def constraint_b(self, X, theta):\n L0 = theta[-7]\n L1 = theta[-6]\n L2 = theta[-5]\n L5 = theta[-2]\n L6 = theta[-1]\n return -2 * L0 * (X[3] ** 2) - 2 * L1 * (X[4] ** 2) - 2 * L2 * X[3] * X[4] + L5*(\n L6 ** 2) * (X[3] ** 2) * np.math.cos(L6 * X[0])\n\n def check_if_states_fulfill_constraint(self, X):\n assert (X.shape[0] == 6) or (X.shape[0] == 9)\n for i in range(X.shape[1]):\n assert np.abs(self.func_z(X[:,i], self.field_param['params']) - X[2,i]) < 1e-14, 'State position is not lying on surface'\n assert np.abs(self.func_z_dot(X[:,i], self.field_param['params']) - X[5,i]) < 1e-14, 'State velocity is not tangential to surface'\n \n def compute_energy(self, X_array, m=3):\n assert (X_array.shape[0] == 6 or X_array.shape[0] == 9), 'array has not the right shape.'\n Energy_kin = 0.5 * m * (X_array[3, :] ** 2 + X_array[4, :] ** 2 + X_array[5, :] ** 2)\n Energy_pot = m * 9.81 * (X_array[2, :]) * np.ones(X_array.shape[1])\n Energy_tot = Energy_kin + Energy_pot\n return Energy_kin, Energy_pot, Energy_tot\n \n def func_ODE(self, t, X, p, flag_constrained=None):\n \"\"\"\n First-order ODE model of particle moving on the surface\n c(x) = L1*X**2 + L2*Y**2 + L3*X*Y + L4*X + L5*Y\n If flag_constrained='a' returns the unconstrained acceleration\n If flag_constrained='a_bar' returns the unconstrained acceleration with nonidealities\n :param X: State vector with\n X[0] := X,\n X[1] := Y,\n X[2] := Z,\n X[3] := X_dot,\n X[4] := Y_dot,\n X[5] := Z_dot,\n\n X[6] := Fx,\n X[7] := Fy,\n X[8] := Fz\n\n :return: X_ddot, Y_ddot, Z_ddot\n \"\"\"\n g = -9.81\n m = 3\n\n if self.field_param['flag_control'] == True:\n Fx = X[6]\n Fy = X[7]\n Fz = X[8]\n else:\n Fx = 0\n Fy = 0\n Fz = 0\n\n M = np.array([[m, 0, 0],\n [0, m, 0],\n [0, 0, m]])\n F = np.array([[Fx],\n [Fy],\n [m * g + Fz]])\n a = np.linalg.solve(M, F)\n\n if flag_constrained == 'a' or flag_constrained == 'prior':\n return a.flatten()\n\n # Damping from Udwadia2002 - On the foundations of analytical mechanics\n a_0 = 0.2\n velocity = np.sqrt(X[3] ** 2 + X[4] ** 2 + X[5] ** 2)\n if velocity == 0:\n a_c_ni = 0\n else:\n a_c_ni = -a_0 * ((velocity ** 2) / np.abs(velocity)) * np.array(\n [[X[3]], [X[4]], [X[5]]]) # Non-ideal acceleration\n #a_c_ni = 0; print('a_c_ni set to 0')\n\n if flag_constrained == 'a_bar':\n return (a + a_c_ni).flatten()\n\n A = np.array([[2 * p[0] * X[0] + p[2] * X[1] + p[3] - p[5]*p[6]*np.math.sin(p[6]*X[0]),\n 2 * p[1] * X[1] + p[2] * X[0] + p[4],\n -1]])\n b = np.array([[-2 * p[0] * (X[3] ** 2) - 2 * p[1] * (X[4] ** 2) - 2 * p[2] * X[3] * X[4] + p[5]*(p[6]**2)*(X[3]**2)*np.math.cos(p[6]*X[0])]])\n\n # Compute constrained acceleration\n M_invAT = np.linalg.solve(M, A.T) # np.inv(M)@A.T\n if A.shape[0] == 1:\n if (A @ M_invAT) == 0:\n K = 0\n else:\n K = M_invAT * (1 / (A @ M_invAT))\n else:\n K = M_invAT @ np.linalg.pinv(A @ M_invAT) # Weighted moore-penrose pseudo inverse\n\n acc = K @ b + (np.eye(3) - K @ A) @ (a + a_c_ni)\n # Stop simulation if constrained is violated\n assert np.abs(A @ acc - b) < 1e-14, 'Computed acceleration violates constraint equation?'\n return acc.flatten() # return np.vstack((acc)).flatten()\n\n def decorator_changeout_for_solveivp(self, func, number_states, flag_u=True, flag_constrained=None):\n def wrapper(t, X, params):\n if flag_u==True:\n X_new = np.hstack((X, params['u'].flatten()))\n else:\n X_new = X.flatten()\n result = func(t, X_new, params['theta'], flag_constrained)\n return np.hstack((X[number_states:2*number_states], result.flatten()))\n return wrapper\n\n\n","repo_name":"AndReGeist/gp_squared","sub_path":"subclass_ODE_mass_on_surface.py","file_name":"subclass_ODE_mass_on_surface.py","file_ext":"py","file_size_in_byte":9306,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"32"} +{"seq_id":"40269198377","text":"'''\nDeploys cloudformation stacks\n'''\nimport traceback\nimport tasks.cloudformation as cfn\n\nprint('Loading function ....')\n\ndef deploy_stack(configs):\n '''\n Get deployment configurations and deploy stacks\n '''\n setup_data = cfn.load_yaml_file(configs)\n print('Listing configuration to setup Stacks in specified regions.......')\n\n try:\n for single_setup_data in setup_data:\n\n # Extract region and environemnt to deploy stack\n stack_region = single_setup_data['region']\n environment = single_setup_data['parameters']['Environment']\n print(f'Region to deploy resource = {stack_region}')\n print(f'Environment to deploy resource = {environment}')\n\n # Extract resource name and region and create a stack name\n resource_name = single_setup_data['resource_name']\n stack_name = environment+'-'+resource_name\n print('Stack name for deployed resource = ' + stack_name)\n\n # Extract the template file to create stack\n template_file = single_setup_data['template_file']\n with open(template_file, 'r') as template:\n template_body = template.read()\n print(f'Template file used to deploy resource = {template_file}')\n\n # Extract the input parameters to create or update stack\n parameter_values = cfn.build_stack_parameters(single_setup_data['parameters'])\n print(f'Parameter key-value for resource stack = {parameter_values}')\n\n # Deploy Stack\n print('provisioning resources.......')\n stacks = cfn.DeploymentManager(single_setup_data)\n stacks.create_or_update_stack(stack_name, template_body, parameter_values)\n print('Stack Deployment Complete!!!')\n\n\n except Exception as error: # pylint: disable=broad-except\n\n print(f'Function failed due to exception.{error}')\n traceback.print_exc()\n print('Stack Deployment Failed!!!')\n\n return True\n","repo_name":"stelligent/aws-anchore-engine-scanner","sub_path":"tasks/deploy_stacks.py","file_name":"deploy_stacks.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"32"} +{"seq_id":"10599380880","text":"from flask import Flask, render_template, request, redirect\nfrom flask_sqlalchemy import SQLAlchemy\nimport web_scraper as wc \nimport language_processing as lp \n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///movies.db'\ndb = SQLAlchemy(app)\n\nclass Cinema(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n description = db.Column(db.String(200), nullable=False)\n genre = db.Column(db.String(20), nullable=False)\n title = db.Column(db.String(20), nullable=False)\n url = db.Column(db.String(20), nullable=False)\n\n def __repr__(self):\n return '<Movie %r>' % self.id\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n movie = None\n if request.method == 'POST':\n title = request.form['title']\n url = wc.UrlPredictor(title) \n user_movie = wc.MovieScraper(url)\n \n user_movie.find_movie_attributes() \n movie_attributes = user_movie.movie_attributes\n\n if (movie_attributes == {}):\n return render_template('index.html') \n \n movie = lp.processInput(\n user_description=movie_attributes['Description'],\n user_genre=movie_attributes['Genre']\n )\n\n return render_template('index.html', movie=movie)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"adenletchworth/Happy-Cinema","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"18376894308","text":"# Найдите корни квадратного уравнения Ax2+Bx+C=0 двумя способами:\n# 1) С помощью математических формул нахождения корней квадратного уравнения\n# 2) С помощью дополнительных библиотек Python\n\nwith open('input_task2.txt', 'r', encoding='utf-8') as file:\n line = file.readline().split() # .split() учитывает пробелы между жанными\n print(line)\n a, b, c = int(line[0]), int(line[1]), int(line[2])\n import sympy\n x = sympy.Symbol('x')\n print(sympy.solve(f'{a}* x ** 2 + {b} * x + {c}'))\n\n# \"\"\"terminal -> pip install sympy\"\"\" сначала скачивается через терминал sympy, потом решается","repo_name":"Nord-Orn/Python_works","sub_path":"Python_Seminar/Seminars/Seminar 4/Task 2 -2.py","file_name":"Task 2 -2.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"23477618686","text":"def gcd(i, j):\r\n if j == 0:\r\n return i\r\n return gcd(j, i%j)\r\n\r\nif __name__ == \"__main__\":\r\n T = int(input())\r\n dp = [0 for _ in range(1001)]\r\n dp[1] = 3\r\n for i in range(2, 1001):\r\n cnt = 0\r\n for j in range(1, i+1):\r\n if i == j:\r\n continue\r\n if gcd(i, j) == 1:\r\n cnt += 2\r\n dp[i] = dp[i-1] + cnt\r\n for _ in range(T):\r\n N = int(input())\r\n print(dp[N])","repo_name":"sangmandu/SangSangPlus","sub_path":"Algorithm/SINGON/2725.py","file_name":"2725.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"32"} +{"seq_id":"42857153402","text":"def overrideCardsMoved(args):\n \"\"\"\n Triggers when one or more cards are simultaneously moved from one location to another via drag-drop.\n \"\"\"\n cards = args.cards\n toGroups = args.toGroups\n indexs = args.indexs\n xs = args.xs\n ys = args.ys\n faceups = args.faceups\n phaseIdx = getCurrentPhase()\n \n for i in range(len(cards)):\n card = args.cards[i]\n fromGroup = card.group\n toGroup = toGroups[i]\n index = indexs[i]\n x = xs[i]\n y = ys[i]\n faceup = faceups[i]\n # debug(\"overrideCardsMoved: {}: {} -> {}, [{}, {}] ({}) {}\", card, fromGroup.name, toGroup.name, x, y, index, faceup)\n \n if isUI(card):\n continue\n \n if toGroup == table:\n if settings[\"PlayAuto\"]:\n # Play from hand or any other allowed group\n if fromGroup == me.hand or (getRule(\"play_removed\") and fromGroup.name == \"Removed pile\"):\n # It may have been played faced down\n if not faceup:\n mute() # Or OCTGN will tell which card it is\n notify(MSG_ACTION_FACE_DOWN, me, fromWhereStr(fromGroup))\n card.moveToTable(x, y, True)\n continue\n # Play a character card\n if isCharacter(card):\n slotIdx = getDropSlotIndex(x)\n if slotIdx == None:\n continue\n myRing = getGlobalVar(\"Ring\", me)\n if myRing[slotIdx] == None:\n play(card, slotIdx = slotIdx)\n else:\n backup(card, target = Card(myRing[slotIdx]))\n # Play other type of card\n else:\n play(card)\n continue\n # Move cards in the table\n elif fromGroup == table:\n cy = card.position[1]\n # Attack\n if me.isActive and phaseIdx == AttackPhase and isCharInRing(card):\n if (y > cy - DragOffsetY, y < cy + DragOffsetY)[bool(me.side + 1)]:\n slotIdx = getDropSlotIndex(x)\n if slotIdx == None:\n continue\n myRing = getGlobalVar(\"Ring\", me)\n # United Attack\n if myRing[slotIdx] != None:\n atkCard = Card(myRing[slotIdx])\n if isAttacking(atkCard, False):\n if isAttacking(card):\n cancelAttack(card, True)\n unitedAttack(card, targets = [atkCard])\n continue\n if hasMarker(card, \"Attack\"):\n alignCard(card)\n else:\n if hasMarker(card, \"United Attack\"):\n cancelAttack(card, True)\n attack(card)\n continue\n # Cancel attack\n elif isAttacking(card):\n cancelAttack(card)\n continue\n # Block\n elif (not me.isActive or tutorial) and phaseIdx == BlockPhase and isCharInRing(card):\n if (y > cy - DragOffsetY, y < cy + DragOffsetY)[bool(me.side + 1)]:\n slotIdx = getDropSlotIndex(x)\n if slotIdx == None:\n continue\n slotIdx = invertSlotIdx(slotIdx, inverted = True)\n targets = None\n oppRing = getGlobalVar(\"Ring\", getOpp())\n if oppRing[slotIdx] != None:\n targets = [Card(oppRing[slotIdx])]\n if isBlocking(card):\n cancelBlock(card, True)\n block(card, targets = targets)\n continue\n # Cancel block\n elif isBlocking(card):\n cancelBlock(card)\n continue\n card.moveToTable(x, y, not faceup)\n card.index = index\n # Move cards to a pile\n else:\n card.moveTo(toGroup, index)\n \n \ndef overrideTurnPassed(args):\n \"\"\"\n Triggers when the player clicks the green \"Pass Turn\" button on the player tabs.\n \"\"\"\n if tutorial:\n tutorial.goNextStep()\n return\n \n player = args.player # The player the turn is being passed to\n setState(None, \"activePlayer\", player._id)\n nextTurn(player)\n\n ","repo_name":"raohmaru/CFC","sub_path":"o8g/Scripts/event_overrides.py","file_name":"event_overrides.py","file_ext":"py","file_size_in_byte":4564,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"1831947266","text":"import re\nimport os\nfrom collections import Counter\nfrom docx import Document\n\nfrom utils import sanitize_string, REQUIRED_COLUMNS\nfrom locales import STRINGS\n\nclass Result:\n def __init__(self, fields):\n self.ok = True\n self.message = None\n\n self.out_fname = ''\n self.fields = fields\n self.match_count = Counter()\n\n self.name = (fields if fields is not None else {}).get('name', None)\n\n def set_error(self, message):\n self.ok = False\n self.message = message\n return self\n\n def add_matches_count(self, text, placeholders):\n for placeholder in placeholders:\n if placeholder in text:\n self.match_count[placeholder] += 1\n\n def __str__(self):\n if self.ok:\n return f'{self.fields[\"name\"]} counter={sum(self.match_count.values())} out={os.path.basename(self.out_fname)}'\n return f'error: {self.message}'\n\n def __repr__(self):\n return self.__str__()\n\n\n_FIELDS_PLACEHOLDERS = {\n 'name': 'NOMBRECOMPLETO',\n 'rut': 'RUT',\n 'home_address': 'DOMICILIO',\n 'date_gen': 'FECHAEMISION',\n 'date_start': 'FECHAINICIO',\n 'date_end': 'FECHATERMINO',\n 'amount_num': 'MONTONUMERO',\n 'amount_words': 'MONTOPALABRAS',\n 'title': 'TITULO',\n 'project': 'PROYECTO',\n}\n_PLACEHOLDER_REGEX = re.compile('|'.join(_FIELDS_PLACEHOLDERS.values()))\n\nassert set(_FIELDS_PLACEHOLDERS.keys()).issubset(REQUIRED_COLUMNS), \"Internal error: field in _FIELDS_PLACEHOLDER is not marked as required\"\n\ndef fill_document(fields, out_folder='generated', template_folder='templates'):\n result = Result(fields)\n\n if \"template\" not in fields:\n return result.set_error(message=STRINGS.get('missing_col', 'template'))\n\n template_fname = os.path.join(template_folder, f'{fields[\"template\"]}.docx')\n\n if not os.path.isfile(template_fname):\n return result.set_error(message=STRINGS.get('template_file_not_found', template_fname))\n\n doc = Document(template_fname)\n\n if any(key not in fields for key in _FIELDS_PLACEHOLDERS.keys()):\n missing_fields = ','.join(key for key in _FIELDS_PLACEHOLDERS.keys() if key not in fields)\n return result.set_error(message=STRINGS.get(\"missing_cols\", missing_fields))\n\n for p in doc.paragraphs:\n for r in p.runs:\n if not re.search(_PLACEHOLDER_REGEX, r.text):\n continue\n\n result.add_matches_count(r.text, _FIELDS_PLACEHOLDERS.values())\n\n replaced = str(r.text)\n for field, placeholder in _FIELDS_PLACEHOLDERS.items():\n replaced = replaced.replace(placeholder, fields[field])\n\n r.text = replaced\n\n out_fname = os.path.join(out_folder, f'{sanitize_string(fields[\"name\"])}.docx')\n os.makedirs(os.path.dirname(out_fname), exist_ok=True)\n doc.save(out_fname)\n\n result.out_fname = out_fname\n\n return result\n","repo_name":"pdpino/ihealth-contracts","sub_path":"src/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":2906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"5488453509","text":"import os\nimport sys\n\n# Fix dependency resolution when invoking the script directly\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\")))\n\nfrom concurrent.futures import ThreadPoolExecutor\nfrom functools import partial\nimport json\nimport pandas as pd\nimport sqlalchemy\n\nimport yfinance as yf\n\n\ndef __createPostgresConnection() -> sqlalchemy.engine.Engine:\n USERNAME = \"postgres\"\n PASSWORD = \"example\"\n HOST = \"127.0.0.1\"\n\n engine = sqlalchemy.create_engine(\n f\"postgresql+psycopg2://{USERNAME}:{PASSWORD}@{HOST}\",\n pool_recycle=3600,\n )\n return engine\n\n\ndef createConnection() -> sqlalchemy.engine.Engine:\n # __createPostgresConnection()\n\n engine = sqlalchemy.create_engine(\n f\"sqlite+pysqlite:///simulationDB.sqlite3\",\n connect_args={\"check_same_thread\": False},\n )\n return engine\n\n\ndef _start(engine: sqlalchemy.engine.Engine, ticker: str) -> None:\n try:\n print(f\"Processing {ticker}\")\n with engine.connect() as db:\n data = pd.read_csv(f\"./historical_data/{ticker}.csv\")\n data.to_sql(ticker, db)\n except Exception as e:\n print(\"Fail\", e)\n ...\n\n\ndef __collect(tickerPath: str) -> None:\n tickers = None\n print(\"Collecting most recent data\")\n with open(tickerPath) as tickerINode:\n tickers = json.load(tickerINode)\n\n tickersLen = len(tickers)\n\n for index, ticker in enumerate(tickers):\n print(f\"Downloading {ticker} ({index}/{tickersLen})\")\n tickerDownloadDataframe = yf.download(ticker)\n tickerDownloadDataframe.to_csv(f\"./historical_data/{ticker}.csv\")\n\n\ndef __save(tickerPath: str) -> None:\n print(\"Seeding database\")\n\n engine = createConnection()\n with open(tickerPath) as f:\n tickers = json.load(f)\n with ThreadPoolExecutor(1) as executor:\n result = executor.map(partial(_start, engine), tickers)\n\n\ndef seed(tickerPath: str) -> None:\n __collect(tickerPath)\n __save(tickerPath)\n","repo_name":"mk5med/python-finance-experiment-framework","sub_path":"src/scripts/seed.py","file_name":"seed.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"29950358377","text":"\ndef palindrome_descendant(num):\n if num < 10:\n return False\n num = str(num)\n if len(num)&1:\n return False\n if num == num[::-1]:\n return True\n res = ''\n for i in range(0,len(num),2):\n res += str(int(num[i])+int(num[i+1]))\n return palindrome_descendant(int(res))\n\n","repo_name":"daniel-reich/ubiquitous-fiesta","sub_path":"MvtxpxtFDrzEtA9k5_21.py","file_name":"MvtxpxtFDrzEtA9k5_21.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"36641590901","text":"import re\nimport json\n\nfrom typing import Dict, List, Set, Optional\nfrom datetime import datetime\n\nfrom .types import HTTPMethod, BlockRule, List\nfrom .misc import get_logger\nfrom .node import Node\n\nCRAWLER_PER_BASE_LIMIT = 2000\n\nHash = int\nUrl = str\nBaseURLCounter = Dict[HTTPMethod, Dict[Url, int]]\n\nclass Crawler:\n def __init__(self, \n init_seed: Optional[Set[Node]] = None,\n seed_file: Optional[str] = None,\n block_rules: List[BlockRule] = []):\n\n self._crawler_unseen: Set[Node] = set()\n\n if init_seed:\n self._crawler_unseen.update(init_seed)\n Crawler.store_init_seed(init_seed)\n\n if seed_file:\n self._crawler_unseen.update(Crawler.parse_init_seed(seed_file))\n\n self._block_rules = block_rules\n self._crawler_seen_full: Set[Hash] = set()\n self._crawler_seen_base: BaseURLCounter = { \n HTTPMethod.GET: {}, \n HTTPMethod.POST: {} \n }\n\n @staticmethod\n def parse_init_seed(filename:str) -> Set[Node]:\n logger = get_logger(__name__)\n\n nodes = set()\n\n with open(filename, \"r\") as f:\n data = json.loads(f.read())\n \n for entry in data:\n method = HTTPMethod[entry[\"method\"].upper()]\n params = {\n HTTPMethod.GET: entry[\"params\"][\"GET\"],\n HTTPMethod.POST: entry[\"params\"][\"POST\"]\n }\n nodes.add(Node(entry[\"url\"], method, params))\n \n logger.info(\"Seed file number of entries %d\", len(nodes))\n logger.debug(\"Read seed file as %s\", nodes)\n\n return nodes\n\n @staticmethod\n def store_init_seed(seed: Set[Node]) -> None:\n logger = get_logger(__name__)\n entries = []\n\n dt = datetime.now()\n filename = f\"./seeds/webFuzz_seed\" + \\\n f\"_{dt.day}-{dt.month}\" + \\\n f\"_{dt.hour}:{dt.minute}.json\"\n\n logger.info(\"Writing seed to %s\", filename)\n\n for node in seed:\n entry = {\n \"url\": node.url,\n \"method\": node.method.name,\n \"params\": {\n \"GET\": node.params[HTTPMethod.GET],\n \"POST\": node.params[HTTPMethod.POST]\n }\n }\n entries.append(entry)\n\n with open(filename, \"w+\") as f:\n json.dump(entries, f, indent=3)\n\n @property\n def pending_requests(self) -> int:\n return len(self._crawler_unseen)\n\n @staticmethod\n def _is_match(rule: BlockRule, node: Node) -> bool:\n if rule.method and not rule.method == node.method:\n return False\n\n if not re.search(rule.url, node.url, re.IGNORECASE):\n return False\n \n if not rule.key and not rule.val:\n return True\n\n for method in node.params:\n for key in node.params[method]:\n if not re.search(rule.key, key, re.IGNORECASE):\n continue\n \n for value in node.params[method][key]:\n if re.search(rule.val, value, re.IGNORECASE):\n return True\n \n return False\n\n \"\"\"\n Check if the request to be sent matches the criteria of a blocked link\n\n :param new_request: the request that we want to check\n :type links: Node\n :return: if the request is allowed to be sent\n :rtype: bool\n \"\"\"\n def _should_block(self, new_request: Node) -> bool:\n logger = get_logger(__name__)\n\n for rule in self._block_rules:\n if Crawler._is_match(rule, new_request):\n logger.info(\"Blocked %s\", new_request)\n return True\n \n return False\n \n \"\"\"\n Add to crawler the new links that\n have been found but check that we\n haven't already visited any one of them first\n\n :param links: the new links to add\n :type links: Set[Node]\n :return: the updated crawler object\n :rtype: Crawler\n \"\"\"\n def __add__(self, links: Set[Node]):\n if not isinstance(links, set):\n raise NotImplementedError()\n\n logger = get_logger(__name__)\n\n if not links:\n return self\n\n # since we do not need to store the whole Node object\n # for keeping track which requests have been sent in the\n # past, but only their hash, we compare hashes\n # instead of nodes, and filter out already seen links\n # TODO: there must be a cleaner way to do this\n hash_of_links = set(map(lambda link: link.__hash__(), links))\n uniq_hashes = hash_of_links - self._crawler_seen_full\n uniq_links:Set[Node] = set(\n filter(\n lambda link: link.__hash__() in uniq_hashes, \n links\n )\n )\n\n self._crawler_unseen = self._crawler_unseen | uniq_links\n\n logger.debug(\"New links found: %s\", uniq_links)\n\n return self\n\n \"\"\"\n Check if the base url of the new request did not surpass the limit.\n Each base url (without the query,fragment string) is only allowed to be sent\n in total CRAWLER_PER_BASE_LIMIT number of times. This is a simple\n way to stop urls with nonce parameter to be constantly sent\n\n :param new_request: the request that we want to check\n :type links: Node\n :return: if the request is allowed to be sent\n :rtype: bool\n \"\"\"\n def _base_url_allows(self, new_request: Node) -> bool:\n logger = get_logger(__name__)\n\n base_dict = self._crawler_seen_base[new_request.method]\n\n if new_request.url not in base_dict:\n base_dict[new_request.url] = 0\n return True\n else:\n base_dict[new_request.url] += 1\n if base_dict[new_request.url] == CRAWLER_PER_BASE_LIMIT:\n logger.warning(\"Base URL %s added to blocklist\", new_request.url)\n \n if base_dict[new_request.url] >= CRAWLER_PER_BASE_LIMIT:\n return False\n\n return True\n\n def __iter__(self):\n return self\n\n \"\"\"\n Get the next node to send a request.\n A new request must firstly pass the \n blocked link criteria, and the per base url\n limit criteria.\n\n :return: the next request to sent\n :rtype: Node\n \"\"\"\n def __next__(self):\n\n while len(self._crawler_unseen) != 0:\n new_request = self._crawler_unseen.pop()\n\n # store only the hash in the set\n # as the whole node is not needed\n self._crawler_seen_full.add(new_request.__hash__())\n\n if self._should_block(new_request):\n continue\n\n if not self._base_url_allows(new_request):\n continue\n\n return new_request\n\n raise StopIteration\n","repo_name":"ovanr/webFuzz","sub_path":"webFuzz/webFuzz/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":6939,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"32"} +{"seq_id":"4608218076","text":"from ppadb.device import Device\nfrom .base.chart import MonitorChart\nfrom perfcat.modules.profiler.temp import MarkTempSampler\nfrom PySide6.QtCharts import QLineSeries\n\nclass TempMonitor(MonitorChart):\n def __init__(\n self,\n parent=None,\n ):\n super().__init__(\n y_axis_name=\"℃\",\n parent=parent,\n )\n self.setObjectName(\"Temperature\")\n self.setToolTip(\"不少设备无法获得温度，会显示为-1\")\n self.mark_temp_sampler = None\n\n self._sample_data = {}\n\n self.create_series(\"整体温度\", QLineSeries(self), lambda v: f\"{v}℃\")\n self.create_series(\"CPU温度\", QLineSeries(self), lambda v: f\"{v}℃\")\n self.create_series(\"GPU温度\", QLineSeries(self), lambda v: f\"{v}℃\")\n self.create_series(\"NPU温度\", QLineSeries(self), lambda v: f\"{v}℃\")\n self.create_series(\"电池温度\", QLineSeries(self), lambda v: f\"{v}℃\")\n\n def sample(self, sec: int, device: Device, package_name: str):\n\n if self.mark_temp_sampler is None:\n self.mark_temp_sampler = MarkTempSampler(device)\n\n temp_data = self.mark_temp_sampler.get_temp()\n\n self._sample_data[sec] = {\n \"整体温度\": temp_data[\"total\"],\n \"CPU温度\":temp_data[\"cpu\"],\n \"GPU温度\": temp_data[\"gpu\"],\n \"NPU温度\": temp_data[\"npu\"],\n \"电池温度\": temp_data[\"battery\"]\n }\n\n for k,v in self._sample_data[sec].items():\n self.add_point(k, sec, v)\n\n def reset_series_data(self):\n self.mark_temp_sampler = None\n self._sample_data = {}\n return super().reset_series_data()\n\n def to_dict(self, all: bool = True) -> dict:\n if all:\n return self._sample_data\n else:\n start = self.record_range[0]\n end = self.record_range[1]\n\n data = {}\n for k,v in self._sample_data.items():\n if start <= k <= end:\n data[k] = v\n return data\n\n def from_dict(self, data: dict):\n for sec, data_table in data.items():\n for k, v in data_table.items():\n self.add_point(k,int(sec), v)\n self.flush()\n","repo_name":"kaluluosi/PerformanceCatcher","sub_path":"src/perfcat/pages/profiler/plugins/temp_monitor.py","file_name":"temp_monitor.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"18847972666","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.path as mpltPath #Para pip\n\n# Adaptado de https://www.fundza.com/vectors/point2line/index.html\ndef pnt2line(punto, inicio_linea, fin_linea):\n\tline_vec = fin_linea - inicio_linea\n\tpnt_vec = punto - inicio_linea\n\tline_len = np.linalg.norm(line_vec)\n\tline_unitvec = line_vec/line_len\n\tpnt_vec_scaled = pnt_vec * 1.0/line_len\n\tt = np.dot(line_unitvec, pnt_vec_scaled) \n\tif t < 0.0:\n\t\tt = 0.0\n\telif t > 1.0:\n\t\tt = 1.0\n\tnearest = line_vec * t\n\tdist = np.linalg.norm(pnt_vec-nearest)\n\treturn dist\n\ndef A(g,elemento,xnl,ynl):\n\tpolygon = mpltPath.Path(elemento)\n\tpip = polygon.contains_points([[xnl,ynl]])\n\tif not pip[0]:\n\t\td = []\n\t\tfor i in range(len(coords)-1):\n\t\t\tpunto = np.array([xnl,ynl])\n\t\t\tinicio_linea = np.array([elemento[i][0],elemento[i][1]])\n\t\t\tfin_linea = np.array([elemento[i+1][0],elemento[i+1][1]])\n\t\t\td.append(pnt2line(punto, inicio_linea, fin_linea))\n\t\tpunto = np.array([xnl,ynl])\n\t\tinicio_linea = np.array([elemento[-1][0],elemento[-1][1]])\n\t\tfin_linea = np.array([elemento[0][0],elemento[0][1]])\n\t\td.append(pnt2line(punto, inicio_linea, fin_linea))\n\t\treturn g(np.min(d))\n\treturn g(0)\n\n\nl = 0.1 #Longitud interna\nLR = 6*l\nmacauley = lambda f, *args: f(*args)/2 + abs(f(*args))/2\nf2p = lambda r: (1-(r/LR))\nf3p = lambda r: (1-(r**2/LR**2))\ng1 = lambda r: np.exp(-r/l) #Función de atenuación biexponencial\ng2 = lambda r: macauley(f2p,r)\ng3 = lambda r: macauley(f3p,r)\ng4 = lambda r: r*g1(r)\n\nG = [g1,g2,g3,g4]\n# r_clasico = lambda xl,yl,xnl,ynl: np.sqrt((xnl-xl)**2+(ynl-yl)**2)\nr = 1 # Radio del polígono\nN = 150 #Numero de puntos para graficar\nn = 6 #Numero de lados del poligono\nh = 2*(r+LR)/N\nth = 2*np.pi/n\ncoords = np.array([[r*np.cos(th*i),r*np.sin(th*i)] for i in range(n)]) #Coordenadas del polígono\n_coords = np.array(coords.tolist()+[coords[0].tolist()]) #Coordenadas del polígono para graficar\n_X = [-(r+LR)+h*i for i in range(N+1)] # Muestreo en X\n_Y = [-(r+LR)+h*i for i in range(N+1)] # Muestreo en Y\nfig = plt.figure()\ncount = 0\nfor i,g in enumerate(G):\n\tcount+=1\n\tX = []\n\tY = []\n\tZ = []\n\tfor x in _X:\n\t\tfor y in _Y:\n\t\t\tX.append(x) # Coordenada x\n\t\t\tY.append(y) # Coordenada y\n\t\t\tz = A(g,coords,x,y) # Valor de la función de atenuación\n\t\t\tZ.append(z)\n\tax = fig.add_subplot(int(np.sqrt(len(G))),int(np.sqrt(len(G))),count,projection='3d')\n\tsurf = ax.plot_trisurf(X,Y,Z,cmap='magma') # Gráfica 3D\n\tcbar = fig.colorbar(surf)\n\nplt.show()\n","repo_name":"ZibraMax/Tesis-NLFEM","sub_path":"test_atenuacion_novedosa.py","file_name":"test_atenuacion_novedosa.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"11797264691","text":"city = [\"I0\", \"I0\", \"S\"]\n\nimport sir\nimport util\nimport random\n\ndef has_an_infected_neighbor(city, position):\n if position == 0:\n if (city[len(city) - 1] == \"I0\" or city[len(city) - 1] == \"I1\") or (city[position + 1] == \"I0\" or city[position + 1] == \"I1\"):\n neighbor_infected = True\n else:\n neighbor_infected = False\n\n elif position == (len(city) - 1):\n if (city[position - 1] == \"I0\" or city[position - 1] == \"I1\") or (city[0] == \"I0\" or city[0] == \"I1\"):\n neighbor_infected = True\n else:\n neighbor_infected = False\n\n else:\n if (city[position - 1] == \"I0\" or city[position - 1] == \"I1\") or (city[position + 1] == \"I0\" or city[position + 1] == \"I1\"):\n neighbor_infected = True\n else:\n neighbor_infected = False\n\n return neighbor_infected\n\nprint(has_an_infected_neighbor(city, 0))\n\ndef gets_infected_at_position(city, position, infection_rate):\n\n if has_an_infected_neighbor(city, position) == True:\n random.seed(sir.TEST_SEED)\n immunity_level = random.random()\n if immunity_level > infection_rate:\n infected = False\n else:\n infected = True\n else:\n infected = False\n\n return infected\n\nprint(gets_infected_at_position(city, 2, 0.5))\n\n","repo_name":"thomaswilwilson/UCCS","sub_path":"cs121-pa1-pandemic-simulation/gets_infected_at_position.py","file_name":"gets_infected_at_position.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"34059059177","text":"import time, num_type_gen, plot_lines, all_num_types_game, single_num_type_game\n\n\ndef set_difficulty():\n\n difficulty = input(\"Вибери рівень складності:\\n1 - простий\\n2 - середній\\n3 - складний\\n\")\n\n while difficulty not in (\"1\", \"2\", \"3\"):\n difficulty = input(\"Неправильне значення. Введіть число від 1 до 3\\n\")\n\n return int(difficulty) \n\n\ndef main():\n plot_lines.vstup()\n plot_lines.study_begin()\n plot_lines.study_end()\n plot_lines.game_rules()\n plot_lines.game_start()\n\n while True:\n n = int(input(\"\"\"Вибери режим гри:\n Введи 1, якщо хочеш вивчити парні числа\n Введи 2, якщо хочеш вивчити вдалі числа\n Введи 3, якщо хочеш вивчити числа Улама\n Введи 4, якщо хочеш перевірити свої знання з усіх чисел\n Введи 5 якщо хочеш завершити гру\n \"\"\"))\n\n if n == 1:\n difficulty = set_difficulty()\n \n plot_lines.even_numbers_start()\n\n won = single_num_type_game.main(difficulty, num_type_gen.even_numbers)\n\n if won:\n plot_lines.even_numbers_win()\n else:\n plot_lines.even_numbers_lose()\n\n elif n == 2:\n difficulty = set_difficulty()\n\n plot_lines.lucky_number_start()\n\n won = single_num_type_game.main(difficulty, num_type_gen.lucky_numbers)\n\n if won:\n plot_lines.lucky_number_win()\n else:\n plot_lines.lucky_number_lose()\n \n\n elif n == 3:\n difficulty = set_difficulty()\n\n plot_lines.ulam_number_start()\n\n won = single_num_type_game.main(difficulty, num_type_gen.numbers_Ulam)\n\n if won:\n plot_lines.ulam_number_win()\n else:\n plot_lines.ulam_number_lose()\n\n elif n == 4:\n difficulty = set_difficulty()\n\n plot_lines.all_numbers_start()\n all_num_types_game.main(difficulty)\n\n elif n == 5:\n print(\"Вітаємо, ти успішно пройшов усі потрібні тобі курси. До зустрічі 😊\")\n return None\n\nmain()","repo_name":"OlehPalka/First_semester_labs","sub_path":"Lab work 7/Comand/Final_game_version.py","file_name":"Final_game_version.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"70899009371","text":"# 9368_연속한 1의 개수\n\n# N개의 0과 1로 이루어진 수열에서 연속한 1의 개수 중 최대 값을 출력\n# 첫 줄에 테스트 케이스 개수 T, 다음 줄에는 수열의 길이 N\n# 다음 줄에는 N개의 0과 1로 구성된 수열이 공백 없이 제공\n\nT = int(input())\n\nfor tc in range(1, T+1):\n n = int(input())\n lst = list(map(int, input()))\n\n one_lst = []\n cnt = 0\n for i in range(n):\n if lst[i] == 1:\n cnt += 1\n one_lst.append(cnt) # 1이 있고, 또 더하면 2가 된 그 상태를 더하는\n elif lst[i] == 0:\n cnt = 0 # 0일때 0으로 초기화\n\n print(f'#{tc} {max(one_lst)}')\n","repo_name":"jeongmin59/Algorithm","sub_path":"SWEA/01_List/9386_연속한 1의 개수.py","file_name":"9386_연속한 1의 개수.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"17669534687","text":"import time\r\nfrom hashlib import sha256\r\n\r\n\r\nclass Block:\r\n\r\n __slots__ = 'nonce', 'prev_hash', 'index','timestamp'\r\n\r\n def __init__(self, index, prev_hash, timestamp=None, nonce=0):\r\n self.prev_hash = prev_hash\r\n self.index = index\r\n self.nonce = nonce\r\n self.timestamp = timestamp or int(time.time())\r\n\r\n def hash(self, nonce=None):\r\n if nonce:\r\n self.nonce = nonce\r\n block_string = '{}{}{}{}'.format(\r\n self.prev_hash, self.index, self.nonce, self.timestamp\r\n )\r\n return sha256(sha256(block_string.encode()).hexdigest().encode('utf8')).hexdigest()\r\n\r\n @property\r\n def as_dict(self):\r\n return {\r\n \"index\": self.index,\r\n \"timestamp\": self.timestamp,\r\n \"prev_hash\": self.prev_hash,\r\n \"hash\": self.hash(),\r\n \"nonce\": self.nonce\r\n \r\n }\r\n\r\n @classmethod\r\n def from_dict(cls, data):\r\n return cls(\r\n data['index'],\r\n data['prev_hash'],\r\n data['timestamp'],\r\n data['nonce']\r\n )","repo_name":"Bhumika0821/BLOCK-CHAIN","sub_path":"block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"33636476313","text":"#Embedded file name: c:/depot/games/branches/release/EVE-TRANQUILITY/carbon/client/script/movement/movementClient.py\nimport svc\nimport const\nimport GameWorld\nimport movement\nimport blue\n\nclass MovementClient(movement.movementService):\n __guid__ = 'svc.movementClient'\n __notifyevents__ = ['ProcessEntityMove', 'OnSessionChanged']\n __dependencies__ = movement.movementService.__dependencies__[:]\n __dependencies__.extend(['gameWorldClient'])\n reportedMissing = False\n TIME_BEFORE_SENDING = 6 * const.SEC / 30\n\n def Run(self, *etc):\n movement.movementService.Run(self)\n self.lastSentTime = 0\n self.unsentPlayerMoves = []\n self.movementServer = sm.RemoteSvc('movementServer')\n self.addressCache = {}\n GameWorld.SetupCLevelMovement()\n\n def OnSessionChanged(self, isRemote, sess, change):\n newworldspaceid = change.get('worldspaceid', (None, None))[1]\n self.LookupWorldSpaceNodeID(newworldspaceid)\n\n def LookupWorldSpaceNodeID(self, newworldspaceid):\n if newworldspaceid and newworldspaceid not in self.addressCache:\n if self.entityService.IsClientSideOnly(newworldspaceid):\n return None\n nodeid = self.movementServer.ResolveNodeID(newworldspaceid)\n if nodeid:\n self.addressCache[newworldspaceid] = nodeid\n else:\n self.LogError('Trying to resolve a unknown worldspaceid to a node', newworldspaceid)\n return self.addressCache.get(newworldspaceid, None)\n\n def GetPlayerEntity(self):\n raise StandardError('Not implemented')\n\n def SetupComponent(self, entity, component):\n movement.movementService.SetupComponent(self, entity, component)\n gw = self.gameWorldClient.GetGameWorld(component.sceneID)\n positionComponent = entity.GetComponent('position')\n if entity.entityID == session.charid:\n remoteNodeID = self.LookupWorldSpaceNodeID(entity.scene.sceneID)\n if remoteNodeID is None:\n remoteNodeID = -1\n component.moveModeManager = GameWorld.MoveModeManager(entity.entityID, component.sceneID, const.movement.AVATARTYPE_CLIENT_LOCALPLAYER, component.moveState, positionComponent, component.physics, component.characterController, GameWorld.PlayerInputMode(), remoteNodeID)\n else:\n component.moveModeManager = GameWorld.MoveModeManager(entity.entityID, component.sceneID, const.movement.AVATARTYPE_CLIENT_NPC, component.moveState, positionComponent, component.physics, component.characterController, GameWorld.ClientRemoteMode(), -1)\n component.InitializeCharacterControllerRefs(positionComponent)\n sm.GetService('navigation')\n\n def RegisterComponent(self, entity, component):\n movement.movementService.RegisterComponent(self, entity, component)\n\n def UnRegisterComponent(self, entity, component):\n movement.movementService.UnRegisterComponent(self, entity, component)","repo_name":"alexcmd/eve","sub_path":"eve-8.21.494548/carbon/client/script/movement/movementClient.py","file_name":"movementClient.py","file_ext":"py","file_size_in_byte":2982,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"32"} +{"seq_id":"15932978268","text":"import sys\nimport subprocess\nfrom PyQt6 import QtWidgets, QtCore, QtGui\n\nclass SystemProfiler(QtWidgets.QWidget):\n def __init__(self):\n super().__init__()\n self.initUI()\n\n def initUI(self):\n self.setGeometry(300, 300, 1900, 800)\n self.setWindowTitle('System Profiler')\n\n self.tree = QtWidgets.QTreeWidget(self)\n self.tree.setHeaderLabel('Categories')\n\n hardware = QtWidgets.QTreeWidgetItem(self.tree)\n hardware.setText(0, 'Hardware')\n hardware.addChild(QtWidgets.QTreeWidgetItem(['SPAudioDataType', 'SPCameraDataType', 'SPCardReaderDataType',\n 'SPiBridgeDataType', 'SPDisplaysDataType', 'SPHardwareDataType',\n 'SPMemoryDataType', 'SPNVMeDataType', 'SPPCIDataType', 'SPParallelSCSIDataType',\n 'SPPowerDataType', 'SPSASDataType', 'SPSerialATADataType',\n 'SPSPIDataType', 'SPSmartCardsDataType', 'SPStorageDataType',\n 'SPThunderboltDataType', 'SPUSBDataType']))\n\n network = QtWidgets.QTreeWidgetItem(self.tree)\n network.setText(0, 'Network')\n network.addChild(QtWidgets.QTreeWidgetItem(['SPBluetoothDataType', 'SPEthernetDataType', 'SPNetworkDataType',\n 'SPAirPortDataType', 'SPNetworkVolumeDataType', 'SPWWANDataType']))\n\n software = QtWidgets.QTreeWidgetItem(self.tree)\n software.setText(0, 'Software')\n software.addChild(QtWidgets.QTreeWidgetItem(['SPApplicationsDataType', 'SPDeveloperToolsDataType',\n 'SPDisabledSoftwareDataType', 'SPExtensionsDataType',\n 'SPFontsDataType', 'SPFrameworksDataType',\n 'SPInstallHistoryDataType', 'SPLegacySoftwareDataType',\n 'SPLogsDataType', 'SPPrefPaneDataType', 'SPPrintersSoftwareDataType',\n 'SPConfigurationProfileDataType', 'SPRawCameraDataType',\n 'SPSoftwareDataType', 'SPStartupItemDataType', 'SPSyncServicesDataType']))\n\n self.textEditTop = QtWidgets.QTextEdit(self)\n self.textEditBottom = QtWidgets.QTextEdit(self)\n\n vbox = QtWidgets.QVBoxLayout()\n vbox.addWidget(self.textEditTop)\n vbox.addWidget(self.textEditBottom)\n\n self.rightWidget = QtWidgets.QWidget(self)\n self.rightWidget.setLayout(vbox)\n\n splitter = QtWidgets.QSplitter(QtCore.Qt.Orientation.Horizontal, self)\n splitter.addWidget(self.tree)\n splitter.addWidget(self.rightWidget)\n splitter.setSizes([400, 1500])\n\n mainLayout = QtWidgets.QHBoxLayout(self)\n mainLayout.addWidget(splitter)\n\n self.tree.clicked.connect(self.on_tree_clicked)\n\n def on_tree_clicked(self, index):\n item_text = self.tree.currentItem().text(0)\n cmd = ['system_profiler', item_text]\n result = subprocess.run(cmd, stdout=subprocess.PIPE, text=True)\n self.textEditTop.setText(result.stdout)\n\nif __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n ex = SystemProfiler()\n ex.show()\n sys.exit(app.exec())\n","repo_name":"harithoppil/system_profiler","sub_path":"main1.py","file_name":"main1.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6322721845","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 22 16:29:23 2021\n\n@author: tomorr\n\"\"\"\n\n\nimport numpy as np\nimport pandas as pd\nimport gurobipy as gp\nfrom gurobipy import GRB\n\n\ndef Data_load(data_name):\n \"\"\"\n Import data\n \"\"\"\n \n if data_name == 'BreastCancerCoimbra':\n data = pd.read_excel('Data/Breast Cancer Coimbra Data Set .xlsx')\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1]==1 else -1 for i in range(data.values[:,-1].shape[0])])\n \n elif data_name == 'BreastCancerPrognostic':\n data = pd.read_excel('Data/Breast Cancer Wisconsin Prognostic .xlsx',header=None)\n data.fillna(method= 'ffill', inplace=True)\n x = data.values[:,1:]\n y = np.array([1 if data.values[i,0]=='N' else -1 for i in range(data.values[:,0].shape[0])])\n\n elif data_name == 'LiverPatient':\n data = pd.read_csv('Data/Indian Liver Patient Dataset (ILPD).csv',engine=\"python\",header=None)\n data[1] = [1 if data.values[i,1] == 'Male' else 0 for i in range(data.values.shape[0])]\n data.fillna(method= 'ffill', inplace=True)\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1]==1 else -1 for i in range(data.values[:,-1].shape[0])])\n \n elif data_name == 'GermanCredit':\n data = pd.read_excel('Data/GermanCredit.xlsx',header=None)\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] ==1 else -1 for i in range(data.values[:,-1].shape[0])])\n \n elif data_name == 'LiverDisorders':\n data = pd.read_excel('Data/LiverDisorders.xlsx',header=None)\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] ==1 else -1 for i in range(data.values[:,-1].shape[0])])\n elif data_name==\"Australian\":\n data = pd.read_csv('Data/Australian Credit Approval.csv')\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] ==1 else -1 for i in range(data.values[:,-1].shape[0])])\n \n elif data_name==\"Diabetes\":\n data = pd.read_csv('Data/Diabetes dataset.csv')\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] ==1 else -1 for i in range(data.values[:,-1].shape[0])])\n \n elif data_name==\"Ionosphere\":\n data = pd.read_csv('Data/Johns Hopkins University Ionosphere database.csv',header=None)\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] =='g' else -1 for i in range(data.values[:,-1].shape[0])])\n elif data_name=='SpamBase':\n data = pd.read_csv('Data/SPAM E-MAIL DATABASE ATTRIBUTES.csv',header=None)\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] ==1 else -1 for i in range(data.values[:,-1].shape[0])])\n elif data_name=='Sonar':\n data = pd.read_csv('Data/Sonar.csv',header=None)\n x = data.values[:,:-1]\n y = np.array([1 if data.values[i,-1] =='R' else -1 for i in range(data.values[:,-1].shape[0])])\n else:\n print('No data')\n \n return x,y\n\n\n\n \ndef FI_calculation(score,y):\n \"\"\"\n Calculate the KL-divergence based FI by Algorithm 1\n Input:\n score: score value h(x) of each test sample x.\n y: true class label.\n \n Output:\n value of FI\n \"\"\"\n \n sample_p = score[y==1] # positive samples\n sample_n = score[y==-1] # negative samples\n \n N_p = sample_p.shape[0]\n N_n = sample_n.shape[0]\n \n S = N_p * N_n #number of samples of ranking errors\n \n error = np.zeros(S) # ranking error\n for i in range(N_p):\n for j in range(N_n):\n error[i * N_n + j] = sample_n[j] - sample_p[i]\n \n \n def expectation(error,k):\n # calculate the empirical expectation given the value of k\n x = 0\n for i in range(error.shape[0]):\n x += np.exp(error[i]/k)\n \n return x/error.shape[0]\n \n \n if (error<0).sum() == 0:\n return 0, error\n elif error.mean()>0:\n return np.Inf, error\n else: \n k_min = 10\n e = expectation(error,k_min)\n while (e <= 1) & (k_min>1e-4) :\n k_min = k_min/2\n e = expectation(error,k_min)\n #print(k_min)\n \n if k_min <= 1e-4:\n return 0,error\n else:\n k_max = k_min * 2\n \n while k_max - k_min > 1e-3:\n k = (k_min + k_max) / 2\n e = expectation(error,k)\n if e <= 1:\n k_max = k\n else:\n k_min = k\n \n #print(k_max,k_min)\n \n return k_max, error\n \n \n \ndef bAUC_calculation(samples, estimate=True):\n\n \"\"\"\n Calculte bAUC given the samples of ranking error\n Formulation can be found in Norton M, Uryasev S. Maximization of auc and buffered auc in binary classification[J]. Mathematical Programming, 2019, 174(1): 575-612.\n \"\"\"\n\n ################# estimate bPOE by simply sorting samples #################\n if estimate==True:\n N=samples.shape[0]\n sorted_samples=sorted(samples)\n \n cvar=sorted_samples[-1]\n probability_level=1\n for i in range(N):\n probability_level = probability_level - 1/float(N)\n cvar = (cvar*(i) + sorted_samples[-(i+1)])/float(i+1)\n if cvar <=0: break\n \n var=sorted_samples[int( probability_level*N ) ]\n bPOE=1-probability_level\n bAUC=probability_level\n # a=1/float(-var)\n gamma=var\n return bAUC\n ######################Get bAUC exactly by solving an LP#############################\n\n \n m = gp.Model()\n m.setParam(\"OutputFlag\",0)\n\n E=[]\n \n for i in range(samples.shape[0]):\n E.append(m.addVar(lb=0,ub=GRB.INFINITY,obj=0,vtype=GRB.CONTINUOUS,name=\"E\"+str(i) ))\n a=m.addVar(lb=0,ub=GRB.INFINITY,obj=0,vtype=GRB.CONTINUOUS,name=\"a\" ) \n\n m.update()\n m.setObjective( (1/float(samples.shape[0]))*gp.quicksum(E[i] for i in range(samples.shape[0]) ),GRB.MINIMIZE)\n \n m.optimize()\n \n for i in range(samples.shape[0]):\n m.addConstr ( E[i] >= a * (samples[i]) + 1 )\n m.addConstr ( E[i] >= 0 )\n \n m.optimize()\n bPOE=m.getObjective().getValue()\n bAUC=1-bPOE\n \n return bAUC\n \n \ndef FI_minimization(N,S,data_sample,lb_p,lb_n,ub_p,ub_n,LogToConsole=False):\n \n \"\"\"\n Train linear classifier by minimizing wasserstein based FI.\n\n Input:\n N: dimension of features.\n data_sample: samples in the empirical distribution of ranking errors.\n S: number of samples in the empirical distribution of ranking errors.\n lb_p, ub_p: lower bound and upper bound for the features of positive samples.\n lb_n, ub_n: lower bound and upper bound for the features of negativee samples.\n\n \n Output:\n w_solu: decision variable for the linear classifier\n \"\"\"\n \n MM = gp.Model()\n MM.Params.LogToConsole=LogToConsole\n\n \n k = MM.addVar(lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"k\")\n p1 = MM.addVars(S,N,lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"p1\")\n p2 = MM.addVars(S,N,lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"p2\")\n q1 = MM.addVars(S,N,lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"q1\")\n q2 = MM.addVars(S,N,lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"q2\")\n \n r = MM.addVars(S,2*N,lb = -GRB.INFINITY,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"r\")\n r_abs = MM.addVars(S,2*N,lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"r\")\n \n w = MM.addVars(N,lb = -GRB.INFINITY,ub = GRB.INFINITY, vtype=GRB.CONTINUOUS,name='w')\n w_abs = MM.addVars(N,lb = 0,ub = GRB.INFINITY, vtype=GRB.CONTINUOUS,name='w')\n \n aux = MM.addVars(S,lb = -GRB.INFINITY,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"aux\")\n \n \n MM.setObjective(k,GRB.MINIMIZE)\n \n MM.addConstr( sum( aux[s] for s in range(S) )\n <= 0 )\n \n MM.addConstrs(sum(- p1[s,n] * lb_p[n] + p2[s,n] * ub_p[n] \n - q1[s,n] * lb_n[n] + q2[s,n] * ub_n[n] \n for n in range(N))\n + sum(r[s,n] * data_sample[s,n]\n for n in range(2*N))\n == aux[s]\n for s in range(S))\n \n MM.addConstrs(- p1[s,n] + p2[s,n] \n == \n - w[n] - r[s,n] \n for s in range(S) for n in range(N))\n \n MM.addConstrs(- q1[s,n] + q2[s,n] \n ==\n w[n] - r[s,N+n] \n for s in range(S) for n in range(N))\n \n \n MM.addConstrs(r_abs[s,n] == gp.abs_(r[s,n]) for s in range(S) for n in range(2*N))\n MM.addConstrs(r_abs[s,n] <= k for s in range(S) for n in range(2*N))\n \n \n\n MM.addConstrs(w_abs[n] == gp.abs_(w[n]) for n in range(N))\n MM.addConstr(gp.quicksum(w_abs[n] for n in range(N)) == 1 )\n\n MM.optimize()\n\n \n w_solu = np.zeros(N)\n for n in range(N):\n w_solu[n] = w[n].x\n\n \n return w_solu\n\n \n\n\n\n\ndef bAUC(N,S,data_sample,LogToConsole=False):\n \n \"\"\"\n Train linear classifier by maximizing bAUC\n Formulation can be found in Norton M, Uryasev S. Maximization of auc and buffered auc in binary classification[J]. Mathematical Programming, 2019, 174(1): 575-612.\n \n Input:\n N: dimension of features.\n data_sample: samples in the empirical distribution of ranking errors.\n S: number of samples in the empirical distribution of ranking errors.\n \n Output:\n w_solu: decision variable for the linear classifier\n \"\"\"\n \n MM = gp.Model()\n MM.Params.LogToConsole=LogToConsole\n \n \n w = MM.addVars(N,lb = -GRB.INFINITY,ub = GRB.INFINITY, vtype=GRB.CONTINUOUS,name='w')\n w_abs = MM.addVars(N,lb = 0,ub = GRB.INFINITY, vtype=GRB.CONTINUOUS,name='w')\n\n r = MM.addVars(S,lb = 0,ub = GRB.INFINITY,vtype=GRB.CONTINUOUS,name=\"r\")\n \n MM.setObjective(gp.quicksum(r[s] for s in range(S)),GRB.MINIMIZE)\n \n MM.addConstrs(r[s] >= \n sum(w[n]*(data_sample[s,N+n]-data_sample[s,n]) for n in range(N)) +1\n for s in range(S))\n \n\n MM.addConstrs(w_abs[n] == gp.abs_(w[n]) for n in range(N))\n MM.addConstr(gp.quicksum(w_abs[n] for n in range(N)) == 1 )\n \n MM.setParam(\"NonConvex\", 2)\n \n MM.optimize()\n \n w_solu = np.zeros(N)\n for n in range(N):\n w_solu[n] = w[n].x\n \n return w_solu\n\n \n\ndef performance(data_name,name,w,N,S_test,data_test):\n \"\"\"\n For section 5.3 Optimization for linear classifiers.\n Calculating the statistic descriptions of ranking errors.\n \n Output: performance table\n \"\"\"\n ranking_error = np.zeros(S_test)\n for s in range(S_test):\n ranking_error[s] = w.dot(data_test[s,N:]) - w.dot(data_test[s,:N])\n \n # ranking_error_pos = ranking_error[ranking_error >= 0]\n \n v_prob = np.mean(ranking_error <= 0)\n v_mean = np.mean(ranking_error[ranking_error >= 0])\n v_std = np.std(ranking_error)\n v_var95 = np.quantile(ranking_error, 0.95)\n v_var99 = np.quantile(ranking_error, 0.99)\n v_cvar95 = np.mean(ranking_error[ranking_error>=v_var95])\n v_cvar99 = np.mean(ranking_error[ranking_error>=v_var99])\n \n perf_df = pd.DataFrame({\"Data_name\":[data_name],\n \"Model_name\": [name], \n \"Probability\": [v_prob],\n \"Mean\": [v_mean],\n \"Std\": [v_std],\n \"VaR%95\": [v_var95],\n \"CVaR%95\": [v_cvar95],\n \"VaR%99\": [v_var99],\n \"CVaR%99\": [v_cvar99]})\n \n return perf_df\n\ndef calculate_error(classifier, x, y):\n score = classifier.decision_function(x)\n\n sample_p = score[y==1] # positive samples\n sample_n = score[y==-1] # negative samples\n \n N_p = sample_p.shape[0]\n N_n = sample_n.shape[0]\n \n S = N_p * N_n #number of samples of ranking errors\n \n error = np.zeros(S) # ranking error\n for i in range(N_p):\n for j in range(N_n):\n error[i * N_n + j] = sample_n[j] - sample_p[i]\n \n return error\n\n\ndef performance_of_error(data_name, name, ranking_error):\n v_prob = np.mean(ranking_error <= 0)\n v_mean = np.mean(ranking_error[ranking_error >= 0])\n v_std = np.std(ranking_error)\n v_var95 = np.quantile(ranking_error, 0.95)\n v_var99 = np.quantile(ranking_error, 0.99)\n v_cvar95 = np.mean(ranking_error[ranking_error>=v_var95])\n v_cvar99 = np.mean(ranking_error[ranking_error>=v_var99])\n \n perf_df = pd.DataFrame({\"Data_name\":[data_name],\n \"Model_name\": [name], \n \"AUC\": [v_prob],\n \"Mean\": [v_mean],\n \"Std\": [v_std],\n \"VaR%95\": [v_var95],\n \"CVaR%95\": [v_cvar95],\n \"VaR%99\": [v_var99],\n \"CVaR%99\": [v_cvar99]})\n \n return perf_df\n\n ","repo_name":"chenyang223/Fragility-index","sub_path":"fragility_index.py","file_name":"fragility_index.py","file_ext":"py","file_size_in_byte":13263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17353174532","text":"#Welcome\r\nprint(\"Welcome to Xs and Os! Xs and Os is a tic tac toe game coded entirely in Python!\")\r\nstart = input(\"Do you want to play? y/n \")\r\nwhile start.upper() != \"Y\" and start.upper() != \"N\":\r\n start = input(\"What?! You have to enter Y or N!\")\r\n\r\nif start.upper() == \"Y\":\r\n #--------------Global varibles--------------#\r\n #Game Board\r\n board = [\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\"]\r\n #Is game still going\r\n game_still_going = True\r\n #Game winner\r\n winner = None\r\n #Who's Turn is it\r\n current_player = \"X\"\r\n\r\n#Play game or quit\r\n def DisplayBoard():\r\n print(\"\\n\")\r\n print(board[0] + \" | \" + board[1] + \" | \" + board[2] + \" \" + \"1\" + \" | \" + \"2\" + \" | \" + \"3\" )\r\n print(board[3] + \" | \" + board[4] + \" | \" + board[5] + \" \" + \"4\" + \" | \" + \"5\" + \" | \" + \"6\" )\r\n print(board[6] + \" | \" + board[7] + \" | \" + board[8] + \" \" + \"7\" + \" | \" + \"8\" + \" | \" + \"9\" )\r\n print(\"\\n\")\r\n\r\n def PlayGame():\r\n DisplayBoard()\r\n while game_still_going:\r\n HandleTurn(current_player)\r\n check_if_game_over()\r\n flip_player()\r\n #Show who won or if it is a tie\r\n if winner == \"X\" or winner == \"O\":\r\n print(winner +\" has won the game!\")\r\n else:\r\n print(\"It's a tie!\")\r\n\r\n def HandleTurn(player):\r\n #Ask for position\r\n print(\"It is \" + player + \"'s turn\")\r\n position = input(\"Choose a position from 1-9: \")\r\n\r\n while position not in [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"] or board[int(position) - 1] != \"-\":\r\n if position not in [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]:\r\n position = input(\"Invalid position. Please choose a position from 1-9: \")\r\n else:\r\n position = input(\"Position already taken. Please choose another position from 1-9: \")\r\n\r\n board[int(position) - 1] = player\r\n DisplayBoard()\r\n\r\n def check_if_game_over():\r\n check_for_winner()\r\n check_if_tie()\r\n\r\n def check_for_winner():\r\n CheckRows()\r\n CheckColumns()\r\n CheckDiagonals()\r\n\r\n def CheckRows():\r\n #Set up global varibles\r\n global game_still_going\r\n global winner\r\n #Check if rows have same value and aren't empty\r\n row_1 = board[0] == board[1] == board[2] != \"-\"\r\n row_2 = board[3] == board[4] == board[5] != \"-\"\r\n row_3 = board[6] == board[7] == board[8] != \"-\"\r\n #Stop game if row winner\r\n if row_1 or row_2 or row_3:\r\n game_still_going = False\r\n #Reveal winner\r\n if row_1:\r\n winner = board[0]\r\n elif row_2:\r\n winner = board[3]\r\n elif row_3:\r\n winner = board[6]\r\n return\r\n\r\n def CheckColumns():\r\n #Set up global varibles\r\n global game_still_going\r\n global winner\r\n #Check if rows have same value and aren't empty\r\n column_1 = board[0] == board[3] == board[6] != \"-\"\r\n column_2 = board[1] == board[4] == board[7] != \"-\"\r\n column_3 = board[2] == board[5] == board[8] != \"-\"\r\n #Stop game if row winner\r\n if column_1 or column_2 or column_3:\r\n game_still_going = False\r\n #Reveal winner\r\n if column_1:\r\n winner = board[0]\r\n elif column_2:\r\n winner = board[1]\r\n elif column_3:\r\n winner = board[2]\r\n return\r\n\r\n def CheckDiagonals():\r\n #Set up global varibles\r\n global game_still_going\r\n global winner\r\n #Check if rows have same value and aren't empty\r\n diagonal_1 = board[0] == board[4] == board[8] != \"-\"\r\n diagonal_2 = board[2] == board[4] == board[6] != \"-\"\r\n #Stop game if row winner\r\n if diagonal_1 or diagonal_2:\r\n game_still_going = False\r\n #Reveal winner\r\n if diagonal_1:\r\n winner = board[0]\r\n elif diagonal_2:\r\n winner = board[2]\r\n return\r\n\r\n def check_if_tie():\r\n #Set up global varibles\r\n global game_still_going\r\n #Check if - is in board\r\n if \"-\" not in board:\r\n game_still_going = False\r\n\r\n def flip_player():\r\n #Set up global varibles\r\n global current_player\r\n #Swap player\r\n if current_player == \"X\":\r\n current_player = \"O\"\r\n else:\r\n current_player = \"X\"\r\n\r\n PlayGame()\r\n\r\nelse:\r\n print(\"\\nSad to see you go! Bye! :(\")\r\n exit()\r\n","repo_name":"DragonRider8128/Python","sub_path":"XandO.py","file_name":"XandO.py","file_ext":"py","file_size_in_byte":4509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29798330327","text":"#!/usr/bin/env python3\nimport itertools\nimport time\nfrom typing import NoReturn\n\nimport board\nimport RPi.GPIO as GPIO\n\nfrom plantmobile.input_device import DistanceSensor\n\n\ndef loop(sensor: DistanceSensor) -> NoReturn:\n out_of_range = False\n for i in itertools.count():\n distance = sensor.read()\n if out_of_range or i % 1 == 0:\n print(\"The distance is : %.2f cm\" % (distance))\n out_of_range = distance == float(\"inf\")\n if out_of_range:\n out_of_range = True\n print(\"sensor is out of range\")\n time.sleep(.1)\n assert False, \"Not reached\"\n\n\nif __name__ == '__main__':\n print('Program is starting...')\n sensor = DistanceSensor(trig_pin=board.D4, echo_pin=board.D17, threshold_cm=10)\n sensor.setup()\n\n try:\n loop(sensor)\n except KeyboardInterrupt:\n sensor.off()\n GPIO.cleanup()\n","repo_name":"adegtiar/plantmobile","sub_path":"scripts/distance_sensor_test.py","file_name":"distance_sensor_test.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73864280088","text":"from itertools import combinations_with_replacement\nfrom fractions import gcd\nfrom math import factorial as fact\n\n# def fact(n):\n# \t'''in case math library doesn't work as usual in foobar'''\n# \tif n == 0:\n# \t\treturn 1\n# \tf = 1\n# \tfor i in range(1,n+1):\n# \t\tf = f*i\n# \treturn f\n\ndef partitions(n,j=1):\n\t'''Generates all partitions of the integer n, meaning all lists of integers whose sums are equal to n'''\n\tyield [n]\n\tfor i in range(j,n//2+1):\n\t\tif n-i >= i:\n\t\t\tfor p in partitions(n-i,i):\n\t\t\t\tyield [i]+p\n\ndef number_of_permuations(lst,n):\n\t'''gets the number of permutations of the type corresponding to a partition. E.g. a partition [1,2] means\n\t\"swap 2 numbers, leave one alone\"'''\n\tres = 1\n\tfor i in range(1,n+1):\n\t\ta = lst.count(i) #how many times is the number there?\n\t\tif a > 0:\n\t\t\tres = res * (i**a) * fact(a)\n\treturn fact(n)//res\n\nh = 12\nw = 12\ns = 2\ntotal = 0\nn = 0\nfor p in partitions(h):\n\tfor q in partitions(w):\n\t\tnh = number_of_permuations(p,h)\n\t\tnw = number_of_permuations(q,w)\n\t\texponent = 0\n\t\tfor i in p:\n\t\t\tfor j in q:\n\t\t\t\texponent += gcd(i,j)\n\n\t\ttotal = total + nh*nw * s**exponent\n\t\tprint(p,q)\ntotal = total//fact(h)//fact(w)\nprint(total)\n","repo_name":"uebling/Foobar","sub_path":"Level 5/extra1.py","file_name":"extra1.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33954391090","text":"from bisect import bisect\n\nn, k = map(int, input().strip().split())\na = list(map(int, input().strip().split()))\nb = sorted(set(a))\nm = len(b)\ntree = [0] * (m + 1)\nf = []\nwhile a:\n i = bisect(b, a.pop())\n j = i\n total = 0\n while j:\n total += tree[j]\n j -= j & -j\n while i <= m:\n tree[i] += 1\n i += i & -i\n f.append(n - len(a) - 1 - total)\nf.sort()\nans = 0\nj = n\nfor i in range(n):\n while j > 0 and f[i] + f[j - 1] >= k:\n j -= 1\n ans += n - max(j, i + 1)\nprint(ans)\n","repo_name":"HBinhCT/Q-project","sub_path":"hackerearth/Data Structures/Advanced Data Structures/Fenwick (Binary Indexed) Trees/Vanya and Array/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"21856067755","text":"import discord\n\nfrom discord.ext import commands\nfrom nerdlandbot.helpers.TranslationHelper import get_culture_from_context as culture\nfrom nerdlandbot.translations.Translations import get_text as translate\n\nempty = '\\u200b'\n\nclass Privacy(commands.Cog, name=\"privacy\"):\n def __init__(self, bot: commands.Bot):\n self.bot = bot\n\n @commands.command(name=\"privacy\", brief=\"privacy_brief\")\n @commands.guild_only()\n async def privacy(self, ctx: commands.Context):\n language = await culture(ctx)\n title_txt = translate(\"privacy_policy_title\", language)\n content_txt = translate(\"privacy_policy_content\", language)\n\n embed = discord.Embed(title=title_txt, description=content_txt)\n await ctx.channel.send(embed=embed)\n\n\ndef setup(bot: commands.Bot):\n bot.add_cog(Privacy(bot))\n","repo_name":"nerdland-unofficial-fans/nerdlandbot","sub_path":"nerdlandbot/commands/privacy.py","file_name":"privacy.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"31"} +{"seq_id":"2773274915","text":"\"\"\"\nSPINN implementation that uses thin stack algorithm.\n\"\"\"\n\nimport torch\nfrom torch import nn\nfrom torch.autograd import Variable\nfrom torch.nn import init\n\nfrom . import basic\n\n\nclass Reducer(nn.Module):\n\n def __init__(self, hidden_dim, tracking_dim):\n super(Reducer, self).__init__()\n\n self.comp_linear = nn.Linear(in_features=2 * hidden_dim + tracking_dim,\n out_features=5 * hidden_dim)\n self.reset_parameters()\n\n def reset_parameters(self):\n init.kaiming_normal(self.comp_linear.weight.data)\n init.constant(self.comp_linear.bias.data, val=0)\n\n def forward(self, left_h, left_c, right_h, right_c, tracking):\n \"\"\"\n Args:\n left_h (Variable): A variable of size\n (batch_size, hidden_dim) which contains hidden states\n of left children.\n left_c (Variable)\n right_h (Variable)\n right_c (Variable)\n tracking (Variable): A variable of size\n (batch_size, tracking_dim) which contains tracking\n vectors.\n\n Returns:\n parent_h, parent_c (Variable): (batch_size, hidden_size)\n vectors which represent hidden and cell states.\n \"\"\"\n\n lrt = torch.cat([left_h, right_h, tracking], dim=1)\n i, fl, fr, o, g = torch.chunk(self.comp_linear(lrt), chunks=5, dim=1)\n parent_c = fl.sigmoid()*left_c + fr.sigmoid()*right_c + i.sigmoid()*g.tanh()\n parent_h = o.sigmoid() * parent_c.tanh()\n return parent_h, parent_c\n\n\nclass SPINN(nn.Module):\n\n def __init__(self, word_dim, hidden_dim, tracking_dim, shift_id, reduce_id):\n super(SPINN, self).__init__()\n self.word_dim = word_dim\n self.hidden_dim = hidden_dim\n self.tracking_dim = tracking_dim\n self.shift_id = shift_id\n self.reduce_id = reduce_id\n\n self.word_linear = nn.Linear(in_features=word_dim,\n out_features=2 * hidden_dim)\n self.reducer = Reducer(hidden_dim=hidden_dim, tracking_dim=tracking_dim)\n self.tracker_cell = nn.LSTMCell(\n input_size=3 * hidden_dim, hidden_size=tracking_dim)\n self.trans_linear = nn.Linear(in_features=tracking_dim, out_features=2)\n self.reset_parameters()\n\n def reset_parameters(self):\n self.reducer.reset_parameters()\n init.kaiming_normal(self.word_linear.weight.data)\n init.constant(self.word_linear.bias.data, val=0)\n init.kaiming_normal(self.tracker_cell.weight_ih.data)\n init.orthogonal(self.tracker_cell.weight_hh.data)\n init.constant(self.tracker_cell.bias_ih.data, val=0)\n init.constant(self.tracker_cell.bias_hh.data, val=0)\n init.kaiming_normal(self.trans_linear.weight.data)\n init.constant(self.trans_linear.bias.data, val=0)\n\n def compute_tracking(self, stack, buffer, buffer_cursor,\n queue, queue_cursor, tracker_state):\n buffer_top, _ = SPINN._pop_from_buffer(\n buffer=buffer, cursor=buffer_cursor)\n right_ind, new_queue_cursor = SPINN._pop_from_queue(\n queue=queue, cursor=queue_cursor)\n left_ind, _ = SPINN._pop_from_queue(\n queue=queue, cursor=new_queue_cursor)\n right = SPINN._get_from_stack(stack=stack, index=right_ind)\n left = SPINN._get_from_stack(stack=stack, index=left_ind)\n right_h, _ = right.chunk(2, dim=1)\n left_h, _ = left.chunk(2, dim=1)\n buffer_top_h, _ = buffer_top.chunk(2, dim=1)\n batch_size = buffer_top_h.size(0)\n if not tracker_state:\n zero_state = Variable(\n buffer_top_h.data.new(batch_size, self.tracking_dim).zero_())\n tracker_state = (zero_state, zero_state)\n tracker_cell_input = torch.cat([buffer_top_h, left_h, right_h], dim=1)\n tracker_state_h, tracker_state_c = self.tracker_cell(\n input=tracker_cell_input, hx=tracker_state)\n tracking = tracker_state_h\n tracker_state_new = (tracker_state_h, tracker_state_c)\n return tracking, tracker_state_new\n\n @staticmethod\n def _pop_from_buffer(buffer, cursor):\n batch_size, _, buffer_dim = buffer.size()\n cursor_expand = (cursor.unsqueeze(1).unsqueeze(2)\n .expand(batch_size, 1, buffer_dim))\n popped = buffer.gather(dim=1, index=cursor_expand).squeeze(1)\n new_cursor = cursor + 1\n return popped, new_cursor\n\n @staticmethod\n def _pop_from_queue(queue, cursor):\n cursor_expand = cursor.unsqueeze(1)\n popped = queue.gather(dim=1, index=cursor_expand - 1).squeeze(1)\n new_cursor = cursor - 1\n return popped, new_cursor\n\n @staticmethod\n def _push_to_queue(queue, cursor, value):\n batch_size = queue.size(0)\n value_expand = Variable(queue.data.new(batch_size, 1).fill_(value + 2))\n cursor_expand = cursor.unsqueeze(1)\n new_queue = queue.scatter(dim=1, index=cursor_expand,\n source=value_expand)\n new_cursor = cursor + 1\n return new_queue, new_cursor\n\n @staticmethod\n def _get_from_stack(stack, index):\n batch_size, _, stack_dim = stack.size()\n index_expand = (index.unsqueeze(1).unsqueeze(2)\n .expand(batch_size, 1, stack_dim))\n return stack.gather(dim=1, index=index_expand).squeeze(1)\n\n @staticmethod\n def _write_to_stack(stack, time, value):\n # Write to time + 2, since the first two time steps of the stack\n # are dummy.\n batch_size, _, stack_dim = stack.size()\n index_tensor = (stack.data.new(batch_size, 1, stack_dim).long()\n .fill_(time + 2))\n index = Variable(index_tensor)\n value_expand = value.unsqueeze(1)\n return stack.scatter(dim=1, index=index, source=value_expand)\n\n def step(self, time, action, stack, buffer, buffer_cursor,\n queue, queue_cursor, tracking):\n \"\"\"\n Args:\n time (int): The current time step value.\n action (Variable): A long variable with shape\n (batch_size,), which contains the transition action\n of each sequence in a batch.\n stack (Variable): A float variable with shape\n (batch_size, num_transitions + 2, 2 * hidden_dim), which\n indicates the stack.\n buffer (Variable): A float variable with shape\n (batch_size, num_transitions, 2 * hidden_dim), which\n indicates the buffer.\n buffer_cursor (Variable): A long variable with shape\n (batch_size,), which contains the current top indices\n of the buffer.\n queue (Variable): A long variable with shape\n (batch_size, num_transitions + 2), which contains back\n pointers to the stack.\n queue_cursor (Variable): A long variable with shape\n (batch_size,), which contains the current end indices\n of the queue.\n tracking (Variable): A float variable with shape\n (batch, tracking_dim), which contains the tracker RNN\n output.\n \"\"\"\n\n # buffer_top: (batch_size, 2 * hidden_dim)\n buffer_top, new_buffer_cursor = SPINN._pop_from_buffer(\n buffer=buffer, cursor=buffer_cursor)\n\n # 1. Compute stack, buffer cursor, queue cursor after shift\n stack_shift = SPINN._write_to_stack(\n stack=stack, time=time, value=buffer_top)\n queue_cursor_shift = queue_cursor\n buffer_cursor_shift = new_buffer_cursor\n\n # 2. Compute stack, buffer cursor, queue cursor after reduce\n right_ind, new_queue_cursor = SPINN._pop_from_queue(\n queue=queue, cursor=queue_cursor)\n left_ind, new_queue_cursor = SPINN._pop_from_queue(\n queue=queue, cursor=new_queue_cursor)\n right = SPINN._get_from_stack(stack=stack, index=right_ind)\n left = SPINN._get_from_stack(stack=stack, index=left_ind)\n right_h, right_c = right.chunk(2, dim=1)\n left_h, left_c = left.chunk(2, dim=1)\n parent_h, parent_c = self.reducer(\n left_h=left_h, left_c=left_c, right_h=right_h, right_c=right_c,\n tracking=tracking)\n parent = torch.cat([parent_h, parent_c], dim=1)\n stack_reduce = SPINN._write_to_stack(\n stack=stack, time=time, value=parent)\n queue_cursor_reduce = new_queue_cursor\n buffer_cursor_reduce = buffer_cursor\n\n # 3. Merge shift and reduce results depending on the transition action\n reduce_mask = torch.eq(action, self.reduce_id)\n reduce_mask_cursor = reduce_mask.long()\n reduce_mask_stack = (reduce_mask.float().unsqueeze(1).unsqueeze(2)\n .expand_as(stack))\n new_queue_cursor = ((1 - reduce_mask_cursor) * queue_cursor_shift\n + reduce_mask_cursor * queue_cursor_reduce)\n new_buffer_cursor = ((1 - reduce_mask_cursor) * buffer_cursor_shift\n + reduce_mask_cursor * buffer_cursor_reduce)\n new_stack = ((1 - reduce_mask_stack) * stack_shift\n + reduce_mask_stack * stack_reduce)\n\n # 4. Update queue\n new_queue, new_queue_cursor = SPINN._push_to_queue(\n queue=queue, cursor=new_queue_cursor, value=time)\n\n return new_stack, new_buffer_cursor, new_queue, new_queue_cursor\n\n def forward(self, tokens, trans=None):\n \"\"\"\n Args:\n tokens (Variable): A float variable with shape\n (batch_size, <= num_transitions, word_dim),\n which contains a word embedding of each token.\n trans (Variable): A long variable with shape\n (batch_size, num_transitions) which contains\n transitions sequences.\n If None, the model uses its own predictions instead.\n\n Returns:\n root (Variable): A hidden state of the root node.\n The size is (batch_size, hidden_dim).\n trans_logits (Variable): Unnormalized probabilities\n of predicted transitions, whose size is\n (batch_size, num_transitions, 2).\n \"\"\"\n\n batch_size = tokens.size(0)\n if trans:\n num_trans = trans.size(1)\n else:\n num_trans = tokens.size(1)*2 - 1\n hidden_dim = self.hidden_dim\n\n # Initialize data structures\n buffer = basic.apply_nd(fn=self.word_linear, input_=tokens)\n # Prepend two dummy timesteps to stack and queue\n stack = Variable(\n buffer.data.new(batch_size, num_trans + 2, 2 * hidden_dim).zero_())\n queue = Variable(\n trans.data.new(batch_size, num_trans + 2).zero_())\n buffer_cursor = Variable(trans.data.new(batch_size).zero_())\n queue_cursor = Variable(trans.data.new(batch_size).fill_(2))\n tracker_state = None\n trans_logits = []\n for t in range(num_trans):\n tracking, tracker_state = self.compute_tracking(\n stack=stack, buffer=buffer, buffer_cursor=buffer_cursor,\n queue=queue, queue_cursor=queue_cursor,\n tracker_state=tracker_state)\n trans_logits_t = self.trans_linear(tracking)\n trans_logits.append(trans_logits_t)\n if trans:\n action_t = trans[:, t]\n else:\n action_t = trans_logits_t.max(1)[1].squeeze(1)\n step_result = self.step(\n time=t, action=action_t, stack=stack,\n buffer=buffer, buffer_cursor=buffer_cursor,\n queue=queue, queue_cursor=queue_cursor, tracking=tracking)\n stack, buffer_cursor, queue, queue_cursor = step_result\n root = stack[:, -1, :]\n root_h, _ = root.chunk(2, dim=1)\n trans_logits = torch.stack(trans_logits, dim=1)\n return root_h, trans_logits\n","repo_name":"jihunchoi/spinn","sub_path":"model/spinn.py","file_name":"spinn.py","file_ext":"py","file_size_in_byte":12062,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"27943973449","text":"class TopicMatcher:\n\n class TopicNode(object):\n __slots__ = 'children', 'content'\n\n def __init__(self):\n self.children = {}\n self.content = None\n\n def __init__(self):\n self._root = self.TopicNode()\n\n def set_topic(self, key, value):\n node = self._root\n if key and key[-1] == '/':\n key = key[:-1]\n for sym in key.split('/'):\n node = node.children.setdefault(sym, self.TopicNode())\n node.content = value\n\n def get_topic(self, key, set_default=None):\n node = self._root\n if key and key[-1] == '/':\n key = key[:-1]\n for sym in key.split('/'):\n node = node.children.get(sym)\n if node is None:\n if set_default is not None:\n self.set_topic(key, set_default)\n return set_default\n return node.content\n\n def filter(self, filter_topic: str):\n \"\"\"\n Return registered topics by filter_topic\n :param filter_topic: str\n :return: [str]\n \"\"\"\n def __rec(lst, node, _all=False, tt=None):\n if not lst and not node.children:\n return [('/'.join(tt), node.content)] if node.content else []\n part = None\n if _all:\n res = []\n for k, ch in node.children.items():\n res += __rec([], ch, _all, tt + [k])\n return res\n elif lst and lst[0] in ['+', '#']:\n part = lst[0]\n lst = lst[1:]\n res = []\n for k, ch in node.children.items():\n res += __rec(lst, ch, _all or part == '#', tt + [k])\n return res\n elif lst and lst[0] in node.children:\n return __rec(lst[1:], node.children[lst[0]], _all,\n tt + [lst[0]])\n return []\n\n if filter_topic and filter_topic[-1] == '/':\n filter_topic = filter_topic[:-1]\n return __rec(filter_topic.split('/'), self._root, False, [])\n\n def matches(self, topic):\n if topic and topic[-1] == '/':\n topic = topic[:-1]\n lst = topic.split('/')\n lst_len = len(lst)\n normal = not topic.startswith('$')\n res = []\n\n def __rec(node, i=0):\n if i == lst_len:\n if node.content:\n res.append(node.content)\n else:\n part = lst[i]\n if part in node.children:\n __rec(node.children[part], i + 1)\n if '+' in node.children and (normal or i > 0):\n __rec(node.children['+'], i + 1)\n if '#' in node.children and (normal or i > 0):\n content = node.children['#'].content\n if content:\n res.append(content)\n __rec(self._root)\n return res\n\n def match(self, topic, default=None):\n res = self.matches(topic)\n if res:\n return res[0]\n return default\n\n def values(self) -> list:\n _values = []\n\n def __step(node):\n if node.content and node.content not in _values:\n _values.append(node.content)\n for child in node.children.values():\n __step(child)\n __step(self._root)\n return _values\n","repo_name":"calcite/zmq_tubes","sub_path":"zmq_tubes/matcher.py","file_name":"matcher.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"2266761365","text":"import gzip\nimport sys\nimport xmltodict\nimport json\nimport jsonpickle\n\nwith gzip.open(sys.argv[1], 'r') as f:\n xml_dict = xmltodict.parse(f.read())\n\npubmed_articles = xml_dict['PubmedArticleSet']['PubmedArticle']\n\nids={}\n\nfor pa in pubmed_articles:\n pmid = pa['MedlineCitation']['PMID']['#text']\n\n article = pa['MedlineCitation']['Article']\n\n nctids = []\n\n\n if 'DataBankList' in article:\n journal = article[\"Journal\"][\"Title\"]\n year = pa[\"PubmedData\"][\"History\"][\"PubMedPubDate\"][-1][\"Year\"]\n\n for k,v in article['DataBankList'].items():\n if k=='DataBank':\n if type(v) == list:\n for vv in v:\n for k1,v1 in vv['AccessionNumberList'].items():\n if v1 == None:\n continue\n\n if type(v1) == str:\n if v1.lower().startswith('nct'):\n nctids.append(v1)\n else:\n for v11 in v1:\n #print(\"l:\",v11)\n if type(v11)==str and v11.lower().startswith('nct'):\n nctids.append(v11)\n else:\n for k1,v1 in v['AccessionNumberList'].items():\n if v1 == None:\n continue\n\n #print(v1, type(v1))\n if type(v1) == str:\n if v1.lower().startswith('nct'):\n nctids.append(v1)\n else:\n \n for v11 in v1:\n #print(\"nl:\",v11)\n if type(v11)==str and v11.lower().startswith('nct'):\n nctids.append(v11)\n if len(nctids) > 0:\n ids[pmid]= { \"nctids\": nctids }\n\n ids[pmid][\"journal\"] = journal\n ids[pmid][\"year\"] = year\n\n if 'MeshHeadingList' in pa['MedlineCitation']:\n ids[pmid][\"MeSH\"] = pa['MedlineCitation']['MeshHeadingList']\n if 'PublicationTypeList' in article:\n ids[pmid][\"PT\"] = article[\"PublicationTypeList\"]\n\n\nwith open('./'+sys.argv[1]+'.json', 'w') as f:\n json.dump(jsonpickle.encode(ids), f)\n","repo_name":"READ-BioMed/clinical-trial-medline-link","sub_path":"extract_linked.py","file_name":"extract_linked.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40358414904","text":"#!/usr/bin/env python3\nimport rospy\nfrom std_msgs.msg import Bool\nimport time\nimport Jetson.GPIO as GPIO\n\n\n# Set the GPIO pin numbering mode\nGPIO.setmode(GPIO.BOARD)\n# Specify the GPIO pin number you want to read from\npin_number = 18\n# Set up the GPIO pin as an input\nGPIO.setup(pin_number, GPIO.IN)\n\n\ndef sender():\n\tpub = rospy.Publisher(\"mission\", Bool, queue_size=10)\n\trospy.init_node('mission_switch', anonymous=True)\n\twhile not rospy.is_shutdown():\n\t\tinput_status = GPIO.input(pin_number)\n\t\tif input_status == GPIO.HIGH:\n\t\t\tdata = False\n\t\telse:\n\t\t\tdata = True\n\t\tpub.publish(data)\n\t\tprint(data)\n\t\ttime.sleep(1)\n\n\nif __name__ == '__main__':\n\ttry:\n\t\tsender()\n\texcept rospy.ROSInterruptException:\n\t\tpass\n","repo_name":"reaf-tamu/REAF22-23","sub_path":"Competition/MissionSwitchPublisher.py","file_name":"MissionSwitchPublisher.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2994158977","text":"# from matplotlib import pyplot as plt\n# import numpy as np\n# from tensorflow.examples.tutorials.mnist import input_data\n#\n# mnist = input_data.read_data_sets('MNIST_data', one_hot = True)\n# first_image = mnist.test.images[0]\n# first_image = np.array(first_image, dtype='float')\n# pixels = first_image.reshape((28, 28))\n# plt.imshow(pixels, cmap='gray')\n# plt.show()\n\n\n\nimport tensorflow as tf\nimport random\n# import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom tensorflow.examples.tutorials.mnist import input_data\nfrom PIL import Image\n\nwith Image.open(\"hopper.jpg\") as im:\n im.rotate(0)\n\nimg=Image.open(\"hopper.jpg\")\n\nimg2 = Image.open('hopper.jpg').convert('L')\n\n\n\ntf.set_random_seed(777) # reproducibility\n\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n\nfirst_image2=mnist.test.images[3]\n\nfirst_image = np.array(first_image2, dtype='float')\npixels = first_image.reshape((28, 28))\n# plt.imshow(pixels, cmap='gray')\n# # plt.imshow(first_image, cmap='gray')\n# plt.show()\n\na = np.mgrid[:5, :5][0]\n\nthird_image=np.fft.fftshift(np.fft.fft2(pixels))\n# for x in range(0,8):\n# for y in range(0,8):\n# third_image[10+x,10+y]=0\n\nfilterSize=10\nfor x in range(0, filterSize):\n for y in range(0, 28):\n third_image[ x, y] = 0\n third_image[27-x, y] = 0\nfor x in range(0, 28):\n for y in range(0, filterSize):\n third_image[x, y] = 0\n third_image[ x,27 - y] = 0\n\nsecond_image=np.fft.fft2(third_image)\nsecond_imageAbs=abs(second_image)\n\n\n#subplot(r,c) provide the no. of rows and columns\nf, axarr = plt.subplots(1,2)\n\n# use the created array to output your multiple images. In this case I have stacked 4 images vertically\naxarr[0].imshow(pixels, cmap='gray')\naxarr[1].imshow(second_imageAbs, cmap='gray')\n\n\nplt.show()\n\n\n\n# # second_image=np.fft.fft2(im)\n# # second_image=np.fft.fftshift(np.fft.fft2(img2))\n# second_image=np.fft.fftshift(np.fft.fft2(pixels))\n#\n#\n# second_imageAbs=np.log10(abs(second_image))\n# # pixels = second_image.reshape((28, 28))\n# plt.imshow(second_imageAbs, cmap='gray')\n#\n# plt.show()","repo_name":"juneho777/DeepLearningZeroToAll-master","sub_path":"DeepLearningZeroToAll-master/JuneTest1.py","file_name":"JuneTest1.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"8881961118","text":"from django import forms\nfrom .models import Product, Review, Order, OrderItem, Customer, Address, Payment, Wishlist, Coupon, Shipping\n\n\nclass AddToCartForm(forms.Form):\n quantity = forms.IntegerField(min_value=1, initial=1)\n\n\nclass AddReviewForm(forms.ModelForm):\n class Meta:\n model = Review\n fields = ['rating', 'comment']\n widgets = {\n 'rating': forms.Select(choices=[(i, str(i)) for i in range(1, 6)])\n }\n\n\nclass OrderForm(forms.ModelForm):\n class Meta:\n model = Order\n fields = ['customer', 'products', 'total_price', 'order_date']\n widgets = {\n 'customer': forms.Select(attrs={'class': 'form-control'}),\n 'products': forms.SelectMultiple(attrs={'class': 'form-control'}),\n 'total_price': forms.NumberInput(attrs={'class': 'form-control'}),\n 'order_date': forms.DateTimeInput(attrs={'class': 'form-control'}),\n }\n\n\nclass OrderItemForm(forms.ModelForm):\n class Meta:\n model = OrderItem\n fields = ['order', 'product', 'quantity']\n widgets = {\n 'order': forms.Select(attrs={'class': 'form-control'}),\n 'product': forms.Select(attrs={'class': 'form-control'}),\n 'quantity': forms.NumberInput(attrs={'class': 'form-control'}),\n }\n\n\nclass CustomerForm(forms.ModelForm):\n class Meta:\n model = Customer\n fields = ['username', 'email']\n widgets = {\n 'username': forms.TextInput(attrs={'class': 'form-control'}),\n 'email': forms.EmailInput(attrs={'class': 'form-control'}),\n }\n\n\nclass ProductForm(forms.ModelForm):\n class Meta:\n model = Product\n fields = ['name', 'price', 'category']\n widgets = {\n 'name': forms.TextInput(attrs={'class': 'form-control'}),\n 'price': forms.NumberInput(attrs={'class': 'form-control'}),\n 'category': forms.Select(attrs={'class': 'form-control'}),\n }\n\n\nclass AddressForm(forms.ModelForm):\n class Meta:\n model = Address\n fields = '__all__'\n widgets = {\n # Add appropriate widgets for address fields\n }\n\n\nclass PaymentForm(forms.ModelForm):\n class Meta:\n model = Payment\n fields = '__all__'\n widgets = {\n # Add appropriate widgets for payment fields\n }\n\n\nclass WishlistForm(forms.ModelForm):\n class Meta:\n model = Wishlist\n fields = '__all__'\n widgets = {\n # Add appropriate widgets for wishlist fields\n }\n\n\nclass CouponForm(forms.ModelForm):\n class Meta:\n model = Coupon\n fields = '__all__'\n widgets = {\n # Add appropriate widgets for coupon fields\n }\n\n\nclass ShippingForm(forms.ModelForm):\n class Meta:\n model = Shipping\n fields = '__all__'\n widgets = {\n # Add appropriate widgets for shipping fields\n }\n\n\n# Add more form classes as needed\n","repo_name":"sourrinn/django-ecommerce","sub_path":"store/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"42411675852","text":"#!/usr/bin/python3\n\nimport click\n\nfrom yaml2pdf.make import make_document_from_directory, make_document_from_yaml\n\n\n@click.group()\ndef ciit():\n pass\n\n\n@ciit.command()\n@click.option(\"--template\", type=str)\n@click.option(\"--yaml-file\", type=click.Path(exists=True))\n@click.option(\"--output-dir\", type=click.Path(exists=True))\ndef from_yaml(template, yaml_file, output_dir):\n make_document_from_yaml(template, yaml_file, output_dir)\n\n\n@ciit.command()\n@click.option(\"--template\", type=str)\n@click.option(\"--directory\", type=click.Path(exists=True))\n@click.option(\"--output-dir\", type=click.Path(exists=True))\ndef from_directory(template, directory, output_dir):\n make_document_from_directory(template, directory, output_dir)\n\n\ndef main():\n ciit(obj={})\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"xdurana/yaml2pdf","sub_path":"yaml2pdf.py","file_name":"yaml2pdf.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32321405239","text":"def find_longest_alindrome(s, start, end):\n ret = [0, None, None]\n while (start >= 0) and (end < len(s)):\n if s[start] == s[end]:\n ret[0] = end - start + 1\n ret[1], ret[2] = start, end\n start -= 1\n end += 1\n else:\n break\n return ret\n \nclass Solution:\n \"\"\"\n @param s: input string\n @return: the longest palindromic substring\n \"\"\"\n def longestPalindrome(self, s):\n # write your code here\n # 有O(n)的算法，不过复杂到难以理解，先弃坑了...\n ret = [0, 0, 1]\n for i in range(len(s)):\n r1 = find_longest_alindrome(s, i, i)\n r2 = find_longest_alindrome(s, i, i + 1)\n if r1[0] > ret[0]:\n ret = r1\n if r2[0] > ret[0]:\n ret = r2\n return s[ret[1]:ret[2] + 1]\n\n# medium: https://www.lintcode.com/problem/longest-palindromic-substring/\n","repo_name":"yingl/LintCodeInPython","sub_path":"longest-palindromic-substring.py","file_name":"longest-palindromic-substring.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":123,"dataset":"github-code","pt":"31"} +{"seq_id":"14867138404","text":"from flask import Flask, render_template, jsonify, request\nimport json\nimport random\nfrom flask_cors import CORS\n\napp = Flask(__name__)\nCORS(app)\nrenter_list = []\nhost_list = []\n\n@app.route('/')\ndef home():\n return render_template('form.html')\n\n@app.route('/insurance')\ndef insurance():\n address = request.args.get('address', default='', type = str)\n name = request.args.get('name', default='', type = str)\n data = {'address': address, 'name': name }\n return render_template('insurance.html', data=data)\n\n@app.route('/summary')\ndef summary():\n address = request.args.get('address', default='', type = str)\n name = request.args.get('name', default='', type = str)\n insured = request.args.get('insured', default='true', type = str)\n data = {'address': address, 'name': name, 'insurance': insured }\n return render_template('summary.html', data=data)\n \n@app.route('/getRenters')\ndef getRenters():\n return jsonify(renter_list)\n\n@app.route('/getHosts')\ndef getHosts():\n return jsonify(host_list)\n \n@app.route('/clearHosts')\ndef clearHosts():\n host_list.clear()\n return json.dumps({'success':True}), 200, {'ContentType':'application/json'} \n\n@app.route('/clearRenters')\ndef clearRenters():\n renter_list.clear()\n return json.dumps({'success':True}), 200, {'ContentType':'application/json'} \n\n@app.route('/addRenter')\ndef addRenter():\n address = request.args.get('address', default='', type = str)\n name = request.args.get('name', default='', type = str)\n email = request.args.get('email', default='', type = str)\n renter_list.append({'name': name, 'address': address, 'email': email })\n policy_string = 'INSPOL' + str(random.randint(10000, 99999))\n return json.dumps({'success':True, 'policyNumber':policy_string}), 200, {'ContentType':'application/json'} \n\n@app.route('/addHost')\ndef addHost():\n address = request.args.get('address', default='', type = str)\n name = request.args.get('name', default='', type = str)\n email = request.args.get('email', default='', type = str)\n host_list.append({'name': name, 'address': address, 'email': email })\n policy_string = 'INSPOL' + str(random.randint(10000, 99999))\n return json.dumps({'success':True, 'policyNumber':policy_string}), 200, {'ContentType':'application/json'} \n\nif __name__ == '__main__':\n app.run(debug=True,host='0.0.0.0',port=5000)","repo_name":"zeigotaro/rebirth-uk-demo","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11862117887","text":"import requests\nfrom lxml import etree\n\ndef get_mid(brand):\n with open('/Users/dingcong/ledi_git/易车/carMid.txt', 'r') as f:\n carMid_json = eval(f.read())\n mid = carMid_json[brand]\n return mid\n\nif __name__ == '__main__':\n header = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36'\n }\n brand = \"奥迪\"\n mid = get_mid(brand)\n #得到车型页数\n url = f'https://car.yiche.com/xuanchegongju/?mid={mid}'\n r = requests.get(url, headers=header)\n tree = etree.HTML(r.text)\n page_num = tree.xpath('//div[@class=\"link-list pg-item\"]//a/text()')\n base_url = f'https://car.yiche.com/xuanchegongju/?mid={mid}&page='\n title_list = []\n carId_list = []\n for i in range(1, len(page_num)+1):\n current_url = base_url + str(i)\n r = requests.get(current_url, headers=header)\n tree = etree.HTML(r.text)\n title_list += tree.xpath('//div[@class=\"search-result-list\"]//div/a/p[1]/text()')\n carId_list += tree.xpath('//div[@class=\"search-result-list\"]//div/@data-id')\n # 判断是否车型名称与车型id匹配\n if len(title_list) == len(carId_list):\n carId_dict = dict(zip(title_list, carId_list))\n with open(f'{brand}carId.txt', 'w') as f:\n f.write(str(carId_dict))\n print(\"车型ID获取完成，已保存\")","repo_name":"1998Don/ledi_git","sub_path":"易车/crawler_carId.py","file_name":"crawler_carId.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2369199432","text":"import pandas as pd\nimport gower\nimport numpy as np\nfrom scipy.cluster.hierarchy import dendrogram, linkage, fcluster\nimport matplotlib.pyplot as plt\nimport mlflow\nimport mlflow.sklearn\n\ndef calcular_outliers(df: pd.DataFrame, col:str):\n \"\"\" Calcula los outliers de una variable\n Args:\n df (DataFrame): Dataset a utilizar\n col (str): Nombre de la columna\n Returns:\n DataFrame: Dataset sin outliers\n DataFrame: Dataset con outliers\"\"\"\n q1 = df[col].quantile(0.25)\n q3 = df[col].quantile(0.75)\n iqr = q3 - q1\n lower = q1 - 1.5 * iqr\n upper = q3 + 1.5 * iqr\n outliers = df[(df[col] < lower) | (df[col] > upper)]\n no_outliers = df[(df[col] >= lower) & (df[col] <= upper)]\n print(f'Se encontraron {len(outliers)} outliers en la variable {col} con un límite inferior de {lower} y un límite superior de {upper}')\n print(f'Se analizará una base con {len(no_outliers)} registros')\n return no_outliers, outliers\n\nlfv = pd.read_csv('./Archivos_Cliente/Base_lfv.csv', encoding='latin1')\ndf_donantes = pd.read_csv('./Archivos_Cliente/Base_donantes_preprocesada.csv', encoding='latin1')\ndf_transacciones = pd.read_csv('./Archivos_Cliente/Transacciones_Individuales_preprocesada.csv', encoding='latin1')\n\n# Hacer una tabla dinámica que muestre por donante en qué canal entró su donación\ntipo_transacciones = df_transacciones.pivot_table(index='FK_ID_Donante', columns='FK_CD_Registro', values='VL_Importe', aggfunc='count').fillna(0)\n# Calcular el número de canales por donante\ntipo_transacciones['Canales_Donacion'] = tipo_transacciones.apply(lambda x: np.count_nonzero(x), axis=1)\n# Calcular el canal principal de donación\ntipo_transacciones['Canal_Principal'] = tipo_transacciones.apply(lambda x: x.idxmax(), axis=1)\n# Agregar variables a la tabla de donantes\nlfv = pd.merge(lfv, df_donantes[['ID_Donante', 'FK_ID_Genero', 'FK_ID_Estado_Civil']], on='ID_Donante', how='left')\nlfv = pd.merge(lfv, tipo_transacciones[['Canales_Donacion', 'Canal_Principal']], left_on='ID_Donante', right_on= 'FK_ID_Donante', how='left')\n\n# Calcular el rango IQR para la variable DLTV\ndonaciones_sin_outlier, outliers = calcular_outliers(lfv, 'DLTV')\n\n# Tomar una muestra aleatoria de donantes\ndonaciones_sin_outlier = donaciones_sin_outlier.sample(n=1000, random_state=1)\n\nexperiment = mlflow.set_experiment(\"cluster_jerarquico\")\n\nwith mlflow.start_run(experiment_id=experiment.experiment_id):\n # Definir variables a utilizar\n model_cols = ['DLTV', 'FK_ID_Genero', 'Canal_Principal', 'VL_Edad', 'Prom_Cuotas_Pagadas', 'Prom_Cuotas_No_Pagadas', 'FK_ID_Estado_Civil']\n gower_dist = gower.gower_matrix(donaciones_sin_outlier[model_cols])\n gower_dist = pd.DataFrame(gower_dist)\n \n # Registre los parámetros\n mlflow.log_param(\"variables\", model_cols)\n\n # Definir el dendograma\n Z = linkage(gower_dist, method='average', metric='euclidean')\n clusters = fcluster(Z, t=4, criterion='maxclust')\n\n # Graficar\n fig, ax = plt.subplots(figsize=(12, 8))\n d = dendrogram(Z, show_leaf_counts=True, leaf_font_size=10, ax=ax)\n ax.set_xlabel('Observaciones', fontsize=10)\n ax.set_yticks(np.arange(0,50,10))\n ax.set_ylabel('Distancia', fontsize=10)\n plt.show()\n \n # Registre el modelo\n mlflow.sklearn.log_model(clusters, \"cluster_jerarquico\")\n\n # Cree y registre la métrica de interés\n # Numero de clusters\n n_clusters = len(np.unique(clusters))\n # Tamaño de clusters\n cluster_size = pd.Series(clusters).value_counts().sort_index()\n # Porcentaje de observaciones en cada cluster\n cluster_pct = cluster_size / len(clusters)\n # DLTV promedio por cluster\n cluster_dltv = donaciones_sin_outlier.groupby(clusters)['DLTV'].mean()\n # Promedio cuotas pagadas por cluster\n cluster_cuotas_pagadas = donaciones_sin_outlier.groupby(clusters)['Prom_Cuotas_Pagadas'].mean()\n # Promedio cuotas no pagadas por cluster\n cluster_cuotas_no_pagadas = donaciones_sin_outlier.groupby(clusters)['Prom_Cuotas_No_Pagadas'].mean()\n # Promedio edad por cluster\n cluster_edad = donaciones_sin_outlier.groupby(clusters)['VL_Edad'].mean()\n # Cuenta de generos por cluster\n cluster_genero = donaciones_sin_outlier.groupby(clusters)['FK_ID_Genero'].value_counts().unstack().fillna(0)\n # Cuenta de canales por cluster\n cluster_canal = donaciones_sin_outlier.groupby(clusters)['Canal_Principal'].value_counts().unstack().fillna(0)\n # Cuenta de estado civil por cluster\n cluster_estado_civil = donaciones_sin_outlier.groupby(clusters)['FK_ID_Estado_Civil'].value_counts().unstack().fillna(0)\n \n # Registrar métricasw en mlflow\n mlflow.log_metric(\"n_clusters\", n_clusters)\n mlflow.log_metric(\"cluster_size\", cluster_size)\n mlflow.log_metric(\"cluster_pct\", cluster_pct)\n mlflow.log_metric(\"cluster_dltv\", cluster_dltv)\n mlflow.log_metric(\"cluster_cuotas_pagadas\", cluster_cuotas_pagadas)\n mlflow.log_metric(\"cluster_cuotas_no_pagadas\", cluster_cuotas_no_pagadas)\n mlflow.log_metric(\"cluster_edad\", cluster_edad)\n mlflow.log_metric(\"cluster_genero\", cluster_genero)\n mlflow.log_metric(\"cluster_canal\", cluster_canal)\n mlflow.log_metric(\"cluster_estado_civil\", cluster_estado_civil)\n\n print(f'El número de clusters es {n_clusters}')\n print(f'El tamaño de los clusters es {cluster_size}')\n print(f'El porcentaje de observaciones en cada cluster es {cluster_pct}')\n print(f'El DLTV promedio por cluster es {cluster_dltv}')\n print(f'El promedio de cuotas pagadas por cluster es {cluster_cuotas_pagadas}')\n print(f'El promedio de cuotas no pagadas por cluster es {cluster_cuotas_no_pagadas}')\n print(f'El promedio de edad por cluster es {cluster_edad}')\n print(f'La cuenta de generos por cluster es {cluster_genero}')\n print(f'La cuenta de canales por cluster es {cluster_canal}')\n print(f'La cuenta de estado civil por cluster es {cluster_estado_civil}')","repo_name":"ggomez1803/Proyecto_DSA","sub_path":"Experimentos/Experimento_cluster_jerarquico.py","file_name":"Experimento_cluster_jerarquico.py","file_ext":"py","file_size_in_byte":5929,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9144163402","text":"from shared.Intcode import Computer\nfrom typing import List, Optional, Dict\n\n\ndef main():\n computer = load()\n replace(computer, {1: 12, 2: 2})\n result = run_program(computer)\n\n print(\"Program halted with %s status. Value in position 0 = %d\" %\n (\"HALT\" if result else \"CRASH\", computer.registers[0]))\n\n\ndef load() -> Computer:\n with open(\"../input/input02.txt\", \"r\") as f:\n return Computer.from_string(f.read())\n\n\ndef replace(computer: Computer, replacements: Dict[int, int]):\n for pos, val in replacements.items():\n computer.registers[pos] = val\n\n\ndef run_program(computer: Computer) -> bool:\n try:\n computer.run()\n return True\n except Exception as e:\n print(str(e))\n return False\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"cgdilley/AdventOfCode2019","sub_path":"extras/[Day02.1x]1202ProgramAlarm-Redux.py","file_name":"[Day02.1x]1202ProgramAlarm-Redux.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40847769121","text":"import sys\nfrom collections import Counter\n\n\nS = list(sys.stdin.readline().strip())\nK = int(input())\n\nlen_S = len(S)\n\ncnt = 0\nfor i in range(len_S-K):\n tmp = []\n for j in range(i, len_S, len_S-K):\n tmp.append(S[j])\n \n counter = Counter(tmp)\n cnt += len(tmp) - counter.most_common()[0][1]\n \nprint(cnt)\n\n\n'''\namamamama\namavckdkz\n\nam avckd\n avckd kz\n\n9-7 2개 \nav avckd\n avckd kz\n \nA v A v A k A k A\n B B B B\n\na a c d z => cdz를 교체\nv v k k => vv혹은 kk를 교체\n \nam amam\n\nam amckz\n\nam amama ma\n'''\n\n \n\n ","repo_name":"euroversedev/BaekJoonOJ_Python","sub_path":"RandomDefense/14254.py","file_name":"14254.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39352599699","text":"def main():\r\n A = int(input())\r\n B = int(input())\r\n C = int(input())\r\n result = A * B * C\r\n arr = [0 for i in range(10)]\r\n length = len(str(result))\r\n for i in range(length - 1, -1, -1):\r\n arr[result // 10 ** i] += 1\r\n result = result % 10 ** i\r\n for i in arr:\r\n print(i)\r\nmain()","repo_name":"yoots50/Baekjoon.py","sub_path":"백준/Bronze/2577. 숫자의 개수/숫자의 개수.py","file_name":"숫자의 개수.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14661888717","text":"class Solution:\n def twoSum(self, nums: List[int], target: int) -> List[int]:\n nums_length = len(nums)\n memo = {}\n for i in range(nums_length):\n complement = target - nums[i]\n if complement in memo.keys():\n return (i, memo[complement])\n memo[nums[i]] = i\n \n # slow AF\n #nums_length = len(nums)\n #for i in range(nums_length):\n # for j in range(i+1, nums_length):\n # if nums[i] + nums[j] == target:\n # return (i, j)","repo_name":"deathweaselx86/leetcode","sub_path":"problems/two_sum/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"34344446638","text":"import tensorflow as tf\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\r\n\r\ndef read_picture(filelist):\r\n \"\"\"\r\n 读取狗图片，并转换为tensor\r\n :param filelist: 文件名+路径的列表\r\n :return:每张图片张量\r\n \"\"\"\r\n # 1.构造文件队列\r\n file_queue = tf.train.string_input_producer(filelist)\r\n # 2.构造读取器-默认读取一张图片\r\n reader = tf.WholeFileReader()\r\n # 读取内容-返回key为文件名，value为文件内容\r\n key,value = reader.read(file_queue)\r\n # 3.对读取数据图片进行解码\r\n image = tf.image.decode_jpeg(value)\r\n # 4.进行批处理-统一图片大小\r\n image_resize = tf.image.resize_images(image,[200,200])\r\n # 注意：一定要把样本形状固定[200,200,3],在批处理时候，要求所有数据形状必须需定义\r\n image_resize.set_shape([200,200,3])\r\n # 进行批处理\r\n image_batch = tf.train.batch([image_resize],batch_size=20,num_threads=1,capacity=20)\r\n return image_batch\r\nif __name__=='__main__':\r\n # 1.找到文件，放入列表\r\n file_name = os.listdir('./data/summary/images') # 返回文件名字列表\r\n # 将文件名与路径拼接为列表\r\n filelist = [os.path.join('./data/summary/images',file) for file in file_name]\r\n image_batch = read_picture(filelist)\r\n # 开启会话\r\n with tf.Session() as sess:\r\n # 定义一个线程协调器\r\n coord = tf.train.Coordinator()\r\n # 开启文件读取线程\r\n threads = tf.train.start_queue_runners(sess,coord=coord)\r\n # 打印读取内容\r\n print(sess.run([image_batch]))\r\n # 关闭线程\r\n coord.request_stop()\r\n coord.join()","repo_name":"Vinsmoke-Joker/machine_learn","sub_path":"read_picture.py","file_name":"read_picture.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70875194327","text":"import sys\nimport unittest\n\nimport make_db\nimport model\n\nstatic_epoch = 1641585162\n\nTEST_BUCKETS_DATA = {\n 'gs://kubernetes-jenkins/logs/': {'prefix': ''},\n 'gs://bucket1/': {'prefix': 'bucket1_prefix'},\n 'gs://bucket2/': {'prefix': 'bucket2_prefix'}\n}\n\n\nclass MockedClient(make_db.GCSClient):\n \"\"\"A GCSClient with stubs for external interactions.\"\"\"\n NOW = static_epoch\n LOG_DIR = 'gs://kubernetes-jenkins/logs/'\n JOB_DIR = LOG_DIR + 'fake/123/'\n ART_DIR = JOB_DIR + 'artifacts/'\n lists = {\n LOG_DIR: [LOG_DIR + 'fake/'],\n LOG_DIR + 'fake/': [JOB_DIR, LOG_DIR + 'fake/122/'],\n LOG_DIR + 'bad-latest/': [LOG_DIR + 'bad-latest/6/'],\n LOG_DIR + 'latest/': [LOG_DIR + 'latest/4/', LOG_DIR + 'latest/3/'],\n 'gs://kubernetes-jenkins/pr-logs/directory/': [],\n ART_DIR: [ART_DIR + 'junit_01.xml'],\n ART_DIR.replace('123', '122'): [],\n }\n gets = {\n JOB_DIR + 'finished.json': {'timestamp': NOW, 'result': 'SUCCESS'},\n JOB_DIR + 'started.json': {'timestamp': NOW - 5},\n LOG_DIR + 'latest/latest-build.txt': '4',\n LOG_DIR + 'bad-latest/latest-build.txt': 'asdf',\n LOG_DIR + 'fake/122/finished.json': {'timestamp': 123},\n ART_DIR + 'junit_01.xml': '''\n <testsuite>\n <testcase name=\"Foo\" time=\"3\" />\n <testcase name=\"Bad\" time=\"4\">\n <failure>stacktrace</failure>\n </testcase>\n <testcase name=\"Lazy\" time=\"0\">\n <skipped />\n </testcase>\n </testsuite>\n '''}\n\n def get(self, path, as_json=True):\n return self.gets.get(path)\n\n def ls(self, path, **_kwargs): # pylint: disable=arguments-differ\n return self.lists[path]\n\n\nclass GCSClientTest(unittest.TestCase):\n \"\"\"Unit tests for GCSClient\"\"\"\n\n # pylint: disable=protected-access\n\n JOBS_DIR = 'gs://kubernetes-jenkins/logs/'\n\n def setUp(self):\n self.client = MockedClient(self.JOBS_DIR)\n\n def test_get_junits(self):\n junits = self.client.get_junits_from_build(self.JOBS_DIR + 'fake/123')\n self.assertEqual(\n sorted(junits),\n ['gs://kubernetes-jenkins/logs/fake/123/artifacts/junit_01.xml'])\n\n def test_get_builds_normal_list(self):\n # normal case: lists a directory\n self.assertEqual((True, ['123', '122']), self.client._get_builds('fake'))\n\n def test_get_builds_latest(self):\n # optimization: does a range based on build-latest.txt\n precise, gen = self.client._get_builds('latest')\n self.assertFalse(precise)\n self.assertEqual(['4', '3', '2', '1'], list(gen))\n\n def test_get_builds_limit(self):\n # optimization: does a range based on build-latest.txt\n precise, gen = self.client._get_builds('latest', build_limit=2)\n self.assertFalse(precise)\n self.assertEqual(['4', '3'], list(gen))\n\n def test_get_builds_latest_fallback(self):\n # fallback: still lists a directory when build-latest.txt isn't an int\n self.assertEqual((True, ['6']), self.client._get_builds('bad-latest'))\n\n def test_get_builds_non_sequential(self):\n # fallback: setting sequential=false causes directory listing\n self.client.metadata = {'sequential': False}\n self.assertEqual((True, ['4', '3']),\n self.client._get_builds('latest'))\n\n def test_get_builds_exclude_list_no_match(self):\n # special case: job is not in excluded list\n self.client.metadata = {'exclude_jobs': ['notfake']}\n self.assertEqual([('fake', '123'), ('fake', '122')], list(self.client.get_builds(set())))\n\n def test_get_builds_exclude_list_match(self):\n # special case: job is in excluded list\n self.client.metadata = {'exclude_jobs': ['fake']}\n self.assertEqual([], list(self.client.get_builds(set())))\n\n def test_get_builds_exclude_list_match_using_regexp(self):\n # special case: job is in excluded list\n self.client.metadata = {'exclude_jobs': ['.*(flaky|flake|fake).*']}\n self.assertEqual([], list(self.client.get_builds(set())))\n # special case: job is in excluded list\n self.client.metadata = {'exclude_jobs': ['.*(flaky|flake).*']}\n self.assertEqual([('fake', '123'), ('fake', '122')], list(self.client.get_builds(set())))\n\n\nclass MainTest(unittest.TestCase):\n \"\"\"End-to-end test of the main function's output.\"\"\"\n JOBS_DIR = GCSClientTest.JOBS_DIR\n\n def test_remove_system_out(self):\n self.assertEqual(make_db.remove_system_out('not<xml<lol'), 'not<xml<lol')\n self.assertEqual(\n make_db.remove_system_out('<a><b>c<system-out>bar</system-out></b></a>'),\n '<a><b>c</b></a>')\n\n @staticmethod\n def get_expected_builds():\n return {\n MockedClient.JOB_DIR.replace('123', '122')[:-1]:\n (None, {'timestamp': 123}, []),\n MockedClient.JOB_DIR[:-1]:\n ({'timestamp': MockedClient.NOW - 5},\n {'timestamp': MockedClient.NOW, 'result': 'SUCCESS'},\n [MockedClient.gets[MockedClient.ART_DIR + 'junit_01.xml']])\n }\n\n def assert_main_output(self, threads, expected=None, db=None,\n client=MockedClient):\n if expected is None:\n expected = self.get_expected_builds()\n if db is None:\n db = model.Database(':memory:')\n make_db.main(db, {self.JOBS_DIR: {}}, threads, True, sys.maxsize, False, client)\n\n result = {path: (started, finished, db.test_results_for_build(path))\n for _rowid, path, started, finished in db.get_builds()}\n\n self.assertEqual(result, expected)\n return db\n\n def test_clean(self):\n self.maxDiff = None\n for threads in [1, 32]:\n self.assert_main_output(threads)\n\n def test_incremental_new(self):\n db = self.assert_main_output(1)\n\n new_junit = '''\n <testsuite>\n <testcase name=\"New\" time=\"8\"/>\n <testcase name=\"Foo\" time=\"2.3\"/>\n </testsuite>\n '''\n\n class MockedClientNewer(MockedClient):\n NOW = static_epoch\n LOG_DIR = 'gs://kubernetes-jenkins/logs/'\n JOB_DIR = LOG_DIR + 'fake/124/'\n ART_DIR = JOB_DIR + 'artifacts/'\n lists = {\n LOG_DIR: [LOG_DIR + 'fake/'],\n LOG_DIR + 'fake/': [JOB_DIR, LOG_DIR + 'fake/123/'],\n ART_DIR: [ART_DIR + 'junit_01.xml'],\n 'gs://kubernetes-jenkins/pr-logs/directory/': [],\n }\n gets = {\n JOB_DIR + 'finished.json': {'timestamp': NOW},\n ART_DIR + 'junit_01.xml': new_junit,\n }\n\n expected = self.get_expected_builds()\n expected[MockedClientNewer.JOB_DIR[:-1]] = (\n None, {'timestamp': MockedClientNewer.NOW}, [new_junit])\n\n self.assert_main_output(1, expected, db, MockedClientNewer)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"kubernetes/test-infra","sub_path":"kettle/make_db_test.py","file_name":"make_db_test.py","file_ext":"py","file_size_in_byte":7016,"program_lang":"python","lang":"en","doc_type":"code","stars":3709,"dataset":"github-code","pt":"31"} +{"seq_id":"17358573361","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 24 13:58:19 2022\n\n@author: imdat\n\"\"\"\n\nimport math\nimport matplotlib.pyplot as plt\nimport xlsxwriter\n\nxCamberPoints = []\nyCamberPoints = []\nxUpperPoints = []\nxLowerPoints = []\nyUpperPoints = []\nyLowerPoints = []\n\ndef welcome():\n hello = \"\"\"This is a NACA Four-Digit Airfoil Generator.\n The first digit specifies the maximum camber(m) in the percentage of the chord,\n the second digit indicates the position of the maximum camber(p) in tenths of the chord,\n the last two numbers provide the maximum thickness(t) of the airfoil in percentage\n of the chord.\"\"\"\n global c ,m ,p ,t ,pn\n \n while True:\n try:\n c = float(input(\"Please enter your chord(airfoil length) in meters:\\t\"))\n m = int(input(\"Please enter your maximum camber value in percentage of the chord:\\t\"))\n p = int(input(\"Please enter your position of the maximum camber value in tenths of the chord:\\t\"))\n if p == 0:\n raise Exception()\n t = int(input(\"Please enter your maximum thickness value in percentage of the chord:\\t\"))\n pn = float(input(\"How many points would you like to have?\\t\"))\n break\n except:\n print(\"\"\"\"Only floating point numbers and integers are allowed.\nAlso, do not forget the position of the maximum camber 'p' cannot be 0\"\"\")\n m /= 100\n p/= 10\n t/= 100\n\ndef calculatePoints():\n def calculateSurface():\n xu = (x - (yt * (math.sin(tetha))))\n xUpperPoints.append(xu)\n xl = (x + (yt * (math.sin(tetha))))\n xLowerPoints.append(xl)\n yu = yc + (yt * (math.cos(tetha)))\n yUpperPoints.append(yu)\n yl = yc - (yt * (math.cos(tetha)))\n yLowerPoints.append(yl)\n x = 0\n while x <= c:\n xCamberPoints.append(x)\n yt = (t / 0.2) * ((0.2969 * (x**0.5)) - (0.1260 * x) - (0.3516 * (x**2))\n + (0.2843 * (x**3)) - (0.1015 * (x**4)))\n if x <= p:\n yc = (m / (p**2) ) * ( (2*p*x) - (x**2) ) \n yCamberPoints.append(yc)\n tetha = math.atan((m / (p**2)) * ((2*p) - (2*x)))\n calculateSurface()\n\n elif x > p:\n yc = ( (m / ((1-p)**2)) ) * ((1-2*p) + (2*p*x) - (x**2))\n yCamberPoints.append(yc)\n tetha = math.atan((m / ((1 - p)**2)) * ((2*p)-(2*x)))\n calculateSurface()\n\n x += c / pn\n xCamberPoints.append(c)\n yCamberPoints.append(0)\n xUpperPoints.append(c)\n yUpperPoints.append(0)\n xLowerPoints.append(c)\n yLowerPoints.append(0)\n\ndef show():\n font = {'family':'Calibri','color':'#363945', 'size' : 15}\n \n def graphic():\n plt.figure(figsize = (13.5,10))\n plt.plot(xCamberPoints, yCamberPoints, ls = \":\", c = \"#D2386C\", label = \"Camber Line\")\n plt.legend()\n plt.plot(xLowerPoints, yLowerPoints, c = \"#0072b5\", label = \"Airfoil Shape\")\n plt.legend()\n plt.plot(xUpperPoints, yUpperPoints, c = \"#0072b5\", label = \"Airfoil Shape\")\n plt.xlabel(\"X axis in meters\", fontdict=font)\n plt.ylabel(\"Y axis in meters\", fontdict=font)\n \n graphic()\n plt.axis(\"equal\")\n plt.title(\"NACA {}{}{} AIRFOIL\".format(int(m*100),int(p*10),int(t*100)), fontdict=font)\n plt.show()\n plt.savefig(\"NACA {}{}{}.png\".format(int(m*100),int(p*10),int(t*100)))\n \n graphic()\n plt.title(\"Expanded NACA {}{}{} AIRFOIL\".format(int(m*100),int(p*10),int(t*100)), fontdict=font)\n plt.show()\n plt.savefig(\"Expanded NACA {}{}{}.png\".format(int(m*100),int(p*10),int(t*100)))\n\ndef excel():\n workbook = xlsxwriter.Workbook(\"NACA{}{}{}.xlsx\".format(int(m*100),int(p*10),int(t*100)))\n worksheet = workbook.add_worksheet(\"{}{}{}\".format(int(m*100),int(p*10),int(t*100)))\n \n bold_format = workbook.add_format({\"bold\":True})\n \n worksheet.write(\"A1\", \"X Camber Points\" , bold_format)\n worksheet.write(\"B1\", \"Y Camber Points\", bold_format)\n worksheet.write(\"C1\", \"X Upper Points\", bold_format)\n worksheet.write(\"D1\", \"Y Upper Points\", bold_format)\n worksheet.write(\"E1\", \"X Lower Points\", bold_format)\n worksheet.write(\"F1\", \"Y Lower Points\", bold_format)\n \n \n rowIndex = 2\n while rowIndex <= len(xCamberPoints)+1:\n indexNumber = rowIndex-2\n worksheet.write(\"A\"+ str(rowIndex), round(xCamberPoints[indexNumber], 4))\n worksheet.write(\"B\"+ str(rowIndex), round(yCamberPoints[indexNumber], 4))\n worksheet.write(\"C\"+ str(rowIndex), round(xUpperPoints[indexNumber], 4))\n worksheet.write(\"D\"+ str(rowIndex), round(yUpperPoints[indexNumber], 4))\n worksheet.write(\"E\"+ str(rowIndex), round(xLowerPoints[indexNumber], 4))\n worksheet.write(\"F\"+ str(rowIndex), round(yLowerPoints[indexNumber], 4))\n rowIndex += 1\n \n workbook.close()\n\nwelcome()\ncalculatePoints()\nshow()\nexcel()","repo_name":"imdatozdagci/Aerospace-Applications-with-Python","sub_path":"nacaFourDigit/nacaFourDigit.py","file_name":"nacaFourDigit.py","file_ext":"py","file_size_in_byte":4910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7445014144","text":"from functools import wraps\nimport time\n\n\n\ndef mydecorator(function):\n @wraps(function)\n def wrapper(*args,**kwargs):\n result = function(*args,**kwargs)\n return result\n return wrapper\n\ndef timeit(func):\n @wraps(func)\n def wrapper(*args,**kwargs):\n print ('**** Starting Timer ****')\n start = time.time()\n \n func(*args,**kwargs)\n \n end = time.time()\n print (f'=== {func.__name__} took {int(end-start)} seconds to complete')\n return wrapper\n\n\n\ndef print_args(func):\n @wraps(func)\n def wrapper(*args,**kwargs):\n print ()\n print('***args:')\n for arg in args:\n print (f' - {arg}')\n print ('***** kwargs:')\n \n for k,v in kwargs.items():\n print (f'- {k}:{v})')\n print ()\n\n func(*args,**kwargs)\n return wrapper \n \n","repo_name":"mig0913/utils","sub_path":"decoratorme.py","file_name":"decoratorme.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"24417582508","text":"from radio.utils.normalize import split_artist_title\n\nfrom .common import timestamp_ms\n\n\nasync def load(session):\n url = 'http://streaming.extrafm.hr/stream/now_playing.php'\n\n response = await session.get(url, params={\n 'the_stream': 'http://streams.extrafm.hr:8110/;',\n '_': timestamp_ms(),\n })\n contents = await response.text()\n\n return split_artist_title(contents, normalize_case=True)\n","repo_name":"ihabunek/radioscraper","sub_path":"loaders/implementations/extrafm.py","file_name":"extrafm.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"31"} +{"seq_id":"69809850650","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n__author__ = 'defaultstr'\n\nfrom django.template import loader, RequestContext\nfrom django import forms\nfrom task_manager.models import *\nfrom task_manager.controllers import TaskManager\nfrom user_system.utils import *\nfrom .utils import *\n\ntry:\n import simplejson as json\nexcept ImportError:\n import json\n\n\nclass QueryDocumentTaskManager(TaskManager):\n \"\"\"\n TaskManager for query-document pair annotation\n \"\"\"\n\n def get_next_task_unit(self, request, user, task):\n \"\"\"\n The default schedule method just returns next task unit that the user has not annotated.\n :param user: user\n :param task: task\n :return: next task unit, None if no new task needs annotation\n \"\"\"\n\n task_units = TaskUnit.objects(task=task)\n task_units = sorted(task_units, key=lambda x: json.loads(x.unit_content)['url'])\n task_unit_tags = [t.tag for t in task_units]\n\n annotations = Annotation.objects(task=task, user=user)\n annotated_tags = set([a.task_unit.tag for a in annotations])\n\n for tag in task_unit_tags:\n if tag in annotated_tags:\n continue\n else:\n return TaskUnit.objects(task=task, tag=tag)[0]\n if len(task_units) > 0:\n self.send_task_finished_emails(request, task, user, admin_emails=['maojiaxin@gmail.com', 'songjingtao1994@sina.com'])\n\n return None\n\n def get_annotation_content(self, request, task, unit_tag):\n \"\"\"\n :param task: task\n :param unit_tag:\n :return: Html fragment that will be inserted to the content block.\n \"\"\"\n try:\n task_unit = TaskUnit.objects.get(task=task, tag=unit_tag)\n jsonObj = json.loads(task_unit.unit_content)\n t = loader.get_template('annotation_task_1_content.html')\n c = RequestContext(\n request,\n {\n 'task_id': task.id,\n 'unit_tag': unit_tag,\n 'query': jsonObj['query'],\n 'html': jsonObj['doc_snippet'],\n })\n return t.render(c)\n except DoesNotExist:\n return '<div>Error! Can\\'t find task unit!</div>'\n\n def get_annotation_description(self, request, task, unit_tag):\n \"\"\"\n :param task:\n :param unit_tag:\n :return: Html fragment that will be inserted to the description block\n \"\"\"\n user = get_user_from_request(request)\n finished_task_num = len(Annotation.objects(user=user, task=task))\n all_task_num = len(TaskUnit.objects(task=task))\n t = loader.get_template('annotation_task_1_description.html')\n c = RequestContext(\n request,\n {\n 'task_name': task.task_name,\n 'task_description': task.task_description,\n 'finished_unit_num': finished_task_num,\n 'all_unit_num': all_task_num,\n }\n )\n return t.render(c)\n\n def get_style(self, request, task, unit_tag):\n \"\"\"\n :param task:\n :param unit_tag:\n :return: CSS fragment that will be inserted to the css block\n \"\"\"\n t = loader.get_template('annotation_task_1.css')\n c = RequestContext(\n request, {}\n )\n return t.render(c)\n\n def validate_annotation(self, request, task, unit_tag):\n try:\n unit_tag = request.POST['unit_tag']\n task_id = request.POST['task_id']\n score = int(request.POST['score'])\n my_task = Task.objects.get(id=task_id)\n if task != my_task:\n return False\n task_unit = TaskUnit.objects.get(task=task, tag=unit_tag)\n return True\n except KeyError:\n return False\n except DoesNotExist:\n return False\n except ValueError:\n return False\n\n def save_annotation(self, request, task, unit_tag):\n try:\n task_unit = TaskUnit.objects.get(task=task, tag=unit_tag)\n user = get_user_from_request(request)\n score = int(request.POST['score'])\n not_available = 'not_available' in request.POST\n a = Annotation()\n a.user = user\n a.task_unit = task_unit\n content = json.loads(task_unit.unit_content)\n\n a.annotation_content = json.dumps(\n {\n 'annotator': user.username,\n 'query': content['query'],\n 'topic_num': content['topic_num'],\n 'docno': content['docno'],\n 'score': score,\n 'not_available': not_available,\n }\n )\n a.task = task\n a.credit = task.credit_per_annotation\n a.save()\n\n # add credit to user\n user.credit += task.credit_per_annotation\n user.save()\n except DoesNotExist:\n return None\n except ValueError:\n return None\n\n def get_annotation_quality(self, task):\n annotations = list(Annotation.objects(task=task))\n ret = {}\n if len(annotations) == 0:\n return ret\n\n ret['weighted kappa'] = compute_weighted_kappa(annotations)\n ret['4-level kappa'] = compute_kappa(annotations)\n ret['Kripendorff\\'s alpha'] = compute_alpha(annotations)\n\n return ret\n\n def get_task_info_html(self, task):\n task_info = ' #task unit: %d </br>' % len(TaskUnit.objects(task=task))\n task_info += ' #annotation: %d </br>' % len(Annotation.objects(task=task))\n task_info += '</br>'.join(\n [' %s: %f ' % (k, v)\n for k, v in sorted(\n self.get_annotation_quality(task).items(), key=lambda x: x[0]\n )\n ]\n )\n return task_info\n\n\n","repo_name":"defaultstr/annotation_platform","sub_path":"annotation_task_1/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":5962,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"28432520485","text":"\n\nFlow_After_Filter_Temp = 152.5\nAmbient_Humidity =13.9\nAir_Ingress_Filter = 0\nAir_Ingress_Mill = 0\nSystem_fan_Heat_Release = 10\nRecirculation_Humidity = 100\nAmbient_Absolute_Pressure =1003.0\nRecirculation_Air_Volumetric_Flow = 9\nGas_Flow = 647\nFuel_property_Density = 0.78\nFuel_Property_Combustion_Water = 1.61\nCombustion_Air_Volumetric_Flow = 2.8\nCombustion_Air_Temp = 55.0\nMoisture= 0.0\nStucco_Flow = 31\nGypsum_Moisture = .5\nHH = 72.0\nAIII = 7.8\nAII = 0\nAIII_back_conversion_Converstional_Ratio = 80\n\n\ndef Recirculation_humidity(Recirculation_Humidity,Recirculation_Air_Volumetric_Flow,Gas_Flow,Fuel_Property_Combustion_Water,Combustion_Air_Volumetric_Flow,Ambient_Humidity,Fuel_property_Density,Flow_After_Filter_Temp,Air_Ingress_Mill,System_fan_Heat_Release ,Moisture,Stucco_Flow,HH,AIII,AII,AIII_back_conversion_Converstional_Ratio,Air_Ingress_Filter,Combustion_Air_Temp, Ambient_Absolute_Pressure,Gypsum_Moisture):\n X = Recirculation_Humidity\n P = Recirculation_Air_Volumetric_Flow\n Q = Gas_Flow\n R = Fuel_Property_Combustion_Water\n S = Combustion_Air_Volumetric_Flow\n T = Ambient_Humidity\n U = Fuel_property_Density\n V = Air_Ingress_Mill\n W = Moisture\n Y = Stucco_Flow\n M = HH\n N = AIII\n O = AII\n AB = AIII_back_conversion_Converstional_Ratio\n BD = Air_Ingress_Filter\n DE = Combustion_Air_Temp\n EF = Ambient_Absolute_Pressure\n FG = Gypsum_Moisture\n GH = System_fan_Heat_Release\n HI =Flow_After_Filter_Temp\n \n Pressure_Pa_To_mmWC = lambda pressure : pressure / 9.80638278\n CS = lambda Ts,Wh : 1000 * (((0.0068 * Ts + 0.0000006 * (Ts ** 2)) / 28.96) + (Wh / 1000) * ((0.0081 * Ts + 0.0000029 * (Ts**2)) / 18.02))\n\n R30 = 100\n A = MVOL( R30,X,Pressure_Pa_To_mmWC(100*EF-101325))\n print(\"Here 1\")\n B= MVOL(DE,T,Pressure_Pa_To_mmWC(100*EF-101325))/(1+T/1000)\n print(\"Here 2\")\n\n \n \n\n R30 = lambda X,V,BD,GH,P,A,Q,U,R,S,B,HI : TEM(((CS(HI,X)*((( P*A/(1+X/1000) + ((Q*U/3600) - (Q*R/3600)) + (S*B))+ V)+BD))+GH)/((( P*A/(1+X/1000) + ((Q*U/3600) - (Q*R/3600)) + (S*B))+ V)+BD),X)\n print(\"R30 \",R30)\n \n\n L10 = ((1-0.01*W)*Y*1000/3600)-(((0.01*W*Y*1000/3600)*(1 - 0.01*(FG))*1000/3600)-(((1-0.01*W)*Y*1000/3600)*(0.01*M/145.148*1.5 + 0.01*(N+O)/136.138*2 + AB/(1-AB)*0.01*N/136.138*0.5)*18.0153))\n G14 = ( X*( P * A / (1+X/1000) )+1000*(Q*R/3600)+(S*B*T)/( ( P * A / (1+X/1000) )+ ((Q*U/3600) - (Q*R/3600)) + (S*B) )) *( ( P * A/ (1+X/1000) )+ ((Q*U/3600) - (Q*R/3600)) + (S*B) ) + T*V + 1000*((((1-0.01*W)*Y*1000/3600)*(0.01*M/145.148*1.5 + 0.01*(N+O)/136.138*2 + AB/(1-AB)*0.01*N/136.138*0.5)*18.0153) + ( ((0.01*W*Y*1000/3600)*(0.01*FG*1000/3600) - (0.01*W*Y*1000/3600) )))/ ( P * A/ (1+X/1000) )+ ((Q*U/3600) - (Q*R/3600)) + (S*B + V )\n print(\"Here 3\")\n X = G14 *( ( ( P * A / (1+X/1000) )+ ((Q*U/3600) - (Q*R/3600)) + (S*B) + V )+T*BD - 1000*L10)/ ( ( P * ( A ) / (1+X/1000) )+ ((Q*U/3600) - (Q*R/3600)) + (S*B+ V ) + BD )\n print(\"Here 4\")\n return abs( X)\n \ndef TEM(Dh, Wh):\n A = 0.0000006 / 28.96 + (Wh / 1000) * 0.0000029 / 18.02\n B = 0.0068 / 28.96 + (Wh / 1000) * 0.0081 / 18.02\n C = -Dh / 1000\n Delta = B ** 2 - 4 * A * C\n TEM = (-B + Delta ** 0.5) / 2 / A\n return TEM\n\nMVOL = lambda Temperature,Humidity,Static_Pressure : (1 + Humidity / 1000) * (273.15 / (273.15 + Temperature) * (101325 + Static_Pressure / 101325) / (0.7735 + Humidity / 1000 * 1.2436))\n\n\n\n\nprint(Recirculation_humidity(Recirculation_Humidity,Recirculation_Air_Volumetric_Flow,Gas_Flow,Fuel_Property_Combustion_Water,Combustion_Air_Volumetric_Flow,Ambient_Humidity,Fuel_property_Density,Flow_After_Filter_Temp,Air_Ingress_Mill,System_fan_Heat_Release ,Moisture,Stucco_Flow,HH,AIII,AII,AIII_back_conversion_Converstional_Ratio,Air_Ingress_Filter,Combustion_Air_Temp, Ambient_Absolute_Pressure,Gypsum_Moisture))\n#print(MVOL( 5,.01,Pressure_Pa_To_mmWC(100*6-101325)))\n\n","repo_name":"arjunmenon-IoT/Saint_gobain_smart_factory","sub_path":"GP_Model_HM/Recirculation Humidity_ Calibration.py","file_name":"Recirculation Humidity_ Calibration.py","file_ext":"py","file_size_in_byte":4062,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"7914924863","text":"#the dataset is there in this repo\nimport psycopg2\nimport csv\nconn = psycopg2.connect(\"dbname=dq user=cenrax\")\ncur = conn.cursor()\nwith open('user_accounts.csv', 'r') as file:\n next(file) # skip csv header\n reader = csv.reader(file)\n for row in reader:\n cur.execute(\"INSERT INTO users VALUES (%s, %s, %s, %s);\", row)\n \nconn.commit()\nconn.close()\n","repo_name":"Cenrax/PostGresWithPython","sub_path":"Checkpoint2.py","file_name":"Checkpoint2.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25268004575","text":"# 8.26: Reorder the index on the DataFrame to 'r2', 'r1',\n# 'r3' using .reindex(). (This method also returns the new,\n# modified DataFrame.)\n\nimport pandas as pd\n\ndf = pd.DataFrame({ 'a': [1, 2, 3],\n 'b': [2.9, 3.5, 4.9],\n 'c': ['yourstr', 'mystr', 'theirstr'] },\n index=['r1', 'r2', 'r3'])\n\n\n","repo_name":"rafaelmvargas/advanced-python","sub_path":"session_08_working_files/inclass_exercises/inclass_8.26.py","file_name":"inclass_8.26.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5333764955","text":"from turtle import Turtle\n\n\nclass Scoreboard(Turtle):\n def __init__(self):\n super().__init__()\n self.color('White')\n self.penup()\n self.hideturtle()\n self.sety(180)\n self.scores = [0, 0]\n self.write_score()\n\n def write_score(self):\n self.clear()\n self.write(f'{self.scores[0]} {self.scores[1]}', align='center', font=(\"Courier\", 100, \"normal\"))\n\n def game_over(self):\n self.goto(0, 0)\n self.color('red')\n self.write('GAME OVER', align='center', font=(\"Courier\", 40, \"normal\"))","repo_name":"loisbaker/100-days-of-code","sub_path":"Day22/scoreboard.py","file_name":"scoreboard.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42784921422","text":"# -*- coding: utf-8 -*-\n\nfrom django.db import models\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom apps.contrib.utils.strings import get_uuid5\n\n\nclass Rol(models.Model):\n \"\"\"Role model.\"\"\"\n\n slug = models.SlugField(\n verbose_name=_(\"Slug\"),\n default=get_uuid5,\n db_index=True,\n unique=True,\n )\n codigo = models.CharField(\n verbose_name=_(\"codigo\"),\n max_length=5,\n )\n nombre = models.CharField(\n verbose_name=_(\"Nombre\"),\n max_length=50,\n )\n descripcion = models.TextField(\n verbose_name=_(\"direccion\"),\n blank=True,\n null=True,\n )\n\n def __str__(self):\n return self.nombre\n\n @property\n def get_codigo(self):\n return self.codigo\n\n class Meta:\n db_table = \"rol\"\n verbose_name = _(\"Rol\")\n verbose_name_plural = _(\"Roles\")\n app_label = \"empresa\"\n\n\n\"\"\"\nclass RolUser(models.Model):\n\n user = models.ForeignKey(\n \"accounts.User\",\n verbose_name=_(\"Usuario\"),\n )\n rol = models.ForeignKey(\"Rol\", verbose_name=_(\"Rol\"))\n\n descripcion = models.TextField(\n verbose_name=_(\"Descripción\"),\n blank=True,\n null=True,\n )\n\n def __str__(self):\n return self.nombre\n\n @property\n def get_nombre(self):\n return self.nombre\n\n class Meta:\n db_table = \"rol\"\n verbose_name = _(\"Rol\")\n verbose_name_plural = _(\"Roles\")\n app_label = \"empresa\"\n\n\"\"\"\n","repo_name":"jorge-DPS/proyecto_JGP","sub_path":"apps/empresa/models/rol.py","file_name":"rol.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11463981739","text":"from flask import Flask, render_template, url_for, request\r\nfrom flask import jsonify\r\nimport flask\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import FunctionTransformer\r\nimport os\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport numpy as np\r\nimport os\r\n\r\n\r\nimport streamlit as st\r\nimport streamlit.components.v1 as components\r\n\r\n \r\napp = flask.Flask('your_flask_env')\r\napp = Flask(__name__)\r\n\r\nloaded_model = keras.models.load_model('model.h5')\r\n\r\nlabel_to_id = {'person_001': 0,\r\n 'person_002': 1,\r\n 'person_003': 2,\r\n 'person_004': 3,\r\n 'person_005': 4,\r\n 'person_006': 5,\r\n 'person_007': 6,\r\n 'person_008': 7,\r\n 'person_009': 8,\r\n 'person_010': 9,\r\n 'person_011': 10,\r\n 'person_012': 11,\r\n 'person_013': 12,\r\n 'person_014': 13,\r\n 'person_015': 14,\r\n 'person_016': 15,\r\n 'person_017': 16,\r\n 'person_018': 17,\r\n 'person_019': 18,\r\n 'person_020': 19,\r\n 'person_021': 20,\r\n 'person_022': 21,\r\n 'person_023': 22,\r\n 'person_024': 23,\r\n 'person_025': 24,\r\n 'person_026': 25,\r\n 'person_027': 26,\r\n 'person_028': 27,\r\n 'person_029': 28,\r\n 'person_030': 29,\r\n 'person_031': 30,\r\n 'person_032': 31,\r\n 'person_033': 32,\r\n 'person_034': 33,\r\n 'person_035': 34,\r\n 'person_036': 35,\r\n 'person_037': 36,\r\n 'person_038': 37,\r\n 'person_039': 38,\r\n 'person_040': 39,\r\n 'person_041': 40,\r\n 'person_042': 41,\r\n 'person_043': 42,\r\n 'person_044': 43,\r\n 'person_045': 44,\r\n 'person_046': 45,\r\n 'person_047': 46,\r\n 'person_048': 47,\r\n 'person_049': 48,\r\n 'person_050': 49,\r\n 'person_051': 50,\r\n 'person_052': 51,\r\n 'person_053': 52,\r\n 'person_054': 53,\r\n 'person_055': 54,\r\n 'person_056': 55,\r\n 'person_057': 56,\r\n 'person_058': 57,\r\n 'person_059': 58,\r\n 'person_060': 59,\r\n 'person_061': 60,\r\n 'person_062': 61,\r\n 'person_063': 62,\r\n 'person_064': 63,\r\n 'person_065': 64,\r\n 'person_066': 65,\r\n 'person_067': 66,\r\n 'person_068': 67,\r\n 'person_069': 68,\r\n 'person_070': 69,\r\n 'person_071': 70,\r\n 'person_072': 71,\r\n 'person_073': 72,\r\n 'person_074': 73,\r\n 'person_075': 74,\r\n 'person_076': 75,\r\n 'person_077': 76,\r\n 'person_078': 77,\r\n 'person_079': 78,\r\n 'person_080': 79,\r\n 'person_081': 80,\r\n 'person_082': 81,\r\n 'person_083': 82,\r\n 'person_084': 83,\r\n 'person_085': 84,\r\n 'person_086': 85,\r\n 'person_087': 86,\r\n 'person_088': 87,\r\n 'person_089': 88,\r\n 'person_090': 89,\r\n 'person_091': 90,\r\n 'person_092': 91,\r\n 'person_093': 92,\r\n 'person_094': 93,\r\n 'person_095': 94,\r\n 'person_096': 95,\r\n 'person_097': 96,\r\n 'person_098': 97,\r\n 'person_099': 98,\r\n 'person_100': 99,\r\n 'person_101': 100,\r\n 'person_102': 101,\r\n 'person_103': 102,\r\n 'person_104': 103,\r\n 'person_105': 104,\r\n 'person_106': 105,\r\n 'person_107': 106,\r\n 'person_108': 107,\r\n 'person_109': 108,\r\n 'person_110': 109,\r\n 'person_111': 110,\r\n 'person_112': 111,\r\n 'person_113': 112,\r\n 'person_114': 113,\r\n 'person_115': 114,\r\n 'person_116': 115,\r\n 'person_117': 116,\r\n 'person_118': 117,\r\n 'person_119': 118,\r\n 'person_120': 119,\r\n 'person_121': 120,\r\n 'person_122': 121,\r\n 'person_123': 122,\r\n 'person_124': 123,\r\n 'person_125': 124,\r\n 'person_126': 125,\r\n 'person_127': 126,\r\n 'person_128': 127,\r\n 'person_129': 128,\r\n 'person_130': 129,\r\n 'person_131': 130,\r\n 'person_132': 131,\r\n 'person_133': 132,\r\n 'person_134': 133,\r\n 'person_135': 134,\r\n 'person_136': 135,\r\n 'person_137': 136,\r\n 'person_138': 137,\r\n 'person_139': 138,}\r\n\r\n#app\r\nimport subprocess\r\n\r\n\r\ndef on_progress(stream, chunk, bytes_remaining):\r\n total_size = stream.filesize\r\n bytes_downloaded = total_size - bytes_remaining\r\n percentage_of_completion = bytes_downloaded / total_size * 100\r\n print('down-'+percentage_of_completion)\r\n\r\n@app.route('/')\r\ndef index():\r\n return render_template('index.html')\r\n@app.route('/model' , methods=['POST'])\r\ndef model():\r\n data=\"as\"\r\n\r\n if flask.request.method == 'POST':\r\n username = flask.request.values.get('data') \r\n image_path = username\r\n try:\r\n image_data = keras.preprocessing.image.load_img(image_path, target_size=(224,224))\r\n image_array = keras.preprocessing.image.img_to_array(image_data)\r\n\r\n\r\n image_array = np.expand_dims(image_array, axis=0)\r\n\r\n # Make prediction\r\n prediction = loaded_model.predict(image_array)\r\n predicted_label = np.argmax(prediction, axis=1)\r\n original_label = list(label_to_id.keys())[list(label_to_id.values()).index(predicted_label[0])]\r\n probability = np.argmax(prediction, axis=-1)\r\n print(prediction[0][probability])\r\n if((prediction[0][probability])<0.8):\r\n data = {\"data\":\"Bad Image\", \"message\":\"Alert! Image you have provided is not seen before\"}\r\n else:\r\n data = {\"data\":original_label, \"message\":\"Congratulations! Our model has successfully predicted the person.\"}\r\n except:\r\n data = {\"data\":\"Enter Valid Path\",\"message\":\"Unfortunately! Image path that you have provided is not working\"}\r\n\r\n return render_template('model.html', data=data)\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)","repo_name":"hassanWaleed/Vein-Recognition-System","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73887758488","text":"import os\nfrom pathlib import Path\nimport time\n\nfrom PySide2 import (QtCore, QtWidgets)\nfrom PySide2.QtWidgets import (QDialog, QFileDialog, QWidget)\n\nimport numpy as np\nimport pandas as pd\nimport pyqtgraph as pg\n\nfrom .WorkdirDialog import Ui_Dialog as WorkdirDialog\nfrom .UploadDialog import Ui_Dialog as UploadDialog\nfrom .PathModeling import Ui_PathModelingDialog as PathModelingDialog\nfrom .DataViewer import Ui_DataViewerDialog as DataViewerDialog\nfrom .Additional import Ui_AdditionalConfigDialog as AdditionalDialog\nfrom .DSP import FIRNLMS,easyFourier\n\nclass WorkdirManager():\n\n def __init__(self, workdir):\n self.wdmandialog = QDialog()\n self.wdmandialog.ui = WorkdirDialog()\n self.wdmandialog.ui.setupUi(self.wdmandialog)\n self.workdir = workdir\n self.metafile = (Path(workdir) / 'metacontrol.feather')\n if self.metafile.exists():\n self.model = self.PandasModel(pd.read_feather(self.metafile))\n self.wdmandialog.ui.tableView.setModel(self.model)\n self.wdmandialog.ui.tableView.setSelectionBehavior(QtWidgets.QTableView.SelectRows)\n self.wdmandialog.ui.bDelete.clicked.connect(self.delete)\n\n def showWorkdirManager(self):\n self.wdmandialog.exec_()\n\n def delete(self):\n selection = self.wdmandialog.ui.tableView.selectionModel().selectedRows()\n rows = []\n for ss in selection:\n rows.append(ss.row())\n metapath = Path(self.workdir) / 'metacontrol.feather'\n df = pd.read_feather(metapath)\n filestodelete = df.iloc[rows].filename.values\n for ff in filestodelete:\n fff = (Path(self.workdir) / ff)\n if fff.exists():\n fff.unlink()\n df = df.drop(rows)\n df = df.reset_index(drop=True)\n df.to_feather(metapath)\n self.model = self.PandasModel(df)\n self.wdmandialog.ui.tableView.setModel(self.model)\n\n class PandasModel(QtCore.QAbstractTableModel):\n \"\"\"\n Class to populate a table view with a pandas dataframe\n \"\"\"\n def __init__(self, data, parent=None):\n QtCore.QAbstractTableModel.__init__(self, parent)\n self._data = data\n\n def rowCount(self, parent=None):\n return len(self._data.values)\n\n def columnCount(self, parent=None):\n return self._data.columns.size\n\n def data(self, index, role=QtCore.Qt.DisplayRole):\n if index.isValid():\n if role == QtCore.Qt.DisplayRole:\n return str(self._data.values[index.row()][index.column()])\n return None\n\n def headerData(self, col, orientation, role):\n if orientation == QtCore.Qt.Horizontal and role == QtCore.Qt.DisplayRole:\n return self._data.columns[col]\n return None\n\n\nclass MyAdditionalDialog(QDialog):\n\n def __init__(self,driver):\n super().__init__()\n self.driver = driver\n self.ui = AdditionalDialog()\n self.ui.setupUi(self)\n self.ui.bSave.clicked.connect(self.checkAndSave)\n self.textFields = [self.ui.textPoly1,self.ui.textPoly2,self.ui.textPoly3,self.ui.textPoly4]\n self.checks = [self.ui.checkEnable1,self.ui.checkEnable2,self.ui.checkEnable3,self.ui.checkEnable4]\n \n def showAdditionalDialog(self):\n for k,pflag in enumerate(self.driver.predistenablemap):\n self.textFields[k].setText(np.array2string(self.driver.predistcoefs[k]))\n self.checks[k].setChecked(pflag)\n self.ui.spinFusionW1.setValue(self.driver.fusionweights[0])\n self.ui.spinFusionW2.setValue(self.driver.fusionweights[1])\n self.exec_()\n\n def checkAndSave(self):\n self.ui.statusLabel.setText(\"\")\n try:\n for k in range(4):\n self.driver.predistenablemap[k] = self.checks[k].isChecked()\n self.driver.predistcoefs[k] = np.fromstring(self.textFields[k].text().strip(\"[]\"),sep=\" \") \n if len(self.driver.predistcoefs[k].shape) > 1:\n self.driver.predistenablemap[k] = False\n self.driver.predistcoefs[k] = np.array([1.0,0.0])\n raise BaseException(\"Must be an array.\")\n if len(self.driver.predistcoefs[k].shape) > 10:\n self.driver.predistenablemap[k]\n self.driver.predistcoefs[k] = np.array([1.0,0.0])\n raise BaseException(\"Maximum order is 9.\")\n self.driver.fusionweights = [self.ui.spinFusionW1.value(),self.ui.spinFusionW2.value()]\n # print(self.driver.predistcoefs)\n # print(self.driver.predistenablemap)\n # print(self.driver.fusionweights)\n # self.driver.openSerial()\n self.driver.handshake()\n for k in range(4):\n self.driver.writePredistConfig(id=k)\n self.driver.writeFusionConfig()\n self.driver.recordAdditionalConfigs()\n self.ui.statusLabel.setText(\"Coefficients checked, saved and written to device.\")\n except BaseException as ex:\n self.ui.statusLabel.setText(f\"Error: {ex}\")\n\n\nclass MyDataViewer(QDialog):\n\n def __init__(self,dataman):\n super().__init__()\n self.dataman = dataman\n self.ui = DataViewerDialog()\n self.ui.setupUi(self)\n self.ui.bOpen.clicked.connect(self.openFile)\n self.ui.bOpenDataInMemory.clicked.connect(self.openDataInMemory)\n self.gwidget = pg.GraphicsLayoutWidget()\n self.ui.plotLayout.addWidget(self.gwidget)\n self.setAcceptDrops(True)\n\n def dragEnterEvent(self, e):\t\t\n if e.mimeData().hasUrls():\n e.accept()\n else:\n e.ignore()\n\n def dropEvent(self, e):\n droppedFile = Path(e.mimeData().text())\n if str(droppedFile).endswith(\".feather\"):\n self.openFile(droppedFile)\n else:\n self.ui.statusLabel.setText(\"File is not in feather format.\")\n\n def showDataViewerDialog(self):\n self.exec_()\n\n def openFile(self,fname=None):\n try: \n if fname:\n filename = [fname]\n else: \n if self.dataman.lastdatafolder:\n if self.dataman.lastdatafolder.is_dir():\n refdir = str(self.dataman.lastdatafolder)\n else: \n refdir = os.getenv('HOME') \n else: \n refdir = os.getenv('HOME') \n filename = QFileDialog.getOpenFileName(self, \"Open File\",\n refdir, 'feather (*.feather)')\n if filename[0] != '':\n auxf = Path(filename[0])\n self.dataman.lastdatafolder = auxf.parent if auxf.exists() else os.getenv('HOME')\n if (filename[0] != ''):\n self.ui.fileName.setText(str(filename[0]))\n self.datafromfile = pd.read_feather(filename[0])\n logs = self.datafromfile[[\"time\",\"log\"]][self.datafromfile[\"log\"].notnull()].values.tolist()\n self.ui.logText.setText(\"\\n\".join( [f\"{aa[0]}: {aa[1]}\" for aa in logs] ))\n if \"ctrl\" not in self.datafromfile.columns:\n self.ui.statusLabel.setText(\"File not valid (Error 1).\")\n return\n self.plotData() \n else:\n self.ui.fileName.setText('')\n self.datafromfile = None\n except BaseException as ex:\n self.ui.statusLabel.setText(f\"Error: {ex}\")\n\n\n def openDataInMemory(self):\n if self.dataman.globalctreadings == 0:\n self.ui.statusLabel.setText(\"Recorded data no found...\")\n elif (not self.dataman.ctrlmode): #or ( self.dataman.ctrlmode and (not self.dataman.taskisctrl) ):\n self.ui.statusLabel.setText(\"Only Control Mode data can be viewed for now.\")\n else:\n self.plotData(frommemory=True)\n\n\n def plotData(self,frommemory=False):\n pens = [pg.mkPen('r', width=1), pg.mkPen('b', width=1)]\n item = self.gwidget.getItem(0,0)\n if item is not None:\n self.gwidget.removeItem(item)\n item2 = self.gwidget.getItem(1,0)\n if item2 is not None:\n self.gwidget.removeItem(item2)\n \n myplot1 = self.gwidget.addPlot(row=0,col=0,labels={\"bottom\":\"Time (s)\"}) \n myplot2 = self.gwidget.addPlot(row=1,col=0,labels={\"bottom\":\"Time (s)\"})\n myplot1.addLegend(offset=(0,0),labelTextSize=\"7pt\")\n myplot2.addLegend(offset=(0,0),labelTextSize=\"7pt\")\n if frommemory:\n limf = self.dataman.globalctreadings\n myplot1.plot(self.dataman.timereads[:limf],self.dataman.dacoutdata[0][0:limf],pen=pens[0],name=\"Peturbation\")\n myplot1.plot(self.dataman.timereads[:limf],self.dataman.dacoutdata[1][0:limf],pen=pens[1],name=\"Control\")\n myplot2.plot(self.dataman.timereads[:limf],self.dataman.xerrodata[0:limf],pen=pens[0],name=\"Error\")\n myplot2.plot(self.dataman.timereads[:limf],self.dataman.xrefdata[0:limf],pen=pens[1],name=\"Reference\")\n else:\n myplot1.plot(self.datafromfile.time,self.datafromfile.perturb,pen=pens[0],name=\"Peturbation\")\n myplot1.plot(self.datafromfile.time,self.datafromfile.ctrl,pen=pens[1],name=\"Control\")\n myplot2.plot(self.datafromfile.time,self.datafromfile.err,pen=pens[0],name=\"Error\")\n myplot2.plot(self.datafromfile.time,self.datafromfile.ref,pen=pens[1],name=\"Reference\")\n \n\nclass MyPathModelingDialog():\n\n def __init__(self,dataman,driver):\n self.dataman = dataman\n self.driver = driver\n self.pdialog = QDialog()\n self.pdialog.ui = PathModelingDialog()\n self.pdialog.ui.setupUi(self.pdialog)\n self.endtime = np.ceil(self.dataman.timereads[self.dataman.globalctreadings-1])\n self.pdialog.ui.spinStartTime.setValue(0.0)\n if self.endtime > 120:\n self.starttime = 20\n elif self.endtime > 60:\n self.starttime = 10\n elif self.endtime > 10:\n self.starttime = 2\n else: \n self.starttime = 0\n self.pdialog.ui.spinEndTime.setMaximum(self.endtime)\n self.pdialog.ui.spinEndTime.setValue(self.endtime)\n self.pdialog.ui.spinStartTime.setMaximum(self.endtime)\n self.pdialog.ui.spinStartTime.setValue(self.starttime)\n self.pdialog.ui.bRunModeling.clicked.connect(self.runModeling)\n self.gwidget = pg.GraphicsLayoutWidget()\n self.pdialog.ui.plotLayout.addWidget(self.gwidget)\n self.pdialog.ui.bOpenFile.clicked.connect(self.openFile)\n self.pdialog.ui.bUploadAndRec.setEnabled(False)\n self.pdialog.ui.bUploadAndRec.clicked.connect(self.recordData)\n self.pdialog.ui.progressBar.setValue(0) \n self.pdialog.ui.bSaveToFile.clicked.connect(self.savePathsToFile) \n self.pdialog.ui.bCheck.clicked.connect(self.checkPaths)\n self.datafromfile = None\n self.dataman.hasPaths = False\n \n def openFile(self):\n filename = QFileDialog.getOpenFileName(self.pdialog, \"Open File\",\n os.getenv('HOME'), 'feather (*.feather)')\n if (filename[0] != ''):\n self.pdialog.ui.fileName.setText(filename[0])\n self.datafromfile = pd.read_feather(filename[0])\n if \"ctrl\" not in self.datafromfile.columns:\n self.pdialog.ui.statusLabel.setText(\"File not valid (Error 1).\")\n return\n self.endtime = self.datafromfile.time.values[-1]\n self.pdialog.ui.spinStartTime.setMaximum(self.endtime)\n self.pdialog.ui.spinEndTime.setMaximum(self.endtime)\n self.pdialog.ui.spinEndTime.setValue(self.endtime)\n self.pdialog.ui.comboSource.setCurrentIndex(1)\n else:\n self.pdialog.ui.fileName.setText('')\n self.datafromfile = None\n\n def checkPaths(self): \n try:\n self.pdialog.ui.statusLabel.setText(\"Wait... May take some time...\")\n # self.driver.openSerial()\n self.driver.handshake()\n wsec,wfbk = self.driver.readPaths()\n self.driver.stopReadings()\n\n pens = [pg.mkPen('r', width=2), pg.mkPen('b', width=2), pg.mkPen('r', width=1), pg.mkPen('b', width=1)]\n item = self.gwidget.getItem(0,0)\n if item is not None:\n self.gwidget.removeItem(item)\n item2 = self.gwidget.getItem(1,0)\n if item2 is not None:\n self.gwidget.removeItem(item2)\n\n myplot1 = self.gwidget.addPlot(row=0,col=0)\n myplot1.addLegend(offset=(0,0),labelTextSize=\"7pt\") \n myplot1.plot(wsec,pen=pens[0],name=\"Sec. Path from Device\")\n myplot2 = self.gwidget.addPlot(row=1,col=0)\n myplot2.addLegend(offset=(0,0),labelTextSize=\"7pt\")\n myplot2.plot(wfbk,pen=pens[1],name=\"Feedback Path from Device\") \n if self.dataman.hasPaths:\n myplot1.plot(self.dataman.secpath,pen=pens[2],name=\"Sec. Path from Memory\")\n myplot2.plot(self.dataman.fbkpath,pen=pens[3],name=\"Feedback Path from Memory\")\n \n\n self.pdialog.ui.statusLabel.setText(\"Paths successfully read.\")\n # sqerror = 0.0\n # for k in range(len(wsec)):\n # sqerror = sqerror + (wsec[k]-self.dataman.secpath[k])**2\n # print(sqerror)\n # sqerror = 0.0\n # for k in range(len(wfbk)):\n # sqerror = sqerror + (wfbk[k]-self.dataman.fbkpath[k])**2\n # print(sqerror)\n except BaseException as ex:\n self.pdialog.ui.statusLabel.setText(f\"Error: {ex}\")\n\n\n def savePathsToFile(self): \n if not self.dataman.hasPaths:\n self.pdialog.ui.statusLabel.setText(\"No paths in memory to be saved.\")\n return\n else:\n self.pdialog.ui.statusLabel.setText(\"\")\n filename = QFileDialog.getSaveFileName(self.pdialog, \"Save File\", os.getenv('HOME'), 'feather (*.feather)')\n if (filename[0] != ''):\n try: \n dftosave = pd.DataFrame({\n 'wsec': self.dataman.secpath,\n 'wfbk': self.dataman.fbkpath\n })\n dftosave.to_feather(filename[0])\n self.pdialog.ui.statusLabel.setText(\"File saved successfully!\")\n except Exception as err:\n self.pdialog.ui.statusLabel.setText(f\"Error: {err}\")\n\n def showPathModelingdDialog(self):\n self.pdialog.exec_()\n\n def recordData(self):\n self.pdialog.ui.statusLabel.setText(\"\")\n try: \n if self.dataman.hasPaths:\n datasize = self.dataman.secpath.shape[0]\n else:\n raise Exception(\"Path data not found in memory.\")\n self.driver.closeSerial()\n time.sleep(0.2)\n self.driver.openSerial()\n self.driver.handshake()\n self.driver.gravaCaminho('s', self.dataman.secpath, self.pdialog.ui.progressBar)\n self.driver.handshake()\n self.driver.gravaCaminho('f', self.dataman.fbkpath, self.pdialog.ui.progressBar)\n self.driver.stopReadings()\n self.driver.openSerial()\n self.driver.handshake()\n self.driver.gravaFlash()\n self.pdialog.ui.statusLabel.setText(\"Successfully uploaded and written in flash!\")\n self.driver.stopReadings()\n except BaseException as ex:\n self.pdialog.ui.statusLabel.setText(f\"Error: {ex}\")\n\n\n def runModeling(self):\n self.dataman.hasPaths = False\n\n psi = float(self.pdialog.ui.textPenalization.text())\n mu = float(self.pdialog.ui.textStepSize.text())\n N = self.pdialog.ui.spinMemSize.value()\n Navg = self.pdialog.ui.spinAveraging.value()\n endtime = self.pdialog.ui.spinEndTime.value()\n starttime = self.pdialog.ui.spinStartTime.value()\n\n try:\n\n if self.pdialog.ui.comboSource.currentIndex() == 0:\n if self.dataman.driver.controlMode and (not self.dataman.driver.taskIsControl) and (self.dataman.globalctreadings > 0):\n limf = self.dataman.globalctreadings\n timemask = (self.dataman.timereads[0:limf] >= starttime) & (self.dataman.timereads[0:limf] <= endtime)\n x = self.dataman.dacoutdata[1][0:limf][timemask]\n dfbk = self.dataman.xrefdata[0:limf][timemask]\n dsec = self.dataman.xerrodata[0:limf][timemask]\n else:\n raise BaseException(\"Path modeling recording not found\")\n else:\n if self.datafromfile is not None:\n timemask = (self.datafromfile.time >= starttime) & (self.datafromfile.time <= endtime)\n x = self.datafromfile.ctrl[timemask].values\n dfbk = self.datafromfile.ref[timemask].values \n dsec = self.datafromfile.err[timemask].values\n else:\n raise BaseException(\"Path modeling recording not found (file not open)\")\n \n\n # dfbk = dfbk - np.mean(dfbk)\n # dsec = dsec - np.mean(dsec) \n\n filt = FIRNLMS(N,mu,psi,None)\n filt.run(x,dfbk)\n self.wfbk = filt.ww\n filt2 = FIRNLMS(N,mu,psi,None)\n filt2.run(x,dsec)\n self.wsec = filt2.ww\n\n pens = [pg.mkPen('r', width=1), pg.mkPen('b', width=1)]\n item = self.gwidget.getItem(0,0)\n if item is not None:\n self.gwidget.removeItem(item)\n item2 = self.gwidget.getItem(1,0)\n if item2 is not None:\n self.gwidget.removeItem(item2)\n myplot1 = self.gwidget.addPlot(row=0,col=0)\n myplot1.plot(self.wsec,pen=pens[0])\n myplot1.plot(self.wfbk,pen=pens[1])\n\n myplot2 = self.gwidget.addPlot(row=1,col=0)\n splfreq = (1/self.dataman.samplingperiod)\n magdb1,freqs1 = easyFourier(self.wsec,splfreq)\n myplot2.plot(freqs1,magdb1,pen=pens[0])\n magdb2,freqs2 = easyFourier(self.wfbk,splfreq)\n myplot2.plot(freqs2,magdb2,pen=pens[1]) \n\n self.dataman.hasPaths = True\n self.dataman.fbkpath = self.wfbk\n self.dataman.secpath = self.wsec\n\n self.pdialog.ui.statusLabel.setText(f\"Success! Paths are available in memory for upload.\")\n self.pdialog.ui.bUploadAndRec.setEnabled(True)\n\n except BaseException as ex:\n self.pdialog.ui.statusLabel.setText(f\"Error: {ex}.\")\n \n\nclass MyUploadDialog():\n\n def __init__(self, driver, dataman):\n self.driver = driver\n self.dataman = dataman\n self.uploaddialog = QDialog()\n self.uploaddialog.ui = UploadDialog()\n self.uploaddialog.ui.setupUi(self.uploaddialog)\n self.uploaddialog.ui.bAbre.clicked.connect(self.abreArquivo)\n self.uploaddialog.ui.progressBar.setValue(0)\n self.uploaddialog.ui.bGravar.clicked.connect(self.gravaDados)\n self.uploaddialog.ui.bGravaFlash.clicked.connect(self.gravaFlash)\n\n def showUploadDialog(self):\n self.uploaddialog.exec_()\n\n def abreArquivo(self):\n filename = QFileDialog.getOpenFileName(self.uploaddialog, \"Abrir Arquivo\",\n os.getenv('HOME'), 'feather (*.feather);;csv (*.csv)')\n if (filename[0] != ''):\n self.uploaddialog.ui.caminhoArquivo.setText(filename[0])\n else:\n self.uploaddialog.ui.caminhoArquivo.setText('')\n\n def gravaFlash(self):\n try:\n self.uploaddialog.ui.status.setText(\"\")\n # self.driver.openSerial()\n self.driver.handshake()\n self.driver.gravaFlash()\n self.uploaddialog.ui.status.setText(\"Successfully written in flash!\")\n self.driver.stopReadings()\n except Exception as err:\n if self.driver.serial.isOpen():\n self.driver.stopReadings()\n self.uploaddialog.ui.status.setText(str(err))\n\n def gravaDados(self):\n try:\n self.uploaddialog.ui.status.setText(\"\")\n fname = self.uploaddialog.ui.caminhoArquivo.text()\n if self.uploaddialog.ui.comboTipo.currentIndex() <= 2:\n if self.uploaddialog.ui.comboTipo.currentIndex() == 0:\n dataf = pd.read_feather(fname)\n if (dataf.columns[0] != 'wsec') or (dataf.columns[1] != 'wfbk'):\n raise Exception(\"Invalid dataframe.\")\n datasize = dataf.wsec.values.shape[0]\n if datasize > 3000:\n raise Exception(\"Path having more than 3000 samples.\")\n wsec = dataf.wsec.values\n wfbk = dataf.wfbk.values\n elif self.uploaddialog.ui.comboTipo.currentIndex() == 1:\n if self.dataman.hasPaths:\n wsec = self.dataman.secpath\n wfbk = self.dataman.fbkpath \n datasize = wsec.shape[0]\n else:\n raise Exception(\"Path data not found in memory.\")\n # self.driver.openSerial()\n self.driver.handshake()\n self.driver.gravaCaminho('s', wsec, self.uploaddialog.ui.progressBar)\n self.driver.handshake()\n self.driver.gravaCaminho('f', wfbk, self.uploaddialog.ui.progressBar)\n self.uploaddialog.ui.status.setText(\"Upload finished!\")\n self.driver.stopReadings()\n else:\n dataf = pd.read_csv(fname)\n datasize = dataf.values.shape[0]\n if datasize > 3000:\n raise Exception(\"Path having more than 3000 samples.\")\n datatitle = dataf.columns[0]\n if (datatitle == 'wsec') and (self.uploaddialog.ui.comboTipo.currentIndex() == 1):\n tipo = 's'\n elif (datatitle == 'wfbk') and (self.uploaddialog.ui.comboTipo.currentIndex() == 2):\n tipo = 'f'\n else:\n raise Exception(\"Incorrect combination between column title and data.\")\n # self.driver.openSerial()\n self.driver.handshake()\n self.driver.gravaCaminho(tipo, dataf.values.reshape((1, -1))[0], self.uploaddialog.ui.progressBar)\n self.uploaddialog.ui.status.setText(\"Upload finished!\")\n self.driver.stopReadings()\n except Exception as err:\n if self.driver.serial.isOpen():\n self.driver.stopReadings()\n self.uploaddialog.ui.status.setText(str(err))\n\n\n\n# class MyAutomatorDialog():\n\n# def __init__(self):\n# self.adialog = QDialog()\n# self.adialog.ui = AutomatorDialog()\n# self.adialog.ui.setupUi(self.adialog)\n\n# def showAutomatorDialog(self):\n# self.adialog.exec_()\n\n# def printMessage(self,msg):\n# self.adialog.ui.messageArea.insertPlainText(msg)","repo_name":"eduardobatista/ActVibSoftware","sub_path":"ActVib/dialogs.py","file_name":"dialogs.py","file_ext":"py","file_size_in_byte":23496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40617351789","text":"\"\"\"\r\n5. APARTMENT BUILDING ADMINISTRATOR\r\nMichael is the administrator of an apartment building and wants to manage the monthly expenses\r\nfor each apartment in the building. In order to complete this task, Michael needs an application to\r\nstore, for a certain month, the expenses for each apartment. Each expense will be stored in the\r\napplication through the following elements: amount, type of the expense (the administrator wants to\r\ngroup the expenses in several predefined categories: such as water, heating, illuminating, gas, others).\r\nMichael needs an application in order to repeatedly execute the following functionalities (each\r\nfunctionality is exemplified):\r\n1. Add a new transaction into the list.\r\ninsert 100, type at 25 – inserts at apartment 25 an expense of 100 RON having the given type\r\n2. Modify expenses from the list.\r\nremove 15 – removes all the expenses at apartment 15\r\nremove from 5 to 10 – removes all the expenses from apartment 5 to apartment 10\r\nremove type – removes all the expenses having the indicated type, from all the apartments\r\nreplace 12, type with 200 – replaces the amount for the expense having the specified type at\r\napartment 12 with 200 RON\r\n3.Write the expenses having different properties.\r\ngreater than 100 - writes all the apartments with an overall expense greater than 100 RON\r\nless than 100 before 15 - writes all the apartments with an overall expense less than 100 for apartments from 1 to 15 \r\nall type – writes all the expenses having the specified type. \r\nbalance 10 – computes the balance (total amount) for apartment 10 \r\n4.Obtain different characteristics of expenses. \r\nsum type – writes the total amount for the expenses having the specified type.\r\n max 25 – writes the maximum expense per type for apartment 25 \r\nasc sort apt – sorts the total expenses/apartment in an ascending order \r\ndesc sort type - sorts the total expenses per type in a descending order\r\n5.Filter\r\nfilter type – retains only the expenses having the specified type. \r\nfilter 300 – retains only the overall expenses greater than 300 RON\r\n6. Undo the last operation\r\nundo – the last operation that has modified the list of expenses is cancelled. \r\n\r\n\"\"\"\r\n\r\n\r\ndef readAtransaction():\r\n \"\"\"\r\n Description:reads the element of a transaction-amount,type,apartment\r\n Input:-\r\n preconditions:-\r\n Output:t or -1\r\n postconditions: t is a list, t=[amount, type, apartment]\r\n -1 if the transaction isn't valid\r\n \"\"\"\r\n print(\"Enter transaction data: \\n\")\r\n amount=float(input(\"amount: \"))\r\n typ=input(\"type: \")\r\n ap=int(input(\"apartment: \"))\r\n if(ap<0 or amount<0):\r\n return -1\r\n else:\r\n t=[amount, typ, ap]\r\n return t\r\ndef verifyExistance(l,typ,ap):\r\n '''\r\n Description: verifies the existance of a transaction\r\n Input:l,typ,ap\r\n preconditions:l-list of transactions, typ-type of transaction, ap-apartment number\r\n Output:pos\r\n postconditions:pos=-1 if the transaction doesn't exist\r\n pos=i, i the position in the list if the transaction exists\r\n '''\r\n pos=-1\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[2]==ap and t[1]==typ):\r\n pos=i\r\n return pos\r\ndef InitialTest(l):\r\n l.append([100.5,'water',14])\r\n l.append([93.9, 'gas', 15])\r\n l.append([20, 'illuminating', 3])\r\n\r\ndef test_verifyExistance():\r\n l=[]\r\n InitialTest(l)\r\n\r\n assert verifyExistance(l,'water',14)==0\r\n assert verifyExistance(l,'gas',3)==-1\r\n assert verifyExistance(l,'illuminating',3)==2\r\ndef addAtransaction(l,t):\r\n \"\"\"\r\n Description: adds a new transaction into the list\r\n Input:l,t\r\n Preconditions:l-list of transactions, t-the transaction to be added\r\n Output:result\r\n Postconditions: result=true if t is added\r\n result=false if t is not added\r\n \"\"\"\r\n pos=verifyExistance(l,t[1],t[2])\r\n if(pos==-1):\r\n l.append(t)\r\n return True\r\n else:\r\n return False\r\ndef test_addAtransaction():\r\n l=[]\r\n InitialTest(l)\r\n\r\n assert addAtransaction(l,[100, 'water', 14])==False\r\n assert addAtransaction(l,[21, 'gas', 2])==True\r\ndef findApartment(l,ap):\r\n \"\"\"\r\n Description: searches for the transaction of a given apartment\r\n Input:l,ap\r\n Preconditions:l-list of transactions, ap-apartment to be found\r\n Output:pos\r\n Postconditions:pos=-1 if there is no transaction for ap in the list\r\n pos=i, i the position in the list if ap exists\r\n \"\"\"\r\n pos=-1\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[2]==ap):\r\n pos=i\r\n return pos\r\ndef test_findApartment():\r\n l=[]\r\n InitialTest(l)\r\n assert findApartment(l,15)==1\r\n assert findApartment(l,21)==-1\r\ndef removeExpenses_ap(l,ap):\r\n \"\"\"\r\n Description: deletes the transaction for a given apartment(if it is in the list)\r\n Input:l,ap\r\n Preconditions:l-list of students\r\n ap-the apartment we need to delete the transaction from\r\n Output:result\r\n Postconditions:result=true, if we found and deleted the transaction\r\n result=false, if the transaction is not in the list\r\n \"\"\"\r\n pos=findApartment(l,ap)\r\n if(pos==-1):\r\n return False\r\n else:\r\n l.pop(pos)\r\n return True\r\ndef test_removeExpenses_ap():\r\n l=[]\r\n InitialTest(l)\r\n assert removeExpenses_ap(l,3)==True\r\n assert removeExpenses_ap(l,6)==False\r\ndef findType(l,typ):\r\n \"\"\"\r\n Description: Searches for a transaction with a given type\r\n Input:l,typ\r\n Preconditions:l-list\r\n type-type of transaction to be deleted\r\n Output:pos\r\n Postconditions:pos=-1, if the type is not in the list\r\n pos=i,i the position of the transacton with the given type\r\n \"\"\"\r\n pos=-1\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[1]==typ):\r\n pos=i\r\n return pos\r\ndef test_findType():\r\n l=[]\r\n InitialTest(l)\r\n assert findType(l,'water')==0\r\n assert findType(l,'heating')==-1\r\ndef removeExpenses_type(l,typ):\r\n \"\"\"\r\n Description:Removes transaction with a given type, if found\r\n Input:l,typ\r\n Preconditions:l-list\r\n typ-type of transaction to be deleted\r\n Output:result\r\n Postconditions:result=true, if we find a transaction of a given type and delete it\r\n result=false, if the transaction is not found, therefore not removed\r\n \"\"\"\r\n pos=findType(l,typ)\r\n if(pos==-1):\r\n return False\r\n else:\r\n l.pop(pos)\r\n return True\r\n\r\ndef test_removeExpenses_type():\r\n l=[]\r\n InitialTest(l)\r\n assert removeExpenses_type(l,'water')==True\r\n assert removeExpenses_type(l,'heating')==False\r\ndef ReplaceExpense(l,typ,ap,su):\r\n \"\"\"\r\n Description:Replaces the amount of a expense with a given type and apartment number\r\n Input:l, typ, ap\r\n Precond:l-list of transactions, typ-type of expense, ap-apartment number\r\n Output:result\r\n Postcond:result=true,if the transaction is found and the amount of the expense is successfully replaced\r\n result=false, if the transaction is not found in the list\r\n \"\"\"\r\n pos=verifyExistance(l,typ,ap)\r\n if(pos==-1):\r\n return False\r\n else:\r\n t=l[pos]\r\n t[0]=su\r\n return True\r\ndef test_ReplaceExpense():\r\n l=[[12.5,'water',12],[113,'gas',15],[145,'illuminating',12]]\r\n \r\n assert ReplaceExpense(l,'water',12,200)==True\r\n assert ReplaceExpense(l,'gas',11,100)==False\r\ndef overall_expense(l,ap):\r\n \"\"\"\r\n Description: Computes the overall expense for a given apartment number\r\n Input:l,ap\r\n Prec:l-list of transactions, ap-the apartment number\r\n Output:su\r\n Post:su=the overall expense for the given apartment\r\n \"\"\"\r\n su=0\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[2]==ap):\r\n su=su+t[0]\r\n return su\r\ndef test_overall_expense():\r\n l=[[13.5,'water',2],[11.5,'gas',2],[3.5,'illuminating',1]]\r\n assert overall_expense(l,2)==25.0\r\n assert overall_expense(l,3)==0\r\n assert overall_expense(l,1)==3.5\r\ndef insertAp(l,ap):\r\n '''\r\n Description: inserts the apartment in the new list if it doesn't exist\r\n Input:l,ap\r\n Prec:l-list of apartments, ap-apartment number\r\n Output: -\r\n Post:-\r\n '''\r\n if(NoApart(l,ap)!=-1):\r\n l.append(ap)\r\ndef printList(l):\r\n '''\r\n Description: prints the list of apartments\r\n Input:l\r\n Prec:l-list of apartments\r\n Output:-\r\n Post:-\r\n '''\r\n if(len(l)>0):\r\n print(l)\r\n else:\r\n print(\"No apartments\")\r\ndef ExpType(l,typ):\r\n '''\r\n Description: Prints all the expenses havin the specified type\r\n Input:l,typ\r\n Prec:l-list of transactions\r\n typ-type of expense\r\n Output:-\r\n Post:-\r\n '''\r\n if(findType(l,typ)!=-1):\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[1]==typ):\r\n print(t)\r\n else:\r\n print(\"no expenses for the given type\")\r\n \r\ndef NoApart(l,ap):\r\n \"\"\"\r\n Description: Verifies if there are expenses registered for a given apartment\r\n Input:l,ap\r\n Prec:l-list of transactions,ap-the given apartment number\r\n Output:result\r\n Post: result=True, if there is no apartment with the given number in the list\r\n result=False, if there is an apartment with the number of the variable ap\r\n \"\"\"\r\n for i in range(0,len(l)):\r\n \r\n if l[i]==ap:\r\n return False\r\n return True\r\ndef test_NoApart():\r\n l=[12,1,3]\r\n assert NoApart(l,1)==False\r\n assert NoApart(l,4)==True\r\ndef TotalExpense_type(l,typ):\r\n \"\"\"\r\n Description: Calculates the total sum for a type of expense\r\n Input:l,typ\r\n Prec: l-list of transactions, typ-type of expense\r\n Output:s\r\n Post:s=0, if there are no expenses for the given type\r\n \r\n \"\"\"\r\n s=0\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[1]==typ):\r\n s=s+t[0]\r\n return s\r\ndef test_TotalExpense_type():\r\n l=[[13.5,'water',2],[11.5,'water',2],[3.5,'illuminating',1]]\r\n assert TotalExpense_type(l,'water')==25.0\r\n assert TotalExpense_type(l,'heating')==0\r\n\r\ndef findMaxE(l,ap):\r\n \"\"\"\r\n Description: Return the maximum expense(type) for the specified apartment\r\n Input:l,ap\r\n Prec:l-list of transactions, ap-the apartment number\r\n Output:typ\r\n Post:typ=\"null\", if there are no expenses registered for the specified apartment\r\n typ=the type of the highest expense\r\n \"\"\"\r\n ma=0\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n typ=\"null\"\r\n ma=0\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[2]==ap):\r\n if(t[0]>ma):\r\n typ=t[1]\r\n ma=t[0]\r\n return typ\r\ndef test_findMaxE():\r\n l=[[13.5,'water',2],[11.5,'gas',2],[3.5,'illuminating',1]]\r\n assert findMaxE(l,2)=='water'\r\n assert findMaxE(l,3)=='null'\r\ndef printMaxAp(l,ap):\r\n '''\r\n Description: Prints the maximum expense for an apartment, if it is in the list\r\n Input:l,ap\r\n Prec:l-list of transactions\r\n ap-apartment number\r\n Output:-\r\n Post:-\r\n\r\n '''\r\n typ=findMaxE(l,ap)\r\n if(typ!=-1):\r\n print(typ)\r\n else:\r\n print(\"no apartment registered\")\r\ndef NoApart2(l,ap):\r\n \"\"\"\r\n Description: Verifies if the apartment exist in the list of total expenses\r\n Input:l,ap\r\n Prec:l-list of total expenses, ap-the number of the apartment\r\n Output:result\r\n Post:result=True, if the apartment doesn't exist\r\n result=False if it exists\r\n \"\"\"\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[1]==ap):\r\n return False\r\n return True\r\n \r\ndef test_NoApart2():\r\n l=[[23.5,3],[35,2],[100,5]]\r\n assert NoApart2(l,3)==False\r\n assert NoApart2(l,7)==True\r\ndef ConstrList(l,a):\r\n '''\r\n Specification: Creates a list containing the total amount of expenses for each apartment\r\n Input:l,a\r\n Prec:l-list of transactions\r\n a-new list\r\n Output:-\r\n Post:-\r\n '''\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n ex=overall_expense(l,t[2])\r\n if NoApart2(a,t[2]):\r\n a.append([ex,t[2]])\r\ndef sort_asc(l):\r\n \"\"\"\r\n Description: Sorts a list of total expenses for apartments in an ascending order\r\n Input: l\r\n Prec: l-list of apartments with total expenses: l=[[total_expense1,ap1],[total_expense2,ap2],...]\r\n Output: -\r\n Post: -\r\n \"\"\"\r\n for i in range(0,len(l)-1):\r\n for j in range(i+1,len(l)):\r\n x=l[i]\r\n y=l[j]\r\n if(x[0]>y[0]):\r\n au=l[i]\r\n l[i]=l[j]\r\n l[j]=au\r\n\r\ndef NoApart3(l,typ):\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n if(t[1]==typ):\r\n return False\r\n return True\r\ndef test_NoApart3():\r\n l=[[2001.5,'water'],[3420,'gas'],[35.5,'illuminating']]\r\n assert NoApart3(l,'water')==False\r\n assert NoApart3(l,'heating')==True\r\ndef ConstrListTyp(l,a):\r\n '''\r\n Specification: Creates a list containing the total amount of expenses for each type\r\n Input:l,a\r\n Prec:l-list of transactions\r\n a-new list\r\n Output:-\r\n Post:-\r\n '''\r\n for i in range(0,len(l)):\r\n t=l[i]\r\n ex=TotalExpense_type(l,t[1])\r\n if NoApart3(a,t[1]):\r\n a.append([ex,t[1]])\r\ndef sort_desc(l):\r\n \"\"\"\r\n Description: Sorts a list of total expenses per type in a descending order\r\n Input: l\r\n Prec: l-list of total expenses for each registered type: l=[[total_expense1,type1],[total_expense2,type2],...]\r\n Output:-\r\n Post:-\r\n \"\"\"\r\n for i in range(0,len(l)-1):\r\n for j in range(i+1,len(l)):\r\n x=l[i]\r\n y=l[j]\r\n if(x[0]<y[0]):\r\n au=l[i]\r\n l[i]=l[j]\r\n l[j]=au\r\ndef printMenu():\r\n str=\" Commands: \\n\"\r\n str=str+ \"\\t 1= add a transaction \\n\"\r\n str=str+ \"\\t 2= Modify expenses from list \\n\"\r\n str=str+ \"\\t 3=Write the expenses having different properties. \\n\"\r\n str=str+ \"\\t 4=Obtain different characteristics of expenses \\n\"\r\n str=str+ \"\\t 0= exit app \\n\"\r\n print (str)\r\ndef printMenu2():\r\n str=\" Commands: \\n\"\r\n str=str+ \"\\t 1=remove all expenses from one ap \\n\"\r\n str=str+ \"\\t 2=remove from ap1 to ap2 \\n\"\r\n str=str+ \"\\t 3=remove type \\n\"\r\n str=str+ \"\\t 4=replace for an apartment the expense, with a given type \\n\"\r\n str=str+ \"\\t 0=exit app \\n\"\r\n print (str)\r\ndef printMenu3():\r\n str=\"Commands: \\n\"\r\n str=str+ \"\\t 1=greater than a sum \\n\"\r\n str=str+ \"\\t 2=less than a sum, before an apartment \\n\"\r\n str=str+ \"\\t 3=all type \\n\"\r\n str=str+ \"\\t 4=balance apartment \\n\"\r\n print(str)\r\ndef printMenu4():\r\n str=\"Commands: \\n\"\r\n str= str+ \"\\t 1=sum type \\n\"\r\n str=str+ \"\\t 2=max ap \\n\"\r\n str=str+ \"\\t 3=asc sort apt \\n\"\r\n str=str+ \"\\t 4=desc sort type \\n\"\r\n print(str)\r\ndef validCommand(cmd):\r\n availableCmd=[\"1\",\"2\",\"3\",\"4\",\"0\"]\r\n return cmd in availableCmd\r\ndef validCommand2(cmd):\r\n availableCmd=[\"1\",\"2\",\"3\",\"4\",\"0\"]\r\n return cmd in availableCmd\r\ndef main():\r\n \r\n \r\n test_verifyExistance()\r\n test_addAtransaction()\r\n test_findApartment()\r\n test_removeExpenses_ap()\r\n test_findType()\r\n test_removeExpenses_ap()\r\n test_ReplaceExpense()\r\n test_overall_expense()\r\n test_NoApart()\r\n test_TotalExpense_type()\r\n test_findMaxE()\r\n test_NoApart2()\r\n test_NoApart3()\r\n l=[]\r\n while True:\r\n printMenu()\r\n cmd=input(\"Enter option:\")\r\n if validCommand(cmd):\r\n if cmd==\"0\":\r\n break\r\n else:\r\n if cmd==\"1\":\r\n tNew=readAtransaction()\r\n while(tNew==-1):\r\n print(\"give a valid transaction\")\r\n tNew=readAtransaction()\r\n \r\n ok=addAtransaction(l,tNew)\r\n if(not ok):\r\n print(\"the transaction exists\")\r\n else:\r\n print(\"transaction added with success\")\r\n \r\n else:\r\n if cmd==\"2\":\r\n printMenu2()\r\n cmd=input(\"Enter command: \")\r\n if validCommand2(cmd):\r\n if cmd==\"0\":\r\n break\r\n elif cmd==\"1\":\r\n n=int(input(\"give apartment number:\"))\r\n if (findApartment(l,n)!=-1):\r\n while(findApartment(l,n)!=-1):\r\n removeExpenses_ap(l,n)\r\n print(\"the expenses were removed successfully\")\r\n else:\r\n print(\"No transactions for apartment 15 were found\")\r\n elif cmd==\"2\":\r\n a=int(input(\"give first ap\"))\r\n b=int(input(\"give second ap\"))\r\n ok=0\r\n \r\n for i in range (a,b+1):\r\n if(findApartment(l,i)!=-1):\r\n while(findApartment(l,i)!=-1):\r\n removeExpenses_ap(l,i)\r\n ok=1\r\n print(\"the expenses have been removed successfully\")\r\n if ok==0:\r\n print(\"there are no transactions for apartments 5 to 10\")\r\n \r\n \r\n \r\n elif cmd==\"3\":\r\n tip=input(\"give type of expense to be removed:\")\r\n if(findType(l,tip)!=-1):\r\n while(findType(l,tip)!=-1):\r\n removeExpenses_type(l,tip)\r\n print(\"the expenses have been removed successfully\")\r\n else:\r\n print(\"there are no expenses for the given type\")\r\n elif cmd==\"4\":\r\n ap=int(input(\"give ap number: \"))\r\n \r\n tip=input(\"Give type of expense to be replaced: \")\r\n su=float(input(\"give new expense: \"))\r\n if(ReplaceExpense(l,tip,ap,su)==True):\r\n print(\"The expense was replaced successfully\")\r\n else:\r\n print(\"There is no transaction in the list for apartment 12, having the indicated type\")\r\n \r\n \r\n else:\r\n print(\"Enter a valid command\")\r\n \r\n elif cmd==\"3\":\r\n printMenu3()\r\n cmd=input(\"Enter option\")\r\n if(validCommand2(cmd)):\r\n if(cmd==\"1\"):\r\n l1=[]\r\n for i in range(len(l)):\r\n t=l[i]\r\n n=float(input(\"Enter sum:\"))\r\n if(overall_expense(l,t[2])>n):\r\n insertAp(l1,t[2])\r\n printList(l1) \r\n \r\n if(cmd==\"2\"):\r\n l1=[]\r\n ap=int(input(\"Before apartment:\"))\r\n n=float(input(\"Less than:\"))\r\n for i in range (0,len(l)):\r\n t=l[i]\r\n if(t[2]<ap and overall_expense(l,t[2])<n):\r\n insertAp(l1,t[2])\r\n printList(l1)\r\n \r\n if(cmd==\"3\"):\r\n typ=input(\"give type:\")\r\n ExpType(l,typ)\r\n \r\n if(cmd==\"4\"):\r\n ap=int(input(\"give apartment\"))\r\n print(overall_expense(l,ap))\r\n else:\r\n print(\"Enter a valid command\")\r\n elif(cmd==\"4\"):\r\n printMenu4()\r\n cmd=input(\"Enter option: \")\r\n if(validCommand2(cmd)):\r\n if(cmd==\"1\"):\r\n typ=input(\"give type: \")\r\n print(TotalExpense_type(l,typ))\r\n \r\n if(cmd==\"2\"):\r\n ap=int(input(\"give apartment:\"))\r\n printMaxAp(l,ap)\r\n if(cmd==\"3\"):\r\n a=[]\r\n ConstrListAp(l,a)\r\n sort_asc(a)\r\n print(a)\r\n \r\n if(cmd==\"4\"):\r\n a=[]\r\n ConstrListTyp(l,a)\r\n sort_desc(a)\r\n print(a)\r\n \r\n else:\r\n print(\"Enter a valid command\")\r\n \r\n else:\r\n print(\"Enter a valid command:\")\r\n print(l)\r\nmain()\r\n","repo_name":"AndraPop29/Faculty","sub_path":"Programmng Fundementals/Lab02-Andra Pop/lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":22278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1089566340","text":"## @file CalcModule.py\n# @author Bill Nguyen\n# @brief Sorts, calculates averages and allocates students entering second year\n# @date January 18, 2019\n\nfrom ReadAllocationData import *\n\n## @brief Sorts list of students in descending order\n# @param S is list of dictionaries generated from the readStdnts function\n# @return List of dictionaries that is sorted in descending order based on gpa\ndef sort(S):\n for j in range(len(S)):\n index = 0\n while index < (len(S) - 1):\n if (S[index][\"gpa\"] > S[index+1][\"gpa\"]) or (S[index][\"gpa\"] == S[index+1][\"gpa\"]):\n index += 1\n else:\n temp = S[index]\n S[index] = S[index + 1]\n S[index+1] = temp\n return S\n\n## @brief Calculates average of all males or females students\n# @param L is a list of dictionaries generated from readStdnts function\n# @param g is a string that has the input male or female\n# @return Average of all males or females students\n\ndef average(L, g):\n sum_g = 0\n count = 0\n if g != \"male\" and g != \"female\":\n return \"in g enter male or female\"\n for student in L:\n if student[\"gender\"] == g:\n sum_g += student[\"gpa\"]\n count += 1\n if count == 0:\n return 0\n else:\n return sum_g/count\n\n## @brief Allocates all students to second year stream\n# @param S a list of dictionaries generated by readStdnts function\n# @param F a list that contains students with free choice\n# @param C a dictionary that contains all department capacities\n# @return a dictionary that allocates all qualified students to their second year stream\ndef allocate(S, F, C):\n choice_dict = { \"civil\": [], \"chemical\": [],\"electrical\": [],\"software\": [],\"materials\": [],\"mechanical\": [],\"engphys\": [] }\n freeChoice_list = []\n S_list = []\n for student in S:\n if (student[\"gpa\"] >= 4.0) and (student[\"macid\"] in F) and (student[\"gpa\"] <= 12.0):\n freeChoice_list.append(student)\n elif (student[\"gpa\"] >= 4.0) and (student[\"gpa\"] <= 12.0):\n S_list.append(student)\n\n freeChoice_list = sort(freeChoice_list)\n \n for student in freeChoice_list:\n for x in student[\"choices\"]:\n if C[x] > len(choice_dict[x]):\n choice_dict[x].append(student)\n break\n \n sorted_list = sort(S_list)\n \n for student in sorted_list:\n for x in student[\"choices\"]:\n if C[x] > len(choice_dict[x]):\n choice_dict[x].append(student)\n break\n \n return choice_dict\n \n","repo_name":"BillNguyen1999/python-year2","sub_path":"A1/src/CalcModule.py","file_name":"CalcModule.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22871332851","text":"from unittest.mock import MagicMock\n\nimport pynguin.configuration as config\nimport pynguin.testcase.defaulttestcase as dtc\nimport pynguin.testcase.statement as stmt\nimport pynguin.testcase.variablereference as vr\nimport pynguin.utils.generic.genericaccessibleobject as gao\n\n\ndef test_field_statement(test_case_mock, variable_reference_mock, field_mock):\n field_statement = stmt.FieldStatement(\n test_case_mock, field_mock, variable_reference_mock\n )\n assert field_statement.field == field_mock\n\n\ndef test_new_source(test_case_mock, variable_reference_mock, field_mock):\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n new_source = MagicMock(vr.VariableReference)\n statement.source = new_source\n assert statement.source == new_source\n\n\ndef test_accessible_object(test_case_mock, variable_reference_mock, field_mock):\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n assert statement.accessible_object() == field_mock\n\n\ndef test_field_statement_eq_same(test_case_mock, variable_reference_mock, field_mock):\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n assert statement.__eq__(statement)\n\n\ndef test_constructor_statement_accept(\n test_case_mock, variable_reference_mock, field_mock\n):\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n visitor = MagicMock(stmt.StatementVisitor)\n statement.accept(visitor)\n\n visitor.visit_field_statement.assert_called_once_with(statement)\n\n\ndef test_get_var_references(test_case_mock, field_mock):\n var = vr.VariableReference(test_case_mock, int)\n statement = stmt.FieldStatement(test_case_mock, field_mock, var)\n assert statement.get_variable_references() == {var, statement.ret_val}\n\n\ndef test_statement_replace(field_mock, test_case_mock):\n ref = vr.VariableReference(test_case_mock, int)\n statement = stmt.FieldStatement(test_case_mock, field_mock, ref)\n new = vr.VariableReference(test_case_mock, int)\n\n statement.replace(ref, new)\n assert statement.source == new\n\n\ndef test_statement_replace_2(field_mock, test_case_mock):\n ref = vr.VariableReference(test_case_mock, int)\n statement = stmt.FieldStatement(test_case_mock, field_mock, ref)\n new = vr.VariableReference(test_case_mock, int)\n\n statement.replace(statement.ret_val, new)\n assert statement.ret_val == new\n\n\ndef test_statement_replace_3(field_mock, test_case_mock):\n ref = vr.VariableReference(test_case_mock, int)\n ref_2 = vr.FieldReference(ref, gao.GenericField(MagicMock, \"foo\", int))\n statement = stmt.FieldStatement(test_case_mock, field_mock, ref_2)\n new = vr.VariableReference(test_case_mock, int)\n\n statement.replace(ref, new)\n assert statement.source.get_variable_reference() == new\n\n\ndef test_primitive_statement_replace_ignore(field_mock):\n test_case = dtc.DefaultTestCase()\n ref = stmt.IntPrimitiveStatement(test_case, 5)\n statement = stmt.FieldStatement(test_case, field_mock, ref.ret_val)\n new = stmt.FloatPrimitiveStatement(test_case, 0).ret_val\n old = statement.source\n statement.replace(new, new)\n assert statement.source == old\n\n\ndef test_field_statement_eq_other_type(\n test_case_mock, variable_reference_mock, field_mock\n):\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n assert not statement.structural_eq(variable_reference_mock, {})\n\n\ndef test_field_statement_eq_clone(test_case_mock, field_mock):\n ref = vr.VariableReference(test_case_mock, float)\n statement = stmt.FieldStatement(test_case_mock, field_mock, ref)\n memo = {ref: ref}\n clone = statement.clone(test_case_mock, memo)\n memo[statement.ret_val] = clone.ret_val\n assert statement.structural_eq(clone, memo)\n\n\ndef test_hash_same(test_case_mock, variable_reference_mock, field_mock):\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n statement2 = stmt.FieldStatement(\n test_case_mock, field_mock, variable_reference_mock\n )\n assert statement.structural_hash() == statement2.structural_hash()\n\n\ndef test_mutate_not(test_case_mock, field_mock, variable_reference_mock):\n config.configuration.search_algorithm.change_parameter_probability = 0.0\n statement = stmt.FieldStatement(test_case_mock, field_mock, variable_reference_mock)\n assert not statement.mutate()\n\n\ndef test_mutate_no_replacements(field_mock, constructor_mock):\n config.configuration.search_algorithm.change_parameter_probability = 1.0\n test_case = dtc.DefaultTestCase()\n const = stmt.ConstructorStatement(test_case, constructor_mock)\n field = stmt.FieldStatement(test_case, field_mock, const.ret_val)\n test_case.add_statement(const)\n test_case.add_statement(field)\n assert not field.mutate()\n\n\ndef test_mutate_success(field_mock, constructor_mock):\n config.configuration.search_algorithm.change_parameter_probability = 1.0\n test_case = dtc.DefaultTestCase()\n const = stmt.ConstructorStatement(test_case, constructor_mock)\n const2 = stmt.ConstructorStatement(test_case, constructor_mock)\n field = stmt.FieldStatement(test_case, field_mock, const.ret_val)\n const3 = stmt.ConstructorStatement(test_case, constructor_mock)\n test_case.add_statement(const)\n test_case.add_statement(const2)\n test_case.add_statement(field)\n test_case.add_statement(const3)\n assert field.mutate()\n assert field.source == const2.ret_val\n","repo_name":"microsoft/codamosa","sub_path":"tests/testcase/statements/test_fieldstatement.py","file_name":"test_fieldstatement.py","file_ext":"py","file_size_in_byte":5526,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"31"} +{"seq_id":"30065727088","text":"from flask import Flask, request\nfrom flask_restful import Resource, Api\nimport requests\nfrom get_salesforce_data import *\nfrom threading import Thread\nimport json\nfrom store_data import update_excel\n\napp = Flask(__name__)\napi = Api(app)\n\n\ndef case_details(api_endpoint, case_number, comment):\n case_fields = get_data(case_number)\n if len(case_fields) == 1:\n out_data = {\n \"blocks\":\n [\n {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"mrkdwn\",\n \"text\": \"Provided Case number does not exist. Please mention the correct one.\"\n }\n }\n ]\n }\n requests.post(url=api_endpoint, headers=const.headers, data=json.dumps(out_data))\n elif len(case_fields) == 2:\n out_data = {\n \"blocks\":\n [\n {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"mrkdwn\",\n \"text\": str(case_fields.get('text'))\n }\n }\n ]\n }\n requests.post(url=api_endpoint, headers=const.headers, data=json.dumps(out_data))\n else:\n update_excel(case_fields, comment)\n out_data = {\n \"blocks\":\n [\n {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"mrkdwn\",\n \"text\": \"Case details are updated in the repository!!:thankyou:\"\n }\n }\n ]\n }\n requests.post(url=api_endpoint, headers=const.headers, data=json.dumps(out_data))\n\n\nclass SlackServer(Resource):\n def post(self):\n api_endpoint = request.values['response_url']\n data = request.values['text'].split()\n case_number = data[0]\n comment = \" \".join(data[1:])\n print(case_number)\n print(comment)\n if data[0].isnumeric() and len(data[0]) == 8:\n thr = Thread(target=case_details, args=[api_endpoint, case_number, comment])\n thr.start()\n return ':mag_right: Your Requested has been submitted.Please wait for a while :mag:'\n else:\n return 'Please enter the valid 8 digit Case Number along with comment For example 00012345 Please \\\n respond to customer'\n\n\napi.add_resource(SlackServer, '/slackserver')\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n\n\n","repo_name":"chetan94k/Capture_Escalated_Cases_Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"37316608722","text":"import math\nimport numpy as np\n\n\ndef distance(c1, c2):\n return np.linalg.norm(c1-c2, 2)\n\n\ndef get_closest(color, palette):\n closest_idx, closest_color = min((x for x in enumerate(palette)), key=lambda x: distance(x[1], color))\n\n return closest_idx, closest_color\n\n\ndef floyd_steinberg(image, palette):\n img_float = image.astype('float')\n palette = np.asarray(palette, 'float')\n\n height, width = image.shape[:2]\n\n dithered = np.zeros((height, width, 3), 'uint8')\n indexes = np.zeros((height, width), 'int')\n\n for y in range(height):\n for x in range(width):\n color = img_float[y, x]\n\n closest_idx, closest_color = get_closest(color, palette)\n\n dithered[y, x] = closest_color\n indexes[y, x] = closest_idx\n\n error = color - closest_color\n\n if x < width - 1:\n img_float[y][x + 1] += error * 7 / 16\n\n if y < height - 1:\n if 0 < x:\n img_float[y + 1][x - 1] += error * 3 / 16\n\n img_float[y + 1][x] += error * 5 / 16\n\n if x < width - 1:\n img_float[y + 1][x + 1] += error * 1 / 16\n\n return dithered, indexes\n\n\ndef ordered(image, palette):\n mask = np.asarray([[ 1, 49, 13, 61, 4, 52, 16, 64],\n [33, 17, 45, 29, 36, 20, 48, 32],\n [ 9, 57, 5, 53, 12, 60, 8, 56],\n [41, 25, 37, 21, 44, 28, 40, 24],\n [ 3, 51, 15, 63, 2, 50, 14, 62],\n [35, 19, 47, 31, 34, 18, 46, 30],\n [11, 59, 7, 55, 10, 58, 6, 54],\n [43, 27, 39, 23, 42, 26, 38, 22]]) / 65.0\n\n # mask = np.asarray([[ 1, 9, 3, 11],\n # [13, 5, 15, 7],\n # [ 4, 12, 2, 10],\n # [16, 8, 14, 6]]) / 17.0\n\n # mask = np.asarray([[7, 9, 5],\n # [2, 1, 4],\n # [6, 3, 8]]) / 10.0\n\n # mask = np.asarray([[1, 3],\n # [4, 2]]) / 5.0\n\n h, w = image.shape[:2]\n reps = math.ceil(h / mask.shape[0]), math.ceil(w / mask.shape[1])\n\n mask = np.tile(mask, reps)[:h, :w] + 0.5\n mask = np.expand_dims(mask, -1)\n\n img_float = mask * image\n indexes = np.zeros((h, w), 'int')\n\n for y in range(h):\n for x in range(w):\n color = img_float[y, x]\n\n closest_idx, closest_color = get_closest(color, palette)\n\n img_float[y, x] = closest_color\n indexes[y, x] = closest_idx\n\n return img_float, indexes\n\n\ndef dither(image, palette, out=None, method='fs'):\n if method == 'fs':\n return floyd_steinberg(image, palette)\n elif method == 'ordered':\n return ordered(image, palette)\n else:\n raise Exception('Unknown method type \\'%s\\'' % str(method))\n\n\n","repo_name":"tomfen/Imagetorio","sub_path":"old python stuff/pydither.py","file_name":"pydither.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"16880538929","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport sys\r\nsys.path.append(\".\")\r\n\r\n\r\nclass HMCN(nn.Module):\r\n def __init__(self, input_dim, voc_size):\r\n super(HMCN, self).__init__()\r\n self.input_dim = input_dim\r\n self.voc_size = voc_size\r\n ## Local hierarchical classifiers\r\n self.lhc = nn.Sequential(\r\n nn.ReLU(),\r\n nn.Linear(input_dim, 2*input_dim),\r\n nn.ReLU(),\r\n nn.Linear(2*input_dim, sum(voc_size)),\r\n )\r\n ## Global hierarchical classifiers\r\n self.ghc = nn.GRU(input_dim, input_dim, batch_first=True)\r\n self.alpha = nn.Parameter(torch.FloatTensor([[0.]*sum(voc_size)]))\r\n self.local_out = nn.ModuleList(\r\n [\r\n nn.Sequential(\r\n nn.ReLU(),\r\n nn.Linear(input_dim, voc_size[i])\r\n )\r\n for i in range(len(voc_size))]\r\n )\r\n\r\n def forward(self, x):\r\n ## Local output\r\n feat1 = self.lhc(x)\r\n p1 = torch.sigmoid(feat1)\r\n ## Global output\r\n x = x.unsqueeze(1)\r\n seq_x = torch.cat([x, x, x], dim=1)\r\n seq_o, _ = self.ghc(seq_x) # (1, seq, dim)\r\n feat2 = []\r\n for i, _ in enumerate(self.local_out):\r\n feat2.append(self.local_out[i](seq_o[:, i, :]))\r\n\r\n feat2 = torch.cat(feat2, dim=-1) # (1, o_n)\r\n p2 = torch.sigmoid(feat2)\r\n p_weights = torch.sigmoid(self.alpha)\r\n\r\n return p1 * p_weights + (1-p_weights) * p2\r\n\r\n\r\nclass RETAIN(nn.Module):\r\n def __init__(self, emb_dim):\r\n super(RETAIN, self).__init__()\r\n self.emb_dim = emb_dim\r\n self.encoder = nn.GRU(emb_dim, emb_dim * 2, batch_first=True)\r\n self.alpha_net = nn.Linear(emb_dim * 2, 1)\r\n self.beta_net = nn.Linear(emb_dim * 2, emb_dim)\r\n\r\n def forward(self, i1_seq):\r\n o1, h1 = self.encoder(i1_seq) # o1:(1, seq, dim*2) hi:(1,1,dim*2)\r\n\r\n ej1 = self.alpha_net(o1) # (1, seq, 1)\r\n bj1 = self.beta_net(o1) # (1, seq, dim)\r\n att_ej1 = torch.softmax(ej1, dim=1)\r\n o1 = (att_ej1 * torch.tanh(bj1) * i1_seq).sum(1) # (1, dim)\r\n\r\n return o1\r\n\r\n\r\nclass CEHMRNet(nn.Module):\r\n def __init__(self,\r\n vocab_size,\r\n emb_dim=64,\r\n device=torch.device('cpu:0'),\r\n ):\r\n r\"\"\"\r\n The designed network in CEHMRNet framework.\r\n :param vocab_size: list, including sizes of diagnosis, procedures and medications.\r\n :param emb_dim: embedding dimension\r\n :param device: the device where program runs\r\n \"\"\"\r\n super(CEHMRNet, self).__init__()\r\n K = len(vocab_size)\r\n self.K = K\r\n self.vocab_size = vocab_size\r\n self.device = device\r\n # Embedding layers\r\n self.embeddings = nn.ModuleList(\r\n [nn.Embedding(vocab_size[i], emb_dim) for i in range(2)])\r\n\r\n # Temporal representation\r\n self.retain = nn.ModuleList([RETAIN(emb_dim) for _ in range(2)])\r\n # Hierarchical classifiers\r\n voc_size = [vocab_size[4], vocab_size[3], vocab_size[2]]\r\n self.hmnc_f = HMCN(64*4, voc_size)\r\n self.dropout = nn.Dropout(p=0.4)\r\n\r\n def forward(self, input):\r\n\r\n def mean_embedding(embedding):\r\n return embedding.mean(dim=1).unsqueeze(dim=0) # (1,1,dim)\r\n if len(input) == 0:\r\n raise Exception(\"Input error!\")\r\n\r\n i1_seq = []\r\n i2_seq = []\r\n for adm in input:\r\n i1_emb = self.dropout(self.embeddings[0](torch.LongTensor(adm[0]).unsqueeze(dim=0).to(self.device)))\r\n i2_emb = self.dropout(self.embeddings[1](torch.LongTensor(adm[1]).unsqueeze(dim=0).to(self.device)))\r\n i1_seq.append(mean_embedding(i1_emb))\r\n i2_seq.append(mean_embedding(i2_emb))\r\n i1_seq = torch.cat(list(reversed(i1_seq)), dim=1) # (1, seq, dim)\r\n i2_seq = torch.cat(list(reversed(i2_seq)), dim=1) # (1, seq, dim)\r\n\r\n o1 = self.retain[0](i1_seq)\r\n o2 = self.retain[1](i2_seq)\r\n\r\n patient_representations = torch.cat([o1, o2], dim=-1) # (seq, dim*2)\r\n\r\n query = patient_representations\r\n\r\n feat = torch.cat([i1_emb.squeeze(0).mean(0).unsqueeze(0), i2_emb.squeeze(0).mean(0).unsqueeze(0), query], dim=-1)\r\n output = self.hmnc_f(feat)\r\n\r\n return output\r\n\r\n","repo_name":"siomkos2020/CEHMR","sub_path":"models/cehmr_net.py","file_name":"cehmr_net.py","file_ext":"py","file_size_in_byte":4520,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"19954778526","text":"'''\n题目描述:\n给定两个单词 word1 和 word2，找到使得 word1 和 word2 相同所需的最小步数，\n每步可以删除任意一个字符串中的一个字符。\n'''\n'''\n解题思路:\n转换为求两个字符串的最长公共子序列问题。\nans = m+n -2*dp[m][n]\n'''\n\n\nclass Solution:\n def minDistance(self, word1: str, word2: str) -> int:\n m = len(word1)\n n = len(word2)\n\n dp = [[0]*(n+1) for _ in range(m+1)]\n\n for i in range(1,m+1):\n for j in range(1, n+1):\n\n if word1[i-1] == word2[j-1]:\n dp[i][j] = dp[i - 1][j - 1] + 1\n else:\n dp[i][j] = max(dp[i-1][j], dp[i][j-1])\n\n return m+n-2*dp[m][n]","repo_name":"JTShuai/LeetCode_Notes","sub_path":"动态规划/583_两个字符串的删除操作.py","file_name":"583_两个字符串的删除操作.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16787171721","text":"import streamlit as st\nimport query_params as qp\n\n# Reset query params; the query params will update throughout the app as qp.update() is called\nqp.reset()\n\n\n# ----- START: MAIN APP -----\n\n# Display the title\nst.markdown('# Query Parameters Demo')\nst.markdown('## Inputs')\n\n\n# Get query params\nquery_params = qp.get_helper(['color_enabled', 'color_index', 'pet_index', 'size_index'], default=0)\n\n# Create a pet selectbox\npets = ['dog', 'cat', 'fish', 'hamster']\npet = st.selectbox('Pet', options=pets, index=query_params['pet_index'], key='pet_select')\n\n# Create a size radio\nenable_options = [False, True]\ncolor_enabled = st.radio('Enable color?', enable_options, index=query_params['color_enabled'], key='color_enable_radio')\n\n# Toggle color selection on and off\n# If color is not enabled, then the query won't appear in the URL. However, it will be remembered in the state!\nif color_enabled:\n # Create a color selectbox\n colors = ['red', 'orange', 'yellow', 'green', 'blue', 'purple']\n color = st.selectbox('Color', options=colors, index=query_params['color_index'], key='color_select')\n\n # Update query params\n qp.update(color_index=colors.index(color))\n\n\n# Create a size radio\nsizes = ['small', 'big']\nsize = st.radio('Text Size', sizes, index=query_params['size_index'], key='size_radio')\n\n# Create the text\nif color_enabled:\n text = 'Hello {} {}!'.format(color, pet)\nelse:\n text = 'Hello {}!'.format(pet)\nif size == 'big':\n text = '## ' + text\n\n# Display the text\nst.markdown('## Result')\nst.markdown(text)\n\n# Update query params\nqp.update(color_enabled=enable_options.index(color_enabled), pet_index=pets.index(pet), size_index=sizes.index(size))\n\n\n# ----- END: MAIN APP -----\n\n\n# Set query params; this actually updates the URL\nqp.set()\n","repo_name":"kmcentush/streamlit_libraries","sub_path":"query_params/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32223091119","text":"\"\"\"exercicio006.py em 2018-09-29. Projeto Practice Python.\n\nPeça ao usuário uma string e imprima se essa string é um palíndromo ou não.\n(Um palíndromo é uma string que se lê de trás para frente e de frente para trás.)\n\n\"\"\"\nfrom cicero import cabecalho\n\n\ndef palindromo() -> object:\n \"\"\"Verifica se o inverso da palavra/frase é igual\n\n :return: impressão do resultado\n :rtype: object\n \"\"\"\n palavra = str(input('Digite uma palavras: ')).strip().lower().replace(' ', '')\n if palavra == palavra[::-1]:\n return print(f'A palavra {palavra} é um palíndromo.')\n else:\n return print(f'A palavra {palavra} NÃO é um palíndromo.')\n\n\nif __name__ == '__main__':\n cabecalho('Palíndromo')\n palindromo()\n","repo_name":"cicerohr/Practice_Python","sub_path":"exercicio006.py","file_name":"exercicio006.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"26107892425","text":"from typing import Iterable, List, Sequence, Tuple, Union\n\nfrom fastestimator.backend._concat import concat\nfrom fastestimator.backend._expand_dims import expand_dims\nfrom fastestimator.backend._get_shape import get_shape\nfrom fastestimator.slicer.slicer import Slicer\nfrom fastestimator.types import Tensor\nfrom fastestimator.util.traceability_util import traceable\n\n\n@traceable()\nclass AxisSlicer(Slicer):\n \"\"\"A slicer which cuts along a given axis.\n\n This slicer cuts volumes along the specified axis, reducing the total dimension of the input by 1. For example, if\n you want to feed a batched channel-first 3D volume [B, C, W, H, D] into a 2D model [B, C, W, H] then you could set\n `axis=-1` or `axis=4`.\n\n Args:\n slice: The input key(s) which this Slicer slices. Data which this slicer does not cut will be replicated across\n the resulting minibatches so that the network ops always have access to all of the batch keys.\n unslice: The input key(s) which this Slicer un-slices. By default (empty tuple) the Slicer will un-slice\n whatever keys were specified in `slice`. If you do not want to un-slice those keys, then pass None or\n manually specify the specific key(s) which you would like this slicer to un-slice.\n axis: The axis along which to cut the data\n mode: What mode(s) to invoke this Slicer in. For example, \"train\", \"eval\", \"test\", or \"infer\". To invoke\n regardless of mode, pass None. To invoke in all modes except for a particular one, you can pass an argument\n like \"!infer\" or \"!train\".\n ds_id: What dataset id(s) to invoke this Slicer in. To invoke regardless of ds_id, pass None. To invoke in all\n ds_ids except for a particular one, you can pass an argument like \"!ds1\".\n \"\"\"\n def __init__(self,\n axis: int,\n slice: Union[None, str, Sequence[str]] = None,\n unslice: Union[None, str, Sequence[str]] = (),\n mode: Union[None, str, Iterable[str]] = None,\n ds_id: Union[None, str, Iterable[str]] = None) -> None:\n super().__init__(slice=slice, unslice=unslice, mode=mode, ds_id=ds_id)\n assert isinstance(axis, int), f\"Axis must be an integer, got {type(axis)}\"\n self.axis = axis\n\n def _slice_batch(self, batch: Tensor) -> List[Tensor]:\n shape = get_shape(batch)\n cut_index: List[Union[slice, int]] = [slice(None) for _ in range(len(shape))]\n slices = []\n for i in range(0, shape[self.axis]):\n cut_index[self.axis] = i\n slices.append(batch[cut_index])\n return slices\n\n def _unslice_batch(self, slices: Tuple[Tensor, ...], key: str) -> Tensor:\n expanded = [expand_dims(elem, self.axis) for elem in slices]\n return concat(expanded, axis=self.axis)\n","repo_name":"fastestimator/fastestimator","sub_path":"fastestimator/slicer/axis_slicer.py","file_name":"axis_slicer.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"31"} +{"seq_id":"36687659739","text":"# -*- coding:utf-8 -*-\nfrom django.shortcuts import render\nfrom models import User,Article\nimport redisC\n\ndef hello(request):\n redis = redisC.rcon()\n context = {}\n status = request.GET.get(\"status\")\n type = request.GET.get(\"type\")\n star = request.GET.get(\"star\")\n sort = request.GET.get(\"sort\")\n if status:\n context['status'] = status\n if type:\n context['type'] = type\n if star:\n context['star'] = star\n if sort:\n context['sort'] = sort\n sortType = None\n if sort == None or sort == 1:\n sortType = 'comment'\n elif sort == 2:\n sortType = 'createTime'\n page = 12\n\n # 登录信息\n if request.session.get(\"user_id\"):\n context['login'] = True\n context['title'] = request.session.get(\"user_title\")\n context['headSrc'] = request.session.get(\"user_head\")\n context['username'] = request.session.get(\"user_name\")\n context['level'] = request.session.get(\"user_level\")\n else:\n if \"username\" in request.COOKIES:\n username = request.COOKIES[\"username\"]\n if username:\n context['username'] = username\n value = User.objects.get(name=username)\n if value:\n context['login'] = True\n context['title'] = value.title\n context['headSrc'] = value.head\n request.session['user_name'] = value.name\n request.session['user_id'] = value.id\n request.session['user_head'] = value.head\n request.session['user_title'] = value.title\n request.session['user_level'] = value.level\n\n # 内容 置顶\n article_list1=Article.objects.filter(star=2)[:5]\n if article_list1:\n context['article_list1'] = article_list1\n # 内容 列表\n article_list2= None\n if type:\n article_list2= Article.objects.filter(type=type).exclude(star=2)[:page]\n elif status:\n article_list2 = Article.objects.filter(status=status).exclude(star=2)[:page]\n elif star:\n article_list2 = Article.objects.filter(star=1)[:page]\n else:\n article_list2 = Article.objects.exclude(star=2).order_by(sortType)[:page]\n if article_list2:\n context['article_list2'] = article_list2\n\n # 回帖周榜\n\n redis.hmget('replyList', \"1\", \"2\", \"3\")\n\n # 本周热议\n\n\n\n return render(request, 'html/index.html', context)\n\n\ndef header(request):\n context = {}\n\n # 登录信息\n if request.session.get(\"user_id\"):\n context['login'] = True\n context['title'] = request.session.get(\"user_title\")\n context['headSrc'] = request.session.get(\"user_head\")\n context['username'] = request.session.get(\"user_name\")\n context['level'] = request.session.get(\"user_level\")\n else:\n if \"username\" in request.COOKIES:\n username = request.COOKIES[\"username\"]\n if username:\n context['username'] = username\n value = User.objects.get(name=username)\n if value:\n context['login'] = True\n context['title'] = value.title\n context['headSrc'] = value.head\n request.session['user_name'] = value.name\n request.session['user_id'] = value.id\n request.session['user_head'] = value.head\n request.session['user_title'] = value.title\n request.session['user_level'] = value.level\n\n return render(request, 'html/common/header.html', context)","repo_name":"lzplzplzp/demo9","sub_path":"demo9/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":3618,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"2188670749","text":"import json\nimport os\n\nfrom features.security import Security\nfrom features.website_manager import WebsiteData\nfrom lib.logger import create_logger\n\nif \"PRE_COMMIT\" in os.environ:\n from integration.features_integration_test import _test_feature\nelse:\n from tests.integration.features_integration_test import _test_feature\n\nsecurity_tags = {\n \"x-frame-options\": [\"same_origin\"],\n \"content-security-policy\": [\"same_origin\"],\n \"x-xss-protection\": [\"1,mode=block\"],\n \"strict-transport-security\": [\"max-age=15768000\"],\n \"referrer-policy\": [\"unsafe-url\"],\n}\n\n\ndef test_start():\n feature = Security\n feature._create_key(feature)\n\n html = {\n \"html\": \"empty_html\",\n \"har\": \"\",\n \"url\": \"\",\n \"headers\": json.dumps(security_tags),\n }\n expected = {\n feature.key: {\n \"values\": [\n \"x-frame-options\",\n \"content-security-policy\",\n \"x-xss-protection\",\n \"strict-transport-security\",\n \"referrer-policy\",\n ],\n \"excluded_values\": [],\n \"runs_within\": 2, # time the evaluation may take AT MAX -> acceptance test}\n }\n }\n\n are_values_correct, runs_fast_enough = _test_feature(\n feature_class=feature, html=html, expectation=expected\n )\n assert are_values_correct and runs_fast_enough\n\n\n\"\"\"\n--------------------------------------------------------------------------------\n\"\"\"\n\n\ndef test_decide():\n _logger = create_logger()\n\n security = Security(_logger)\n\n website_data = WebsiteData()\n website_data.values = [\n \"x-frame-options\",\n \"content-security-policy\",\n \"vary\",\n \"x-xss-protection\",\n \"referrer-policy\",\n ]\n expected_decision = True\n expected_probability = 1.0\n\n security.expected_headers = security_tags\n\n decision, probability = security._decide(website_data=website_data)\n\n assert probability == expected_probability\n assert decision == expected_decision\n","repo_name":"codecentric/metadata_picker","sub_path":"tests/integration/security_test.py","file_name":"security_test.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5148265329","text":"import torch\nimport torch.nn as nn\n\nclass RMSELoss(torch.nn.Module):\n def __init__(self):\n super(RMSELoss,self).__init__()\n\n def forward(self,x,y):\n assert x.size(0) == y.size(0)\n len = x.size(0)\n loss = 0\n for i in range(len):\n loss += (x[i]-y[i])*(x[i]-y[i])\n return loss/len\n\n# 多交叉损失函数\ndef multi_category_focal_loss2(pred, target, gamma=2, alpha=0.25):\n \"\"\"\n :param pred: 预测值\n :param target: 真实值\n :param gamma:\n :param alpha:\n :return:\n \"\"\"\n pred = pred.view(-1, 2)\n target = target.view(-1, 1)\n target = target.type(torch.FloatTensor)\n # target = target.type(torch.cuda.FloatTensor)\n eps = 1e-7\n t = 1.0 - target\n pt = (1.0 - pred) * t + eps\n weight = alpha * t + (1.0 - alpha) * (1.0 - t)\n weight = weight * t + eps\n weight = weight * t + eps\n loss = weight * (torch.pow((1.0 - pt), gamma)) * torch.log(pt)\n loss = -1 * loss.sum(1)\n return loss.mean()\n\n\n\n\nif __name__ == \"__main__\":\n rmse = RMSELoss()\n # x = torch.randint(0,3, (64, 4))\n # y = torch.randn((64, 1))\n x = torch.Tensor([0, 0, 2, 0])\n y = torch.Tensor([0, 1, 0, 0])\n loss = rmse(x, y)\n print(loss)","repo_name":"oranger99/emo_is_all_you_need","sub_path":"rmseloss.py","file_name":"rmseloss.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"31"} +{"seq_id":"8676498753","text":"\"\"\"Script to launch ensemble on test set results.\"\"\"\nimport argparse\nimport json\nfrom collections import defaultdict\nfrom functools import partial\nfrom pathlib import Path\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport pandas as pd\nimport SimpleITK as sitk # noqa: N813\nfrom absl import logging\nfrom omegaconf import OmegaConf\nfrom tqdm import tqdm\n\nfrom imgx.datasets import (\n DIR_TFDS_PROCESSED_MAP,\n IMAGE_SPACING_MAP,\n NUM_CLASSES_MAP,\n)\nfrom imgx.exp.eval import (\n get_jit_segmentation_metrics,\n get_non_jit_segmentation_metrics_per_step,\n)\n\nlogging.set_verbosity(logging.INFO)\n\n\ndef parse_args() -> argparse.Namespace:\n \"\"\"Parse arguments.\"\"\"\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--log_dir\",\n type=Path,\n help=\"Folder of wandb.\",\n default=None,\n )\n args = parser.parse_args()\n\n return args\n\n\ndef vote_ensemble(test_dir: Path, dir_tfds: Path, num_classes: int) -> None:\n \"\"\"Ensemble prediction via voting.\n\n Args:\n test_dir: path having predictions.\n dir_tfds: path of tfds data, having ground truth.\n num_classes: number of classes in labels.\n \"\"\"\n # get seed dirs and sort by seeds\n lst_seed_dir = sorted(\n test_dir.glob(\"seed_*/\"), key=lambda x: int(x.stem.split(\"_\")[-1])\n )\n num_seeds = len(lst_seed_dir)\n\n # map relative_path to list of full path, corresponding to seeds\n path_dict = defaultdict(list)\n for seed_dir in lst_seed_dir:\n for x in seed_dir.glob(\"**/*.nii.gz\"):\n rel_path = x.relative_to(seed_dir)\n path_dict[rel_path].append(x)\n\n # vote to ensemble\n logging.info(\"Calculating ensemble predictions.\")\n for rel_path, pred_paths in path_dict.items():\n # a list of shape (D, W, H)\n mask_preds = [\n sitk.GetArrayFromImage(sitk.ReadImage(x)) for x in pred_paths\n ]\n # (D, W, H, num_classes, num_seeds)\n mask_onehot = jax.nn.one_hot(\n jnp.stack(mask_preds, axis=-1), num_classes=num_classes, axis=-2\n )\n # vote (D, W, H)\n mask_pred = jnp.argmax(jnp.sum(mask_onehot, axis=-1), axis=-1).astype(\n \"uint16\"\n )\n\n # copy meta data\n uid = pred_paths[0].stem.split(\"_\")[0]\n volume_mask_true = sitk.ReadImage(\n dir_tfds / f\"{uid}_mask_preprocessed.nii.gz\"\n )\n volume_mask_pred = sitk.GetImageFromArray(mask_pred)\n volume_mask_pred.CopyInformation(volume_mask_true)\n\n # save\n out_path = test_dir / f\"ensemble_{num_seeds}\" / rel_path\n out_path.parent.mkdir(parents=True, exist_ok=True)\n sitk.WriteImage(\n image=volume_mask_pred,\n fileName=out_path,\n useCompression=True,\n )\n\n\ndef evaluate_ensemble_prediction(\n dir_path: Path, dir_tfds: Path, num_classes: int, spacing: jnp.ndarray\n) -> None:\n \"\"\"Evaluate the saved predictions from ensemble.\n\n Args:\n dir_path: path having predictions.\n dir_tfds: path of tfds data, having ground truth.\n num_classes: number of classes in labels.\n spacing: spacing for voxels.\n \"\"\"\n num_steps = int(dir_path.name.split(\"_\")[1])\n uids = [\n x.name.split(\"_\")[0] for x in (dir_path / \"step_0\").glob(\"*.nii.gz\")\n ]\n lst_df_scalar = []\n for uid in tqdm(uids, total=len(uids)):\n # (D, W, H)\n mask_true = sitk.GetArrayFromImage(\n sitk.ReadImage(dir_tfds / f\"{uid}_mask_preprocessed.nii.gz\")\n )\n # (D, W, H, num_classes)\n mask_true = jax.nn.one_hot(mask_true, num_classes=num_classes, axis=-1)\n # (1, W, H, D, num_classes)\n mask_true = jnp.transpose(mask_true, axes=(2, 1, 0, 3))[None, ...]\n\n pred_paths = [\n dir_path / f\"step_{i}\" / f\"{uid}_mask_pred.nii.gz\"\n for i in range(num_steps)\n ]\n # a list of shape (D, W, H)\n mask_preds = [\n sitk.GetArrayFromImage(sitk.ReadImage(x)) for x in pred_paths\n ]\n # (D, W, H, num_classes, num_steps)\n mask_pred = jax.nn.one_hot(\n jnp.stack(mask_preds, axis=-1), num_classes=num_classes, axis=-2\n )\n # (1, W, H, D, num_classes, num_steps)\n mask_pred = jnp.transpose(mask_pred, axes=(2, 1, 0, 3, 4))[None, ...]\n\n # metrics\n scalars_jit = jax.vmap(\n partial(\n get_jit_segmentation_metrics,\n mask_true=mask_true,\n spacing=spacing,\n ),\n in_axes=-1,\n out_axes=-1,\n )(mask_pred)\n scalars_nonjit = get_non_jit_segmentation_metrics_per_step(\n mask_pred=mask_pred,\n mask_true=mask_true,\n spacing=spacing,\n )\n scalars = {**scalars_jit, **scalars_nonjit}\n\n # flatten per step\n scalars_flatten = {}\n for k, v in scalars.items():\n for i in range(v.shape[-1]):\n scalars_flatten[f\"{k}_step_{i}\"] = v[..., i]\n scalars_flatten[k] = v[..., -1]\n scalars = scalars_flatten\n scalars = jax.tree_map(lambda x: np.asarray(x).tolist(), scalars)\n scalars[\"uid\"] = [uid]\n lst_df_scalar.append(pd.DataFrame(scalars))\n\n # assemble metrics\n df_scalar = pd.concat(lst_df_scalar)\n df_scalar = df_scalar.sort_values(\"uid\")\n df_scalar.to_csv(dir_path / \"metrics_per_sample.csv\", index=False)\n\n # average over samples in the dataset\n scalars = df_scalar.drop(\"uid\", axis=1).mean().to_dict()\n scalars = {\"test_\" + k: v for k, v in scalars.items()}\n scalars[\"num_images_in_total\"] = len(df_scalar)\n with open(dir_path / \"mean_metrics.json\", \"w\", encoding=\"utf-8\") as f:\n json.dump(scalars, f, sort_keys=True, indent=4)\n\n\ndef main() -> None: # pylint:disable=R0915\n \"\"\"Main function.\"\"\"\n args = parse_args()\n\n config = OmegaConf.load(args.log_dir / \"files\" / \"config_backup.yaml\")\n if config.task.name != \"diffusion\":\n raise ValueError(\"Ensemble is only for diffusion.\")\n\n data_config = config.data\n dir_tfds = DIR_TFDS_PROCESSED_MAP[data_config.name]\n spacing = jnp.array(IMAGE_SPACING_MAP[data_config.name])\n num_classes = NUM_CLASSES_MAP[data_config[\"name\"]]\n\n test_dir = args.log_dir / \"files\" / \"test_evaluation\"\n\n # no ensemble if 1 seed only\n lst_seed_dir = sorted(\n test_dir.glob(\"seed_*/\"), key=lambda x: int(x.stem.split(\"_\")[-1])\n )\n if len(lst_seed_dir) == 1:\n logging.info(\"Ensemble not performed as there is one seed only.\")\n return\n\n # ensemble\n vote_ensemble(test_dir=test_dir, dir_tfds=dir_tfds, num_classes=num_classes)\n # evaluate\n for dir_path in test_dir.glob(\"ensemble_*/sample_*_steps\"):\n logging.info(f\"Evaluating ensemble predictions metrics for {dir_path}.\")\n evaluate_ensemble_prediction(\n dir_path=dir_path,\n dir_tfds=dir_tfds,\n num_classes=num_classes,\n spacing=spacing,\n )\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"mathpluscode/ImgX-DiffSeg","sub_path":"imgx/run_test_ensemble.py","file_name":"run_test_ensemble.py","file_ext":"py","file_size_in_byte":7072,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"31"} +{"seq_id":"30704742141","text":"import numpy as np\nimport pandas as pd\nimport os\nimport copy\nimport math\nfrom scipy import integrate \n\n\nbasepath=os.path.abspath(os.path.dirname(__file__))\npath=basepath+'/HWData3.txt'\n\na=np.loadtxt(path)\nA=np.mat(a)\n\n\nrow=A.shape[0]\ncol=A.shape[1]\nesp=0.000001\ncorrect=0\n\n\n\ndef cal_para(x):\n mean=np.zeros((1,col-1))\n for i in range(col-1):\n mean[0,i]=np.mean(x[:,i])\n \n cov=np.cov(x.T)\n return mean,cov\n\n\n\n\n\n\n\ndef cal_density(x1,x2,x3,x4,mean,cov,n=4):\n #求概率密度\n x=np.zeros((1,col-1))\n x[0,0]=x1\n x[0,1]=x2\n x[0,2]=x3\n x[0,3]=x4\n det_cov=np.linalg.det(cov)\n cov_=np.linalg.inv(cov)\n para=1/(pow((2*np.pi),n/2)*pow(det_cov,0.5))\n exponent=-0.5*(x-mean).dot(cov_).dot((x-mean).T)\n\n return para*pow(np.e,(exponent[0,0]))\n \n\n\ndef cal_probability(mean,x,cov,n=4):\n #根据概率密度求积分的概率\n #积分下限\n a1=x[0,0]-cov[0,0]\n a2=x[0,1]-cov[1,1]\n a3=x[0,2]-cov[2,2]\n a4=x[0,3]-cov[3,3]\n #积分下限\n b1=x[0,0]+cov[0,0]\n b2=x[0,1]+cov[1,1]\n b3=x[0,2]+cov[2,2]\n b4=x[0,3]+cov[3,3]\n\n return cal_density(x[0,0],x[0,1],x[0,2],x[0,3],mean,cov)\n \n probability=integrate.nquad(cal_density,[[a1,b1],[a2,b2],[a3,b3],[a4,b4]],args=(mean,cov))\n return probability[0] \n\n\n\n\nfor t in range(5):\n #五折交叉检验\n\n X1=A[0:50,0:4].copy()\n X2=A[50:100,0:4].copy()\n X3=A[100:150,0:4].copy()\n\n X1=np.delete(X1,[10*t,10*t+1,10*t+2,10*t+3,10*t+4,10*t+5,10*t+6,10*t+7,10*t+8,10*t+9], 0)\n X2=np.delete(X2,[10*t,10*t+1,10*t+2,10*t+3,10*t+4,10*t+5,10*t+6,10*t+7,10*t+8,10*t+9], 0)\n X3=np.delete(X3,[10*t,10*t+1,10*t+2,10*t+3,10*t+4,10*t+5,10*t+6,10*t+7,10*t+8,10*t+9], 0)\n\n \n test1=A[t*10:t*10+10,0:4].copy()\n test2=A[50+t*10:60+t*10,0:4].copy()\n test3=A[100+t*10:110+t*10,0:4].copy()\n\n \n\n mean1,cov1=cal_para(X1)\n mean2,cov2=cal_para(X2)\n mean3,cov3=cal_para(X3)\n\n\n\n #测试\n \n\n for test in test1:\n p1=cal_probability(mean1,test,cov1)\n p2=cal_probability(mean2,test,cov2)\n p3=cal_probability(mean3,test,cov3)\n print(p1,p2,p3)\n if(p1>p2)and(p1>p3):\n correct=correct+1\n \n for test in test2:\n p1=cal_probability(mean1,test,cov1)\n p2=cal_probability(mean2,test,cov2)\n p3=cal_probability(mean3,test,cov3)\n print(p1,p2,p3)\n if(p2>p1)and(p2>p3):\n correct=correct+1\n\n \n for test in test3:\n p1=cal_probability(mean1,test,cov1)\n p2=cal_probability(mean2,test,cov2)\n p3=cal_probability(mean3,test,cov3)\n print(p1,p2,p3)\n if(p3>p2)and(p3>p1):\n correct=correct+1\n \n\n\nprint(correct/150)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n","repo_name":"HollySwift17/Machine-Learning-practice","sub_path":"Parameter estimation &Non-parametric estimation/ML.py","file_name":"ML.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"33348560050","text":"# -*- coding: utf-8 -*-\r\n\"\"\" \r\n@Time    : 2021/7/13 14:53\r\n@Author  : HCF\r\n@FileName: dataload.py\r\n@SoftWare: PyCharm\r\n\"\"\"\r\nimport numpy as np\r\nimport os.path as osp\r\nfrom PIL import Image\r\nimport random\r\nfrom paddle.io import Dataset\r\nimport os\r\nimport h5py\r\nimport cv2\r\nimport xml.etree.ElementTree as et\r\nfrom mesh_to_sdf import mesh_to_sdf\r\nimport trimesh\r\n\r\n#导入环境图 mesh to sdf 相机参数\r\nclass BatchLoaderMyreal(Dataset):\r\n def __init__(self, dataRoot, shapeRoot = None,\r\n imHeight = 360, imWidth = 480,\r\n envHeight = 256, envWidth = 512,\r\n isRandom=False, phase='TRAIN', rseed = 1,\r\n isLoadCam = False, isLoadEnvmap = False, tag=None, classNum=12,\r\n camNum = 10, shapeRs = 0, shapeRe = 1500, volumeSize=32, batchSize = None, isOptim = False, ignore = [],\r\n isLoadSDF = True, grid_res = 8, bounding_radius = 1.1):\r\n\r\n self.dataRoot = dataRoot\r\n self.shapeRoot = shapeRoot\r\n self.imHeight = imHeight\r\n self.imWidth = imWidth\r\n self.envHeight = envHeight\r\n self.envWidth = envWidth\r\n self.phase = phase.upper()\r\n self.isLoadCam = isLoadCam\r\n self.isLoadEnvmap = isLoadEnvmap\r\n self.camNum = camNum\r\n self.shapeRs = shapeRs\r\n self.shapeRe = shapeRe\r\n self.isLoadSDF = isLoadSDF\r\n self.grid_res = grid_res\r\n self.bounding_radius = bounding_radius\r\n self.tag = tag\r\n self.classNum = classNum\r\n\r\n if batchSize is None:\r\n batchSize = camNum\r\n self.batchSize = min(batchSize , 10)\r\n else:\r\n self.batchSize = batchSize\r\n\r\n # self.minX, self.maxX = -1.1, 1.1\r\n # self.minY, self.maxY = -1.1, 1.1\r\n # self.minZ, self.maxZ = -1.1, 1.1\r\n # self.volumeSize = volumeSize\r\n # y, x, z = np.meshgrid(\r\n # np.linspace(self.minX, self.maxX, volumeSize),\r\n # np.linspace(self.minY, self.maxY, volumeSize),\r\n # np.linspace(self.minZ, self.maxZ, volumeSize))\r\n # x = x[:, :, :, np.newaxis ]\r\n # y = y[:, :, :, np.newaxis ]\r\n # z = z[:, :, :, np.newaxis ]\r\n # coord = np.concatenate([x, y, z], axis=3 )\r\n\r\n # shapeList = sorted(glob.glob(osp.join(dataRoot) ))\r\n if isLoadCam:\r\n self.originArr = []\r\n self.lookatArr = []\r\n self.upArr = []\r\n for n in range(max(0, shapeRs ), min(classNum, shapeRe) ):\r\n if n in ignore:\r\n continue\r\n shape = osp.join(shapeRoot, self.tag )\r\n if not osp.isdir(shape ):\r\n continue\r\n camFileName = osp.join(shape, 'cam%d.txt' % camNum )\r\n with open(camFileName, 'r') as camIn:\r\n camLines = camIn.readlines()\r\n viewNum = int(camLines[0].strip() )\r\n origins = []\r\n lookats = []\r\n ups = []\r\n for n in range(0, viewNum ):\r\n originStr = camLines[3*n+1 ].strip().split(' ')\r\n lookatStr = camLines[3*n+2 ].strip().split(' ')\r\n upStr = camLines[3*n+3 ].strip().split(' ')\r\n\r\n origin = np.array([float(x) for x in originStr ])[np.newaxis, :]\r\n lookat = np.array([float(x) for x in lookatStr ])[np.newaxis, :]\r\n up = np.array([float(x) for x in upStr])[np.newaxis, :]\r\n\r\n origins.append(origin.astype(np.float32 ) )\r\n lookats.append(lookat.astype(np.float32 ) )\r\n ups.append(up.astype(np.float32 ) )\r\n\r\n origins = np.concatenate(origins, axis=0 )\r\n lookats = np.concatenate(lookats, axis=0 )\r\n ups = np.concatenate(ups, axis=0 )\r\n\r\n self.originArr.append(origins )\r\n self.lookatArr.append(lookats )\r\n self.upArr.append(ups )\r\n\r\n if isLoadEnvmap:\r\n self.envList = []\r\n self.scaleList = []\r\n envListUnique = []\r\n for n in range(max(0, shapeRs ), min(classNum, shapeRe ) ):\r\n if n in ignore:\r\n continue\r\n shape = osp.join(shapeRoot, 'Shape__%d' % n )\r\n if not osp.isdir(shape ):\r\n continue\r\n xmlFile = osp.join(shape, 'im.xml')\r\n # Create rendering file for Depth maps\r\n tree = et.parse(xmlFile )\r\n root = tree.getroot()\r\n\r\n shapes = root.findall('emitter')\r\n assert(len(shapes ) == 1 )\r\n for shape in shapes:\r\n strings = shape.findall('string')\r\n assert(len(strings) == 1 )\r\n for st in strings:\r\n envFileName = st.get('value')\r\n\r\n envFileName = envFileName.replace('/home/zhl/CVPR20/TransparentShape','/mnt/data3/lzc/transparent')\r\n if not osp.isfile(envFileName):\r\n print(envFileName)\r\n # if not envFileName.find('1640')==-1:\r\n # print(envFileName)\r\n floats = shape.findall('float')\r\n assert(len(floats) == 1 )\r\n for f in floats:\r\n scale = float(f.get('value') )\r\n self.envList.append(envFileName )\r\n self.scaleList.append(scale )\r\n\r\n if envFileName not in envListUnique:\r\n envListUnique.append(envFileName )\r\n print(\"Number of environment maps %d\" % (len(envListUnique ) ) )\r\n\r\n\r\n\r\n if rseed is not None:\r\n random.seed(rseed)\r\n\r\n # Permute the image list\r\n self.count = camNum\r\n self.perm = list(range(self.count ) )\r\n if isRandom:\r\n random.shuffle(self.perm)\r\n\r\n\r\n\r\n\r\n def __len__(self):\r\n return len(self.perm)\r\n\r\n def __getitem__(self, ind):\r\n # normalize the normal vector so that it will be unit length\r\n origins = []\r\n lookats = []\r\n ups = []\r\n\r\n envs = []\r\n shapeId = ind\r\n batchDict = {}\r\n batchDict['data_path'] = osp.join(self.dataRoot, self.tag)\r\n for imId in self.perm:\r\n if self.isLoadCam:\r\n origin = self.originArr[shapeId ][imId ]\r\n lookat = self.lookatArr[shapeId ][imId ]\r\n up = self.upArr[shapeId ][imId]\r\n\r\n origins.append(origin[np.newaxis, :] )\r\n lookats.append(lookat[np.newaxis, :] )\r\n ups.append(up[np.newaxis, :] )\r\n\r\n if self.isLoadEnvmap:\r\n envFileName = self.envList[shapeId ]\r\n scale = self.scaleList[shapeId ]\r\n env = cv2.imread(envFileName, -1)\r\n if env is None:\r\n print(envFileName)\r\n env = env[:, :, ::-1]\r\n env = cv2.resize(env, (self.envWidth, self.envHeight ), interpolation=cv2.INTER_LINEAR)\r\n env = np.ascontiguousarray(env )\r\n env = env.transpose([2, 0, 1]) * scale\r\n\r\n envs.append(env[np.newaxis, :] )\r\n\r\n\r\n\r\n if self.isLoadCam:\r\n origins = np.concatenate(origins, axis=0 )\r\n lookats = np.concatenate(lookats, axis=0 )\r\n ups = np.concatenate(ups, axis=0 )\r\n\r\n batchDict['origin'] = origins\r\n batchDict['lookat'] = lookats\r\n batchDict['up'] = ups\r\n\r\n if self.isLoadEnvmap:\r\n envs = np.concatenate(envs, axis=0 )\r\n batchDict['env'] = envs\r\n\r\n #读取sdf文件\r\n if self.isLoadSDF:\r\n shapePath = osp.join(self.shapeRoot, \"Shape__%d\" % (shapeId + self.shapeRs))\r\n batchDict['shape_path'] = shapePath\r\n\r\n gt_sdfName = osp.join(shapePath, 'object_sdf_%d.npy'%(self.grid_res))\r\n if osp.isfile(gt_sdfName):\r\n batchDict['gt_grid'] = np.load(gt_sdfName).astype(np.float)\r\n else:\r\n gtName = osp.join(shapePath, 'meshGT_transform.ply')\r\n # gtName = osp.join(shapePath, 'object-1500000.obj')\r\n gtmesh = trimesh.load(gtName)\r\n linear_space = np.linspace(-self.bounding_radius, self.bounding_radius, self.grid_res)\r\n grid_x, grid_y, grid_z = np.meshgrid(linear_space, linear_space, linear_space)\r\n coords = np.stack((grid_x, grid_y, grid_z), axis=3)\r\n query_points = coords.reshape((-1, 3))\r\n gtsdfs = mesh_to_sdf(gtmesh, query_points, surface_point_method='sample', sign_method='normal',\r\n bounding_radius=None, scan_count=100,\r\n scan_resolution=400, sample_point_count=10000000, normal_sample_count=20)\r\n gtsdfs = np.reshape(gtsdfs, grid_x.shape).transpose((1, 0, 2))\r\n batchDict['gt_grid'] = gtsdfs\r\n np.save(gt_sdfName, gtsdfs)\r\n\r\n return batchDict\r\n\r\n def loadHDR(self, imName, scale):\r\n if not osp.isfile(imName ):\r\n print('Error: %s does not exist.' % imName )\r\n assert(False )\r\n image = cv2.imread(imName, -1 )[:, :, ::-1]\r\n image = cv2.resize(image, (self.imWidth, self.imHeight ), interpolation=cv2.INTER_LINEAR)\r\n image = np.ascontiguousarray(image )\r\n imMean = np.mean(image )\r\n\r\n if scale is None:\r\n if self.phase == 'TRAIN':\r\n scale = (np.random.random() * 0.2 + 0.4) / imMean\r\n else:\r\n scale = 0.5 / imMean\r\n image = (image*scale).transpose([2, 0, 1] )\r\n return image, scale\r\n\r\n def loadImage(self, imName, isGama = False):\r\n if not os.path.isfile(imName):\r\n print('Fail to load {0}'.format(imName) )\r\n im = np.zeros([3, self.imSize, self.imSize], dtype=np.float32)\r\n return im\r\n\r\n im = Image.open(imName)\r\n im = self.imResize(im)\r\n im = np.asarray(im, dtype=np.float32)\r\n if isGama:\r\n im = (im / 255.0) ** 2.2\r\n im = 2 * im - 1\r\n else:\r\n im = (im - 127.5) / 127.5\r\n if len(im.shape) == 2:\r\n im = im[:, np.newaxis]\r\n im = np.transpose(im, [2, 0, 1])\r\n return im\r\n\r\n def imResize(self, im):\r\n w0, h0 = im.size\r\n if w0 != self.imHeight or h0 != self.imWidth:\r\n im = im.resize( (self.imWidth, self.imHeight ), Image.ANTIALIAS)\r\n return im","repo_name":"LRussianStand/SDFRecontruction","sub_path":"dataload.py","file_name":"dataload.py","file_ext":"py","file_size_in_byte":10679,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"33041160294","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\"\"\"Utilities.\"\"\"\n\nimport codecs\nimport json\nimport os\nimport os.path\nimport pprint\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass\nfrom datetime import date, datetime\nfrom decimal import Decimal\nfrom io import BytesIO, TextIOWrapper\nfrom typing import Any, Callable, Generator, List, Optional, Tuple, Union\n\nimport yaml\nfrom google.api_core.exceptions import BadRequest, NotFound\nfrom google.cloud import bigquery\n\nQueryParameter = Union[\n bigquery.ArrayQueryParameter,\n bigquery.ScalarQueryParameter,\n bigquery.StructQueryParameter,\n]\n\nTABLE_EXTENSIONS = {\n \"ndjson\": bigquery.SourceFormat.NEWLINE_DELIMITED_JSON,\n \"csv\": bigquery.SourceFormat.CSV,\n \"backup_info\": bigquery.SourceFormat.DATASTORE_BACKUP,\n \"export_metadata\": bigquery.SourceFormat.DATASTORE_BACKUP,\n \"avro\": bigquery.SourceFormat.AVRO,\n \"parquet\": bigquery.SourceFormat.PARQUET,\n \"orc\": bigquery.SourceFormat.ORC,\n}\n\n\n@dataclass\nclass Table:\n \"\"\"Define info needed to create a table for a generated test.\"\"\"\n\n name: str\n source_format: str\n # a tuple means read via `load(*source_path)` and format as source_format\n # a string means source_path is already in source_format\n source_path: Union[str, Tuple[str, str]]\n # post_init fields\n schema: Optional[List[bigquery.SchemaField]] = None\n\n def __post_init__(self):\n \"\"\"Fill in calculated fields if not provided.\"\"\"\n if self.schema is None:\n if isinstance(self.source_path, str):\n resource_dir, resource = os.path.split(self.source_path)\n full_name, _ = resource.rsplit(\".\", 1)\n else:\n resource_dir, full_name = self.source_path\n try:\n table_dir, _ = os.path.split(resource_dir)\n self.schema = [\n bigquery.SchemaField.from_api_repr(field)\n for field in load(table_dir, f\"{full_name}.schema\")\n ]\n except FileNotFoundError:\n pass\n\n\nclass NDJsonDecodeError(Exception):\n \"\"\"ndjson decode error.\"\"\"\n\n pass\n\n\nclass JsonDecodeError(Exception):\n \"\"\"JSON decode error.\"\"\"\n\n pass\n\n\n@contextmanager\ndef dataset(bq: bigquery.Client, dataset_id: str):\n \"\"\"Context manager for creating and deleting the BigQuery dataset for a test.\"\"\"\n try:\n result = bq.get_dataset(dataset_id)\n except NotFound:\n result = bq.create_dataset(dataset_id)\n try:\n yield result.reference\n finally:\n bq.delete_dataset(dataset_id, delete_contents=True)\n\n\ndef default_encoding(obj):\n \"\"\"Add custom logic for serializing rows into JSON for BigQuery.\"\"\"\n if isinstance(obj, date) or isinstance(obj, datetime):\n return obj.isoformat()\n return obj\n\n\ndef load_table(bq: bigquery.Client, dataset: bigquery.Dataset, table: Table):\n \"\"\"Load table for a test.\"\"\"\n destination = dataset.table(table.name)\n job_config = bigquery.LoadJobConfig(\n source_format=table.source_format,\n write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE,\n )\n\n if table.schema is None:\n # autodetect schema if not provided\n job_config.autodetect = True\n else:\n job_config.schema = table.schema\n # look for time_partitioning_field in provided schema\n for field in job_config.schema:\n if field.description == \"time_partitioning_field\":\n job_config.time_partitioning = bigquery.TimePartitioning(\n field=field.name\n )\n break # stop because there can only be one time partitioning field\n\n if isinstance(table.source_path, str):\n with open(table.source_path, \"rb\") as file_obj:\n job = bq.load_table_from_file(file_obj, destination, job_config=job_config)\n else:\n mem_file = BytesIO()\n for row in load(*table.source_path):\n mem_file.write(json.dumps(row, default=default_encoding).encode() + b\"\\n\")\n mem_file.seek(0)\n job = bq.load_table_from_file(mem_file, destination, job_config=job_config)\n\n try:\n job.result()\n except BadRequest:\n # print the first 5 rows for debugging errors\n for row in job.errors[:5]:\n print(row)\n raise\n\n\ndef load_view(\n bq: bigquery.Client, dataset: bigquery.Dataset, view_name: str, view_query: str\n):\n \"\"\"Load view for a test.\"\"\"\n view = bigquery.Table(dataset.table(view_name))\n view.view_query = view_query.format(\n project=dataset.project, dataset=dataset.dataset_id\n )\n bq.create_table(view)\n\n\ndef read(*paths: str, decoder: Optional[Callable] = None, **kwargs):\n \"\"\"Read a file and apply decoder if provided.\"\"\"\n with open(os.path.join(*paths), **kwargs) as f:\n return decoder(f) if decoder else f.read()\n\n\ndef ndjson_load(file_obj: TextIOWrapper) -> List[Any]:\n \"\"\"Decode newline delimited json from file_obj.\"\"\"\n res = []\n for i, line in enumerate(file_obj):\n try:\n res.append(json.loads(line))\n except json.JSONDecodeError as e:\n raise NDJsonDecodeError(\n f\"Line {i+1} column {e.colno} of file {file_obj.name}, {e.msg}\"\n )\n\n return res\n\n\ndef json_load(file_obj: TextIOWrapper) -> List[Any]:\n \"\"\"Decode json from file_obj.\"\"\"\n try:\n return json.load(file_obj)\n except json.JSONDecodeError as e:\n raise JsonDecodeError(\n f\"Line {e.lineno} column {e.colno} of file {file_obj.name}, {e.msg}\"\n )\n\n\ndef load(resource_dir: str, *basenames: str, **search: Optional[Callable]) -> Any:\n \"\"\"Read the first matching file found in resource_dir.\n\n Calls read on paths under resource_dir with a name sans extension in\n basenames and an extension and decoder in search.\n\n :param resource_dir: directory to check for files\n :param basenames: file names to look for, without an extension\n :param search: mapping of file extension to decoder\n :return: first response from read() that doesn't raise FileNotFoundError\n :raises FileNotFoundError: when all matching files raise FileNotFoundError\n \"\"\"\n search = search or {\n \"yaml\": yaml.full_load,\n \"json\": json_load,\n \"ndjson\": ndjson_load,\n }\n not_found: List[str] = []\n for basename in basenames:\n for ext, decoder in search.items():\n try:\n return read(resource_dir, f\"{basename}.{ext}\", decoder=decoder)\n except FileNotFoundError:\n not_found.append(f\"{basename}.{ext}\")\n raise FileNotFoundError(f\"[Errno 2] No such files in '{resource_dir}': {not_found}\")\n\n\ndef get_query_params(resource_dir: str) -> Generator[QueryParameter, None, None]:\n \"\"\"Attempt to load the first query params found in resource_dir.\"\"\"\n try:\n params = load(resource_dir, \"query_params\")\n except FileNotFoundError:\n params = []\n for param in params:\n if {\"name\", \"type\", \"type_\", \"value\"}.issuperset(param.keys()):\n # this is a scalar query param\n param[\"type_\"] = param.pop(\"type\", param.pop(\"type_\", \"STRING\"))\n yield bigquery.ScalarQueryParameter(**param)\n else:\n # attempt to coerce to some type of query param\n try:\n yield bigquery.StructQueryParameter.from_api_repr(param)\n except KeyError:\n try:\n yield bigquery.ArrayQueryParameter.from_api_repr(param)\n except KeyError:\n # this is a different format for scalar param than above\n yield bigquery.ScalarQueryParameter.from_api_repr(param)\n\n\ndef coerce_result(*elements: Any) -> Generator[Any, None, None]:\n \"\"\"Recursively coerce elements to types available in json.\n\n Coerce date and datetime to string using isoformat.\n Coerce bigquery.Row to dict using comprehensions.\n Coerce bytes to base64 encoded strings.\n Omit dict keys named \"generated_time\".\n Omit columns with null results to simplify `expect` files.\n \"\"\"\n for element in elements:\n if isinstance(element, (dict, bigquery.Row)):\n yield {\n key: (\n list(coerce_result(*value))\n if isinstance(value, list)\n else next(coerce_result(value))\n )\n for key, value in element.items()\n # drop generated_time column\n if key not in (\"generated_time\",) and value is not None\n }\n elif isinstance(element, (date, datetime)):\n yield element.isoformat()\n elif isinstance(element, Decimal):\n yield str(element)\n elif isinstance(element, bytes):\n yield codecs.encode(element, \"base64\").decode().strip()\n else:\n yield element\n\n\ndef get_differences(expected, result, path=\"\", sep=\".\"):\n \"\"\"Get the differences between two JSON-like python objects.\n\n For complicated objects, this is a big improvement over pytest -vv\n \"\"\"\n differences = []\n\n if expected is not None and result is None:\n differences.append((\"Expected exists but not Result\", path))\n if expected is None and result is not None:\n differences.append((\"Result exists but not Expected\", path))\n if expected is None and result is None:\n return differences\n\n exp_is_dict, res_is_dict = isinstance(expected, dict), isinstance(result, dict)\n exp_is_list, res_is_list = isinstance(expected, list), isinstance(result, list)\n if exp_is_dict and not res_is_dict:\n differences.append((\"Expected is dict but not Result\", path))\n elif res_is_dict and not exp_is_dict:\n differences.append((\"Result is dict but not Expected\", path))\n elif not exp_is_dict and not res_is_dict:\n if exp_is_list and res_is_list:\n for i in range(max(len(expected), len(result))):\n if i >= len(result):\n differences.append(\n (f\"Result missing element {expected[i]}\", path + sep + str(i))\n )\n elif i >= len(expected):\n differences.append(\n (\n f\"Result contains extra element {result[i]}\",\n path + sep + str(i),\n )\n )\n else:\n differences += get_differences(\n expected[i], result[i], path + sep + str(i)\n )\n elif expected != result:\n differences.append((f\"Expected={expected}, Result={result}\", path))\n else:\n exp_keys, res_keys = set(expected.keys()), set(result.keys())\n in_exp_not_res, in_res_not_exp = exp_keys - res_keys, res_keys - exp_keys\n\n for k in in_exp_not_res:\n differences.append((\"In Expected, not in Result\", path + sep + k))\n for k in in_res_not_exp:\n differences.append((\"In Result, not in Expected\", path + sep + k))\n\n for k in exp_keys & res_keys:\n differences += get_differences(expected[k], result[k], path + sep + k)\n\n return differences\n\n\ndef print_and_test(expected, result):\n \"\"\"Print objects and differences, then test equality.\"\"\"\n pp = pprint.PrettyPrinter(indent=2)\n\n print(\"\\nExpected:\")\n pp.pprint(expected)\n\n print(\"\\nActual:\")\n pp.pprint(result)\n\n print(\"\\nDifferences:\")\n print(\"\\n\".join([\" - \".join(v) for v in get_differences(expected, result)]))\n\n assert result == expected\n","repo_name":"mozilla/bigquery-etl","sub_path":"bigquery_etl/pytest_plugin/sql_test.py","file_name":"sql_test.py","file_ext":"py","file_size_in_byte":11769,"program_lang":"python","lang":"en","doc_type":"code","stars":210,"dataset":"github-code","pt":"31"} +{"seq_id":"73342980247","text":"import pytest\nimport tempfile\nimport pandas as pd\n\nfrom indico_toolkit.metrics import CompareModels\n\n\ndef test_get_data_df(extraction_model_group_id, extraction_model_id, indico_client):\n comp = CompareModels(\n indico_client,\n extraction_model_group_id,\n extraction_model_id,\n extraction_model_group_id,\n extraction_model_id,\n )\n comp.get_data()\n assert isinstance(comp.df, pd.DataFrame)\n assert comp.df.shape[0] > 0\n assert len(comp.non_overlapping_fields) == 0\n\n\n@pytest.fixture(scope=\"module\")\ndef compare_obj(indico_client):\n model_1 = pd.DataFrame()\n model_1[\"precision_1\"] = [0.5, 0.5, 0.5]\n model_1[\"f1Score_1\"] = [0.5, 0.5, 0.5]\n model_1[\"field_name\"] = [\"a\", \"b\", \"c\"]\n model_2 = pd.DataFrame()\n model_2[\"precision_2\"] = [1, 1, 1]\n model_2[\"f1Score_2\"] = [1, 1, 1]\n model_2[\"field_name\"] = [\"a\", \"b\", \"c\"]\n comp = CompareModels(indico_client, 1, 1, 1, 2)\n comp.df = pd.merge(model_1, model_2, on=\"field_name\")\n comp.overlapping_fields = set([\"a\", \"b\", \"c\"])\n return comp\n\n\ndef test_get_metric_differences(compare_obj):\n df = compare_obj.get_metric_differences()\n assert df.shape[1] == 4\n assert set(df.columns) == set(\n [\"f1Score_1\", \"f1Score_2\", \"field_name\", \"difference\"]\n )\n for val in df[\"difference\"]:\n assert val == 0.5\n","repo_name":"IndicoDataSolutions/Indico-Solutions-Toolkit","sub_path":"tests/metrics/test_compare_models.py","file_name":"test_compare_models.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"31"} +{"seq_id":"9537487136","text":"#!/usr/bin/env python\n\nimport os\nimport re\n\n# Type conversion\ncutypes = {\"integer\":\"Int32\", \"doublereal\":\"Float64\", \"logical\":\"Int32\",\n \"char\":\"Uint8\"}\n\ncutest = os.getenv('CUTEST', \"\")\nif cutest is \"\":\n print(\"ERROR: Variable CUTEST not set. Verify your CUTEst installation.\")\n exit(1)\n\n# The function definitions are being read from the C include file.\n# This should probably be improved to obtain more information about the\n# functions, but this is easier.\nfunctions = []\nwith open(cutest+\"/include/cutest.h\") as file:\n # Each new function starts with\n # void CUTEST_xxx\n start_function = False\n for line in file:\n if start_function:\n function += \" \" + line.strip()\n if \"void CUTEST\" in line:\n start_function = True\n function = line.strip()\n if start_function and \";\" in line:\n start_function = False\n functions.append(function)\n\nprint(\"const libname = \\\"libCUTEstJL.so\\\"\\n\") \ns=\" \"\nfor function in functions:\n # Get function name\n name = re.search('CUTEST_([a-z]*)', function).group(1)\n # Get function arguments\n args = re.search('\\((.*)\\)', function).group(1).split(',')\n matches = [re.search('([a-z]*) \\*(.*)', arg.strip()) for arg in args]\n # Create a list of [type,variable name]\n vars = [[m.group(1),m.group(2)] for m in matches]\n\n print(\"function \"+name+\" (\"+', '.join([v[1] for v in vars])+\")\")\n print(s+\"ccall((\\\"cutest_\"+name+\"_\\\", libname), Void,\")\n if len(vars) == 1:\n print(s+s+\"(Ptr{\"+cutypes[vars[0][0]]+\"},),\")\n else:\n print(s+s+\"(\"+', '.join([\"Ptr{\"+cutypes[v[0]]+\"}\" for v in vars])+\"),\")\n print(s+s+', '.join([v[1] for v in vars]) + \")\")\n print(\"end\\n\")\n","repo_name":"abelsiqueira/old-CUTEst.jl","sub_path":"cutest-converter.py","file_name":"cutest-converter.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1571982791","text":"import serial\n\nclass Connection:\n def __init__(self, port='/dev/ttyUSB0', baud=9600):\n self.port = port\n self.baud = baud\n self.data = ''\n\n def connect(self):\n try:\n conn = serial.Serial(self.port, self.baud)\n print('Conectado com sucesso.\\n')\n return conn\n except:\n print('Sem conexão na porta ' + str(self.port) + '.\\n')\n return 0\n","repo_name":"the-harry/pet_feeder","sub_path":"edge/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"16417530580","text":"## draft space for creating a more complex study definition for learning disabilities research\n# semi based on ethnicity research repo\n# note the original default study definition from the template is now stored under \"study_definiton_example.py\"\n\n# cohort extractor\nfrom cohortextractor import (\n StudyDefinition,\n patients,\n codelist_from_csv,\n codelist,\n filter_codes_by_category,\n combine_codelists\n )\n\n# import all codelists from codelists.py file\nfrom codelists import *\n\n# import all dictionaries from dictionaries.py file\nfrom dictionaries import dict_stp\n\n## STUDY POPULATION\n\nstudy = StudyDefinition(\n\n default_expectations = {\n \"date\": {\"earliest\": \"1970-01-01\", \"latest\": \"today\"}, # date range for simulation\n \"rate\": \"uniform\",\n \"incidence\": 0.2\n },\n\n population = patients.registered_with_one_practice_between(\n \"2019-02-01\", \"2020-02-01\"\n ),\n\n dereg_date = patients.date_deregistered_from_all_supported_practices(\n on_or_before=\"2020-12-01\",\n date_format = \"YYYY-MM\",\n return_expectations = {\"date\": {\"earliest\":\"2020-02-01\"}},\n ),\n\n # DEMOGRAPHIC COVARIATES\n # AGE\n age = patients.age_as_of(\n \"today\",\n return_expectations = {\n \"rate\": \"universal\",\n \"int\": {\"distribution\": \"population_ages\"},\n }\n ),\n\n # SEX\n sex = patients.sex(return_expectations={\n \"rate\": \"universal\",\n \"category\": {\"ratios\":{\"M\": 0.49, \"F\":0.51}},\n }\n ),\n\n # DEPRIVIATION\n imd=patients.address_as_of(\n \"today\",\n returning=\"index_of_multiple_deprivation\",\n round_to_nearest=100,\n return_expectations={\n \"rate\": \"universal\",\n \"category\": {\"ratios\": {\"1\": 0.2, \"2\": 0.2, \"3\": 0.2, \"4\": 0.2, \"5\":0.2}},\n }\n ),\n\n # GEOGRAPHIC REGION CALLED STP\n stp = patients.registered_practice_as_of(\n \"2020-01-01\",\n returning=\"stp_code\",\n return_expectations={\n \"category\": {\"ratios\": dict_stp},\n }\n ),\n\n # # https://codelists.opensafely.org/codelist/opensafely/severe-and-profound-learning-disability-flags/44ef542a/#full-list\n severe_and_profound_learning_disability=patients.with_these_clinical_events(\n severe_and_profound_learning_disability_codes,\n on_or_before= \"today\",\n returning = \"binary_flag\",\n return_expectations={\"incidence\": 0.02}, # ~1.8% of England population have a learning disability\n ),\n\n\n\n intel_dis_incl_downs_syndrome=patients.with_these_clinical_events(\n intellectual_disability_including_downs_syndrome_codes,\n on_or_before= \"today\",\n returning=\"binary_flag\",\n return_expectations={\"incidence\": 0.02,}, # ~1.8% of England population have a learning disability\n )\n)\n","repo_name":"ralmond-nhs-e-i/research-template-learning-disabilities-tester","sub_path":"analysis/study_definition.py","file_name":"study_definition.py","file_ext":"py","file_size_in_byte":2731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3101680960","text":"# https://inf-ege.sdamgia.ru/problem?id=29672\n\nf = open('inf_22_10_20_24.txt')\nlines = f.readlines()\nf.close()\n\nk = 0\n\nfor line in lines:\n if line.count('E') > line.count('A'):\n k += 1\n\nprint(k)\n\n","repo_name":"permCoding/elective-course-21","sub_path":"tasks/task24/01/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18138148899","text":"\"\"\"\nNutrition Module for Bot\n\nClasses:\n NutritionModule - bot module it\n FoodItem - food item with it's properties\n FoodHistory - dated collection of instances of FoodItem class\n NutritionFacts - contains nutrients values, has overwritten arithmetics\n\"\"\"\nimport logging\nimport re\nimport time\n\nimport mysql.connector\nfrom mysql.connector.connection import MySQLConnection\nfrom mysql.connector.cursor import MySQLCursor\nfrom telegram.ext import CommandHandler, MessageHandler, CallbackQueryHandler, ConversationHandler, ContextTypes, filters\nfrom telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update\nfrom config import MYSQL_HOST, MYSQL_PASSWORD, MYSQL_USER\nfrom typing import cast\n\n\nclass Object:\n pass\n\n\nwhile True:\n try:\n database = mysql.connector.connect(\n host=MYSQL_HOST,\n user=MYSQL_USER,\n password=MYSQL_PASSWORD,\n )\n break\n\n except mysql.connector.errors.DatabaseError as e:\n logging.log(\n logging.INFO,\n \"Nutrition module waits for database to start\",\n )\n logging.log(\n logging.INFO,\n e.msg\n )\n time.sleep(1)\n pass\n\ndbcursor: MySQLCursor = database.cursor()\n\n\n# food_history = FoodHistory()\nasync def food_stats_callback(update: Update,\n context: ContextTypes.DEFAULT_TYPE):\n global dbcursor\n dbcursor.execute(\"USE nutrition_module\")\n logging.log(\n logging.INFO,\n f\"{update.message.from_user.name} Requested Food Stats\",\n )\n try:\n dbcursor.execute(\"\"\"SELECT\n time(time), name, grams,\n calories * grams / 100 AS calories,\n protein * grams / 100 AS protein,\n fats * grams / 100 AS fats,\n carbohydrates * grams / 100 AS carbohydrates\n FROM history_303245273\n INNER JOIN food_dictionary\n ON food_dictionary.id = history_303245273.food_id\n WHERE date(time) = current_date();\"\"\")\n today_records = dbcursor.fetchall()\n\n dbcursor.execute(\"\"\"SELECT\n SUM(calories * grams / 100) AS calories,\n SUM(protein * grams / 100) AS protein,\n SUM(fats * grams / 100) AS fats,\n SUM(carbohydrates * grams) / 100 AS carbohydrates\n FROM history_303245273\n INNER JOIN food_dictionary\n ON food_dictionary.id = history_303245273.food_id\n WHERE date(time) = current_date();\"\"\")\n today_totals = dbcursor.fetchone()\n message = \"\\n\".join([\n \"*--TODAY STATS--*\",\n \"*Eaten:*\",\n \"\\n\".join(\n map(\n lambda x: \"{}: {} {}g {}cal ({}p {}f {}c)\".format(*x).\n zfill(2),\n today_records,\n )),\n \"*Total:* {}cal\\nprotein - {}g\\nfats - {}g\\ncarbs - {}g\".format(\n *today_totals).zfill(2),\n ])\n except Exception:\n message = \"Shrek: *growls* You haven't eaten any food today!\"\n\n await update.message.reply_text(message, parse_mode=\"markdown\")\n logging.log(logging.INFO, f\"Responded:\\n{message}\")\n\n\nasync def add_food_entry_callback(update: Update,\n context: ContextTypes.DEFAULT_TYPE):\n global dbcursor\n chat_data = cast(dict, context.chat_data)\n\n dbcursor.execute(\"USE nutrition_module\")\n dbcursor = dbcursor\n user_id = update.message.from_user.id\n user_name = update.message.from_user.name\n text = update.message.text\n chat_id = update.message.chat_id\n chat_data[\"food_item\"] = Object()\n\n chat_data[\"user_id\"] = user_id\n\n dbcursor.execute(\"SELECT COUNT(1) FROM users WHERE user_id = %s\",\n (user_id, ))\n user_exists = bool(dbcursor.fetchone()[0])\n\n if not user_exists:\n query = \"\"\"CREATE TABLE history_%s (\n id INT NOT NULL AUTO_INCREMENT,\n time datetime not null default(current_timestamp),\n food_id INT NOT NULL,\n grams INT,\n PRIMARY KEY (id),\n FOREIGN KEY (food_id) REFERENCES food_dictionary(id)\n )\"\"\"\n dbcursor.execute(query, (user_id, ))\n dbcursor.execute(\n \"INSERT INTO users (user_id, user_name) VALUES (%s, %s)\",\n (user_id, user_name),\n )\n database.commit()\n\n match = re.match(r\"^add food *(\\w+) *(\\d+gr)\", text)\n food = chat_data[\"food_item\"]\n if match:\n food.name = match[1]\n food.grams = int(match[2][:-2])\n\n dbcursor.execute(\"SELECT * FROM food_dictionary WHERE name = %s\",\n (food.name, ))\n buttons = []\n for food_item in dbcursor.fetchall():\n (\n food_id,\n name,\n calories,\n protein,\n fats,\n carbs,\n added_by_user,\n ) = food_item\n button = InlineKeyboardButton(\n f\"{name}: {calories}cal ({protein}p, {fats}f, {carbs}c)\",\n callback_data=f\"FOOD_ID:{food_id}\",\n )\n buttons.append([button])\n buttons.append([\n InlineKeyboardButton(\"Add my own food item\",\n callback_data=\"ADD_NEW\")\n ])\n\n await context.bot.send_message(\n chat_id,\n \"Which food do you want to add?\",\n reply_markup=InlineKeyboardMarkup(buttons),\n )\n return \"CHOOSE_ITEM\"\n\n match = re.match(r\"^add food *(\\w+)\", text)\n if match:\n food.name = match[1]\n\n await context.bot.send_message(chat_id, \"Specify grams\")\n return \"SPECIFY_GRAMS\"\n\n await context.bot.send_message(chat_id, \"Specify food name\")\n return \"SPECIFY_NAME\"\n\n\nasync def add_food_state_name_callback(update: Update,\n context: ContextTypes.DEFAULT_TYPE):\n chat_data = cast(dict, context.chat_data)\n\n text = update.message.text\n chat_id = update.message.chat_id\n food = chat_data[\"food_item\"]\n\n food.name = text.strip()\n await context.bot.send_message(chat_id, \"Specify grams\")\n return \"SPECIFY_GRAMS\"\n\n\nasync def add_food_state_grams_callback(update: Update,\n context: ContextTypes.DEFAULT_TYPE):\n global dbcursor\n chat_data = cast(dict, context.chat_data)\n\n text = update.message.text\n chat_id = update.message.chat_id\n food = chat_data[\"food_item\"]\n dbcursor = dbcursor\n\n food.grams = int(text.strip())\n\n dbcursor.execute(\"SELECT * FROM food_dictionary WHERE name = %s\",\n (food.name, ))\n buttons = []\n for food_item in dbcursor.fetchall():\n (\n food_id,\n name,\n calories,\n protein,\n fats,\n carbs,\n added_by_user,\n ) = food_item\n button = InlineKeyboardButton(\n f\"{name}: {calories}cal ({protein}p, {fats}f, {carbs}c)\",\n callback_data=f\"FOOD_ID:{food_id}\",\n )\n buttons.append([button])\n buttons.append([\n InlineKeyboardButton(\"Add my own food item\", callback_data=\"ADD_NEW\")\n ])\n\n await context.bot.send_message(\n chat_id,\n \"Which food do you want to add?\",\n reply_markup=InlineKeyboardMarkup(buttons),\n )\n return \"CHOOSE_ITEM\"\n\n\nasync def add_food_state_choose_item_callback(\n update: Update, context: ContextTypes.DEFAULT_TYPE):\n global dbcursor\n chat_data = cast(dict, context.chat_data)\n food = chat_data[\"food_item\"]\n user_id = chat_data[\"user_id\"]\n\n answer = update.callback_query.data\n\n await update.callback_query.message.edit_reply_markup()\n\n match = re.match(r\"FOOD_ID:(\\d+)\", answer)\n if match:\n food_id = int(match[1])\n dbcursor.execute(\n \"INSERT INTO history_%s (food_id, grams) VALUES (%s, %s)\",\n (user_id, food_id, food.grams),\n )\n database.commit()\n dbcursor.execute(\"SELECT name FROM food_dictionary WHERE id = %s\",\n (food_id, ))\n await update.callback_query.message.edit_text(\n f\"{dbcursor.fetchone()[0]} was successfully added\")\n return -1\n\n if answer == \"ADD_NEW\":\n await update.callback_query.message.edit_text(\"Ok. Specify calories\")\n return \"SPECIFY_CALORIES\"\n\n\nasync def add_food_state_calories_callback(update: Update,\n context: ContextTypes.DEFAULT_TYPE):\n chat_data = cast(dict, context.chat_data)\n text = update.message.text\n chat_id = update.message.chat_id\n food = chat_data[\"food_item\"]\n\n food.calories = int(text.strip())\n await context.bot.send_message(chat_id,\n \"Specify protein, fats and carbohydrates\")\n return \"SPECIFY_PFC\"\n\n\nasync def add_food_state_pfc_callback(update: Update,\n context: ContextTypes.DEFAULT_TYPE):\n global dbcursor\n chat_data = cast(dict, context.chat_data)\n text = update.message.text\n chat_id = update.message.chat_id\n food = chat_data[\"food_item\"]\n user_id = chat_data[\"user_id\"]\n\n match = re.match(\n r\"([+-]?([0-9]*[.])?[0-9]+) ([+-]?([0-9]*[.])?[0-9]+) ([+-]?([0-9]*[.])?[0-9]+)\",\n text,\n )\n if match:\n food.protein = float(match[1])\n food.fats = float(match[3])\n food.carbohydrates = float(match[5])\n\n dbcursor.execute(\n \"\"\"INSERT INTO food_dictionary\n (name, calories, protein, fats, carbohydrates, added_by_user)\n VALUES (%s, %s, %s, %s, %s, %s)\"\"\",\n (\n food.name,\n food.calories,\n food.protein,\n food.fats,\n food.carbohydrates,\n user_id,\n ),\n )\n database.commit()\n\n food_id = dbcursor.lastrowid\n\n dbcursor.execute(\n \"INSERT INTO history_%s (food_id, grams) VALUES (%s, %s)\",\n (user_id, food_id, food.grams),\n )\n database.commit()\n await context.bot.send_message(chat_id, \"Food was successfully added\")\n return -1\n\n\nadd_food_handler = ConversationHandler(\n entry_points=[CommandHandler(\"add_food\", add_food_entry_callback)],\n states={\n \"SPECIFY_NAME\": [\n MessageHandler(\n filters.TEXT,\n callback=add_food_state_name_callback,\n )\n ],\n \"SPECIFY_GRAMS\": [\n MessageHandler(\n filters.TEXT,\n callback=add_food_state_grams_callback,\n )\n ],\n \"CHOOSE_ITEM\":\n [CallbackQueryHandler(callback=add_food_state_choose_item_callback)],\n \"SPECIFY_CALORIES\": [\n MessageHandler(\n filters.TEXT,\n callback=add_food_state_calories_callback,\n )\n ],\n \"SPECIFY_PFC\":\n [MessageHandler(filters.TEXT, callback=add_food_state_pfc_callback)],\n },\n fallbacks=[CommandHandler(\"add_food\", add_food_entry_callback)],\n)\n\nfood_stats_handler = CommandHandler(\"food_stats\", food_stats_callback)\n","repo_name":"Meta-Gigachad/assistant-telegram-bot","sub_path":"handlers/nutrition.py","file_name":"nutrition.py","file_ext":"py","file_size_in_byte":11201,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"25011712135","text":"'''\nWrite a program which accept number from user and return difference between summation of all its factors and non factors\nInput : 12\nOutput : -34 (16 - 50) \n'''\n\ndef FindDiff(num):\n if(num<0):\n num = -num;\n sumfactor = 0;\n sumnonfactor = 0;\n for i in range(1,num):\n if(num%i == 0):\n sumfactor = sumfactor + i;\n else:\n sumnonfactor = sumnonfactor + i;\n return (sumfactor - sumnonfactor);\n\ndef main():\n no = int(input(\"Enter number: \"));\n result = FindDiff(no);\n print(result);\n\nif __name__ == \"__main__\":\n main();","repo_name":"Aditya-A-Pardeshi/Coding-Hands-On","sub_path":"4 Python_Programs/1 Problems on numbers/15_DiffOf_SumOfFactors_SumOfNonFactors/Demo.py","file_name":"Demo.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"31736921948","text":"from flask import Flask, render_template, make_response, request\nimport re\nimport PyPDF2\nimport os\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n if request.method == 'POST':\n # Check if the POST request has a file part\n if 'file' not in request.files:\n return render_template('index.html', error='No file selected')\n\n file = request.files['file']\n\n # Check if the file is a PDF\n if not file.filename.endswith('.pdf'):\n return render_template('index.html', error='Please select a PDF file')\n\n # Save the file to the server\n file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)\n file.save(file_path)\n\n # Open the PDF file in read-binary mode\n with open(file_path, 'rb') as file:\n # Create a PDF reader object\n pdf_reader = PyPDF2.PdfReader(file)\n\n # Get the number of pages in the PDF file\n num_pages = len(pdf_reader.pages)\n\n # Loop through each page and extract the text\n for page_num in range(num_pages):\n if page_num < 2: # to print only page 1 and 2\n # Get the page object\n page_obj = pdf_reader.pages[page_num]\n\n # Extract the text from the page\n text = page_obj.extract_text()\n\n # Search for the required text\n match = re.search(r'Start balance\\s*£\\s*\\d+,\\d+\\.\\d+\\s*Money in\\s*£\\s*\\d+,\\d+\\.\\d+\\s*Money out\\s*£\\s*\\d+,\\d+\\.\\d+\\s*End balance\\s*£\\s*\\d+,\\d+\\.\\d+', text)\n\n # Check if the text is found\n if match:\n # Render the template with the matched text\n response= make_response(render_template('index.html', balance=match.group()))\n\n response.set_cookie('balance', match.group())\n\n return response\n\n # If the text is not found, render the template with an error message\n return render_template('index.html', error='Cannot find the required text')\n\n # Render the index.html file with an empty form\n return render_template('index.html')\n\nif __name__ == '__main__':\n app.config['UPLOAD_FOLDER'] = 'pdfs'\n app.run(debug=True)\n","repo_name":"MasudAbdulle/sdpPocketpal","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"30361347329","text":"# value = []\n#\n#\n# def LinearSearch(List, x, y):\n# flag = False\n# for i in range(0, y):\n# if List[i] == x:\n# flag = True\n# value.append(i)\n# if flag:\n# return 1\n# else:\n# return -1\n#\n#\n# L = [1, 2, 2, 4, 5, 8, 9, 8]\n# y = len(L)\n# x = int(input())\n# result = LinearSearch(L, x, y)\n#\n# if result == 1:\n# for i in value:\n# print(i)\n# else:\n# print(\"Not Find\")\n\ndef linearSearch(array, n, x):\n\n # Going through array sequentially\n for i in range(0, n):\n if array[i] == x:\n return i\n return -1\n\n\narray = [2, 4, 0, 1, 9]\nx = int(input())\nn = len(array)\nresult = linearSearch(array, n, x)\nif result == -1:\n print(\"Element not found\")\nelse:\n print(\"Element found at index: \", result)\n","repo_name":"Abir835/Problem_solve","sub_path":"Algorithm/Search/Linear_Search.py","file_name":"Linear_Search.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40169590359","text":"import os\nimport cv2\nimport numpy as np\nimport torch\nimport onnxruntime\n\n__labels = [\n \"FEMALE_GENITALIA_COVERED\",\n \"FACE_FEMALE\",\n \"BUTTOCKS_EXPOSED\",\n \"FEMALE_BREAST_EXPOSED\",\n \"FEMALE_GENITALIA_EXPOSED\",\n \"MALE_BREAST_EXPOSED\",\n \"ANUS_EXPOSED\",\n \"FEET_EXPOSED\",\n \"BELLY_COVERED\",\n \"FEET_COVERED\",\n \"ARMPITS_COVERED\",\n \"ARMPITS_EXPOSED\",\n \"FACE_MALE\",\n \"BELLY_EXPOSED\",\n \"MALE_GENITALIA_EXPOSED\",\n \"ANUS_COVERED\",\n \"FEMALE_BREAST_COVERED\",\n \"BUTTOCKS_COVERED\",\n]\n\n\ndef _read_image(image_path, input_width, input_height):\n # From ultralytics\n img = cv2.imread(image_path)\n img_height, img_width = img.shape[:2]\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = cv2.resize(img, (input_width, input_height))\n image_data = np.array(img) / 255.0\n image_data = np.transpose(image_data, (2, 0, 1))\n image_data = np.expand_dims(image_data, axis=0).astype(np.float32)\n return image_data, img_width, img_height\n\n\ndef _postprocess(output, img_width, img_height, input_width, input_height):\n outputs = np.transpose(np.squeeze(output[0]))\n rows = outputs.shape[0]\n boxes = []\n scores = []\n class_ids = []\n x_factor = img_width / input_width\n y_factor = img_height / input_height\n\n for i in range(rows):\n classes_scores = outputs[i][4:]\n max_score = np.amax(classes_scores)\n if max_score >= 0.5:\n class_id = np.argmax(classes_scores)\n x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3]\n left = int((x - w / 2) * x_factor)\n top = int((y - h / 2) * y_factor)\n width = int(w * x_factor)\n height = int(h * y_factor)\n class_ids.append(class_id)\n scores.append(max_score)\n boxes.append([left, top, width, height])\n\n indices = cv2.dnn.NMSBoxes(boxes, scores, 0.5, 0.5)\n\n detections = []\n for i in indices:\n box = boxes[i]\n score = scores[i]\n class_id = class_ids[i]\n detections.append(\n {\"class\": __labels[class_id], \"score\": float(score), \"box\": box}\n )\n\n return detections\n\n\nclass NudeDetector:\n def __init__(self):\n self.onnx_session = onnxruntime.InferenceSession(\n os.path.join(\"files\", \"best.onnx\"),\n providers=[\"CUDAExecutionProvider\", \"CPUExecutionProvider\"],\n )\n model_inputs = self.onnx_session.get_inputs()\n input_shape = model_inputs[0].shape\n self.input_width = input_shape[2]\n self.input_height = input_shape[3]\n self.input_name = model_inputs[0].name\n\n def detect(self, image_path):\n preprocessed_image, image_width, image_height = _read_image(\n image_path, self.input_width, self.input_height\n )\n outputs = self.onnx_session.run(None, {self.input_name: preprocessed_image})\n detections = _postprocess(\n outputs, image_width, image_height, self.input_width, self.input_height\n )\n\n return detections\n\n\nif __name__ == \"__main__\":\n detector = NudeDetector()\n detections = detector.detect(\"/Users/praneeth.bedapudi/Desktop/images.jpeg\")\n","repo_name":"OPTML-Group/Diffusion-MU-Attack","sub_path":"src/tasks/utils/metrics/nudenet/detector.py","file_name":"detector.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"32732017599","text":"\"\"\"\nCustom layers to build an ALAE generative network.\n\nAuthor: Simon Thomas\nDate: Jul-6-2020\n\n\nPYTHON SOFTWARE VERSIONS:\n- tensorflow 2.2.0\n- tensorflow-addons 0.10.0\n- scikit-image 0.17.2\n- numpy 1.17.2\n- matplotlib 3.1.3\n- Keras 2.3.1\n- h5py 2.10.0\n\n\nCUDA\n- cudatoolkit 10.1.243\n- cudnn 7.6.5\n\"\"\"\n\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nimport numpy as np\n\nfrom tensorflow_addons.layers import InstanceNormalization\nfrom tensorflow.keras.layers import Dense, LeakyReLU, Conv2D, Flatten, Add, Cropping2D, Layer\nfrom tensorflow.keras.layers import UpSampling2D, Reshape, AveragePooling2D, Input\nfrom tensorflow.keras.models import Model\n\n# ------------------- FIXES BUG ---------------------------- #\n# See https://github.com/tensorflow/tensorflow/issues/34983\n# and comment from Papageno2\n#tf.config.experimental_run_functions_eagerly(True)\n# ---------------------------------------------------------- #\n\nnormal = tf.initializers.RandomNormal\nones = tf.initializers.ones\n\n\ndef crop_noise(noise_tensor, size, block):\n \"\"\"\n Crops the noise_tensor to the target size.\n \"\"\"\n cut = (noise_tensor.shape[1] - size) // 2\n crop = Cropping2D(cut, name=f\"G_Noise_Crop_block_{block}\")(noise_tensor)\n return crop\n\n\nclass DenseEQ(Dense):\n \"\"\"\n Standard dense layer but includes learning rate equilization\n at runtime as per Karras et al. 2017. Includes learning rate multiplier,\n but defaults to 1.0. Only needed for the mapping network.\n\n Inherits Dense layer and overides the call method.\n \"\"\"\n def __init__(self, lrmul=1.0, **kwargs):\n if 'kernel_initializer' in kwargs:\n raise Exception(\"Cannot override kernel_initializer\")\n self.lrmul=lrmul\n super().__init__(kernel_initializer=normal(0, 1/self.lrmul), **kwargs)\n\n def build(self, input_shape):\n super().build(input_shape)\n # The number of inputs\n n = np.product([int(val) for val in input_shape[1:]])\n # He initialisation constant\n self.c = np.sqrt(2/n)*self.lrmul\n\n def call(self, inputs):\n output = K.dot(inputs, self.kernel*self.c) # scale kernel\n if self.use_bias:\n output = K.bias_add(output, self.bias*self.lrmul, data_format='channels_last')\n if self.activation is not None:\n output = self.activation(output)\n return output\n\n\nclass Conv2DEQ(Conv2D):\n \"\"\"\n Standard Conv2D layer but includes learning rate equilization\n at runtime as per Karras et al. 2017.\n\n Inherits Conv2D layer and overrides the call method, following\n https://github.com/keras-team/keras/blob/master/keras/layers/convolutional.py\n\n from the tf-keras branch.\n\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Requires usual Conv2D inputs e.g.\n - filters, kernel_size, strides, padding\n \"\"\"\n\n if 'kernel_initializer' in kwargs:\n raise Exception(\"Cannot override kernel_initializer\")\n super().__init__(kernel_initializer=normal(0, 1), **kwargs)\n\n def build(self, input_shape):\n super().build(input_shape)\n # The number of inputs\n n = np.product([int(val) for val in input_shape[1:]])\n # He initialisation constant\n self.c = np.sqrt(2 / n)\n\n def call(self, inputs):\n outputs = K.conv2d(\n inputs,\n self.kernel * self.c, # scale kernel\n strides=self.strides,\n padding=self.padding,\n data_format=self.data_format,\n dilation_rate=self.dilation_rate)\n\n if self.use_bias:\n outputs = K.bias_add(\n outputs,\n self.bias,\n data_format=self.data_format)\n\n if self.activation is not None:\n return self.activation(outputs)\n return outputs\n\n\nclass Fade(Add):\n \"\"\"\n A Fade layer which performs a weighted sum of the\n TWO inputs. Set alpha through training with:\n K.set_value(layer.alpha, new_alpha)\n \"\"\"\n # init with default value\n def __init__(self, alpha=0.0, **kwargs):\n super(Fade, self).__init__(**kwargs)\n self.alpha = tf.Variable(alpha,\n name='ws_alpha',\n trainable=False,\n dtype=tf.float32,\n aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,\n synchronization=tf.VariableSynchronization.ON_READ\n )\n\n # output a weighted sum of inputs\n def _merge_function(self, inputs):\n # only supports a weighted sum of two inputs\n assert (len(inputs) == 2)\n # ((1-a) * input1) + (a * input2)\n output = ((1.0 - self.alpha) * inputs[0]) + (self.alpha * inputs[1])\n return output\n\n\nclass AdaInstanceNormalization(Layer):\n \"\"\"\n This is the AdaInstanceNormalization layer used by manicman199 available at\n https://github.com/manicman1999/StyleGAN-Keras\n\n This is used in StyleGAN version 1 as well as ALAE.\n \"\"\"\n\n def __init__(self,\n axis=-1,\n momentum=0.99,\n epsilon=1e-3,\n center=True,\n scale=True,\n **kwargs):\n super(AdaInstanceNormalization, self).__init__(**kwargs)\n self.axis = axis\n self.momentum = momentum\n self.epsilon = epsilon\n self.center = center # always done\n self.scale = scale # always done\n\n def build(self, input_shape):\n\n dim = input_shape[0][self.axis]\n if dim is None:\n raise ValueError('Axis ' + str(self.axis) + ' of '\n 'input tensor should have a defined dimension '\n 'but the layer received an input with shape ' +\n str(input_shape[0]) + '.')\n\n super(AdaInstanceNormalization, self).build(input_shape)\n\n def call(self, inputs, training=None):\n input_shape = K.int_shape(inputs[0])\n reduction_axes = list(range(0, len(input_shape)))\n\n beta = inputs[1]\n gamma = inputs[2]\n\n if self.axis is not None:\n del reduction_axes[self.axis]\n\n del reduction_axes[0]\n mean = K.mean(inputs[0], reduction_axes, keepdims=True)\n stddev = K.std(inputs[0], reduction_axes, keepdims=True) + self.epsilon\n normed = (inputs[0] - mean) / stddev\n\n return normed * gamma + beta\n\n def get_config(self):\n config = {\n 'axis': self.axis,\n 'momentum': self.momentum,\n 'epsilon': self.epsilon,\n 'center': self.center,\n 'scale': self.scale\n }\n base_config = super(AdaInstanceNormalization, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))\n\n def compute_output_shape(self, input_shape):\n return input_shape[0]\n\n\nclass Bias(Layer):\n \"\"\"\n A simple bias layer used in StyleGAN2 after\n the Mod/Demod layer\n \"\"\"\n def __init__(self, units, **kwargs):\n super().__init__(**kwargs)\n self.units = units\n\n def build(self, input_shape):\n # Conv bias\n self.bias = self.add_weight(\"bias\",\n shape=[self.units, ],\n initializer=ones\n )\n\n def call(self, inputs, **kwargs ):\n return inputs + self.bias\n\n\nclass ModulationConv2D(Layer):\n \"\"\"\n Modulation/Demodulation Convolutional layer, including learning rate equilization\n at runtime as per Karras et al. 2017 & 2019. (ProGAN & StyleGan2)\n\n Inspired by https://github.com/moono/stylegan2-tf-2.x/blob/master/stylegan2/custom_layers.py\n\n Look at tf-keras branch at\n https://github.com/keras-team/keras/blob/master/keras/layers/convolutional.py\n\n for implementation details.\n\n \"\"\"\n\n def __init__(self, filters, kernel_size, style_fmaps, block, demodulate=False, **kwargs):\n super().__init__(**kwargs)\n self.filters = filters\n self.kernel_size = kernel_size\n self.style_fmaps = style_fmaps # this is z_dim\n self.block = block\n self.demodulate = demodulate\n\n def build(self, input_shape):\n \"\"\"\n Input shape is a list of input shapes.\n\n x_shape = input_shape[0] - the shape of the feature map\n w_shape = input_shape[1] - the shape of the style vector\n \"\"\"\n x_shape, w_shape = input_shape[0], input_shape[1]\n\n # print(\"Build X-shape:\", x_shape)\n\n # Conv Kernel\n self.kernel = self.add_weight(\"kernel\",\n shape=(self.kernel_size[0], self.kernel_size[1],\n x_shape[-1], self.filters),\n initializer=normal(0, 1),\n trainable=True\n )\n\n # Equilized learning rate constant\n n = np.product([int(val) for val in x_shape[1:]])\n # He initialisation constant\n self.c = np.sqrt(2 / n)\n\n # add modulation layer\n self.modulate = DenseEQ(units=x_shape[-1], name=f\"modulation_{self.block}\")\n\n def scale_weights(self, style):\n \"\"\"\n Scales and transforms the weights using the style vector\n\n B - BATCH\n k - kernel\n I - Input Features\n O - Output Features\n\n \"\"\"\n # convolution kernel weights for fused conv\n weight = self.kernel * self.c # [kkIO]\n weight = weight[np.newaxis] # [BkkIO]\n\n # modulation\n style = self.modulate(style) # [BI] - includes the bias\n weight *= style[:, np.newaxis, np.newaxis, :, np.newaxis] # [BkkIO]\n\n # demodulation\n if self.demodulate:\n # demodulate with the L2 Norm of the weights (statistical assumption)\n d = tf.math.rsqrt(tf.reduce_sum(tf.square(weight), axis=[1, 2, 3]) + 1e-8) # [BO]\n weight *= d[:, np.newaxis, np.newaxis, np.newaxis, :] # [BkkIO]\n\n # weight: reshape, prepare for fused operation\n new_weight_shape = [tf.shape(weight)[1], tf.shape(weight)[2], tf.shape(weight)[3], -1] # [kkI(BO)]\n weight = tf.transpose(weight, [1, 2, 3, 0, 4]) # [kkIBO]\n weight = tf.reshape(weight, shape=new_weight_shape) # [kkI(BO)]\n return weight\n\n def call(self, inputs, **kwargs):\n x = inputs[0]\n style = inputs[1]\n\n # Transform the weights using the style vector\n weights = self.scale_weights(style)\n\n # Prepare inputs: reshape minibatch to convolution groups\n rows = x.shape[1]\n cols = x.shape[2]\n x = tf.reshape(x, [1, -1, rows, cols])\n\n # Perform convolution\n x = tf.nn.conv2d(x, weights, data_format='NCHW', strides=[1, 1, 1, 1], padding='SAME')\n\n # x: reshape back to batches\n x = tf.reshape(x, [-1, self.filters, tf.shape(x)[2], tf.shape(x)[3]])\n\n # x: reshape to [BHWO]\n x = tf.transpose(x, [0, 2, 3, 1])\n\n return x\n\n def get_config(self):\n config = super(ModulationConv2D, self).get_config()\n config.update({\n 'filters': self.filters,\n 'kernel': self.kernel,\n 'style_fmaps': self.style_fmaps,\n 'demodulate': self.demodulate,\n 'up': self.up,\n 'down': self.down\n })\n return config\n\n\nclass MeanAndStDev(Layer):\n \"\"\"\n This is the Instance Normalization transformation which\n concatenates mu and sigma to later be mapped to w.\n\n This is used in the encoder introduced by\n \"\"\"\n def __init__(self, **kwargs):\n super(MeanAndStDev, self).__init__(**kwargs)\n\n def build(self, input_shape):\n super(MeanAndStDev, self).build(input_shape)\n\n def call(self, inputs, **kwargs):\n m = K.mean(inputs, axis=[1, 2], keepdims=True)\n std = K.std(inputs, axis=[1, 2], keepdims=True)\n statistics = K.concatenate([m, std], axis=1)\n return statistics\n\n\n\nclass EncoderBlock(Model):\n \"\"\"\n Encoder block using instance normalisation to extract style\n as introduced in the ALAE by Podgorskiy et al (2020).\n \"\"\"\n def __init__(self, filters, block, z_dim, **kwargs):\n \"\"\"\n :param filters: the number of convolution filters (fixed 3x3 size)\n :param block: the block number for naming\n :param z_dim: the z-dimension for mapping features to style vector\n \"\"\"\n super(EncoderBlock, self).__init__(**kwargs)\n # Attributes\n self.filters = filters\n self.block = block\n self.z_dim = z_dim\n\n # Trainable Layers\n self.conv1 = Conv2DEQ(filters=filters, kernel_size=(3, 3), padding=\"same\", name=f\"E_block_{block}_Conv_1\")\n self.act1 = LeakyReLU(0.2, name=f\"E_block_{block}_Act_1\")\n self.msd = MeanAndStDev(name=f\"E_block_{block}_msd\")\n self.in1 = InstanceNormalization(name=f\"E_block_{block}_IN_1\", center=False, scale=False)\n self.in2 = InstanceNormalization(name=f\"E_block_{block}_IN_2\", center=False, scale=False)\n self.conv2 = Conv2DEQ(filters=filters, kernel_size=(3, 3), padding=\"same\", name=f\"E_block_{block}_Conv_2\")\n self.act2 = LeakyReLU(0.2, name=f\"E_block_{block}_Act_2\")\n self.downsample = AveragePooling2D(name=f\"E_block_{block}_DownSample\")\n self.mapStyle1 = DenseEQ(units=z_dim, name=f\"E_block_{block}_style_1\")\n self.mapStyle2 = DenseEQ(units=z_dim, name=f\"E_block_{block}_style_2\")\n self.flatten = Flatten(name=f\"E_block_{block}_flatten\")\n\n def call(self, inputs, **kwargs):\n # Convolution 1\n x = self.conv1(inputs)\n x = self.act1(x)\n\n # Instance Normalisation 1\n style1 = self.flatten(self.msd(x))\n x = self.in1(x)\n\n # Convolution 2\n x = self.conv2(x)\n if self.block > 1:\n x = self.downsample(x)\n x = self.act2(x)\n\n # Instance Normalisation 2\n style2 = self.flatten(self.msd(x))\n x = self.in2(x)\n\n # Affine transform to style vectors\n w1 = self.mapStyle1(style1)\n w2 = self.mapStyle2(style2)\n\n return x, w1, w2\n\n\nclass EncoderBlockRouter(Model):\n \"\"\"\n This is a wrapper that makes progressive growing possible\n with the complex input/output routing of the encoder block.\n \"\"\"\n def __init__(self, filters, block, z_dim, **kwargs):\n \"\"\"\n :param filters: a list of filters for the block - [input_filters, block_filters] e.g. [16, 32]\n :param block: the block number for naming\n :param z_dim: the z-dimension for mapping to a style vector\n \"\"\"\n super(EncoderBlockRouter, self).__init__(**kwargs)\n # Attributes\n self.filters = filters\n self.block = block\n self.z_dim = z_dim\n\n # Parameters\n self.conv1 = Conv2DEQ(filters=self.filters[0], kernel_size=(3, 3), padding=\"same\")\n self.act1 = LeakyReLU(alpha=0.2)\n self.encode = EncoderBlock(self.filters[1], self.block, self.z_dim, name=f\"E_block_{block}_encoder\")\n\n def call(self, inputs, **kwargs):\n x = inputs\n\n x = self.conv1(x)\n x = self.act1(x)\n\n x, w1, w2 = self.encode(x)\n\n return x, w1, w2\n\n\nclass GeneratorBlockModDemod(Model):\n \"\"\"\n Generator block using Modulation and Demodulation of the\n weights to inject style (introduced in StyleGAN version 2).\n \"\"\"\n def __init__(self, filters, block, z_dim, **kwargs):\n super(GeneratorBlockModDemod, self).__init__(**kwargs)\n # Attributes\n self.filters = filters\n self.block = block\n self.z_dim = z_dim\n self.dim = 2 ** (block + 1)\n\n # Parameters\n self.upsample = UpSampling2D(name=f\"G_block_{block}_UpSample\")\n self.noise = Conv2DEQ(filters=filters, kernel_size=1, padding='same', name=f\"G_block_{block}_noise_bias\")\n # phase 1\n self.conv1 = ModulationConv2D(filters, kernel_size=(3, 3), style_fmaps=self.z_dim,\n block=self.block, demodulate=False, name=f\"G_block_{block}_ModulationConv_1\")\n self.bias1 = Bias(units=self.filters, name=f\"G_block_{block}_bias_1\")\n self.addNoise1 = Add(name=f\"G_block_{block}_Add_1\")\n self.act1 = LeakyReLU(0.2, name=f\"G_block_{block}_Act_1\")\n\n if self.block > 1: # > 4x4\n # phase 2\n self.conv2 = ModulationConv2D(filters, kernel_size=(3, 3), style_fmaps=self.z_dim,\n block=self.block, demodulate=False, name=f\"G_block_{block}_ModulationConv_1\")\n self.bias2 = Bias(units=self.filters, name=f\"G_block_{block}_bias_2\")\n self.addNoise2 = Add(name=f\"G_block_{block}_Add_2\")\n self.act2 = LeakyReLU(0.2, name=f\"G_block_{block}_Act_2\")\n\n def call(self, inputs, **kwargs):\n # Unpack inputs\n input_tensor, noise_tensor, style_tensor = inputs\n\n # Get noise image for level\n noise_tensor = crop_noise(noise_tensor, self.dim, self.block)\n\n if self.block > 1:\n x = self.upsample(input_tensor)\n else:\n x = input_tensor\n\n # Phase 1\n noise = self.noise(noise_tensor)\n x = self.conv1([x, style_tensor])\n x = self.bias1(x)\n x = self.addNoise1([x, noise])\n x = self.act1(x)\n\n if self.block == 1: # 4x4 block\n return x\n\n # Phase 2\n x = self.conv2([x, style_tensor])\n x = self.bias2(x)\n x = self.addNoise2([x, noise])\n x = self.act2(x)\n\n return x\n\n\nclass GeneratorBlockAdaIN(Model):\n \"\"\"\n Generator block using adaptive instance normalisation to inject style,\n as per StyleGAN version 1\n \"\"\"\n def __init__(self, filters, block, z_dim=None, **kwargs):\n super(GeneratorBlockAdaIN, self).__init__(**kwargs)\n # Attributes\n self.filters = filters\n self.block = block\n self.dim = 2 ** (block + 1)\n self.z_dim = z_dim # not necessary for this layer - helps with ModDemo block\n\n # Trainable Layers\n self.upsample = UpSampling2D(name=f\"G_block_{block}_UpSample\", interpolation=\"bilinear\")\n # phase 1\n self.beta1 = DenseEQ(units=filters, name=f\"G_block_{block}_beta1\")\n self.beta1r = Reshape([1, 1, filters], name=f\"G_block_{block}_beta1_reshape\")\n self.gamma1 = DenseEQ(units=filters, name=f\"G_block_{block}_gamma1\")\n self.gamma1r = Reshape([1, 1, filters], name=f\"G_block_{block}_gamma1_reshape\")\n self.noise1 = Conv2DEQ(filters=filters, kernel_size=1, padding='same', name=f\"G_block_{block}_noise_bias1\")\n self.conv1 = Conv2DEQ(filters=filters, kernel_size=3, padding='same', name=f\"G_block_{block}_decoder_conv1\")\n self.AdaIn1 = AdaInstanceNormalization(name=f\"G_block_{block}_AdaIN_1\")\n self.addNoise1 = Add(name=f\"G_block_{block}_Add_1\")\n self.act1 = LeakyReLU(0.2, name=f\"G_block_{block}_Act_1\")\n\n # phase 2\n self.beta2 = DenseEQ(units=filters, name=f\"G_block_{block}_beta2\")\n self.beta2r = Reshape([1, 1, filters], name=f\"G_block_{block}_beta2_reshape\")\n self.gamma2 = Dense(units=filters, name=f\"G_block_{block}_gamma2\")\n self.gamma2r = Reshape([1, 1, filters], name=f\"G_block_{block}_gamma2_reshape\")\n self.noise2 = Conv2DEQ(filters=filters, kernel_size=1, padding='same', name=f\"G_block_{block}_noise_bias2\")\n self.conv2 = Conv2DEQ(filters=filters, kernel_size=3, padding='same', name=f\"G_block_{block}_decoder_conv2\")\n self.AdaIn2 = AdaInstanceNormalization(name=f\"G_block_{block}_AdaIN_2\")\n self.addNoise2 = Add(name=f\"G_block_{block}_Add_2\")\n self.act2 = LeakyReLU(0.2, name=f\"G_block_{block}_Act_2\")\n\n def call(self, inputs, **kwargs):\n # Unpack inputs\n input_tensor, noise_tensor, style_tensor = inputs\n\n # Get noise image for level\n noise_tensor = crop_noise(noise_tensor, self.dim, self.block)\n\n if self.block > 1:\n x = self.upsample(input_tensor)\n else:\n x = input_tensor\n\n # Phase 1\n beta = self.beta1r(self.beta1(style_tensor))\n gamma = self.gamma1r(self.gamma1(style_tensor))\n noise = self.noise1(noise_tensor)\n x = self.conv1(x)\n x = self.AdaIn1([x, beta, gamma])\n x = self.addNoise1([x, noise])\n x = self.act1(x)\n\n # Phase 2\n beta = self.beta2r(self.beta2(style_tensor))\n gamma = self.gamma2r(self.gamma2(style_tensor))\n noise = self.noise2(noise_tensor)\n x = self.conv2(x)\n x = self.AdaIn2([x, beta, gamma])\n x = self.addNoise2([x, noise])\n x = self.act2(x)\n\n return x\n\n\nclass tRGB(Layer):\n \"\"\"\n Linear transformation from feature space to rgb space, using a 1x1 convolution\n \"\"\"\n def __init__(self, block):\n super(tRGB, self).__init__()\n self.block = block\n self.transform = Conv2DEQ(filters=3, kernel_size=(1, 1), padding=\"same\", name=f\"G_block_{block}_tRGB\")\n\n def build(self, input_shape):\n super(tRGB, self).build(input_shape)\n\n def call(self, inputs, **kwargs):\n return self.transform(inputs)\n\n\n# Progressive Growing Functions\ndef expand_encoder(old_model, filters, block, z_dim):\n \"\"\"\n Expands the old model by a factor of 2. Size is not explicit and\n is determined by doubling the size of the input of the old_model\n\n :param old_model: the old \"straight through\" encoder model\n :param filters: a list of filters required EncoderBlockRouter - [input_filers, block_filters]\n :param block: the block number for naming\n :param z_dim: the z-dimension for mapping features to style vectors\n :return: straight_e (E_s), merged_e (E_m)\n \"\"\"\n # Create new input\n dim = int(old_model.get_input_at(0).shape[1] * 2)\n c = old_model.get_input_at(0).shape[-1]\n e_in = Input(shape=(dim, dim, c), name=\"E_new_input\")\n\n x_new, new_w1, new_w2 = EncoderBlockRouter(filters=filters, block=block,\n z_dim=z_dim, name=f\"encoder_block_router_{block}\")(e_in)\n\n # Save values\n x = x_new\n ws = [new_w1, new_w2]\n\n # Pass through remaining layers of old model\n for b in range(block - 1, 0, -1):\n if b == block - 1 and b != 0:\n x, w1, w2 = old_model.get_layer(f\"encoder_block_router_{b}\").encode(x)\n else:\n x, w1, w2 = old_model.get_layer(f\"E_block_{b}_encoder\")(x)\n\n ws.extend([w1, w2])\n\n e_out = Add(name=\"E_Final_Sum_Straight\")(ws)\n\n straight_e = Model(inputs=[e_in], outputs=[e_out], name=f\"E_straight_{dim}\")\n\n # CREATE MERGE MODEL\n\n # Downsample input\n e_old_in = AveragePooling2D(name=\"downsample\")(e_in)\n\n x = old_model.layers[1].layers[0](e_old_in)\n x_old = old_model.layers[1].layers[1](x)\n\n # Merge x into old model\n x = Fade(name=\"Fade_E\")([x_old, x_new])\n\n # Pass through remaining layers of old model\n w_old = []\n for b in range(block - 1, 0, -1):\n if b == block - 1 and b != 0:\n x, w1, w2 = old_model.get_layer(f\"encoder_block_router_{b}\").encode(x)\n else:\n x, w1, w2 = old_model.get_layer(f\"E_block_{b}_encoder\")(x)\n\n w_old.extend([w1, w2])\n\n # Merge new W into old model\n w_old = Add(name=\"E_Sum_Old_W\")(w_old)\n w_new = Add(name=\"E_Sum_New_W\")([new_w1, new_w2, w_old])\n e_out = Fade(name=\"Fade_E_w\")([w_old, w_new])\n\n merged_e = Model(inputs=[e_in], outputs=[e_out], name=\"E_merged\")\n\n return straight_e, merged_e\n\n\ndef expand_generator(old_model, block, filters, z_dim, noise_dim, block_type=\"AdaIN\"):\n \"\"\"\n Expands the old model by increasing the output by a factor of 2. Size is not explicit and\n is determined by doubling the last feature map size.\n\n :param old_model: the old \"straight through\" generator model to expand\n :param block: the block number for naming\n :param filters: the number of convolution filters (all 3x3 size)\n :param z_dim: the number of z-dimensions to feed style vectors\n :param block_type: the type of generator block to use. Default is \"AdaIN\" else \"ModDeMod\n :return: straight_g, merged_g\n \"\"\"\n # Pre conditions\n assert (block > 1)\n GeneratorBlock = GeneratorBlockAdaIN if block_type == \"AdaIN\" else GeneratorBlockModDemod\n\n # Create new inputs\n w_inputs = [Input(shape=(z_dim,), name=f\"G_w_input_{i + 1}\") for i in range(block)]\n noise_input = Input(shape=(noise_dim, noise_dim, 1), name=\"G_noise_input\")\n constant_input = Input(shape=(1, 1), name=\"G_constant_input\")\n\n # Pass through old model up to tRGB\n noise = noise_input\n constant = constant_input\n x = old_model.get_layer(f\"G_base\")(constant)\n x = old_model.get_layer(\"G_base_reshape\")(x)\n for b in range(block - 1):\n style = w_inputs[b]\n x = old_model.get_layer(f\"G_block_{b + 1}_style\")([x, noise, style])\n\n # Get old RGB and upsample\n old_out = old_model(w_inputs[:block - 1] + [noise_input, constant])\n old_out = UpSampling2D()(old_out)\n\n # Add new block\n style = w_inputs[block - 1]\n x = GeneratorBlock(filters=filters, block=block, z_dim=z_dim, name=f\"G_block_{block}_style\")([x, noise, style])\n\n # Transform to RGB\n new_out = tRGB(block)(x)\n\n # STRAIGHT MODEL\n g_inputs = w_inputs + [noise_input, constant_input]\n straight_g = Model(inputs=g_inputs, outputs=[new_out], name=f\"G_straight_{block}\")\n\n # MERGE MODEL\n g_out = Fade(name=\"Fade_G\")([old_out, new_out])\n\n merged_g = Model(inputs=g_inputs, outputs=[g_out], name=f\"G_merged_{block}\")\n\n return straight_g, merged_g\n\n\n","repo_name":"smthomas-sci/StyleALAE","sub_path":"layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":25852,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"22858052404","text":"import time\nfrom pyscf import lib\nfrom pyscf.lib import logger\n\nfrom mpi4py import MPI\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\nsize = comm.Get_size()\n\nINQUIRY = 50040\nclass omp(lib.with_omp_threads):\n def __init__(self, nthreads=None, interval=0.05):\n self.interval = interval\n if nthreads is None:\n nthreads = size\n if rank == 0:\n lib.with_omp_threads.__init__(self, nthreads)\n\n def __enter__(self):\n if rank == 0:\n lib.with_omp_threads.__enter__(self)\n\n def __exit__(self, type, value, traceback):\n if rank == 0:\n lib.with_omp_threads.__exit__(self, type, value, traceback)\n for i in range(1,size):\n comm.send('Done', i, tag=INQUIRY)\n else:\n while True:\n time.sleep(self.interval)\n if comm.Iprobe(source=0, tag=INQUIRY):\n task = comm.recv(source=0, tag=INQUIRY)\n if task == 'Done':\n break\n\n\nclass Logger(logger.Logger):\n def __init__(self, stdout, verbose):\n if rank == 0:\n logger.Logger.__init__(self, stdout, verbose)\n else:\n logger.Logger.__init__(self, stdout, 0)\n\n\ndef static_partition(tasks):\n segsize = (len(tasks)+size-1) // size\n start = rank * segsize\n stop = min(len(tasks), start+segsize)\n return tasks[start:stop]\n","repo_name":"sunqm/ctfccsd","sub_path":"ctf_helper.py","file_name":"ctf_helper.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31995877493","text":"import os\nimport pandas as pd\nimport numpy as np\nimport logging\nfrom detect_delimiter import detect\n\n\nclass consistencyCheck:\n \"\"\"\n check consistency and timing of data\n \"\"\"\n\n def __init__(self, qcloader):\n data_path = os.path.join(qcloader.input_dir, qcloader.config_data['Consistency_Check']['file'])\n self.data_path = data_path\n self.data_name = os.path.basename(data_path)\n self.data_df = qcloader.readFile(data_path)\n self.config_data = qcloader.config_data\n self.input_dir = qcloader.input_dir\n self.output_dir = qcloader.output_dir\n self.logFile = qcloader.logFile\n\n def checkDuplicates(self):\n \"\"\"\n Check for duplicates based on unique keys\n \"\"\"\n unique_keys = self.config_data['Consistency_Check']['Duplicates'][\"Unique_keys\"]\n data_df = self.data_df.astype(str)\n dups = data_df[unique_keys].duplicated(keep=False)\n duplicated_df = data_df[dups]\n n = duplicated_df.shape[0]\n if n == 0:\n print('No duplicate rows detected in ',self.config_data['Consistency_Check']['file'])\n logging.info('No duplicate rows detected in' + self.config_data['Consistency_Check']['file'])\n else:\n print(n, 'rows are duplicates in', self.config_data['Consistency_Check']['file'])\n logging.error(str(n) + ' rows are duplicates in ' + self.config_data['Consistency_Check']['file'])\n print(duplicated_df)\n duplicated_file = os.path.join(self.output_dir,\n ''.join((self.data_name.split('.', 1)[0], '_duplicates.csv')))\n duplicated_df.to_csv(duplicated_file)\n logging.info('Duplicates saved to ' + duplicated_file)\n\n def checkOverlap(self):\n \"\"\"\n Check for overlapping times between rows.\n Specifically, look for overlapping times in Sleep and Steps categories\n \"\"\"\n cat_id_name = self.config_data['Consistency_Check']['Overlap'][\"Category\"]\n sleep_id_name = self.config_data['Consistency_Check']['Overlap'][\"Sleep\"]\n steps_id_name = self.config_data['Consistency_Check']['Overlap'][\"Steps\"]\n categories = [sleep_id_name, steps_id_name]\n if (sleep_id_name == 'nan') & (steps_id_name == 'nan'):\n print('No Sleep or Steps categories specified for', self.config_data['Consistency_Check']['file'])\n logging.info('No Sleep or Steps categories specified for ' + self.config_data['Consistency_Check']['file'])\n\n else:\n stdate_id_name = self.config_data['Consistency_Check']['Overlap'][\"Start_Date\"]\n sttime_id_name = self.config_data['Consistency_Check']['Overlap'][\"Start_Time\"]\n endate_id_name = self.config_data['Consistency_Check']['Overlap'][\"End_Date\"]\n entime_id_name = self.config_data['Consistency_Check']['Overlap'][\"End_Time\"]\n sub_id_name = self.config_data['Consistency_Check']['Overlap'][\"Subject_ID\"]\n meas_id_name = self.config_data['Consistency_Check']['Overlap'][\"Measurement\"]\n data_df = self.data_df.astype(str)\n if ((stdate_id_name == 'nan') | (sttime_id_name == 'nan')) | \\\n ((endate_id_name == 'nan') | (entime_id_name == 'nan')):\n print('Date and time columns not specified for', self.config_data['Consistency_Check']['file'])\n logging.info('Date and time columns not specified for ' + self.config_data['Consistency_Check']['file'])\n else:\n categories = [x for x in categories if x != 'nan']\n overlap_index_list = []\n data_df = data_df.reset_index()\n for c in categories:\n subj_groups = data_df[data_df[cat_id_name] == c].groupby([sub_id_name, meas_id_name])\n for group in subj_groups:\n idx = group[1]['index'].reset_index(drop=True)\n st_df = pd.to_datetime(group[1][stdate_id_name] + ' ' + group[1][sttime_id_name])\n en_df = pd.to_datetime(group[1][endate_id_name] + ' ' + group[1][entime_id_name])\n for i in range(1, len(idx)):\n delta_t = (st_df[idx[i]] - en_df[idx[i - 1]]).total_seconds()\n if delta_t < 0:\n overlap_index_list.append((data_df.loc[idx[i], sub_id_name], idx[i - 1], idx[i], c,\n data_df.loc[idx[i], meas_id_name]))\n if len(overlap_index_list) > 0:\n df_overlap = pd.DataFrame(overlap_index_list,\n columns=['SubjID', 'Previous', 'Next', 'Category', 'Measure'])\n print('Error: The following rows have overlapping times:')\n print(df_overlap)\n overlapFile = os.path.join(self.output_dir, ''.join((os.path.splitext(\n os.path.basename(self.data_path))[0], '_overlap.csv')))\n\n df_overlap.to_csv(overlapFile, index=False)\n logging.error('Overlapping times saved to ' + overlapFile)\n else:\n print('No overlapping times detected for',self.config_data['Consistency_Check']['file'])\n logging.info('No overlapping times detected for ' + self.config_data['Consistency_Check']['file'])\n\n def measurementStatistics(self):\n \"\"\"\n Compute 5 summary statistics for numeric data\n \"\"\"\n data_df = self.data_df\n meas_id_name = self.config_data['Consistency_Check']['Measurement_Statistics'][\"Measurement\"]\n value_id_name = self.config_data['Consistency_Check']['Measurement_Statistics'][\"Value\"]\n self.data_df[value_id_name] = pd.to_numeric(data_df[value_id_name])\n desc_groups = data_df.groupby(meas_id_name)[value_id_name].describe()\n descFile = os.path.join(self.output_dir, ''.join((os.path.splitext(\n os.path.basename(self.data_path))[0], '_desc_stats.csv')))\n desc_groups.to_csv(descFile)\n logging.info('Descriptive stats saved to ' + descFile)\n print(desc_groups)\n\n def countCombinations(self):\n \"\"\"\n Count unique combinations of specified column values\n \"\"\"\n data_df = self.data_df\n cols = self.config_data['Consistency_Check']['Count_Combinations'][\"Columns\"]\n combinations_df = data_df.groupby(cols).size().reset_index().rename(columns={0: 'count'})\n combinationsFile = os.path.join(self.output_dir, ''.join((os.path.splitext(\n os.path.basename(self.data_path))[0], '_combinations_count.csv')))\n combinations_df.to_csv(combinationsFile)\n logging.info('Unique combinations saved to ' + combinationsFile)\n print(combinations_df)\n\n def crossCheck(self):\n \"\"\"\n Check if other input files contain the same values for specified columns\n :return:\n \"\"\"\n data_df = self.data_df\n cross_file = self.config_data['Consistency_Check']['Cross_Check'][\"cross_file\"]\n cross_file_path = os.path.join(self.input_dir, cross_file)\n file = open(cross_file_path, \"r\")\n head = file.readline()\n file.close()\n delim = detect(head)\n cross_df = pd.read_csv(cross_file_path, sep=delim)\n checkCols = self.config_data['Consistency_Check']['Cross_Check'][\"Columns\"]\n for col in checkCols:\n data_df_vals = np.unique(data_df[col])\n cross_df_vals = np.unique(cross_df[col])\n indata = [value for value in data_df_vals if value not in cross_df_vals]\n incross = [value for value in cross_df_vals if value not in data_df_vals]\n if len(indata) > len(incross):\n msg = self.data_name + ' has more values for ' + col + ' than ' + cross_file\n print(msg)\n logging.error(msg)\n\n elif len(incross) > len(indata):\n msg = cross_file + ' has more values for ' + col + ' than ' + self.data_name\n print(msg)\n logging.error(msg)\n else:\n msg = 'Both ' + self.data_name + ' and ' + cross_file + ' have the same values for ' + col\n print(msg)\n logging.info(msg)\n","repo_name":"andrewkoneksa/data_management","sub_path":"qclib/consistencyCheck.py","file_name":"consistencyCheck.py","file_ext":"py","file_size_in_byte":8398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4228521959","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n####### - Usefuls links\n# https://cryptobook.nakov.com/secure-random-generators/secure-random-generators-csprng\n# https://en.wikipedia.org/wiki/List_of_random_number_generators\n\nimport os\nimport ressources.bytesManager as bm\nfrom ressources.utils import millerRabin\nfrom secrets import randbits\n\n\ndef xorshiftperso(evenOrodd: int = 0, nBits: int = 512):\n \"\"\"\n Personal implementation of a random number generator using XORshift\n\n nBits: number of bits needed (e.g 2048 bits to output 2048 bits lenght's number).\n\n return even or odd number:\n - 0 odd\n - 1 even\n - both\n \"\"\"\n\n assert nBits > 23\n bytesT = int(nBits / 8)\n\n # Unpredictable random seed\n state1, state2 = os.urandom(bytesT), os.urandom(bytesT)\n\n a, b = bm.bytes_to_int(state1), bm.bytes_to_int(state2)\n\n a ^= bm.bytes_to_int(bm.circularRotation(state1, 0, 23))\n b ^= bm.bytes_to_int(bm.circularRotation(state2, 1, 17))\n\n # Generate full bytes of 1 of the size of the array\n size = int(\"0x\" + \"\".join([\"FF\" for _ in range(0, nBits)]), 16)\n # For n bits\n a &= size\n b &= size\n\n r = b + a\n\n if evenOrodd and r & 1:\n # Bitwsing and with \"-2\" (number with all bits set to one except lowest one)\n # always kills just the LSB of your number and forces it to be even\n r &= -2\n elif not evenOrodd and not (r & 1):\n # Force the number to the next odd\n r |= 1\n\n return r\n\n\ndef randomInt(evenOrodd: int = 0, n: int = 512):\n \"\"\"\n Return a random integer using secrets module.\n\n For even or odd number:\n - 0 odd\n - 1 even\n - 2 both\n \"\"\"\n r = randbits(n)\n\n if evenOrodd and r & 1:\n # Bitwsing and with \"-2\" (number with all bits set to one except lowest one)\n # always kills just the LSB of your number and forces it to be even\n r &= -2\n elif not evenOrodd and not (r & 1):\n # Force the number to the next odd\n r |= 1\n\n return r\n\n\ndef randomPrime(nBits: int = 512, gen=None, condition=lambda p: p == p, k: int = 1, Verbose=False):\n \"\"\"\n Return generated prime numbers with bitlength nBits.\n Stops after the generation of k prime numbers.\n\n You can verify a condition with condition method.\n \"\"\"\n\n # Generate random odd number of nBits\n assert nBits >= 8 and nBits <= 4096\n\n if not gen:\n gen = randomInt\n\n def find(Verbose: bool):\n maybe = gen(0, nBits)\n\n if Verbose:\n print(\"Finding one who respect given condition(s).\")\n\n while not condition(maybe):\n maybe = gen(0, nBits)\n\n if Verbose:\n print(\"Found !\")\n\n return maybe\n\n maybe = find(Verbose)\n\n b = k\n primes = []\n\n while k > 0:\n\n if millerRabin(maybe):\n primes.append(maybe)\n k -= 1\n\n maybe = find(Verbose)\n\n if b:\n return primes[0]\n\n return primes\n\n\ndef safePrime(nBits: int = 1024, randomFunction=xorshiftperso, easyGenerator: bool = False):\n \"\"\"\n The number 2p + 1 associated with a Sophie Germain prime is called a safe prime.\n In number theory, a prime number p is a Sophie Germain prime if 2p + 1 is also prime\n\n nBits: number of bits wanted for output prime number.\n\n Based on:\n https://eprint.iacr.org/2003/186.pdf\n\n The primes to be generated need to be 1024 bit to 2048 bit long for good cryptographical uses.\n\n Multiprocessing safe prime computing using cpu_percentage of your cpus.\n\n Return (safe_prime,sophieGermain_prime) tuple's\n \"\"\"\n\n from multiprocessing import Pool, cpu_count, Manager\n import signal # https://docs.python.org/3/library/signal.html\n\n manager = Manager()\n\n c = int((85 / 100) * cpu_count())\n\n original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)\n poule = Pool(c)\n signal.signal(signal.SIGINT, original_sigint_handler)\n\n flag = manager.Value(\"i\", 0) # Can be shared between processes.\n\n return_list = manager.list([])\n\n data = [(nBits, randomFunction, easyGenerator, False, flag, return_list) for _ in range(c)]\n\n # Permit to quit safe prime generation with exit signal\n try:\n poule.starmap(safePrime_worker, data)\n except KeyboardInterrupt:\n poule.terminate()\n return False\n else:\n poule.close()\n\n return list(return_list)[0]\n\n\ndef safePrime_worker(nBits: int = 1024, randomFunction=None, easyGenerator: bool = False, Verbose: bool = False, flag=None, returnL: list = []):\n \"\"\"\n Function executed on each process for safe prime generation\n \"\"\"\n\n import ressources.interactions as it\n from multiprocessing import Manager\n\n if not flag:\n flag = Manager().Value(\"i\", 0)\n\n if easyGenerator:\n if Verbose:\n print(\"Easy generator choosen.\")\n\n p_filter = lambda p: p % 3 == 2 and (p % 12 == 1 or p % 12 == 11) # p = 1 mod 12 make 11 as primitive root\n\n else:\n\n p_filter = lambda p: p % 3 == 2\n\n while not bool(flag.value):\n # For faster searching\n # Calculate 2q +1 and (q-1)//2\n # Return Sophie Germain's prime according to what is prime.\n\n if randomFunction is None:\n randomFunction = randomInt\n\n q = randomPrime(nBits, randomFunction, p_filter, 1)\n\n p1 = 2 * q + 1\n\n p2 = (q - 1) // 2\n\n if Verbose:\n it.clear()\n print(f\"Prime {q} candidate's.\")\n\n if millerRabin(p1):\n\n if Verbose:\n print(\"q is prime and 2q +1 too.\")\n print(f\"\\nSophie Germain prime's: {q}\\n\")\n\n sophieGermain_prime, safe_prime = q, p1\n\n # Safe prime found\n flag.value = 1\n returnL.append((safe_prime, sophieGermain_prime))\n\n elif millerRabin(p2):\n\n if Verbose:\n print(\"q is prime and (q-1)/2 too.\")\n print(f\"\\nSophie Germain prime's: {p2}\\n\")\n\n sophieGermain_prime, safe_prime = p2, q\n\n # Safe prime found\n flag.value = 1\n returnL.append((safe_prime, sophieGermain_prime))\n\n else:\n if Verbose:\n print(\"But 2 * him + 1 and (him - 1) / 2 doesn't seem to be primes...\\n\")\n\n\ndef genSafePrimes(n: int, L: list, nBits: int, randomFunction=None):\n \"\"\"\n Generate n tuples of distincts safe primes number's and append them into a list L.\n Randomly choosing easy generator or not.\n \"\"\"\n import random as rd\n\n for _ in range(n):\n # bool(rd.getrandbits(1)) faster than rd.choice([True,False])\n s = safePrime(nBits, randomFunction, bool(rd.getrandbits(1)))\n\n if not s: # s is false due to interruption of research\n return s\n\n if s not in L:\n L.append(s)\n\n return L\n","repo_name":"theogobinet/Katsumi","sub_path":"ressources/prng.py","file_name":"prng.py","file_ext":"py","file_size_in_byte":6835,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"31"} +{"seq_id":"38514756154","text":"\ndef check_armstring_number(a):\n su = 0 \n temp = a\n while temp >0 :\n rem = temp %10\n su = su + pow(rem,3)\n temp = temp//10\n if su==a:\n print('Armstrong Number')\n else:\n print('Not Armstrong Number')\n","repo_name":"mehra-rohit/Data-Structures-and-Algorithms","sub_path":"Algorithms/armstrong_number.py","file_name":"armstrong_number.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40550088166","text":"carpo_student_version = '0.0.8'\n\nimport os, stat\nprint('1. Installing carpo-student version ' + carpo_student_version)\nos.system('pip install carpo-student==' + carpo_student_version)\n\nrun_file_name = 'RUN_JUPYTER_LAB.command'\nprint('2. Installing ' + run_file_name)\n\nfile_content = '''\ncd \"{}\";\njupyter lab\n'''\nroot_dir = os.getcwd()\noutput_file = os.path.join(root_dir, run_file_name)\nwith open(output_file, 'w') as fp:\n fp.write(file_content.format(root_dir))\nst = os.stat(output_file)\nos.chmod(output_file, stat.S_IWRITE | stat.S_IREAD | stat.S_IEXEC)\n\nprint('\\t', run_file_name,'is created.')\nprint('\\t In the future, to run Jupyter Lab, double click on this file.')","repo_name":"vtphan/carpo","sub_path":"installation-script/install_carpo_osx.py","file_name":"install_carpo_osx.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"72549898007","text":"T = int(input())\n\n\ndef is32(n):\n try:\n bitstring=bin(n)\n except (TypeError, ValueError):\n return False\n \n if len(bin(n)[2:]) <=32:\n return True\n else:\n return False\n\n\ndef mat(A):\n if len(A) == 1:\n return A[0]\n else:\n i = 1\n B = []\n for i in range(1,len(A)):\n B.append(A[i-1]-A[i]) \n #print B\n return mat(B)\n\nif T<=5:\n for _ in range(T):\n s = int(input())\n t = map(int,raw_input().split())\n check = [] \n for i in t:\n check.append(is32(i))\n if False not in check:\n if len(t)==s:\n print (mat(t)%1000000007)\n","repo_name":"Dawny33/Code","sub_path":"Hackerrank/CSIndia/Q-2/arrsimp.py","file_name":"arrsimp.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"948301081","text":"\"\"\"\n******\n11줄\n\"\"\"\n\n# a = \"*\" * 6\n# for _ in range(0,11):\n# print(a)\n\n\n\"\"\"\nfor 문을 이용해서 \n*****\n****\n***\n**\n*\n\"\"\"\n\na = \"*\"\nb = 6\nfor i in range(0,5):\n if b <=6:\n b = b - 1\n print(a * b)\n #희귀답안\n\n# a = \"*\"\n# for i in reversed(range(1,6)):\n# print(a * i) #명품답안\n\n\"\"\"\n*******\n* *\n* *\n* *\n* *\n*******\n\n\"\"\"\n\na = \"*\"\nb = 0\nfor i in range(0,6):\n if b ==0:\n print(a * 7)\n b = b + 1\n if b >= 1 and b <=4:\n print(a + \" \" * 5 + a)\n if b==5:\n print(a * 7)\n\n\"\"\"\nfor loop을 통해서 \n\n666666\n55555\n4444\n333\n22\n1\n22\n333\n4444\n55555\n만들기\n\"\"\"\n\na = 7\nb = 0\n\n# for i in range(0,5):\n# a = a-1\n# print(str(a) * a)\n# if a == 2:\n# for a in range(0,5):\n# a = a + 1\n# print(str(a) * a)\n #40점짜리\n\n # for a in range(0,5):\n # b = b + 1\n # print(str(b) * b)\n\nfor i in range(0,10):\n if i <5:\n a = a-1\n print(str(a) * a)\n if i >= 5: #else 라고 써도 됨\n b = b + 1\n print(str(b) * b)\n\n\n\n #print(reversed(str(a)* a))\n\n # sixtoone = str(a) * a\n # c = reversed(sixtoone)\n # print(str(c))\n\n\n\"\"\"\n0\n01\n012\n0123\n01234\n012345\n\"\"\"\n\na = \"0\"\nb = 1\nc = 0\n\nfor i in range(0,8):\n c = c + 1\n d = \"1\" * c\n b = b + int(d)\n print(str(a)+str(b-1))\n\n#for i in range(6):\n# for j in range(i+1):\n# print(j,end=\"\")\n# print()\n#모범답안\n\n\"\"\"\n1~100까지의 수 중에서 홀수와 홀수의 합을 실행 결과와 같이\n출력하는 프로그램을 작성하시오.\n(1+3+5+...+99)\n\"\"\"\na = 0\n\nfor i in range(0,100):\n if i % 2 ==1:\n a = a + i\nprint(a)","repo_name":"suyeon1109/coding-academy","sub_path":"dailywork/looppractice.py","file_name":"looppractice.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"24662773488","text":"# Merge Similar Items\n# You are given two 2D integer arrays, items1 and items2, representing two sets of items. Each array items has the following properties:\n# items[i] = [valuei, weighti] where valuei represents the value and weighti represents the weight of the ith item.\n# The value of each item in items is unique.\n# Return a 2D integer array ret where ret[i] = [valuei, weighti], with weighti being the sum of weights of all items with value valuei.\n# Note: ret should be returned in ascending order by value.\n\ndef mergeSimilarItems (items1, items2) :\n # idValues = {}\n # for id, value in items1 :\n # idValues[id] = idValues.get(id, 0) + value\n # for id, value in items2 :\n # idValues[id] = idValues.get(id, 0) + value\n # sortedValues = [[id, value] for id, value in idValues.items()]\n # return sorted(sortedValues)\n items1, items2 = sorted(items1), sorted(items2)\n left, right = 0, 0\n result = []\n while (left < len(items1) or right < len(items2)) :\n if (right == len(items2) or (left < len(items1) and items1[left][0] < items2[right][0])) :\n result.append(items1[left])\n left += 1\n elif (left == len(items1) or (right < len(items2) and items1[left][0] > items2[right][0])) :\n result.append(items2[right])\n right += 1\n else :\n result.append([items1[left][0], items1[left][1] + items2[right][1]])\n left += 1\n right += 1\n return result\nprint(mergeSimilarItems(items1 = [[1,1],[4,5],[3,8]], items2 = [[3,1],[1,5]]))","repo_name":"vibhatsu08/leetcode-python","sub_path":"mergeSimilarItems.py","file_name":"mergeSimilarItems.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69804539289","text":"import collections\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\ndef findDuplicateSubtrees_dfs(root: TreeNode) -> list:\n count = collections.Counter()\n ans = []\n def collect(node):\n if not node: return \"#\"\n serial = \"{},{},{}\".format(node.val, collect(node.left), collect(node.right))\n count[serial] += 1\n if count[serial] == 2:\n ans.append(node)\n return serial\n \n collect(root)\n return ans\n\ndef findDuplicateSubtrees_uid(root: TreeNode) -> list:\n trees = collections.defaultdict()\n trees.default_factory = trees.__len__\n count = collections.Counter()\n ans = []\n def lookup(node):\n if node:\n uid = trees[node.val, lookup(node.left), lookup(node.right)]\n count[uid] += 1\n if count[uid] == 2:\n ans.append(node)\n return uid\n lookup(root)\n return ans","repo_name":"DengBoCong/Algorithm","sub_path":"core/tmp/Python/tree/find_duplicate_subtrees.py","file_name":"find_duplicate_subtrees.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"31"} +{"seq_id":"22086716505","text":"from django.test import TestCase\nfrom rest_framework.test import APIClient\n\n\nclass TestAccountView(TestCase):\n def setUp(self):\n self.admin_data = {\n \"username\": \"admin\",\n \"password\": \"1234\",\n \"is_superuser\": True,\n \"is_staff\": True,\n }\n\n self.client = APIClient()\n\n def test_create_admin_account(self):\n expected_account_creation_response = {\n \"id\": 1,\n \"username\": \"admin\",\n \"is_superuser\": True,\n \"is_staff\": True,\n }\n\n actual_account_creation_response = self.client.post(\n \"/api/accounts/\",\n self.admin_data,\n format=\"json\",\n ).json()\n\n self.assertDictEqual(\n actual_account_creation_response,\n expected_account_creation_response,\n )\n\n def test_create_duplicated_account(self):\n self.client.post(\n \"/api/accounts/\",\n self.admin_data,\n format=\"json\",\n ).json()\n\n duplicate_account_creation_response = self.client.post(\n \"/api/accounts/\",\n self.admin_data,\n format=\"json\",\n )\n\n self.assertTrue(duplicate_account_creation_response.status_code, 409)\n\n def test_login_user(self):\n self.client.post(\n \"/api/accounts/\",\n self.admin_data,\n format=\"json\",\n ).json()\n\n login_response = self.client.post(\n \"/api/login/\",\n {\n \"username\": self.admin_data[\"username\"],\n \"password\": self.admin_data[\"password\"],\n },\n format=\"json\",\n )\n\n self.assertIn(\"token\", login_response.json().keys())\n\n def test_login_inexisting_user(self):\n login_response = self.client.post(\n \"/api/login/\",\n {\"username\": \"alala\", \"password\": \"ofdjsf\"},\n format=\"json\",\n )\n\n self.assertEqual(login_response.status_code, 401)\n","repo_name":"enias-oliveira/parking-lot","sub_path":"authentication/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3509853412","text":"import numpy as np \nimport matplotlib.pyplot as plt \nimport matplotlib\n\n# calculates how many lines to skip when calculating velocity\ndef dn(dLPD, dT, v_SP, dOUT, ALPHA=10):\n\tdenominator = dT*v_SP*dOUT*alpha\n\treturn int(dLPD/denominator)\n\n# output format in the log file\n# 0.Step 1.Atoms 2.f_brlc[1] 3.f_brlc[2] 4.f_brlc[3] 5.f_trlc[1]\n# 6.f_trlc[2] 7.f_trlc[3] 8.c_3[1] 9.c_3[2] 10.c_3[3] 11.c_4[1]\n# 12.c_4[2] 13.c_4[3] 14.f_pull[1] 15.f_pull[2] 16.f_pull[3]\n# 17.f_pull[4] 18.f_pull[5] 19.f_pull[6] 20.f_pull[7] \n\n#c_3[1], c_3[2], and c_3[3] are the x-, y- and z-dir position of the center...\n# ...of mass of the bottom (shearing) block, respectively,\n#c_4[1], c_4[2], and c_4[3] are the x-, y- and z-dir position of the center...\n# ...of mass of the top (fixed) block, respectively,\n#f_pull[1] is the x-component of the spring force (the spring is 1-D in x-dir, so...\n# ...it only has x-components of force and position), and f_pull[5] is the x-dir...\n# ....load point position (the end of the spring not attached to the block).\n\nprint('started...')\n\nk = '54e2' # N/m\nk_gouge = 54.4946245293753 # N/m\ndelta_lpd = 5.5e-2 # cm\nv_sp = 10 # cm/s\ndt = 9.33e-9 # s\ndelta_out = 1000\nalpha = 25\nderivative_steps = dn(delta_lpd,dt,v_sp,delta_out,alpha)\nnum_bins = 150\nsigma_n = 50e6 # Pa\ngouge_thickness = 6e-2 # m\ngouge_length = 16e-2 # m\ngouge_depth = 1e-3 # m\nA = gouge_length*gouge_depth # m^2\n\noutput_textfile = 'velocity_histograms/k'+k+'_veloctity_histogram.txt'\nfile = 'k'+k+'.txt'\n\ntry: sim_data = np.loadtxt(file)\nexcept: sim_data = np.loadtxt('res99'+file)\nsim_length = len(sim_data)\n\n# number of time steps between outputs\nnum_dt = sim_data[1,0] - sim_data[0,0] \n# array of friction values\nfriction = np.array(sim_data[0:sim_length,2]/sim_data[0:sim_length,3])\n\n# caluculate the instantaneous derivative as a function of time\n############\ntime_window = derivative_steps*dt\nvelocities = []\ndfdts = []\nfor i, j in zip(range(0, friction.size-derivative_steps, derivative_steps),\n\trange(derivative_steps, friction.size, derivative_steps)):\n\tinstantaneous_dfdt = (friction[j]-friction[i])/time_window\n\tdfdts.append(instantaneous_dfdt)\n\n# time array with centered derivative increments\ntime = np.arange(\n\t0, friction.size-derivative_steps, derivative_steps) + derivative_steps/2\n# convert time to mega seconds\ntime /= 1e6\n\ninelastic_velocities = -sigma_n*A*np.array(dfdts)/k_gouge + v_sp/100 # m/s\n\nprint('max inelastic velocity: '+str(np.max(inelastic_velocities))+' m/s')\n\nplt.rc('font', family='serif', serif = \"cmr10\", size=20)\nplt.rcParams['mathtext.fontset']='cm'\nplt.figure(figsize=(10,6))\nmatplotlib.rcParams['axes.unicode_minus'] = False\n\nplt.errorbar(x=time, y=inelastic_velocities, linewidth=1, fmt='')\n\n#plt.legend(loc='lower right', fontsize=12)\nplt.xlabel('time $t$ (Ms)')\nplt.ylabel('\\ninstantaneous inelastic x-velocity $d\\delta_x/dt$ (m/s)')\nplt.title('\\nnumber of log file lines between $v$ calculations = '+str(\n\tderivative_steps)+'\\n$v_{sp}$=10cm/s, $k_{sp}=$'+k+'N/m\\n')\nplt.tight_layout(pad=0)\nplt.grid(linestyle='--')\n#plt.ylim(bottom=0)\n#plt.xscale('log')\nplt.show()\n\nhist, bins = np.histogram(inelastic_velocities, bins=np.logspace(-5, 8, num_bins))\nhist = np.array(hist).astype(np.float32)\ncenters = []\nfor edge1, edge2 in zip(bins[:-1], bins[1:]):\n\tcenters += [edge1 + ((edge2-edge1)/2)]\n\nwith open(output_textfile, 'w') as ttt:\n\tfor x,y in zip(centers,hist):\n\t\tttt.write(str(x)+' '+str(y)+'\\n')\n\nplt.rc('font', family='serif', serif = \"cmr10\", size=20)\nplt.rcParams['mathtext.fontset']='cm'\nplt.figure(figsize=(10,6))\nmatplotlib.rcParams['axes.unicode_minus'] = False\n\nplt.stem(centers, hist, 'r', use_line_collection=True)\n\nplt.xscale('log')\nplt.yscale('log')\n#plt.xlim(left=1e-4)\n#plt.legend(loc='best')\nplt.xlabel('instantaneous inelastic x-velocity $d\\delta_x/dt$ (m/s)\\n')\nplt.ylabel('\\noccurences')\nplt.title('Histogram of instantaneous inelastic velocities, bins='+str(\n\tnum_bins)+'\\nnumber of log file lines between $v$ calculations = '+str(\n\tderivative_steps)+'\\n$v_{sp}$=10cm/s, $k_{sp}=$'+k+'N/m\\n')\nplt.tight_layout(pad=0)\nplt.grid(linestyle='--')\nplt.show()\n","repo_name":"balessio/granularMechanics","sub_path":"velocity_histogram_kgouge.py","file_name":"velocity_histogram_kgouge.py","file_ext":"py","file_size_in_byte":4100,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"25655781436","text":"from reg_time_spider import *\n\nurl = 'https://data.stats.gov.cn/easyquery.htm?cn=E0103'\nparams = {}\nheaders = {'User-Agent': 'Mozilla/5.0(Windows;U;Windows NT6.1;en-US;rv:1.9.1.6) Geko/20091201 Firefox/3.5.6'}\nparams['m'] = 'QueryData'\nparams['dbcode'] = 'fsnd'\nparams['rowcode'] = 'zb'\nparams['colcode'] = 'sj'\nparams['k1'] = str(get_time())\n\n# 读取和写入\n\nrequests.packages.urllib3.disable_warnings()\nprint('农、林、牧、渔业总产值及指数.xlsx')\nstart_get('A0D05', 'result/农、林、牧、渔业总产值及指数.xlsx', url=url, params=params, headers=headers)\nprint('农用塑料薄膜使用量.xlsx')\nstart_get('A0D0L', 'result/农用塑料薄膜使用量.xlsx', url=url, params=params, headers=headers)\nprint('主要农业机械年末拥有量.xlsx')\nstart_get('A0D0G', 'result/主要农业机械年末拥有量.xlsx', url=url, params=params, headers=headers)\nprint('有效灌溉面积、农用化肥施用量、农村水电站及用电量.xlsx')\nstart_get('A0D0H', 'result/有效灌溉面积、农用化肥施用量、农村水电站及用电量.xlsx', url=url, params=params, headers=headers)\nprint('农林牧渔业增加值.xlsx')\nstart_get('A0D06', 'result/农林牧渔业增加值.xlsx', url=url, params=params, headers=headers)\nprint('农村水电建设和发电量.xlsx')\nstart_get('A0D0I', 'result/农村水电建设和发电量.xlsx', url=url, params=params, headers=headers)\nprint('全体及分城乡居民收支基本情况（2013-）.xlsx')\nstart_get('A0A00', 'result/others/全体及分城乡居民收支基本情况（2013-）.xlsx', url=url, params=params, headers=headers)\nprint('每万人口卫生技术人员数.xlsx')\nstart_get('A0O03', 'result/others/每万人口卫生技术人员数.xlsx', url=url, params=params, headers=headers)\nprint('互联网主要指标发展情况.xlsx')\nstart_get('A0G0J', 'result/others/互联网主要指标发展情况.xlsx', url=url, params=params, headers=headers)\nprint('城乡居民社会养老保险情况.xlsx')\nstart_get('A0S0B', 'result/others/城乡居民社会养老保险情况.xlsx', url=url, params=params, headers=headers)\nprint('农产品生产价格指数(上年=100).xlsx')\nstart_get('A0907', 'result/others/农产品生产价格指数(上年=100).xlsx', url=url, params=params, headers=headers)","repo_name":"ssociopath/rural-data-spider","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11033971790","text":"from typing import Any, Dict, List, Type, TypeVar, Union\n\nfrom attrs import define as _attrs_define\nfrom attrs import field as _attrs_field\n\nfrom ..types import UNSET, Unset\n\nT = TypeVar(\"T\", bound=\"MainAccountInformation\")\n\n\n@_attrs_define\nclass MainAccountInformation:\n \"\"\"\n Attributes:\n created (Union[Unset, str]):\n loyalty_title (Union[Unset, str]):\n position (Union[Unset, str]):\n \"\"\"\n\n created: Union[Unset, str] = UNSET\n loyalty_title: Union[Unset, str] = UNSET\n position: Union[Unset, str] = UNSET\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n created = self.created\n loyalty_title = self.loyalty_title\n position = self.position\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update({})\n if created is not UNSET:\n field_dict[\"created\"] = created\n if loyalty_title is not UNSET:\n field_dict[\"loyalty_title\"] = loyalty_title\n if position is not UNSET:\n field_dict[\"position\"] = position\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n created = d.pop(\"created\", UNSET)\n\n loyalty_title = d.pop(\"loyalty_title\", UNSET)\n\n position = d.pop(\"position\", UNSET)\n\n main_account_information = cls(\n created=created,\n loyalty_title=loyalty_title,\n position=position,\n )\n\n main_account_information.additional_properties = d\n return main_account_information\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties\n","repo_name":"wiese-m/tibia-data-api-client","sub_path":"tibia_data_api_client/models/main_account_information.py","file_name":"main_account_information.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73140566808","text":"'''\n2019/01/14 初十 情人节\n吴恩达作业\n'''\n\n'''\n单变量线性回归\n案例：开分店，根据城市的人口预测利润\n\n训练数据集：\n人口数据,利润 ---》 X，Y\n6.11 17.59\n5.52 9.13\n8.51 13.66\n7.00 11.85\n\n得到线性回归函数：h(x)=W0 + W1*x 最大程度拟合数据的分布\n求出 W1 W2\n代价函数cost funtion J=(1/2m)sun(h(i)-y(i))^2 ---》计算每个样本的误差再求和\n把每个训练样本的x代入h,与实际值y求差值（即x一样时，y的差值）。把所有的样本代入求平方和，再除m求平均，除2时因为求导时产生2，除不除2没关系\n目标：最小化代价函数\n\n第一步：确定代价函数\n第二步：最小化代价函数\n\n（1）初始化参数W0 W1 为0\n（2）改变W0 W1 减小代价函数，直到最小值\n\n梯度下降法：求最值\nW = W -α*(∂J/∂W) 偏微分---》梯度方向\n迭代到某一时刻，代价函数的值不变或者变化很小时迭代完成\n\nh(x)=W0+W1*x = X*W\nw=[W0\n W1]\nX=[1 x]\n代价函数：J=(1/2m)sun(X*W-y(i))^2\n梯度下降公式：\nW0=W0-α*(1/m)sum(h(xi)-y(i))x(i)0\nW1=W1-α*(1/m)sum(h(xi)-y(i))x(i)1\nw=[W0\n W1]\nW=W-αX^T(X*W-y)\n维度：X(m,n)\n y(m,1)\ntheta(n,1)\n'''\n\n\nimport numpy as np #科学计算库，处理多维数据\nimport pandas as pd #基于numpy，二维数据，提供行标列标\nimport matplotlib.pyplot as plt\n#matplotlib 2D绘图库\n#matplot.pyplot 提供类似MATLAB的绘图框架\n\n#sed 字符串分隔符\n#header 指定第几行作为列名，没有指定列名默认header=0\n#names 指定列名\n\n#读取数据\ndata = pd.read_csv('data',names=['population','porfit'])\ndata.head()\ndata.tail()\ndata.describe()#均值方差等\ndata.info()\n\ndata.plot.scatter('population','porfit',label='population')\nplt.show()\n\n#添加1列\ndata.insert(0,'ones',1)\nX=data.iloc[:,0:-1]\ny=data.iloc[:,-1]\n#转成数组\nX=X.values\ny=y.values\ny=y.reshape(y.shape[0],1)\n\ndef costFuntion(X,y,theta):\n '''\n 损失函数\n :param X:\n :param y:\n :param theta:\n :return:\n '''\n inner = np.power(X@theta - y,2)\n return np.sum(inner)/(2*len(X))\n\n#初始化\ntheta = np.zeros((2,1))\ncost_init = costFuntion(X,y,theta)\n\n#梯度下降\ndef gradlientDescent(X,y,theta,alpha,iters):\n costs = []\n for i in range(iters):\n theta = theta - (X.T @ (X @ theta - y)) *alpha/len(X)\n cost = costFuntion(X,y,theta)\n costs.append(cost)\n return theta,costs\n\n#训练\nalpha = 0.01\niters = 2000 #迭代2000次\ntheta,costs=gradlientDescent(X,y,theta,alpha,iters)\n\n# fig,ax=plt.subplots(2,3)#fig画布，ax实例\n# ax1 = ax[0,0]\nfig,ax = plt.subplot\nax.plot(np.arange(iters),costs)\nax.set(xlabel='iter',ylabel='cost',title='cost and iter')\nplt.show()\n\n\nx = np.linspace(y.min(),y.max(),100)\ny_ = theta[0,0]+theta[1,0]*x\nfig,ax=plt.subplot()\nax.scatter(X[:,1],y,label='train data')\nax.plot(x,y_,label='pridict')\nax.legend()\nax.set(xlabel='population',ylabel='porfit',title='cost and iter')\n\n\n#用正规方程\ndef normalEquation(X,y):\n theta = np.linalg.inv(X.T @ X)@X.T @y\n return theta\n'''\n多变量线性回归\n\n数据预处理：特征归一化\n消除特征值之间量纲的影响，各特征值处于同一数量级\n提升模型的收敛速度 ---》 等高线圆\n提升模型的精度\n\n归一化方法：\n1.归一化到标准到正态分布 z=(x(i)-μ)/σ μ特征的均值 σ特征的标准差 量化后的特征分布在[-1,1]区间\n2.z=(x(i)-min(x))/(max(x)-min(x)) 量化后特征分布到[0,1]\n\n'''\n#读取数据\ndata = pd.read_csv('txt',names=['size','bedrooms','price'])\ndata.head()\n\n#特征归一化 --》 数据的量级在相同的范围\ndef normalize_feature(data):\n return (data-data.mean())/data.std()\n\ndata = normalize_feature(data)\n\ndata.plot.scatter('size','price',label='size')\ndata.plot.scatter('bedroom','price',label='size')\nplt.show()\n\n#添加全为1的列\ndata.insert(0,'ones',1)\n\n#构造数据集\nX=data.iloc[:,0:-1]\ny=data.iloc[:,-1]\n\n#转为数组\nX = X.values\ny=y.values\ny=y.reshape(y.shape,1)\n\ntheta = np.zeros((3,1))\ncost_init = costFuntion(X,y,theta)\n\n#不同alpha的效果\nalpha = [0.003,0.03,0.01,0.001,0.0001]\niters = 2000 #迭代2000次\n\nfig,ax = plt.subplots()\nfor i in alpha:\n _,costs = gradlientDescent(X,y,theta,i,iters)\n ax.plot(np.arange(iters),costs,label=i)\nax.legend()\nax.set(xlabel='iter',ylabel='cost',title='cost and iter')\nplt.show()\n\n\n'''\n正规方程 一下子算出w\nw=(X^T X)^-1 X^T y \n如果不可逆，一般考虑两种情况：或求伪逆矩阵\n1.移除冗杂特征，一些特征存在线性依赖，删除几个\n2.特征太多时，删除一些特征。对于小样本数据使用正则化\n\nnumpy.linalg模块包含线性代数的函数，求逆矩阵，特征值，解线性方程组，求解行列式\ninv函数计算逆矩阵\n \n梯度下降：\n需要选择学习率\n需要多次迭代\n特征值范围相差太大时要特征缩放\n当特征数n很大时，能工作很好\n\n正规方程：\n不需要选择学习率\n不需要多次迭代\n不需要特征缩放\n当特征数n很大时，运算很慢，因为求逆矩阵的时间复杂度是O(n^3)\n\n何时选择梯度下降，正规方程：\nn<10000时，用正规方程，n>=10000时，用梯度下降\n一些复杂算法只能用梯度下降\n \n \n \n \n'''\n\n\n\n\n\n\n\n\n\n","repo_name":"lhgcs/PythonDemoRepository","sub_path":"deepLearning/wuenda_homework/wed_work.py","file_name":"wed_work.py","file_ext":"py","file_size_in_byte":5277,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25280547960","text":"def answer_finder():\n n, k = [int(x) for x in input().split()]\n if k==1:\n if n%4 ==0:\n print('On')\n else:\n print('Ambiguous')\n else:\n if n%4==0:\n print('Off')\n else:\n print('On')\n\ntest_cases = int(input())\n\nwhile test_cases:\n test_cases -= 1\n answer_finder()","repo_name":"naveen701526/Code-Chef-Contest-Problems","sub_path":"0-1000 Beginner Level/DARLIG.py","file_name":"DARLIG.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74668135449","text":"import queue\nimport threading\nimport tkinter as tk\nfrom tkinter import filedialog, messagebox, simpledialog\n\nimport cv2\nfrom PIL import Image, ImageTk\n\nfrom config import VIEWER_HEIGHT\nfrom utils import VideoReader, process_video\n\n\nclass VideoPlayerApp:\n def __init__(self, root):\n self.root = root\n self.root.title(\"Video Player with Frame Counter\")\n self.vid_reader = None\n self.playing = False\n self.stop_signal = threading.Event()\n self.video_thread = None\n self.start_frame = 0\n self.queue = queue.Queue()\n self.video_info_label = tk.Label(root, text=\"\")\n self.video_info_label.pack(pady=10)\n self.btn_open = tk.Button(\n root, text=\"Open Video\", command=self.open_video\n )\n self.btn_open.pack(pady=10)\n self.canvas = tk.Canvas(root, bg=\"black\")\n self.canvas.pack(pady=20)\n self.label_frame_num = tk.Label(root, text=\"Frame: 0\")\n self.label_frame_num.pack(pady=10)\n self.btn_start = tk.Button(\n root, text=\"Start\", command=self.start_video\n )\n self.btn_start.pack(side=tk.LEFT, padx=10)\n self.btn_stop = tk.Button(root, text=\"Stop\", command=self.stop_video)\n self.btn_stop.pack(side=tk.LEFT, padx=10)\n self.btn_reset = tk.Button(\n root, text=\"Reset\", command=self.reset_video\n )\n self.btn_reset.pack(side=tk.LEFT, padx=10)\n self.btn_process_save = tk.Button(\n root, text=\"Process & Save\", command=self.process_and_save\n )\n self.btn_process_save.pack(pady=10)\n\n def open_video(self):\n filepath = filedialog.askopenfilename()\n if not filepath:\n return\n self.vid_reader = VideoReader(filepath)\n original_width, original_height = self.vid_reader.get_size()\n aspect_ratio = original_width / original_height\n if original_height > VIEWER_HEIGHT:\n new_height = VIEWER_HEIGHT\n new_width = int(new_height * aspect_ratio)\n else:\n new_height = int(original_height)\n new_width = int(original_width)\n self.new_size = (new_width, new_height)\n self.canvas.config(width=new_width, height=new_height)\n video_name = self.vid_reader.get_name()\n total_frames = self.vid_reader.get_frame_count()\n info_text = f\"Video: {video_name} | Total Frames: {total_frames}\"\n self.video_info_label[\"text\"] = info_text\n self.update_video()\n\n def start_video(self):\n self.stop_video()\n self.root.after(100, self._start_video_thread)\n self.root.after(100, self.display_from_queue)\n\n def _start_video_thread(self):\n if not self.video_thread or not self.video_thread.is_alive():\n self.stop_signal.clear()\n self.video_thread = threading.Thread(target=self.update_video)\n self.video_thread.start()\n\n def stop_video(self):\n self.stop_signal.set()\n if self.video_thread and self.video_thread.is_alive():\n self.video_thread.join()\n\n def reset_video(self):\n new_start_frame = simpledialog.askinteger(\"Input\", \"Reset to frame:\")\n if new_start_frame is not None:\n self.start_frame = new_start_frame\n self.vid_reader.cap.set(cv2.CAP_PROP_POS_FRAMES, self.start_frame)\n self.label_frame_num[\"text\"] = f\"Frame: {self.start_frame}\"\n\n def update_video(self):\n if not self.vid_reader:\n return\n while self.vid_reader.cap.isOpened() and not self.stop_signal.is_set():\n ret, frame = self.vid_reader.cap.read()\n if ret:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame = cv2.resize(frame, self.new_size)\n self.queue.put(frame)\n else:\n break\n\n def display_from_queue(self):\n try:\n frame = self.queue.get_nowait()\n self.photo = ImageTk.PhotoImage(image=Image.fromarray(frame))\n self.canvas.create_image(0, 0, anchor=tk.NW, image=self.photo)\n self.label_frame_num[\n \"text\"\n ] = f\"Frame: {int(self.vid_reader.get_current_frame())}\"\n self.root.after(1, self.display_from_queue)\n except queue.Empty:\n self.root.after(100, self.display_from_queue)\n\n def process_and_save(self):\n output_video_name = simpledialog.askstring(\n \"Input\", \"Output video name:\"\n )\n base_output_dir = filedialog.askdirectory(\n title=\"Select Base Output Directory\"\n )\n if not base_output_dir:\n messagebox.showwarning(\"Warning\", \"No directory selected!\")\n return\n start_frame = simpledialog.askinteger(\"Input\", \"Start frame:\")\n end_frame = simpledialog.askinteger(\"Input\", \"End frame:\")\n if all(\n [\n output_video_name,\n base_output_dir,\n start_frame is not None,\n end_frame is not None,\n ]\n ):\n threading.Thread(\n target=process_video,\n args=(\n self.vid_reader,\n output_video_name,\n base_output_dir,\n start_frame,\n end_frame,\n ),\n ).start()\n messagebox.showinfo(\"Info\", \"Processing started!\")\n\n\nif __name__ == \"__main__\":\n root = tk.Tk()\n app = VideoPlayerApp(root)\n root.mainloop()\n","repo_name":"shuzo57/CreateDataset","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70663162327","text":"#!/usr/bin/env python\n\nimport rospy\nfrom std_msgs.msg import Bool\nfrom dbw_mkz_msgs.msg import ThrottleCmd, SteeringCmd, BrakeCmd, SteeringReport\nfrom styx_msgs.msg import Lane, Waypoint\nfrom geometry_msgs.msg import TwistStamped, PoseStamped\nimport math\n\nfrom twist_controller import Controller\nfrom yaw_controller import YawController\nfrom pid import PID\n\nimport time\nimport numpy as np\n\nLOOKAHEAD_WPS = 30 # Number of waypoints we will publish. You can change this number\n\n\n'''\nYou can build this node only after you have built (or partially built) the `waypoint_updater` node.\n\nYou will subscribe to `/twist_cmd` message which provides the proposed linear and angular velocities.\nYou can subscribe to any other message that you find important or refer to the document for list\nof messages subscribed to by the reference implementation of this node.\n\nOne thing to keep in mind while building this node and the `twist_controller` class is the status\nof `dbw_enabled`. While in the simulator, its enabled all the time, in the real car, that will\nnot be the case. This may cause your PID controller to accumulate error because the car could\ntemporarily be driven by a human instead of your controller.\n\nWe have provided two launch files with this node. Vehicle specific values (like vehicle_mass,\nwheel_base) etc should not be altered in these files.\n\nWe have also provided some reference implementations for PID controller and other utility classes.\nYou are free to use them or build your own.\n\nOnce you have the proposed throttle, brake, and steer values, publish it on the various publishers\nthat we have created in the `__init__` function.\n\n'''\n\nclass DBWNode(object):\n def __init__(self):\n rospy.init_node('dbw_node')\n\n vehicle_mass = rospy.get_param('~vehicle_mass', 1736.35)\n fuel_capacity = rospy.get_param('~fuel_capacity', 13.5)\n brake_deadband = rospy.get_param('~brake_deadband', .1)\n decel_limit = rospy.get_param('~decel_limit', -5)\n accel_limit = rospy.get_param('~accel_limit', 1.)\n wheel_radius = rospy.get_param('~wheel_radius', 0.2413)\n wheel_base = rospy.get_param('~wheel_base', 2.8498)\n steer_ratio = rospy.get_param('~steer_ratio', 14.8)\n max_lat_accel = rospy.get_param('~max_lat_accel', 3.)\n max_steer_angle = rospy.get_param('~max_steer_angle', 8.)\n\n self.steer_pub = rospy.Publisher('/vehicle/steering_cmd',\n SteeringCmd, queue_size=1)\n self.throttle_pub = rospy.Publisher('/vehicle/throttle_cmd',\n ThrottleCmd, queue_size=1)\n self.brake_pub = rospy.Publisher('/vehicle/brake_cmd',\n BrakeCmd, queue_size=1)\n\n # TODO: Create `TwistController` object\n self.controller = Controller(\n YawController(wheel_base, steer_ratio, 0.001, max_lat_accel, max_steer_angle),\n wheel_radius * vehicle_mass)\n # min_speed = 0.001: in YawController.get_angle, it is divided by a radius that is \n # proportional to current speed. To avoid division by zero, we need a minimum speed.\n\n # Some important variables:\n self.dbw_enabled = False\n self.curr_linear_velocity = 0.0\n self.curr_angular_velocity = 0.0\n self.des_linear_velocity = 0.0\n self.des_angular_velocity = 0.0\n\n self.previous_timestamp = rospy.get_rostime().secs\n self.current_timestamp = 0.0\n self.vel_cur = 0.0\n\n self.delta_t = 0.0\n\n # TODO: Subscribe to all the topics you need to\n\n rospy.Subscriber('/vehicle/dbw_enabled', Bool, self.dbw_enabled_cb, queue_size=1)\n\n rospy.Subscriber('/current_velocity', TwistStamped, self.current_velocity_cb, queue_size=1)\n rospy.Subscriber('/twist_cmd', TwistStamped, self.twist_cmd_cb, queue_size=1)\n \n self.loop()\n\n def loop(self):\n rate = rospy.Rate(20) # 20Hz\n\n # counter = 0 # just for testing throttle _AND_ brake\n # des_linear_velocity = self.des_linear_velocity\n\n while not rospy.is_shutdown():\n # TODO: Get predicted throttle, brake, and steering using `twist_controller`\n # You should only publish the control commands if dbw is enabled\n current_time = rospy.get_rostime()\n current_secs = current_time.secs\n current_nsecs = current_time.nsecs\n\n self.current_timestamp = current_secs + current_nsecs/1000000000.0\n self.delta_t = (self.current_timestamp - self.previous_timestamp)\n self.previous_timestamp = self.current_timestamp\n\n #self.dbw_enabled = True\n\n #if counter>100: # just for testing throttle _AND_ brake\n # counter = 0.\n # if des_linear_velocity == 0.:\n # des_linear_velocity = self.des_linear_velocity\n # else:\n # des_linear_velocity = 0.\n # rospy.logwarn(\"speed = \"+str(des_linear_velocity))\n\n if self.dbw_enabled:\n #self.controller.reload_params()\n throttle, brake, steering = self.controller.control(\n self.delta_t, self.des_linear_velocity, self.des_angular_velocity,\n self.curr_linear_velocity)\n\n self.publish(throttle, brake, steering)\n else:\n self.controller.init(self.curr_linear_velocity)\n\n rate.sleep()\n # counter = counter + 1 # just for testing throttle _AND_ brake\n \n\n def publish(self, throttle, brake, steer):\n tcmd = ThrottleCmd()\n tcmd.enable = True\n tcmd.pedal_cmd_type = ThrottleCmd.CMD_PERCENT\n tcmd.pedal_cmd = throttle\n self.throttle_pub.publish(tcmd)\n\n scmd = SteeringCmd()\n scmd.enable = True\n scmd.steering_wheel_angle_cmd = steer\n self.steer_pub.publish(scmd)\n\n bcmd = BrakeCmd()\n bcmd.enable = True\n bcmd.pedal_cmd_type = BrakeCmd.CMD_TORQUE\n bcmd.pedal_cmd = brake\n self.brake_pub.publish(bcmd)\n\n def dbw_enabled_cb(self, msg):\n\n if (msg.data == True):\n\n self.dbw_enabled = True\n rospy.logwarn(\"DBW_ENABLED\")\n else:\n self.dbw_enabled = False\n rospy.logwarn(\"DBW_DISABLED\")\n\n\n def current_velocity_cb(self, message):\n # \"\"\"From the incoming message extract the velocity message \"\"\"\n self.curr_linear_velocity = message.twist.linear.x\n self.curr_angular_velocity = message.twist.angular.z\n\n def twist_cmd_cb(self, message):\n # \"\"\"From the incoming message extract the desired velocity (twist) message \"\"\"\n self.des_linear_velocity = message.twist.linear.x\n self.des_angular_velocity = message.twist.angular.z\n\nif __name__ == '__main__':\n DBWNode()","repo_name":"alex-n-braun/carla.hal","sub_path":"ros/src/twist_controller/dbw_node.py","file_name":"dbw_node.py","file_ext":"py","file_size_in_byte":6885,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"70901869528","text":"from torch import nn\nimport torch.nn.functional as F\n\nclass NonLinearClassifier(nn.Module):\n def __init__(self, feat_dim=512, num_classes=10):\n super(NonLinearClassifier, self).__init__()\n self.fc1 = nn.Linear(feat_dim, feat_dim)\n self.dropout = nn.Dropout(p=0.2)\n self.fc2 = nn.Linear(feat_dim, feat_dim)\n self.fc3 = nn.Linear(feat_dim, num_classes)\n\n def forward(self, features):\n features = F.relu(self.fc1(features))\n features = F.relu(self.fc2(self.dropout(features)))\n return self.fc3(features)\n","repo_name":"CGCL-codes/AdvEncoder","sub_path":"model/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"31"} +{"seq_id":"74167058328","text":"import pymongo\nfrom DBinfo.DBinfo import mongo\n\nconn = pymongo.MongoClient(mongo['server'], mongo['port'])\ndb = conn[mongo['db_name']]\n\n\ndef get_collection(collection_type, name_collection='', db_name=db):\n if( collection_type == \"message_box\" or collection_type == \"task_board\"):\n full_name_collection = collection_type + name_collection\n else:\n full_name_collection = collection_type\n return db_name[full_name_collection]\n\n\nclass QueryMongo:\n \"\"\"\n Класс для запросов к монго коллекциям\n :param:collection_type- тип коллекции\n :param:name_collection- название коллекции, если коллекция дочерняя\n :param:data- данные для записи или удаления\n :param:update_data- данные для замены\n :param:query_type- бинарная переменная, если 1, как по умолчанию, то еденичный запрос, если 0, то множественный\n \"\"\"\n def __init__(self, collection_type='', name_collection='', data=(), update_data={}, query_type=1):\n self.name_collection = get_collection(collection_type, name_collection)\n self.data = data\n self.type = query_type\n self.update_data = update_data\n\n def insert(self):\n if self.type:\n return self.name_collection.insert_one(self.data).inserted_id\n else:\n return self.name_collection.insert.inserted_id\n\n def select(self):\n if self.type:\n return self.name_collection.find_one(self.data)\n else:\n return self.name_collection.find()\n\n def delete(self):\n if self.type:\n return self.name_collection.delete_one(self.data)\n else:\n return self.name_collection.drop()\n\n def update(self):\n if self.type:\n return self.name_collection.update_one(self.data, self.update_data)\n else:\n return self.name_collection.update_many(self.data, self.update_data)\n","repo_name":"ded-evsey/btool_back","sub_path":"QyeryClass/Mongodb.py","file_name":"Mongodb.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"27180325658","text":"print('Gerador de PA')\r\n\r\nprimeiro = int(input('Digite o primeiro termo: '))\r\nrazao = int(input('Digite a Razão:'))\r\ncont = 1\r\ntermo = primeiro\r\nmais = 10\r\ntotal = 0\r\nwhile mais != 0:\r\n total += mais\r\n while cont <= total:\r\n print('{} => '.format(termo), end='')\r\n termo += razao\r\n cont += 1\r\n print('pausa')\r\n mais = int(input('Quantos termos mais queres ver?: \"0\" encerra o programa '))\r\nprint()\r\nprint('Foram calculados {} termos.'.format(total))\r\nprint('fim')\r\n","repo_name":"Amonvix/Aulas_Python","sub_path":"Exercicio/ex062.py","file_name":"ex062.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74317827289","text":"r\"\"\"Main file for the init2winit project.\n\n\"\"\"\n\nimport functools\nimport json\nimport os\nimport struct\nimport sys\nimport time\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\n\nfrom flax import jax_utils\nfrom init2winit import hyperparameters\nfrom init2winit import utils\nfrom init2winit.dataset_lib import datasets\nfrom init2winit.init_lib import initializers\nfrom init2winit.model_lib import models\nfrom init2winit.trainer_lib import trainers\nimport jax\nfrom jax import lax\nfrom ml_collections.config_dict import config_dict\nimport numpy as np\nimport tensorflow as tf\nfrom vizier import pyvizier\n\ngfile = tf.io.gfile\n\n# For internal compatibility reasons, we need to pull this function out.\nmakedirs = tf.io.gfile.makedirs\n\n\n# Enable flax xprof trace labelling.\nos.environ['FLAX_PROFILE'] = 'true'\n\nflags.DEFINE_string('trainer', 'standard', 'Name of the trainer to use.')\nflags.DEFINE_string('model', 'fully_connected', 'Name of the model to train.')\nflags.DEFINE_string('loss', 'cross_entropy', 'Loss function.')\nflags.DEFINE_string('metrics', 'classification_metrics',\n 'Metrics to be used for evaluation.')\nflags.DEFINE_string('initializer', 'noop', 'Must be in [noop, meta_init].')\nflags.DEFINE_string('experiment_dir', None,\n 'Path to save weights and other results. Each trial '\n 'directory will have path experiment_dir/worker_id/.')\nflags.DEFINE_string('dataset', 'mnist', 'Which dataset to train on.')\nflags.DEFINE_string('data_selector', 'noop', 'Which data selector to use.')\nflags.DEFINE_integer('num_train_steps', None, 'The number of steps to train.')\nflags.DEFINE_integer(\n 'num_tf_data_prefetches', -1, 'The number of batches to to prefetch from '\n 'network to host at each step. Set to -1 for tf.data.AUTOTUNE.')\nflags.DEFINE_integer(\n 'num_device_prefetches', 0, 'The number of batches to to prefetch from '\n 'host to device at each step.')\nflags.DEFINE_integer(\n 'num_tf_data_map_parallel_calls', -1, 'The number of parallel calls to '\n 'make from tf.data.map. Set to -1 for tf.data.AUTOTUNE.'\n)\nflags.DEFINE_integer('eval_batch_size', None, 'Batch size for evaluation.')\nflags.DEFINE_bool('eval_use_ema', None, 'If True evals will use ema of params.')\nflags.DEFINE_integer(\n 'eval_num_batches', None,\n 'Number of batches for evaluation. Leave None to evaluate '\n 'on the entire validation and test set.')\nflags.DEFINE_integer(\n 'test_num_batches', None,\n 'Number of batches for eval on test set. Leave None to evaluate '\n 'on the entire test set.')\nflags.DEFINE_integer('eval_train_num_batches', None,\n 'Number of batches when evaluating on the training set.')\nflags.DEFINE_integer('eval_frequency', 1000, 'Evaluate every k steps.')\nflags.DEFINE_string(\n 'hparam_overrides', '', 'JSON representation of a flattened dict of hparam '\n 'overrides. For nested dictionaries, the override key '\n 'should be specified as lr_hparams.base_lr.')\nflags.DEFINE_string(\n 'callback_configs', '', 'JSON representation of a list of dictionaries '\n 'which specify general callbacks to be run during eval of training.')\nflags.DEFINE_list(\n 'checkpoint_steps', [], 'List of steps to checkpoint the'\n ' model. The checkpoints will be saved in a separate'\n 'directory train_dir/checkpoints. Note these checkpoints'\n 'will be in addition to the normal checkpointing that'\n 'occurs during training for preemption purposes.')\nflags.DEFINE_string('external_checkpoint_path', None,\n 'If this argument is set, the trainer will initialize'\n 'the parameters, batch stats, optimizer state, and training'\n 'metrics by loading them from the checkpoint at this path.')\n\nflags.DEFINE_string(\n 'early_stopping_target_name',\n None,\n 'A string naming the metric to use to perform early stopping. If this '\n 'metric reaches the value `early_stopping_target_value`, training will '\n 'stop. Must include the dataset split (ex: validation/error_rate).')\nflags.DEFINE_float(\n 'early_stopping_target_value',\n None,\n 'A float indicating the value at which to stop training.')\nflags.DEFINE_enum(\n 'early_stopping_mode',\n None,\n enum_values=['above', 'below'],\n help=(\n 'One of \"above\" or \"below\", indicates if we should stop when the '\n 'metric is above or below the threshold value. Example: if \"above\", '\n 'then training will stop when '\n '`report[early_stopping_target_name] >= early_stopping_target_value`.'))\nflags.DEFINE_integer(\n 'early_stopping_min_steps',\n 0,\n help='Only allows early stopping after at least this many steps.',\n)\nflags.DEFINE_list(\n 'eval_steps', [],\n 'List of steps to evaluate the model. Evaluating implies saving a '\n 'checkpoint for preemption recovery.')\nflags.DEFINE_string(\n 'hparam_file', None, 'Optional path to hparam json file for overriding '\n 'hyperparameters. Hyperparameters are loaded before '\n 'applying --hparam_overrides.')\nflags.DEFINE_string(\n 'training_metrics_config', '',\n 'JSON representation of the training metrics config.')\n\nflags.DEFINE_integer('worker_id', 1,\n 'Client id for hparam sweeps and tuning studies.')\n\nFLAGS = flags.FLAGS\n\n\ndef _write_trial_meta_data(meta_data_path, meta_data):\n d = meta_data.copy()\n d['timestamp'] = time.time()\n with gfile.GFile(meta_data_path, 'w') as f:\n f.write(json.dumps(d, indent=2))\n\n\n@functools.partial(jax.pmap, axis_name='hosts')\ndef _sum_seeds_pmapped(seed):\n return lax.psum(seed, 'hosts')\n\n\ndef _create_synchronized_rng_seed():\n rng_seed = np.int64(struct.unpack('q', os.urandom(8))[0])\n rng_seed = _sum_seeds_pmapped(jax_utils.replicate(rng_seed))\n rng_seed = np.sum(rng_seed)\n return rng_seed\n\n\ndef _run(\n trainer_cls,\n dataset_name,\n data_selector_name,\n eval_batch_size,\n eval_use_ema,\n eval_num_batches,\n test_num_batches,\n eval_train_num_batches,\n eval_frequency,\n checkpoint_steps,\n num_tf_data_prefetches,\n num_device_prefetches,\n num_tf_data_map_parallel_calls,\n early_stopping_target_name,\n early_stopping_target_value,\n early_stopping_mode,\n early_stopping_min_steps,\n eval_steps,\n hparam_file,\n hparam_overrides,\n initializer_name,\n model_name,\n loss_name,\n metrics_name,\n num_train_steps,\n experiment_dir,\n worker_id,\n training_metrics_config,\n callback_configs,\n external_checkpoint_path,\n):\n \"\"\"Function that runs a Jax experiment. See flag definitions for args.\"\"\"\n model_cls = models.get_model(model_name)\n initializer = initializers.get_initializer(initializer_name)\n dataset_builder = datasets.get_dataset(dataset_name)\n data_selector = datasets.get_data_selector(data_selector_name)\n dataset_meta_data = datasets.get_dataset_meta_data(dataset_name)\n input_pipeline_hps = config_dict.ConfigDict(dict(\n num_tf_data_prefetches=num_tf_data_prefetches,\n num_device_prefetches=num_device_prefetches,\n num_tf_data_map_parallel_calls=num_tf_data_map_parallel_calls,\n ))\n\n merged_hps = hyperparameters.build_hparams(\n model_name=model_name,\n initializer_name=initializer_name,\n dataset_name=dataset_name,\n hparam_file=hparam_file,\n hparam_overrides=hparam_overrides,\n input_pipeline_hps=input_pipeline_hps)\n\n # Note that one should never tune an RNG seed!!! The seed is only included in\n # the hparams for convenience of running hparam trials with multiple seeds per\n # point.\n rng_seed = merged_hps.rng_seed\n if merged_hps.rng_seed < 0:\n rng_seed = _create_synchronized_rng_seed()\n xm_experiment = None\n xm_work_unit = None\n if jax.process_index() == 0:\n logging.info('Running with seed %d', rng_seed)\n rng = jax.random.PRNGKey(rng_seed)\n\n # Build the loss_fn, metrics_bundle, and flax_module.\n model = model_cls(merged_hps, dataset_meta_data, loss_name, metrics_name)\n trial_dir = os.path.join(experiment_dir, str(worker_id))\n meta_data_path = os.path.join(trial_dir, 'meta_data.json')\n meta_data = {'worker_id': worker_id, 'status': 'incomplete'}\n if jax.process_index() == 0:\n logging.info('rng: %s', rng)\n makedirs(trial_dir)\n # Set up the metric loggers for host 0.\n metrics_logger, init_logger = utils.set_up_loggers(trial_dir, xm_work_unit)\n hparams_fname = os.path.join(trial_dir, 'hparams.json')\n logging.info('saving hparams to %s', hparams_fname)\n with gfile.GFile(hparams_fname, 'w') as f:\n f.write(merged_hps.to_json())\n _write_trial_meta_data(meta_data_path, meta_data)\n else:\n metrics_logger = None\n init_logger = None\n try:\n epoch_reports = list(\n trainer_cls(\n trial_dir,\n model,\n dataset_builder,\n initializer,\n num_train_steps,\n merged_hps,\n rng,\n eval_batch_size,\n eval_use_ema,\n eval_num_batches,\n test_num_batches,\n eval_train_num_batches,\n eval_frequency,\n checkpoint_steps,\n early_stopping_target_name,\n early_stopping_target_value,\n early_stopping_mode,\n early_stopping_min_steps,\n eval_steps,\n metrics_logger,\n init_logger,\n training_metrics_config=training_metrics_config,\n callback_configs=callback_configs,\n external_checkpoint_path=external_checkpoint_path,\n dataset_meta_data=dataset_meta_data,\n loss_name=loss_name,\n metrics_name=metrics_name,\n data_selector=data_selector,\n ).train()\n )\n logging.info(epoch_reports)\n meta_data['status'] = 'done'\n except utils.TrainingDivergedError as err:\n meta_data['status'] = 'diverged'\n raise err\n finally:\n if jax.process_index() == 0:\n _write_trial_meta_data(meta_data_path, meta_data)\n\n\ndef main(unused_argv):\n # Don't let TF see the GPU, because all we use it for is tf.data loading.\n tf.config.experimental.set_visible_devices([], 'GPU')\n\n # TODO(gdahl) Figure out a better way to handle passing more complicated\n # flags to the binary.\n training_metrics_config = None\n if FLAGS.training_metrics_config:\n training_metrics_config = json.loads(FLAGS.training_metrics_config)\n if FLAGS.callback_configs:\n callback_configs = json.loads(FLAGS.callback_configs)\n else:\n callback_configs = []\n\n checkpoint_steps = [int(s.strip()) for s in FLAGS.checkpoint_steps]\n eval_steps = [int(s.strip()) for s in FLAGS.eval_steps]\n if jax.process_index() == 0:\n makedirs(experiment_dir)\n log_dir = os.path.join(experiment_dir, 'r=3/')\n makedirs(log_dir)\n log_path = os.path.join(\n log_dir, 'worker{}_{}.log'.format(FLAGS.worker_id, jax.process_index()))\n with gfile.GFile(log_path, 'a') as logfile:\n utils.add_log_file(logfile)\n if jax.process_index() == 0:\n logging.info('argv:\\n%s', ' '.join(sys.argv))\n logging.info('device_count: %d', jax.device_count())\n logging.info('num_hosts : %d', jax.process_count())\n logging.info('host_id : %d', jax.process_index())\n logging.info('checkpoint_steps: %r', checkpoint_steps)\n logging.info('eval_steps: %r', eval_steps)\n\n trainer_cls = trainers.get_trainer_cls(FLAGS.trainer)\n _run(\n trainer_cls=trainer_cls,\n dataset_name=FLAGS.dataset,\n data_selector_name=FLAGS.data_selector,\n eval_batch_size=FLAGS.eval_batch_size,\n eval_use_ema=FLAGS.eval_use_ema,\n eval_num_batches=FLAGS.eval_num_batches,\n test_num_batches=FLAGS.test_num_batches,\n eval_train_num_batches=FLAGS.eval_train_num_batches,\n eval_frequency=FLAGS.eval_frequency,\n checkpoint_steps=checkpoint_steps,\n num_tf_data_prefetches=FLAGS.num_tf_data_prefetches,\n num_device_prefetches=FLAGS.num_device_prefetches,\n num_tf_data_map_parallel_calls=FLAGS.num_tf_data_map_parallel_calls,\n early_stopping_target_name=FLAGS.early_stopping_target_name,\n early_stopping_target_value=FLAGS.early_stopping_target_value,\n early_stopping_mode=FLAGS.early_stopping_mode,\n early_stopping_min_steps=FLAGS.early_stopping_min_steps,\n eval_steps=eval_steps,\n hparam_file=FLAGS.hparam_file,\n hparam_overrides=FLAGS.hparam_overrides,\n initializer_name=FLAGS.initializer,\n model_name=FLAGS.model,\n loss_name=FLAGS.loss,\n metrics_name=FLAGS.metrics,\n num_train_steps=FLAGS.num_train_steps,\n experiment_dir=experiment_dir,\n worker_id=FLAGS.worker_id,\n training_metrics_config=training_metrics_config,\n callback_configs=callback_configs,\n external_checkpoint_path=FLAGS.external_checkpoint_path,\n )\n\n\nif __name__ == '__main__':\n flags.mark_flag_as_required('experiment_dir')\n app.run(main)\n","repo_name":"google/init2winit","sub_path":"init2winit/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12901,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"31"} +{"seq_id":"12174147211","text":"\n\n# [\"AN\", \"CF\", \"MJ\", \"RT\", \"NA\"]\t[5, 3, 2, 7, 5]\t\"TCMA\"\n# [\"TR\", \"RT\", \"TR\"]\t[7, 1, 3]\t\"RCJA\"\n# 1 < 앞 캐릭터에 3점 7 -> 뒷캐릭터 3점\n\n# R / T, C / F, J / M, A / N\n\ndef solution(survey, choices):\n answer = ''\n\n board = {\n 'R': 0,\n 'T': 0,\n 'C': 0,\n 'F': 0,\n 'J': 0,\n 'M': 0,\n 'A': 0,\n 'N': 0\n }\n\n for sur, choice in zip(survey, choices):\n front, back = sur\n score = 4 - choice\n if score < 0:\n board[back] += abs(score)\n elif score > 0:\n board[front] += abs(score)\n xx = iter(board.items())\n board = [(x, next(xx)) for x in xx]\n for front, back in board:\n char = front[0] if front[1] >= back[1] else back[0]\n answer += char\n return answer\n\n\nsolution([\"AN\", \"CF\", \"MJ\", \"RT\", \"NA\"], [5, 3, 2, 7, 5]) # \"TCMA\"\n","repo_name":"nellaG/ps","sub_path":"mbti.py","file_name":"mbti.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22184863851","text":"from pox.core import core\nimport pox.openflow.libopenflow_01 as of\nfrom pox.lib.revent import *\nfrom collections import defaultdict\nfrom pox.openflow.discovery import Discovery\nfrom pox.lib.util import dpidToStr\nfrom pox.lib.recoco import Timer\nimport time\n\nlog = core.getLogger()\n\n# Might be nice if we made this accessible on core...\n#_adj = defaultdict(lambda:defaultdict(lambda:[]))\n\ndef _calc_spanning_tree ():\n \"\"\"\n Calculates the actual spanning tree\n\n Returns it as dictionary where the keys are DPID1, and the\n values are tuples of (DPID2, port-num), where port-num\n is the port on DPID1 connecting to DPID2.\n \"\"\"\n def flip (link):\n return Discovery.Link(link[2],link[3], link[0],link[1])\n\n adj = defaultdict(lambda:defaultdict(lambda:[]))\n switches = set()\n # Add all links and switches\n for l in core.openflow_discovery.adjacency:\n adj[l.dpid1][l.dpid2].append(l)\n switches.add(l.dpid1)\n switches.add(l.dpid2)\n\n # Cull links -- we want a single symmetric link connecting nodes\n for s1 in switches:\n for s2 in switches:\n if s2 not in adj[s1]:\n continue\n if not isinstance(adj[s1][s2], list):\n continue\n assert s1 is not s2\n good = False\n for l in adj[s1][s2]:\n if flip(l) in core.openflow_discovery.adjacency:\n # This is a good one\n adj[s1][s2] = l.port1\n adj[s2][s1] = l.port2\n good = True\n break\n if not good:\n del adj[s1][s2]\n if s1 in adj[s2]:\n # Delete the other way too\n del adj[s2][s1]\n\n q = []\n more = set(switches)\n\n done = set()\n\n tree = defaultdict(set)\n\n while True:\n q = sorted(list(more)) + q\n more.clear()\n if len(q) == 0: break\n v = q.pop(False)\n if v in done: continue\n done.add(v)\n for w,p in adj[v].items():\n if w in tree: continue\n more.add(w)\n tree[v].add((w,p))\n tree[w].add((v,adj[w][v]))\n\n if False:\n log.debug(\"*** SPANNING TREE ***\")\n for sw,ports in tree.items():\n #print \" \", dpidToStr(sw), \":\", sorted(list(ports))\n #print \" \", sw, \":\", [l[0] for l in sorted(list(ports))]\n log.debug((\" %i : \" % sw) + \" \".join([str(l[0]) for l in\n sorted(list(ports))]))\n log.debug(\"*********************\")\n\n return tree\n\n\n# Keep a list of previous port states so that we can skip some port mods\n# If other things mess with port states, these may not be correct. We\n# could also refer to Connection.ports, but those are not guaranteed to\n# be up to date.\n_prev = defaultdict(lambda : defaultdict(lambda : None))\n\n# If True, we set ports down when a switch connects\n_noflood_by_default = False\n\n# If True, don't allow turning off flood bits until a complete discovery\n# cycle should have completed (mostly makes sense with _noflood_by_default).\n_hold_down = False\n\n\ndef _handle_ConnectionUp (event):\n # When a switch connects, forget about previous port states\n _prev[event.dpid].clear()\n\n if _noflood_by_default:\n con = event.connection\n log.debug(\"Disabling flooding for %i ports\", len(con.ports))\n for p in con.ports.values():\n if p.port_no >= of.OFPP_MAX: continue\n _prev[con.dpid][p.port_no] = False\n pm = of.ofp_port_mod(port_no=p.port_no,\n hw_addr=p.hw_addr,\n config = of.OFPPC_NO_FLOOD,\n mask = of.OFPPC_NO_FLOOD)\n con.send(pm)\n _invalidate_ports(con.dpid)\n\n if _hold_down:\n t = Timer(core.openflow_discovery.send_cycle_time + 1, _update_tree,\n kw={'force_dpid':event.dpid})\n\n\ndef _handle_LinkEvent (event):\n # When links change, update spanning tree\n\n (dp1,p1),(dp2,p2) = event.link.end\n if _prev[dp1][p1] is False:\n if _prev[dp2][p2] is False:\n # We're disabling this link; who cares if it's up or down?\n #log.debug(\"Ignoring link status for %s\", event.link)\n return\n\n _update_tree()\n\n\ndef _update_tree (force_dpid = None):\n \"\"\"\n Update spanning tree\n\n force_dpid specifies a switch we want to update even if we are supposed\n to be holding down changes.\n \"\"\"\n\n # Get a spanning tree\n tree = _calc_spanning_tree()\n log.debug(\"Spanning tree updated\")\n\n # Connections born before this time are old enough that a complete\n # discovery cycle should have completed (and, thus, all of their\n # links should have been discovered).\n enable_time = time.time() - core.openflow_discovery.send_cycle_time - 1\n\n # Now modify ports as needed\n try:\n change_count = 0\n for sw, ports in tree.items():\n con = core.openflow.getConnection(sw)\n if con is None: continue # Must have disconnected\n if con.connect_time is None: continue # Not fully connected\n\n if _hold_down:\n if con.connect_time > enable_time:\n # Too young -- we should hold down changes.\n if force_dpid is not None and sw == force_dpid:\n # .. but we'll allow it anyway\n pass\n else:\n continue\n\n tree_ports = [p[1] for p in ports]\n for p in con.ports.values():\n if p.port_no < of.OFPP_MAX:\n flood = p.port_no in tree_ports\n if not flood:\n if core.openflow_discovery.is_edge_port(sw, p.port_no):\n flood = True\n if _prev[sw][p.port_no] is flood:\n #print sw,p.port_no,\"skip\",\"(\",flood,\")\"\n continue # Skip\n change_count += 1\n _prev[sw][p.port_no] = flood\n #print sw,p.port_no,flood\n #TODO: Check results\n\n pm = of.ofp_port_mod(port_no=p.port_no,\n hw_addr=p.hw_addr,\n config = 0 if flood else of.OFPPC_NO_FLOOD,\n mask = of.OFPPC_NO_FLOOD)\n con.send(pm)\n\n _invalidate_ports(con.dpid)\n if change_count:\n log.info(\"%i ports changed\", change_count)\n except:\n _prev.clear()\n log.exception(\"Couldn't push spanning tree\")\n\n\n_dirty_switches = {} # A map dpid_with_dirty_ports->Timer\n_coalesce_period = 2 # Seconds to wait between features requests\n\ndef _invalidate_ports (dpid):\n \"\"\"\n Registers the fact that port info for dpid may be out of date\n\n When the spanning tree adjusts the port flags, the port config bits\n we keep in the Connection become out of date. We don't want to just\n set them locally because an in-flight port status message could\n overwrite them. We also might not want to assume they get set the\n way we want them. SO, we do send a features request, but we wait a\n moment before sending it so that we can potentially coalesce several.\n\n TLDR: Port information for this switch may be out of date for around\n _coalesce_period seconds.\n \"\"\"\n if dpid in _dirty_switches:\n # We're already planning to check\n return\n t = Timer(_coalesce_period, _check_ports, args=(dpid,))\n _dirty_switches[dpid] = t\n\ndef _check_ports (dpid):\n \"\"\"\n Sends a features request to the given dpid\n \"\"\"\n _dirty_switches.pop(dpid,None)\n con = core.openflow.getConnection(dpid)\n if con is None: return\n con.send(of.ofp_barrier_request())\n con.send(of.ofp_features_request())\n log.debug(\"Requested switch features for %s\", str(con))\n\n\ndef launch (no_flood = False, hold_down = False):\n global _noflood_by_default, _hold_down\n if no_flood is True:\n _noflood_by_default = True\n if hold_down is True:\n _hold_down = True\n\n def start_spanning_tree ():\n core.openflow.addListenerByName(\"ConnectionUp\", _handle_ConnectionUp)\n core.openflow_discovery.addListenerByName(\"LinkEvent\", _handle_LinkEvent)\n log.debug(\"Spanning tree component ready\")\n core.call_when_ready(start_spanning_tree, \"openflow_discovery\")\n","repo_name":"noxrepo/pox","sub_path":"pox/openflow/spanning_tree.py","file_name":"spanning_tree.py","file_ext":"py","file_size_in_byte":7697,"program_lang":"python","lang":"en","doc_type":"code","stars":606,"dataset":"github-code","pt":"31"} +{"seq_id":"37422737211","text":"stream_port_start = 50110\n# Be careful with port span.\n# Number of ports to use in a video stream. 1 port for control, 2**x for\n# sending images.\nstream_port_span = (2**5) + 1\n\ndef gen_stream_port(start, span):\n port = start\n while True:\n yield port\n port += span\n\nstream_port_gen = gen_stream_port(stream_port_start, stream_port_span)\n\nsettings = {\n 'sensor/vision/plugin/Streamer': {\n 'command_map': {\n '1': {\n 'func': lambda fp : fp.im,\n 'hint': 'Raw image'\n },\n '2': {\n 'func': lambda fp : fp.hsv,\n 'hint': 'HSV'\n },\n 'q': {\n 'func': lambda fp : fp.im_blue,\n 'hint': 'Blue channel'\n },\n 'w': {\n 'func': lambda fp : fp.im_green,\n 'hint': 'Green channel'\n },\n 'e': {\n 'func': lambda fp : fp.im_red,\n 'hint': 'Red channel'\n },\n 'r': {\n 'func': lambda fp : fp.im_hue,\n 'hint': 'Hue channel'\n },\n 't': {\n 'func': lambda fp : fp.im_saturation,\n 'hint': 'Saturation channel'\n },\n 'y': {\n 'func': lambda fp : fp.im_value,\n 'hint': 'Value channel'\n },\n 'a': {\n 'func': lambda fp : fp.filtered_red,\n 'hint': 'InRange red'\n },\n 's': {\n 'func': lambda fp : fp.filtered_orange,\n 'hint': 'InRange orange'\n },\n 'd': {\n 'func': lambda fp : fp.filtered_yellow,\n 'hint': 'InRange yellow'\n },\n 'f': {\n 'func': lambda fp : fp.filtered_green,\n 'hint': 'InRange green'\n },\n 'g': {\n 'func': lambda fp : fp.filtered_blue,\n 'hint': 'InRange blue'\n },\n 'z': {\n 'func': lambda fp : fp.flooded_red,\n 'hint': 'InRange red'\n },\n 'x': {\n 'func': lambda fp : fp.flooded_orange,\n 'hint': 'InRange orange'\n },\n 'c': {\n 'func': lambda fp : fp.flooded_yellow,\n 'hint': 'InRange yellow'\n },\n 'v': {\n 'func': lambda fp : fp.flooded_green,\n 'hint': 'InRange green'\n },\n 'b': {\n 'func': lambda fp : fp.flooded_blue,\n 'hint': 'InRange blue'\n },\n 'i': {\n 'func': lambda fp : fp.hud,\n 'hint': 'Hud'\n },\n }\n },\n 'sensor/vision/control': {\n #'port': 20054,\n 'listen': [],\n 'release': {'path': None},\n 'mock': {'path': None},\n 'maintenance_interval': 5,\n 'max_failed_frames': 150,\n 'vision_processors': [\n 'sensor/vision/cam_down',\n #'sensor/vision/cam_front'\n ]\n },\n 'sensor/vision/cam_fake': {\n 'name': 'cam_fake',\n 'recorded_video': '/home/cevans/Videos/RoboSub/Obstacles/pathOutOfStartGate640x480.mp4',\n 'recorded_video_': '/home/cevans/Videos/RoboSub/Obstacles/tollBooth640x480.mp4',\n 'stream_port': stream_port_gen.next(),\n 'port_span': stream_port_span,\n 'stream_type': 'recorded_video',\n 'codec' : 'MJPG',\n 'enable': True,\n 'log': [\n 'raw',\n 'processed'\n ],\n 'fps': 8,\n 'width': 640,\n 'height': 480,\n 'release': {'path': None},\n 'mock': {'path': None},\n 'plugins': ['Path'] # Tester module can be added for debug\n },\n 'sensor/vision/cam_front': {\n 'name': 'cam_front',\n 'stream_port': stream_port_gen.next(),\n 'port_span': stream_port_span,\n 'stream_type': 'device',\n 'symlink': '/dev/cam_front',\n 'codec': 'MJPG',\n 'enable': True,\n 'log': [\n 'raw',\n 'processed'\n ],\n 'fps': 10,\n 'width': 640,\n 'height': 480,\n 'release': {'path': None},\n 'mock': {'path': None},\n 'plugins': ['Streamer', 'VideoLogger']\n },\n 'sensor/vision/cam_down': {\n 'name': 'cam_down',\n 'stream_port': stream_port_gen.next(),\n 'port_span': stream_port_span,\n 'stream_type': 'device',\n 'symlink': '/dev/cam_down',\n 'codec' : 'MJPG',\n 'enable': False,\n 'log': [\n 'raw',\n 'processed'\n ],\n 'fps': 10,\n 'width': 640,\n 'height': 480,\n 'release': {'path': None},\n 'mock': {'path': None},\n 'plugins': ['Path', 'Streamer', 'VideoLogger']\n },\n 'sensor/vision/cam_left': {\n 'symlink': '/dev/cam_left',\n 'stream_port': stream_port_gen.next(),\n 'port_span': stream_port_span,\n 'codec' : 'MJPG',\n 'enable': False,\n 'log': [\n 'raw',\n 'processed'\n ],\n 'fps': 5,\n 'width': 640,\n 'height': 480,\n 'release': {'path': None},\n 'mock': {'path': None},\n 'plugins': []\n },\n 'sensor/vision/cam_right': {\n 'symlink': '/dev/cam_right',\n 'stream_port': stream_port_gen.next(),\n 'port_span': stream_port_span,\n 'codec' : 'MJPG',\n 'enable': False,\n 'log': [\n 'raw',\n 'processed'\n ],\n 'fps': 5,\n 'width': 640,\n 'height': 480,\n 'release': {'path': None},\n 'mock': {'path': None},\n 'plugins': []\n }\n}\n","repo_name":"pi19404/robosub-1","sub_path":"src/sensor/vision/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":5761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18278268312","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.model_selection import GridSearchCV\nimport warnings\n\nimport utils\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.calibration import CalibratedClassifierCV\n\nwarnings.filterwarnings('ignore')\ndata = pd.read_csv('data/RecentData.csv')\n\n########################################################################################\n# Split training and testing data and preprocessing\n########################################################################################\ndata_test = data[(data['Tx date and time'] > '2018-09-30') & (data['Tx date and time'] <= '2018-11-30')]\ndata_train = data[(data['Tx date and time'] <= '2018-09-30') & (data['Tx date and time'] > '2016-12-31')]\n\ndata_train = data_train.drop(['Tx date and time'], axis=1)\n# X_train = data_train.drop(['Label', 'Tx date and time'], axis=1)\n# y_train = data_train['Label']\n\nX_train, y_train = utils.preprocessunEqualDistribution(data_train, 7)\n\nX_test = data_test.drop(['Label', 'Tx date and time'], axis=1)\ny_test = data_test['Label']\n\ntuned_parameters = [{'n_estimators': [2, 4, 6, 8, 10, 12, 14, 16, 18, 20],\n 'max_depth': [1, 2, 3, 5, 10, 20, 50]}]\nrf_clf = GridSearchCV(RandomForestClassifier(), tuned_parameters, cv=5, scoring='recall_macro')\n\nrf_clf.fit(X_train, y_train)\nbest_n = rf_clf.best_params_['n_estimators']\nbest_depth = rf_clf.best_params_['max_depth']\nprint(best_n, best_depth)\n\ncalibrated_classifier = CalibratedClassifierCV(RandomForestClassifier(n_estimators=best_n, max_depth=best_depth),\n method='isotonic', cv=5)\ncalibrated_classifier.fit(X_train, y_train)\n\ny_pred = calibrated_classifier.predict(X_test)\ny_prob = calibrated_classifier.predict_proba(X_test)\n\nconf = confusion_matrix(y_test, y_pred)\nsns.heatmap(conf, annot=True, cmap=\"Greens\", fmt='g', cbar_kws={'label': 'Number of Transactions'})\nconf_img_name = 'observations/distribution/50-50_conf_matrix.png'\nplt.savefig(conf_img_name)\nplt.show()\n\nlabel_df = pd.DataFrame(data=dict(y_test=y_test, y_pred=y_pred, y_prob=y_prob[:, 1]))\n\ntrue_positive = label_df[(label_df['y_test'] == 1) & (label_df['y_pred'] == 1)]\nfalse_positive = label_df[(label_df['y_test'] == 0) & (label_df['y_pred'] == 1)]\nfalse_negative = label_df[(label_df['y_test'] == 1) & (label_df['y_pred'] == 0)]\ntrue_negative = label_df[(label_df['y_test'] == 0) & (label_df['y_pred'] == 0)]\n\nprint(true_positive.shape)\nprint(true_negative.shape)\nprint(false_positive.shape)\nprint(false_negative.shape)\n\n\ndef displayStats(tp, tn, fp, fn, lower, upper):\n print('\\n\\n\\n')\n print('lower: ', lower)\n print('upper: ', upper)\n total = label_df.shape[0]\n\n # print('tp_shape', tp.shape)\n # print('fn_shape', fn.shape)\n # print('tn_shape', tn.shape)\n # print('fp_shape', fp.shape)\n\n fp_mu = [fp[(fp['y_prob'] < 0.5)]]\n print('fp < .5', fp_mu)\n\n res = {'tp_mu': [tp[(tp['y_prob'] >= 0.5) & (tp['y_prob'] < upper)].shape[0],\n (tp[(tp['y_prob'] >= 0.5) & (tp['y_prob'] < upper)].shape[0] / total) * 100],\n 'tp_ut': [tp[(tp['y_prob'] >= upper) & (tp['y_prob'] <= 1.0)].shape[0],\n (tp[(tp['y_prob'] >= upper) & (tp['y_prob'] <= 1.0)].shape[0] / total) * 100],\n 'fp_mu': [fp[(fp['y_prob'] >= 0.5) & (fp['y_prob'] < upper)].shape[0],\n (fp[(fp['y_prob'] >= 0.5) & (fp['y_prob'] < upper)].shape[0] / total) * 100],\n 'fp_ut': [fp[(fp['y_prob'] >= upper) & (fp['y_prob'] <= 1.0)].shape[0],\n (fp[(fp['y_prob'] >= upper) & (fp['y_prob'] <= 1.0)].shape[0] / total) * 100],\n 'tn_bl': [tn[(tn['y_prob'] >= 0.0) & (tn['y_prob'] <= lower)].shape[0],\n (tn[(tn['y_prob'] >= 0.0) & (tn['y_prob'] <= lower)].shape[0] / total) * 100],\n 'tn_lm': [tn[(tn['y_prob'] > lower) & (tn['y_prob'] < 0.5)].shape[0],\n (tn[(tn['y_prob'] > lower) & (tn['y_prob'] < 0.5)].shape[0] / total) * 100],\n 'fn_bl': [fn[(fn['y_prob'] >= 0.0) & (fn['y_prob'] <= lower)].shape[0],\n (fn[(fn['y_prob'] >= 0.0) & (fn['y_prob'] <= lower)].shape[0] / total) * 100],\n 'fn_lm': [fn[(fn['y_prob'] > lower) & (fn['y_prob'] <= 0.5)].shape[0],\n (fn[(fn['y_prob'] > lower) & (fn['y_prob'] <= 0.5)].shape[0] / total) * 100]}\n\n for k, v in res.items():\n print(k, v)\n\n print('\\n\\n\\n')\n\n\ndisplayStats(true_positive, true_negative, false_positive, false_negative, 0.3, 0.7)\ndisplayStats(true_positive, true_negative, false_positive, false_negative, 0.4, 0.6)\n","repo_name":"TarunISCO/unir_fraud","sub_path":"calibrated_model.py","file_name":"calibrated_model.py","file_ext":"py","file_size_in_byte":4702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"46405661337","text":"import turtle\nimport random\n\nwn = turtle.Screen()\nwn.title(\"our game name\")\nwn.bgcolor(\"green\")\nwn.setup(width=800, height=600)\nwn.tracer(0)\n\n# player\nplayer = turtle.Turtle()\nplayer.speed(0)\nplayer.shape(\"square\")\nplayer.color(\"white\")\nplayer.penup()\nplayer.goto(0, -250)\nplayer.direction = \"stop\"\n\n# list of burgers\nburgers = []\n\n# add burgers\nfor _ in range(20):\n burger = turtle.Turtle()\n burger.speed(0)\n burger.shape(\"circle\")\n burger.color(\"blue\")\n burger.penup()\n burger.goto(0, 250)\n burger.speed = random.randint(1, 4)\n burgers.append(burger)\n\n\n# functions\ndef go_left():\n player.direction = \"left\"\n\n\ndef go_right():\n player.direction = \"right\"\n\n\n# keyboard binding (listening to keyboard)\nwn.listen()\nwn.onkeypress(go_left, \"Left\")\nwn.onkeypress(go_right, \"Right\")\n\n# main game loop\nwhile True:\n\n # update screen\n wn.update()\n\n # player moving\n if player.direction == \"left\":\n x = player.xcor()\n x -= 3\n player.setx(x)\n\n if player.direction == \"right\":\n x = player.xcor()\n x += 3\n player.setx(x)\n\n # moving burgers\n for burger in burgers:\n y = burger.ycor()\n y -= burger.speed\n burger.sety(y)\n\n # check for off-screen\n if y < -300:\n x = random.randint(-380, 380)\n y = random.randint(300, 400)\n burger.goto(x, y)\n\n # check for collision\n if burger.distance(player) < 20:\n x = random.randint(-380, 380)\n y = random.randint(300, 400)\n burger.goto(x, y)\n\nwn.mainloop()\n","repo_name":"sforro/Project-1-5","sub_path":"Final Project.py","file_name":"Final Project.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31070509506","text":"from django.contrib import admin\nfrom django.urls import path, re_path, include\nfrom django.views.generic import RedirectView\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('books/', include('app_book.urls')),\n path('users/', include('django.contrib.auth.urls')),\n path('users/', include(\"app_user.urls\")),\n path('home/', include('frontend.urls')),\n path('dashboard/', include('frontend.urls')),\n re_path(r'^\\S*$', RedirectView.as_view(url='/home/', permanent=True)),\n]\n","repo_name":"GraindCheack/BookShop","sub_path":"book_shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70080421207","text":"# -*- coding: utf-8 -*-\n\nfrom dolmen.viewlet import testing\nfrom cromlech.browser.testing import TestView, TestHTTPRequest\nfrom zope.testing.cleanup import cleanUp\n\nfrom cromlech.browser import IViewSlot\nfrom zope.location import Location\nfrom zope.component import getAdapters\n\n\ndef setup_module(module):\n testing.grok(\"dolmen.app.layout.master\")\n\n\ndef teardown_module(module):\n cleanUp()\n\n\ndef test_registered_managers():\n\n context = Location()\n request = TestHTTPRequest()\n \n view = TestView(context, request)\n managers = list(getAdapters((context, request, view), IViewSlot))\n\n assert len(managers) == 6\n assert set(dict(managers).keys()) == set([\n 'abovebody',\n 'belowbody',\n 'footer',\n 'header',\n 'resources',\n 'top',\n ])\n","repo_name":"trollfot/dolmen.app.layout","sub_path":"src/dolmen/app/layout/tests/test_master_components.py","file_name":"test_master_components.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13075864717","text":"#! /usr/bin/env python\n##################################################\n# Author: Noemie Jaquier, 2019\n# License: MIT\n# Contact: noemie.jaquier@idiap.ch\n##################################################\n\nimport numpy as np\nimport GPy\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection\nimport os.path\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom scipy.io import loadmat # loading data from matlab\nfrom utils.gmr import Gmr\nfrom utils.gmr import plot_gmm\nfrom utils.gp_coregionalize_with_mean_regression import GPCoregionalizedWithMeanRegression\nfrom utils.gmr_mean_mapping import GmrMeanMapping\nfrom utils.gmr_kernels import Gmr_based_kernel\nimport argparse\nimport time\nfrom fastdtw import fastdtw\nimport warnings \nwarnings.filterwarnings(\"ignore\")\n\n# GMR-based GPR on 3D trajectories with time as input\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--data_name', default='Spiral', type=str)\n parser.add_argument('--n_gaussian', default=10, type=int)\n parser.add_argument('--n_test_execution', default=5, type=int)\n args = parser.parse_args()\n data_name = args.data_name\n\n demos = np.load('3D_robot_periodic/'+args.data_name+'.npy')[:,:,:3]\n # fig = plt.figure()\n # ax = plt.axes(projection='3d')\n # ax.set_aspect('equal')\n # ax.plot(demos[0][:,0], demos[0][:,1], demos[0][:,2], color='blue')\n # plt.show()\n\n # Parameters\n nb_data = demos[0].shape[0]\n nb_data_sup = 0\n nb_samples = 1\n dt = 0.0033\n input_dim = 1\n output_dim = 3\n in_idx = [0]\n out_idx = [1, 2, 3]\n nb_states = args.n_gaussian\n\n nb_prior_samples = args.n_test_execution\n nb_posterior_samples = 1\n\n # Create time data\n demos_t = [np.arange(demos[i].shape[0])[:, None] + 1 for i in range(nb_samples)]\n # Stack time and position data\n demos_tx = [np.hstack([demos_t[i]*dt, demos[i]]) for i in range(nb_samples)]\n\n # Stack demos\n demos_np = demos_tx[0]\n for i in range(1, nb_samples):\n demos_np = np.vstack([demos_np, demos_tx[i]])\n\n X = demos_np[:, 0][:, None]\n Y = demos_np[:, 1:]\n\n # Train data for GPR\n X_list = [np.hstack((X, X)) for i in range(output_dim)]\n Y_list = [Y[:, i][:, None] for i in range(output_dim)]\n\n # Test data\n Xt = dt * np.arange(demos[0].shape[0] + nb_data_sup)[:, None]\n nb_data_test = Xt.shape[0]\n Xtest, _, output_index = GPy.util.multioutput.build_XY([np.hstack((Xt, Xt)) for i in range(output_dim)])\n\n\n begin_train = time.time()\n # GMM\n gmr_model = Gmr(nb_states=nb_states, nb_dim=input_dim + output_dim, in_idx=in_idx, out_idx=out_idx)\n gmr_model.init_params_kbins(demos_np.T, nb_samples=nb_samples)\n gmr_model.gmm_em(demos_np.T)\n\n # GMR prediction\n mu_gmr = []\n sigma_gmr = []\n for i in range(Xt.shape[0]):\n mu_gmr_tmp, sigma_gmr_tmp, H_tmp = gmr_model.gmr_predict(Xt[i])\n mu_gmr.append(mu_gmr_tmp)\n sigma_gmr.append(sigma_gmr_tmp)\n\n mu_gmr = np.array(mu_gmr)\n sigma_gmr = np.array(sigma_gmr)\n\n # Define GPR likelihood and kernels\n likelihoods_list = [GPy.likelihoods.Gaussian(name=\"Gaussian_noise_%s\" %j, variance=0.01) for j in range(output_dim)]\n # kernel_list = [GPy.kern.RBF(1, variance=1., lengthscale=0.1) for i in range(gmr_model.nb_states)]\n kernel_list = [GPy.kern.Matern52(1, variance=1., lengthscale=5.) for i in range(gmr_model.nb_states)]\n\n # Fix variance of kernels\n for kernel in kernel_list:\n kernel.variance.fix(1.)\n kernel.lengthscale.constrain_bounded(0.01, 10.)\n\n # Bound noise parameters\n for likelihood in likelihoods_list:\n likelihood.variance.constrain_bounded(0.001, 0.05)\n\n # GPR model\n K = Gmr_based_kernel(gmr_model=gmr_model, kernel_list=kernel_list)\n mf = GmrMeanMapping(2*input_dim+1, 1, gmr_model)\n\n m = GPCoregionalizedWithMeanRegression(X_list, Y_list, kernel=K, likelihoods_list=likelihoods_list, mean_function=mf)\n\n # Parameters optimization\n m.optimize('bfgs', max_iters=100, messages=True)\n\n # Print model parameters\n #print(m)\n print(\" \")\n print(\"******* Data \"+ data_name +\" *******\")\n print(\" \")\n print(\"#######################################\")\n print(\"Training time\",time.time() - begin_train)\n print(\"#######################################\")\n # GPR prior (no observations)\n\n begin_prior = time.time()\n prior_traj = []\n prior_mean = mf.f(Xtest)[:, 0]\n prior_kernel = m.kern.K(Xtest)\n prior_time = time.time() - begin_prior\n total_sampling_time = 0.0\n for i in range(nb_prior_samples):\n one_sample_begin = time.time()\n prior_traj_tmp = np.random.multivariate_normal(prior_mean, prior_kernel)\n prior_traj.append(np.reshape(prior_traj_tmp, (output_dim, -1)))\n total_sampling_time += time.time() - one_sample_begin\n print(\"#######################################\")\n print(\"Testing time\",total_sampling_time/nb_prior_samples + prior_time)\n print(\"#######################################\")\n prior_kernel_tmp = np.zeros((nb_data_test, nb_data_test, output_dim * output_dim))\n for i in range(output_dim):\n for j in range(output_dim):\n prior_kernel_tmp[:, :, i * output_dim + j] = prior_kernel[i * nb_data_test:(i + 1) * nb_data_test, j * nb_data_test:(j + 1) * nb_data_test]\n prior_kernel_rshp = np.zeros((nb_data_test, output_dim, output_dim))\n for i in range(nb_data_test):\n prior_kernel_rshp[i] = np.reshape(prior_kernel_tmp[i, i, :], (output_dim, output_dim))\n\n\n\n\n # Priors\n fig = plt.figure(figsize=(10, 10))\n ax = plt.axes(projection='3d')\n ax.set_aspect('equal')\n # plt.plot(mu_gmr[:, 0], mu_gmr[:, 1], color=[0.20, 0.54, 0.93], linewidth=3.)\n # plt.scatter(mu_gmr[0, 0], mu_gmr[0, 1], color=[0.20, 0.54, 0.93], marker='X', s=80)\n # plot_gmm(mu_gmr, prior_kernel_rshp, alpha=0.05, color=[0.64, 0.27, 0.73])\n ax.plot(demos[0][:,0], demos[0][:,1], demos[0][:,2], color='blue', label='Demonstration')\n total_dtw = 0.0\n for i in range(nb_prior_samples):\n ax.plot(prior_traj[i][0], prior_traj[i][1], prior_traj[i][2], linewidth=1., label='Reproduction')\n ax.scatter3D(prior_traj[i][0, 0], prior_traj[i][1, 0], prior_traj[i][2,0], marker='X', s=80)\n # np.save('results/'+letter+'/'+letter+str(args.n_gaussian)+'.npy', prior_traj[i])\n # np.save('results/'+letter+'/demo.npy', demos[0])\n distance, path = fastdtw(demos[0, :, :3], prior_traj[i].T)\n total_dtw += distance\n print(\"#######################################\")\n print(\"DTW\",total_dtw / nb_prior_samples)\n print(\"#######################################\")\n # plt.xlabel('$y_1$', fontsize=30)\n # plt.ylabel('$y_2$', fontsize=30)\n # plt.locator_params(nbins=3)\n # plt.tick_params(labelsize=20)\n # plt.tight_layout()\n # plt.savefig('results/'+letter+'/GMRbGP_gmm_'+str(args.n_gaussian)+'priors_datasup.png')\n ax.legend()\n plt.show()","repo_name":"farhadnawaz/GMR_GP_Imitation_Learning","sub_path":"GMR_based_GPR3D.py","file_name":"GMR_based_GPR3D.py","file_ext":"py","file_size_in_byte":6984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70791297369","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass MLP:\n def __init__(self,hidden_node=3):\n self.input_node=1;self.hidden_node=hidden_node; self.output_node=1\n self.w1=np.random.rand(self.hidden_node,self.input_node)\n self.b1=np.random.rand(self.hidden_node,1)\n self.w2=np.random.rand(self.output_node,self.hidden_node)\n self.b2=np.random.rand(self.output_node,1)\n\n def sigmoid(self,x):\n return (1+np.exp(-x))\n\n def d_sigmoid(self,x):\n return self.sigmoid(x)*(1-self.sigmoid(x))\n\n def train(self,train_X,train_y,alpha=0.1,max_iter=500):\n np.random.seed(0)\n input_node=self.input_node; hidden_node=self.hidden_node\n output_node=self.output_node; alpha=alpha; max_iter=max_iter\n\n for iter in range(1,max_iter):\n for i in range(n_train):\n z1=np.dot(self.w1,train_x[i].reshape(1,1))+self.b1\n a1=self.sigmoid(z1)\n z2=np.dot(self.w2,a1)+self.b2\n y_hat=z2\n y_hat_list[i]=y_hat\n e=0.5*(train_y[i]-y_hat)**2\n dy=-(train_y[i]-y_hat)\n dz2=1\n dw2=a1.T\n delta_w2=dy*dz2*dw2\n delta_b2=dy*e\n da1=self.w2.T\n dz1=self.d_sigmoid(z1)\n dw1=train_x[i].T\n delta_w1=dy*dz2*da1*dz1*dw1\n delta_b1=dy*dz2*da1*dz1\n self.w2-=alpha*delta_w2\n self.b2 -=alpha*delta_b2\n self.w1-=alpha*delta_w1\n self.b1 -=alpha*delta_b1\n\n def predict(self,test_x):\n for i in range(n_test):\n z1=np.dot(self.w1,test_x[i].reshape(1,1))+self.b1\n a1=self.sigmoid(z1)\n z2=np.dot(self.w2,a1)+self.b2\n y_hat=z2\n y_hat_list[i]=y_hat\n return y_hat_list\n\n\n\nn_train=20\ntrain_x=np.linspace(0,np.pi*2,n_train)\ntrain_y=np.sin(train_x)\n\nn_test=60\ntest_x=np.linspace(0,np.pi*2,n_test)\ntest_y=np.sin(test_x)\ny_hat_list=np.zeros(n_test)\n\nmlp=MLP(hidden_node=4)\nmlp.train(train_x,train_y,max_iter=600)\nplt.plot(test_x,test_y,label='ground truth')\n\ny_hat_list=mlp.predict(test_x)\nplt.plot(test_x,y_hat_list,'-r',label='prediction')\nplt.legend()\nplt.show()","repo_name":"YoungSeok-Choi/Machine-Running-practice","sub_path":"9주차_2_MLP학습.py","file_name":"9주차_2_MLP학습.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7158841100","text":"#!/usr/bin/env python3\n\nimport argparse\nimport os\nimport subprocess\n\nparser = argparse.ArgumentParser(description='Vcb_Post_Analysis Command')\nparser.add_argument('-e', dest='era', default=\"2018\")\nparser.add_argument('-ch', dest='channel', default=\"Mu\")\nargs = parser.parse_args()\n\nif args.era==\"2016a\": args.era=\"2016preVFP\"\nif args.era==\"2016b\": args.era=\"2016postVFP\"\n\ntarget=f\"Vcb_Histos_{args.era}_{args.channel}_All.root\"\n\ntry: os.remove(target)\nexcept OSError:\n pass\n\nroot_list = os.listdir(\".\")\nroot_list = [root_file for root_file in root_list if root_file.endswith(\".root\")] \nroot_list = [root_file for root_file in root_list if not root_file.find(f\"Vcb_Histos_{args.era}_{args.channel}_\")]\nprint(root_list)\n\nhadd_cmd = f\"hadd {target} \"\nfor root_file in root_list:\n hadd_cmd += root_file + \" \"\n\nprint(hadd_cmd)\nos.system(hadd_cmd)\n","repo_name":"diracyoon/Vcb_Post_Analysis","sub_path":"Python/Hadd_Vcb_Histos.py","file_name":"Hadd_Vcb_Histos.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71111965529","text":"import datetime as dt\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nfrom mplfinance.original_flavor import candlestick_ohlc\nimport matplotlib.dates as mdates\nimport pandas as pd\nimport numpy as np\nimport pandas_datareader.data as web\n#---------------\ndef get_close_date(pd,date):\n while (1):\n date_close = pd.loc[pd['Date']==date]\n if (date_close.empty)==False:\n return date_close\n date+=1\n \ndef plot(minimum,length,new_df_ohlc,num=365):\n total=[]\n special_date=[0,91,182,242]\n special_date_2=[121,274]\n \n for k in range(num):\n \n analysis = pd.DataFrame()\n start=minimum+k\n print(k)\n for i in range(length-362):\n j=i%365\n \n now=start+i\n\n if j in special_date:\n if k==364:\n ax1.axvline(x=now,color='b',linewidth=0.5)\n date_close = get_close_date(new_df_ohlc,now)\n analysis = pd.concat([analysis,date_close])\n elif j in special_date_2:\n if k==364:\n ax1.axvline(x=now,color='r',linewidth=0.5)\n date_close = get_close_date(new_df_ohlc,now)\n analysis = pd.concat([analysis,date_close])\n\n \n close = analysis.filter(['Close'])\n date = analysis.filter(['Date'])\n analysis['close_shift']=(close-close.shift(1))/close.shift(1)\n analysis['date_shift']=date-date.shift(1)\n analysis['slope']=((1+analysis['close_shift'])**(1/analysis['date_shift'])-1)*100\n analysis['abs_slope'] = abs(analysis['slope'])\n temp_point=[0,0]\n for index, row in analysis.iterrows():\n if temp_point!=[0,0]:\n if k==364:\n ax1.plot([temp_point[0],row['Date']], [temp_point[1],row['Close']],'black',linewidth=1)\n temp_point=[row['Date'],row['Close']]\n else:\n temp_point=[row['Date'],row['Close']]\n #ax1.plot([734000,734091], [18,30],'m-',linewidth=1)\n total.append(analysis['abs_slope'].sum())\n\n total = np.array(total)\n sort_index = np.argsort(total)\n #print(total)\n sort_index_reverse=sort_index[::-1]\n cycle_max_relationship=sort_index_reverse[0]\n #print(cycle_max_relationship)\n return cycle_max_relationship,-np.sort(-total)\n\nimport os\nimport csv\n\nstyle.use('ggplot')\ndirectory = 'stock_dfs/'\nfile_list=[]\nfor filename in os.listdir(directory):\n if filename.endswith(\".csv\"):\n file_list.append(filename)\n\nstock_date_pair=[]\narray_pair=[]\nfor file in file_list:\n print(file)\n df = pd.read_csv('stock_dfs/'+file, parse_dates=True, index_col=0)\n \n new_df_ohlc=df[['Open','High','Low','Close','Volume']]\n new_df_ohlc.reset_index(inplace=True)\n new_df_ohlc['Date'] = new_df_ohlc.index\n df_volume = new_df_ohlc['Volume']\n\n\n\n minimum=new_df_ohlc.iloc[0]['Date']\n maximum=new_df_ohlc.iloc[-1]['Date']\n length = int(maximum-minimum)\n\n ax1 = plt.subplot2grid((6,1), (0,0), rowspan=5, colspan=1)\n ax2 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1)\n\n\n candlestick_ohlc(ax1, new_df_ohlc.values, colorup='g')\n ax2.fill_between(df_volume.index, df_volume.values, 0)\n cycle_date,array = plot(minimum,length,new_df_ohlc)\n print(cycle_date)\n print(array)\n stock_date_pair.append(cycle_date)\n array_pair.append(array)\n\n with open('output.csv', 'w+', newline='') as csvfile:\n # 建立 CSV 檔寫入器\n writer = csv.writer(csvfile)\n\n writer.writerow(['ticker', 'cycle_date'])\n for i in range(len(stock_date_pair)):\n writer.writerow([file_list[i],stock_date_pair[i], array_pair[i]])\n\n \n\n#---------------------\nplt.show()\n","repo_name":"justinkwan1216/secret","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10185306295","text":"# Extract images according to a certain interval\n\nimport os\nimport shutil\n\ndef extract_images(source_folder, destination_folder, interval):\n # Create destination folder (if it doesn't exist)\n if not os.path.exists(destination_folder):\n os.makedirs(destination_folder)\n\n # Get all picture files in the source folder and sort by file name\n image_files = sorted([file for file in os.listdir(source_folder) if file.endswith('.jpg') or file.endswith('.png')])\n\n # Extract pictures at regular intervals starting from the first picture and copy to the destination folder\n for i in range(0, len(image_files), interval):\n image_name = image_files[i]\n source_path = os.path.join(source_folder, image_name)\n destination_path = os.path.join(destination_folder, image_name)\n shutil.copyfile(source_path, destination_path)\n '''\n # copy .npy\n source_path_npy = os.path.join(source_folder, image_name.replace('.jpg','.npy'))\n destination_path_npy = os.path.join(destination_folder, image_name.replace('.jpg','.npy'))\n shutil.copyfile(source_path_npy, destination_path_npy)\n \n # copy .mat\n path = r\"/home/chuanzhi/mnt_3T/zyt/people/UCF-QNRF_ECCV18 (2)/UCF-QNRF_ECCV18/val\"\n imageName = image_name.split('.')\n mat_path = os.path.join(path,imageName[0]+'_ann'+'.mat')\n destination_path_mat = os.path.join(destination_folder+'mat',image_name.replace('.jpg','.mat'))\n print(mat_path)\n \n shutil.copyfile(mat_path, destination_path_mat)\n print(f\"The picture has been copied: {image_name}\")\n\t'''\n# 使用示例\nsource_folder = r'/home/chuanzhi/mnt_3T/zyt/people/UCF-Train-Val-Test/val' # The path of the folder where the source image is located\ndestination_folder = r'/home/chuanzhi/mnt_3T/zyt/people/select-UCF/val' # The destination folder path where the pictures are stored after extraction\ninterval = 10 # Extraction interval\n\nextract_images(source_folder, destination_folder, interval)\n","repo_name":"johnhamtom/zyt_Fish_Countig_Network","sub_path":"carDataset/Code/selectFew-shot.py","file_name":"selectFew-shot.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9460625681","text":"from modifiedNaive import ModifiedNaive\n\nclass ModifiedBayes(ModifiedNaive):\n\n\tdef __init__(self):\n\t\tModifiedNaive.__init__(self)\n\n\t'''\n\tCalculates the discrimination score by subtracting the probability of being in the privileged group\n\twith a C+ classification minus the probability of being in the underprivileged group in the C+ classification.\n\t\tCHigherSHigher (float) - the probability of being in the priviliged group given a C+ classification\n\t\tCHigherSLower (float) - the probability of being in the underpriviliged group given a C+ classification\n\tReturns: the discrimination score\n\t'''\n\tdef calculateDiscriminationScore(self, CHigherSHigher, CHigherSLower):\n\t\treturn CHigherSHigher - CHigherSLower\n\n\t'''\n\tBased on the parameter C+ passed into the modify() function, we manually match up the two possible classifications\n\twith the keys \"higher\" and \"lower\" inside a dictionary so we can refer to them later.\n\t\tclassificationDict (dict) - an empty dictionary \n\t\tCHigher (str) - positive classification\n\t\tclassesList (list) - list of possible classifications\n\t'''\n\tdef assignClassifications(self, classificationDict, CHigher, classesList):\n\t\tif (str(classesList[0]) == CHigher):\n\t\t\tclassificationDict[\"higher\"] = classesList[0]\n\t\t\tclassificationDict[\"lower\"] = classesList[1]\n\t\telse:\n\t\t\tclassificationDict[\"higher\"] = classesList[1]\n\t\t\tclassificationDict[\"lower\"] = classesList[0]\n\n\t'''\n\tAssigns the keys \"higher\" and \"lower\" to the two possible sensitive attribute values based on which of the two \n\thas a higher count. S+ (\"higher\") is the privileged group. We do this based on counts instead of as a manual \n\tparameter because there isn't an 'ideal' sensitive attribute category like there is with classifications.\n\t\tdataSet (DataSet) - the dataset\n\t\tdataFrame (DataFrame) - the dataframe\n\t\tsensitivityDict (dict) - an empty dict \n\t'''\n\tdef assignSensitivity(self, dataSet, dataFrame, sensitivityDict):\n\t\tsensitiveAttrCatList = self.getAttributeCategories(dataFrame, dataSet.protectedAttribute)\n\t\tSx = dataFrame.loc[dataFrame[dataSet.protectedAttribute] == sensitiveAttrCatList[0], dataSet.protectedAttribute].count()\n\t\tSy = dataFrame.loc[dataFrame[dataSet.protectedAttribute] == sensitiveAttrCatList[1], dataSet.protectedAttribute].count()\n\t\tif (Sx > Sy):\n\t\t\tsensitivityDict[\"higher\"] = sensitiveAttrCatList[0]\n\t\t\tsensitivityDict[\"lower\"] = sensitiveAttrCatList[1]\n\t\telse:\n\t\t\tsensitivityDict[\"higher\"] = sensitiveAttrCatList[1]\n\t\t\tsensitivityDict[\"lower\"] = sensitiveAttrCatList[0]\n\n\t'''\n\tCounts up the number of elements in a particular column that match the classification value located in the \n\tclassDict passed in with the key \"higher\" (AKA - C+).\n\t\tdataFrame (DataFrame) - the dataframe\n\t\tcolumn (str) - a column header\n\t\tclassDict (dict) - a dictionary containing keys of \"higher\" and \"lower\" and values of the class labels\n\tReturns: the number of elements in the column that match C+\n\t'''\n\tdef calculateNumPos(self, dataFrame, column, classDict):\n\t\treturn dataFrame.loc[dataFrame[column] == classDict[\"higher\"], column].count()\n\n\t'''\n\tA function that can be called in the while loop to keep track/ watch how the counts are changing with each iteration\n\t'''\n\tdef printCounts(self, dataSet, CHigherSLowerCount, CLowerSLowerCount, CHigherSHigherCount, CLowerSHigherCount, higherOrLowerSensitiveAttributeDict, higherOrLowerClassificationDict):\n\t\tdataFrame = dataSet.dataFrame\n\t\tprint(\"c+s- count:\", CHigherSLowerCount)\n\t\tprint(\"c-s- count:\", CLowerSLowerCount)\n\t\tprint(\"c+s+ count:\", CHigherSHigherCount)\n\t\tprint(\"c-s+ count:\", CLowerSHigherCount)\n\t\tprint(\"bayes classification column c+s- count: \", self.countIntersection(dataFrame, dataSet.protectedAttribute, higherOrLowerSensitiveAttributeDict[\"lower\"], \"Bayes Classification\", higherOrLowerClassificationDict[\"higher\"]))\n\t\tprint(\"bayes classification column c-s- count: \", self.countIntersection(dataFrame, dataSet.protectedAttribute, higherOrLowerSensitiveAttributeDict[\"lower\"], \"Bayes Classification\" , higherOrLowerClassificationDict[\"lower\"]))\n\t\tprint(\"bayes classification column c+s+ count: \", self.countIntersection(dataFrame, dataSet.protectedAttribute, higherOrLowerSensitiveAttributeDict[\"higher\"], \"Bayes Classification\", higherOrLowerClassificationDict[\"higher\"]))\n\t\tprint(\"bayes classification column c-s+ count: \", self.countIntersection(dataFrame, dataSet.protectedAttribute, higherOrLowerSensitiveAttributeDict[\"higher\"], \"Bayes Classification\", higherOrLowerClassificationDict[\"lower\"]))\n\n\t'''\n\tSpace saving function for modify() that prints out probabilities\n\t'''\n\tdef printProbabilities(self, CHigherSLower, CLowerSLower, CHigherSHigher, CLowerSHigher):\n\t\tprint(\"c+s- prob:\", CHigherSLower)\n\t\tprint(\"c-s- prob:\", CLowerSLower)\n\t\tprint(\"c+s+ prob:\", CHigherSHigher)\n\t\tprint(\"c-s+ prob:\", CLowerSHigher)\n\n\t'''\n\tTrains the model using modify.\n\t\tdataSet (DataSet) - the dataset\n\t\tCHigher (str) - C+\n\t'''\n\tdef train(self, dataSet, CHigher):\n\t\tModifiedNaive.train(self, dataSet, self.model)\n\t\tself.modify(dataSet, CHigher)\n\t\t\n\t'''\n\tClassifies the dataset and modifies the model until the discrimination score is 0\n\t\tdataSet (DataSet) - the dataset\n\t\tCHigher (str) - C+\n\t'''\n\tdef modify(self, dataSet, CHigher):\n\t\tdataFrame = dataSet.trainDataFrame\n\t\tprotected = dataSet.protectedAttribute\n\t\tgroundTruth = dataSet.trueLabels\n\t\tsensitiveAttributeModelIndex = dataSet.trainHeaders.index(protected) #need to know index of sensitive attribute in the model\n\n\t\tdataFrame = self.classify(dataSet, \"train\")\n\n\t\t#Assign dictionary values based on CHigher parameter\n\t\tclassesList = self.getAttributeCategories(dataFrame, dataSet.trueLabels)\n\t\thigherOrLowerClassificationDict = {}\n\t\tself.assignClassifications(higherOrLowerClassificationDict, CHigher, classesList)\n\n\t\t#Assign the two sensitive attribute categories as S+ and S-\n\t\thigherOrLowerSensitiveAttributeDict = {}\n\t\tself.assignSensitivity(dataSet, dataFrame, higherOrLowerSensitiveAttributeDict)\n\n\t\t#calculate the number of people in the dataset that are actually classified as C+ (in the ground truth column - the real number from the data)\n\t\tactualNumPos = self.calculateNumPos(dataFrame, groundTruth, higherOrLowerClassificationDict)\n\n\t\t#Compute counts for C+S-,C-S+,C+S+,and C-S- based on counts from the original groundTruth column\n\t\tCHigherSLowerCount = self.countIntersection(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"lower\"], groundTruth, higherOrLowerClassificationDict[\"higher\"])\n\t\tCLowerSHigherCount = self.countIntersection(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"higher\"], groundTruth , higherOrLowerClassificationDict[\"lower\"])\n\t\tCHigherSHigherCount = self.countIntersection(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"higher\"],groundTruth, higherOrLowerClassificationDict[\"higher\"])\n\t\tCLowerSLowerCount = self.countIntersection(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"lower\"], groundTruth, higherOrLowerClassificationDict[\"lower\"])\n\t\t#Compute baseline probabilities based on the corresponding counts above, which will be used to calculate the preliminary disc score\n\t\tCHigherSLower = CHigherSLowerCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"lower\"])\n\t\tCHigherSHigher = CHigherSHigherCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"higher\"])\n\t\tCLowerSLower = CLowerSLowerCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"lower\"])\n\t\tCLowerSHigher = CLowerSHigherCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"higher\"])\n\n\t\t#Calculate the preliminary discrimination score -- disc = P(C+ | S+) - P(C+ | S-)\n\t\tdisc = self.calculateDiscriminationScore(CHigherSHigher, CHigherSLower)\n\n\t\twhile (disc > 0.0):\n\t\t\t#Calculate numPos -- the number of instances that we classify people as C+\n\t\t\tnumPos = self.calculateNumPos(dataFrame, \"Bayes Classification\", higherOrLowerClassificationDict)\n\t\t\t\n\t\t\tweightOfChange = 0.01 #Value by which we will be modifiying the counts\n\n\t\t\t#Uncomment if desired: prints out current artificial counts we're modifiying and current actual counts in bayes classification column\n\t\t\t#self.printCounts(dataSet, CHigherSLowerCount, CLowerSLowerCount, CHigherSHigherCount, CLowerSHigherCount, higherOrLowerSensitiveAttributeDict, higherOrLowerClassificationDict)\n\n\t\t\tif (numPos < actualNumPos): #We have more positive C+ labels we can assign\n\n\t\t\t\t#Slightly increase the count for C+S- and slightly decrease the count for C-S-\n\t\t\t\tCHigherSLowerCount = CHigherSLowerCount + (weightOfChange * CLowerSHigherCount)\n\t\t\t\tCLowerSLowerCount = CLowerSLowerCount - (weightOfChange * CLowerSHigherCount)\n\n\t\t\t\t#Update the probabilities based on these new counts\n\t\t\t\tCHigherSLower = CHigherSLowerCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"lower\"])\n\t\t\t\tCLowerSLower = CLowerSLowerCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"lower\"])\n\n\t\t\t\t#Overwrite the old probabilities in the model\n\t\t\t\tself.model[sensitiveAttributeModelIndex][higherOrLowerSensitiveAttributeDict[\"lower\"]][higherOrLowerClassificationDict[\"higher\"]] = CHigherSLower\n\t\t\t\tself.model[sensitiveAttributeModelIndex][higherOrLowerSensitiveAttributeDict[\"lower\"]][higherOrLowerClassificationDict[\"lower\"]] = CLowerSLower\n\n\t\t\telse: #we have assigned more positive C+ labels than we should be\n\t\t\t\n\t\t\t\t#Slightly increase the count for the C-S+ and slightly decrease the count for C+S+ \n\t\t\t\tCLowerSHigherCount = CLowerSHigherCount + (weightOfChange * CHigherSLowerCount)\n\t\t\t\tCHigherSHigherCount = CHigherSHigherCount - (weightOfChange * CHigherSLowerCount)\n\n\t\t\t\t#Update the probabilities based on these new counts\n\t\t\t\tCLowerSHigher = CLowerSHigherCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"higher\"])\n\t\t\t\tCHigherSHigher = CHigherSHigherCount / self.countAttr(dataFrame, protected, higherOrLowerSensitiveAttributeDict[\"higher\"])\n\t\t\t\t\n\t\t\t\t#Overwrite the old probabilities in the model\n\t\t\t\tself.model[sensitiveAttributeModelIndex][higherOrLowerSensitiveAttributeDict[\"higher\"]][higherOrLowerClassificationDict[\"lower\"]] = CLowerSHigher\n\t\t\t\tself.model[sensitiveAttributeModelIndex][higherOrLowerSensitiveAttributeDict[\"higher\"]][higherOrLowerClassificationDict[\"higher\"]] = CHigherSHigher\n\n\t\t\t#reclassify and recompute the new discrimination score\n\t\t\tdataFrame = self.classify(dataSet, \"train\")\n\t\t\tdisc = self.calculateDiscriminationScore(CHigherSHigher, CHigherSLower)","repo_name":"isakson/FairnessSensitiveAlgorithms","sub_path":"ModifiedBayes.py","file_name":"ModifiedBayes.py","file_ext":"py","file_size_in_byte":10485,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"19639970517","text":"import os\nimport mindspore #import torch\nfrom mindspore.dataset import GeneratorDataset\nimport mindspore.dataset as ds\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.io as sio\nimport cv2\nimport random\nimport moxing as mox\nimport re\nfrom option import args\n\ndef make_dataset():\n hazyImages = []\n clearImages = []\n \n #dataset =\"C:/Users/83893/PycharmProjects/mindspore/dataset/ITS_v2\"\n #dataset = \"/hhlv1/hhlKDDN/dataset/ITS_v2\"\n #print(os.file.exist(dataset))\n \n dataset = \"/cache/dataset/\"\n os.makedirs(dataset, exist_ok=True)\n dataroot = args.data_url\n mox.file.copy_parallel(dataroot, dataset)\n \n for i in range(1,1399):\n clearImages.append(dataset+\"clear/\"+str(i)+\".png\")\n #hazyImages.append(dataset+\"trains/\"+str(i)+\"_1.png\")\n fl = os.listdir(dataset+\"hazy\")\n for item in fl:\n if re.match(str(i)+\"_1_\",item):\n hazyImages.append(dataset+\"hazy/\"+item)\n break\n \n\n indices = np.arange(len(clearImages))#np.arange(99)#\n np.random.shuffle(indices)\n clearShuffle = []\n hazyShuffle = []\n\n for i in range(len(indices)):\n index = indices[i]\n clearShuffle.append(clearImages[index])\n hazyShuffle.append(hazyImages[index])\n\n return clearShuffle, hazyShuffle\n\n\n\ndef gammaA(image, gamma_value):\n '''\n lum = image[:,:,0]*0.299 + image[:,:,1]*0.587 + image[:,:,2]*0.114\n avgLum = np.mean(lum)\n gamma_value = 2*(0.5+avgLum)\n '''\n gammaI = (image + 1e-10) ** gamma_value\n #print(gamma_value)\n return gammaI\n\n\ndef random_rot(images):\n randint = random.randint(0, 4)\n if randint == 0:\n for i in range(len(images)):\n images[i] = cv2.rotate(images[i], cv2.ROTATE_90_CLOCKWISE)\n elif randint == 1:\n for i in range(len(images)):\n images[i] = cv2.rotate(images[i], cv2.ROTATE_180)\n elif randint == 2:\n for i in range(len(images)):\n images[i] = cv2.rotate(images[i], cv2.ROTATE_90_COUNTERCLOCKWISE)\n else:\n pass\n return images\n\n\ndef random_crop(images, sizeTo=256):\n w = images[0].shape[1]\n h = images[0].shape[0]\n w_offset = random.randint(0, max(0, w - sizeTo - 1))\n h_offset = random.randint(0, max(0, h - sizeTo - 1))\n\n for i in range(len(images)):\n images[i] = images[i][h_offset:h_offset + sizeTo, w_offset:w_offset + sizeTo, :]\n return images\n\n\ndef random_flip(images):\n if random.random() < 0.5:\n for i in range(len(images)):\n images[i] = cv2.flip(images[i], 1)\n if random.random() < 0.5:\n for i in range(len(images)):\n images[i] = cv2.flip(images[i], 0)\n return images\ndef image_resize(images, siezeTo=(256,256)):\n for i in range(len(images)):\n images[i] = cv2.resize(images[i], siezeTo)\n return images\n\ndef normImge(image, num=1.):\n if len(image.shape) > 2:\n for i in range(3):\n img = image[:,:,i]\n max = np.max(img)\n min = np.min(img)\n image[:, :, i] = (img - min)/(max - min + 1e-8)\n else:\n max = np.max(image)\n min = np.min(image)\n image = (image - min) / (max - min + 1e-8) * num\n return image\n\n\nclass dehazeDataloader:#原文这里transform是True\n def __init__(self, args,train=True, transform=False, num_parallel_workers = 8,sample = None):\n #super.__init__(source=generator_multidimensional, column_names=[\"multi_dimensional_data\"],num_parallel_workers = num_parallel_workers,sample = sample )\n clearImages, hazyImages = make_dataset()\n self.images = hazyImages\n self.clearImages = clearImages\n self._transform = transform\n\n def __getitem__(self, index):\n Ix = Image.open(self.images[index]).convert('RGB')\n Ix = np.array(Ix, dtype=np.float64) / 255.\n\n Jx = Image.open(self.clearImages[index]).convert('RGB')\n Jx = np.array(Jx, dtype=np.float64) / 255.\n\n images = [Ix, Jx]\n\n images = random_crop(images, 256)\n # images = image_resize(images, (256, 256))\n\n images = random_rot(images)\n images = random_flip(images)\n\n [Ix, Jx] = images\n\n if self._transform is not None:\n Ix, Jx = self.transform(Ix, Jx)\n\n return Ix, Jx\n\n def __len__(self):\n return len(self.images)\n#这里的transform还没解决\n def transform(self, Ix, Jx):\n #plt.imshow(img, cmap=plt.cm.gray), plt.show()\n Ix = Ix.transpose([2, 0, 1])#.float()\n #Ix = mindspore.tensor.from_numpy(Ix).float()\n\n Jx = Jx.transpose([2, 0, 1])#.float()\n #Jx = mindspore.tensor.from_numpy(Jx).float()\n return Ix, Jx\n\n\nif __name__ ==\"__main__\":\n\n trainLoader = dehazeDataloader(train=True, transform=True)\n\n for index, (Ix, Jx) in enumerate(trainLoader):\n\n #print(Ix.shape)\n #(3,256,256)\n Ix = Ix.transpose([1, 2, 0])\n Jx = Jx.transpose([1, 2, 0])\n print(Ix.shape)#(256,256,3)\n print(Jx.shape)\n plt.subplot(221), plt.imshow(Ix)\n plt.subplot(222), plt.imshow(Jx)\n plt.show()","repo_name":"dmcv-ecnu/MindSpore_ModelZoo","sub_path":"KDDN_mindspore/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":5133,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"31"} +{"seq_id":"39685425408","text":"#!/usr/bin/env python3\r\n\r\nfrom collections import deque\r\n\r\n\r\ndef sort_012(arr):\r\n \"\"\"\r\n Given an input array consisting on only 0, 1, and 2, sort the array in a single traversal.\r\n\r\n Args:\r\n input_list(list): List to be sorted\r\n \"\"\"\r\n l = 0\r\n i = 0\r\n j = 0\r\n n = len(arr) - 1\r\n\r\n mid = (l + n) // 2\r\n\r\n while j <= n:\r\n if arr[j] < mid:\r\n arr[i], arr[j] = arr[j], arr[i]\r\n i += 1\r\n j += 1\r\n elif arr[j] > mid:\r\n arr[j], arr[n] = arr[j], arr[n]\r\n n -= 1\r\n else:\r\n j += 1\r\n return arr\r\n\r\ndef hash_table_sort_012(arr):\r\n flag = {\r\n 0: [],\r\n 1: [],\r\n 2: [],\r\n }\r\n\r\n for n in arr:\r\n flag[n].append(n)\r\n \r\n return flag[0] + flag[1] + flag[2]\r\n\r\ndef queue_sort_012(arr):\r\n q = [1] * len(arr)\r\n l = 0\r\n h = len(arr) - 1\r\n m = 0\r\n for n in arr:\r\n if n == 0:\r\n m = q[l]\r\n q[l] = n\r\n q[l + 1] = m\r\n l += 1\r\n elif n == 2:\r\n m = q[h]\r\n q[h] = n\r\n q[h - 1] = m\r\n h -= 1\r\n # else:\r\n # q[l + 1] = q[l]\r\n # q[l] = n\r\n # print(q)\r\n\r\n return q\r\n\r\ndef test_function(test_case):\r\n sorted_array = queue_sort_012(test_case)\r\n print(sorted_array)\r\n if sorted_array == sorted(test_case):\r\n print(\"Pass\")\r\n else:\r\n print(\"Fail\")\r\n\r\n\r\nif __name__ == '__main__':\r\n test_function([0, 0, 2, 2, 2, 1, 1, 1, 2, 0, 2])\r\n test_function([2, 1, 2, 0, 0, 2, 1, 0, 1, 0, 0, 2, 2, 2, 1, 2, 0, 0, 0, 2, 1, 0, 2, 0, 0, 1])\r\n test_function([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2])\r\n","repo_name":"andres-root/algond","sub_path":"algoritms/dutch_flag/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7879540599","text":"import time\nfrom random import uniform\nfrom functools import partial, wraps\nfrom typing import Callable, Optional\n\n\ndef retry(\n func: Callable = None,\n retries: int = 10,\n delay: int = 60,\n jitter: Optional[int] = None,\n):\n if func is None:\n return partial(retry, retries=retries, delay=delay, jitter=jitter)\n\n @wraps(func)\n def decorator(*args, **kwargs):\n for i in range(retries):\n try:\n return func(*args, **kwargs)\n except Exception as error:\n if jitter:\n jitter_value = uniform(-1 * jitter, jitter)\n else:\n jitter_value = 0\n print(\n f\"Function {func.__name__} failed with error: {error}. Retrying in {round(delay+jitter_value, 2)} seconds...\"\n )\n time.sleep(delay + jitter_value)\n raise Exception(f\"Function {func.__name__} failed after {retries} retries.\")\n\n return decorator\n","repo_name":"silasge/datalake-nba","sub_path":"datalake_nba/utils/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74940082966","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution(object):\n def findBottomLeftValue(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: int\n \"\"\"\n row = [root]\n while row:\n ret = row[0].val\n newrow = []\n for node in row:\n if node.left:\n newrow.append(node.left)\n if node.right:\n newrow.append(node.right)\n row = newrow\n return ret\n","repo_name":"linmounong/leetcode","sub_path":"2017/513.py","file_name":"513.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"20362784107","text":"from rest_framework import serializers\n\nfrom apps.printing.models import Check, Point, Printer\n\n\nclass PrintSerializer(serializers.Serializer):\n order = serializers.JSONField()\n point_id = serializers.IntegerField()\n\n def validate_order(self, order: dict) -> dict:\n \"\"\"\n Check that order does not exists\n \"\"\"\n order_id = order.get(\"id\")\n\n if not order_id:\n raise serializers.ValidationError(\"Must have an id\")\n\n if Check.objects.filter(order__id=order_id).exists():\n raise serializers.ValidationError(\n f\"Checks for order #{order_id} already exists\"\n )\n\n return order\n\n def validate_point_id(self, point_id: int) -> int:\n \"\"\"\n Check that point has printers\n \"\"\"\n if not Printer.objects.filter(point_id=point_id).exists():\n raise serializers.ValidationError(\"Printers not found\")\n return point_id\n\n\nclass CheckSerializer(serializers.ModelSerializer):\n class Meta:\n model = Check\n fields = [\"id\", \"printer_id\", \"type\", \"order\", \"status\", \"pdf_file\"]\n","repo_name":"Worrik/sheepfish_test_task","sub_path":"apps/printing/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71204058649","text":"import sys\n\nimport pydash\n\nfrom string import ascii_lowercase\nfrom queue import Queue\nimport re\n\nimport util\n\nfilename = 'input/day10.txt'\n\n\ndef main():\n problem1()\n problem2()\n\n\ndef problem1():\n p = util.parse_ints(filename)\n s = 0\n for i in range(100000):\n s += 1\n apply_velocity(p)\n b = bounds(p)\n [x1, x2, y1, y2] = b\n if x2 - x1 < 100 and y2 - y1 < 100:\n print(s)\n show(p, b)\n\n\ndef apply_velocity(points):\n for p in points:\n p[0] += p[2]\n p[1] += p[3]\n\n\ndef bounds(points):\n m = sys.maxsize\n x1, x2, y1, y2 = m, -m, m, -m\n for p in points:\n x1 = min(x1, p[0])\n x2 = max(x2, p[0])\n y1 = min(y1, p[1])\n y2 = max(y2, p[1])\n return [x1, x2, y1, y2]\n\n\ndef show(points, bounds):\n [x1, x2, y1, y2] = bounds\n canvas = []\n for i in range(y2 - y1 + 2):\n canvas.append([\" \"] * (x2 - x1 + 1))\n\n for p in points:\n x = p[0] - x1\n y = p[1] - y1\n canvas[y][x] = \"#\"\n\n canvas = list(map(lambda l: \"\".join(l), canvas))\n for line in canvas:\n print(line)\n print(\"-----\")\n\n\ndef problem2():\n pass\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"dschlyter/advent-of-code","sub_path":"python/2018/day10.py","file_name":"day10.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34992698804","text":"import argparse\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nfrom torchvision import transforms\n\nfrom CNNMRF_origin import CNNMRF\n\n\ndef get_synthesis_image(synthesis, denorm, device):\n cpu_device = torch.device('cpu')\n image = synthesis.clone().squeeze().to(cpu_device)\n\n image = denorm(image)\n return image.to(device).clamp_(0, 1)\n\n\ndef unsample_synthesis(height, width, synthesis, device):\n synthesis = F.interpolate(synthesis, size=[height, width], mode='bilinear')\n synthesis = synthesis.clone().detach().requires_grad_(True).to(device)\n return synthesis\n\n\ndef main(config, cropped, synthesis_in, dir):\n # cropped is 512*512 with a hole in center\n # synthesis is 64*64\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n print(device)\n \"transform and denorm transform\"\n # VGGNet was trained on ImageNet where images are normalized by mean=[0.485, 0.456, 0.406]\n # and std=[0.229, 0.224, 0.225].\n transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))])\n denorm_transform = transforms.Normalize(mean=(-2.12, -2.04, -1.80), std=(4.37, 4.46, 4.44))\n\n \"resize image in several level for training\"\n size = 256\n # cropped=F.interpolate(cropped,[size,size],mode=\"bilinear\")\n # size = 128\n synthesis_in = F.interpolate(synthesis_in, [size, size], mode=\"bilinear\")\n\n cropped.to(device)\n synthesis_in.to(device)\n\n pyramid_content_image = []\n pyramid_style_image = []\n for i in range(config.num_res):\n cropped_sub = F.interpolate(cropped, scale_factor=1 / pow(2, config.num_res - 1 - i), mode='bilinear').to(\n device)\n synthesis_in_sub = F.interpolate(synthesis_in, scale_factor=1 / pow(2, config.num_res - 1 - i),\n mode='bilinear').to(device)\n pyramid_style_image.append(cropped_sub)\n pyramid_content_image.append(synthesis_in_sub)\n\n # return pyramid_content_image[2]\n \"start training\"\n global iter\n iter = 0\n\n # create cnnmrf model\n cnnmrf = CNNMRF(style_image=pyramid_style_image[0], content_image=pyramid_content_image[0], device=device,\n style_weight=config.style_weight, tv_weight=config.tv_weight,\n content_weight=config.content_weight, gpu_chunck_size=config.gpu_chunck_size,\n mrf_synthesis_stride=config.mrf_synthesis_stride,\n mrf_style_stride=config.mrf_style_stride).to(device)\n\n # Sets the module in training mode.\n cnnmrf.train()\n for i in range(0, config.num_res):\n # synthesis = torch.rand_like(content_image, requires_grad=True)\n if i == 0:\n # in lowest level init the synthesis from content resized image\n synthesis = pyramid_content_image[0].clone().to(device)\n synthesis.requires_grad_(True)\n else:\n # in high level init the synthesis from unsampling the upper level synthesis\n synthesis = unsample_synthesis(pyramid_content_image[i].shape[2], pyramid_content_image[i].shape[3],\n synthesis, device)\n cnnmrf.update_style_and_content_image(style_image=pyramid_style_image[i],\n content_image=pyramid_content_image[i])\n # max_iter (int): maximal number of iterations per optimization step\n # image = get_synthesis_image(synthesis, denorm_transform, device)\n # image = F.interpolate(image.unsqueeze(0), size=pyramid_content_image[2].shape[2:4], mode='bilinear')\n # return image\n\n optimizer = optim.LBFGS([synthesis], lr=1, max_iter=config.max_iter)\n \"--------------------\"\n\n def closure():\n global iter\n optimizer.zero_grad()\n loss = cnnmrf(synthesis)\n loss.backward(retain_graph=True)\n # print loss\n if (iter + 1) % 10 == 0:\n print('res_%d_iteration_%d: %f' % (i + 1, iter + 1, loss.item()))\n # save image\n if (iter + 1) % config.sample_step == 0 or iter + 1 == config.max_iter:\n image = get_synthesis_image(synthesis, denorm_transform, device)\n image = F.interpolate(image.unsqueeze(0), size=pyramid_content_image[i].shape[2:4], mode='bilinear')\n torchvision.utils.save_image(image.squeeze(), dir + '/res_%d_result_%d.jpg' % (i + 1, iter + 1))\n print('save image: res_%d_result_%d.jpg' % (i + 1, iter + 1))\n iter += 1\n if iter == config.max_iter:\n iter = 0\n return loss\n\n \"------------\"\n optimizer.step(closure)\n\n image = get_synthesis_image(synthesis, denorm_transform, device)\n image = F.interpolate(image.unsqueeze(0), size=pyramid_content_image[2].shape[2:4], mode='bilinear')\n return image\n\n\ndef texture(cropped, synthesis, dir):\n parser = argparse.ArgumentParser()\n parser.add_argument('--content_path', type=str, default='./dataset/content.jpg')\n parser.add_argument('--style_path', type=str, default='./dataset/style.jpg')\n parser.add_argument('--max_iter', type=int, default=100)\n parser.add_argument('--sample_step', type=int, default=50)\n parser.add_argument('--content_weight', type=float, default=1)\n parser.add_argument('--style_weight', type=float, default=0.6)\n parser.add_argument('--tv_weight', type=float, default=0.35)\n parser.add_argument('--num_res', type=int, default=3)\n parser.add_argument('--gpu_chunck_size', type=int, default=256)\n parser.add_argument('--mrf_style_stride', type=int, default=2)\n parser.add_argument('--mrf_synthesis_stride', type=int, default=2)\n config = parser.parse_args()\n print(config)\n setting = str(config)\n setting.replace(', ', '\\n')\n with open(dir + '/setting.txt', 'a') as file_handle: # .txt可以不自己新建,代码会自动新建\n file_handle.write(setting) # 写入\n file_handle.write('\\n')\n return main(config, cropped, synthesis, dir)\n\n# if __name__ == '__main__':\n# parser = argparse.ArgumentParser()\n# parser.add_argument('--content_path', type=str, default='./dataset/content.jpg')\n# parser.add_argument('--style_path', type=str, default='./dataset/style.jpg')\n# parser.add_argument('--max_iter', type=int, default=100)\n# parser.add_argument('--sample_step', type=int, default=50)\n# parser.add_argument('--content_weight', type=float, default=1)\n# parser.add_argument('--style_weight', type=float, default=0.4)\n# parser.add_argument('--tv_weight', type=float, default=0.1)\n# parser.add_argument('--num_res', type=int, default=4)\n# parser.add_argument('--gpu_chunck_size', type=int, default=256)\n# parser.add_argument('--mrf_style_stride', type=int, default=2)\n# parser.add_argument('--mrf_synthesis_stride', type=int, default=2)\n# config = parser.parse_args()\n# print(config)\n# main(config)\n","repo_name":"Oliiveralien/Inpainting-review","sub_path":"2017CVPR-High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis/High-Res Neural Inpainting-Pytorch/process_texture.py","file_name":"process_texture.py","file_ext":"py","file_size_in_byte":7082,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"31"} +{"seq_id":"32369642315","text":"from collections import deque\n\nn, l, r = map(int, input().split())\nboard = [list(map(int, input().split())) for _ in range(n)]\ndx, dy = [-1,1,0,0], [0,0,-1,1]\nK = 0\n\n\ndef bfs(ix, jx, visited):\n deq = deque()\n deq.append([ix, jx])\n\n union = set()\n union.add((ix, jx))\n\n visited[ix][jx] = 1\n sum_ = board[ix][jx]\n while deq:\n x, y = deq.popleft()\n for a in range(4):\n nx = dx[a] + x\n ny = dy[a] + y\n\n if 0 <= nx < n and 0 <= ny < n and not visited[nx][ny] and l <= abs(board[nx][ny]-board[x][y]) <= r:\n sum_ += board[nx][ny]\n union.add((nx, ny))\n deq.append([nx, ny])\n visited[nx][ny] = 1\n\n if len(union) == 1:\n return False, False\n return union, sum_\n\n\nwhile K < 2000:\n\n visited = [[0]*n for _ in range(n)]\n union_arr = []\n sum_arr = []\n for i in range(n):\n for j in range(n):\n u, s = bfs(i, j, visited)\n if u and s:\n union_arr.append(u)\n sum_arr.append(s)\n\n if not union_arr:\n print(K)\n break\n\n for i, j in zip(union_arr, sum_arr):\n tmp_ans = j // len(i)\n for k in i:\n board[k[0]][k[1]] = tmp_ans\n\n K += 1\n","repo_name":"tpqls0327/Algorithm","sub_path":"Baekjoon/BFSDFS/16234_인구이동_D.py","file_name":"16234_인구이동_D.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25525295002","text":"print('welcome to edureka bank')\nrestart=('y')\nchances=3\nbalance=900\nwhile chances>=0:\n pin=int(input('please enter your 4 digit pin:'))\n if pin==(1234):\n print('you enter your pin correctly/n')\n while restart not in ('n','no','NO','N'):\n print('please press 1 for your balance/n')\n print('please press 2 for your withdrawal/n')\n print('please press 3 for your pay in/n')\n print('please press 4 for your return card/n')\n option=int(input('what would yu like to choose?'))\n if option==1:\n print('your balance is $',balance )\n restart=input('would you like to go back?')\n if restart in ('n','no','NO','N'):\n print('thank you for banking with edurika')\n break\n elif option==2:\n option2=('y')\n withdrawl=float(input('how much would you like to withdrawl?/n$10/$20/$40/$60/$80/$100 for other enter 1'))\n if withdrawl in [10,20,40,60,80,100]:\n balance=balance-withdrawl\n print('/n your balance is now $',balance)\n restart=input('what would you like to do?')\n if restart in('n','no','NO','N'):\n print('thank you for banking with edurika')\n break\n elif withdrawl!=[10,20,40,60,80,100]:\n print('invalid amount,please retry/n')\n restart=('y')\n elif withdrawl==1:\n withdrawl=float(input('please enter the desired amount:'))\n elif option==3:\n pay_in=float(input('how much would you like to pay in?'))\n balance=balance+pay_in\n print('/n your balance is now $',balance)\n restart=input('would you like to go back?')\n if restart in ('n','no','NO','N'):\n print('thank you for banking with edurika')\n break\n elif option==4:\n print('please wait while your card is returned..../n')\n print('thank you for banking with edurika')\n break\n else:\n print('please enter correct number./n')\n restart=('y')\n elif pin!=('1234'):\n print('incorrect password')\n chances=chances-1\n if chances==0:\n print('/n no more tries,contact support@edurika.com')\n break\n \n \n \n\n","repo_name":"mdirfancse2023/Atm-Machine","sub_path":"Atm Machine.py","file_name":"Atm Machine.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"4954006890","text":"############################################################\n# 연도는 1982년부터 2022년 사이로 월은 3월 부터 8월 사이\n# 1. 홈팀이 MBC일 때 승 column에서 H의 개수\n# 2. 홈팀이 MBC일 때 승 column에서 A의 개수\n# 3. 방문팀이 MBC일 때 승 column에서 A의 개수\n# 4. 방문팀이 MBC일 때 승 column에서 H의 개수\n# [1] ‘1’과 ‘3’의 합 & ‘2’과 ‘4’의 합 (따로)\n# 5. 홈 팀이 MBC일 때 scorelist 짝수 항의 합\n# 6. 홈 팀이 MBC일 때 scorelist 홀수 항의 합\n# 7. 방문팀이 MBC일 때 scorelist 홀수 항의 합\n# 8. 방문팀이 MBC일 때 scorelist 짝수 항의 합\n# [2] ‘5’와 ’7’의 합 & ‘6’과 ‘8’의 합 (따로)\n############################################################\n\n\nfrom collections import defaultdict\nfrom datetime import datetime\nimport pickle\nimport pandas as pd\nimport openpyxl\n\nimport os\n\n\n# constants\nFILE_NAME = \"data\"\nMONTH_RANGE = range(3, 8 + 1)\nTEAM = \"MBC\"\nOUTPUT_NAME = (\n lambda num: f\"output/{datetime.now().strftime('%Y%m%d-%H%M%S')}-{num}.xlsx\"\n)\n\n# TEAM = input(\"Enter team: \")\n\n\ndef 득실점(data: pd.DataFrame, month_range=MONTH_RANGE):\n data = data.loc[data[\"month\"].isin(month_range)]\n\n count: dict[str, dict[int, list[int]]] = defaultdict(\n lambda: defaultdict(lambda: [0, 0])\n )\n\n for _, row in data.iterrows():\n scores = (\n row[\"scorelist\"]\n if isinstance(row[\"scorelist\"], list)\n else [row[\"scorelist\"]]\n )\n for i, score in enumerate(map(int, scores)):\n if i % 2 == 0:\n count[row[\"홈팀\"]][row[\"year\"]][0] += score\n count[row[\"방문팀\"]][row[\"year\"]][1] += score\n else:\n count[row[\"홈팀\"]][row[\"year\"]][1] += score\n count[row[\"방문팀\"]][row[\"year\"]][0] += score\n\n result: dict[str, list[tuple[int, int, int]]] = dict()\n for k, v in count.items():\n result[k] = [(nk, nv[0], nv[1]) for nk, nv in v.items()]\n\n return result\n\n\ndef 승패횟수(data: pd.DataFrame, month_range=MONTH_RANGE):\n data = data.loc[data[\"month\"].isin(month_range)]\n\n 홈 = data.loc[data[\"승\"] == \"H\"]\n 원정 = data.loc[data[\"승\"] == \"A\"]\n\n 홈_승리 = 홈.rename(columns={\"홈팀\": \"팀\"})\n 원정_승리 = 원정.rename(columns={\"방문팀\": \"팀\"})\n\n 홈_패배 = 원정.rename(columns={\"홈팀\": \"팀\"})\n 원정_패배 = 홈.rename(columns={\"방문팀\": \"팀\"})\n\n count: dict[str, dict[int, list[int]]] = defaultdict(\n lambda: defaultdict(lambda: [0, 0])\n )\n\n for 승리 in [홈_승리, 원정_승리]:\n for _, row in 승리.iterrows():\n count[row[\"팀\"]][row[\"year\"]][0] += 1\n\n for 패배 in [홈_패배, 원정_패배]:\n for _, row in 패배.iterrows():\n count[row[\"팀\"]][row[\"year\"]][1] += 1\n\n result: dict[str, list[tuple[int, int, int]]] = dict()\n for k, v in count.items():\n result[k] = [(nk, nv[0], nv[1]) for nk, nv in v.items()]\n\n return result\n\n\ndef main():\n # 파일 읽어옴\n with open(FILE_NAME, \"rb\") as f:\n data = pd.DataFrame(pickle.load(f))\n\n result1 = 승패횟수(data)\n result2 = 득실점(data)\n\n # 파일로 저장\n if not os.path.isdir(\"./output\"):\n os.mkdir(\"./output\")\n\n def write_excel(ws, data):\n for d in data:\n ws.append(d)\n\n wb = openpyxl.Workbook()\n\n for k, v in result1.items():\n ws = wb.create_sheet(str(k))\n ws.append([\"팀\", \"승리\", \"패배\"])\n write_excel(ws, v)\n\n del wb[\"Sheet\"]\n wb.save(OUTPUT_NAME(1))\n\n wb = openpyxl.Workbook()\n\n for k, v in result2.items():\n ws = wb.create_sheet(str(k))\n ws.append([\"팀\", \"득점\", \"실점\"])\n write_excel(ws, v)\n\n del wb[\"Sheet\"]\n wb.save(OUTPUT_NAME(2))\n\n # pandas로 나타내기\n frame1 = [\n f'{k}\\n{pd.DataFrame(v, columns=[\"연도\", \"승리\", \"패배\"])}'\n for k, v in result1.items()\n ]\n frame2 = [\n f'{k}\\n{pd.DataFrame(v, columns=[\"연도\", \"득점\", \"실점\"])}'\n for k, v in result2.items()\n ]\n print(*frame1, sep=\"\\n=========\\n\")\n print(\"*****************************************************\")\n print(*frame2, sep=\"\\n=========\\n\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"kdm1jkm/Shihezi","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"21817111173","text":"from imageai.Detection import VideoObjectDetection\r\nimport os\r\nimport json\r\n\r\nclass object_Detection_video(object):\r\n def __init__(self):\r\n print(\"Initializing class .......\")\r\n self.execution_path = os.getcwd()\r\n self.detector = VideoObjectDetection()\r\n #self.detector.setModelTypeAsRetinaNet()\r\n #self.detector.setModelPath( os.path.join(self.execution_path , \"resnet50_coco_best_v2.1.0.h5\"))\r\n self.detector.setModelTypeAsTinyYOLOv3()\r\n self.detector.setModelPath( os.path.join(self.execution_path , \"yolo-tiny.h5\"))\r\n self.detector.loadModel()\r\n print(\"Loading model..........\")\r\n\r\n def predict(self,X,feature_name):\r\n self.detections = self.detector.detectObjectsFromVideo(input_file_path=os.path.join( self.execution_path, X),output_file_path=os.path.join(self.execution_path, \"traffic_mini_detected_1\"), frames_per_second=29, log_progress=True)\r\n return self.detections","repo_name":"lakshikaparihar/object-detection-seldon","sub_path":"object_Detection_video.py","file_name":"object_Detection_video.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"17633631247","text":"\n\ndef get_layer_params2d(*, opacity=.7, **kwargs):\n params = {\n 'pickable': True,\n 'extruded': False,\n 'opacity': opacity,\n 'get_hexagon': '_pdk_h3cell',\n 'get_fill_color': '_pdk_fill_color',\n 'get_line_width': '_pdk_line_width',\n }\n\n return params\n\n\ndef get_layer_params3d(*, opacity=.7, wireframe=False, elevation_scale=20, **kwargs):\n params = {\n 'pickable': True,\n 'extruded': True,\n 'opacity': opacity,\n 'wireframe': wireframe,\n 'elevation_scale': elevation_scale,\n 'get_hexagon': '_pdk_h3cell',\n 'get_fill_color': '_pdk_fill_color',\n 'get_elevation': '_pdk_elevation',\n }\n\n return params\n\ndef get_layer_params(rows, **kwargs):\n if '_pdk_elevation' in rows[0]:\n return get_layer_params3d(**kwargs)\n else:\n return get_layer_params2d(**kwargs)\n","repo_name":"ajfriend/pydeck-h3","sub_path":"src/pydeck_h3/params/layer.py","file_name":"layer.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"23853887795","text":"import os\nos.chdir(\"D:/YANG/JOBS/Latest profile/Postdoctoral fellow-Lund university/Assignment\")\n\n\ndef extract(inF):\n f=open(inF,\"r\")\n lines=f.readlines()\n f.close()\n\n outF=inF[:-4]+\".csv\"\n f=open(outF,\"w\")\n f.write(\"No. of Epoch, Batch size, Learning rate, Regulazizer, Optimizer, Average Accuracy, Std\\n\")\n\n for i in range(len(lines)):\n line=lines[i]\n if line.find(\"epoch\")>=0:\n line=line.replace(\" =\",\"\")\n paras=line[:-1].split()\n print(paras)\n print(i, \" epoch PARAS LENGTH \", len(paras))\n f.write(paras[1]+\",\"+paras[4]+\",\"+paras[6]+\",\"+paras[8]+\",\"+paras[10]+\",\")\n if line.find(\"Average\")>=0:\n paras = line[:-1].split()\n print(paras)\n print(i, \" acc PARAS LENGTH \", len(paras))\n f.write(paras[-1]+\",\")\n\n if line.find(\"Std\")>=0:\n paras = line[:-1].split()\n print(paras)\n print(i, \" std PARAS LENGTH \", len(paras))\n f.write(paras[-1]+\"\\n\")\n\n f.close()\n\ndef extract2(inF):\n f=open(inF,\"r\")\n lines=f.readlines()\n f.close()\n\n outF=inF[:-4]+\".csv\"\n f=open(outF,\"w\")\n f.write(\"Kernel, gamma, C, Average Accuracy, Std\\n\")\n\n for i in range(len(lines)-1):\n line=lines[i]\n if line.find(\"Average acc of 5 runs\")>=0:\n lineK = lines[i-2].replace(\" =\", \"\")\n paras = lineK[:-1].split()\n print(\"with kernel \", paras, \" \", len(paras))\n f.write(paras[2] + \",\" + paras[4] + \",\" + paras[6]+\",\")\n\n line=line.replace(\" =\",\"\")\n paras=line[:-1].split()\n print(\"average acc \", paras, \" \", len(paras))\n f.write(paras[-1]+\",\")\n\n lineS = lines[i+1].replace(\" =\", \"\")\n paras = lineS[:-1].split()\n print(\"std of 5 runs \", paras, \" \", len(paras))\n f.write(paras[-1] + \"\\n\")\n\n\n\n f.close()\n\n# extract(\"Report1-with country.txt\")\n# extract(\"Report1-withOUT country.txt\")\n# extract(\"Report2-with country.txt\")\n# extract(\"Report2-withOUT country.txt\")\n\nextract2(\"SVM-Report-with countryt.txt\")\nextract2(\"SVM-Report-withOUT countryt.txt\")\n\n","repo_name":"khucnam/MFPS_CNN","sub_path":"Source code/MSPF_CNN/Extract_result_Lund_Uni.py","file_name":"Extract_result_Lund_Uni.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70149402650","text":"import streamlit as st\nimport requests\nfrom urllib.parse import quote_plus\n\ndef get_magnet_links(search_term):\n url = f'https://apibay.org/q.php?q={quote_plus(search_term)}'\n response = requests.get(url)\n \n if response.status_code == 200:\n data = response.json()\n \n if data:\n # Filter out results without the necessary fields\n valid_results = [result for result in data if all(key in result for key in ['info_hash', 'seeders', 'leechers'])]\n \n # Sort the valid results based on the number of seeds and peers in descending order\n sorted_data = sorted(valid_results, key=lambda x: (int(x['seeders']), int(x['leechers'])), reverse=True)\n \n # Get the top 10 magnet links\n top_10_links = [f\"magnet:?xt=urn:btih:{result['info_hash']}\" for result in sorted_data[:10]]\n \n return top_10_links\n return [\"Magnet links not found\"]\n\n# Set page title and configure layout\nst.set_page_config(page_title='ShadowFlare.io', layout='wide')\n\n# Add a title and description\nst.title('ShadowFlare')\nst.write(\"Discover and retrieve magnet links for your files.\")\n\n# Input for the user to enter the name of the file\nsearch_term = st.text_input('Enter the name of the file you want:')\n\n# Button to trigger the magnet link retrieval\nif st.button('Get Top 10 Magnet Links'):\n # Display a loading message while retrieving the magnet links\n with st.spinner('Fetching Top 10 Magnet Links...'):\n magnet_urls = get_magnet_links(search_term)\n \n # Display the retrieved top 10 magnet links\n for index, magnet_url in enumerate(magnet_urls, start=1):\n st.write(f'Magnet URL {index}: {magnet_url}')\n\n# Footer with concise information, links, and torrenting disclaimer\nst.sidebar.write(\"\"\"\n## About Me\n**Samir Sengupta**\n\nPassionate data scientist specializing in machine learning and data analysis. Check out my work on [GitHub](https://github.com/SamirSengupta) and connect with me on [LinkedIn](https://www.linkedin.com/in/samirsengupta/).\n\n**Disclaimer**\nDownloading copyrighted material without permission is against the law. This tool is intended for legal use only. Be responsible and respect intellectual property rights.\n\"\"\")\n","repo_name":"SamirSengupta/ShadowFlare","sub_path":"shadowflare.py","file_name":"shadowflare.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10483161924","text":"from HW_18.dao.movie import MovieDAO\n\n\nclass MovieService:\n\n def __init__(self, dao: MovieDAO):\n self.dao = dao\n\n def get_one(self, m_id):\n return self.dao.get_one(m_id)\n\n def get_all(self):\n return self.dao.get_all()\n\n def create(self, data):\n return self.dao.create(data)\n\n def update(self, data):\n m_id = data.get(\"id\")\n movie = self.get_one(m_id)\n movie.id = data.get('id')\n movie.title = data.get('title')\n movie.description = data.get('description')\n movie.trailer = data.get('trailer')\n movie.year = data.get('year')\n movie.rating = data.get('rating')\n movie.genre_id = data.get('genre_id')\n movie.director_id = data.get('director_id')\n self.dao.update(data)\n\n def update_partial(self, data):\n m_id = data.get(\"id\")\n movie = self.get_one(m_id)\n if \"id\" in data:\n movie.id = data.get('id')\n if \"title\" in data:\n movie.title = data.get('title')\n if \"description\" in data:\n movie.description = data.get('description')\n if \"trailer\" in data:\n movie.trailer = data.get('trailer')\n if \"year\" in data:\n movie.year = data.get('year')\n if \"rating\" in data:\n movie.rating = data.get('rating')\n if \"genre_id\" in data:\n movie.genre_id = data.get('genre_id')\n if \"director_id\" in data:\n movie.director_id = data.get('director_id')\n self.dao.update(data)\n\n def delete(self, m_id):\n self.dao.delete(m_id)\n","repo_name":"farit235/Sky_Pro_Course","sub_path":"HW_18/service/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"35652733430","text":"\"\"\"\nZhen Lu 2017/07/15 <albert.lz07@gmail.com>\n\nA script to calculate the ratio of mass flow rate between oxidizer and fuel \nfor the PaSR calculation.\n\nSet a mixing extent factor x,\nfuel stream is made of\n (1-x/2) FUEL + nu x/2 AIR\noxydizer stream is name fo\n nu (1-x/2) AIR + x/2 FUEL\n\nThe ratio of mass flow rates is calculated as\n\n nu(1-x/2)*M_air + phi*x/2*M_fuel\nr_m = -----------------------------------\n phi*(1-x/2)*M_fuel+nu*x/2*M_air\n\n\"\"\"\n\nimport numpy as np\n\n# target equivalence ratio\nphi = 1\n# stoichiometric coefficient\nnu = 0.5\n# Mass of one mole fuel H2:N2 1:1\nm_fuel = 2+28\n# Mass of stoichiometric oxidizer\nm_air = 32 + 3.76*28\n\nfor x in np.arange(0.1,1.0,0.1):\n f_f = phi*(1-x/2)\n f_o = nu*x/2\n o_f = phi*x/2\n o_o = nu*(1-x/2)\n r_m = (o_o*m_air+o_f*m_fuel)/(f_f*m_fuel+f_o*m_air)\n\n print(x,r_m,f_f,f_o,o_f,o_o)\n","repo_name":"Combustion-Zhen/PDF_FlameIndex","sub_path":"PaSR/PaSR_H2/calc_mass_rate.py","file_name":"calc_mass_rate.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"3356448260","text":"from heapq import heappush, heappop\nimport sys\ninput = sys.stdin.readline\nN = int(input())\nclass_lst = [tuple(map(int, input().split())) for _ in range(N)]\nclass_lst.sort(key=lambda x:(x[1],x[2]))\nheap = []\nheappush(heap, [class_lst[0][2],class_lst[0][0]])\nclass_lst = class_lst[1:]\nfor c in class_lst:\n if heap[0][0] <= c[1]:\n heappush(heap, [c[2],c[0]]+heappop(heap))\n else:\n heappush(heap, [c[2],c[0]])\nprint(len(heap))\nans = [0]*N\ncnt = 1\nfor h in heap:\n for i in range(len(h)//2):\n ans[h[i*2+1]-1] = cnt\n cnt += 1\nprint(*ans, sep='\\n')\n","repo_name":"SSAFY-algamza/ssafy-algorithm-study","sub_path":"f1rstf1y9/BOJ/BOJ_1379_강의실 2.py","file_name":"BOJ_1379_강의실 2.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19886317854","text":"from jina import Executor, DocumentArray, requests, Document\nfrom transformers import PegasusForConditionalGeneration, PegasusTokenizer\nimport torch\n\n\nclass PegasusSummarizer(Executor):\n \"\"\"Pegasus Transformer executor class for summarizing text\"\"\"\n\n def __init__(\n self,\n pretrained_model_name_or_path: str = 'google/pegasus-large',\n pooling_strategy: str = 'mean',\n layer_index: int = -1,\n *args,\n **kwargs,\n ):\n super().__init__(*args, **kwargs)\n self.pretrained_model_name_or_path = pretrained_model_name_or_path\n self.pooling_strategy = pooling_strategy\n self.layer_index = layer_index\n self.tokenizer = PegasusTokenizer.from_pretrained(\n self.pretrained_model_name_or_path\n )\n self.model = PegasusForConditionalGeneration.from_pretrained(\n self.pretrained_model_name_or_path\n )\n self.model.to(torch.device('cpu'))\n\n @requests\n def encode(self, docs: 'DocumentArray', **kwargs):\n \n batch = self.tokenizer(docs.texts, truncation=True, padding='longest', return_tensors=\"pt\")\n\n translated = self.model.generate(**batch)\n # translated = model.generate(**batch)\n tgt_text = self.tokenizer.batch_decode(translated, skip_special_tokens=True)\n print(tgt_text[0])\n return DocumentArray(Document(text=tgt_text[0]))\n","repo_name":"myntoralex/PegasusSummarizerExecutor","sub_path":"executor.py","file_name":"executor.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6101523231","text":"import os\nfrom turtle import width\nfrom src.Basic_Func import Basic\nfrom src.Bonus_Func import Bonus\nfrom src.User import User\nfrom termcolor import colored\n\n\nclass Shell:\n \"\"\"\n В этом классе находятся статические методы.\n Главные это parser_runner и run.\n В них находится реализация запуска и парсер командной строки.\n \"\"\"\n\n @staticmethod\n def print_extra_files(a: list, n: int):\n \"\"\"\n Если в команде есть лишние аргументы, то печатает их и игнорирует.\n Это функция предназначена для избежания дубликатов.\n \"\"\"\n print('{0}: there extra operand(s) {1}. They(It) will be ignored.'.\n format(colored(a[0], attrs=['bold']),\n colored(str([a[idx] for idx in range(n, len(a))]), 'yellow', attrs=['bold'])))\n\n @staticmethod\n def print_not_enough(s: str):\n print('{0}: the operand specifying the file is omitted'.format(colored(s, attrs=['bold'])))\n\n @staticmethod\n def parser_runner(args: str):\n \"\"\"\n Функция:\n 1) Парсит аргументов командной строки.\n 2) Запускает код, если нет ошибок.\n Если все было без ошибок то, вызывает функции из класса Basic или Bonus:\n Basic.ls() - если команда была ls.\n Basic.pwd() - если команда была pwd.\n Basic.cd(<given_path>) - если была команда cd.\n Basic.cp(<source_file>, <destination_file>) - если была команда cp.\n Basic.mv(<source_file>, <destination_file>) - если была команда mv.\n Basic.rm(<file_name>) - если была команда rm.\n Basic.rmdir(<dir_name>) - если была команда rmdir.\n Basic.mkdir(<new_dir>) - если была команда mkdir.\n Bonus.exec_files_runner(<[args]>) - если была команда run.\n иначе выводит, что неизвестная команда, и предлагает ознакомиться с командами с помощью\n команды Basic.help().\n Если после какой-то команды, кол-во аргументов этой команды больше чем ожидалось, то предупредит об этом\n и будет работать просто игнорируя лишние аргументы.\n \"\"\"\n basic = Basic()\n bonus = Bonus()\n b = args.split()\n a = []\n i = 0\n while i < len(b):\n if b[i] != '':\n if b[i][0] == \"'\":\n s = b[i][1:]\n if b[i][-1] == \"'\":\n s = b[i][1:len(b[i]) - 1]\n else:\n s += ' '\n i += 1\n while i < len(b) and b[i][-1] != \"'\":\n s += b[i] + ' '\n i += 1\n if i < len(b):\n s += b[i][0:len(b[i]) - 1]\n a.append(s)\n else:\n a.append(b[i])\n i += 1\n if a[0] == 'run':\n a[1] = os.path.abspath(a[1])\n bonus.exec_files_runner(a[1:])\n elif a[0] == 'help' or a[0] == 'ls' or a[0] == 'pwd' or a[0] == 'clear' or a[0] == 'exit':\n if len(a) > 1:\n Shell.print_extra_files(a, 1)\n if a[0] == 'ls':\n basic.ls()\n elif a[0] == 'pwd':\n basic.pwd()\n elif a[0] == 'exit':\n return 1\n elif a[0] == 'help':\n basic.help()\n else:\n os.system('clear')\n elif a[0] == 'cd' or a[0] == 'rm' or a[0] == 'rmdir' \\\n or a[0] == 'mkdir':\n if len(a) < 2:\n Shell.print_not_enough(a[0])\n else:\n if len(a) > 2:\n Shell.print_extra_files(a, 2)\n if a[0] == 'cd':\n basic.cd(a[1])\n elif a[0] == 'rm':\n basic.rm(a[1])\n elif a[0] == 'rmdir':\n basic.rmdir(a[1])\n else:\n basic.mkdir(a[1])\n elif a[0] == 'cp' or a[0] == 'mv':\n print(11)\n if len(a) < 3:\n Shell.print_not_enough(a[0])\n else:\n if len(a) > 3:\n Shell.print_extra_files(a, 3)\n if a[0] == 'cp':\n basic.cp(a[1], a[2])\n else:\n basic.mv(a[1], a[2])\n else:\n print('Incorrect command. '\n 'Type {0} to see which commands '\n '(with their syntax and description) there are.'.\n format(colored('help',\n 'yellow', attrs=['bold'])))\n\n @staticmethod\n def welcome():\n len = 50\n width = 48\n spaces = 14\n print(' ' + len * '=')\n print('Ⅱ' + width * ' ' + ' Ⅱ')\n print('Ⅱ' + width * ' ' + ' Ⅱ')\n print(colored(spaces * ' ' +\n 'Welcome To My OWN SHELL',\n 'green', attrs=['bold']))\n print('Ⅱ' + width * ' ' + ' Ⅱ')\n print('Ⅱ' + width * ' ' + ' Ⅱ')\n print(' ' + len * '=')\n\n @staticmethod\n def run():\n users = User()\n os.system('clear')\n Shell.welcome()\n user = users.getUser()\n while True:\n print(colored(user, 'cyan', attrs=['bold']), end=' ')\n print(colored(\n '~' +\n os.path.abspath(os.curdir)[len(users.getUserName()) + 6:] +\n '$',\n 'blue', attrs=['bold']),\n end=' ')\n cmd = input()\n if Shell.parser_runner(cmd) == 1:\n os.system('clear')\n break\n","repo_name":"alliseeisgold/Shell","sub_path":"src/Shell.py","file_name":"Shell.py","file_ext":"py","file_size_in_byte":6374,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72418210008","text":"# -*- coding: utf-8 -*-\nimport util, area, sys, util_test\n\nMarkBg = \".\" # the sigh of background area, no active pixel\nMarkFg = \"O\" # if active pixel is in the mark\n\ndef markstats_init():\n return {\"keywords_len_max\": 0}\n\n# return with info about Marks\ndef marks_info_table(Prg, Marks, WantedIdNums=None, OutputType=\"txt\", MarkParserFuns=list()):\n Result = list()\n\n Source = Marks.items()\n Errors = list()\n MarkStats = markstats_init()\n\n NotImportantInfoKeys = dict()\n\n if WantedIdNums and isinstance(WantedIdNums, list):\n MarksWanted = dict()\n for Id in WantedIdNums:\n if Id in Marks:\n MarksWanted[Id] = Marks[Id]\n else:\n Errors.append(\"unknown wanted Mark id:\" + str(Id))\n Source = MarksWanted.items()\n\n for MarkId, Mark in Source:\n markstats_insert_id(MarkStats, MarkId)\n\n for MarkParserFun in MarkParserFuns:\n MarkParserFun(Prg, Marks, MarkId, MarkStats)\n\n if OutputType == \"txt\":\n Result.append(\"\\n\") # empty line\n Result.append(\"Mark id: \" + str(MarkId))\n Result.append(\"\") # empty line\n Result.append(mark_to_string(Mark))\n\n if MarkStats[MarkId]:\n Stats = list()\n for K, V in MarkStats[MarkId].items():\n if K in NotImportantInfoKeys:\n continue\n\n Vformatted = str(V)\n Kformatted = \"{txt: >{fill}}\".format(txt=K, fill=MarkStats[\"keywords_len_max\"]) # maybe f strings?\n if isinstance(V, str) and \"\\n\" in V:\n Vformatted = util.txt_multiline_insert_prefix(V, Prefix=\" \" * (MarkStats[\"keywords_len_max\"] + 2))\n\n Stats.append(str(Kformatted) + \": \" + Vformatted)\n Result.append(\"\")\n Result.append(\"\\n\".join(Stats))\n\n\n elif OutputType == \"html\":\n Result.append(\"TODO: html info display\")\n\n elif OutputType == \"latex\":\n Result.append(\"TODO: latex info display\")\n\n\n ResultToStr = [str(Val) for Val in Result]\n\n if OutputType == \"txt\":\n return \"\\n\".join(ResultToStr) + \"\\n\" + \"\\n\".join(Errors)\n\n elif OutputType == \"html\":\n return \"\\n\".join(ResultToStr) + \"\\n\" + \"\\n\".join(Errors)\n\n elif OutputType == \"latex\":\n return \"\\n\".join(ResultToStr) + \"\\n\" + \"\\n\".join(Errors)\n\n\n# TESTED from mark_to_string\n# in a Mark:\n# - original coordinates with pixel values\n# - its a simple set of pixels\n#\n# area: relative coordinates with 0,0 start coord\n# - the bg pixels exists in the strutcture\n# - an area can represent an unreal set of points,\n# for example area_convex. Those pixels don't exists\ndef mark_to_area(Mark):\n Area = area.make_empty(Mark[\"Width\"], Mark[\"Height\"], MarkBg)\n # print(Mark)\n area.coords_insert_from_mark(Area, Mark, MarkFg, Xshift= -Mark[\"Xmin\"], Yshift= -Mark[\"Ymin\"])\n return Area\n\n# TESTED\ndef mark_to_string(Mark):\n return area.to_string(mark_to_area(Mark))\n\ndef mark_from_string_util_test(StringNameFromUtil, Caller=\"? mark_from_string_util_test\"):\n Txt, Width, MarkChar = util_test.Data(StringNameFromUtil)\n return mark_from_string(Txt, Width, MarkChar, Caller=Caller)\n\n# the strings are long lines without separators.\n# Width show us the exact place of splitting.\n# Without Width it's not exact that were we should split lines\ndef mark_from_string(Txt, Width, MarkChar, MarkValueInserted=0, Caller=\"?\"):\n # print(\"\\nCaller: \", Caller)\n # print(\"mark_from_string params TXT:\", Txt, type(Txt), )\n # print(\"mark_from_string params WIDTH:\", Width, type(Width))\n # print(\"mark_from_string params MARKC:\", MarkChar, type(MarkChar))\n\n if len(Txt) % Width != 0:\n sys.exit(\"mark_from_string, incorrect width: string length / Width has a remainder\")\n\n Mark = {\"Coords\":dict()}\n Xmin=Ymin=Xmax=Ymax=None\n\n for Id, Char in enumerate(Txt):\n Y = Id // Width\n X = Id % Width\n if Char == MarkChar:\n if Xmin is None:\n Xmin = Xmax = X\n Ymin = Ymax = Y\n Mark[\"Coords\"][(X,Y)] = MarkValueInserted\n if X < Xmin: Xmin = X\n if Y < Ymin: Ymin = Y\n if X > Xmax: Xmax = X\n if Y > Ymax: Ymax = Y\n\n Mark[\"Width\"] = Xmax - Xmin + 1\n Mark[\"Height\"] = Ymax - Ymin + 1\n Mark[\"Xmin\"] = Xmin\n Mark[\"Ymin\"] = Ymin\n Mark[\"Xmax\"] = Xmax\n Mark[\"Ymax\"] = Ymax\n return Mark\n\n# TESTED\ndef markstats_insert_id(MarkStats, MarkId):\n if MarkId not in MarkStats:\n MarkStats[MarkId] = dict()\n\n# TESTED\ndef mark_area_convex(Prg, Mark, PointsWanted=False):\n AreaConvex = area.make_empty(Mark[\"Width\"], Mark[\"Height\"], MarkBg)\n\n ConnectionPointLines = list()\n # naive implementation, it based on Mark's special attributes: there aren't gaps in marks.\n # I want to revise it later.\n # TODO: maybe if we have more time: Gift Wrapping Algorithm (Convex Hull)\n # maybe there is better solution\n # connect all points with each other\n PixelCoords = [P for P in Mark[\"Coords\"].keys()]\n while PixelCoords:\n FromX, FromY = PixelCoords.pop(0)\n for ToX, ToY in PixelCoords:\n Points = util.coords_connect_fromA_toB_with_points(FromX, FromY, ToX, ToY)\n if PointsWanted:\n ConnectionPointLines.append(Points)\n for ConnectionPointX, ConnectionPointY in Points:\n # -Xmin, -Ymin: A mark contains pixels with relative coords: the minimum is in the left/top corner\n AreaConvex[ConnectionPointX - Mark[\"Xmin\"] ][ConnectionPointY - Mark[\"Ymin\"] ] = MarkFg\n\n if PointsWanted:\n return AreaConvex, ConnectionPointLines\n return AreaConvex\n","repo_name":"BalazsNyiro/deepcopy","sub_path":"src/mark_util.py","file_name":"mark_util.py","file_ext":"py","file_size_in_byte":5797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72675025047","text":"def main(args):\n return 0\n\nif __name__ == '__main__':\n print(\"new a queue\")\n queue = ThreadSafeQueue(max_size=100) \n print(\"new a queue\")\n \n\n\n# Thread safe queue\n\nimport threading\nimport time\n\nclass ThreadSafeException(Exception):\n\tpass\n\nclass ThreadSafeQueue(object):\n\tdef __init__(self, max_size=0):\n\t\tself.max_size = max_size\n\t\tself.queue = []\n\t\tself.lock = threading.Lock()\n\t\tself.condition = threading.Condition()\n\t\t\n\tdef size(self):\n\t\tself.lock.require()\n\t\tsize = len(self.queue)\n\t\tself.lock.release()\n\t\treturn size\n\t\t\n\tdef push(self, value):\n\t\tif(self.max_size != 0 and self.size() > self.max_size):\n\t\t\treturn ThreadSafeException()\n\t\tself.lock.acquire()\n\t\tself.queue.append(value)\n\t\tself.lock.release()\n\t\tself.condition.acquire()\n\t\tself.condition.notify()\n\t\tself.condition.release()\n\t\tpass\n\t\t\n\tdef pop(self, block=False, timeout=0):\n\t\tif(self.size() == 0):\n\t\t\tif(block):\n\t\t\t\tself.condition.acquire()\n\t\t\t\tself.condition.wait(timeout)\n\t\t\telse:\n\t\t\t\treturn None\n\t\tif(self.size() == 0):\n\t\t\treturn None\n\t\tself.lock.acquire()\n\t\titem = self.queue.pop()\n\t\tself.lock.release()\n\t\treturn item\n\n\t\n","repo_name":"KangShanR/blogs","sub_path":"_drafts/python/OS/ThreadSafeQueue.py","file_name":"ThreadSafeQueue.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"10302498841","text":"\"\"\"\nThis module contains the class for video preprocessing app for the e2eVideo pipeline.\n\"\"\"\nimport os\nimport glob\nimport random\nimport subprocess\nimport streamlit as st\nfrom utils import colored_text, get_subdirectories, get_parent_directory\n\n\nclass VideoPreprocessing:\n \"\"\"\n This class contains the video preprocessing app for the e2eVideo pipeline.\n \"\"\"\n\n def __init__(self, starting_path, output_folder):\n self.paths = {\n \"starting_path\": starting_path,\n \"output_folder\": output_folder,\n \"selected_folder\": None,\n }\n self.options = {\n \"video_format\": None,\n \"image_format\": None,\n \"sampling_mode\": None,\n \"num_frames\": None,\n }\n self.cmd_list = None\n\n def folder_selector_ui(self, input_path, button_id=0):\n \"\"\"\n This function displays a folder selector user interface.\n Parameters\n ----------\n input_path : str\n The path to the starting directory.\n Returns\n -------\n selected_path : str\n The path to the selected directory.\n \"\"\"\n if \"subdirs\" not in st.session_state:\n st.session_state.subdirs = get_subdirectories(input_path)\n st.session_state.selected_path = input_path\n\n st.write(\"Data directory: \", input_path)\n\n selected_dir = st.selectbox(\n \"Select video folder\",\n st.session_state.subdirs,\n key=f\"video_folder_selector_{button_id}\",\n )\n column = st.columns(3)\n\n if column[0].button(\"Select folder\", key=f\"select_folder_button_{button_id}\"):\n new_path = os.path.join(st.session_state.selected_path, selected_dir)\n # if it is a correct folder, update the selected path\n if os.path.isdir(new_path):\n st.session_state.selected_path = new_path\n else:\n return st.session_state.selected_path\n if get_subdirectories(new_path):\n st.session_state.subdirs = get_subdirectories(new_path)\n # show button to show subdirectories\n if column[1].button(\n \"Open Folder\", key=f\"show_subdirs_button_{button_id}\"\n ):\n st.session_state.selected_path = new_path\n st.session_state.subdirs = get_subdirectories(new_path)\n st.write(\"Selected folder:\", st.session_state.selected_path)\n else:\n colored_text(\"There are no subdirectories in this folder.\", \"gray\")\n st.write(\"Selected folder:\", st.session_state.selected_path)\n\n if column[2].button(\"Go up\", key=f\"go_up_button_{button_id}\"):\n parent_dir = get_parent_directory(st.session_state.selected_path)\n st.session_state.selected_path = parent_dir[0]\n st.session_state.subdirs = get_subdirectories(parent_dir[0])\n return st.session_state.selected_path\n\n def get_frames(self):\n \"\"\"\n A fuction to get frames from videos.\n \"\"\"\n self.paths[\"selected_folder\"] = self.folder_selector_ui(\n self.paths[\"starting_path\"]\n )\n self.cmd_list = [\n \"python\",\n \"../video_preprocessing.py\",\n \"--videos_folder\",\n self.paths[\"selected_folder\"],\n ]\n\n if not os.path.isdir(self.paths[\"output_folder\"]):\n os.mkdir(self.paths[\"output_folder\"])\n colored_text(\"New frames directory is created!\", \"green\")\n colored_text(\"Frames will be saved in the following directory:\", \"gray\")\n st.write(self.paths[\"output_folder\"])\n\n self.cmd_list.extend([\"--output_folder\", self.paths[\"output_folder\"]])\n\n self.options[\"video_format\"] = st.selectbox(\n \"Select video format\",\n (\"mp4\", \"avi\", \"mov\", \"wmv\", \"flv\", \"mkv\", \"webm\", \"m4v\", \"3gp\"),\n key=\"video_format_selectbox\",\n )\n self.options[\"image_format\"] = st.selectbox(\n \"Select image format\",\n (\n \"jpg\",\n \"png\",\n \"bmp\",\n \"tiff\",\n \"gif\",\n \"webp\",\n \"ico\",\n \"raw\",\n \"eps\",\n \"psd\",\n \"svg\",\n ),\n key=\"image_format_selectbox\",\n )\n self.options[\"sampling_mode\"] = st.selectbox(\n \"Select sampling mode\",\n (\"every_frame\", \"per_second\", \"fixed_frames\"),\n key=\"sampling_mode_selectbox\",\n )\n self.cmd_list.extend(\n [\n \"--video_format\",\n self.options[\"video_format\"],\n \"--image_format\",\n self.options[\"image_format\"],\n \"--sampling_mode\",\n self.options[\"sampling_mode\"],\n ]\n )\n\n if self.options[\"sampling_mode\"] == \"fixed_frames\":\n self.options[\"num_frames\"] = st.number_input(\n \"Number of frames\", min_value=1, max_value=1000, value=10, step=1\n )\n self.cmd_list.extend([\"--num_frames\", str(self.options[\"num_frames\"])])\n\n if \"extract_frames_clicked\" not in st.session_state:\n st.session_state.extract_frames_clicked = False\n\n if st.button(\"Extract Frames\", key=\"extract_frames_button\"):\n st.session_state.extract_frames_clicked = True\n results = subprocess.run(self.cmd_list, capture_output=True, check=False)\n if results.returncode == 0:\n output = results.stdout.decode(\"utf-8\")\n st.text_area(\"Output\", value=output, height=200)\n st.success(\"Frames extracted successfully!\")\n else:\n st.error(\"Error extracting frames!\")\n error = results.stderr.decode(\"utf-8\")\n st.write(f\"Error: \\n {error}\")\n\n if st.session_state.extract_frames_clicked:\n if st.button(\"Show Example Frames\", key=\"show_example_button\"):\n frames_subdir = get_subdirectories(self.paths[\"output_folder\"])\n random_frames_subdir = random.sample(frames_subdir, k=3)\n for subdir in random_frames_subdir:\n frames = glob.glob(\n os.path.join(\n self.paths[\"output_folder\"],\n subdir,\n \"*.\" + self.options[\"image_format\"],\n )\n )\n frames.sort()\n selected_frames = frames[:3]\n st.image(selected_frames, width=500)\n","repo_name":"simulamet-host/video_analytics","sub_path":"e2evideo/frontend/video_preprocessing_app.py","file_name":"video_preprocessing_app.py","file_ext":"py","file_size_in_byte":6718,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"15347607291","text":"import pytest\nimport doctest\nfrom pgmob.backup import FileBackup, FileRestore\nfrom pgmob.errors import PostgresShellCommandError\n\n\ndef cleanup_file(container, file):\n container.exec_run(f\"rm -f '{file}'\")\n\n\n@pytest.fixture\ndef backup(container):\n class TestBackup:\n files = []\n\n @staticmethod\n def dump(db, path):\n TestBackup.files.append(path)\n assert container.exec_run(f\"pg_dump -U postgres -d '{db}' -Fc -f '{path}'\").exit_code == 0\n return path\n\n yield TestBackup\n\n for file in TestBackup.files:\n container.exec_run(f\"rm -f '{file}'\")\n\n\nclass TestBackupFile:\n def test_file_base_path_backup(self, connect, db_with_table, container):\n path = \"/tmp/\" + db_with_table\n cluster = connect()\n cleanup_file(container, path)\n backup = FileBackup(cluster=cluster, base_path=\"/tmp\")\n backup.backup(database=db_with_table, path=db_with_table)\n assert cluster.run_os_command(f\"ls {path}\").text == path\n cleanup_file(container, path)\n\n def test_nonexistent_base_path_backup(self, connect, db_with_table):\n path = \"/nonexistingpath/\" + db_with_table\n cluster = connect()\n backup = FileBackup(cluster=cluster, base_path=\"/nonexistingpath/\")\n pytest.raises(\n PostgresShellCommandError,\n backup.backup,\n database=db_with_table,\n path=db_with_table,\n )\n\n def test_file_absolute_backup(self, connect, db_with_table, container):\n path = \"/tmp/\" + db_with_table\n cluster = connect()\n cleanup_file(container, path)\n backup = FileBackup(cluster=cluster)\n backup.backup(database=db_with_table, path=path)\n assert cluster.run_os_command(f\"ls {path}\").text == path\n cleanup_file(container, path)\n\n def test_nonexistent_absolute_backup(self, connect, db_with_table):\n path = \"/nonexistingpath/\" + db_with_table\n cluster = connect()\n backup = FileBackup(cluster=cluster)\n pytest.raises(\n PostgresShellCommandError,\n backup.backup,\n database=db_with_table,\n path=path,\n )\n\n\nclass TestRestoreFile:\n def test_nonexistent_restore(self, connect, new_db):\n cluster = connect()\n restore = FileRestore(cluster=cluster, base_path=\"/tmp\")\n pytest.raises(\n PostgresShellCommandError,\n restore.restore,\n database=new_db,\n path=\"foo\",\n )\n\n def test_nonexistent_absolute_restore(self, connect, new_db):\n cluster = connect()\n restore = FileRestore(cluster=cluster)\n pytest.raises(\n PostgresShellCommandError,\n restore.restore,\n database=new_db,\n path=\"/tmp/foo\",\n )\n\n def test_file_base_path_restore(self, connect, db_with_table, backup, new_db, container):\n path = \"/tmp/\" + db_with_table\n backup.dump(db_with_table, path)\n cluster = connect()\n restore = FileRestore(cluster=cluster, base_path=\"/tmp\")\n restore.restore(database=new_db, path=db_with_table)\n db = connect(db=new_db)\n assert \"test\" in db.tables\n cleanup_file(container, path)\n\n\ndef test_doctest(doctest_globs_factory):\n from pgmob import backup as backup_module\n\n results = doctest.testmod(m=backup_module, globs=doctest_globs_factory(backup_module))\n assert results.failed == 0\n","repo_name":"dataplat/pgmob","sub_path":"src/tests/functional/test_backup.py","file_name":"test_backup.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73440662807","text":"\"\"\"Implementation of Rule L049.\"\"\"\nfrom typing import List, Optional, Union\n\nfrom sqlfluff.core.parser import KeywordSegment, WhitespaceSegment\nfrom sqlfluff.core.rules.base import LintResult, LintFix, RuleContext\nfrom sqlfluff.core.rules.doc_decorators import document_fix_compatible\nfrom sqlfluff.rules.L006 import Rule_L006\nimport sqlfluff.core.rules.functional.segment_predicates as sp\n\n\nCorrectionListType = List[Union[WhitespaceSegment, KeywordSegment]]\n\n\n@document_fix_compatible\nclass Rule_L049(Rule_L006):\n \"\"\"Comparisons with NULL should use \"IS\" or \"IS NOT\".\n\n **Anti-pattern**\n\n In this example, the ``=`` operator is used to check for ``NULL`` values.\n\n .. code-block:: sql\n\n SELECT\n a\n FROM foo\n WHERE a = NULL\n\n\n **Best practice**\n\n Use ``IS`` or ``IS NOT`` to check for ``NULL`` values.\n\n .. code-block:: sql\n\n SELECT\n a\n FROM foo\n WHERE a IS NULL\n \"\"\"\n\n def _eval(self, context: RuleContext) -> Optional[List[LintResult]]:\n \"\"\"Relational operators should not be used to check for NULL values.\"\"\"\n # Context/motivation for this rule:\n # https://news.ycombinator.com/item?id=28772289\n # https://stackoverflow.com/questions/9581745/sql-is-null-and-null\n if len(context.segment.segments) <= 2:\n return None\n\n # Allow assignments in SET clauses\n if context.parent_stack and context.parent_stack[-1].is_type(\n \"set_clause_list\", \"execute_script_statement\"\n ):\n return None\n\n # Allow assignments in EXEC clauses\n if context.segment.is_type(\"set_clause_list\", \"execute_script_statement\"):\n return None\n\n segment = context.functional.segment\n # Iterate through children of this segment looking for equals or \"not\n # equals\". Once found, check if the next code segment is a NULL literal.\n\n children = segment.children()\n operators = segment.children(sp.is_name(\"equals\", \"not_equal_to\"))\n if len(operators) == 0:\n return None\n\n results: List[LintResult] = []\n # We may have many operators\n for operator in operators:\n after_op_list = children.select(start_seg=operator)\n null_literal = after_op_list.first(sp.is_code())\n # if the next bit of code isnt a NULL then we are good\n if not null_literal.all(sp.is_name(\"null_literal\")):\n continue\n\n sub_seg = null_literal.get()\n assert sub_seg, \"TypeGaurd: Segement Must exist Must exist\"\n self.logger.debug(\n \"Found NULL literal following equals/not equals @%s: %r\",\n sub_seg.pos_marker,\n sub_seg.raw,\n )\n edit = _create_base_is_null_sequence(\n is_upper=sub_seg.raw[0] == \"N\",\n operator_name=operator.name,\n )\n prev_seg = after_op_list.first().get()\n next_seg = children.select(stop_seg=operator).last().get()\n if self._missing_whitespace(prev_seg, before=True):\n whitespace_segment: CorrectionListType = [WhitespaceSegment()]\n edit = whitespace_segment + edit\n if self._missing_whitespace(next_seg, before=False):\n edit = edit + [WhitespaceSegment()]\n res = LintResult(\n anchor=operator,\n fixes=[\n LintFix.replace(\n operator,\n edit,\n )\n ],\n )\n results.append(res)\n\n return results or None\n\n\ndef _create_base_is_null_sequence(\n is_upper: bool,\n operator_name: str,\n) -> CorrectionListType:\n is_seg = KeywordSegment(\"IS\" if is_upper else \"is\")\n not_seg = KeywordSegment(\"NOT\" if is_upper else \"not\")\n if operator_name == \"equals\":\n return [is_seg]\n\n return [\n is_seg,\n WhitespaceSegment(),\n not_seg,\n ]\n","repo_name":"kevingao-twg/dbt-learn","sub_path":"work_env/Lib/site-packages/sqlfluff/rules/L049.py","file_name":"L049.py","file_ext":"py","file_size_in_byte":4039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"8603424982","text":"from authentication.api import *\nfrom django.urls import path\n\n\n\nurlpatterns = [\n path(\"register/\", RegisterAPIView.as_view(), name=\"register\"),\n path(\"login/\", LoginAPIView.as_view(), name=\"login\"),\n path(\"user/\", AuthUserAPIView.as_view(), name=\"user\"),\n]\n ","repo_name":"lurdfab/rest_you","sub_path":"authentication/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42577041002","text":"import datetime\n\nTODAYS_KEYWORD = {\n 'Monday': {'word': 'walrus',\n 'sentence': 'A walrus is a large flippered marine mammal.'},\n 'Tuesday': {'word': 'wombat',\n 'sentence': 'A wombat is a short-legged muscular quadruped.'},\n 'Wednesday': {'word': 'donkey',\n 'sentence': 'A donkey is a domesticated member of the Equidae family.'},\n 'Thursday': {'word': 'gazelle',\n 'sentence': 'A gazelle is a swift animal, mostly found in Africa.'},\n 'Friday': {'word': 'snake',\n 'sentence': 'A snake is a legless, carnivorous reptile.'},\n 'Saturday': {'word': 'tiger',\n 'sentence': 'A tiger is a big scary cat.'},\n 'Sunday': {'word': 'swan',\n 'sentence': 'A swan is a kind of like a big duck. Most of them are white.'}\n}[datetime.datetime.now().strftime(\"%A\")]\n","repo_name":"timols/spellwalrus","sub_path":"calling/keywords.py","file_name":"keywords.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"8470081687","text":"from concurrent import futures\nimport logging\n\nimport grpc\nimport cafe_pb2\nimport cafe_pb2_grpc\nimport pandas as pd\n\n# Open the CSV with menu items\ndata = pd.read_csv(\"./data/menu.csv\")\ndata_hours = pd.read_csv(\"./data/opening_hours.csv\")\n\n\nclass Cafe(cafe_pb2_grpc.MenuServicer):\n\n # Get product information\n def Pricing(self, request, context):\n product = data.loc[data['Menu Items'] == request.product]\n\n # If the product doesn't exist, return an error with a message\n if len(product) == 0:\n available_products = data['Menu Items']\n context.set_details = 'This product does not exist. Select one of these: ' + ', '.join(available_products)\n context.set_code(grpc.StatusCode.INVALID_ARGUMENT)\n return cafe_pb2.ProductInfo()\n\n # If found, return the product information\n return cafe_pb2.ProductInfo(product=product['Menu Items'].item(), price=product['Price'].item(),\n flavors=product['Flavors'].item(), availability=product['Availability'].item())\n\n def SpecialOfTheDay(self, request, context):\n # Validate the day number\n if request.weekday > 7 or request.weekday <= 0:\n context.set_details = 'Invalid day of the week, select between 1 and 7.'\n context.set_code(grpc.StatusCode.INVALID_ARGUMENT)\n return cafe_pb2.SpecialDrink()\n\n drinks = data.loc[data['Type'] == 'Drink']\n\n # Select randomly from the drinks\n drink_of_the_day = drinks.sample(n=1)\n\n # Drink of the day always costs only 10 * (day of the week)% of the original price\n special_price = drink_of_the_day['Price'].item() * (0.10 * request.weekday)\n return cafe_pb2.SpecialDrink(product=drink_of_the_day['Menu Items'].item(),\n special_price=round(special_price))\n\n def OpeningHours(self, request, context):\n idx = request.weekday - 1\n\n # Validate the day number\n if idx > 7 or idx < 0:\n context.set_details = 'Invalid day of the week, select between 1 and 7.'\n context.set_code(grpc.StatusCode.INVALID_ARGUMENT)\n return cafe_pb2.HoursInfo()\n\n opens_at = data_hours['Opening Hours'][idx]\n print(opens_at)\n\n return cafe_pb2.HoursInfo(opens_at=opens_at)\n\n\ndef serve():\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n cafe_pb2_grpc.add_MenuServicer_to_server(Cafe(), server)\n cafe_pb2_grpc.add_BusinessServicer_to_server(Cafe(), server)\n server.add_insecure_port('[::]:50051')\n server.start()\n server.wait_for_termination()\n\n\nif __name__ == '__main__':\n logging.basicConfig()\n serve()\n","repo_name":"haixei/python-grpc","sub_path":"Cafe/cafe_server.py","file_name":"cafe_server.py","file_ext":"py","file_size_in_byte":2714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71430595288","text":"def create_communicator(\n communicator_name='flat', mpi_comm=None, dynamic=False, debug=False):\n\n if mpi_comm is None:\n import mpi4py.MPI\n mpi_comm = mpi4py.MPI.COMM_WORLD\n\n if communicator_name == 'flat':\n from dlframeworks.chainer.communicators.kfac_communicators\\\n .flat_communicator import FlatCommunicator\n return FlatCommunicator(\n mpi_comm=mpi_comm, dynamic=dynamic, debug=debug)\n else:\n raise ValueError(\n 'Unrecognized communicator: \"{}\"'.format(communicator_name))\n","repo_name":"crest-deep/dlframeworks","sub_path":"dlframeworks/chainer/communicators/kfac_communicators/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"15964413616","text":"from __future__ import absolute_import\nfrom __future__ import with_statement\n\nfrom celery.worker.hub import (\n DummyLock,\n BoundedSemaphore,\n Hub,\n)\n\nfrom mock import Mock, call, patch\n\nfrom celery.tests.utils import Case\n\n\nclass File(object):\n\n def __init__(self, fd):\n self.fd = fd\n\n def fileno(self):\n return self.fd\n\n def __eq__(self, other):\n if isinstance(other, File):\n return self.fd == other.fd\n return NotImplemented\n\n\nclass test_DummyLock(Case):\n\n def test_context(self):\n mutex = DummyLock()\n with mutex:\n pass\n\n\nclass test_BoundedSemaphore(Case):\n\n def test_acquire_release(self):\n x = BoundedSemaphore(2)\n\n c1 = Mock()\n x.acquire(c1, 1)\n self.assertEqual(x.value, 1)\n c1.assert_called_with(1)\n\n c2 = Mock()\n x.acquire(c2, 2)\n self.assertEqual(x.value, 0)\n c2.assert_called_with(2)\n\n c3 = Mock()\n x.acquire(c3, 3)\n self.assertEqual(x.value, 0)\n self.assertFalse(c3.called)\n\n x.release()\n self.assertEqual(x.value, 1)\n c3.assert_called_with(3)\n\n def test_bounded(self):\n x = BoundedSemaphore(2)\n for i in xrange(100):\n x.release()\n self.assertEqual(x.value, 2)\n\n def test_grow_shrink(self):\n x = BoundedSemaphore(1)\n self.assertEqual(x.initial_value, 1)\n cb1 = Mock()\n x.acquire(cb1, 1)\n cb1.assert_called_with(1)\n self.assertEqual(x.value, 0)\n\n cb2 = Mock()\n x.acquire(cb2, 2)\n self.assertFalse(cb2.called)\n self.assertEqual(x.value, 0)\n\n cb3 = Mock()\n x.acquire(cb3, 3)\n self.assertFalse(cb3.called)\n\n x.grow(2)\n cb2.assert_called_with(2)\n cb3.assert_called_with(3)\n self.assertEqual(x.value, 3)\n self.assertEqual(x.initial_value, 3)\n\n self.assertFalse(x._waiting)\n x.grow(3)\n for i in xrange(x.initial_value):\n self.assertTrue(x.acquire(Mock()))\n self.assertFalse(x.acquire(Mock()))\n x.clear()\n\n x.shrink(3)\n for i in xrange(x.initial_value):\n self.assertTrue(x.acquire(Mock()))\n self.assertFalse(x.acquire(Mock()))\n self.assertEqual(x.value, 0)\n\n for i in xrange(100):\n x.release()\n self.assertEqual(x.value, x.initial_value)\n\n def test_clear(self):\n x = BoundedSemaphore(10)\n for i in xrange(11):\n x.acquire(Mock())\n self.assertTrue(x._waiting)\n self.assertEqual(x.value, 0)\n\n x.clear()\n self.assertFalse(x._waiting)\n self.assertEqual(x.value, x.initial_value)\n\n\nclass test_Hub(Case):\n\n @patch('kombu.utils.eventio.poll')\n def test_start_stop(self, poll):\n hub = Hub()\n hub.start()\n poll.assert_called_with()\n\n hub.stop()\n hub.poller.close.assert_called_with()\n\n def test_init(self):\n hub = Hub()\n cb1 = Mock()\n cb2 = Mock()\n hub.on_init.extend([cb1, cb2])\n\n hub.init()\n cb1.assert_called_with(hub)\n cb2.assert_called_with(hub)\n\n def test_fire_timers(self):\n hub = Hub()\n hub.timer = Mock()\n hub.timer._queue = []\n self.assertEqual(hub.fire_timers(min_delay=42.324,\n max_delay=32.321), 32.321)\n\n hub.timer._queue = [1]\n hub.scheduler = Mock()\n hub.scheduler.next.return_value = 3.743, None\n self.assertEqual(hub.fire_timers(), 3.743)\n\n e1, e2, e3 = Mock(), Mock(), Mock()\n entries = [e1, e2, e3]\n\n reset = lambda: [m.reset() for m in [e1, e2, e3]]\n\n def se():\n if entries:\n return None, entries.pop()\n return 3.982, None\n hub.scheduler.next = Mock()\n hub.scheduler.next.side_effect = se\n\n self.assertEqual(hub.fire_timers(max_timers=10), 3.982)\n for E in [e3, e2, e1]:\n E.assert_called_with()\n reset()\n\n entries[:] = [Mock() for _ in xrange(11)]\n keep = list(entries)\n self.assertEqual(hub.fire_timers(max_timers=10, min_delay=1.13), 1.13)\n for E in reversed(keep[1:]):\n E.assert_called_with()\n reset()\n self.assertEqual(hub.fire_timers(max_timers=10), 3.982)\n keep[0].assert_called_with()\n\n def test_update_readers(self):\n hub = Hub()\n P = hub.poller = Mock()\n\n read_A = Mock()\n read_B = Mock()\n hub.update_readers({10: read_A, File(11): read_B})\n\n P.register.assert_has_calls([\n call(10, hub.READ | hub.ERR),\n call(File(11), hub.READ | hub.ERR),\n ], any_order=True)\n\n self.assertIs(hub.readers[10], read_A)\n self.assertIs(hub.readers[11], read_B)\n\n hub.remove(10)\n self.assertNotIn(10, hub.readers)\n hub.remove(File(11))\n self.assertNotIn(11, hub.readers)\n P.unregister.assert_has_calls([\n call(10), call(File(11)),\n ])\n\n def test_can_remove_unknown_fds(self):\n hub = Hub()\n hub.poller = Mock()\n hub.remove(30)\n hub.remove(File(301))\n\n def test_remove__unregister_raises(self):\n hub = Hub()\n hub.poller = Mock()\n hub.poller.unregister.side_effect = OSError()\n\n hub.remove(313)\n\n def test_update_writers(self):\n hub = Hub()\n P = hub.poller = Mock()\n\n write_A = Mock()\n write_B = Mock()\n hub.update_writers({20: write_A, File(21): write_B})\n\n P.register.assert_has_calls([\n call(20, hub.WRITE),\n call(File(21), hub.WRITE),\n ], any_order=True)\n\n self.assertIs(hub.writers[20], write_A)\n self.assertIs(hub.writers[21], write_B)\n\n hub.remove(20)\n self.assertNotIn(20, hub.writers)\n hub.remove(File(21))\n self.assertNotIn(21, hub.writers)\n P.unregister.assert_has_calls([\n call(20), call(File(21)),\n ])\n\n def test_enter__exit(self):\n hub = Hub()\n P = hub.poller = Mock()\n hub.init = Mock()\n\n on_close = Mock()\n hub.on_close.append(on_close)\n\n with hub:\n hub.init.assert_called_with()\n\n read_A = Mock()\n read_B = Mock()\n hub.update_readers({10: read_A, File(11): read_B})\n write_A = Mock()\n write_B = Mock()\n hub.update_writers({20: write_A, File(21): write_B})\n self.assertTrue(hub.readers)\n self.assertTrue(hub.writers)\n self.assertFalse(hub.readers)\n self.assertFalse(hub.writers)\n\n P.unregister.assert_has_calls([\n call(10), call(11), call(20), call(21),\n ], any_order=True)\n\n on_close.assert_called_with(hub)\n\n def test_scheduler_property(self):\n hub = Hub(timer=[1, 2, 3])\n self.assertEqual(list(hub.scheduler), [1, 2, 3])\n","repo_name":"jiangningCX/cpython_forum","sub_path":"build/celery/celery/tests/worker/test_hub.py","file_name":"test_hub.py","file_ext":"py","file_size_in_byte":6992,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"27679699778","text":"import psutil\nfrom netifaces import interfaces, ifaddresses, AF_INET\nimport socket\nimport os\n\ndef get_ipaddrs_by_psutil():\n ifaces = psutil.net_if_addrs()\n ipaddrs = {}\n\n for interface in ifaces.iterkeys():\n if interface != \"lo\":\n for addr in ifaces[interface]:\n if addr.family == 2:\n ipaddrs[interface] = addr.address\n return ipaddrs\n\n\ndef get_ipaddrs_by_netiface():\n ipaddrs = {}\n\n for interface in interfaces():\n\n if interface not in ipaddrs:\n ipaddrs[interface] = []\n\n links = ifaddresses(interface)\n\n if AF_INET in links:\n for link in links[AF_INET]:\n ipaddrs[interface].append(link['addr'])\n\n return ipaddrs\n\n\ndef get_ipaddrs():\n\n ipaddrs = {}\n\n try:\n ipaddrs = get_ipaddrs_by_psutil()\n except AttributeError:\n ipaddrs = get_ipaddrs_by_netiface()\n\n return ipaddrs\n\n\ndef get_hostname():\n return socket.gethostname()\n","repo_name":"psistats/linux-client","sub_path":"psistats/net.py","file_name":"net.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"16063612832","text":"#mport string\n#from secret import MSG\nimport os\nos.system('clear')\n\nf = open('./msg.enc', 'r')\n'''\ndef encryption(msg):\n ct = []\n for char in msg:\n ct.append((123 * char + 18) % 256)\n return bytes(ct)\n'''\n\nplaintext = \"\"\n\nsecret = f.read()\ncipher = bytes.fromhex(secret)\n\nfor i in cipher: # for every char in cipher\n for brute in range(33, 126):\n if((123 * brute + 18) % 256) == i: # if it's a char\n plaintext += chr(brute)\n break\n \nprint(plaintext)\n'''\nct = encryption(MSG)\nf = open('./msg.enc','w')\nf.write(ct.hex())\nf.close()\n'''\n","repo_name":"jon-brandy/hackthebox","sub_path":"Categories/Cryptography/BabyEncryption/modAsciibrute.py","file_name":"modAsciibrute.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"31"} +{"seq_id":"71311646487","text":"import socket\nimport cv2\nimport numpy\n\nHOST = '0.0.0.0'\nPORT = 5001\n\n#socket 수신 버퍼를 읽어서 반환하는 함수\ndef recvall(sock, count):\n buf = b''\n while count:\n newbuf = sock.recv(count)\n if not newbuf: return None\n buf += newbuf\n count -= len(newbuf)\n return buf\n\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_socket.bind((HOST, PORT))\nserver_socket.listen(True)\nprint(\"클라이언트의 접속을 기다리고있습니다.\")\nclient_socket, addr = server_socket.accept()\nprint(\"클라이언트 {}가 접속했습니다.\".format(addr))\n\nwhile True:\n length = recvall(client_socket, 16) # 이미지 길이 먼저 수신\n stringData = recvall(client_socket, int(length)) # 이미지 수신\n data = numpy.frombuffer(stringData, dtype='uint8') # unsigned int\n\n data = data.reshape(1080, 1920, 3)\n\n cv_img = cv2.cvtColor(data, cv2.COLOR_RGB2BGR)\n # cv_img = cv2.resize(cv_img, (960, 540))\n cv2.imshow('CLIENT', cv_img)\n cv2.waitKey(1)\n\ncv2.destroyAllWindows()","repo_name":"bpeak/py-sock-examples","sub_path":"samples/screen/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32661353898","text":"from .date_convert import date_conversion\nimport requests\nimport datetime\nimport phpserialize\n\n\ndef download_and_save_data(fincode):\n URL = 'http://company.accordwebservices.com/Company/GetNSEBSESingleGraph'\n\n PARAMS = {\n 'Type': 'H',\n 'FINCODE': fincode,\n 'STK': 'BSE',\n 'DateOption': 'Y',\n 'DateCount': 10,\n 'StartDate': '',\n 'EndDate': '',\n 'token': 'So9q86WSaEBQERJJD3jRry2CxfpXdIVC'\n }\n\n r = requests.get(url = URL, params = PARAMS)\n\n data = r.json()\n\n for datum in data[\"Table\"]:\n date_time = date_conversion(datum[\"Date\"])\n datum[\"Date\"] = str(datetime.datetime.strptime(date_time, \"%m/%d/%Y %I:%M:%S %p\"))\n datum.pop('pe')\n\n file_name = f\"historical_prices/{str(fincode)}_historical_price.prmt\"\n with open(file_name, \"wb\") as f:\n f.write(phpserialize.dumps(data))\n\n return True\n","repo_name":"kapilnchauhan77/FinWebsite","sub_path":"pythonFiles/HISTORICAL_DATA_SAVING_FROM_API/data_pipeline/data_dwnld_save.py","file_name":"data_dwnld_save.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13744652508","text":"from django.http import HttpResponse\nfrom django.core.mail import send_mail\nfrom django.template.loader import render_to_string\nfrom django.conf import settings\n\nfrom .models import Customer\nfrom client_profile.models import UserProfile\n\nimport time\n\n\nclass StripeWH_Handler:\n \"\"\"Handle Stripe webhooks\"\"\"\n\n def __init__(self, request):\n self.request = request\n\n def _send_confirmation_email(self, order):\n \"\"\"Send the user a confirmation email\"\"\"\n cust_email = order.email\n subject = render_to_string(\n 'checkout/confirmation_emails/confirmation_email_subject.txt',\n {'order': order})\n body = render_to_string(\n 'checkout/confirmation_emails/confirmation_email_body.txt',\n {'order': order, 'contact_email': settings.DEFAULT_FROM_EMAIL})\n\n send_mail(\n subject,\n body,\n settings.DEFAULT_FROM_EMAIL,\n [cust_email]\n )\n\n def handle_event(self, event):\n \"\"\"\n Handle a generic/unknown/unexpected webhook event\n \"\"\"\n return HttpResponse(\n content=f'Unhandled webhook received: {event[\"type\"]}',\n status=200)\n\n def handle_payment_intent_succeeded(self, event):\n \"\"\"\n Handle the payment_intent.succeeded webhook from Stripe\n \"\"\"\n intent = event.data.object\n pid = intent.id\n bag = intent.metadata.bag\n save_info = intent.metadata.save_info\n grand_total = round(intent.charges.data[0].amount / 100, 2)\n\n # Clean data in the shipping details\n\n profile = None\n username = intent.metadata.username\n if username != 'AnonymousUser':\n profile = UserProfile.objects.get(user__username=username)\n profile.save()\n\n order_exists = False\n attempt = 1\n while attempt <= 5:\n try:\n order = Customer.objects.get(\n grand_total=grand_total,\n original_bag=bag,\n stripe_pid=pid,\n )\n order_exists = True\n break\n except Customer.DoesNotExist:\n attempt += 1\n time.sleep(1)\n if order_exists:\n self._send_confirmation_email(order)\n return HttpResponse(\n content=f'Webhook received: {event[\"type\"]} | SUCCESS: Verified order already in database',\n status=200)\n\n self._send_confirmation_email(Customer)\n return HttpResponse(\n content=f'Webhook received: {event[\"type\"]} | SUCCESS: Created order in webhook',\n status=200)\n\n def handle_payment_intent_payment_failed(self, event):\n \"\"\"\n Handle the payment_intent.payment_failed webhook from Stripe\n \"\"\"\n return HttpResponse(\n content=f'Webhook received: {event[\"type\"]} | ERROR: {e}',\n status=200)\n","repo_name":"Justwhittaker/PRO4-Mealdeals","sub_path":"memberships/webhook_handler.py","file_name":"webhook_handler.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"23824039561","text":"\"\"\"\nTests for course shifts.\nRun them by\n paver test_system -s lms -t <path>/course_shifts/tests/test_shifts.py --settings=test\n\n'course_shifts' must be added to INSTALLED_APPS in test.py\n\"\"\"\n# pylint: disable=no-member\nimport datetime\nfrom django.db import IntegrityError\nfrom nose.plugins.attrib import attr\nfrom student.tests.factories import UserFactory\nfrom xmodule.modulestore.tests.django_utils import TEST_DATA_MIXED_MODULESTORE, ModuleStoreTestCase\nfrom xmodule.modulestore.tests.factories import ToyCourseFactory\n\nfrom ..manager import CourseShiftManager\nfrom ..models import CourseShiftGroup, CourseShiftGroupMembership, CourseUserGroup, CourseShiftSettings\n\n\ndef date_shifted(days):\n return (datetime.datetime.now() + datetime.timedelta(days=days)).date()\n\n\n@attr(shard=2)\nclass TestCourseShiftGroup(ModuleStoreTestCase):\n \"\"\"\n Test the course shifts feature\n \"\"\"\n MODULESTORE = TEST_DATA_MIXED_MODULESTORE\n\n def setUp(self):\n \"\"\"\n Make sure that course is reloaded every time--clear out the modulestore.\n \"\"\"\n super(TestCourseShiftGroup, self).setUp()\n date = datetime.datetime.now()\n self.course = ToyCourseFactory.create(start=date)\n self.course_key = self.course.id\n\n def _no_groups_check(self):\n \"\"\"\n Checks that there is no groups.\n Used at start and anywhere needed\n \"\"\"\n groups = CourseUserGroup.objects.filter(course_id=self.course_key)\n self.assertTrue(\n len(groups) == 0,\n \"Course has user groups at start\"\n )\n shift_groups = CourseShiftGroup.get_course_shifts(self.course_key)\n self.assertTrue(\n len(shift_groups) == 0,\n \"Course has shift groups at start\"\n )\n\n def _delete_all_shifts(self, key=None):\n if not key:\n key = self.course_key\n shift_groups = CourseShiftGroup.get_course_shifts(key)\n for x in shift_groups:\n x.delete()\n\n def test_creates_cug(self):\n \"\"\"\n Checks that CourseUserGroup is created when CSG created\n \"\"\"\n self._no_groups_check()\n\n name = \"test_shift_group\"\n test_shift_group, created = CourseShiftGroup.create(name, self.course_key)\n\n groups = CourseUserGroup.objects.filter(course_id=self.course_key)\n correct = len(groups) == 1 and groups.first().name == name\n self.assertTrue(correct, \"Should be only 'test_shift_group' user group, found:{}\".format(\n str([x.name for x in groups])\n ))\n\n shift_groups = CourseShiftGroup.get_course_shifts(self.course_key)\n correct = len(shift_groups) == 1 and test_shift_group in shift_groups\n self.assertTrue(\n correct,\n \"Should be only {}, found:{}\".format(\n str(test_shift_group),\n str(shift_groups)\n ))\n\n self._delete_all_shifts()\n\n def test_deletes_cug(self):\n \"\"\"\n Checks that CourseUserGroup us deleted hen CSG deleted\n \"\"\"\n self._no_groups_check()\n test_shift_group, created = CourseShiftGroup.create(\"test_shift_group\", self.course_key)\n test_shift_group.delete()\n self._no_groups_check()\n\n def test_deleted_by_cug_delete(self):\n \"\"\"\n Checks that CourseShiftGroup is deleted when CourseUserGroup is deleted\n \"\"\"\n self._no_groups_check()\n test_shift_group, created = CourseShiftGroup.create(\"test_shift_group\", self.course_key)\n test_shift_group.course_user_group.delete()\n self._no_groups_check()\n\n def test_create_same_course_and_date_error(self):\n \"\"\"\n Checks that error raised for CSG creation with same course_key and\n start_date, BUT DIFFERENT name\n \"\"\"\n self._no_groups_check()\n test_shift_group, created = CourseShiftGroup.create(\"test_shift_group\", self.course_key)\n\n with self.assertRaises(IntegrityError) as context_manager:\n test_shift_group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.course_key)\n self._delete_all_shifts()\n\n def test_create_same_course_dif_date_ok(self):\n \"\"\"\n Checks that error NOT raised for CSG creation with same course_key\n but different date\n \"\"\"\n self._no_groups_check()\n test_shift_group, created = CourseShiftGroup.create(\"test_shift_group\", self.course_key)\n test_shift_group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.course_key,\n start_date=date_shifted(1))\n groups = CourseShiftGroup.get_course_shifts(self.course_key)\n correct = test_shift_group2 in groups and \\\n test_shift_group in groups and \\\n len(groups) == 2\n\n self.assertTrue(correct, \"Should be test_shift_group and test_shift_group2, found:{}\".format(\n str(groups)\n ))\n self._delete_all_shifts()\n\n def test_create_same_course_and_date_copy(self):\n \"\"\"\n Checks that copy returned for CSG creation with same course_key and\n start_date, AND SAME name\n \"\"\"\n self._no_groups_check()\n name = \"test_shift_group\"\n test_shift_group, created = CourseShiftGroup.create(name, self.course_key)\n test_shift_group2, created2 = CourseShiftGroup.create(name, self.course_key)\n\n self.assertFalse(created2, \"shift groups should be same: {}\".format(\n str(test_shift_group),\n str(test_shift_group2)\n ))\n self._delete_all_shifts()\n\n def test_same_name_different_date_error(self):\n \"\"\"\n Checks that error raised for CSG creation with same course_key and name,\n but different start_date\n \"\"\"\n self._no_groups_check()\n name = \"test_shift_group\"\n test_shift_group, created = CourseShiftGroup.create(name, self.course_key)\n with self.assertRaises(ValueError) as context_manager:\n test_shift_group2, created2 = CourseShiftGroup.create(name, self.course_key, start_date=date_shifted(1))\n message_list = [\"Shift already exists with different start_date\"]\n message_right = list(x in str(context_manager.exception) for x in message_list)\n self.assertTrue(all(message_right), \"Message:{}\".format(str(context_manager.exception)))\n\n self._delete_all_shifts()\n\n\n@attr(shard=2)\nclass TestCourseShiftGroupMembership(ModuleStoreTestCase):\n MODULESTORE = TEST_DATA_MIXED_MODULESTORE\n\n def setUp(self):\n \"\"\"\n Make sure that course is reloaded every time--clear out the modulestore.\n \"\"\"\n super(TestCourseShiftGroupMembership, self).setUp()\n date = datetime.datetime.now()\n self.course = ToyCourseFactory.create(start=date)\n self.course_key = self.course.id\n self.user = UserFactory(username=\"test\", email=\"a@b.com\")\n self.group, created = CourseShiftGroup.create(\"test_shift_group\", self.course_key)\n\n self.second_course = ToyCourseFactory.create(org=\"neworg\")\n self.second_course_key = self.second_course.id\n\n def _delete_all_memberships(self):\n memberships = CourseShiftGroupMembership.objects.all()\n for m in memberships:\n m.delete()\n\n def _check_no_memberships(self):\n mems = CourseShiftGroupMembership.objects.all()\n self.assertTrue(len(mems) == 0)\n\n def test_membership_creation(self):\n \"\"\"\n Tests shifts transfer to group pushes user to CourseShiftGroup\n \"\"\"\n membership = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n self.assertTrue(self.user in self.group.users.all())\n self._delete_all_memberships()\n\n def test_membership_deletion(self):\n \"\"\"\n Tests membership deletion and transfer to None removes user from Group\n \"\"\"\n transferred = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n self.assertEqual(transferred , True)\n membership = CourseShiftGroupMembership.get_user_membership(self.user, self.course_key)\n membership.delete()\n self.assertTrue(len(self.group.users.all()) == 0)\n\n transferred = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n self.assertEqual(transferred, True)\n transferred = CourseShiftGroupMembership.transfer_user(self.user, self.group, None)\n self.assertEqual(transferred, True)\n self.assertTrue(len(self.group.users.all()) == 0)\n\n def test_membership_course_user_unique(self):\n \"\"\"\n Tests that there can't be two membership for user in same course_key\n \"\"\"\n group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.course_key,\n start_date=date_shifted(1))\n CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n with self.assertRaises(ValueError) as context_manager:\n CourseShiftGroupMembership.objects.create(user=self.user, course_shift_group=group2)\n group2.delete()\n self._delete_all_memberships()\n\n def test_user_membership_two_courses(self):\n \"\"\"\n Tests that user can have two memberships in two different courses\n \"\"\"\n group2, created = CourseShiftGroup.create(\"test_shift_group\", self.second_course_key)\n membership = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n membership2 = CourseShiftGroupMembership.transfer_user(self.user, None, group2)\n mems = CourseShiftGroupMembership.objects.all()\n self.assertTrue(len(mems) == 2, \"Must be 2 memberships, found: {}\".format(\n str(mems)\n ))\n self._delete_all_memberships()\n\n def test_two_users_for_course_membership(self):\n \"\"\"\n Tests that there can be two users in CourseShiftGroup\n \"\"\"\n user2 = UserFactory(username=\"test2\", email=\"a2@b.com\")\n membership = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n membership = CourseShiftGroupMembership.transfer_user(user2, None, self.group)\n mems = CourseShiftGroupMembership.objects.all()\n self.assertTrue(len(mems) == 2, \"Must be 2 memberships, found: {}\".format(\n str(mems)\n ))\n self._delete_all_memberships()\n\n def test_membership_unchangable(self):\n \"\"\"\n Tests that membership can't be changed\n \"\"\"\n transferred = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n self.assertEqual(transferred, True)\n group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.second_course_key)\n membership = CourseShiftGroupMembership.get_user_membership(self.user, self.course_key)\n membership.course_shift_group = group2\n with self.assertRaises(ValueError):\n membership.save()\n group2.delete()\n self._delete_all_memberships()\n\n def test_membership_transfer_valid(self):\n \"\"\"\n Tests transfer from None, to shift group from the same course, to None\n \"\"\"\n self.assertTrue(len(self.group.users.all()) == 0)\n\n membership = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n self.assertTrue(self.user in self.group.users.all())\n\n group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.course_key, start_date=date_shifted(1))\n membership = CourseShiftGroupMembership.transfer_user(self.user, self.group, group2)\n self.assertTrue(self.user in group2.users.all())\n self.assertTrue(len(self.group.users.all()) == 0)\n\n membership = CourseShiftGroupMembership.transfer_user(self.user, group2, None)\n self.assertTrue(len(self.group.users.all()) == 0)\n self.assertTrue(len(group2.users.all()) == 0)\n group2.delete()\n\n def test_transfer_intercourse_error(self):\n \"\"\"\n Tests user can't be transfered between to the shift from\n the different course\n \"\"\"\n group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.second_course_key)\n membership = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n with self.assertRaises(ValueError):\n membership = CourseShiftGroupMembership.transfer_user(self.user, self.group, group2)\n group2.delete()\n\n def test_transfer_from_error(self):\n \"\"\"\n Tests that transfer raises error when shift_from is incorrect\n \"\"\"\n group2, created = CourseShiftGroup.create(\"test_shift_group2\", self.course_key, start_date=date_shifted(1))\n\n with self.assertRaises(ValueError) as context_manager:\n membership = CourseShiftGroupMembership.transfer_user(self.user, self.group, group2)\n message_list = [\"User's membership is\", \"None\", \"not\"]\n message_right = list(x in str(context_manager.exception) for x in message_list)\n self.assertTrue(all(message_right), \"Message:{}\".format(str(context_manager.exception)))\n\n membership = CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n\n with self.assertRaises(ValueError) as context_manager:\n membership = CourseShiftGroupMembership.transfer_user(self.user, group2, None)\n message_list = [\"User's membership is\", \"test_shift_group2\", \"test_shift_group\", \"not\"]\n message_right = list(x in str(context_manager.exception) for x in message_list)\n self.assertTrue(all(message_right), \"Message:{}\".format(str(context_manager.exception)))\n\n self._delete_all_memberships()\n\n def test_get_user_membership(self):\n membership = CourseShiftGroupMembership.get_user_membership(self.user, self.course_key)\n self.assertIsNone(membership)\n\n CourseShiftGroupMembership.transfer_user(self.user, None, self.group)\n membership = CourseShiftGroupMembership.get_user_membership(self.user, self.course_key)\n self.assertTrue(membership.course_shift_group == self.group, \"Membershift group:{}\".format(\n str(membership.course_shift_group)\n ))\n\n\nclass EnrollClsFields(object):\n _ENROLL_BEFORE = 7\n _ENROLL_AFTER = 0\n _PERIOD = 20\n _COURSE_DATE_START = 14\n\n\n@attr(shard=2)\nclass TestCourseShiftSettings(ModuleStoreTestCase, EnrollClsFields):\n \"\"\"\n Test the course shifts settings\n \"\"\"\n MODULESTORE = TEST_DATA_MIXED_MODULESTORE\n\n def setUp(self):\n \"\"\"\n Make sure that course is reloaded every time--clear out the modulestore.\n \"\"\"\n super(TestCourseShiftSettings, self).setUp()\n date = datetime.datetime.now() - datetime.timedelta(days=self._COURSE_DATE_START)\n self.course = ToyCourseFactory.create(start=date)\n self.course_key = self.course.id\n self._no_groups_check()\n\n def tearDown(self):\n self._delete_groups()\n\n def _settings_setup(self, period=None, autostart=False):\n \"\"\"\n Not included into setUp because should be tests\n \"\"\"\n if not period:\n period = self._PERIOD\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n settings.is_shift_enabled = True\n settings.enroll_before_days = self._ENROLL_BEFORE\n settings.enroll_after_days = self._ENROLL_AFTER\n settings.is_autostart = autostart\n settings.autostart_period_days = period\n settings.save()\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n self.assertTrue(settings.enroll_before_days == self._ENROLL_BEFORE)\n self.assertTrue(settings.enroll_after_days == self._ENROLL_AFTER)\n return\n\n def _delete_groups(self):\n shift_groups = CourseShiftGroup.objects.all()\n for x in shift_groups:\n x.delete()\n\n def _number_of_shifts(self, custom_period):\n \"\"\"\n Calculates how many shifts should be according\n to the current settings\n \"\"\"\n course_started_days_ago = self._COURSE_DATE_START\n # enroll_before effectively shifts start date here\n # E.g. course started 15.01, period is 10, enroll_before is 5\n # First shift is created immediately,second is created at 20.01,\n # next one is created at 30.01.\n\n course_started_days_ago += self._ENROLL_BEFORE\n shifts_number = int(course_started_days_ago / custom_period)\n shifts_number += 1\n return shifts_number\n\n def _no_groups_check(self):\n \"\"\"\n Checks that there is no groups.\n Used at start and anywhere needed\n \"\"\"\n shift_groups = CourseShiftGroup.get_course_shifts(self.course_key)\n self.assertTrue(\n len(shift_groups) == 0,\n \"Course has shift groups at start:{}\".format(shift_groups)\n )\n\n def test_settings_generation_and_saving(self):\n \"\"\"\n Tests that settings got by get_course_settings saved correctly\n \"\"\"\n self._settings_setup(autostart=False)\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n\n self.assertTrue(settings.is_shift_enabled == True)\n self.assertTrue(settings.is_autostart == False)\n self.assertTrue(settings.autostart_period_days == self._PERIOD)\n\n def test_autostart_generation_one(self):\n \"\"\"\n Single start should be generated - default shift at start\n \"\"\"\n custom_period = 30\n self._settings_setup(period=custom_period, autostart=True)\n course_shifts = CourseShiftGroup.get_course_shifts(self.course_key)\n shifts_number = self._number_of_shifts(custom_period)\n self.assertTrue(len(course_shifts) == shifts_number,\n \"Must be {} shift, found: {}\".format(shifts_number, str(course_shifts)))\n\n def test_autostart_generation_two(self):\n \"\"\"\n Two shifts must be generated automatically, default and one more\n \"\"\"\n custom_period = 12\n self._settings_setup(period=custom_period, autostart=True)\n shifts_number = self._number_of_shifts(custom_period)\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n course_shifts = CourseShiftGroup.get_course_shifts(self.course_key)\n self.assertTrue(len(course_shifts) == shifts_number, \"Must be {} shifts, found:{}\".format(\n shifts_number,\n str(course_shifts)))\n\n def test_autostart_generation_three(self):\n \"\"\"\n Three shifts must be generated automatically\n \"\"\"\n self._no_groups_check()\n custom_period = 8\n self._settings_setup(period=custom_period, autostart=True)\n shifts_number = self._number_of_shifts(custom_period)\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n course_shifts = CourseShiftGroup.get_course_shifts(self.course_key)\n self.assertTrue(len(course_shifts) == shifts_number, \"Must be {} shifts, found:{}\".format(\n shifts_number,\n str(course_shifts)))\n\n def test_turn_off_autostart(self):\n \"\"\"\n Checks that when autostart is turned off\n shifts aren't created\n \"\"\"\n self._no_groups_check()\n self._settings_setup(autostart=False, period=8)\n self._no_groups_check()\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n self._no_groups_check()\n\n\n@attr(shard=2)\nclass TestCourseShiftManager(ModuleStoreTestCase, EnrollClsFields):\n def setUp(self):\n super(TestCourseShiftManager, self).setUp()\n date = datetime.datetime.now() - datetime.timedelta(days=14)\n self.course = ToyCourseFactory.create(start=date)\n self.course_key = self.course.id\n self.shift_settings = CourseShiftSettings.get_course_settings(self.course_key)\n self.shift_settings.is_shift_enabled = True\n self.shift_settings.is_autostart = False\n self.shift_settings.save()\n self.user = UserFactory(username=\"test\", email=\"a@b.com\")\n self._no_groups_check()\n\n def tearDown(self):\n self._delete_groups()\n\n def _settings_setup(self, period=None, autostart=False):\n \"\"\"\n Not included into setUp because should be tests\n \"\"\"\n if not period:\n period = self._PERIOD\n settings = CourseShiftSettings.get_course_settings(self.course_key)\n settings.is_shift_enabled = True\n settings.enroll_before_days = self._ENROLL_BEFORE\n settings.enroll_after_days = self._ENROLL_AFTER\n settings.is_autostart = autostart\n settings.autostart_period_days = period\n settings.save()\n return\n\n def _delete_groups(self):\n for x in CourseShiftGroup.objects.all():\n x.delete()\n\n def _no_groups_check(self):\n \"\"\"\n Checks that there is no groups.\n Used at start and anywhere needed\n \"\"\"\n shift_groups = CourseShiftGroup.get_course_shifts(self.course_key)\n correct = len(shift_groups) == 0\n message = str(shift_groups)\n if not correct:\n self._delete_groups()\n self.assertTrue(\n correct,\n message\n )\n\n def test_get_user_course_shift(self):\n \"\"\"\n Tests method get_user_course_shift\n \"\"\"\n self._settings_setup()\n user = self.user\n shift_manager = CourseShiftManager(course_key=self.course_key)\n shift_group = shift_manager.get_user_shift(user)\n self.assertFalse(shift_group, \"User shift group is {}, should be None\".format(str(shift_group)))\n\n test_a_shift_group, created = CourseShiftGroup.create(\"test_shift_group_t1\", self.course_key)\n shift_manager.enroll_user(user, test_a_shift_group)\n shift_group = shift_manager.get_user_shift(user)\n self.assertTrue(shift_group == test_a_shift_group, \"User shift group is {}, should be {}\".format(\n str(shift_group),\n str(test_a_shift_group)\n ))\n self._delete_groups()\n self._no_groups_check()\n\n def test_get_user_course_shift_disabled(self):\n self._settings_setup()\n user = self.user\n test_a_shift_group, created = CourseShiftGroup.create(\"test_shift_group_t2\", self.course_key)\n CourseShiftGroupMembership.transfer_user(user, None, test_a_shift_group)\n\n self.shift_settings.is_shift_enabled = False\n self.shift_settings.save()\n shift_manager = CourseShiftManager(self.course_key)\n shift_group = shift_manager.get_user_shift(user)\n self.assertTrue(shift_group is None, \"User shift group is {}, should be None\".format(str(shift_group)))\n\n self.shift_settings.is_shift_enabled = True\n\n def test_get_active_shifts(self):\n \"\"\"\n Tests method get_active_shifts without user\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n\n group1, created = CourseShiftGroup.create(\"test_group\", self.course_key)\n group2, created = CourseShiftGroup.create(\"test_group2\", self.course_key, start_date=date_shifted(days=5))\n\n course_shifts = shift_manager.get_active_shifts()\n correct = (group1 in course_shifts) and (group2 in course_shifts) and (len(course_shifts) == 2)\n self.assertTrue(correct, \"Shifts should be {} and {}, found {}\".format(\n str(group1),\n str(group2),\n str(course_shifts)\n ))\n\n def test_create_shift(self):\n \"\"\"\n Tests manager.create_shift\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n test_group = shift_manager.create_shift()\n groups = shift_manager.get_all_shifts()\n correct = test_group in groups and len(groups) == 1\n self.assertTrue(correct, \"Should be only {}, found: {}\".format(\n str(test_group),\n str(groups)\n ))\n\n test_group_same = shift_manager.create_shift()\n groups = shift_manager.get_all_shifts()\n correct = test_group_same in groups and len(groups) == 1\n self.assertTrue(correct, \"Should be only {}, found: {}\".format(\n str(test_group),\n str(groups)\n ))\n self.assertTrue(test_group_same == test_group, \"Groups different: {} and {}\".format(\n str(test_group),\n str(test_group_same)\n ))\n\n test_group_other = shift_manager.create_shift(date_shifted(1))\n groups = shift_manager.get_all_shifts()\n correct = test_group_same in groups \\\n and test_group_other in groups \\\n and len(groups) == 2\n self.assertTrue(correct, \"Should be {} and {}, found: {}\".format(\n str(test_group),\n str(test_group_other),\n str(groups)\n ))\n\n def test_create_shift_different_name_error(self):\n \"\"\"\n Checks error at shift creation with same date\n but different name\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n test_group = shift_manager.create_shift()\n with self.assertRaises(IntegrityError):\n test_group_error = shift_manager.create_shift(name=\"same_date_different_name\")\n\n def test_create_shift_different_date_error(self):\n \"\"\"\n Checks error at shift creation with same name\n but different date.\n Checks that for same name and same date error not raised\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n\n test_group = shift_manager.create_shift()\n name = test_group.name\n with self.assertRaises(ValueError) as context_manager:\n test_group_error = shift_manager.create_shift(name=name, start_date=date_shifted(1))\n message_list = [\"Shift already exists with different start_date\"]\n message_right = list(x in str(context_manager.exception) for x in message_list)\n self.assertTrue(all(message_right), \"Message:{}\".format(str(context_manager.exception)))\n\n test_group2 = shift_manager.create_shift()\n self.assertTrue(test_group == test_group2, \"Different groups: {} {}\".format(\n str(test_group),\n str(test_group2)\n ))\n self._delete_groups()\n self._no_groups_check()\n\n def test_get_active_groups(self):\n \"\"\"\n Checks get_active_groups without user\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n self._no_groups_check()\n group = shift_manager.create_shift(date_shifted(-20))\n active_groups = shift_manager.get_active_shifts()\n self.assertTrue(len(active_groups) == 0, \"Should be empty, found {}\".format(str(active_groups)))\n\n group2 = shift_manager.create_shift(date_shifted(1))\n active_groups = shift_manager.get_active_shifts()\n correct = len(active_groups) == 1 and group2 in active_groups\n self.assertTrue(correct, \"Should be {}, found {}\".format(\n str(group2),\n str(active_groups)\n ))\n self._delete_groups()\n self._no_groups_check()\n\n def test_get_active_groups_user(self):\n \"\"\"\n Checks get_active_groups with user.\n Old groups are inactive but if has membership, later groups are active\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n group = shift_manager.create_shift(date_shifted(-20))\n group2 = shift_manager.create_shift(date_shifted(-30))\n\n active_user_groups = shift_manager.get_active_shifts(self.user)\n correct = len(active_user_groups) == 0\n self.assertTrue(correct, \"Active user groups: {}\".format(\n str(active_user_groups)\n ))\n\n shift_manager.enroll_user(self.user, group2, forced=True)\n active_user_groups = shift_manager.get_active_shifts(self.user)\n correct = len(active_user_groups) == 1 and group in active_user_groups\n self.assertTrue(correct, \"Active user groups: {}\".format(\n str(active_user_groups)\n ))\n self._delete_groups()\n self._no_groups_check()\n\n def test_get_active_groups_future(self):\n \"\"\"\n Checks that future groups are inactive\n without user and with user that has older\n membership\n \"\"\"\n self._settings_setup()\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n group = shift_manager.create_shift(start_date=date_shifted(-1))\n shift_manager.enroll_user(self.user, group, forced=True)\n\n group_future = shift_manager.create_shift(start_date=date_shifted(14))\n\n self.assertTrue(group_future.start_date == date_shifted(14),\n \"Start date is {}, must be {}\".format(\n str(group_future.start_date),\n str(date_shifted(14))\n ))\n active_groups = shift_manager.get_active_shifts()\n active_user_groups = shift_manager.get_active_shifts(self.user)\n\n correct = len(active_user_groups) == 0 and len(active_user_groups) == 0\n self.assertTrue(correct, \"Active groups: {}; \\nActive user groups: {}\".format(\n str(active_groups),\n str(active_user_groups)\n ))\n self._delete_groups()\n self._no_groups_check()\n\n def test_enroll_user(self):\n \"\"\"\n Tests method sign_user_on_shift.\n Valid scenarios\n \"\"\"\n self._no_groups_check()\n user = self.user\n shift_manager = CourseShiftManager(self.course_key)\n\n group1 = shift_manager.create_shift()\n group2 = shift_manager.create_shift(date_shifted(days=-5))\n\n shift_manager.enroll_user(user, group1)\n shift_group = shift_manager.get_user_shift(user)\n self.assertTrue(shift_group == group1, \"User shift group is {}, should be {}\".format(\n str(shift_group),\n str(group1)\n ))\n\n shift_manager.enroll_user(\n user=user,\n shift=group2\n )\n shift_group = shift_manager.get_user_shift(user)\n self.assertTrue(shift_group == group2, \"User shift group is {}, should be {}\".format(\n str(shift_group),\n str(group2)\n ))\n\n shift_manager.enroll_user(user, shift=group1)\n shift_group = shift_manager.get_user_shift(user)\n self.assertTrue(shift_group == group1, \"User shift group is {}, should be {}\".format(\n str(shift_group),\n str(group1)\n ))\n self._delete_groups()\n\n def test_enroll_user_error_course_key(self):\n \"\"\"\n Checks that error is raised when enroll_user\n gets shift from other course\n \"\"\"\n self._no_groups_check()\n user = self.user\n shift_manager = CourseShiftManager(self.course_key)\n\n other_course = ToyCourseFactory.create(org=\"neworg\")\n other_course_key = other_course.id\n other_manager = CourseShiftManager(other_course_key)\n other_manager.settings.is_shift_enabled = True\n other_manager.settings.is_autostart = False\n other_group = other_manager.create_shift()\n\n with self.assertRaises(ValueError):\n shift_manager.enroll_user(user, other_group)\n self._delete_groups()\n\n def test_enroll_user_error_inactive(self):\n \"\"\"\n Checks that enroll_user raises error\n if shift is not active\n \"\"\"\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n group = shift_manager.create_shift(date_shifted(-20))\n active_groups = shift_manager.get_active_shifts(self.user)\n self.assertTrue(\n not (group in active_groups),\n \"Active groups : {}\".format(str(active_groups))\n )\n with self.assertRaises(ValueError):\n shift_manager.enroll_user(self.user, group)\n self._delete_groups()\n\n def test_enroll_user_inactive_forced(self):\n \"\"\"\n Checks that no error raised when enroll_user\n is used in forced mode for inactive shift\n \"\"\"\n self._no_groups_check()\n shift_manager = CourseShiftManager(self.course_key)\n group = shift_manager.create_shift(date_shifted(-20))\n active_groups = shift_manager.get_active_shifts()\n self.assertTrue(\n not (group in active_groups),\n \"Active groups : {}\".format(str(active_groups))\n )\n shift_manager.enroll_user(self.user, group, forced=True)\n user_shift = shift_manager.get_user_shift(self.user)\n self.assertTrue(\n user_shift == group,\n \"User shift:{}, should be {}\".format(\n str(user_shift),\n str(group)\n )\n )\n\n def test_unenroll_user(self):\n \"\"\"\n Tests that enroll with None leads to unenrollment\n \"\"\"\n shift_manager = CourseShiftManager(self.course_key)\n group = shift_manager.create_shift(date_shifted(-5))\n\n shift_manager.enroll_user(self.user, None)\n current_shift = shift_manager.get_user_shift(self.user)\n self.assertTrue(\n current_shift is None,\n \"Current shift should be None, but it is {}\".format(str(current_shift))\n )\n shift_manager.enroll_user(self.user, group)\n shift_manager.enroll_user(self.user, None)\n self.assertTrue(\n current_shift is None,\n \"Current shift should be None, but it is {}\".format(str(current_shift))\n )\n","repo_name":"zimka/course_shifts","sub_path":"course_shifts/tests/test_shifts.py","file_name":"test_shifts.py","file_ext":"py","file_size_in_byte":33935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"845007767","text":"from tkinter import *\nfrom config.Constantes_config import *\n\ndef leer_linea_all_time(archivo):\n # Recive el archivo csv, lee una linea del mismo y la devuelve con formato acorde.\n # Hecha por Oriz Omar, Agustin Conti.\n fin_archivo = False\n linea = archivo.readline()\n registro = ['NAN','NAN','NAN',0,0]\n if linea:\n registro = linea.rstrip().split(',')\n registro[POS_ACIERTOS_REGISTRO] = int(registro[POS_ACIERTOS_REGISTRO]) \n registro[POS_INTENTOS_REGISTRO] = int(registro[POS_INTENTOS_REGISTRO])\n else:\n fin_archivo = True\n return registro, fin_archivo\n\ndef gen_dict_all_time_ordenado():\n # Lee el historial all time y genera un diccionario ordenado a partir de este.\n # Hecha por Oriz Omar, Agustin Conti.\n archivo = open('CSVs/historial_all_time.csv','r')\n registro, fin_archivo = leer_linea_all_time(archivo)\n dict_all_time = {}\n while not fin_archivo:\n if registro[POS_NOMBRE_REGISTRO] not in dict_all_time:\n dict_all_time[registro[POS_NOMBRE_REGISTRO]] = [registro[POS_INTENTOS_REGISTRO], registro[POS_ACIERTOS_REGISTRO], INICIAR_CANT_PARTIDAS]\n else:\n dict_all_time[registro[POS_NOMBRE_REGISTRO]][INTENTOS] += registro[POS_INTENTOS_REGISTRO]\n dict_all_time[registro[POS_NOMBRE_REGISTRO]][ACIERTOS] += registro[POS_ACIERTOS_REGISTRO]\n dict_all_time[registro[POS_NOMBRE_REGISTRO]][PARTIDAS_JUGADAS] += 1\n\n registro,fin_archivo = leer_linea_all_time(archivo)\n archivo.close()\n return sorted(dict_all_time.items(), key = lambda item: (item[1][ACIERTOS], - item[1][INTENTOS]) , reverse = True)\n\ndef coordinar_scroll_con_frame(lienzo):\n # Resetear la region de scroll para encompazar el frame.\n # Hecha por Oriz Omar, Agustin Conti.\n lienzo.configure(scrollregion = lienzo.bbox(\"all\"))\n\ndef poblar_frame(frame_ranking, trofeo_ganador, laurel_derecho, laurel_izquierdo, medalla_plata, medalla_bronce):\n # Recibe el frame, la lista de j. ordenada y la imagen trofeo. Se encarga de mostrar en la interfaz la tabla de ranking \n # con todas sus estadísticas.\n # Hecha por Oriz Omar, Agustin Conti.\n\n #---------------------------------- label fijos--------------------------------------------\n espacio = Label(frame_ranking, text = ' ', bg = '#F2F3F4', pady = 15, padx = 10)\n espacio.grid(column = 0, row = 0)\n\n nombre_jugador = Label(frame_ranking, text = 'Nombre del Jugador', font = (\"Lucida Console\", 11), bg = '#F2F3F4', borderwidth = \"1\", pady = 15, padx = 10)\n nombre_jugador.grid(column = 2, row = 0)\n\n aciertos_jugador = Label(frame_ranking, text = 'Aciertos', font = (\"Lucida Console\", 11), bg = '#F2F3F4', borderwidth = \"1\", pady = 15, padx = 10)\n aciertos_jugador.grid(column = 4, row = 0)\n\n intentos_jugador = Label(frame_ranking, text = 'Intentos', font = (\"Lucida Console\", 11), bg = '#F2F3F4', borderwidth = \"1\", pady = 15, padx = 10)\n intentos_jugador.grid(column = 6, row = 0)\n\n promedio_aciertos_por_intento = Label(frame_ranking, text = '% Aciertos por intento', font = (\"Lucida Console\", 11), bg = '#F2F3F4', borderwidth = \"1\", pady = 15, padx = 10)\n promedio_aciertos_por_intento.grid(column = 8, row = 0)\n\n promedio_intentos_partida = Label(frame_ranking, text = 'Intentos por partida', font = (\"Lucida Console\", 11), bg = '#F2F3F4', borderwidth = \"1\", pady = 15, padx = 10)\n promedio_intentos_partida.grid(column = 10, row = 0)\n\n trofeo = Label(frame_ranking, image = trofeo_ganador, bg = '#F2F3F4', height = 110, width = 125)\n trofeo.grid(column = 0, row = 1)\n\n laurel_i = Label(frame_ranking, image = laurel_izquierdo, bg = '#F2F3F4', height = 100, width = 50)\n laurel_i.grid(column = 1, row = 1)\n\n laurel_d = Label(frame_ranking, image = laurel_derecho, bg = '#F2F3F4', height = 100, width = 50)\n laurel_d.grid(column = 3, row = 1)\n\n medalla_p = Label(frame_ranking, image = medalla_plata, bg = '#F2F3F4', height = 50, width = 50)\n medalla_p.grid(column = 0, row = 2)\n\n medalla_b = Label(frame_ranking, image = medalla_bronce, bg = '#F2F3F4', height = 50, width = 50)\n medalla_b.grid(column = 0, row = 3)\n\n\n #---------------------------------- label generados--------------------------------------------\n columna_actual = 2\n fila_actual = 1\n lugar = 4 # la numeracion empieza por el cuarto lugar (trofeos)\n tamanio_letra = 30\n posicion = 0 #(de la lista de jugadores ordenada final.)\n lista_jugadores_ordenada_final = gen_dict_all_time_ordenado()\n\n for jugador, estadisticas in lista_jugadores_ordenada_final:\n\n if posicion >= 3 and columna_actual == 0:\n temp_label = Label(frame_ranking, text = f'{lugar}º', font = (\"Lucida Console\", tamanio_letra-2), bg = '#F2F3F4', borderwidth = \"1\", relief = \"solid\")\n temp_label.grid(column = columna_actual, row = fila_actual)\n columna_actual += 2\n lugar += 1 \n\n temp_label = Label(frame_ranking, text = f'{jugador}', font = (\"Lucida Console\", tamanio_letra), bg = '#F2F3F4')\n temp_label.grid(column = columna_actual, row = fila_actual)\n\n temp_label = Label(frame_ranking, text = f'{estadisticas[ACIERTOS]}', font = (\"Lucida Console\", tamanio_letra), bg = '#F2F3F4')\n temp_label.grid(column = columna_actual + 2, row = fila_actual)\n\n temp_label = Label(frame_ranking, text = f'{estadisticas[INTENTOS]}', font = (\"Lucida Console\", tamanio_letra), bg = '#F2F3F4')\n temp_label.grid(column = columna_actual + 4, row = fila_actual)\n\n if estadisticas[INTENTOS] > 0:\n temp_label = Label(frame_ranking, text = f'{(estadisticas[ACIERTOS] / estadisticas[INTENTOS]) * 100:.2f}%', font = (\"Lucida Console\", tamanio_letra), bg = '#F2F3F4')\n else:\n temp_label = Label(frame_ranking, text ='No tuvo intentos', font = (\"Lucida Console\", tamanio_letra - 15), bg = '#F2F3F4')\n temp_label.grid(column = columna_actual + 6, row = fila_actual)\n \n temp_label = Label(frame_ranking, text = f'{estadisticas[INTENTOS] / estadisticas[PARTIDAS_JUGADAS]:.2f}', font = (\"Lucida Console\", tamanio_letra), bg = '#F2F3F4')\n temp_label.grid(column = columna_actual + 8, row = fila_actual)\n \n \n if tamanio_letra > 10:\n tamanio_letra -= 3\n\n if posicion >= 2:\n columna_actual = 0\n else:\n columna_actual = 2\n\n fila_actual += 1\n posicion += 1\n return fila_actual\n\ndef cerrar_all_time(ver_all_time, raiz_all_time):\n # Recibe el boton ver all time y la raiz de all time. cierra la interfaz y habilita el botón.\n # Hecha por Oriz Omar.\n raiz_all_time.destroy()\n ver_all_time.config(state = NORMAL, bg = 'gold')\n\ndef desabilitarX():\n # Hecha por Oriz Omar.\n pass\n\ndef ranking_all_time(raiz_ranking_fin, ver_all_time):\n # Interfaz del ranking all time almacenado en el Csv.\n # Hecha por Omar Oriz, Agustin Conti.\n #---------------------------------- raíz--------------------------------------------\n raiz_all_time = Toplevel(raiz_ranking_fin)\n raiz_all_time.protocol(\"WM_DELETE_WINDOW\", desabilitarX)\n raiz_all_time.title(\"RANKING ALL-TIME!\")\n raiz_all_time.attributes('-topmost', True)\n raiz_all_time.iconbitmap('Imagenes/laureles.ico')\n raiz_all_time.resizable(0, 0)\n raiz_all_time.geometry(\"1050x400\")\n raiz_all_time.config(bg=\"#F2F3F4\")\n\n #---------------------------------- Scroll--------------------------------------------\n barra_scroll = Scrollbar(raiz_all_time, orient = \"vertical\",)\n #---------------------------------- Lienzo--------------------------------------------\n lienzo = Canvas(raiz_all_time)\n #---------------------------------- frame--------------------------------------------\n frame_ranking = Frame(lienzo)\n frame_ranking.config(bg = '#F2F3F4')\n #---------------------------------- Configuracion del scroll--------------------------------------------\n barra_scroll.config(command = lienzo.yview)\n barra_scroll.pack(side = \"right\", fill = \"y\")\n\n #---------------------------------- Configuracion del lienzo--------------------------------------------\n lienzo.configure(yscrollcommand = barra_scroll.set, bg = '#F2F3F4') # parametro de configuracion yscrollcommand seteando la barra_scroll como scroller.\n lienzo.pack(side=\"left\", fill = \"both\", expand = True)\n lienzo.create_window((1, 1), window = frame_ranking, anchor = \"n\")\n\n #---------------------------------- relacionar el frame con la barra de scroll.--------------------------------------------\n frame_ranking.bind(\"<Configure>\", lambda event, lienzo = lienzo: coordinar_scroll_con_frame(lienzo))\n\n #---------------------------------- Imagenes --------------------------------------------\n trofeo_ganador = PhotoImage(file = 'Imagenes/trofeo_transparente.png')\n laurel_derecho = PhotoImage(file = 'Imagenes/laurel_derecho.png')\n laurel_izquierdo = PhotoImage(file = 'Imagenes/laurel_izquierdo.png')\n medalla_plata = PhotoImage(file = 'Imagenes/medalla_silver.png')\n medalla_bronce = PhotoImage(file = 'Imagenes/medalla_bronce.png')\n\n #---------------------------------- Poblar Frame --------------------------------------------\n ultima_fila = poblar_frame(frame_ranking, trofeo_ganador, laurel_derecho, laurel_izquierdo, medalla_plata, medalla_bronce)\n\n #---------------------------------- Botones --------------------------------------------\n fila_actual = ultima_fila + 1\n salir_del_juego = Button(frame_ranking, text = 'Cerrar ventana', command = lambda: cerrar_all_time(ver_all_time,raiz_all_time), bg = 'pale violet red', fg = 'dark slate blue', activebackground = 'violetred3')\n salir_del_juego.grid(column = 10, row = fila_actual, pady = 10) \n\n\n raiz_all_time.mainloop()","repo_name":"salluzziluca/TP_Fosiles","sub_path":"interfaces/Interfaz_all_time.py","file_name":"Interfaz_all_time.py","file_ext":"py","file_size_in_byte":9833,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"35902951939","text":"import os\nfrom setuptools import setup, find_packages\n\n# Meta info\nproject_root = os.path.dirname(os.path.realpath(__file__))\nwith open(f\"{project_root}/VERSION\", \"r\") as version_file:\n version = version_file.readline().strip()\nwith open(f\"{project_root}/README.md\", \"r\") as readme_file:\n readme = readme_file.read()\n\nsetup(\n name=\"event-processor\",\n version=version,\n author=\"Nicolas Marier\",\n author_email=\"software@nmarier.com\",\n url=\"https://github.com/marier-nico/event-processor\",\n project_urls={\n \"Documentation\": \"https://event-processor.readthedocs.io/en/latest/\",\n \"Source\": \"https://github.com/marier-nico/event-processor\",\n \"Tracker\": \"https://github.com/marier-nico/event-processor/issues\"\n },\n description=\"Pythonic event-processing library based on decorators\",\n long_description=readme,\n long_description_content_type=\"text/markdown\",\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Topic :: Software Development :: Libraries\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n ],\n keywords=\"event decorators development\",\n\n # Packages and depencies\n package_dir={\"\": \"src\"},\n packages=find_packages(where=\"src\"),\n install_requires=[],\n extras_require={\"pydantic\": [\"pydantic >= 1.8.2,< 3.0\"]},\n package_data={\"\": [\"VERSION\"], \"event_processor\": [\"py.typed\"]},\n\n # Other configurations\n zip_safe=True,\n platforms=\"any\",\n)\n","repo_name":"marier-nico/event-processor","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"37151614801","text":"import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport scipy.stats\n\n\ndef sim_matrix_training(text_embeds, vid_embeds_pooled, pooling_type):\n \"\"\"\n Computes the similarity matrix using pooled video frames\n \n Output\n sims: num_texts x num_vids\n \"\"\"\n text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)\n vid_embeds_pooled = vid_embeds_pooled / vid_embeds_pooled.norm(dim=-1, keepdim=True)\n\n if pooling_type == 'avg':\n sims = torch.mm(text_embeds, vid_embeds_pooled.t())\n \n else:\n # num_texts x embed_dim x num_vids\n vid_embeds_pooled = vid_embeds_pooled.permute(1,2,0)\n # num_texts x 1 x embed_dim\n text_embeds = text_embeds.unsqueeze(1)\n \n sims = torch.bmm(text_embeds, vid_embeds_pooled).squeeze(1)\n\n return sims\n\n\ndef sim_matrix_inference(text_embeds_per_video_id, vid_embeds_pooled_per_video_id, pooling_type):\n \"\"\"\n Computes the similarity matrix using pooled video frames using all texts per video\n\n Output\n sims: num_vids x max_text_per_vid x num_vids\n \"\"\"\n text_embeds_per_video_id = text_embeds_per_video_id / text_embeds_per_video_id.norm(dim=-1, keepdim=True)\n vid_embeds_pooled_per_video_id = vid_embeds_pooled_per_video_id / vid_embeds_pooled_per_video_id.norm(dim=-1, keepdim=True)\n\n if pooling_type == 'avg':\n # text_embeds_per_video_id -> num_vids x max_text_per_vid x embed_dim\n # vid_embeds_pooled_per_video_id -> num_vids x embed_dim\n\n sims = text_embeds_per_video_id @ vid_embeds_pooled_per_video_id.t()\n\n else:\n # text_embeds_per_video_id -> num_vids x max_text_per_vid x embed_dim\n # vid_embeds_pooled_per_video_id -> num_vids x num_vids x max_text_per_vid x embed_dim\n num_vids, max_text_per_vid, embed_dim = text_embeds_per_video_id.shape\n\n # num_vids x max_text_per_vid x embed_dim x num_vids\n vid_embeds_pooled_per_video_id = vid_embeds_pooled_per_video_id.permute(1,2,3,0)\n vid_embeds_pooled_per_video_id = vid_embeds_pooled_per_video_id.view(num_vids*max_text_per_vid, embed_dim, num_vids)\n # num_vids x max_text_per_vid x 1 x embed_dim\n text_embeds_per_video_id = text_embeds_per_video_id.unsqueeze(2)\n text_embeds_per_video_id = text_embeds_per_video_id.view(num_vids*max_text_per_vid, 1, embed_dim)\n\n sims = torch.bmm(text_embeds_per_video_id, vid_embeds_pooled_per_video_id)\n sims = sims.view(num_vids, max_text_per_vid, 1, num_vids).squeeze(2)\n \n return sims\n\n\ndef generate_embeds_per_video_id(text_embeds, vid_embeds_pooled, all_vid_ids, pooling_type):\n # Construct dictionary of text embeds per unique video id\n text_embeds_per_video_id = {}\n\n for idx, v_id in enumerate(all_vid_ids):\n if v_id in text_embeds_per_video_id:\n text_embeds_per_video_id[v_id].append(text_embeds[idx])\n else:\n text_embeds_per_video_id[v_id] = [text_embeds[idx]]\n\n for v_id in text_embeds_per_video_id:\n text_embeds_per_video_id[v_id] = torch.stack(text_embeds_per_video_id[v_id])\n\n # num_vids x max_text_per_vid x embed_dim\n text_embeds_per_video_id = pad_and_stack_dict_to_tensor(text_embeds_per_video_id,\n text_embeds_per_video_id.keys(), text_embeds.shape[-1])\n\n if pooling_type == 'avg':\n # num_vids x embed_dim\n vid_embeds_pooled_per_video_id = vid_embeds_pooled\n\n else:\n # Construct dictionary of video embeds for each text per video_id\n vid_embeds_pooled_per_video_id = []\n\n for i in range(vid_embeds_pooled.shape[0]):\n vid_embeds_pooled_per_video_id.append({})\n for idx, v_id in enumerate(all_vid_ids):\n if v_id in vid_embeds_pooled_per_video_id[i]:\n vid_embeds_pooled_per_video_id[i][v_id].append(vid_embeds_pooled[i, idx, :])\n else:\n vid_embeds_pooled_per_video_id[i][v_id] = [vid_embeds_pooled[i, idx, :]]\n\n for i in range(len(vid_embeds_pooled_per_video_id)):\n for v_id in vid_embeds_pooled_per_video_id[i]:\n vid_embeds_pooled_per_video_id[i][v_id] = torch.stack(vid_embeds_pooled_per_video_id[i][v_id])\n\n # num_vids x max_text_per_vid x embed_dim\n vid_embeds_pooled_per_video_id[i] = pad_and_stack_dict_to_tensor(vid_embeds_pooled_per_video_id[i],\n vid_embeds_pooled_per_video_id[i].keys(), vid_embeds_pooled.shape[-1])\n\n # num_vids x num_vids x max_text_per_vid x embed_dim\n vid_embeds_pooled_per_video_id = torch.stack(vid_embeds_pooled_per_video_id)\n\n return text_embeds_per_video_id, vid_embeds_pooled_per_video_id\n\n\ndef t2v_metrics(sims):\n # Permute sims so it represents a sequence of text-video similarity matrices.\n # Then obtain the double argsort to position the rank on the diagonal\n stacked_sims = sims.permute(1,0,2)\n \n sims_sort = torch.argsort(stacked_sims, dim=-1, descending=True)\n sims_sort_2 = torch.argsort(sims_sort, dim=-1, descending=False)\n\n ranks = torch.flatten(torch.diagonal(sims_sort_2, dim1=1, dim2=2))\n \n # Now we need to extract valid ranks, as some belong to inf padding values\n valid_check = torch.flatten(torch.diagonal(sims, dim1 = 0, dim2 = 2))\n mask = ~ torch.logical_or(torch.isinf(valid_check), torch.isnan(valid_check))\n valid_ranks = ranks[mask]\n\n return compute_metrics(valid_ranks.numpy())\n\n\ndef v2t_metrics(sims):\n # Code to avoid nans\n sims[sims!=sims] = float('-inf')\n # Forms a similarity matrix\n sims, _ = torch.max(sims, dim = 1)\n sims = sims.t()\n\n sims_sort = torch.argsort(sims, dim=-1, descending=True)\n sims_sort_2 = torch.argsort(sims_sort, dim=-1, descending=False)\n\n ranks = torch.diag(sims_sort_2).numpy() # diagonal\n\n return compute_metrics(ranks)\n\n\ndef compute_metrics(lst):\n metrics = {}\n metrics[\"R1\"] = 100 * float(np.sum(lst == 0)) / len(lst)\n metrics[\"R5\"] = 100 * float(np.sum(lst < 5)) / len(lst)\n metrics[\"R10\"] = 100 * float(np.sum(lst < 10)) / len(lst)\n metrics[\"R50\"] = 100 * float(np.sum(lst < 50)) / len(lst)\n metrics[\"R100\"] = 100 * float(np.sum(lst < 100)) / len(lst)\n metrics[\"MedR\"] = np.median(lst) + 1\n metrics[\"MeanR\"] = np.mean(lst) + 1\n #stats = [metrics[x] for x in (\"R1\", \"R5\", \"R10\")]\n #metrics[\"geometric_mean_R1-R5-R10\"] = scipy.stats.mstats.gmean(stats)\n return metrics\n\n\ndef pad_and_stack_dict_to_tensor(input, order, d=512):\n max_length = max([input[k].shape[0] for k in input])\n \n padded_input = {k: torch.cat([input[k], torch.full((max_length - input[k].shape[0], d), \n float(\"-inf\"), device = input[k].device)]) for k in input}\n \n padded_stacked_input = torch.stack([padded_input[k] for k in order], dim = 0)\n return padded_stacked_input\n","repo_name":"layer6ai-labs/xpool","sub_path":"modules/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":6878,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"31"} +{"seq_id":"8868457318","text":"from fastapi import APIRouter, File, UploadFile\nfrom fastapi.param_functions import Depends\nfrom sqlalchemy.orm.session import Session\nfrom starlette.responses import HTMLResponse, JSONResponse\nimport csv, os, re\nfrom ..dependencies import get_db\nfrom ..models import Address, Company\n\n\nrouter = APIRouter(prefix='/api')\n\n@router.post(\"/import-from-csv/\")\nasync def import_from_csv(db: Session = Depends(get_db), file: UploadFile = File(...)):\n if not file.filename.endswith('.csv'):\n return JSONResponse({'error': 'File type not supported'}, status_code=400)\n try:\n os.mkdir(\"uploads\")\n except:\n print('folder already exists')\n file_name = os.getcwd()+\"/uploads/\"+file.filename.replace(\" \", \"-\")\n with open(file_name,'wb+') as f:\n f.write(await file.read())\n f.close()\n customers = []\n with open(file_name, newline='') as csvfile:\n csv_reader = csv.DictReader(csvfile)\n for row in csv_reader:\n if not row[\"Bill to 4\"]:\n pattern_1 = re.compile(r\"\\d{5}\")\n zip_code = pattern_1.findall(row[\"Bill to 3\"])\n if len(zip_code) == 0:\n zip_code = ''\n else:\n zip_code = zip_code[0]\n pattern_2 = re.compile(r'^(.+?),')\n city = pattern_2.findall(row[\"Bill to 3\"])\n if len(city) == 0:\n city = None\n else:\n city = city[0]\n pattern_3 = re.compile(r'[A-Z]{2}')\n state = pattern_3.findall(row[\"Bill to 3\"])\n if len(state) == 0:\n state = None\n else:\n state = state[0]\n customers.append({\n \"name\": row[\"Customer\"],\n \"phone_number\": \"1\" + row[\"Main Phone\"].replace('-', ''),\n \"contact_email\": row[\"Main Email\"],\n \"address\": {\n \"to_from\": row[\"Bill to 1\"],\n \"street\": row[\"Bill to 2\"],\n \"zip\": zip_code,\n \"city\": city,\n \"state\": state\n }\n })\n for customer in customers:\n company = Company(name=customer['name'], phone_number=customer['phone_number'], contact_email=customer['contact_email'])\n company.address = Address(**customer['address'])\n db.add(company)\n db.commit()\n db.refresh(company)\n return {'customers': customers}\n\n@router.get(\"/upload-html/\")\nasync def main():\n content = \"\"\"\n <body>\n <form action=\"/api/import-from-csv/\" enctype=\"multipart/form-data\" method=\"post\">\n <input name=\"file\" type=\"file\">\n <input type=\"submit\">\n </form>\n </body>\n \"\"\"\n return HTMLResponse(content=content)\n\n","repo_name":"cameronthecoder/jake-hauling-backend","sub_path":"app/internal/companies_utils.py","file_name":"companies_utils.py","file_ext":"py","file_size_in_byte":2884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"30360467699","text":"import string\n\ndef part_one(rs_data):\n score = 0\n for rucksack in rs_data:\n compartment1 = rucksack[ 0 : len(rucksack)//2 ]\n compartment2 = rucksack[ len(rucksack)//2 : ]\n for component in compartment1:\n if component in compartment2:\n score += int(priority_dictionary[component])\n break\n print(score)\n\ndef part_two(rs_data):\n score = 0\n for i in range(0, len(rs_data), 3):\n rucksack_group = rs_data[i:i+3]\n for component in rucksack_group[0]:\n if component in rucksack_group[1] and component in rucksack_group[2]:\n score += priority_dictionary[component]\n break\n print(score)\n\nif __name__ == \"__main__\":\n priority_dictionary = {}\n for i, letter in enumerate(string.ascii_lowercase):\n priority_dictionary[letter] = i + 1 # Start at 1\n for i, letter in enumerate(string.ascii_uppercase):\n priority_dictionary[letter] = i + 1 + 26\n\n with open(\"input.txt\", \"r\") as outfile:\n rucksack_data: list[str] = outfile.read().strip().splitlines()\n\n part_one(rucksack_data)\n part_two(rucksack_data)\n","repo_name":"adamtry/advent-of-code-2022","sub_path":"Day03: Rucksack Reorganization/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"17861520056","text":"from Crypto.Cipher import AES\nfrom cStringIO import StringIO\nfrom Crypto import Random\nfrom Crypto.Random import random\n\nimport core\n\n\nclass AESCipherCBC(object):\n\n VALID_BLOCK_SIZES = (16, 24, 32)\n\n def __init__(self, key, iv=None):\n \"\"\"\n :param key: The key. Must be of length of (16, 24, 32).\n :type key: basestring\n :param iv: Initialization Vector for CBC. If None will be all zeros.\n :type iv: basestring \n \"\"\"\n self._cipher = AES.new(key, mode=AES.MODE_ECB)\n self._prev = iv if iv is not None else \"\\x00\" * len(key)\n self._block_size = len(key)\n\n def encrypt(self, plaintext):\n blocks = (plaintext[i: i + self._block_size]\n for i in range(0, len(plaintext), self._block_size))\n ciphertext = StringIO()\n for block in blocks:\n ciphertext.write(self._encrypt_block(block))\n return ciphertext.getvalue()\n\n def _encrypt_block(self, plaintext):\n l = len(plaintext)\n if l > self._block_size:\n raise ValueError(\"Cannot encrypt block with size %s\" % (l,))\n if l < self._block_size:\n plaintext = core.pad_pkcs7(plaintext, self._block_size)\n xored = core.xor_buffer(plaintext, self._prev)\n self._prev = self._cipher.encrypt(xored)\n return self._prev\n\n def decrypt(self, ciphertext):\n blocks = (ciphertext[i: i + self._block_size]\n for i in range(0, len(ciphertext), self._block_size))\n plaintext = StringIO()\n for block in blocks:\n plaintext.write(self._decrypt_block(block))\n return plaintext.getvalue()\n\n def _decrypt_block(self, ciphertext):\n if len(ciphertext) != self._block_size:\n raise ValueError(\"Cannot decrypt block with size %s\" %\n (len(ciphertext),))\n plaintext = self._cipher.decrypt(ciphertext)\n plaintext = core.xor_buffer(plaintext, self._prev)\n self._prev = ciphertext\n return plaintext\n\n\ndef aes_encrypt(plaintext, mode=AES.MODE_ECB):\n randfile = Random.new()\n prefix = randfile.read(random.randint(5, 10))\n suffix = randfile.read(random.randint(5, 10))\n key = randfile.read(AES.block_size)\n iv = randfile.read(AES.block_size)\n cipher = AES.new(key, mode=mode, IV=iv)\n return cipher.encrypt(pad_to_block(prefix + plaintext + suffix))\n\n\ndef ecb_cbc_oracle(encrypt_func):\n \"\"\"\n Detect AES encryption mode by feeding a large, repeating input that, if\n used in ECB mode, will produce at least two identical blocks.\n \n :param encrypt_func: A function that accept plaintext and encrypts it in\n either ECB or CBC mode.\n :return: `AES.MODE_ECB` or `AES.MODE_CBC`\n \"\"\"\n plaintext = \"a\" * 10 * AES.block_size\n ciphertext = encrypt_func(plaintext)\n blocks = [ciphertext[i: i + AES.block_size]\n for i in range(0, len(ciphertext), AES.block_size)]\n unique_blocks = set(blocks)\n return AES.MODE_ECB if len(unique_blocks) != len(blocks) else AES.MODE_CBC\n\n\ndef pad_to_block(plaintext):\n length = len(plaintext)\n remainder = length % AES.block_size\n if remainder == 0:\n return plaintext\n padded = core.pad_pkcs7(plaintext, length + (AES.block_size - remainder))\n return padded\n","repo_name":"avrahamshukron/cryptopals","sub_path":"aes.py","file_name":"aes.py","file_ext":"py","file_size_in_byte":3305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19150993079","text":"import random\n\nNUM_POCET = 3 #3 #zadanie maximalnej velkosti cisla na hadanie\nMAX_GUESSES = 10 #zadanie maximalneho poctu pokuv pre hadnutie cisla\n\n\n#telo hlavnej funkcie\ndef main():\n while True:\n taj_cislo = getSecretNum() #vytvorenie tajneho cisla\n print(\"Myslim si cislo\")\n print(\"Mas {} pokusov na uhadnutie cisla.\".format(MAX_GUESSES))\n akt_pokus = 1\n\n #pokial je pocet zivotou mensi ako pocet hadanych cisel hra bude pokracovat\n while akt_pokus <= MAX_GUESSES:\n pokus = ''\n #urcenie ake napovedy maju byt vypisane pri zadani cisla\n # ak ani jedno cislo nie je rovne ziadnemu cisli v trojcisli co bolo zadane ale je zadane nieco ine ako cislo\n while len(pokus) != NUM_POCET or not pokus.isdecimal():\n print(\"Pokus cislo {} => \".format(akt_pokus))\n pokus = input(\"> \")\n \n napovedy = getNapovedy(pokus, taj_cislo)\n print(napovedy)\n akt_pokus += 1\n\n #ak je hadane cislo rovne typovanemu\n if pokus == taj_cislo:\n print(\"Spravne cislo!\")\n break\n\n #ak hrac prekroci pocet pokusov\n if akt_pokus > MAX_GUESSES:\n print(\"Pozor! Prekrocil si maximalny pocet pokusov!\")\n print(\"Spravna odpovd bola: {}\".format(taj_cislo))\n\n #spytanie sa hraca ci chce hrat dalsiu hru\n print(\"Chces hrat dalsiu hru? (yes alebo no)\")\n if not input(\"> \").lower().startswith(\"y\"):\n break\n print(\"Dakujem za hru!\")\n\n#generuje niekolko ciferne cilo ale generuje nahodne usporiadany string s danym mnozstvom cisel\ndef getSecretNum():\n cisla = list(\"0123456789\") #vytvorenie listu obsahujuci vsetky cisla od 0 po 9\n random.shuffle(cisla) #pomiesanie cisel v liste hodnot\n\n #z pomiesaneho listu sa vytiahne mnozstvo cisel podla NUM_POCET. Takto sa vytvori string obsahujuci cisla a podla mnozstva moze ziskat max 10 cif. cislo\n taj_cislo = ''\n for i in range(NUM_POCET):\n taj_cislo += str(cisla[i])\n return taj_cislo\n\n#Vytvorenie funkcie pre tvorbu napovedy\ndef getNapovedy(pokus, taj_cislo):\n #ak je napveda rovnaka ako tajne cislo\n if pokus == taj_cislo:\n return \"Spravne!\"\n \n #vytvorenie prazdnej napovedy\n napoveda = []\n\n #tvorba napovedy\n for i in range(len(pokus)):\n #ak sa jedno cislo nachadza v casti hladaneho cisla a je na spravnom mieste\n if pokus[i] == taj_cislo[i]:\n napoveda.append(\"Fermi\")\n\n #ak sa jedno cislo nachadza v casti hadaneho cisla ale nie je na spravnom mieste\n elif pokus[i] in taj_cislo:\n napoveda.append(\"Pico\")\n \n #ak sa v typovanom cisle nenachadza ani jedna cislica\n if len(napoveda) == 0:\n return 'Bagels'\n else:\n napoveda.sort()\n return \" \".join(napoveda)\n \n#spustenie programu\nif __name__ == '__main__':\n main()\n\n","repo_name":"JakubDanihel/bagels_python3","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2960,"program_lang":"python","lang":"sk","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11420791015","text":"import socket\nimport os\nimport zlib\nfrom time import sleep\n\n# Function to find the Checksum of Sent Message\ndef findChecksum(SentMessage, k):\n \n # Dividing sent message in packets of k bits.\n c1 = SentMessage[0:k]\n c2 = SentMessage[k:2*k]\n c3 = SentMessage[2*k:3*k]\n c4 = SentMessage[3*k:4*k]\n \n # Calculating the binary sum of packets\n Sum = bin(int(c1, 2)+int(c2, 2)+int(c3, 2)+int(c4, 2))[2:]\n \n # Adding the overflow bits\n if(len(Sum) > k):\n x = len(Sum)-k\n Sum = bin(int(Sum[0:x], 2)+int(Sum[x:], 2))[2:]\n if(len(Sum) < k):\n Sum = '0'*(k-len(Sum))+Sum\n \n # Calculating the complement of sum\n Checksum = ''\n for i in Sum:\n if(i == '1'):\n Checksum += '0'\n else:\n Checksum += '1'\n return Checksum\n\ndef checkReceiverChecksum(ReceivedMessage, k, Checksum):\n \n # Dividing sent message in packets of k bits.\n c1 = ReceivedMessage[0:k]\n c2 = ReceivedMessage[k:2*k]\n c3 = ReceivedMessage[2*k:3*k]\n c4 = ReceivedMessage[3*k:4*k]\n \n # Calculating the binary sum of packets + checksum\n ReceiverSum = bin(int(c1, 2)+int(c2, 2)+int(Checksum, 2) +\n int(c3, 2)+int(c4, 2)+int(Checksum, 2))[2:]\n \n # Adding the overflow bits\n if(len(ReceiverSum) > k):\n x = len(ReceiverSum)-k\n ReceiverSum = bin(int(ReceiverSum[0:x], 2)+int(ReceiverSum[x:], 2))[2:]\n \n # Calculating the complement of sum\n ReceiverChecksum = ''\n for i in ReceiverSum:\n if(i == '1'):\n ReceiverChecksum += '0'\n else:\n ReceiverChecksum += '1'\n return ReceiverChecksum\n\n\n\n\n\n\n\nserverIP = \"10.0.0.189\"\nserverPort = 5001\n\nprint(\"1 - Enviar arquivos para teste\")\nprint(\" Obs.: Você precisa ter um arquivo 'teste.txt' na sua máquina\")\nprint(\"2 - Chat Cliente-Servidor\")\n\noption = int(input())\n\nif option == 1:\n k = 8\n gap = \"<gap>\"\n fileName = \"teste.txt\"\n fileSize = os.path.getsize(fileName)\n\n\n udpSocketClient = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Socket do cliente para enviar arquivos para o servidor\n\n destination = (serverIP, serverPort)\n\n udpSocketClient.connect(destination)\n udpSocketClient.send(f\"{fileName}{gap}{fileSize}\".encode('utf-8'))\n\n\n with open(fileName, \"rb\") as file_:\n while True:\n bytesRead = file_.read(4096) # Lê os bytes do arquivo\n \n if not bytesRead: # Se não tem mais bytes, acabou o arquivo, então para de enviar\n print(\"File sended!\")\n udpSocketClient.sendall('file_download_exit'.encode('utf-8'))\n break\n\n udpSocketClient.sendall(bytesRead) # Sendall é uma variação do socket.send(), só que fica enviando até terminar tudo\n sleep(0.001)\n\n udpSocketClient.close()\n file_.close()\n\nelif option == 2:\n\n flag = 1\n\n udpSocketClient = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Socket do cliente para enviar mensagens para o servidor\n\n destination = (serverIP, serverPort) # \"Socket\" do servidor que o cliente vai enviar mensagem\n\n while flag == 1:\n\n # Envio das mensagens do cliente\n clientMessage = input()\n if clientMessage == \"SAIR\":\n flag = 0\n\n udpSocketClient.sendto(bytes(clientMessage,\"utf8\"), destination) # Com a função \"bytes\", converto a mensagem do cliente de string para bytes para ser enviada pelo \"sendTo\"\n\n # Recebimento das mensagens do servidor\n serverMessage, source = udpSocketClient.recvfrom(1024)\n serverMessage = serverMessage.decode('ASCII')\n\n print(source[0], \":\", serverMessage)\n\n udpSocketClient.close()","repo_name":"Hbprado/IF678","sub_path":"Entrega1/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74979186968","text":"\"\"\" Helpers and Project-Wide functions used in the different modules \"\"\"\nimport re\nfrom inspect import getsource\nfrom copy import copy\nimport networkx as nx\n\n###########\n# Helpers #\n###########\n\ndef subfinder(mylist, pattern):\n \"\"\" Little helper to find patterns in lists \"\"\"\n matches = []\n for i in range(len(mylist)):\n if mylist[i] == pattern[0] and mylist[i:i+len(pattern)] == pattern:\n matches.append(pattern) # Add the pattern to the list\n return matches\n\ndef shallow_copy(dic):\n \"\"\" Returns a shallow copy of a dictionnary \"\"\"\n res = {}\n for key in dic.keys():\n res[key] = copy(dic[key])\n return res\n\n#############\n# Def / Ref #\n#############\n\ndef find_vars(graph, node):\n \"\"\" Returns the list of all the vars used on edges of type (node, child) \"\"\"\n children = list(graph.successors(node)) # Our functions are defined on edges, not nodes\n if children != []:\n cond_vars = [] # Variables used in conditions\n cmd_vars_assign = [] # Variables defined of assigned\n cmd_vars_read = [] # Variables referenced for definition\n for child in children:\n code = getsource(graph.adj[node][child][\"cmd\"]) # Get the source code of the lambda function\n code_list = code.split(\"dec=\")[1].strip().split(\"cmd=\") # Parse the code to get the variable names\n cond_string, cmd_string = code_list\n\n cond_vars += [var.replace(\"'\", \"\") for var in re.findall(r\"\\'[A-Za-z0-9]\\'+\", cond_string)] # RegEx\n\n if re.findall(r\"None\", cmd_string) == ['None']:\n cmd_vars_assign_string, cmd_vars_read_string = cmd_string.split(\":\")\n else:\n cmd_vars_assign_string, cmd_vars_read_string = cmd_string.split(\":\")[1:]\n cmd_vars_assign += [var.replace(\"'\", \"\") for var in re.findall(r\"\\'[A-Za-z0-9]\\'+\", cmd_vars_assign_string)] # RegEx\n cmd_vars_read += [var.replace(\"'\", \"\") for var in re.findall(r\"\\'[A-Za-z0-9]\\'+\", cmd_vars_read_string)] # RegEx\n return [cond_vars, cmd_vars_assign, cmd_vars_read]\n\n else: # No definition or usage\n return [[], [], []]\n\ndef Ref(graph, node):\n \"\"\" Returns the list of all the vars REFFERED TO on edges of type (node, child) \"\"\"\n cond_vars, cmd_vars_assign, cmd_vars_read = find_vars(graph, node)\n return list(set(cond_vars + cmd_vars_read))\n\ndef Def(graph, node):\n \"\"\" Returns the list of all the vars DEFINED on edges of type (node, child) \"\"\"\n cond_vars, cmd_vars_assign, cmd_vars_read = find_vars(graph, node)\n return list(set(cmd_vars_assign))\n\ndef all_vars(graph):\n vars_set = set()\n for node in graph.nodes:\n vars_list = find_vars(graph, node)\n for vars in vars_list:\n vars_set = vars_set.union(vars)\n return(list(vars_set))\n\ndef create_test_set(vars, r):\n if vars == []:\n return []\n elif len(vars) == 1:\n return [{vars[0] : i} for i in range(-r, r)]\n else:\n head = vars[0]\n tail = vars[1:]\n partial_result = create_test_set(tail, r)\n result = []\n for partial_test in partial_result:\n for i in range(-r, r):\n test = partial_test.copy()\n test.update({head : i})\n result.append(test)\n return result\n\n\n###################\n# Loops and paths #\n###################\n\ndef compute_all_loops(graph):\n \"\"\" Get all the possible loops \"\"\"\n loops = list(nx.simple_cycles(graph))\n # As explained, this function is used for time saving, and could be reimplemented\n return loops\n\ndef is_i_loop(path, loops, i):\n \"\"\" Test if the path contains i iterations of each loop maximum \"\"\"\n tst = []\n for loop in loops:\n tst.append(len(subfinder(path, loop))) # Append the max number of iterations of this loop\n\n correct = (max([0] + tst) <= i) # Check if no loop is repeated more than i times\n\n return correct\n\ndef compute_all_paths(graph, i, limit):\n \"\"\" Get all the possible paths \"\"\"\n if limit < 0: # We reached the end of recursion\n return [[]]\n elif limit == 0: # We are at the end of the recursion, return the last node\n return [[i]]\n elif list(graph.successors(i)) == []:\n return [[i]] # This node has no successors, return it\n else:\n paths = []\n for k in graph.successors(i): # Iterate on every child\n paths_k = compute_all_paths(graph, k, limit=limit - 1)\n for path_k in paths_k:\n paths += [[i] + path_k] # Create paths by recursion, one layer at a time\n return paths\n\ndef simple_paths(graph, u, v):\n \"\"\" Returns the simple paths aka with 1-loops \"\"\"\n max_node = max(list(graph.nodes)) # Maximum node in the graph, for the limit\n loops = compute_all_loops(graph) # Get all the loops in the graph\n max_loop_size = max([0] + [len(loop) for loop in loops]) # Get the maximum loop size for the limit\n # Get all the paths starting from u, with maximum limit at 1 iteration per loop + the height of the graph\n all_possible_paths = compute_all_paths(graph, u, limit=max_loop_size * len(loops) + max_node)\n\n possible_paths = [path for path in all_possible_paths if path[len(path) - 1] == max_node]\n possible_paths = [path for path in possible_paths if is_i_loop(path, loops, 1)]\n\n # Cut the paths when they reach v\n result_paths = []\n for path in possible_paths:\n for index, char in enumerate(path):\n if char == v:\n result_paths.append(path[:index + 1]) # Only keep the beginning of the path\n break\n\n return [list(a) for a in list(set(tuple(path) for path in result_paths))]\n\n##########################\n# Conditions / Décisions #\n##########################\n\ndef get_decision(decision_tuple, dico):\n \"\"\"\n Get the decision from the conditions \n decision_tuple = \n ([\n lambda dic: dic['x'] >= 1,\n lambda dic: dic['y'] <= 2\n ], lambda a, b: a and b)\n \"\"\"\n cond_outcome = []\n for e in decision_tuple[0]:\n cond_outcome.append(e(dico))\n \n if len(cond_outcome) == 0: # Lambda has to be evaluated on something\n cond_outcome = [True]\n\n result = decision_tuple[1](*cond_outcome)\n\n return result\n\ndef get_condition_values(decision_tuple, dico):\n \"\"\" Get the values for every condition \"\"\"\n cond_outcome = []\n for e in decision_tuple[0]:\n cond_outcome.append(e(dico))\n\n return cond_outcome # Return a list of booleans corresponding to each condition\n\n############\n# Browsers #\n############\n\ndef browse_graph(dico, graph):\n \"\"\" Execute the test in the dict and returns the path \"\"\"\n tmp_node = 1 # Starting node\n path = [1] # Initial path\n while tmp_node != max(graph.nodes): # While we still have nodes to visit\n successors = list(graph.successors(tmp_node)) # Children\n i = 0\n not_found = True\n while not_found:\n v = successors[i] # Find successors of the node\n if get_decision(graph.adj[tmp_node][v]['dec'], dico):\n graph.adj[tmp_node][v]['cmd'](dico) # Execute the command\n tmp_node = v # Switch node\n path += [v] # Update path\n not_found = False # Break out of the loop\n i += 1\n\n return (path)\n\ndef browse_graph_verbose(dico, graph):\n \"\"\" Execute the test in the dict and returns the path + dic values \"\"\"\n tmp_node = 1 # Starting node\n path = {1: [shallow_copy(dico)]} # Initial path\n while tmp_node != max(graph.nodes): # While we still have nodes to visit\n successors = list(graph.successors(tmp_node)) # Children\n i = 0\n not_found = True\n while not_found:\n v = successors[i] # Find successors of the node\n if get_decision(graph.adj[tmp_node][v]['dec'], dico):\n graph.adj[tmp_node][v]['cmd'](dico) # Execute the command\n tmp_node = v # Switch node\n try:\n path[v].append(shallow_copy(dico)) # Update path\n except KeyError:\n path[v] = [shallow_copy(dico)]\n not_found = False # Break out of the loop\n i += 1\n\n return (path)\n","repo_name":"Charleess/Verification-Formelle","sub_path":"src/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":8211,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"73699405847","text":"class Solution:\n def searchInsert(self, nums: List[int], target: int) -> int:\n low, high = 0, len(nums) - 1\n\n while low <= high:\n mid = (low + high) // 2\n if nums[mid] == target:\n return mid\n elif nums[mid] > target:\n high = mid - 1\n elif nums[mid] < target:\n low = low + 1\n\n return low\n\n\"\"\"\nResults:\nBinary Search 二分查找\nRuntime: 68 ms, faster than 34.86% of Python3 online submissions for Search Insert Position.\nMemory Usage: 14.5 MB, less than 45.46% of Python3 online submissions for Search Insert Position.\n\"\"\"","repo_name":"buptwxd2/leetcode","sub_path":"Round_1/35. Search Insert Position/solution_3.py","file_name":"solution_3.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69922011289","text":"from __future__ import print_function\nfrom __future__ import division\nimport pandas as pd\nfrom statsmodels.tools import eval_measures\nimport statsmodels.formula.api as smf\nimport numpy as np\nimport statsmodels.api as sm\n\nfrom matplotlib import pyplot as plt\n\n# just for the sake of this blog post!\nfrom warnings import filterwarnings\nfilterwarnings('ignore')\n\ndef shift(df,n):\n df = df.shift(n)\n df.fillna(method='bfill', inplace=True)\n return df\n\ndef preprocess_data(data_path, labels_path=None):\n # load data and set index to city, year, weekofyear\n df = pd.read_csv(data_path, index_col=[0, 1, 2])\n\n df['reanalysis_relative_humidity_percent_shifted'] = shift(df['reanalysis_relative_humidity_percent'], 8)\n df['precipitation_amt_mm_shifted'] = shift(df['precipitation_amt_mm'], 8)\n df['precipitation_amt_mm_shifted2'] = shift(df['precipitation_amt_mm'],2)\n df['reanalysis_precip_amt_kg_per_m2_shifted'] = shift(df['reanalysis_precip_amt_kg_per_m2'], 8)\n df['reanalysis_dew_point_temp_k_shifted'] = shift(df['reanalysis_dew_point_temp_k'], 11)\n\n #reanalysis_dew_point_temp_k\n #reanalysis_max_air_temp_k\n #reanalysis_min_air_temp_k\n\n # select features we want\n features_sj = ['reanalysis_specific_humidity_g_per_kg',\n 'reanalysis_dew_point_temp_k',\n 'station_avg_temp_c',\n 'station_max_temp_c',\n 'reanalysis_relative_humidity_percent',\n 'reanalysis_relative_humidity_percent_shifted',\n 'precipitation_amt_mm_shifted',\n 'reanalysis_precip_amt_kg_per_m2_shifted',\n 'reanalysis_dew_point_temp_k_shifted',\n 'precipitation_amt_mm_shifted2']\n\n features_iq = ['reanalysis_specific_humidity_g_per_kg',\n 'reanalysis_dew_point_temp_k',\n 'station_avg_temp_c',\n 'station_min_temp_c',\n 'precipitation_amt_mm_shifted',\n 'precipitation_amt_mm_shifted2',\n 'reanalysis_min_air_temp_k']\n\n df_sj = df[features_sj]\n df_iq = df[features_iq]\n\n #print('features: ', df.head(5))\n #print('labels : ', sj_train_labels.shape)\n\n # fill missing values\n df_sj.fillna(method='ffill', inplace=True)\n df_iq.fillna(method='ffill', inplace=True)\n #print('features: ', df.head(5))\n # add labels to dataframe\n if labels_path:\n labels = pd.read_csv(labels_path, index_col=[0, 1, 2])\n df_sj = df_sj.join(labels)\n df_iq = df_iq.join(labels)\n\n # separate san juan and iquitos\n sj = df_sj.loc['sj']\n iq = df_iq.loc['iq']\n\n return sj, iq\n\n#sj_train, iq_train = preprocess_data('data-processed_pani/dengue_features_train_removed_94_anom.csv',labels_path=\"data-processed_pani/dengue_labels_train_removed_94_anom.csv\")\n#sj_train, iq_train = preprocess_data('data-processed_pani/dengue_features_train.csv',labels_path=\"data-processed_pani/dengue_labels_train.csv\")\nsj_train, iq_train = preprocess_data('data-processed_pani/dengue_features_train.csv',labels_path=\"data-processed_pani/dengue_labels_train_filled_94_anom.csv\")\n\n#print(sj_train.describe())\n\nsj_train_subtrain = sj_train.head(800)\nsj_train_subtest = sj_train.tail(sj_train.shape[0] - 800)\n\niq_train_subtrain = iq_train.head(400)\niq_train_subtest = iq_train.tail(iq_train.shape[0] - 400)\n\nsj_correlations = sj_train_subtrain.corr()\niq_correlations = iq_train_subtrain.corr()\n\n\ndef get_best_model(train, test, model_formula ):\n # Step 1: specify the form of the model\n\n grid = 10 ** np.arange(-8, -3, dtype=np.float64)\n\n best_alpha = []\n best_score = 1000\n\n # Step 2: Find the best hyper parameter, alpha\n for alpha in grid:\n model = smf.glm(formula=model_formula,\n data=train,\n family=sm.families.NegativeBinomial(alpha=alpha))\n\n results = model.fit()\n predictions = results.predict(test).astype(int)\n score = eval_measures.meanabs(predictions, test.total_cases)\n\n if score < best_score:\n best_alpha = alpha\n best_score = score\n\n print('best alpha = ', best_alpha)\n print('best score = ', best_score)\n\n # Step 3: refit on entire dataset\n full_dataset = pd.concat([train, test])\n model = smf.glm(formula=model_formula,\n data=full_dataset,\n family=sm.families.NegativeBinomial(alpha=best_alpha))\n\n fitted_model = model.fit()\n return fitted_model\n\nmodel_formula_sj = \"total_cases ~ 1 + \" \\\n \"reanalysis_specific_humidity_g_per_kg + \" \\\n \"reanalysis_dew_point_temp_k + \" \\\n \"station_max_temp_c + \" \\\n \"reanalysis_relative_humidity_percent + \"\\\n \"reanalysis_relative_humidity_percent_shifted + \"\\\n \"precipitation_amt_mm_shifted + \"\\\n \"reanalysis_precip_amt_kg_per_m2_shifted + \"\\\n \"reanalysis_dew_point_temp_k_shifted + \"\\\n \"precipitation_amt_mm_shifted2 + \"\\\n \"station_avg_temp_c\"\n\nmodel_formula_iq = \"total_cases ~ 1 + \" \\\n \"reanalysis_specific_humidity_g_per_kg + \" \\\n \"reanalysis_dew_point_temp_k + \" \\\n \"station_min_temp_c + \" \\\n \"reanalysis_min_air_temp_k +\" \\\n \"station_avg_temp_c\"\n\nprint('....................For SJ.............................')\nsj_best_model = get_best_model(sj_train_subtrain, sj_train_subtest, model_formula_sj)\nprint('\\n')\nprint('....................For IQ.............................')\niq_best_model = get_best_model(iq_train_subtrain, iq_train_subtest, model_formula_iq)\nprint('\\n')\n\nfigs, axes = plt.subplots(nrows=2, ncols=1)\n\n# plot sj\nsj_train['fitted'] = sj_best_model.fittedvalues\nsj_train.fitted.plot(ax=axes[0], label=\"Predictions\")\nsj_train.total_cases.plot(ax=axes[0], label=\"Actual\")\n\n# plot iq\niq_train['fitted'] = iq_best_model.fittedvalues\niq_train.fitted.plot(ax=axes[1], label=\"Predictions\")\niq_train.total_cases.plot(ax=axes[1], label=\"Actual\")\n\nplt.suptitle(\"Dengue Predicted Cases vs. Actual Cases\")\nplt.legend()\nplt.show()\n\nsj_test, iq_test = preprocess_data('data-processed_pani/dengue_features_test.csv')\n\nsj_predictions = sj_best_model.predict(sj_test).astype(int)\niq_predictions = iq_best_model.predict(iq_test).astype(int)\n\nsubmission = pd.read_csv(\"data-processed_pani/submission_format.csv\",\n index_col=[0, 1, 2])\n\nsubmission.total_cases = np.concatenate([sj_predictions, iq_predictions])\nsubmission.to_csv(\"data-processed_pani/test6.csv\")\n","repo_name":"Crystal-Solutions/dm_project","sub_path":"mosquito_model_pani.py","file_name":"mosquito_model_pani.py","file_ext":"py","file_size_in_byte":6671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40635902026","text":"# A script to show that python can be used for querying the pulsar cluster using pulsarSQL\nimport prestodb\nimport schedule\nimport time\nfrom datetime import datetime\n\n# A function to query presto every 10 seconds \ndef queryjob():\n\n conn=prestodb.dbapi.connect(\n host='ec2-54-186-138-107.us-west-2.compute.amazonaws.com',\n port=8080,\n user='Govi')\n cur = conn.cursor()\n cur.execute('SELECT mnemonic,avg(profitorloss) FROM pulsar.\"public/default\".ProcessedData group by mnemonic')\n rows = cur.fetchall()\n \n\nschedule.every(10).seconds.do(queryjob)\n\nwhile 1:\n schedule.run_pending()\n time.sleep(1)\n\n","repo_name":"govardhan1194/StockItUp","sub_path":"PulsarSQL/Presto.py","file_name":"Presto.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"31"} +{"seq_id":"74536709849","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar 2 02:04:19 2018\n\n@author: Apurva\n\"\"\"\n\nimport os\nimport collections\nimport sys\nimport re\nimport copy\nimport math\n\nclasses = [\"ham\", \"spam\"]\nstop_words = []\n\ntraining_set = dict()\ntrainSetWithoutStopWord = dict()\ntraining_set_vocab = []\ntrainSetWithoutStopWord_vocab = []\n\ntest_set = dict()\ntestSetWithoutStopWord = dict()\n\nweights = {'weight_zero': 0.0}\nweightsWithoutStopWord = {'weight_zero': 0.0}\n\nlearningRate = .001\nregularizationConst = 0.0\n\ndef getData(dataSet, directory, target):\n for dir_entry in os.listdir(directory):\n dir_entry_path = os.path.join(directory, dir_entry)\n if os.path.isfile(dir_entry_path):\n with open(dir_entry_path, 'r') as text_file:\n text = text_file.read()\n dataSet.update({dir_entry_path: Mail(text, bagOfWords(text), target)})\n\ndef bagOfWords(text):\n bagsofwords = collections.Counter(re.findall(r'\\w+', text))\n return dict(bagsofwords)\n\ndef setStopWords():\n stopWords = []\n with open('stop_words.txt', 'r') as txt:\n stopWords = (txt.read().splitlines())\n return stopWords\n\n\ndef deleteStopWords(stopWords, data_set):\n dataSetWithoutStopWords = copy.deepcopy(data_set)\n for i in stopWords:\n for j in dataSetWithoutStopWords:\n if i in dataSetWithoutStopWords[j].getWordFreqs():\n del dataSetWithoutStopWords[j].getWordFreqs()[i]\n return dataSetWithoutStopWords\n\t\n\t\t\t\t\ndef getVocabulary(data_set):\n vocab = []\n for i in data_set:\n for j in data_set[i].getWordFreqs():\n if j not in vocab:\n vocab.append(j)\n return vocab\n\n\ndef learnWeights(trainingSet, weights_param, iter, reg):\n for x in range(0, iter):\n print(\"iteration \", x)\n counter = 1\n for w in weights_param:\n sum = 0.0\n for i in trainingSet:\n y = 0.0\n if trainingSet[i].getTarget() == classes[1]:\n y = 1.0\n if w in trainingSet[i].getWordFreqs():\n sum += float(trainingSet[i].getWordFreqs()[w]) * (y - sigmoidFunc(classes[1], weights_param, trainingSet[i]))\n weights_param[w] += ((learningRate * sum) - (learningRate * float(reg) * weights_param[w]))\n\n\ndef sigmoidFunc(target, weights_param, doc):\n if target == classes[0]:\n sum_wx_0 = weights_param['weight_zero']\n for i in doc.getWordFreqs():\n if i not in weights_param:\n weights_param[i] = 0.0\n sum_wx_0 += weights_param[i] * float(doc.getWordFreqs()[i])\n return 1.0 / (1.0 + math.exp(float(sum_wx_0)))\n\n elif target == classes[1]:\n sum_wx_1 = weights_param['weight_zero']\n for i in doc.getWordFreqs():\n if i not in weights_param:\n weights_param[i] = 0.0\n sum_wx_1 += weights_param[i] * float(doc.getWordFreqs()[i])\n return math.exp(float(sum_wx_1)) / (1.0 + math.exp(float(sum_wx_1)))\n\n\ndef logisticCalculation(data_point, weights_param):\n score = {}\n score[0] = sigmoidFunc(classes[0], weights_param, data_point)\n score[1] = sigmoidFunc(classes[1], weights_param, data_point)\n if score[1] > score[0]:\n return classes[1]\n else:\n return classes[0]\n\n\nclass Mail:\n text = \"\"\n word_freqs = {'weight_zero': 1.0}\n\n target = \"\"\n learned_class = \"\"\n\n def __init__(self, text, counter, target):\n self.text = text\n self.word_freqs = counter\n self.target = target\n\n def getText(self):\n return self.text\n\n def getWordFreqs(self):\n return self.word_freqs\n\n def getTarget(self):\n return self.target\n\n def getLearnedClass(self):\n return self.learned_class\n\n def setLearnedClass(self, guess):\n self.learned_class = guess\n\n\ndef main(lambdaConstant, numIter):\n if len(sys.argv) < 3:\n print(\"There should be 2 arguments -- lambda noOfIterations\")\n sys.exit(1)\n\t\t\n num_iterations = int(numIter)\n training_spam_dir = \"./train/spam/\"\n training_ham_dir = \"./train/ham/\"\n test_spam_dir = \"./test/spam/\"\n test_ham_dir = \"./test/ham/\"\n\t\n getData(training_set, training_spam_dir, classes[1])\n getData(training_set, training_ham_dir, classes[0])\n getData(test_set, test_spam_dir, classes[1])\n getData(test_set, test_ham_dir, classes[0])\n regularizationConst = lambdaConstant\n\n stop_words = setStopWords()\n\n trainSetWithoutStopWord = deleteStopWords(stop_words, training_set)\n testSetWithoutStopWord = deleteStopWords(stop_words, test_set)\n\n training_set_vocab = getVocabulary(training_set)\n trainSetWithoutStopWord_vocab = getVocabulary(trainSetWithoutStopWord)\n\n for i in training_set_vocab:\n weights[i] = 0.0\n for i in trainSetWithoutStopWord_vocab:\n weightsWithoutStopWord[i] = 0.0\n\n learnWeights(training_set, weights, num_iterations, regularizationConst)\n learnWeights(trainSetWithoutStopWord, weightsWithoutStopWord, num_iterations, regularizationConst)\n\n\n numberOfCorrectClassification = 0.0\n for i in test_set:\n test_set[i].setLearnedClass(logisticCalculation(test_set[i], weights))\n if test_set[i].getLearnedClass() == test_set[i].getTarget():\n numberOfCorrectClassification += 1.0\n\n numberOfCorrectClassification_withoutStopWords = 0.0\n for i in testSetWithoutStopWord:\n testSetWithoutStopWord[i].setLearnedClass(logisticCalculation(testSetWithoutStopWord[i], weightsWithoutStopWord))\n if testSetWithoutStopWord[i].getLearnedClass() == testSetWithoutStopWord[i].getTarget():\n numberOfCorrectClassification_withoutStopWords += 1.0\n\n print (\"Logistic Regression: Number of Correct Guesses before removing the stop words:\\t%d/%s\" % (numberOfCorrectClassification, len(test_set)))\n print (\"Logistic Regression: Accuracy before removing stop words:\\t\\t\\t%.4f%%\" % (100.0 * float(numberOfCorrectClassification) / float(len(test_set))))\n \n print (\"Logistic Regression: Number of Correct guesses after removing stop the words:\\t\\t%d/%s\" % (numberOfCorrectClassification_withoutStopWords, len(testSetWithoutStopWord)))\n print (\"Logistic Regression: Accuracy after removing stop words:\\t\\t\\t%.4f%%\" % (100.0 * float(numberOfCorrectClassification_withoutStopWords) / float(len(testSetWithoutStopWord))))\n\n\nif __name__ == '__main__':\n main(sys.argv[1], sys.argv[2])","repo_name":"ApurvaMithal/EmailClassification","sub_path":"LogisticRegression.py","file_name":"LogisticRegression.py","file_ext":"py","file_size_in_byte":6433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39745094990","text":"import simplegui\nimport math\n\n# global variables for frame\n\nWIDTH = 800\nHEIGHT = 500\n\n# point list\n\npoint1 = (0, HEIGHT / 2 + 10)\npoint2 = (0, HEIGHT / 2 - 10)\npoint3 = (50, point2[1])\npoint4 = (50, point1[1])\npoint_list = [point1, point2, point3, point4]\n\nmidpoint = (0, HEIGHT / 2)\n\nmagnet_point = (600, 250)\n\n# global variables\nparticle_group = set([])\nxy_group = [midpoint]\nxy_group_group = []\n\ninit_vel = [1.0, 0.0]\npart_mass = 50.0\npart_charge = 1.0\n\n#part_mass_m = 1.0\npart_charge_m = 1.0\n\n# Particle class\nclass Particle:\n \n def __init__(self, pos, vel, accel, charge, mass, magn, magn_bool):\n self.pos = [pos[0], pos[1]]\n self.vel = [vel[0], vel[1]]\n self.accel = [accel[0], accel[1]]\n self.charge = charge\n self.mass = mass\n self.magn = magn\n self.magn_bool = False\n \n def update_old(self):\n self.pos[0] += self.vel[0]\n self.pos[1] += self.vel[1]\n \n self.vel[0] += self.accel[0]\n self.vel[1] += self.accel[1]\n \n def update(self):\n r = point_dist(self.pos, magnet_point)\n B = (self.magn / (r ** 3)) * 10 ** 6\n \n qbm = ((self.charge * B) / self.mass)\n \n \n self.vel[0] += self.vel[1] * qbm #+ self.accel[0]\n self.vel[1] += -1.0 * self.vel[0] * qbm #+ self.accel[1]\n \n self.pos[0] += self.vel[0]\n self.pos[1] += self.vel[1]\n \n def draw(self, canvas):\n canvas.draw_circle(self.pos, 5, 2, \"Black\")\n \n def line_d(self, canvas):\n global xy_group_group, xy_group, particle_group\n\n if self.pos[0] <= WIDTH and self.pos[1] <= HEIGHT:\n xy_group.append(tuple(self.pos))\n #canvas.draw_polyline(xy_group, 5, \"Red\")\n for pos in xy_group:\n canvas.draw_circle(pos, 1, 1, \"Black\", \"Black\")\n\n else:\n if len(xy_group) > 1:\n xy_group_group.append(list(xy_group)) \n xy_group = []\n particle_group.pop(0)\n \n \n# draw handler for canvas\ndef draw(canvas):\n #TEST\n canvas.draw_circle(magnet_point, 3, 2, \"Blue\")\n \n #if len(xy_group_group) > 0:\n # print len(xy_group_group)\n \n # draw rectangle\n canvas.draw_polygon(point_list, 10, \"Red\", \"Red\")\n \n # draw particles\n part_draw(canvas, particle_group)\n \n # draw lines\n for part in particle_group:\n part.line_d(canvas)\n \n line_draw(canvas, xy_group_group)\n\n\n# helper functions\ndef part_draw(canvas, group):\n for particle in group:\n particle.update()\n particle.draw(canvas)\n\ndef line_draw(canvas, group):\n for line in group:\n #canvas.draw_polyline(line, 5, \"Purple\")\n for pos in line:\n canvas.draw_circle(pos, 1, 1, \"Purple\", \"Purple\")\n \ndef part_var_update():\n global part\n part = Particle(midpoint, [2.0 * init_vel[0], init_vel[1]], [0.0, 0.1], part_charge, part_mass, 1.0, True)\n\ndef key_down(key):\n global part\n if key == simplegui.KEY_MAP['space']:\n particle_group.add(part)\n \ndef vel_inph_x(text_input):\n global init_vel\n \n init_vel[0] = float(text_input)\n \n part_var_update()\n \ndef vel_inph_y(text_input):\n global init_vel\n \n init_vel[1] = float(text_input)\n \n part_var_update()\n \ndef part_m(text_input):\n global part_mass, part\n \n part_mass_m = float(text_input)\n \n part_mass = part_mass * part_mass_m\n \n part_var_update() \n \ndef part_ch(text_input): \n global part_charge\n \n part_charge_m = float(text_input)\n \n part_charge = part_charge * part_charge_m\n \n part_var_update()\n \ndef point_dist(point1, point2):\n #distance = math.sqrt(((point2[0] - point1[0]) ** 2) + ((point2[1] - point1[1]) ** 2))\n #test for x only\n distance = math.sqrt((point2[0] - point1[0]) ** 2)\n return distance\n\n# Create a frame and assign callbacks to event handlers\nframe = simplegui.create_frame(\"Home\", WIDTH, HEIGHT)\nframe.set_canvas_background(\"White\")\nframe.set_draw_handler(draw)\nframe.set_keydown_handler(key_down)\nframe.add_input(\"init vel X\", vel_inph_x, 50)\nframe.add_input(\"init vel Y\", vel_inph_y, 50)\nframe.add_input(\"particle mass\", part_m, 50)\nframe.add_input(\"particle charge\", part_ch, 50)\n\n# particle list\npart = Particle(midpoint, [2.0 * init_vel[0], init_vel[1]], [0.0, 0.1], part_charge, part_mass, 1.0, True)\n\n# Start the frame animation\nframe.start()","repo_name":"marcrowo/codeskulptor-particle","sub_path":"codeskulptor-particle.py","file_name":"codeskulptor-particle.py","file_ext":"py","file_size_in_byte":4495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34827618884","text":"import yaml\nimport pprint\nimport sys\n\nfrom skull import txn as Txn\nfrom skull import txndata as TxnData\nfrom skull import logger as Logger\nfrom skull import http\n\nfrom common import protos as Protos\nfrom common import metrics as Metrics\nfrom common.proto import *\n\n##\n# Module Init Entry, be called when start phase\n#\n# @param config A parsed yamlObj\n#\ndef module_init(config):\n print(\"py module init\")\n Logger.info('0', 'config: {}'.format(pprint.pformat(config)))\n\n Logger.trace('py module init: trace test')\n Logger.debug('py module init: debug test')\n Logger.info('1', 'py module init: info test')\n Logger.warn('2', 'py module init: warn test', 'no suggestion')\n Logger.error('3', 'py module init: error test', 'no solution')\n Logger.fatal('4', 'py module init: fatal test', 'no solution')\n return True\n\n##\n# Module Release Function, be called when shutdown phase\n#\ndef module_release():\n print(\"py module release\")\n return\n\n##\n# Input data unpack function, be called if this module is the 'first' module in\n# the workflow and there is input data incoming\n#\n# @param txn Transaction context which is used for getting shared transaction\n# data or invoking service `iocall`\n# @param data Input data\n#\n# @return How many bytes be consumed\n#\ndef module_unpack(txn, data):\n #print \"py module unpack: {}\".format(data)\n #Logger.info('5', 'receive data: {}'.format(data))\n\n requestHandler = http.Request(data)\n request = None\n\n try:\n request = requestHandler.parse()\n except http.RequestIncomplete as e:\n print(\"request body incomplete, need more data: {}\".format(e))\n return 0\n except Exception as e:\n print(\"request parsing failed: {}\".format(e))\n return -1\n\n print(\"request: {}\".format(request))\n\n # Store data into txn sharedData\n example_msg = txn.data()\n example_msg.data = data\n return len(data)\n\n##\n# Input data unpack function, be called if this module is the 'last' module in\n# the workflow (It would no effect if there is no response needed)\n#\n# @param txn Transaction context which is used for getting shared transaction\n# data or invoking service `iocall`\n# @param data Input data\n#\n# @return How many bytes be consumed\n#\ndef module_pack(txn, txndata):\n print(\"py module pack\")\n\n # Increase counters\n mod_metrics = Metrics.module()\n mod_metrics.response.inc(1)\n\n mod_dymetrics = Metrics.transaction('test')\n mod_dymetrics.response.inc(1)\n\n # Assemble response\n if txn.status() != Txn.Txn.TXN_OK:\n txndata.append('error')\n else:\n responseHandler = http.Response()\n response = responseHandler.response\n\n response.status = 200\n response.headerlist = [('Content-type', 'text/html')]\n response.body = 'test'\n\n res_str = responseHandler.getFullContent()\n txndata.append(res_str)\n\n\n##\n# Module Runnable Entry, be called when this module be picked up in current\n# workflow\n#\n# @param txn Transaction context\n#\n# @return - True if no error\n# - False if error occurred\ndef module_run(txn):\n print(\"py module run\")\n\n # Increase counters\n mod_metrics = Metrics.module()\n mod_metrics.request.inc(1)\n\n mod_dymetrics = Metrics.transaction('test')\n mod_dymetrics.request.inc(1)\n return True\n","repo_name":"finaldie/skull","sub_path":"src/user-py/share/module_http.py","file_name":"module_http.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"31"} +{"seq_id":"10027180117","text":"# -*- codeing = utf-8 -*-\n#@Time :2020/11/16 10:09\n#@Author :张士澜\n#@File :bs4_test.py\n#@Software :PyCharm\n\n\nfrom bs4 import BeautifulSoup\n\nif __name__ == '__main__':\n fp = open('./douban.html','r',encoding='utf-8')\n soup = BeautifulSoup(fp,'lxml')\n #print(soup.a)\n # print(soup.find('a'))\n # print(soup.find('div',class_ = 'tagslist').string)\n # print(soup.find_all('a'))\n # print(soup.select('.mod'))\n print(soup.select('.tagslist > ul a')[0]['href'])","repo_name":"zhangshilan/python_demo","sub_path":"demo_spider_2/dataparser_test/bs4_test.py","file_name":"bs4_test.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73542073368","text":"from watchlist import app, db\nfrom flask import request, redirect, url_for, render_template, flash\nfrom flask_login import login_user, login_required, logout_user, current_user\nfrom watchlist.models import *\n\n\n# 主页视图\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n if request.method == 'POST': # 判断是否是 POST 请求\n if not current_user.is_authenticated: # 如果当前用户未认证\n return redirect(url_for('index')) # 重定向到主页\n # 获取表单数据\n title = request.form['title'] # 传入表单对应输入字段的 name 值\n year = request.form['year'] # 传入表单对应输入字段的 name 值\n # 验证数据\n if not title or not year or len(year) > 4 or len(title) > 60:\n flash('Invalid input.') # 显示错误信息\n return redirect(url_for('index')) # 重定向回主页\n # 保存表单数据到数据库\n movie = Movie(title=title, year=year) # 创建记录\n db.session.add(movie) # 添加到数据库会话\n db.session.commit() # 提交数据库会话\n flash('Item created.') # 显示成功创建的提示\n return redirect(url_for('index'))\n\n # user = User.query.first() # 读取用户记录\n movies = Movie.query.all() # 读取电影记录\n # return render_template('index.html', user=user, movies=movies)\n return render_template('index.html', movies=movies)\n\n\n# 登录视图\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n if request.method == 'POST':\n username = request.form['username']\n password = request.form['password']\n\n if not username or not password:\n flash('Invalid input.')\n return redirect(url_for('login'))\n\n user = User.query.first()\n\n # 验证用户名和密码是否一致\n if username == user.username and user.validate_password(password):\n login_user(user) # 登录用户\n flash('Login success.')\n return redirect(url_for('index'))\n\n flash('Invalid username or password.') # 如果验证失败，显示错误消息\n return redirect(url_for('login')) # 重定向到登录页面\n\n return render_template('login.html')\n\n\n# 登出视图\n@app.route('/logout')\n@login_required # 用于视图保护\ndef logout():\n logout_user() # 登出用户\n flash('Goodbye.')\n return redirect(url_for('index')) # 重定向回首页\n\n\n# 设置页面\n@app.route('/settings', methods=['GET', 'POST'])\n@login_required\ndef settings():\n if request.method == 'POST':\n name = request.form['name']\n\n if not name or len(name) > 20:\n flash('Invalid input.')\n return redirect(url_for('settings'))\n\n # current_user.name = name\n # current_user 会返回当前登录用户的数据库记录对象\n # 等同于下面的用法\n user = User.query.first()\n user.name = name\n db.session.commit()\n flash('Settings updated.')\n return redirect(url_for('index'))\n\n return render_template('settings.html')\n\n\n# 编辑条目视图\n@app.route('/movie/edit/<int:movie_id>', methods=['GET', 'POST'])\n@login_required\ndef edit(movie_id):\n movie = Movie.query.get_or_404(movie_id)\n\n if request.method == 'POST': # 处理编辑表单的提交请求\n title = request.form['title']\n year = request.form['year']\n\n if not title or not year or len(year) > 4 or len(title) > 60:\n flash('Invalid input.')\n return redirect(url_for('edit', movie_id=movie_id)) # 重定向回对应的编辑页面\n\n movie.title = title # 更新标题\n movie.year = year # 更新年份\n db.session.commit() # 提交数据库会话\n flash('Item updated.')\n return redirect(url_for('index')) # 重定向回主页\n\n return render_template('edit.html', movie=movie) # 传入被编辑的电影记录\n\n\n# 删除条目视图\n@app.route('/movie/delete/<int:movie_id>', methods=['post'])\n@login_required # 登录保护\ndef delete(movie_id):\n movie = Movie.query.get_or_404(movie_id) # 获取电影记录\n db.session.delete(movie) # 删除对应的记录\n db.session.commit() # 提交数据库会话\n flash('Item deleted.')\n return redirect(url_for('index')) # 重定向回主页\n\n\n@app.route('/hello')\ndef hello():\n # return 'Welcome to My Watchlist!'\n return '<h2>Hello DuXin!</h2><img src=\"http://helloflask.com/totoro.gif\">'\n\n\n@app.route('/user/<name>')\ndef user_page(name):\n return 'User:%s ' % name\n\n\n@app.route('/test')\ndef test_url_for():\n print(url_for('hello'))\n print(url_for('user_page', name='ChenPengKang'))\n print(url_for('user_page', name='DuXin'))\n print(url_for('test_url_for'))\n print(url_for('test_url_for', num=2))\n return 'Test page'\n","repo_name":"cmk271314/watchlist","sub_path":"watchlist/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4860,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25752750641","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n\"\"\"\n\nfrom Bio import SeqIO\nimport argparse\nfrom pathlib import Path\nimport sys\nfrom collections import defaultdict\n\n\ndef disjoint_kmers(ref, asm, verbose=False):\n n_ref_only = 0\n n_asm_only = 0\n for kmer, occ in ref.items():\n if kmer not in asm:\n n_ref_only += 1\n if verbose:\n print('ref_only', kmer, occ)\n for kmer, occ in asm.items():\n if kmer not in ref:\n n_asm_only += 1\n if verbose:\n print('asm_only', kmer, occ)\n return n_ref_only, n_asm_only\n\n\ndef unique_kmers_in_ref(ref, asm):\n n_unique_kmers = 0\n n_unique_kmers_contained = 0\n for kmer, occ in ref.items():\n if len(occ) == 1:\n n_unique_kmers += 1\n if kmer in asm:\n n_unique_kmers_contained += 1\n return n_unique_kmers, n_unique_kmers_contained\n\n\ndef same_count(ref, asm, verbose=False):\n n_same = 0\n n_different = 0\n for kmer in (set(ref.keys()) | set(asm.keys())):\n if len(ref[kmer]) == len(asm[kmer]):\n n_same += 1\n else:\n n_different += 1\n if verbose:\n print('different', kmer, ref[kmer], asm[kmer])\n return n_same, n_different\n\n\ndef count_kmers_with_position(seqs, k):\n kmers = defaultdict(list)\n for h, seq in enumerate(seqs.values()):\n # forward\n s = sanitize(str(seq.seq))\n for i in range(len(s) - k + 1):\n kmer = s[i:i+k]\n kmers[kmer].append((h, i))\n # backward\n s = sanitize(str(seq.seq.reverse_complement()))\n for i in range(len(s) - k + 1):\n kmer = s[i:i+k]\n kmers[kmer].append((h, i))\n return kmers\n\n\ndef sanitize(seq: str):\n return seq.upper().replace('N', '')\n\n\ndef main():\n parser = argparse.ArgumentParser(description='unique k-mer analysis')\n parser.add_argument('ref', type=Path, help='FASTA')\n parser.add_argument('asm', type=Path, help='FASTA')\n parser.add_argument('--k', type=int, default=40)\n args = parser.parse_args()\n k = args.k\n\n ref = count_kmers_with_position(\n SeqIO.to_dict(SeqIO.parse(args.ref, \"fasta\")), k)\n asm = count_kmers_with_position(\n SeqIO.to_dict(SeqIO.parse(args.asm, \"fasta\")), k)\n\n n_ref = len(ref.keys())\n n_asm = len(asm.keys())\n print('n_ref={}'.format(n_ref))\n print('n_asm={}'.format(n_asm))\n\n n_both = len(set(ref.keys()) | set(asm.keys()))\n print('n_both={}'.format(n_both))\n\n n_unique_kmers, n_unique_kmers_contained = unique_kmers_in_ref(ref, asm)\n print('n_unique={} n_unique_contained={} {}%'.format(n_unique_kmers,\n n_unique_kmers_contained, n_unique_kmers_contained / max(1, n_unique_kmers) * 100))\n\n n_ref_only, n_asm_only = disjoint_kmers(ref, asm)\n print('n_ref_only={} {}%'.format(n_ref_only, n_ref_only / n_ref * 100))\n print('n_asm_only={} {}%'.format(n_asm_only, n_asm_only / max(1, n_asm) * 100))\n\n n_same, n_different = same_count(ref, asm)\n print('n_same={} {}%'.format(n_same, n_same / n_both * 100))\n print('n_different={} {}%'.format(n_different, n_different / n_both * 100))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"ryought/dbgphmm","sub_path":"scripts/kir/unique_kmer.py","file_name":"unique_kmer.py","file_ext":"py","file_size_in_byte":3219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7156544009","text":"import pandas as pd\n\n #general info dataframe \ndef general_info(general_info_list):\n from pathlib import Path\n my_file=Path(\"../Project_3_Swapout/csv_data/general_info.csv\")\n if my_file ==True:\n general_info_df=pd.read_csv(my_file)\n general_info_df.append(general_info_list)\n else:\n \n #create empty general_info_df\n general_info_df = pd.DataFrame(columns=[\n \"first_name\", \n \"last_name\", \n \"email\", \n \"year\", \n \"make\", \n \"model\", \n \"miles\", \n \"certificate\"\n ])\n #set Path\n Path = (\"../Project_3_Swapout/csv_data/general_info.csv\")\n #creat empty general_info csv\n general_info_df.to_csv(Path)\n\n#private info dataframe\ndef private_info(private_info_list):\n my_file=\"../Project_3_Swapout/csv_data/private_info.csv\"\n if my_file.exist():\n private_info_df=pd.read_csv(my_file)\n private_info_df.append(private_info_list)\n else:\n #create empty private_info_df\n private_info_df = pd.DataFrame(columns=[\n \"digital_address\", \n \"account_password\", \n \"mailing_address\"\n ])\n #set Path\n Path = (\"../Project_3_Swapout/csv_data/private_info.csv\")\n private_info_df.to_csv(Path)\n\ngeneral_info([1,2,3,4,5,6,7,8,9])","repo_name":"mightlee123/Swapout","sub_path":"data_to_csv.py","file_name":"data_to_csv.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"23193056933","text":"# encoding=utf-8\r\n\"\"\"in response to https://notepad-plus-plus.org/community/topic/17024/\r\n\r\nThis will take the next item from a list (er, immutable tuple, really)\r\n\r\nThis is based on the `editor.replace()` example in pythonscript docs.\r\n\"\"\"\r\nfrom Npp import *\r\nimport re\r\n\r\ncounter = 0\r\nsearch_for_string = 'text0'\r\nloopy_replacements = ('iteration1', 'other text', '3rd text chain')\r\n\r\ndef forum_post17024_select_replacement(m):\r\n \"\"\"this will select the next item from the loopy_replacements\"\"\"\r\n global counter\r\n global loopy_replacements\r\n l = len(loopy_replacements)\r\n\r\n chosen = loopy_replacements[counter % l]\r\n counter = counter + 1\r\n\r\n return chosen\r\n\r\neditor.replace( search_for_string , forum_post17024_select_replacement , re.IGNORECASE )\r\n#editor.replace( 'text0' , get_counter , re.IGNORECASE )","repo_name":"pryrt/nppStuff","sub_path":"pythonScripts/nppCommunity/17xxx/17024-replace-from-loop.py","file_name":"17024-replace-from-loop.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"27270290015","text":"class Solution:\n def gameOfLife(self, board: List[List[int]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n n,m = len(board),len(board[0])\n sit = [[0 for _ in range(m)] for j in range(n)]\n dire = [(-1,-1),(-1,0),(-1,1),(0,-1),(0,1),(1,-1),(1,0),(1,1)]\n n,m = len(board),len(board[0])\n for i in range(n):\n for j in range(m):\n count = 0\n sit[i][j] = board[i][j]\n for x,y in dire:\n xn = i+x\n yn = j+y\n if 0<=xn<n and 0<=yn<m and board[xn][yn]==1:\n count += 1\n #if i==0 and j==2:print((i,j),count)\n #　print((i,j),count)\n if count<2 or count>3:\n sit[i][j] = 0\n elif count==3:\n sit[i][j] = 1\n else: continue\n for i in range(n):\n for j in range(m):\n board[i][j] = sit[i][j]\n","repo_name":"colinchang2019/LeetCode_Train","sub_path":"Leetcode289.py","file_name":"Leetcode289.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72442810969","text":"#!/bin/python3\n\ndef is_plaindrome(n):\n is_palind = 1\n n = list(str(n))\n if len(n) == 6:\n for i in range(len(n) // 2):\n if n[i] != n[len(n) - i - 1]:\n return 0\n return is_palind\n\n#calculate palindromes which is the product of two 3-digit numbers in advance\npalindrome_compound = []\nfor i in range(100, 1000):\n for j in range(i, 1000):\n if is_plaindrome(i * j) == 1:\n palindrome_compound.append(i * j)\n\nresult = []\n\nt = int(input().strip())\nfor i in range(t):\n n = int(input().strip())\n while n > 100000:\n n -= 1\n if n in palindrome_compound:\n result.append(str(n))\n break\n \nprint('\\n'.join(result))\n","repo_name":"intothedeep/Project-Euler","sub_path":"problem004.py","file_name":"problem004.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"34576165931","text":"\nfrom const import *\nimport pygame\n\nfrom square import Square\nfrom chesspiece import *\nfrom dragger import Dragger\n\n\nclass ChessBoard:\n def __init__(self):\n self.squares = [[0,0,0,0,0,0,0,0]for col in range (COLUMNS)]\n self._createBoard()\n self._addChessPiece(\"white\")\n self._addChessPiece(\"black\")\n self.dragger = Dragger()\n\n def showBoard(self, surface):\n for row in range(ROWS):\n for col in range (COLUMNS):\n if (row+col) % 2 == 0:\n color = (234,235,200) #ligth colour\n else:\n color = (119, 154, 88) #dark colour\n \n rect = (col*SQUARE_SIZE, row*SQUARE_SIZE,SQUARE_SIZE,SQUARE_SIZE)\n pygame.draw.rect(surface,color,rect)\n \n def showPieces(self, surface):\n for row in range(ROWS):\n for col in range (COLUMNS):\n if self.squares[row][col].has_piece():\n piece = self.squares[row][col].piece\n\n if piece is not self.dragger.piece:\n img = pygame.image.load(piece.texture)\n img_center = col*SQUARE_SIZE + SQUARE_SIZE//2, row*SQUARE_SIZE + SQUARE_SIZE//2\n piece.texture_rect = img.get_rect(center = img_center)\n surface.blit(img,piece.texture_rect)\n \n def _createBoard(self):\n for row in range (ROWS):\n for col in range (COLUMNS):\n self.squares[row][col]= Square(row,col)\n\n def _addChessPiece(self,team):\n row_pawns, row_other_pieces = (6,7) if team == \"white\" else (1,0)\n\n #adding pawns\n for col in range(COLUMNS):\n self.squares[row_pawns][col] = Square(row_pawns,col, Pawn(team))\n \n #adding knigths\n self.squares[row_other_pieces][1] = Square(row_other_pieces, 1, Knight(team))\n self.squares[row_other_pieces][6] = Square(row_other_pieces, 6, Knight(team))\n\n #adding bishops\n self.squares[row_other_pieces][2] = Square(row_other_pieces, 2, Bishop(team))\n self.squares[row_other_pieces][5] = Square(row_other_pieces, 5, Bishop(team))\n\n #adding rooks\n self.squares[row_other_pieces][0] = Square(row_other_pieces, 0, Rook(team))\n self.squares[row_other_pieces][7] = Square(row_other_pieces, 7, Rook(team))\n\n #adding king and queen\n self.squares[row_other_pieces][4] = Square(row_other_pieces, 4, King(team))\n self.squares[row_other_pieces][3] = Square(row_other_pieces, 3, Queen(team))\n","repo_name":"gerardocalabrese/Chess","sub_path":"source/chessboard.py","file_name":"chessboard.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"20523186203","text":"import numpy\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D, MaxPool2D\nfrom keras import backend as K\nimport matplotlib.pyplot as plt\n\n\nfrom keras.utils import np_utils\nfrom matplotlib import pyplot\nfrom sklearn.model_selection import KFold\nfrom tensorflow.python.keras import Sequential\nfrom tensorflow.python.keras.optimizers import SGD\n\ndef input_data():\n\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\n\n \n X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')\n X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')\n\n \n y_train = np_utils.to_categorical(y_train)\n y_test = np_utils.to_categorical(y_test)\n num_classes = y_test.shape[1]\n\n \n X_train = X_train.astype('float32')\n X_test = X_test.astype('float32')\n \n X_train = (X_train / 255.0)\n X_test = (X_test / 255.0)\n\n return X_test, y_test, X_train, y_train\n\n\n\ndef create_model():\n \n model = Sequential()\n model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))\n model.add(MaxPooling2D((2, 2)))\n model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))\n model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))\n model.add(MaxPooling2D((2, 2)))\n model.add(Flatten())\n model.add(Dense(100, activation='relu', kernel_initializer='he_uniform'))\n model.add(Dense(10, activation='softmax'))\n \n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n return model\n\n\ndef evaluate_model(X_train, y_Train, n_folds=5):\n\n accuracy, data = list(), list()\n\n \n kfold = KFold(n_folds, shuffle=True, random_state=1)\n\n for x_train, x_test in kfold.split(X_train):\n \n model = create_model()\n \n trainX, trainY, testX, testY = X_train[x_train], y_Train[x_train], X_train[x_test], y_Train[x_test]\n \n data_fit = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=10, batch_size=32)\n \n _, acc = model.evaluate(testX, testY, verbose=0)\n \n accuracy.append(acc)\n data.append(data_fit)\n return accuracy, data\n\n\n\ndef summarize_diagnostics(data):\n for i in range(len(data)):\n \n pyplot.subplot(2, 1, 1)\n pyplot.title('Cross Entropy Loss')\n pyplot.plot(data[i].history['loss'], color='red', label='green')\n pyplot.plot(data[i].history['val_loss'], color='orange', label='test')\n \n pyplot.subplot(2, 1, 2)\n pyplot.title('Classification Accuracy')\n pyplot.plot(data[i].history['accuracy'], color='blue', label='train')\n pyplot.plot(data[i].history['val_accuracy'], color='orange', label='test')\n pyplot.show()\n\n\n\ndef summarize_performance(acc):\n \n print('Accuracy: mean=%.3f std=%.3f, n=%d' % (numpy.mean(acc) * 100, numpy.std(acc) * 100, len(acc)))\n\n \n pyplot.boxplot(acc)\n pyplot.show()\n\n\ndef test(X_train, model):\n test_images = X_train[1:5]\n test_images = test_images.reshape(test_images.shape[0], 28, 28)\n\n for i, test_image in enumerate(test_images, start=1):\n org_image = test_image\n test_image = test_image.reshape(1, 28, 28, 1)\n prediction = model.predict_classes(test_image, verbose=0)\n\n print(\"Predicted digit: {}\".format(prediction[0]))\n plt.subplot(220 + i)\n plt.axis('off')\n plt.title(\"Predicted digit: {}\".format(prediction[0]))\n plt.imshow(org_image, cmap=plt.get_cmap('gray'))\n\n plt.show()\n\n\ndef run():\n X_test, y_test, X_train, y_train = input_data()\n\n \n model = create_model()\n model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)\n \n test(X_train, model)\n\n\n model.save(\"model.h5\")","repo_name":"Coding-Ninjas-Club-SRM/AI-ML-MINI-PROJECT-3","sub_path":"Om/cnn.py","file_name":"cnn.py","file_ext":"py","file_size_in_byte":3921,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"39780094500","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 ('bar', '0015_auto_20160303_0925'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Account',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=200)),\n ('is_default', models.BooleanField(default=False)),\n ('costed', models.BooleanField(default=False)),\n ],\n ),\n migrations.CreateModel(\n name='AccountTransaction',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('summ', models.DecimalField(max_digits=8, decimal_places=2)),\n ('account', models.ForeignKey(to='bar.Account')),\n ('day', models.ForeignKey(to='bar.WorkDay')),\n ('sale_offer', models.ForeignKey(to='bar.SaleOffer')),\n ],\n ),\n ]\n","repo_name":"silverozzo/tango3","sub_path":"bar/migrations/0016_account_accounttransaction.py","file_name":"0016_account_accounttransaction.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29460791902","text":"import cadquery as cq\nimport math\n\nfrom cqindustry import (\n jersey_shape,\n barrier_straight,\n barrier_cross,\n barrier_curved,\n barrier_diagonal,\n taper_barrier\n)\n\nbarrier_height = 20\n\nj_shape = jersey_shape(\n width = 20,\n height = barrier_height,\n base_height = 4,\n middle_width_inset = -5,\n middle_height = 4,\n top_width_inset = -1\n)\n\nj_shape_template = jersey_shape(\n width = 23,\n height = barrier_height+3,\n base_height = 4+3,\n middle_width_inset = -5,\n middle_height = 4,\n top_width_inset = -1\n)\n\nj_shape_cut = jersey_shape(\n width = 20.4,\n height = barrier_height+.4,\n base_height = 4+.4,\n middle_width_inset = -5,\n middle_height = 4,\n top_width_inset = -1\n)\n\n\nbarrier = barrier_straight(\n j_shape = j_shape,\n length = 75\n).translate((0,0,barrier_height/2))\n\nbarrier_two = barrier_straight(\n j_shape = j_shape_template.toPending(),\n length = 6\n).rotate((0,1,0),(0,0,0),90)\n\nbarrier_cut = barrier_straight(\n j_shape = j_shape_cut.toPending(),\n length = 6\n).rotate((0,1,0),(0,0,0),90)\n\nbarrier_standin = barrier_straight(\n j_shape = j_shape.toPending(),\n length = 6\n).rotate((0,1,0),(0,0,0),90)\n\npip_height = 2.1\npip_radius = 1.55\nmagnet_template_cut = (\n cq.Workplane(\"XY\")\n .cylinder(6,pip_radius)\n)\n\nbarrier_template = (\n cq.Workplane(\"XY\")\n .add(barrier_two)\n .cut(magnet_template_cut.translate((\n (barrier_height/2)-pip_radius-1.5,\n (20/2)-pip_radius-2,\n 0\n )))\n .cut(magnet_template_cut.translate((\n ((barrier_height/2)-pip_radius-1.5),\n -1*((20/2)-pip_radius-2),\n 0\n )))\n .cut(barrier_cut.translate((0,0,3)))\n #.add(barrier_standin.translate((0,0,3)))\n)\n\nscene = (\n cq.Workplane(\"XY\")\n .add(barrier_template)\n)\n\n#show_object(scene)\n\ncq.exporters.export(barrier_template, 'stl/barrier_magnet_template.stl')\n","repo_name":"medicationforall/cqindustry","sub_path":"example/barrier_magnet_template.py","file_name":"barrier_magnet_template.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"39360309239","text":"age = int(input('나이를 입력하세요 : '))\ntime = int(input('눈을 감고 양팔을 벌리고 외발로 서있을 수 있는 시간을 입력하세요. : '))\nage_1 = 0\nage = int(age / 10) * 10\n\nif time >= 80 :\n age_1 = 20\nif time >= 75 and time < 80 :\n age_1 = 30\nif time >= 50 and time < 75 :\n age_1 = 40\nif time >= 35 and time < 50 :\n age_1 = 50\nif time >= 10 and time < 35 :\n age_1 = 60\nif time >= 5 and time < 10 :\n age_1 = 70\nif time < 5 :\n age_1 = '80대 이상'\n\nprint ('당신의 나이는 ',age,' 대 이지만, 균형 나이는 ',age_1,' 대 입니다.')\n\n \n","repo_name":"yoots50/Python","sub_path":"1-1/유태석2_2019_중간고사_연습.py","file_name":"유태석2_2019_중간고사_연습.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25754846069","text":"# Step 1. Import the necessary libraries\nimport pandas as pd\nimport numpy as np\n# Step 2. Create the 3 DataFrames based on the following raw data\nraw_data_1 = {\n 'subject_id': ['1', '2', '3', '4', '5'],\n 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],\n 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n\nraw_data_2 = {\n 'subject_id': ['4', '5', '6', '7', '8'],\n 'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],\n 'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n\nraw_data_3 = {\n 'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n 'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}\n# Step 3. Assign each to a variable called data1, data2, data3\ndata1 = pd.DataFrame(raw_data_1)\ndata2 = pd.DataFrame(raw_data_2)\ndata3 = pd.DataFrame(raw_data_3)\n# Step 4. Join the two dataframes along rows and assign all_data\nall_data = pd.concat([data1,data2], axis=0)\n# Step 5. Join the two dataframes along columns and assing to all_data_col\nall_data_col = pd.concat([data1,data2], axis=1)\n# Step 6. Print data3\ndata3\n# Step 7. Merge all_data and data3 along the subject_id value\npd.merge(left = all_data , right = data3 , how = 'left' , on = 'subject_id' )\n# Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2\npd.merge(left = data1 , right = data2, how = 'inner' , on = 'subject_id' )\n# Step 9. Merge all values in data1 and data2, with matching records from both sides where available.\npd.merge(left = data1 , right = data2 , how = 'outer', on = 'subject_id')\n","repo_name":"ecandamo/Training","sub_path":"pandas_exercises/05_Merge/Fictitous Names/Esteban-Solutions.py","file_name":"Esteban-Solutions.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6886435562","text":"import os\nimport cv2\nimport numpy as np\nfrom PIL import Image\nfrom skimage.io import imread, imsave\nfrom skimage.measure import label, regionprops\n# from pdf2image import convert_from_path\n# import filetype\nimport time\nimport math\nfrom transliterate import translit\n\nImage.MAX_IMAGE_PIXELS = 933120000\n \n\ndef get_file_name_and_path(file):\n return os.path.basename(file).split('.')[0], os.path.dirname(file)\n\n\ndef rotate_image(img, angle):\n im = Image.open(img)\n out = im.rotate(angle, expand=True)\n out.save('final_result.png')\n\n# def create_jpeg_from_pdf(pdf_file_path): \n# file_name_without_extension, file_path = get_file_name_and_path(pdf_file_path)\n# jpg_file = convert_from_path(pdf_file_path, 500)[0]\n# jpg_file.save(f'{file_path}/{file_name_without_extension}_JPEG.jpg', 'JPEG')\n\n\ndef get_image_area(image): \n img = Image.open(image)\n height, width = img.size\n area = height * width\n return area\n\n\ndef crop_image(image_path, min_x, min_y, max_x, max_y, cropped_image_path):\n image = imread(image_path)\n cropped = image[min_x:max_x,min_y:max_y]\n imsave(cropped_image_path, cropped)\n\ndef delete_qr_code(image):\n img = cv2.imread(image)\n qr = cv2.QRCodeDetector().detect(img)\n if qr[0]:\n points = qr[1][0]\n\n for point in points:\n for coord in point:\n coord += 100\n points_array = np.array(points)\n\n cv2.fillPoly(img, pts = np.int32([points_array]), color=(255,255,255))\n image_qr_cut_name = f'{image}_qr_cut.jpg'\n \n cv2.imwrite(image_qr_cut_name, img)\n\n return (True, image_qr_cut_name)\n return (False, False)\n\n\ndef cut_floor_plan_from_image(image):\n qr_cut, path = delete_qr_code(image)\n if qr_cut:\n image = path \n source_image_area = get_image_area(image)\n\n pim = Image.open(image) \n pgr = pim.convert('L')\n thr = pgr.point(lambda p: p < 230 and 255)\n\n nim = np.array(thr)\n\n label_image=label(nim)\n # height, width = Image.open(image).size\n \n rp = regionprops(label_image)\n rp_less_then_image = [region for region in rp if region.bbox_area < source_image_area * 0.8]\n rp_sorted_80 = sorted(rp_less_then_image, key=lambda x: x.area, reverse=True)\n \n # rp_sorted_with_distance = sorted(rp_sorted, key=lambda x: math.dist(x.centroid, (height/2, width/2)))\n \n region = rp_sorted_80[0]\n min_x, min_y, max_x, max_y = region.bbox\n\n\n\n image_file_name, image_file_path = get_file_name_and_path(image)\n # result_files_dir = f'{image_file_path}/floor_plans'\n image_file_name = translit(image_file_name, 'ru', reversed=True)\n result_image_name = f'{image_file_name}_plan.png'\n\n crop_image(image, min_x, min_y, max_x, max_y, result_image_name)\n\n return os.path.basename(result_image_name)","repo_name":"alchupin/alchupin-crop_floor_plan_from_img","sub_path":"api/img_processing.py","file_name":"img_processing.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4666903279","text":"import Leap\nimport mouse\nimport time \nimport math\nimport keyboard\nimport pyautogui\nfrom Leap import Gesture \n\n# BaseBehavior Class:\n# This is a parent class that provides a blueprint for other behavior classes.\n# All specific behavior classes should inherit from this class and override its methods as needed.\nclass BaseBehavior(object):\n \n # Class variables for screen dimensions\n screen_width = 1920 # Default screen width value\n screen_height = 1080 # Default screen height value\n start_time = time.time()\n exitingStreetView = False\n switchingPlanet = False\n currentPlanet = \"Earth\"\n planetSwitchTime = time.time()\n autoSwitchTime = time.time()\n last_hand_detected_time = None\n\n def __init__(self, name):\n \"\"\"Constructor for the BaseBehavior class.\n \n Args:\n - name (str): Name of the behavior.\n \"\"\"\n self.name = name\n\n def execute(self, frame):\n \"\"\"Method to execute the behavior. \n This method should be overridden in derived classes to implement specific behavior.\n \n Args:\n - frame: The frame data from the UltraLeap device.\n \n Raises:\n - NotImplementedError: If the method is not overridden in the derived class.\n \"\"\"\n raise NotImplementedError(\"This method should be overridden in derived classes.\")\n \n # Common movement functions \n\n def navigate_to(self, destination):\n \"\"\"Navigate to a specific destination in Google Earth.\n \n This function simulates keypresses and mouse interactions to navigate to \n a specified destination in Google Earth.\n \n Args:\n - destination (str): The name or address of the destination in Google Earth.\n \n Note: This function currently has hardcoded behavior to navigate to the \n Arizona Science Center, this should be updated if other destinations are intended.\n \"\"\"\n # Simulate Ctrl + F to focus on the search bar\n pyautogui.hotkey('/')\n time.sleep(0.5) # Wait for a short duration to ensure the search bar is focused\n \n # Type the destination\n pyautogui.write(destination)\n time.sleep(0.5) # Wait again before pressing Enter\n \n # Simulate Enter key to initiate the search\n pyautogui.press('enter')\n \n # Right-click after searching (Exit out of search to allow for keyboard controls)\n pyautogui.rightClick()\n\n def sigmoid(self, x):\n \"\"\"Computes the sigmoid of the input value.\n \n The sigmoid function maps any input value to an output value between 0 and 1.\n \n Args:\n - x (float): Input value.\n \n Returns:\n - float: The sigmoid of the input value.\n \"\"\"\n return 1 / (1 + math.exp(-x))\n\n def movement_speed(self, value):\n \"\"\"Calculate movement speed based on input value.\n \n Args:\n - value (float): Input value.\n \n Returns:\n - float: The calculated movement speed.\n \"\"\"\n return float(value) / 90.0\n\n def exponential_zoom(self, distance, zoom_in_threshold=120, zoom_out_threshold=140, scale_factor=15, power=4):\n \"\"\"Calculate an exponential zoom factor based on the distance from a sensor using a sigmoid curve.\n \n Args:\n - distance (float): Current distance to the sensor.\n - zoom_in_threshold (float, optional): Maximum distance for zooming in. Default is 160.\n - zoom_out_threshold (float, optional): Minimum distance for starting to zoom out. Default is 300.\n - scale_factor (float, optional): Determines the sharpness of the sigmoid curve. Higher values give a sharper curve. Default is 10.\n - power (float, optional): Power to which the normalized distance is raised to slow down zoom near thresholds. Default is 3.\n \n Returns:\n - float: A zoom factor. Positive values indicate zooming in, while negative values indicate zooming out.\n \"\"\"\n \n # For zooming in\n if distance <= zoom_in_threshold:\n normalized_distance = (float(distance) / zoom_in_threshold) ** power\n return (1 - self.sigmoid(scale_factor * (normalized_distance - 0.5))) * 1.5\n \n # For zooming out\n elif distance > zoom_out_threshold:\n normalized_distance = ((distance - zoom_out_threshold) / (zoom_in_threshold - zoom_out_threshold)) ** power\n #print(\" \" + str(self.exitingStreetView) + \" + \" + str(self.inStreetView()))\n if(self.inStreetView() and self.exitingStreetView == False): \n self.exitStreetView()\n\n return (-self.sigmoid(scale_factor * (normalized_distance - 0.5))) * 1.5\n \n # No zooming for distances between the two thresholds\n return 0\n\n def inStreetView(self):\n \"\"\"Check if Google Earth is in \"Street View\" mode based on the pixel color of the \"Exit Street View\" button.\n\n Returns:\n - bool: True if in Street View, False otherwise.\n \"\"\"\n \n # Coordinates of the pixel to check (this needs to be adjusted to where the \"Exit Street View\" button is)\n x, y = 1893, 38 # Assuming top-right corner, but this might need fine-tuning\n\n # Capture the pixel color at the specified location\n pixel_color = pyautogui.screenshot().getpixel((x, y))\n #print(pixel_color)\n # Define the expected color of the \"Exit Street View\" button (assuming white, but this might need adjustment)\n # The values might need fine-tuning based on the exact color in your Google Earth version\n expected_color = (252, 252, 252) \n\n # Compare the captured pixel color to the expected color\n return pixel_color == expected_color\n \n def exitStreetView(self):\n start_time = time.time()\n exitingStreetView = True\n pyautogui.press('esc')\n \n def _move_to_planets(self, offset=0):\n pyautogui.moveTo(103, 13)\n pyautogui.click()\n pyautogui.moveTo(232, 398 - offset)\n pyautogui.click()\n pyautogui.moveTo(491, 397 - offset)\n\n def _move_to_moon(self, offset=0):\n self._move_to_planets(offset)\n pyautogui.moveTo(491, 478 - offset)\n pyautogui.click()\n pyautogui.moveTo(self.screen_width / 2.0, self.screen_height / 2.0)\n\n def _move_to_mars(self, offset=0):\n self._move_to_planets(offset)\n pyautogui.moveTo(491, 453 - offset)\n pyautogui.click()\n pyautogui.moveTo(self.screen_width / 2.0, self.screen_height / 2.0)\n\n def _move_to_earth(self, offset=0):\n self._move_to_planets(offset)\n pyautogui.click()\n pyautogui.moveTo(self.screen_width / 2.0, self.screen_height / 2.0)\n self.last_hand_detected_time = time.time()\n\n def _switch_to_target_planet(self, target_planet):\n \"\"\"Private method to switch to a given target planet with the required offset.\"\"\"\n offset = 0 if self.currentPlanet == \"Earth\" else 20\n\n planet_actions = {\n \"Moon\": self._move_to_moon,\n \"Mars\": self._move_to_mars,\n \"Earth\": self._move_to_earth\n }\n\n if target_planet in planet_actions and self.currentPlanet != target_planet:\n planet_actions[target_planet](offset)\n self.currentPlanet = target_planet\n\n def switch_planets(self, target_planet):\n if round(time.time() - self.planetSwitchTime) > 7 and self.switchingPlanet:\n self.switchingPlanet = False\n\n if not self.switchingPlanet:\n self.switchingPlanet = True\n self.planetSwitchTime = time.time()\n self._switch_to_target_planet(target_planet)\n\n def rotate_planets(self):\n next_planet = {\n \"Earth\": \"Mars\",\n \"Mars\": \"Moon\",\n \"Moon\": \"Earth\"\n }\n\n target_planet = next_planet.get(self.currentPlanet, \"Earth\") # Default to Earth if something goes wrong\n self._switch_to_target_planet(target_planet)\n \n def relase_keys(self):\n pyautogui.keyUp('up')\n pyautogui.keyUp('down')\n pyautogui.keyUp('left')\n pyautogui.keyUp('right')\n\n\n# HandTiltBehavior Class:\n# This class defines the behavior when a hand is tilted.\n# It inherits from the BaseBehavior class and provides specific implementations for hand tilting behavior.\nclass HandTiltBehavior(BaseBehavior):\n\n def __init__(self):\n \"\"\"Constructor for the HandTiltBehavior class.\n Initializes the name of the behavior to \"handTilt\".\n \"\"\"\n super(HandTiltBehavior, self).__init__(\"handTilt\")\n\n def execute(self, frame):\n \"\"\"Execute the hand tilting behavior based on the frame data from the UltraLeap device.\n \n Args:\n - frame: The frame data from the UltraLeap device.\n \n Note: The exact behavior executed is determined by the logic inside this method.\n \"\"\"\n X_DISTANCE = 10 # Threshold for hand motion (adjust as needed)\n center_x, center_y = self.screen_width / 2, self.screen_height / 2\n auto_navigate = False\n # Constants for hand position-based motion\n X_CENTER = 0 # The X position when the hand is directly over the sensor\n Z_CENTER = 0 # The Z position when the hand is directly over the sensor\n DEAD_ZONE_RADIUS = 75 # Radius around the center where no motion is triggered\n\n # Get current mouse position\n x, y = mouse.get_position()\n\n # If no hands are detected and auto navigation is not active, navigate to the Arizona Science Center\n if len(frame.hands) == 0 and not auto_navigate:\n auto_navigate = True\n self.navigate_to_arizona_science_center()\n\n # If hands are detected, turn off auto navigation\n elif len(frame.hands) != 0: \n auto_navigate = False\n\n # If mouse position is near the edges of the screen, reset it to the center\n if (x < X_DISTANCE or x > self.screen_width - X_DISTANCE or \n y < X_DISTANCE or y > self.screen_height - X_DISTANCE):\n mouse.move(center_x, center_y)\n\n # Iterate through each hand detected in the frame\n for hand in frame.hands:\n handType = \"Left Hand\" if hand.is_left else \"Right Hand\"\n normal = hand.palm_normal\n direction = hand.direction\n \n # Convert hand orientation (pitch, roll, yaw) from radians to degrees\n pitch = direction.pitch * Leap.RAD_TO_DEG\n roll = normal.roll * Leap.RAD_TO_DEG\n yaw = direction.yaw * Leap.RAD_TO_DEG\n\n # Adjust the mouse wheel based on the palm's vertical position to control zoom\n mouse.wheel(delta=self.exponential_zoom(hand.palm_position[1]))\n\n # Control movement based on hand orientation\n self.control_movement(pitch, roll, yaw)\n\n # Supporting Function(s)\n \n def control_movement(self, pitch, roll, yaw):\n \"\"\"Control movement based on hand orientation.\n \n Args:\n - pitch (float): Pitch of the hand in degrees.\n - roll (float): Roll of the hand in degrees.\n - yaw (float): Yaw of the hand in degrees.\n \"\"\"\n \n # Calculate movement speeds based on hand orientation\n forward_backward_speed = self.movement_speed(pitch)\n left_right_speed = self.movement_speed(roll)\n turn_speed = self.movement_speed(yaw)\n threshold = 0.1\n\n # Forward and Backward movement based on pitch (using arrow keys)\n if forward_backward_speed < -threshold: # Negative pitch indicates forward movement\n pyautogui.keyDown('up')\n time.sleep(-forward_backward_speed)\n pyautogui.keyUp('up')\n elif forward_backward_speed > threshold: # Positive pitch indicates backward movement\n pyautogui.keyDown('down')\n time.sleep(forward_backward_speed)\n keyboard.release('down')\n\n # Left and Right movement based on roll (using arrow keys)\n if left_right_speed > threshold: # Positive roll indicates left movement\n pyautogui.keyDown('left')\n time.sleep(left_right_speed)\n keyboard.release('left')\n elif left_right_speed < -threshold: # Negative roll indicates right movement\n keyboard.press('right')\n time.sleep(-left_right_speed)\n keyboard.release('right')\n \n # Turning left and right based on yaw (using mouse movement)\n if turn_speed < -threshold: # Negative yaw indicates turning left\n mouse.press(button='middle')\n mouse.move(-100 * turn_speed, 0)\n mouse.release(button='middle')\n elif turn_speed > threshold: # Positive yaw indicates turning right\n mouse.press(button='middle')\n mouse.move(100 * turn_speed, 0)\n mouse.release(button='middle')\n\n# HandSlideBehavior Class:\n# This class defines the behavior when a hand slides.\n# It inherits from the BaseBehavior class and provides specific implementations for hand sliding behavior.\nclass HandSlideBehavior(BaseBehavior):\n\n def __init__(self):\n \"\"\"Constructor for the HandSlideBehavior class.\n Initializes the name of the behavior to \"handSlide\" and other related properties.\n \"\"\"\n self.auto_navigate = False\n self.timeout = 30\n self.alt = False\n BaseBehavior.currentPlanet = \"Earth\"\n super(HandSlideBehavior, self).__init__(\"handSlide\")\n\n def execute(self, frame):\n \"\"\"Execute the hand sliding behavior based on the frame data from the UltraLeap device.\n \n Args:\n - frame: The frame data from the UltraLeap device.\n \n Note: The exact behavior executed is determined by the logic inside this method.\n \"\"\"\n if(round(time.time() - BaseBehavior.start_time) > 30 and BaseBehavior.exitingStreetView == True):\n #print(\"Exited Street View\")\n BaseBehavior.exitingStreetView = False\n\n if(BaseBehavior.exitingStreetView != True and BaseBehavior.switchingPlanet != True):\n \n if(keyboard.is_pressed('m')):\n print(\"Moving to Mars\")\n self.switch_planets(\"Mars\")\n if(keyboard.is_pressed('l')):\n self.switch_planets(\"Moon\")\n if(keyboard.is_pressed('e')):\n self.switch_planets(\"Earth\")\n\n if frame.hands:\n BaseBehavior.last_hand_detected_time = time.time() \n self.auto_navigate = False\n\n hand = frame.hands[0]\n\n hand_x = hand.palm_position[0] # Left and right\n hand_z = hand.palm_position[2] # Forward and backward\n\n # Constants for hand position-based motion\n dead_zone = 20 # A zone in the center where there's no movement\n outer_limits = 200\n\n # Check for slide\n if all(-outer_limits < coord < outer_limits for coord in [hand_x, hand_z]):\n\n if self.alt:\n # Using the exponential_zoom function for zooming\n zoomStrength = self.exponential_zoom(hand.palm_position[1])\n mouse.wheel(delta=zoomStrength)\n self.alt = False \n\n else:\n if -outer_limits < hand_x < -dead_zone:\n keyboard.release('right') # Ensure right arrow key is not pressed\n keyboard.press('left') # Press left arrow key for left movement\n elif dead_zone < hand_x < outer_limits:\n keyboard.release('left') # Ensure left arrow key is not pressed\n keyboard.press('right') # Press right arrow key for right movement\n else:\n keyboard.release('left')\n keyboard.release('right')\n\n if -outer_limits < hand_z < -dead_zone:\n keyboard.release('down') # Ensure down is not pressed\n keyboard.press('up') # Keep up pressed\n elif dead_zone < hand_z < outer_limits:\n keyboard.release('up') # Ensure up is not pressed\n keyboard.press('down') # Keep down pressed\n else:\n # In the dead zone: release any keys and do not trigger any movement\n keyboard.release('up')\n keyboard.release('down')\n self.alt = True\n else:\n self.relase_keys()\n\n # If no hands are detected and auto navigation is not active, and 30 seconds have passed since the last hand was detected\n elif self.auto_navigate == False and BaseBehavior.last_hand_detected_time and round(time.time() - BaseBehavior.last_hand_detected_time) == 30 and BaseBehavior.currentPlanet == \"Earth\":\n self.navigate_to(\"Arizona Science Center\")\n self.auto_navigate = True\n \n elif(BaseBehavior.last_hand_detected_time and int(round(time.time() - BaseBehavior.last_hand_detected_time)) != 0 and int(round(time.time() - BaseBehavior.last_hand_detected_time)) % 120 == 0):\n self.rotate_planets()\n \n if not frame.hands:\n self.relase_keys()\n return\n\n # If none of the above conditions are met, release all movement keys\n else:\n self.relase_keys()\n return\n","repo_name":"JoshuaShunk/Ultraleap-Google-Earth-Controller-","sub_path":"behaviors.py","file_name":"behaviors.py","file_ext":"py","file_size_in_byte":17879,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"22984350677","text":"from sqlalchemy import create_engine, text\n\nengine = create_engine('sqlite:///thunderbird_manufacturing.db')\nconn = engine.connect()\n\n\ndef customer_for_id(customer_id):\n stmt = text(\"SELECT * FROM CUSTOMER WHERE CUSTOMER_ID = :id\")\n return conn.execute(stmt, id=customer_id).first()\n\n\nprint(customer_for_id(3))","repo_name":"thomasnield/oreilly_programming_with_sql","sub_path":"code/section_iii/3_1_passing_parameters.py","file_name":"3_1_passing_parameters.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"31"} +{"seq_id":"22798495803","text":"#import multiple libraries\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import animation\nimport time\n\n#CONSTANTS\n#---------\niterations=1000 #quantity of \"parts\" that the flower is going to be divided by (the larger it becomes, the smoother it looks, the more time it takes)\nfps = 100 #frames per second on the animation\nmake_animated = True #animation boolean\nsleep_seconds = 10 #time the flower pauses after being plotted\ncolor = 'red' #specify the color of the rose\n\n#CODE\n#----\nfirst = True #boolean to check when to pause\nfig = plt.figure() #initialize the figure\nax = plt.axes(xlim=(-1,1), ylim=(-1,1)) #set limits of the axes\nrose, = ax.plot([],[],color) #initialize the rose, variable\nuser_input = input(\"Type in a number (in case of a fraction in its lowest terms [a/b]): \") #ask for k\ncoeffs = user_input.split(\"/\") #split the user input by the fraction slash\nnum = float(coeffs[0]) #assign numerator value\nif len(coeffs)>1: #check for a denominator\n\tden = float(coeffs[1]) #assign denominator value\nelse:\n\tden = 1 #if there's no denominator give a value of 1\nk=num/den #assign the value to k\nnum_odd = False\nden_odd = False\nif num%2 != 0: #check if the numerator is odd\n\tnum_odd = True #assign value True to boolean checking if the numerator is odd\nif den%2 != 0: #check if the denominator is odd\n\tden_odd = True #assign value True to boolean checking if the denominator is odd\n\nif num_odd and den_odd: #check if both numerator and denominator are odd\n\tangle = np.pi*den #assign the value of the angle to close\nelse:\n\tangle = 2*np.pi*den #assign the value of the angle to close\nthetas = np.linspace(0, angle, iterations) #creation of all the values that are going to be computed\nx = np.cos(k*thetas)*np.cos(thetas) #multiply by the cosinus to get all x coordinates\ny = np.cos(k*thetas)*np.sin(thetas) #multiply by the sinus to get all y coordinates\n\n\ndef initialize(): #definition of initialize\n\trose.set_data([],[]) #establish the array on the rose variable\n\treturn rose,\n\n\ndef animate(i): #definition for the animation\n\tglobal first #modification of global variable final\n\trose.set_data(x[0:i], y[0:i]) #add one more value of x and y to the rose array\n\tif i == 0 and not first: #check if the flower has been completed and it's not the first plotting\n\t\ttime.sleep(sleep_seconds) #sleep for the specified time\n\tif first: #check if it is the very start of the plotting\n\t\tfirst=False; #know it's not the first time\n\treturn rose,\n\n\nif make_animated: #if animated\n\tanimation.FuncAnimation(fig, animate, init_func=initialize, frames=iterations, interval=1000/fps, blit=True) #call the animation with all functions and user specifications\nelse:\n\tplt.plot(x,y, color) #plot the flower directly\nplt.show() #show plots\n","repo_name":"MiguelSanchezP/Rhodoneas","sub_path":"rhodoneas.py","file_name":"rhodoneas.py","file_ext":"py","file_size_in_byte":2742,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"29573871346","text":"\"\"\"\nArquivo para criação da classe Aviao\n\"\"\"\nfrom auxiliar import tela, larguraTela, alturaTela, fuel_message, display_message\nimport time\nimport math\nimport pygame\n\n\nclass Airplane(object):\n\n def __init__(self, airX, airY, airW, airH, airImg):\n self.airX = airX # Posição do avião no eixo X\n self.airY = airY # Posição do avião no eixo Y\n self.airW = airW # Largura da imagem do avião\n self.airH = airH # Altura da imagem do avião\n self.airImg = airImg # Imagem do avião\n self.airVelX = 0 # Velocidade HORIZONTAL\n self.airVelY = 0 # Velocidade VERTICAL\n self.airAX = 0 # Aceleração HORIZONTAL\n self.airAY = 0 # Aceleração VERTICAL\n self.gravity = 9.86 # Aceleração gravitacional do avião\n self.massa = 30000 # Massa do avião\n self.peso = self.massa * self.gravity # Força peso\n self.fuel = 100 # Combustível\n self.count_f = 0 # Contador para o combustível\n self.airVelTotal = 0 # Velocidade total do avião\n self.Frx = 0 # Força resultante em X\n self.Fry = 0 # Força resultante em Y\n self.d = 1.2 # Densidade do ar quando a temperatura é de 5ºC\n self.A = 1000 # Área das asas avião\n self.Cd = 0.031 # Coeficiente de arrasto\n self.D = 0 # Força de arrasto\n self.Cs = 0.031 # Coeficiente de arrasto\n self.S = 0 # Força de sustentação\n self.teste_decolagem = False # Teste se o avião já decolou\n self.teste_combustivel = False # Para evitar bugs no contador de combustível\n\n def draw(self, angulo):\n tela.blit(pygame.transform.rotate(self.airImg, angulo), (self.airX, self.airY))\n\n def forca(self, angulo, tracao):\n # Cálculo do ângulo em radianos\n angulo = angulo * math.pi / 180\n # Cálculo do modulo da velocidade resultante\n self.airVelTotal = (self.airVelX ** 2 + self.airVelY ** 2) ** (1 / 2)\n\n # Cálculo da força de arrasto\n self.D = (1 / 2) * self.Cd * self.d * self.A * (self.airVelTotal ** 2)\n # Cálculo da força de sustentação\n self.S = (1 / 2) * self.Cs * self.d * self.A * (self.airVelTotal ** 2)\n\n # Se a velocidade VERTICAL é zero\n if self.airVelY == 0:\n self.Frx = tracao * math.cos(angulo) - self.D\n self.Fry = self.peso - self.S - tracao * math.sin(angulo)\n\n # Se a velocidade VERTICAL é diferente de zero\n elif self.airVelY != 0:\n self.Frx = tracao * math.cos(angulo) - self.D * (self.airVelX / self.airVelTotal) - self.S * \\\n (self.airVelY / self.airVelTotal)\n self.Fry = self.peso - tracao * math.sin(angulo) + self.D * (self.airVelY / self.airVelTotal) - \\\n self.S * (self.airVelX / self.airVelTotal)\n\n # Cálculo da aceleração no eixo X\n self.airAX = self.Frx / self.massa\n # Cálculo da aceleração no eixo X\n self.airAY = self.Fry / self.massa\n\n # Atualiza a posição HORIZONTAL do avião\n def atualizaX(self):\n # atualiza velocidade horizontal\n self.airVelX += self.airAX * (1 / 60)\n\n # verificar se a velociade é máxima\n if self.airVelX >= 500:\n self.airVelX = 500\n elif self.airVelX <= -500:\n self.airVelX = -500\n\n # atualiza posição horizontal\n self.airX += self.airVelX * (1 / 60) + 0.5 * self.airAX * ((1 / 60) ** 2)\n\n # limita direita\n if self.airX > 400:\n self.airX = 400\n # self.airVelX = 0\n # limita esquerda\n elif self.airX <= 0:\n self.airX = 0\n # self.airVelX = 0\n return True\n\n # Atualiza a posição VERTICAL do avião\n def atualizaY(self):\n # atualiza velocidade vertical\n self.airVelY += self.airAY * (1 / 60)\n\n # verificar se a velociade é máxima\n if self.airVelY >= 500:\n self.airVelY = 500\n elif self.airVelY <= -500:\n self.airVelY = -500\n\n # atualiza posição vertical\n self.airY += self.airVelY * (1 / 60) + 0.5 * self.airAY * ((1/60) ** 2)\n\n # limita inferiormente\n if self.airY > 390:\n self.airY = 390\n self.airVelY = 0\n\n # limita superiormente\n elif self.airY < 20:\n self.airY = 20\n self.airVelY = 0\n\n # Se o avião saiu da posição inicial\n if self.airVelY < 0 and self.airVelX != 0:\n self.teste_combustivel = True\n # Verificar se o avião decolou\n if self.airY < 350:\n self.teste_decolagem = True\n\n return True\n\n # Atualiza o combustível do avião\n def combustivel(self):\n # Primeiramente, verifica se o avião levantou voo\n if self.teste_combustivel:\n # Atualizar o valor do combustível\n if self.count_f >= 100:\n self.count_f = 0\n else:\n self.count_f += 0.001\n self.fuel -= 0.01\n\n # Verifica se tem aceleração em alguma direção\n if self.airAX != 0 or self.airAY != 0:\n self.fuel -= 0.02\n\n # Verifica se o combustível acabou\n if self.fuel <= 0:\n self.airAY = 0\n self.airAX = 0\n self.fuel = 0\n display_message(\"Sem combustível!!!!\", (255, 255, 255))\n\n # Se chegou na base da tela sem combustível\n if self.airY >= 389:\n return True\n\n # Mostra o combustível\n fuel_message(\"Combustível: {:.2f} %\".format(self.fuel), (255, 255, 255))\n\n # Colisão do avião\n def collide(self):\n # Chegou ao final da tela depois de decolar\n if self.airY > 389 and self.teste_decolagem is True:\n return True\n","repo_name":"gcb2708/PI_1B_vF","sub_path":"classe_aviao.py","file_name":"classe_aviao.py","file_ext":"py","file_size_in_byte":6390,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39889981000","text":"from flask import Flask, render_template\nimport requests\nimport json\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n\n response = requests.get(\n 'https://www.st1.se/fuel/stationPrices/pricesPerStation/'\n )\n\n data = response.text\n\n parsed = json.loads(data)[\"data\"][\"station_prices\"]\n updated_at = json.loads(data)[\"data\"][\"updated_at\"]\n\n stations = {}\n\n for keys in parsed:\n newName = parsed[keys][\"name\"].strip(\"St1\")\n stations[parsed[keys][\"name\"]] = newName,[parsed[keys][\"prices\"][\"b95\"][\"price_with_tax\"],parsed[keys][\"prices\"][\"diesel\"][\"price_with_tax\"],parsed[keys][\"prices\"][\"e85\"][\"price_with_tax\"]]\n\n return render_template('index.html', stations=stations, updated_at=updated_at)\n \nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=3000)\n","repo_name":"Snorlena/Fuel_Prices","sub_path":"flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69804246169","text":"from typing import List\n\n\ndef projectionArea(grid: List[List[int]]) -> int:\n rows, cols = len(grid), len(grid[0])\n row_max, col_max = [0] * rows, [0] * rows\n res = 0\n for row in range(rows):\n for col in range(cols):\n if grid[row][col] != 0:\n res += 1\n row_max[row] = max(row_max[row], grid[row][col])\n col_max[col] = max(col_max[col], grid[row][col])\n\n return res + sum(row_max) + sum(col_max)\n\n\n\n\n\n","repo_name":"DengBoCong/Algorithm","sub_path":"core/ProjectionAreaOf3dShapes/ProjectionAreaOf3dShapes.py","file_name":"ProjectionAreaOf3dShapes.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"31"} +{"seq_id":"40106491197","text":"import argparse\nimport csv\nimport json\nimport logging\nfrom pathlib import Path\nfrom typing import Set\n\nimport pandas as pd\nfrom tqdm import tqdm\nfrom utils import load_xlel_tsv\n\n\ndef load_langs(file_path: Path) -> Set:\n with open(file_path, \"r\") as rf:\n data = json.load(rf)\n langs = set(list(data.values()))\n return langs\n\n\ndef skip_langs(wiki_inlinks: pd.DataFrame, langs: Set):\n\n valid_rows, skipped_rows = [], []\n skip_labels = []\n\n for _, row in tqdm(wiki_inlinks.iterrows()):\n if row[\"Wikipedia Language\"] in langs:\n valid_rows += [row]\n else:\n skipped_rows += [row]\n skip_labels += [f\"UNSUPPORTED_LANG_{row['Wikipedia Language']}\"]\n\n skipped_df = pd.DataFrame(skipped_rows)\n skipped_df[\"Comment\"] = skip_labels\n\n valid_df = pd.DataFrame(valid_rows)\n\n return skipped_df, valid_df\n\n\nif __name__ == \"__main__\":\n\n logging.basicConfig(\n format=\"%(asctime)s - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n handlers=[logging.StreamHandler()],\n )\n\n parser = argparse.ArgumentParser(description=\"\")\n parser.add_argument(\"data\", type=Path, help=\"Path to XLEL tsv\")\n parser.add_argument(\"langs\", type=Path, help=\"list of valid languages\")\n parser.add_argument(\"out\", type=Path)\n parser.add_argument(\n \"--skipped-out\", type=Path, default=None, help=\"path to write skipped links\"\n )\n\n args = parser.parse_args()\n\n langs = load_langs(args.langs)\n\n wiki_inlinks = load_xlel_tsv(\n args.data,\n return_keys=[\n \"Wikidata Item\",\n \"Wikipedia Language\",\n \"Wikipedia Title\",\n \"Wikipedia Inlink Title\",\n \"Context\",\n ],\n )\n\n skipped_df, valid_df = skip_langs(wiki_inlinks, langs)\n skipped_df.to_csv(args.skipped_out, sep=\"\\t\", index=False, quoting=csv.QUOTE_NONE)\n valid_df.to_csv(args.out, sep=\"\\t\", index=False, quoting=csv.QUOTE_NONE)\n","repo_name":"adithya7/xlel-wd","sub_path":"data_collection/postprocess/postprocess_inlink_langs.py","file_name":"postprocess_inlink_langs.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"12047614900","text":"import sys\nsys.path.append(\"..\") \nfrom sklearn.cluster import KMeans\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.linear_model import LogisticRegression\nimport numpy as np\nimport torch\nimport torchvision.transforms as transforms\nfrom vgg import VGG16\nfrom utils import *\nfrom PIL import Image\nimport numpy as np\nimport torchvision.transforms as transforms\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--begin_repeat', type=int, default=2, help=' begin repeat num')\nargs = parser.parse_args()\n\nrepeat = 5\nbegin_repeat = args.begin_repeat\ninput_channel = 3\nbatch_size = 128\nsave_path = '/public/data1/users/leishiye/neural_code/results/training_time/result_list_training_process_'\nload_path = '/public/data1/users/leishiye/neural_code/models/training_time/model_training_process_'\ndepth = 1\n\ndataset = 'tinyimagenet'\n\nnum_classes = 200\nn_clusters = num_classes\n\nwidth_list = width_list = [64, 128]\noutput_epoch_list = [0, 1, 2, 3, 6, 8, 10, 13, 17, 20, 25, 30, 35, 40]\n\n# data loading\ndata_transforms = {\n 'train': transforms.Compose([\n transforms.RandomHorizontalFlip(0.5),\n transforms.ToTensor(),\n transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n ]),\n 'test': transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),\n ])\n}\n\nclass Dataset():\n def __init__(self, x, y, transform=None):\n assert(len(x) == len(y))\n print(x.shape)\n self.x = np.random.randint(256, size=x.shape) # random inputs\n #self.x = x\n self.y = y\n self.transform = transform\n\n def __getitem__(self, idx):\n x, y = self.x[idx], self.y[idx]\n if self.transform is not None:\n x = self.transform( Image.fromarray(np.uint8(x)))\n # x = self.transform(x)\n return x, y\n\n def __len__(self):\n return len(self.x)\n\n\ndef TinyImageNet(root='./path', train=True, transform=None):\n if train:\n path = '{}/tiny-imagenet/train.npz'.format(root)\n else:\n path = '{}/tiny-imagenet/test.npz'.format(root)\n\n data = np.load(path)\n\n return Dataset(x=data['images'], y=data['labels'], transform=transform)\n\ndata_dir = '/public/data1/users/leishiye/datasets'\nimage_datasets = dict()\nimage_datasets['train'] = TinyImageNet(data_dir, train=True, transform=data_transforms['train'])\nimage_datasets['test'] = TinyImageNet(data_dir, train=False, transform=data_transforms['test'])\ndataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['train', 'test']}\ndataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}\n\nprint(\"dataset size: \")\nprint(dataset_sizes)\n\ntrainloader = dataloaders['train']\ntestloader = dataloaders['test']\n\nprint('dataset: ' + dataset)\nprint('depth: ' + str(depth))\n\n# Load model\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nuse_cuda = torch.cuda.is_available()\n\nfor iter in np.linspace(begin_repeat-1, begin_repeat + repeat-2, repeat).astype('int'):\n print('repeat: ' + str(iter + 1))\n for num_neuron in width_list:\n print('layer width: ' + str(num_neuron))\n result_list = []\n\n # training according to training epoch list\n for training_epoch in output_epoch_list:\n net = torch.load(load_path + str(training_epoch) + '_width_' + str(num_neuron) + '_' + dataset + '_depth_' + str(depth) + '_iter' + str(iter + 1)).to(device)\n \"\"\"\n # evaluation\n train_acc = test(net, trainloader, epoch=1)\n print(\"train accuracy: \", train_acc)\n test_acc = test(net, testloader, epoch=1)\n print(\"test accuracy: \", test_acc)\n \"\"\"\n net.eval()\n # compute activation code\n train_activation_codes, train_label_scalar = compute_conv_code_list(trainloader, net)\n test_activation_codes, test_label_scalar = compute_conv_code_list(testloader, net)\n\n train_activation_codes = train_activation_codes[1]\n test_activation_codes = test_activation_codes[1]\n \n # compute redundancy ratio\n test_redundancy_ratio = (test_activation_codes.shape[0] - np.unique(test_activation_codes, axis=0).shape[\n 0]) / dataset_sizes['test']\n train_redundancy_ratio = (train_activation_codes.shape[0] - np.unique(train_activation_codes, axis=0).shape[\n 0]) / dataset_sizes['train']\n\n print(\"random train redundancy ratio: \" + str(train_redundancy_ratio))\n print(\"random test redundancy ratio: \" + str(test_redundancy_ratio))\n \n \"\"\"\n # compute clustering accuracy with kmeans\n train_cluster_result = KMeans(n_clusters=200, random_state=9).fit_predict(train_activation_codes)\n train_clustering_accuracy_kmeans = compute_clustering_accuracy(train_cluster_result, train_label_scalar, n_cluster=n_clusters)\n\n test_cluster_result = KMeans(n_clusters=n_clusters, random_state=9).fit_predict(test_activation_codes)\n test_clustering_accuracy_kmeans = compute_clustering_accuracy(test_cluster_result, test_label_scalar, n_cluster=n_clusters)\n\n print(\"train_clustering_accuracy_kmeans: \" + str(train_clustering_accuracy_kmeans))\n print(\"test_clustering_accuracy_kmeans: \" + str(test_clustering_accuracy_kmeans))\n\n # compute clusterisng accuracy with KNN\n neigh = KNeighborsClassifier(n_neighbors=9, metric='hamming').fit(train_activation_codes,\n train_label_scalar)\n knn_pred_result = neigh.predict(test_activation_codes)\n smstr = np.nonzero(test_label_scalar - knn_pred_result)\n knn_accuracy = 1 - np.shape(smstr[0])[0] / test_label_scalar.shape[0]\n\n print(\"knn_accuracy: \" + str(knn_accuracy))\n\n # compute multiclass logisticRegression\n logistic_classifier = OneVsOneClassifier(LogisticRegression(solver='liblinear', random_state=9)).fit(train_activation_codes,\n train_label_scalar)\n logistic_pred_result = logistic_classifier.predict(test_activation_codes)\n smstr = np.nonzero(test_label_scalar - logistic_pred_result)\n logistic_accuracy = 1 - np.shape(smstr[0])[0] / test_label_scalar.shape[0]\n\n print(\"logistic_accuracy: \" + str(logistic_accuracy))\n \"\"\"\n result_list.extend([train_redundancy_ratio, test_redundancy_ratio])\n \n # save\n save_list(result_list,\n save_path + dataset + '_random_input_depth_' + str(depth) + '_width_' + str(num_neuron) + '_iter' + str(iter + 1))","repo_name":"LeavesLei/activation-code","sub_path":"TinyImagenet/run_tinyimagenet_random_input.py","file_name":"run_tinyimagenet_random_input.py","file_ext":"py","file_size_in_byte":6980,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"16298963618","text":"from django.contrib import admin\nfrom .models import Product, Category\n\n\nclass ProductAdmin(admin.ModelAdmin):\n list_display = (\n 'name',\n 'category',\n 'price',\n 'image_url',\n 'sku',\n )\n\n ordering = ('name',) # to do a reverse ordering just stick a minus \n # infront of the word 'name'\n\n\nclass CategoryAdmin(admin.ModelAdmin):\n list_display = (\n 'friendly_name',\n 'name',\n )\n\n\n# Register your models here.\nadmin.site.register(Product, ProductAdmin)\nadmin.site.register(Category, CategoryAdmin)\n\n","repo_name":"natashacmyers/cononleystore","sub_path":"products/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"30893235820","text":"import os\r\nimport numpy\r\n\r\ndef make_vector(index_list, wv, directory):\r\n\tf = open(directory+\"/mcc.dat\")\r\n\tlines = f.readlines()\r\n\tf.close()\r\n\tprint(\"complete: read \"+ directory)\r\n\r\n\tword_list = []\r\n\ttf_idf = []\r\n\tfor line in lines:\r\n\t\tpoint = line.find(\":\")\r\n\t\tpoint2 = line.find(\"\\n\")\r\n\t\tword = line[:point]\r\n\t\tvalue = line[point+1:point2]\r\n\t\ttry:\r\n\t\t\ttf_idf.append(float(value))\r\n\t\t\tword_list.append(word)\r\n\t\texcept:\r\n\t\t\tcontinue\r\n\t#print(\"complete: make tf-idf vector\")\r\n\t\r\n\twv_list = []\r\n\tdel_list = []\r\n\tfor word in word_list:\r\n\t\ttry:\r\n\t\t\tn = index_list.index(word)\r\n\t\t\twv_list.append(wv[n])\r\n\t\texcept:\r\n\t\t\tdel_list.append(word)\r\n\r\n\t# delete word not in word2vec\r\n\tfor word in del_list:\r\n\t\tn = word_list.index(word)\r\n\t\tdel word_list[n]\r\n\t\tdel tf_idf[n]\r\n\r\n\tvec_tf_idf = numpy.array(tf_idf)\r\n\tvec_wv = numpy.array(wv_list)\r\n\t#print(\"complete: make word vector\")\r\n\r\n\r\n\ttf_idf_wv = numpy.dot(vec_tf_idf, vec_wv)\r\n\t#print(\"complete: make tf-idf-wv vector\")\r\n\r\n\tf = open(directory+'/tfidfwv.dat', 'w')\r\n\ti = 0\r\n\tfor n in tf_idf_wv:\r\n\t\tf.write('w'+str(i)+':'+str(n)+'\\n')\r\n\t\ti += 1\r\n\tf.close()\r\n\tprint(\"complete: write vector file for \"+directory)\r\n\r\n\treturn\r\n\r\ndef path_find(index_list, wv, directory):\r\n\tdirectories = os.listdir(directory)\r\n\tfor name in directories:\r\n\t\tif name == \"mcc.dat\":\r\n\t\t\tmake_vector(index_list, wv, directory)\r\n\t\telif name == \"tfidfwv.dat\":\r\n\t\t\tcontinue\r\n\t\telse:\r\n\t\t\tpath_find(index_list, wv, directory+\"/\"+name)\r\n\r\n\treturn\r\n\r\nif __name__ == '__main__':\r\n\r\n\tdirectory = input(\"Insert top folder directory: \")\r\n\r\n\tf = open('vec.txt')\r\n\tlines = f.readlines()\r\n\tf.close()\r\n\r\n\tindex_list = []\r\n\twv = []\r\n\tfor line in lines:\r\n\t\tpoint = line.find(\" \")\r\n\t\tword = line[:point]\r\n\t\tweight = line[point+1:].split()\r\n\t\ti = 0\r\n\t\tfor n in weight:\r\n\t\t\tweight[i] = float(n)\r\n\t\t\ti += 1\r\n\t\tindex_list.append(word)\r\n\t\twv.append(weight)\r\n\tdel index_list[0]\r\n\tdel wv[0]\r\n\tprint(\"complete: read word2vec output\")\r\n\r\n\tpath_find(index_list, wv, directory)","repo_name":"pch8944/DS_Word2Vec","sub_path":"tf-idf-wv.py","file_name":"tf-idf-wv.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29895807941","text":"# coding: utf8\n\nimport unittest\nfrom io import open\n\nfrom pyxpdf.xpdf import Document\n\nfrom .common_imports import (\n InitGlobalTextCase,\n PropertyTextCase,\n file_in_sample_dir,\n file_in_test_dir,\n)\n\n\nclass PageTestCase(InitGlobalTextCase, PropertyTextCase):\n mandarin_pdf = file_in_sample_dir(\"nonfree\", \"mandarin.pdf\")\n mandarin_prop = {\n \"artbox\": (0.0, 0.0, 612.0, 792.0),\n \"bleedbox\": (0.0, 0.0, 612.0, 792.0),\n \"crop_height\": 792.0,\n \"crop_width\": 612.0,\n \"cropbox\": (0.0, 0.0, 612.0, 792.0),\n \"index\": 0,\n \"is_cropped\": True,\n \"label\": \"1\",\n \"media_height\": 792.0,\n \"media_width\": 612.0,\n \"mediabox\": (0.0, 0.0, 612.0, 792.0),\n \"rotation\": 0,\n \"trimbox\": (0.0, 0.0, 612.0, 792.0),\n }\n\n find_char = u\"通\"\n find_result = [\n (\n 122.69618999999997,\n 149.79401400000003,\n 132.71618999999998,\n 160.57553400000006,\n ),\n (282.53913, 183.51330000000004, 292.55913, 194.11446),\n (72.0, 473.912424, 82.02, 484.513584),\n ]\n\n def setUp(self):\n super(PageTestCase, self).setUp()\n self.doc = Document(self.mandarin_pdf)\n\n def test_page_properties(self):\n for prop, val in self.mandarin_prop.items():\n self.assertProperty(self.doc[0], prop, val, setter=False)\n\n def test_page_text(self):\n with open(file_in_test_dir(\"mandarin_first.txt\"), \"r\", encoding=\"utf-8\") as fp:\n self.assertEqual(self.doc[0].text(), fp.read())\n\n def test_page_find(self):\n self.assertEqual(self.find_result[0], self.doc[9].find_text(self.find_char))\n self.assertEqual(\n self.find_result[1], self.doc[9].find_text(self.find_char, direction=\"next\")\n )\n self.assertEqual(\n self.find_result[2], self.doc[9].find_text(self.find_char, direction=\"next\")\n )\n\n\ndef test_suite():\n suite = unittest.TestSuite()\n suite.addTests([unittest.makeSuite(PageTestCase)])\n return suite\n\n\nif __name__ == \"__main__\":\n print(\"to test use test.py %s\" % __file__)\n","repo_name":"ashutoshvarma/pyxpdf","sub_path":"src/pyxpdf/tests/test_page.py","file_name":"test_page.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"31"} +{"seq_id":"2235576280","text":"import sys\nfrom cx_Freeze import setup, Executable\n\n# Dependencies are automatically detected, but it might need fine tuning.\nbuild_exe_options = {\"packages\": [\"os\"], \"includes\": [\"tkinter\"], \"includes\": [\"subprocess\"], \"includes\": [\"random\"], \"includes\": [\"watchdog\"], \"includes\": [\"PIL\"], \"includes\": [\"shutil\"], \"includes\": [\"threading\"]}\n\n# GUI applications require a different base on Windows (the default is for\n# a console application).\nbase = None\nif sys.platform == \"win32\":\n base = \"Win32GUI\"\n\nsetup(\n name=\"Catcher\",\n version=\"0.1\",\n description=\"Minha 1° Aplicação!\",\n options={\"build_exe\": build_exe_options},\n executables=[Executable(\"Catcher.py\", base=base)]\n)","repo_name":"Ralph20s/Catcher","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"4433580660","text":"import sys, heapq\nsys.stdin = open(\"특정한최단경로_input.txt\")\ninput = sys.stdin.readline\n\ndef dijkstra(graph, start):\n\n distances = {node : float('inf') for node in graph}\n distances[start] = 0\n queue = []\n heapq.heappush(queue, [distances[start], start])\n\n while queue:\n currentDistance, currentNode = heapq.heappop(queue)\n\n if currentDistance > distances[currentNode]: continue\n\n for adjacent, weight in graph[currentNode].items():\n distance = currentDistance + weight\n\n if distance < distances[adjacent]:\n distances[adjacent] = distance\n heapq.heappush(queue, [distance, adjacent])\n\n return distances\n\n\ndef AtoB(graph, mustA, mustB):\n toA = dijkstra(graph, 1)\n toB = dijkstra(graph, mustA)\n toN = dijkstra(graph, mustB)\n\n if mustA not in toA or mustB not in toB or N not in toN:\n return -1\n else:\n tot = toA[mustA] + toB[mustB] + toN[N]\n return (tot if tot < float('inf') else -1)\n\ndef BtoA(graph, mustA, mustB):\n toB = dijkstra(graph, 1)\n toA = dijkstra(graph, mustB)\n toN = dijkstra(graph, mustA)\n\n if mustA not in toA or mustB not in toB or N not in toN:\n return -1\n else:\n tot = toA[mustA] + toB[mustB] + toN[N]\n return (tot if tot < float('inf') else -1)\n\nN, E = map(int, input().split())\ngraph = {i : {} for i in range(1, N+1)}\n\nfor _ in range(E):\n a, b, c = map(int, input().split())\n if b in graph[a] and c >= graph[a][b]: continue\n graph[a][b] = c\n graph[b][a] = c\n\nmustA, mustB = map(int, input().split())\nresult1 = AtoB(graph, mustA, mustB)\nresult2 = BtoA(graph, mustA, mustB)\n\nif result1 == -1 and result2 == -1:\n print(-1)\nelif result1 >= 0 and result2 >= 0:\n print(min(result1, result2))\nelse:\n print(max(result1, result2))","repo_name":"yoonwoo123/Algorithm","sub_path":"200826프로/특정한최단경로.py","file_name":"특정한최단경로.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74444554647","text":"from bs4 import BeautifulSoup\nimport requests\n\nresponse = requests.get(\"https://wow.gamepedia.com/Battle_for_Azeroth_inscription_techniques\")\n\nresponse.raise_for_status()\npage = response.text\n\nbs = BeautifulSoup(page, \"html.parser\")\ntables = bs.find_all(\"table\", \"sortable\")\n\n\n","repo_name":"kvosbur/WowAuctionHelper","sub_path":"GatherData/GetInscriptionData.py","file_name":"GetInscriptionData.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"12378153948","text":"from django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\n\nfrom .models import Departamento, Categoria, Item\n\ndef quem_somos(request):\n template_name = \"core/quem_somos.html\"\n context = {}\n return render(request, template_name, context)\n\n\ndef contato(request):\n template_name = \"core/contato.html\"\n context = {}\n return render(request, template_name, context)\n\n\ndef home(request):\n departamentos = Departamento.objects.filter(ativo=True)\n produtos = Item.objects.filter(ativo=True)\n template_name = \"core/home.html\"\n context = {\n 'departamentos': departamentos,\n 'produtos': produtos\n }\n return render(request, template_name, context)\n\n\ndef itens_categoria(request, categoria_id):\n departamentos = Departamento.objects.filter(categorias__isnull=False, ativo=True).distinct()\n produtos = Item.objects.filter(categoria_id=categoria_id)\n template_name = \"core/itens_categoria.html\"\n categoria = Categoria.objects.get(pk=categoria_id)\n context = {\n 'departamentos': departamentos,\n 'produtos': produtos,\n 'categoria': categoria\n }\n return render(request, template_name, context)","repo_name":"Alquimar/edcar_distribuidora","sub_path":"edcar_distribuidora/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13277534012","text":"def alerter(inputs, windowSize, allowedIncrease):\n # preSum for average\n preSum = [0]\n for val in inputs:\n preSum.append(preSum[-1] + val)\n\n maxVal, maxIndex, maxCnt = float('-inf'), -1, 0\n i, j = 0, 0\n preAverage = 0\n\n while j <= len(inputs):\n if (j - i + 1 > windowSize):\n # condition 2\n average = (preSum[j] - preSum[i]) / windowSize\n if preAverage and average / preAverage >= allowedIncrease: return True\n preAverage = average\n if maxIndex == i:\n # update maxVal\n for idx in range(i + 1, j):\n if inputs[idx] > maxVal:\n maxVal = inputs[idx]\n maxIndex = idx\n maxCnt = 0\n # condition 1\n if maxVal / average >= allowedIncrease:\n maxCnt += 1\n if maxCnt == (windowSize if len(inputs) - maxIndex > windowSize else len(inputs) - maxIndex): return True\n i += 1\n elif inputs[j] > maxVal:\n maxVal = inputs[j]\n maxIndex = j\n maxCnt = 0\n print(maxVal, maxIndex)\n j += 1\n \n return False\n\ndef alerter2(inputs, windowSize, allowedIncrease):\n if not inputs or not windowSize: return False\n i, j = 0, windowSize - 1\n preSum = [0]\n for val in inputs:\n preSum.append(preSum[-1] + val)\n\n preAverage = float(\"inf\")\n maxVal, maxIdx, maxCnt = float('-inf'), -1, 0\n windowCnt = 0\n while j < len(inputs):\n if j - windowSize + 1 > maxIdx:\n maxVal, maxIdx = float('-inf'), -1\n for z in range(i, j + 1):\n if inputs[z] > maxVal:\n maxVal = inputs[z]\n maxIdx = z\n maxCnt = 0\n windowCnt = getWindow(len(inputs), z, windowSize)\n elif inputs[j] > maxVal:\n maxVal = inputs[j]\n maxIdx = j\n maxCnt = 0\n windowCnt = getWindow(len(inputs), j, windowSize)\n # condition 2\n average = (preSum[j + 1] - preSum[i]) / windowSize\n if not preAverage or average / preAverage > allowedIncrease: return True\n preAverage = min(preAverage, average)\n # condition 1\n if not average or maxVal / average > allowedIncrease:\n maxCnt += 1\n if maxCnt == windowCnt: return True\n\n j += 1\n i += 1\n return False\n\ndef getWindow(size, idx, window):\n if idx <= window - 1:\n return idx + 1\n elif idx >= size - window:\n return size - idx\n return window\n\nprint(alerter2([10,20,2,40,80,90,100], 4, 2))","repo_name":"SuperMartinYang/learning_algorithm","sub_path":"interview_exam/Wepay/wepay.py","file_name":"wepay.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40393452331","text":"import os.path\r\nimport pickle\r\nfrom rest_framework.generics import GenericAPIView\r\nfrom rest_framework.parsers import MultiPartParser # for file upload\r\n\r\nfrom .models import File # import File model\r\nfrom .serializer import FileSerializer # import FileSerializer\r\nfrom django.http import JsonResponse # for returning JSON response\r\nfrom rest_framework import status # for returning status codes\r\nfrom drf_yasg.utils import swagger_auto_schema # for swagger documentation\r\nfrom rest_framework.permissions import IsAuthenticated # for authentication\r\nfrom core.utils.util import recc_images # for extracting colors\r\nfrom pathlib import Path # for getting the path of the file\r\nfrom PIL import Image\r\nfrom django.core.cache import cache\r\nimport imagehash\r\n\r\n\r\nimg_files_list = pickle.load(open(\"core/utils/img_files.pkl\", \"rb\"))\r\n\r\nclass FileUploadView(GenericAPIView):\r\n serializer_class = FileSerializer\r\n parser_classes = (MultiPartParser,)\r\n permission_classes = [IsAuthenticated]\r\n\r\n @swagger_auto_schema(\r\n tags=['upload'],\r\n operation_summary='Upload a file and predict images'\r\n )\r\n\r\n def post(self, request): # for uploading file\r\n file = request.FILES.get('file')\r\n # print(file)\r\n if not file:\r\n return JsonResponse({\"error\": \"No file uploaded.\"}, status=status.HTTP_400_BAD_REQUEST)\r\n \r\n try:\r\n res = File.objects.create(file=file) # create a file object\r\n except Exception as e:\r\n return JsonResponse({\"File error\": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\r\n print(res.file.path)\r\n # Check if the image recommendations are already cached in Redis\r\n image_hash = str(imagehash.average_hash(Image.open(os.path.join(Path(__file__).resolve().parent.parent, 'media', res.file.name)))) \r\n print(image_hash) \r\n cache_key = f\"recommended_images_{image_hash}\"\r\n # print(cache_key)\r\n recommended_images = cache.get(cache_key)\r\n print(recommended_images )\r\n if not recommended_images:\r\n try:\r\n img_indicess = recc_images(os.path.join(Path(__file__).resolve().parent.parent, 'media', res.file.name))\r\n recommended_images = []\r\n for image_path in img_indicess:\r\n image_number = int(image_path.split('\\\\')[-1].split('.')[0])\r\n recommended_images.append(image_number)\r\n\r\n cache.set(cache_key, recommended_images, timeout=60*5)\r\n\r\n except FileNotFoundError:\r\n return JsonResponse({\"error\": \"File not found.\"}, status=status.HTTP_404_NOT_FOUND)\r\n except Exception as e:\r\n return JsonResponse({\"error\": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\r\n \r\n uploaded_file_path = os.path.join('media', res.file.name)\r\n response_data = {\r\n 'uploaded_image': uploaded_file_path,\r\n 'recommended_images': recommended_images,\r\n }\r\n return JsonResponse({\r\n \"success\": \"true\",\r\n \"code\": 200,\r\n \"result\": response_data\r\n }, status=status.HTTP_200_OK)\r\n ","repo_name":"arjyo851/TrendSpotter","sub_path":"backend/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15978751311","text":"#! /usr/bin/env python\n# coding=utf-8\n\nimport numpy as np\nimport tensorflow as tf\nimport core.utils as utils\nimport core.common as common\nimport core.backbone as backbone\nfrom core.config import cfg\n\n\nclass YOLOV3(object):\n \"\"\"Implement tensoflow yolov3 here\"\"\"\n def __init__(self, input_data, trainable):\n\n self.trainable = trainable\n self.classes = utils.read_class_names(cfg.YOLO.CLASSES)\n self.num_class = len(self.classes)\n self.strides = np.array(cfg.YOLO.STRIDES)\n # self.anchors = utils.get_anchors(cfg.YOLO.ANCHORS)\n # self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE\n # self.iou_loss_thresh = cfg.YOLO.IOU_LOSS_THRESH\n self.upsample_method = cfg.YOLO.UPSAMPLE_METHOD\n\n try:\n self.conv_l = self.__build_nework(input_data)\n except:\n raise NotImplementedError(\"Can not build up yolov3 network!\")\n\n def __build_nework(self, input_data):\n\n input_data = backbone.darknet53(input_data, self.trainable)\n\n # input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv52')\n # input_data = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, 'conv53')\n # input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv54')\n # input_data = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, 'conv55')\n # input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv56')\n #\n # conv_lobj_branch = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, name='conv_lobj_branch')\n # conv_l = common.convolutional(input_data, (1, 1, 1024, self.num_class),\n # trainable=self.trainable, name='conv_l', activate=False, bn=False)\n\n conv_l = tf.layers.flatten(input_data)\n conv_l = tf.reshape(conv_l, (-1, 8 * 8 * 128))\n conv_l = tf.layers.dense(conv_l, units=1024, activation=tf.nn.leaky_relu, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.1))\n conv_l = tf.layers.dropout(conv_l, rate=0.4)\n conv_l = tf.layers.dense(conv_l, units=512, activation=tf.nn.leaky_relu, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.1))\n conv_l = tf.layers.dropout(conv_l, rate=0.4)\n conv_l = tf.layers.dense(conv_l, units=self.num_class, name='conv_l', activation=tf.nn.softmax)\n\n # input_data = common.convolutional(input_data, (1, 1, 1024, 256), self.trainable, 'conv57')\n # input_data = common.upsample(input_data, name='upsample0', method=self.upsample_method)\n #\n # with tf.variable_scope('route_1'):\n # input_data = tf.concat([input_data, route_2], axis=-1)\n\n # input_data = common.convolutional(input_data, (1, 1, 768, 256), self.trainable, 'conv58')\n # input_data = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, 'conv59')\n # input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv60')\n # input_data = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, 'conv61')\n # input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv62')\n #\n # conv_mobj_branch = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, name='conv_mobj_branch')\n # conv_m = common.convolutional(input_data, (1, 1, 768, self.num_class),\n # trainable=self.trainable, name='conv_m', activate=False, bn=False)\n\n # conv_m = tf.layers.flatten(input_data)\n # conv_m = tf.reshape(conv_m, (-1, 26 * 26 * 768))\n # conv_m = tf.layers.dense(conv_m, units=self.num_class, name='conv_m', activation=tf.nn.relu)\n #\n # input_data = common.convolutional(input_data, (1, 1, 768, 128), self.trainable, 'conv63')\n # input_data = common.upsample(input_data, name='upsample1', method=self.upsample_method)\n #\n # with tf.variable_scope('route_2'):\n # input_data = tf.concat([input_data, route_1], axis=-1)\n\n # input_data = common.convolutional(input_data, (1, 1, 384, 128), self.trainable, 'conv64')\n # input_data = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, 'conv65')\n # input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv66')\n # input_data = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, 'conv67')\n # input_data = common.convolutional(input_data, (1, 1, 384, 128), self.trainable, 'conv68')\n #\n # conv_sobj_branch = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, name='conv_sobj_branch')\n # conv_s = common.convolutional(input_data, (1, 1, 384, self.num_class),\n # trainable=self.trainable, name='conv_s', activate=False, bn=False)\n\n # conv_s = tf.layers.flatten(input_data)\n # conv_s = tf.reshape(conv_s, (-1, 52 * 52 * 384))\n # conv_s = tf.layers.dense(conv_s, units=self.num_class, name='conv_s', activation=tf.nn.relu)\n\n return conv_l\n\n def decode(self, conv_output):\n \"\"\"\n return tensor of shape [batch_size, output_size, output_size, anchor_per_scale, 5 + num_classes]\n contains (x, y, w, h, score, probability)\n \"\"\"\n\n conv_shape = tf.shape(conv_output)\n batch_size = conv_shape[0]\n output_size = conv_shape[1]\n\n conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, self.num_class))\n\n conv_raw_prob = conv_output[:, :, :, :]\n pred_prob = tf.sigmoid(conv_raw_prob)\n\n return pred_prob\n\n def focal(self, target, actual, alpha=1, gamma=2):\n focal_loss = alpha * tf.pow(tf.abs(target - actual), gamma)\n return focal_loss\n\n def bbox_giou(self, boxes1, boxes2):\n\n boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,\n boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)\n boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,\n boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)\n\n boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]),\n tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis=-1)\n boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]),\n tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis=-1)\n\n boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])\n boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])\n\n left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])\n right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])\n\n inter_section = tf.maximum(right_down - left_up, 0.0)\n inter_area = inter_section[..., 0] * inter_section[..., 1]\n union_area = boxes1_area + boxes2_area - inter_area\n iou = inter_area / union_area\n\n enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2])\n enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])\n enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\n enclose_area = enclose[..., 0] * enclose[..., 1]\n giou = iou - 1.0 * (enclose_area - union_area) / enclose_area\n\n return giou\n\n def bbox_iou(self, boxes1, boxes2):\n\n boxes1_area = boxes1[..., 2] * boxes1[..., 3]\n boxes2_area = boxes2[..., 2] * boxes2[..., 3]\n\n boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,\n boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)\n boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,\n boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)\n\n left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])\n right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])\n\n inter_section = tf.maximum(right_down - left_up, 0.0)\n inter_area = inter_section[..., 0] * inter_section[..., 1]\n union_area = boxes1_area + boxes2_area - inter_area\n iou = 1.0 * inter_area / union_area\n\n return iou\n\n def loss_layer(self, conv, label):\n\n # label = tf.argmax(label, axis=1)\n # conv = tf.argmax(conv, axis=1)\n #\n # prob_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=label, logits=conv)\n # prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1]))\n\n # prob_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=label, logits=conv))\n\n prob_loss = tf.keras.losses.categorical_crossentropy(y_true=label, y_pred=conv)\n prob_loss = tf.reduce_mean(prob_loss)\n\n return prob_loss\n\n def compute_loss(self, label):\n\n # with tf.name_scope('smaller_loss'):\n # loss_s = self.loss_layer(self.conv_s, label)\n #\n # with tf.name_scope('medium_loss'):\n # loss_m = self.loss_layer(self.conv_m, label)\n #\n # with tf.name_scope('bigger_loss'):\n # loss_l = self.loss_layer(self.conv_l, label)\n\n loss = self.loss_layer(self.conv_l, label)\n\n return loss\n\n\n","repo_name":"conglanjun/ming_data_work","sub_path":"core/yolov3.py","file_name":"yolov3.py","file_ext":"py","file_size_in_byte":9375,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"25456315251","text":"''' Write a python program to sort dictionary by values (Ascending/ Descending) '''\n\n# input dictonary\ndict1 = {1:'one', 3:'three',4:'four', 2:'two'} \n\n# before sorting the dictonary\nprint('Before sorting :', dict1) \n \n# convet the given dictondary into list. items() method is used to return the list\nl = list(dict1.items()) \n\n# sorting the list\nl.sort() \n\n# converting the list into dictonary\ndict2 = dict(l)\n\n# print the sorted list as ascending order \nprint('Ascending order is',dict2)\n\n\n#sorting in reverse order\nl.sort(reverse=True) \n\n# converting the list into dictonary\ndict3 = dict(l)\n\n# print the sorted list as decending order \nprint('Descending order is',dict3)","repo_name":"jerinraju868/Python_Backend_Internship","sub_path":"Jan 9/pg_3.py","file_name":"pg_3.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36078170378","text":"import numpy as np\nimport pandas as pd\n\nfrom scipy.cluster import hierarchy\nimport sklearn\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import PredefinedSplit\nfrom sklearn.base import BaseEstimator\nfrom sklearn.feature_selection import SelectorMixin\nfrom scipy.stats import chi2_contingency\nimport scipy.spatial.distance as ssd\n\nDATE_INSERTED = 'Date Inserted'\nAUROC = 'roc_auc'\nBACC = 'balanced_accuracy'\nMEASURES = [BACC, AUROC]\n\n\nclass SelectHierarchicalClustering(SelectorMixin, BaseEstimator):\n \"\"\"\n A transformer that clusters the features in X according to dist_matrix, and selects a feature from each cluster with\n the highest chi2 score of X[feature] versus y\n \"\"\"\n\n def __init__(self, dist_matrix, threshold=1):\n\n self.dist_matrix = dist_matrix\n self.threshold = threshold\n\n def _corr_linkage(self, method='average'):\n\n linkage = hierarchy.linkage(self.dist_matrix, method=method)\n\n return linkage\n\n def _hierarchical_clustering(self, linkage):\n \"\"\" Perform hierarchical clustering\n\n :param linkage: linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method)\n :return: a list of lists, each list represents a cluster and contains the indexes of features belonging\n to the cluster\n \"\"\"\n\n # array of len(X) - array[i] is the cluster number to which sample i belongs\n cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance')\n\n cluster_id_to_feature_idx = {}\n for idx, cluster_id in enumerate(cluster_ids):\n cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx)\n\n return list(cluster_id_to_feature_idx.values())\n\n def fit(self, X, y):\n \"\"\"\n Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects a feature from each\n cluster with the highest chi2 score versus y.\n The attribute self.n_features_ represents the number of features selected (=number of clusters)\n The attribute self.selected_features_ is a list of indexes that correspond to the selected features\n \"\"\"\n linkage = self._corr_linkage()\n clusters = self._hierarchical_clustering(linkage)\n\n chi2_vals, __ = sklearn.feature_selection.chi2(X, y)\n chi2_vals = pd.Series(chi2_vals)\n\n # fitted attributes\n self.n_features_ = X.shape[1]\n self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters]\n self.clusters_ = clusters\n\n print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}')\n\n return self\n\n def _get_support_mask(self):\n \"\"\"\n Get the boolean mask indicating which features are selected\n Returns\n -------\n mask : boolean array of shape [# input features]\n An element is True iff its corresponding feature is selected for\n retention.\n \"\"\"\n\n # Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing\n # underscore) and otherwise raises a NotFittedError with the given message.\n sklearn.utils.validation.check_is_fitted(self)\n\n mask = np.zeros((self.n_features_, ), dtype=bool)\n\n mask[self.selected_features_] = 1\n\n return mask\n\n\ndef get_split_value(genome_id, train_genome_ids, validation_genome_ids):\n\n if genome_id in train_genome_ids:\n return -1\n elif genome_id in validation_genome_ids:\n return 0\n return None\n\n\ndef split_by_insertion_date(genomes_data, proportion=0.2):\n \"\"\"\n Returns a predefined split of genomes_data to train and validation according to proportion, where the validation\n are genomes with the latest insertion date\n\n :param genomes_data: a GenomesData object\n :param proportion: the proportion size of validation\n :return: PredefinedSplit object\n \"\"\"\n\n metadata = genomes_data.metadata\n # late-> early\n genomes_sorted_by_insertion = metadata.sort_values(DATE_INSERTED, ascending=False)\n\n total_number_of_genomes = len(genomes_data)\n validation_size = round(total_number_of_genomes * proportion)\n\n validation_genome_ids = genomes_sorted_by_insertion.index[:validation_size]\n train_genome_ids = genomes_sorted_by_insertion.index[validation_size:]\n\n fold = [get_split_value(genome_id, train_genome_ids, validation_genome_ids)\n for genome_id in genomes_data.genomes]\n\n return PredefinedSplit(fold)\n\n\ndef grid_search_results_to_df(grid_search, param_name, decimals=3):\n\n df = pd.DataFrame(grid_search.cv_results_)\n df.index = df['param_' + param_name]\n metrics = ['mean_test_'+metric for metric in MEASURES]\n df = df[metrics]\n df = df.round(decimals)\n\n return df\n\n\ndef perform_fs_k_best(train_dataset, k_range, split=None, random_state=0, return_train_score=False):\n \"\"\"\n Perform cross validation with various k values and a RF classifier. k represents top k features with the highest\n chi2 scores between each feature and the target labels.\n The classifier scores are computed according to MEASURES. The best parameter k is selected according to AUROC score.\n\n :param train_dataset: GenomesData object representing the train dataset\n :param k_range: a range of k values\n :param split: an object that represents the splits to train and validation\n (e.g. sklearn.model_selection.PredefinedSplit). If split is None, performs 5-fold stratified cross\n validation\n :param random_state: a random state for the RF classifier\n :param return_train_score: specifies if the GridSearchCV should also calculate classifier score on train\n :return: A fitted GridSearchCV object\n \"\"\"\n pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)),\n ('k_best', SelectKBest(score_func=chi2)),\n ('rf', RandomForestClassifier(random_state=random_state))])\n\n param_grid = {\n 'k_best__k': k_range,\n }\n\n scoring = MEASURES\n search = GridSearchCV(pipeline, param_grid, cv=split, scoring=scoring, refit=AUROC,\n return_train_score=return_train_score)\n search.fit(train_dataset.data, train_dataset.y)\n\n print(f'Best roc_auc score is: {search.best_score_}')\n\n return search\n\n\ndef get_fs_pipeline(X_train_dist_mat, k, threshold, random_state=0):\n\n pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)),\n ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)),\n ('cluster', SelectHierarchicalClustering(X_train_dist_mat, threshold=threshold)),\n ('rf', RandomForestClassifier(random_state=random_state))])\n\n return pipeline\n\n\ndef perform_fs_clusters(train_dataset, X_train_dist_mat, t_range, split=None, random_state=0,\n return_train_score=False):\n \"\"\"\n Perform cross validation with various t values and a RF classifier. t represents a threshold for clustering.\n If the number of features is k, a threshold of 0 will leave k features (there will be k clusters).\n The higher the t, features with greater distance will be merged to the same cluster, thus a smaller number of\n features will be selected.\n The classifier scores are computed according to MEASURES. The best parameter t is selected according to AUROC score.\n\n :param train_dataset: GenomesData object representing the train dataset\n :param X_train_dist_mat: A precomputed distance matrix for the train set according to split\n :param t_range: a range of t values\n :param split: an object that represents the splits to train and validation\n (e.g. sklearn.model_selection.PredefinedSplit). If split is None, performs 5-fold stratified cross\n validation\n :param random_state: a random state for the RF classifier\n :param return_train_score: specifies if the GridSearchCV should also calculate classifier score on train\n :return: A fitted GridSearchCV object\n \"\"\"\n\n pipeline = get_fs_pipeline(X_train_dist_mat, k=450, threshold=1, random_state=random_state)\n\n param_grid = {\n 'cluster__threshold': t_range,\n }\n\n scoring = MEASURES\n search = GridSearchCV(pipeline, param_grid, cv=split, scoring=scoring, refit=AUROC,\n return_train_score=return_train_score)\n search.fit(train_dataset.data, train_dataset.y)\n\n print(f'Best roc_auc score is: {search.best_score_}')\n\n return search\n\n\ndef phi_coef(x, y):\n \"\"\"Calculates phi coefficient between features\n\n Parameters:\n X - feature x column\n y - feature y column\n\n Returns:\n corr - phi coefficient value\n \"\"\"\n\n confusion_matrix = pd.crosstab(x, y)\n chi2 = chi2_contingency(confusion_matrix)[0]\n n = confusion_matrix.sum().sum()\n corr = np.sqrt(chi2 / n)\n\n return corr\n\n\ndef perform_fs_first_step(X_train, y_train, feature_names, k=100):\n \"\"\"select the k features with the highest chi-square scores\n between each feature and the target labels\n\n\n Parameters:\n X_train - dataframe represents the training genomes feature vectors\n y_train - dataframe represents the training genomes labels\n feature_names - feature namess of X_train\n k - number of features to select.\n\n Returns:\n X_train_fs - dataframe represents the training genomes feature vectors (reduced size feature vectors which\n consists of the k selected features)\n \"\"\"\n\n fs = SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)\n fs.fit(X_train, y_train)\n fs_selected_indexes = fs.get_support()\n X_train_fs = pd.DataFrame(X_train[:, fs_selected_indexes].toarray(),\n columns=feature_names[fs_selected_indexes])\n\n return X_train_fs\n\n\ndef create_corr_matrix(X_train_raw, y_train, k=450):\n \"\"\"\n Create a correlation matrix of size kxk between each two k best features X_train_raw. The best k features are\n selected according to chi2 score between each feature and the target labels.\n\n :param X_train_raw: X train strings of genes\n :param y_train: train labels\n :param k: k features to select according to chi2 score\n :return: kxk correlation matrix between each two k best features X_train_raw. Matrix values are between 0-1.\n A value of 0 represents no correlation between the corresponding features, a value of 1 represents a perfect\n correlation between the corresponding features.\n \"\"\"\n\n vectorizer = CountVectorizer(lowercase=False, binary=True)\n X_train = vectorizer.fit_transform(X_train_raw, y_train)\n feature_names = np.array(vectorizer.get_feature_names())\n\n X_train_k_best = perform_fs_first_step(X_train, y_train, feature_names, k=k)\n\n X_train_corr_mat = X_train_k_best.corr(method=phi_coef)\n\n return X_train_corr_mat\n\n\ndef feature_corr_to_dist_matrix(feature_corr_matrix):\n \"\"\"Transforms the correlation matrix feature_corr_matrix to a condensed distance matrix\"\"\"\n\n feature_corr_dist_matrix = 1 - feature_corr_matrix\n\n feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix)\n\n return feature_corr_dist_matrix_condensed\n\n\n\n","repo_name":"shakedna1/wspc_rep","sub_path":"Notebooks/Code/feature_selection.py","file_name":"feature_selection.py","file_ext":"py","file_size_in_byte":11607,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"22646497351","text":"#handling the web talble\n#code by sunil savale\n#date - 15/09/2021\n\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver import ActionChains\nimport time\n\n\n#launch the Browser\ndriver = webdriver.Chrome(ChromeDriverManager().install())\n\n#navigate the URL\ndriver.get('https://courses.letskodeit.com/practice')\n\n#inspectng the webtable\ntable = driver.find_elements_by_xpath(\".//table[@id='product']/tbody/tr[position()]\")\ntable1 = driver.find_elements_by_xpath(\".//table[@id='product']/tbody/tr[position()>2]\")\n\nselect = driver.find_elements_by_id(\"multiple-select-example\")\naction = ActionChains(driver)\n\nmytabel= []\n#for the get table we need to use for loop\nfor ele in table:\n print(ele.text)\n mytabel.append(ele.text)\nprint('*'*50)\nfor ele2 in table1:\n print(ele2.text)\nprint('*'*50)\nfor x in select:\n print(x.text)\n\n#click on apple web element by using action class\ntarget = driver.find_element_by_xpath(\".//option[text()='Apple']\")\naction.click(target).perform()\ntime.sleep(4)\n\n\ndriver.quit()","repo_name":"sunilsavale/practiceselenium","sub_path":"WebtableHandling.py","file_name":"WebtableHandling.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39339163233","text":"from discord.enums import ChannelType\n\nguilds = [859079215574810674]\nusers = [704845945325748354]\n\ndef check(ctx):\n if ctx.channel.type == ChannelType.private:\n return ctx.author.id in users\n\n return ctx.guild.id in guilds\n\nasync def error(ctx):\n await ctx.send(f'Sorry, command `{ctx.command.name}` is not available for now')\n\n\n","repo_name":"BayuDC/mofunami","sub_path":"utils/special_command.py","file_name":"special_command.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"8750183224","text":"\"\"\" TolTEC HWP Control Program - Logger\n ===================================\n \n This subtasks sets up the logging and opens a port for\n external logging messages. The message format for external\n messages is:\n LEVEL\\tSender\\tMessage\n The port that the logger is listening to is specified in\n the config file.\n\"\"\"\n\nimport logging\nimport socket\n\nclass LoggerControl():\n \"\"\" Logger receiver: sets up logging then opens a port\n for external log messages.\n \"\"\"\n \n def __init__(self, config, name=''):\n \"\"\" Constructor: Set up variables\n \"\"\"\n self.name = name\n self.config = config # configuration\n self.log = logging.getLogger('control.logger')\n \n def __call__(self):\n \"\"\" Object call: does the log thing\n \"\"\"\n ### Set up logging\n # Set root log level\n logging.getLogger().setLevel(logging.DEBUG)\n logformat = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n # Set stream logger\n shand = logging.StreamHandler()\n shand.setFormatter(logging.Formatter(logformat))\n shand.setLevel(self.config['logging']['level'])\n logging.getLogger().addHandler(shand)\n # Set up optional config file\n if len(self.config['logging']['logfile']):\n fhand = logging.FileHandler(self.config['logging']['logfile'])\n fhand.setFormatter(logging.Formatter(logformat))\n fhand.setLevel(logging.DEBUG)\n logging.getLogger().addHandler(fhand)\n # Test Message\n self.log.info('Logging is set up')\n ### Set up port server\n # Get log socket\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n port = int(self.config['logging']['port'])\n sock.bind(('localhost',port))\n sock.listen(5)\n # Endless loop getting messages\n while(True):\n # Get new connection\n conn, addr = sock.accept()\n #print('Conected with %s at address %s' % (addr[0],str(addr[1])))\n # Get the message\n reply = conn.recv(1024)\n #print(' Got message: %s' % reply)\n # Close the connection\n conn.close()\n ### Make log message\n # Split incoming message\n split = reply.decode().split('\\t')\n if len(split) < 2:\n lvl = 'INFO'\n lgr = self.config['logging']['deflogger']\n msg = split[0]\n elif len(split) < 3:\n lvl = split[0]\n lgr = self.config['logging']['deflogger']\n msg = split[1]\n else:\n lvl = split[0]\n lgr = split[1]\n msg = '\\t'.join(split[2:])\n # Get logging level\n log=logging.getLogger(lgr)\n if lvl.upper() in ['DEBUG','DEBUGGING']: log.debug(msg)\n elif lvl.upper() in ['INFO','INFORMATION']: log.info(msg)\n elif lvl.upper() in ['WARN','WARNING']: log.warn(msg)\n elif lvl.upper() in ['ERR','ERROR']: log.error(msg)\n elif lvl.upper() in ['CRIT','CRITICAL']: log.critical(msg)\n else: log.info(msg)\n\n\"\"\" Communicate to this via Telnet or the following simple program:\n\nimport socket\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.connect(('127.0.0.1', 50773))\nmessage = 'error\\tpipe.logtest.client\\tTest Message'\ns.sendall(message)\n# to receive also data=s.recv(bufsize) # bufsize can be 100 or so\ns.close()\n\n\"\"\"\n","repo_name":"toltec-astro/TolTECHWP","sub_path":"controller/loggercontrol.py","file_name":"loggercontrol.py","file_ext":"py","file_size_in_byte":3592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"11777477122","text":"# -*- coding: utf-8 -*-\n# __author__ = zok \n# Email = 362416272@qq.com\n# Date: 2019/3/9 Python: 3.7\n\nimport re\n\nfrom utils.utils import get_page\n\n\"\"\"\nProxyMetaClass为元类，实现了，自动获取并调用 方法名前缀为 crawl_的方法\n\"\"\"\n\n\nclass ProxyMetaClass(type):\n \"\"\"\n 元类\n 判断方法开头是否是crawl,是就将其加入__CrawlFunc__属性中\n \"\"\"\n def __new__(cls, name, bases, attrs):\n count = 0\n attrs['__CrawlFunc__'] = []\n for k, v in attrs.items():\n if 'crawl_' in k:\n attrs['__CrawlFunc__))'].append(k) # ���方法名添加到 属性中\n count += 1\n attrs['__CrawlFuncCount__'] = count # 方法数量\n return type.__new__(cls, name, bases, attrs)\n\n\nclass Crawler(object, metaclass=ProxyMetaClass):\n\n def get_proxies(self, callback):\n \"\"\"\n 获取代理\n :param callback:\n :return:\n \"\"\"\n proxies = []\n for proxy in eval(\"self.{}()\".format(callback)):\n print('成功获取到代理', proxy)\n proxies.append(proxy)\n return proxies\n\n def crawl_ip3366(self):\n for page in range(1, 4):\n start_url = 'http://www.ip3366.net/free/?stype=1&page={}'.format(page)\n html = get_page(start_url)\n ip_address = re.compile('<tr>\\s*<td>(.*?)</td>\\s*<td>(.*?)</td>')\n # \\s * 匹配空格，起到换行作用\n re_ip_address = ip_address.findall(html)\n for address, port in re_ip_address:\n result = address + ':' + port\n yield result.replace(' ', '')\n\n def crawl_kuaidaili(self):\n for i in range(1, 4):\n start_url = 'http://www.kuaidaili.com/free/inha/{}/'.format(i)\n html = get_page(start_url)\n if html:\n ip_address = re.compile('<td data-title=\"IP\">(.*?)</td>')\n re_ip_address = ip_address.findall(html)\n port = re.compile('<td data-title=\"PORT\">(.*?)</td>')\n re_port = port.findall(html)\n for address, port in zip(re_ip_address, re_port):\n address_port = address + ':' + port\n yield address_port.replace(' ', '')\n","repo_name":"wkunzhi/ProxyPool","sub_path":"get_proxy/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"2144328709","text":"import asyncio\nfrom typing import Callable, Any, Coroutine, Optional\nfrom models import TopicMessage\nfrom uuid import uuid4\n\nfrom simple_websocket_server import WebSocket\n\nTopicCallbackType = Callable[[str, Any], Coroutine]\n\nclass MultiThreadTopicSocketManager(WebSocket):\n def __init__(self, server, sock, address):\n super().__init__(server, sock, address)\n self.topic_callbacks = {}\n self.message_queue = {}\n self.on_disconnect_callbacks = []\n\n def add_topic_callback(self, topic: str, callback: TopicCallbackType):\n self.topic_callbacks[topic] = callback\n\n def remove_topic_callback(self, topic: str):\n del self.topic_callbacks[topic]\n\n def add_on_disconnect_callback(self, callback: Callable):\n self.on_disconnect_callbacks.append(callback)\n\n def handle_close(self):\n print('Socket closed', self.address)\n for callback in self.on_disconnect_callbacks:\n callback(self)\n\n def handle(self):\n if self.data == \"ping\":\n self.send_message(\"pong\")\n return\n # Then self.data is a topic message\n try:\n message = TopicMessage.parse_raw(self.data)\n # print('Have handlers for topics:', list(self.topic_callbacks.keys()))\n except Exception as e:\n print('Error when parsing topic message', e)\n return\n if message.topic in self.topic_callbacks:\n try:\n if message.topic not in self.message_queue:\n self.message_queue[message.topic] = []\n self.message_queue[message.topic].append(message.message)\n except Exception as e:\n print('Error when adding message to message queue', message.topic, message.message)\n print(e)\n\n async def handle_callbacks(self):\n \"\"\"\n Calls the callbacks foe every message in the message_queue\n \"\"\"\n messages = list(self.message_queue.items())\n for topic, messages in messages:\n # print(\"Handling\", len(messages), \"messages for topic\", topic)\n for message in messages:\n if topic in self.topic_callbacks:\n asyncio.ensure_future(self.topic_callbacks[topic](topic, message))\n # Remove the message from the queue\n self.message_queue[topic].remove(message)\n # If there are no more messages for the topic, remove the topic\n if len(self.message_queue[topic]) == 0:\n del self.message_queue[topic]\n\n def send(self, message: Any, topic: str):\n \"\"\"\n Sends a message as a topic\n \"\"\"\n self.send_message(TopicMessage(topic=topic, message=message).json())\n\n async def await_response_once(self, topic: str):\n \"\"\"\n Waits for a message with the topic and returns its message\n This converts the callback into a future to make it easier to use with await\n \"\"\"\n future = asyncio.Future()\n\n async def callback(_, message):\n future.set_result(message)\n\n self.add_topic_callback(topic, callback)\n res = await future\n self.remove_topic_callback(topic)\n return res\n\n async def send_and_await_response(self, message: Any, topic: Optional[str] = None) -> Any:\n \"\"\"\n Sends a message and waits for a response\n \"\"\"\n if topic is None:\n topic = str(uuid4())\n self.send(message, topic)\n return await self.await_response_once(topic)\n\n\n\nclass TopicSocketManager(WebSocket):\n def __init__(self, server, sock, address):\n super().__init__(server, sock, address)\n self.topic_callbacks = {}\n\n def add_topic_callback(self, topic: str, callback: TopicCallbackType):\n self.topic_callbacks[topic] = callback\n\n def remove_topic_callback(self, topic: str):\n del self.topic_callbacks[topic]\n\n def handle(self):\n if self.data == \"ping\":\n self.send_message(\"pong\")\n return\n # Then self.data is a topic message\n message = TopicMessage.parse_raw(self.data)\n print('Got topic message', message.topic, message.message)\n # print('Have handlers for topics:', list(self.topic_callbacks.keys()))\n if message.topic in self.topic_callbacks:\n try:\n asyncio.ensure_future(self.topic_callbacks[message.topic](message.topic, message.message))\n except Exception as e:\n print('Error in topic callback while handling topic message', message.topic, e)\n\n def connected(self):\n print(self.address, 'connected')\n\n def handle_close(self):\n print(self.address, 'closed')\n\n async def await_response_once(self, topic: str):\n \"\"\"\n Waits for a message with the topic and returns its message\n This converts the callback into a future to make it easier to use with await\n \"\"\"\n future = asyncio.Future()\n\n async def callback(_, message):\n future.set_result(message)\n\n self.add_topic_callback(topic, callback)\n res = await future\n self.remove_topic_callback(topic)\n return res\n\n async def send(self, message: Any, topic: str):\n \"\"\"\n Sends a message as a topic\n \"\"\"\n self.send_message(TopicMessage(topic=topic, message=message).json())\n\n async def send_and_await_response(self, message: Any, topic: Optional[str] = None) -> Any:\n \"\"\"\n Sends a message and waits for a response\n \"\"\"\n if topic is None:\n topic = str(uuid4())\n self.send_message(TopicMessage(topic=topic, message=message).json())\n return await self.await_response_once(topic)","repo_name":"Veldrovive/task-planning","sub_path":"server/socket_manager_v2.py","file_name":"socket_manager_v2.py","file_ext":"py","file_size_in_byte":5750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18089943927","text":"\"\"\" Patch for plone.app.contentrules\n\"\"\"\nfrom plone.contentrules.engine.interfaces import IRuleExecutor\nfrom plone.contentrules.engine.interfaces import IRuleStorage\nfrom plone.contentrules.engine.interfaces import StopRule\nfrom Products.CMFCore.interfaces import ISiteRoot\nfrom zope.component import queryUtility\nfrom Acquisition import aq_inner\nfrom Acquisition import aq_parent\nfrom plone.app.contentrules.handlers import init, _status\n\n\ndef execute(context, event):\n \"\"\" Execute all rules relative to the context, and bubble as appropriate.\n #134252 patch content rules engine to go beyond site folder.\n \"\"\"\n # Do nothing if there is no rule storage or it is not active\n storage = queryUtility(IRuleStorage)\n if storage is None or not storage.active:\n return\n init()\n\n rule_filter = _status.rule_filter\n\n # Stop if someone else is already executing. This could happen if,\n # for example, a rule triggered here caused another event to be fired.\n # We continue if we are in the context of a 'cascading' rule.\n\n if rule_filter.in_progress and not rule_filter.cascade:\n return\n\n # Tell other event handlers to be equally kind\n rule_filter.in_progress = True\n\n # Prepare to break hard if a rule demanded execution be stopped\n try:\n\n # Try to execute rules in the context. It may not work if the context\n # is not a rule executor, but we may still want to bubble events\n executor = IRuleExecutor(context, None)\n if executor is not None:\n executor(event, bubbled=False, rule_filter=rule_filter)\n\n # Do not bubble beyond the site root\n is_site = context.id == 'SITE'\n if not ISiteRoot.providedBy(context) or is_site:\n parent = aq_parent(aq_inner(context))\n while parent is not None:\n parent_is_site = parent.id == 'SITE'\n executor = IRuleExecutor(parent, None)\n if executor is not None:\n executor(event, bubbled=True, rule_filter=rule_filter)\n # 134252 set parent to none only if parent id != SITE\n if ISiteRoot.providedBy(parent) and not parent_is_site:\n parent = None\n else:\n parent = aq_parent(aq_inner(parent))\n\n except StopRule:\n pass\n\n # We are done - other events that occur after this one will be allowed to\n # execute rules again\n rule_filter.in_progress = False\n","repo_name":"eea/Products.EEAPloneAdmin","sub_path":"Products/EEAPloneAdmin/patches/patch_plone_app_contentrules_handlers.py","file_name":"patch_plone_app_contentrules_handlers.py","file_ext":"py","file_size_in_byte":2482,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"11905187385","text":"import matplotlib\r\nmatplotlib.use('TkAgg')\r\n\r\nimport random\r\nimport operator\r\nimport csv\r\nimport agentframework\r\nimport matplotlib.animation \r\nimport matplotlib.pyplot\r\nimport requests\r\nimport bs4\r\nimport tkinter\r\n\r\n\r\n\r\n\"\"\"Note to visualise the code in this file the code %matplotlib qt must be inputted in to the ipython console first. Or alternatively\r\nthe code can be ran in the command prompt\"\"\"\r\n\r\n\"\"\"https://www.youtube.com/watch?v=8exB6Ly3nx0 this excellent resource had info on combining GUI with matplotlib data\"\"\"\r\n\r\nenvironment = []\r\nagents = []\r\nnum_of_agents = 10\r\nnum_of_iterations = 100\r\nneighbourhood = 20\r\n\r\n\r\nfig = matplotlib.pyplot.figure(figsize=(7, 7))\r\nax = fig.add_axes([0, 0, 1, 1])\r\n\r\ncarry_on = True\r\n\r\ndef run():\r\n animation = matplotlib.animation.FuncAnimation(fig, update, frames=gen_function, repeat=False)\r\n canvas.draw() #show did not work see here for solution https://stackoverflow.com/questions/50165115/unable-to-call-canvas-show\r\n \r\n\r\n\r\n \r\n \r\nroot = tkinter.Tk()\r\nroot.wm_title(\"Model\")\r\n#Open window having dimension 700x700\r\nroot.geometry('700x700')\r\nmenu_bar = tkinter.Menu(root)\r\nroot.config(menu=menu_bar)\r\nroot.configure(background=\"grey\")\r\n\r\n\r\nmy_button = tkinter.Button(root, text=\"Run model\", command=run, bg='blue')#https://pythonexamples.org/python-tkinter-button-background-color/#:~:text=You%20can%20change%20the%20background,bg%20property%20as%20shown%20below.&text=The%20default%20color%20of%20Tkinter%20Button%20is%20grey.\r\nmy_button.pack(side=tkinter.TOP)#https://www.youtube.com/watch?v=Uk2FivOD8qo got idea from here\r\n\r\n\r\ncanvas = matplotlib.backends.backend_tkagg.FigureCanvasTkAgg(fig, master=root)\r\ncanvas._tkcanvas.pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)\r\n\r\n\r\n\r\nf = open('in.csv', newline='')\r\n#Note that the correct directory must be navigated to in the terminal else the full file path will be needed\r\nreader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)\r\nfor row in reader: # A list of rows\r\n rowlist = []\r\n for value in row: # A list of value\r\n rowlist.append(value)\r\n environment.append(rowlist)\r\n \r\n # Make the agents.\r\nfor i in range(num_of_agents):\r\n agents.append(agentframework.agent(environment, agents)) \r\n \r\ndef update(frame_number):\r\n \r\n fig.clear() \r\n global carry_on\r\n \r\n \r\n \r\n # Move the agents.\r\n for j in range(num_of_iterations):\r\n for i in range(num_of_agents):\r\n agents[i].move()\r\n agents[i].eat()\r\n agents[i].share_with_neighbours(neighbourhood)\r\n \r\n \r\n \r\n #show agents \r\n matplotlib.pyplot.xlim(0, 100)\r\n matplotlib.pyplot.ylim(0, 100)\r\n matplotlib.pyplot.imshow(environment)\r\n \r\n \r\n for i in range(num_of_agents):\r\n matplotlib.pyplot.scatter(agents[i]._x, agents[i]._y)\r\n\r\n\r\n\"\"\"In the gen_function function we are assigning the number of iterations the animation will go through. a=0\r\nmeans starting at 0 and carry_on while a =<10 leads to 10 iterations being called as the anmation will carry\r\non\"\"\"\r\ndef gen_function(b = [0]):\r\n store = 0\r\n global carry_on #Not actually needed as we're not assigning, but clearer\r\n while (store < 10) & (carry_on) :\r\n yield store\t\t\t# Returns control and waits next call.\r\n store = store + 1\r\n\r\n#Extras!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\r\n\"\"\"below is an attempt to carry on with the animation until store reaches 10\"\"\"\r\n\r\n\r\n \r\ntkinter.mainloop()\r\n\r\n","repo_name":"jord9762/Leeds_uni_core_programming_skills_tasks","sub_path":"Final_model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3472,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"43157346625","text":"from aima.core.logic.common import TermVisitor, TokenTypes, ImplicationTerm, AndTerm, NotTerm, OrTerm\n\n__author__ = 'Ivan Mushketik'\n__docformat__ = 'restructuredtext en'\n\nclass SymbolsCollector(TermVisitor):\n def __init__(self):\n self.symbols = set()\n\n def clear(self):\n self.symbols = set()\n\n def collect_symbols(self, root_term):\n self.clear()\n root_term.accept_visitor(self)\n\n return self.symbols\n\n def visit_symbol_term(self, term):\n self.symbols.add(term.name)\n\n\nclass Model(TermVisitor):\n def __init__(self):\n self.calculation_result = {}\n self.symbols_table = {}\n\n def clear(self):\n self.calculation_result = {}\n self.symbols_table = {}\n\n def extend(self, symbol, value):\n m = Model()\n m.symbols_table = self.symbols_table.copy()\n m.symbols_table[symbol] = value\n\n return m\n\n def is_true(self, root_term):\n self.calculation_result = {}\n root_term.accept_visitor(self)\n\n return self.calculation_result.get(root_term) == True\n\n def visit_false_term(self, term):\n self.calculation_result[term] = False\n\n def visit_true_term(self, term):\n self.calculation_result[term] = True\n\n def visit_symbol_term(self, term):\n self.calculation_result[term] = self.symbols_table.get(term.name)\n\n def visit_function_term(self, term):\n v1 = self.calculation_result[term.children[0]]\n if v1 == None:\n self.calculation_result[term] = None\n elif term.type == TokenTypes.NOT:\n self.calculation_result[term] = not self.calculation_result[term.children[0]]\n else:\n\n v2 = self.calculation_result[term.children[1]]\n if v2 == None:\n self.calculation_result[term] = None\n if term.type == TokenTypes.AND:\n self.calculation_result[term] = v1 and v2\n\n elif term.type == TokenTypes.OR:\n self.calculation_result[term] = v1 or v2\n\n elif term.type == TokenTypes.IMPLICATION:\n self.calculation_result[term] = (not v1) or v2\n\n elif term.type == TokenTypes.BICONDITIONAL:\n self.calculation_result[term] = ((not v1) or v2) and ((not v2) or v1)\n\n def get_assigned_symbols(self):\n return self.symbols_table.keys()\n\n def flip(self, symbol):\n self.symbols_table[symbol] = not self.symbols_table[symbol]\n\n def __str__(self):\n return \"Model: \" + str(self.symbols_table)\n\nclass CNFTransformer:\n \"\"\"\n Transformer that transformer any expression in propositional logic into CNF expression\n \"\"\"\n def transform(self, root_term):\n \"\"\"\n Transform expression presented by root term\n\n :param root_term (Term): root term of the expression\n :return (Term): root term of transformed expression\n \"\"\"\n return self._transform_term(root_term)\n\n def _transform_term(self, term):\n if term.type == TokenTypes.NOT:\n return self._transform_not_expression(term)\n elif term.type == TokenTypes.BICONDITIONAL:\n return self._transform_biconditional_expression(term)\n elif term.type == TokenTypes.IMPLICATION:\n return self._transform_implication(term)\n elif term.type == TokenTypes.OR:\n return self._transform_or(term)\n elif term.type == TokenTypes.AND:\n return AndTerm(self._transform_term(term.children[0]),\n self._transform_term(term.children[1]))\n else:\n return term\n\n def _transform_not_expression(self, term):\n not_child = term.children[0]\n # Check if we can remove double NOT\n if not_child.type == TokenTypes.NOT:\n return self.transform(not_child.children[0])\n # De Morgan rule NOT (A OR B) == (NOT A) AND (NOT B)\n elif not_child.type == TokenTypes.OR:\n return AndTerm(self.transform(NotTerm(not_child.children[0])),\n self.transform(NotTerm(not_child.children[1])))\n # De Morgan rule NOT (A AND B) == (NOT A) OR (NOT B)\n elif not_child.type == TokenTypes.AND:\n return self.transform(OrTerm(self.transform(NotTerm(not_child.children[0])),\n self.transform(NotTerm(not_child.children[1]))))\n\n # No rules, just return NOT expression\n return term\n\n def _transform_or(self, term):\n left_child = term.children[0]\n right_child = term.children[1]\n\n # If we have A OR (B AND C)\n if not left_child.is_function() and right_child.type == TokenTypes.AND:\n and_expression = right_child\n symbol = left_child\n # If we have (B AND C) OR A\n elif not right_child.is_function() and left_child.type == TokenTypes.AND:\n and_expression = left_child\n symbol = right_child\n else:\n # Just return OR term\n return term\n\n and_left_child = and_expression.children[0]\n and_right_child = and_expression.children[1]\n\n # OR distribution; A OR (B AND C) == (A OR B) AND (A OR C)\n return AndTerm(self.transform(OrTerm(and_left_child, symbol)),\n self.transform(OrTerm(and_right_child, symbol)))\n\n\n def _transform_implication(self, term):\n first_child = NotTerm(term.children[0])\n second_child = term.children[1]\n\n # Implication transformation; A => B == (NOT A) OR B\n return self.transform(OrTerm(self.transform(first_child), second_child))\n\n def _transform_biconditional_expression(self, term):\n first_implication = ImplicationTerm(self.transform(term.children[0]),\n self.transform(term.children[1]))\n\n second_implication = ImplicationTerm(self.transform(term.children[1]),\n self.transform(term.children[0]))\n\n # Biconditional term transformation; A <=> B == (A => B) AND (B => A)\n return AndTerm(self.transform(first_implication), self.transform(second_implication))\n\n\nclass CNFClauseGatherer(TermVisitor):\n \"\"\"\n Select set of expressions separated by AND in CNF\n \"\"\"\n def __init__(self):\n self.clauses = set()\n\n def collect(self, root_term):\n # If we have expressions like A OR B, D, NOT C; just return the root term\n if root_term.type != TokenTypes.AND:\n return {root_term}\n else:\n self.clauses = set()\n root_term.accept_visitor(self)\n\n return self.clauses\n\n def visit_function_term(self, term):\n if term.type == TokenTypes.AND:\n left_child = term.children[0]\n right_child = term.children[1]\n\n if left_child.type != TokenTypes.AND:\n self.clauses.add(left_child)\n if right_child.type != TokenTypes.AND:\n self.clauses.add(right_child)\n\nclass CNFOrGatherer(TermVisitor):\n \"\"\"\n Gather all symbols in CNF clause.\n \"\"\"\n def __init__(self):\n self.symbols = set()\n self.not_symbols = set()\n\n def collect(self, root_term):\n \"\"\"\n Collect all symbols in CNF clause. If we have A OR B OR NOT C, this will return {A, B}, {C}.\n\n :param root_term: root term of the CNF clause\n :return set(Term), set(Term): pair of set of terms, where fist element of pair is symbols without NOT, and second\n set is a set of inverted symbols\n \"\"\"\n if root_term.type == TokenTypes.IDENTIFIER:\n return {root_term}, set()\n\n self.symbols = set()\n self.not_symbols = set()\n\n root_term.accept_visitor(self)\n\n return self.symbols, self.not_symbols\n\n def visit_function_term(self, term):\n if term.type == TokenTypes.OR:\n left_child = term.children[0]\n right_child = term.children[1]\n\n self._add_child(left_child)\n self._add_child(right_child)\n elif term.type == TokenTypes.NOT:\n self._add_child(term)\n\n def _add_child(self, term):\n if term.type == TokenTypes.NOT:\n self.not_symbols.add(term.children[0])\n elif term.type == TokenTypes.IDENTIFIER:\n self.symbols.add(term)\n\n\n\n \n\n \n ","repo_name":"mushketyk/aima-python","sub_path":"aima/core/logic/propositional/visitors.py","file_name":"visitors.py","file_ext":"py","file_size_in_byte":8334,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"31"} +{"seq_id":"1551337041","text":"import os\nimport sys\n\nmode = \"gradient\"\nstart_T=2\nend_T=2.6\nT_steps=30\n\n#Ls=[40,60,80,100]\nLs=[40]\nmcs=5000\nstart_sampling=mcs/5\nrandom_start = 1 #1 is true, else is false\n\nfor L in Ls:\n args = mode+\" \"+str(L)+\" \"+str(mcs)+\" \"+str(start_sampling)+\" \"+str(random_start)+\" \"+str(start_T)+\" \"+str(end_T)+\" \"+str(T_steps)\n os.system(\"./main.out\" + \" \" +args)","repo_name":"GauteHolen/Fys3150_MyCode","sub_path":"Projects/Project4/run_gradient.py","file_name":"run_gradient.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34573933059","text":"import os\nimport uuid\nimport pytest\nimport flask\n\nfrom tools import readFile\nfrom tools import writeFile\n\nfrom tools import causeOSError\nfrom tools import causePermissionError\nfrom tools import causeSameFileError\nfrom tools import causeShUtilError\n\nfrom dmci.api import App\n\nMOCK_XML = b\"<xml />\"\nMOCK_XML_MOD = b\"<xml mod />\"\n\n\n@pytest.fixture(scope=\"function\")\ndef client(tmpDir, tmpConf, mockXsd, monkeypatch):\n \"\"\"Create an instance of the API.\"\"\"\n workDir = os.path.join(tmpDir, \"api\")\n rejectDir = os.path.join(tmpDir, \"api\", \"rejected\")\n if not os.path.isdir(workDir):\n os.mkdir(workDir)\n\n monkeypatch.setattr(\"dmci.CONFIG\", tmpConf)\n tmpConf.distributor_cache = workDir\n tmpConf.rejected_jobs_path = rejectDir\n tmpConf.mmd_xsd_path = mockXsd\n tmpConf.path_to_parent_list = mockXsd\n\n app = App()\n assert app._conf.distributor_cache == workDir\n\n with app.test_client() as client:\n yield client\n\n return\n\n\n@pytest.mark.api\ndef testApiApp_Init(tmpConf, tmpDir, monkeypatch):\n \"\"\"Test if app fails if distributor_cache and mmd_xsd_path are not\n given in the config.\n \"\"\"\n monkeypatch.setattr(\"dmci.CONFIG\", tmpConf)\n\n tmpConf.distributor_cache = None\n tmpConf.mmd_xsd_path = None\n with pytest.raises(SystemExit) as sysExit:\n App()\n\n assert sysExit.type == SystemExit\n assert sysExit.value.code == 1\n\n tmpConf.distributor_cache = tmpDir\n tmpConf.mmd_xsd_path = None\n with pytest.raises(SystemExit) as sysExit:\n App()\n assert sysExit.type == SystemExit\n assert sysExit.value.code == 1\n\n # Check Invalid XML Schema\n failXsd = os.path.join(tmpDir, \"app_invalid.xsd\")\n writeFile(failXsd, \"blablabla\")\n tmpConf.mmd_xsd_path = failXsd\n with pytest.raises(SystemExit) as sysExit:\n App()\n\n# END Test testApiApp_Init\n\n\n@pytest.mark.api\ndef testApiApp_EndPoints(client):\n \"\"\"Test requests to endpoints not in use.\"\"\"\n assert client.get(\"/\").status_code == 404\n assert client.get(\"/v1/\").status_code == 404\n\n # Get method is not allowed\n assert client.get(\"/v1/insert\").status_code == 405\n assert client.get(\"/v1/update\").status_code == 405\n assert client.get(\"/v1/validate\").status_code == 405\n\n # Bare delete command is not allowed\n assert client.post(\"/v1/delete\").status_code == 404\n\n# END Test testApiApp_EndPoints\n\n\nclass MockException:\n def __init__(self, *args, **kwargs):\n raise Exception\n\n\n@pytest.mark.api\ndef testApiApp_EndPoints_Exception(tmpDir, tmpConf, mockXsd, monkeypatch):\n \"\"\"Test requests to endpoints Exception.\"\"\"\n workDir = os.path.join(tmpDir, \"api\")\n rejectDir = os.path.join(tmpDir, \"api\", \"rejected\")\n if not os.path.isdir(workDir):\n os.mkdir(workDir)\n if not os.path.isdir(rejectDir):\n os.mkdir(rejectDir)\n monkeypatch.setattr(\"dmci.CONFIG\", tmpConf)\n\n tmpConf.distributor_cache = workDir\n tmpConf.rejected_jobs_path = rejectDir\n tmpConf.mmd_xsd_path = mockXsd\n tmpConf.path_to_parent_list = mockXsd\n monkeypatch.setattr(\"lxml.etree.XMLSchema\", MockException)\n with pytest.raises(SystemExit):\n App()\n\n# END Test testApiApp_EndPoints_Exception\n\n\n@pytest.mark.api\ndef testApiApp_InsertUpdateRequests(client, monkeypatch):\n \"\"\"Test api insert and update requests.\"\"\"\n assert isinstance(client, flask.testing.FlaskClient)\n\n # Test sending 3MB of data\n tooLargeFile = bytes(3000000)\n assert client.post(\"/v1/insert\", data=tooLargeFile).status_code == 413\n assert client.post(\"/v1/update\", data=tooLargeFile).status_code == 413\n\n # Test sending 0B of data\n noFile = bytes(0)\n assert client.post(\"/v1/insert\", data=noFile).status_code == 202\n assert client.post(\"/v1/update\", data=noFile).status_code == 202\n\n # Fail caching the file\n with monkeypatch.context() as mp:\n mp.setattr(\"builtins.open\", causeOSError)\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 507\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 507\n\n # Data is valid\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML))\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 200\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 200\n\n # Data is valid and gets modified by validate\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML_MOD))\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 200\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 200\n\n # first _persist_file fails\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML))\n mp.setattr(\"dmci.api.app.App._persist_file\", lambda *a: (\"Failed to write the file\", 666))\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 666\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 666\n\n # first _persist_file works\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML))\n mp.setattr(\"dmci.api.app.App._persist_file\", lambda *a: (\"Everything is OK\", 200))\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 200\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 200\n\n # Data is not valid\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (False, \"\", MOCK_XML))\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 400\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 400\n\n # Data is valid, distribute fails\n with monkeypatch.context() as mp:\n f = [\"A\", \"B\"]\n s = [\"C\"]\n e = [\"Reason A\", \"Reason B\"]\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML))\n mp.setattr(\"dmci.api.app.Worker.distribute\", lambda *a: (False, False, [], f, s, e))\n\n response = client.post(\"/v1/insert\", data=MOCK_XML)\n assert response.status_code == 500\n assert response.data == (\n b\"The following distributors failed: A, B\\n\"\n b\" - A: Reason A\\n\"\n b\" - B: Reason B\\n\"\n b\"The following jobs were skipped: C\\n\"\n )\n\n response = client.post(\"/v1/update\", data=MOCK_XML)\n assert response.status_code == 500\n assert response.data == (\n b\"The following distributors failed: A, B\\n\"\n b\" - A: Reason A\\n\"\n b\" - B: Reason B\\n\"\n b\"The following jobs were skipped: C\\n\"\n )\n\n# END Test testApiApp_InsertRequests\n\n\n@pytest.mark.api\ndef testApiApp_PersistAgainAfterModification(client, monkeypatch):\n\n outputs = iter([(\"Everything is OK\", 200), (\"Failure in persisting\", 666),\n (\"Everything is OK\", 200), (\"Failure in persisting\", 666)])\n\n @staticmethod\n def fake_output(data, full_path):\n return next(outputs)\n\n with monkeypatch.context() as mp:\n # Data is valid but failure to persist again after modifications\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML_MOD))\n mp.setattr(\"dmci.api.app.App._persist_file\", fake_output)\n assert client.post(\"/v1/insert\", data=MOCK_XML).status_code == 666\n assert client.post(\"/v1/update\", data=MOCK_XML).status_code == 666\n\n# END Test testApiApp_PersistAgainAfterModification\n\n\n@pytest.mark.api\ndef testApiApp_DeleteRequests(client, monkeypatch):\n \"\"\"Test api delete request.\"\"\"\n assert isinstance(client, flask.testing.FlaskClient)\n testUUID = \"test:7278888a-96a5-4ee5-845a-2051bb8994c8\"\n\n # Invalid UUID\n assert client.post(\"/v1/delete/blabla\").status_code == 400\n # Valid UUID, but no namespace\n assert client.post(\"/v1/delete/7278888a96a54ee5845a2051bb8994c8\").status_code == 400\n\n # Distribute fails\n with monkeypatch.context() as mp:\n f = [\"A\", \"B\"]\n s = [\"C\"]\n e = [\"Reason A\", \"Reason B\"]\n mp.setattr(\"dmci.api.app.Worker.distribute\", lambda *a: (False, False, [], f, s, e))\n\n response = client.post(\"/v1/delete/%s\" % testUUID, data=MOCK_XML)\n assert response.status_code == 500\n assert response.data == (\n b\"The following distributors failed: A, B\\n\"\n b\" - A: Reason A\\n\"\n b\" - B: Reason B\\n\"\n b\"The following jobs were skipped: C\\n\"\n )\n\n # Distribute ok\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.distribute\", lambda *a: (True, True, [], [], [], []))\n response = client.post(\"/v1/delete/%s\" % testUUID, data=MOCK_XML)\n assert response.status_code == 200\n assert response.data == b\"Everything is OK\\n\"\n\n# END Test testApiApp_DeleteRequests\n\n\n@pytest.mark.api\ndef testApiApp_ValidateRequests(client, monkeypatch):\n \"\"\"Test api validate request.\"\"\"\n assert isinstance(client, flask.testing.FlaskClient)\n\n # Test sending 3MB of data\n tooLargeFile = bytes(3000000)\n assert client.post(\"/v1/validate\", data=tooLargeFile).status_code == 413\n\n # Fail caching the file\n with monkeypatch.context() as mp:\n mp.setattr(\"builtins.open\", causeOSError)\n assert client.post(\"/v1/validate\", data=MOCK_XML).status_code == 507\n\n # Data is valid\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (True, \"\", MOCK_XML))\n assert client.post(\"/v1/validate\", data=MOCK_XML).status_code == 200\n\n # Data is not valid\n with monkeypatch.context() as mp:\n mp.setattr(\"dmci.api.app.Worker.validate\", lambda *a: (False, \"\", MOCK_XML))\n assert client.post(\"/v1/validate\", data=MOCK_XML).status_code == 400\n\n# END Test testApiApp_ValidateRequests\n\n\n@pytest.mark.api\ndef testApiApp_PersistFile(tmpDir, monkeypatch):\n \"\"\"Test the persistent file writer function.\"\"\"\n assert App._persist_file(MOCK_XML, None)[1] == 507\n\n outFile = os.path.join(tmpDir, \"app_persist_file.xml\")\n\n with monkeypatch.context() as mp:\n mp.setattr(\"builtins.open\", causeOSError)\n assert App._persist_file(MOCK_XML, outFile)[1] == 507\n assert not os.path.isfile(outFile)\n\n assert App._persist_file(MOCK_XML, outFile)[1] == 200\n assert os.path.isfile(outFile)\n\n# END Test testApiApp_PersistFile\n\n\n@pytest.mark.api\ndef testApiApp_CheckMetadataId():\n testUUID = \"7278888a-96a5-4ee5-845a-2051bb8994c8\"\n correct_UUID = uuid.UUID(testUUID)\n\n # Correct with namespace\n assert App._check_metadata_id(\"test:\"+testUUID) == (\"test\", correct_UUID, None)\n\n # Without namespace\n assert App._check_metadata_id(testUUID) == (None, None,\n \"Input must be structured as <namespace>:<uuid>.\")\n # With namespace, but not in accordance with the defined env_string\n assert App._check_metadata_id(\"test:\"+testUUID, env_string=\"TEST\") == (None, None,\n \"Dataset metadata_id \"\n \"namespace is wrong: \"\n \"test\")\n\n # With namespace, defined env_string, but present in call\n assert App._check_metadata_id(\"test.TEST:\"+testUUID, env_string=\"TEST\") == (\"test.TEST\",\n correct_UUID,\n None)\n\n # Test with namespace, but malformed UUID\n out = App._check_metadata_id(\"test:blabla\")\n assert out[0] is None\n assert out[1] is None\n assert out[2] == \"Cannot convert to UUID: blabla\"\n\n out = App._check_metadata_id(\"test:wrong:7278888a96a54ee5845a2051bb8994c8\")\n assert out[0] is None\n assert out[1] is None\n assert out[2] == \"Input must be structured as <namespace>:<uuid>.\"\n\n\n@pytest.mark.api\ndef testApiApp_HandlePersistFile(caplog, fncDir, monkeypatch):\n \"\"\"Test the persistent file handler function.\"\"\"\n rejectDir = os.path.join(fncDir, \"rejected\")\n testFile = os.path.join(fncDir, \"test.xml\")\n rejectFile = os.path.join(rejectDir, \"test.xml\")\n errorFile = os.path.join(rejectDir, \"test.txt\")\n\n os.mkdir(rejectDir)\n assert os.path.isdir(rejectDir)\n\n # Status OK\n # =========\n\n # Invalid full_path\n caplog.clear()\n assert App._handle_persist_file(True, None) is False\n assert \"Failed to unlink processed file\" in caplog.text\n\n # Valid full path, and delete\n writeFile(testFile, \"<xml />\")\n assert os.path.isfile(testFile)\n assert App._handle_persist_file(True, testFile) is True\n assert not os.path.isfile(testFile)\n\n # Status NOK\n # ==========\n\n # Fail move to rejected samefile error\n writeFile(testFile, \"<xml />\")\n assert os.path.isfile(testFile)\n caplog.clear()\n with monkeypatch.context() as mp:\n mp.setattr(\"shutil.copy\", causeSameFileError)\n assert App._handle_persist_file(False, testFile, testFile, \"Error\") is False\n assert \"Source and destination represents the same file\" in caplog.text\n\n # Fail move to rejected permission error\n writeFile(testFile, \"<xml />\")\n assert os.path.isfile(testFile)\n caplog.clear()\n with monkeypatch.context() as mp:\n mp.setattr(\"shutil.copy\", causePermissionError)\n assert App._handle_persist_file(False, testFile, rejectFile, \"Error\") is False\n assert \"Permission denied\" in caplog.text\n\n # Fail move to rejected generic error\n writeFile(testFile, \"<xml />\")\n assert os.path.isfile(testFile)\n caplog.clear()\n with monkeypatch.context() as mp:\n mp.setattr(\"shutil.copy\", causeShUtilError)\n assert App._handle_persist_file(False, testFile, rejectFile, \"Error\") is False\n assert \"Something failed moving the rejected file\" in caplog.text\n\n # Successful move to rejected\n writeFile(testFile, \"<xml />\")\n assert os.path.isfile(testFile)\n assert App._handle_persist_file(False, testFile, rejectFile, \"Error\") is True\n assert not os.path.isfile(testFile)\n assert os.path.isfile(rejectFile)\n assert os.path.isfile(errorFile)\n assert readFile(errorFile) == \"Error\"\n\n\n@pytest.mark.api\ndef testApiApp_HandlePersistFile_fail2write_reason(caplog, fncDir, monkeypatch):\n \"\"\" Test that _handle_persist_file catches the error if it fails\n to open the reject file (that should provide the reason for\n failing to write the persist file to the rejected folder).\n \"\"\"\n import builtins\n\n # Fail writing error file\n rejectDir = os.path.join(fncDir, \"rejected\")\n full_path = os.path.join(fncDir, \"test.xml\")\n reject_path = os.path.join(rejectDir, \"test.xml\")\n\n os.mkdir(rejectDir)\n assert os.path.isdir(rejectDir)\n\n writeFile(full_path, \"<xml />\")\n assert os.path.isfile(full_path)\n caplog.clear()\n\n original_open = builtins.open\n\n def patched_open(*args, **kwargs):\n if \"rejected/test.xml\" in args[0]:\n mp.setattr(\"builtins.open\", causeOSError)\n\n return original_open(*args, **kwargs)\n\n with monkeypatch.context() as mp:\n mp.setattr(\"builtins.open\", patched_open)\n assert App._handle_persist_file(False, full_path, reject_path, \"Some reason\") is False\n assert \"Failed to write rejected reason to file\" in caplog.text\n\n# END Test testApiApp_HandlePersistFile\n","repo_name":"metno/discovery-metadata-catalog-ingestor","sub_path":"tests/test_api/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":15566,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"35731163076","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ConvBN2d(nn.Module):\n def __init__(self,\n inChannel,\n outChannel,\n kernelSize=3,\n stride=1,\n padding=0,\n dilation = 1,\n **kwargs):\n super(ConvBN2d, self).__init__()\n\n self.conv = nn.Conv2d(\n in_channels=inChannel,\n out_channels=outChannel,\n kernel_size=kernelSize,\n stride=stride,\n padding=padding,\n dilation=dilation,\n **kwargs)\n \n self.bn = nn.BatchNorm2d(num_features=outChannel)\n \n def forward(self, x):\n x = self.conv(x)\n x = self.bn(x)\n \n return x","repo_name":"Sazareame/sazaretorch","sub_path":"model/component/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31928444660","text":"import pip\r\nfrom Description import requrements,allImports\r\n\r\nclass setup:\r\n def __init__(self):\r\n self.requrements=requrements\r\n try:\r\n allImports()\r\n except:\r\n self.Install()\r\n \r\n def Installing(self,package):\r\n if hasattr(pip, 'main'):\r\n pip.main(['install', package])\r\n else:\r\n pip._internal.main(['install', package])\r\n\r\n def Install(self):\r\n for i in self.requrements:\r\n self.Installing(i)\r\n return True\r\n\r\n","repo_name":"Bisheshsingh/AI-ML","sub_path":"MNIST recognition/Setup.py","file_name":"Setup.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6609231141","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n # http://localhost:8000/checkout\n path('', views.checkout_payment, name=\"checkout-payment\"),\n path('confirm', views.confirm, name=\"confirm\"),\n path('save', views.save, name='save'),\n path('', views.index, name=\"checkout-index\"),\n]","repo_name":"RanAegisdottir/FireSale","sub_path":"FireSale/checkout/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11765818489","text":"from kivy.app import App\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy.uix.label import Label\nfrom kivy.uix.image import Image\nfrom kivy.uix.button import Button\nfrom kivy.uix.textinput import TextInput\n\n\nclass PatientPoseApp(App):\n def build(self):\n self.window = GridLayout()\n self.window.cols = 1\n\n # Create text fields grid layout\n self.top_grid = GridLayout(row_force_default=True,\n row_default_height=50,\n )\n self.top_grid.cols = 2\n\n # Create buttons grid layout\n self.button_grid = GridLayout()\n self.button_grid.cols = 3\n\n # Set window size\n self.window.size_hint = (0.9, 0.9)\n self.window.pos_hint = {\"center_x\": 0.5, \"center_y\": 0.5}\n\n # Set top grid size\n self.top_grid.size_hint = (0.9, 0.9)\n self.top_grid.pos_hint = {\"center_x\": 0.5, \"center_y\": 0.5}\n \n # Set button grid size\n self.button_grid.size_hint = (0.9, 0.9)\n self.button_grid.pos_hint = {\"center_x\": 0.5, \"center_y\": 0.5}\n\n # Add widgets to window\n\n # Logo widget\n self.window.add_widget(Image(source=\"./resources/logo.png\",\n color = (1,1,1,1)))\n\n # Data path widgets\n self.train_label = Label(text=\"Train folder: \",\n font_size=20,\n color='#00FFCE',\n size_hint_x = None,\n width = 250\n )\n self.test_label = Label(text=\"Test folder: \",\n font_size=20,\n color='#00FFCE',\n size_hint_x = None,\n width = 250\n )\n\n # Text input widgets\n self.train_path = TextInput(multiline=False,\n font_size=18,\n padding_y = (15, 10)\n # size_hint = (1, None),\n # height=50,\n #width=300\n )\n self.test_path = TextInput(multiline=False,\n font_size=18,\n padding_y = (15, 10)\n # size_hint = (1, None),\n # height=50,\n #width=300\n )\n\n self.top_grid.add_widget(self.train_label)\n self.top_grid.add_widget(self.train_path)\n self.top_grid.add_widget(self.test_label)\n self.top_grid.add_widget(self.test_path)\n\n # Button widgets\n self.train_button = Button(text=\"TRAIN MODEL\",\n font_size=18,\n size_hint_y = None, # important to set this\n height=50,\n )\n self.test_button = Button(text=\"TEST MODEL\",\n font_size=18,\n size_hint_y = None,\n height=50,\n )\n self.export_button = Button(text=\"EXPORT MODEL\",\n font_size=18,\n size_hint_y = None,\n height=50,\n )\n self.button_grid.add_widget(self.train_button)\n self.button_grid.add_widget(self.test_button)\n self.button_grid.add_widget(self.export_button)\n\n\n # Add secondary grids to the main window grid\n self.window.add_widget(self.top_grid)\n self.window.add_widget(self.button_grid)\n\n return self.window\n\nif __name__ == \"__main__\":\n PatientPoseApp().run()","repo_name":"pytholic/Patient-Pose-App","sub_path":"Implementation/app_basic.py","file_name":"app_basic.py","file_ext":"py","file_size_in_byte":3984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72891852568","text":"import logging\nimport requests\nimport pandas as pd\nimport streamlit as st\n\n\nlogging.getLogger(\"streamlit_searchbox\").setLevel(logging.DEBUG)\ndata = pd.read_csv('supermarket_train.csv', delimiter=';')\ndevice_ids = data['device_id'].unique()\nfiltered = data.loc[data['device_id'] == 352398080391651]\nnames = filtered['name'].unique()\nnameToItemId = dict(zip(data.name, data.item_id))\nitemIdToName = dict(zip(data.item_id, data.name))\nst.set_page_config(page_title='Формирование чека', layout=\"wide\", initial_sidebar_state=\"auto\", page_icon=\"📖\")\ntitle_ = st.empty()\ntitle_.title('Универсальная рекомендательная система. Команда Link Bizkit')\nuploaded_file = st.file_uploader(\"Загрузите тренировочный датасет:\")\nif uploaded_file is not None:\n df = pd.read_csv(uploaded_file, sep='\\t')\n requests.post('https://a20391-b090.s.d-f.pw/train_model', json={\"dataset\":df.to_json( orient=\"index\")})\n\n\n\ndef filter_names():\n print(\"change names\")\n state.NAMES = data.loc[data['device_id'] == device_id]['name'].unique()\n print(len(data.loc[data['device_id'] == device_id]['name'].unique()))\n\n\ncol1, col2, = st.columns(2)\n#\nwith col1:\n device_id = st.selectbox(\n 'Идентификатор кассы',\n device_ids,\n on_change = filter_names)\n col1_2 = st.empty()\n\n\n\n\n\nst.header = \"Поиск по документации\"\nstate = st.session_state\n\n\n\nif 'ITEMS' not in state:\n state.ITEMS = []\n state.NAMES = data.loc[data['device_id'] == device_id]['name'].unique()\n\n\nst.markdown(\"---\")\n\ndef _set_items():\n state.ITEMS = state.item_choice\n items =[]\n for item in state.item_choice:\n items.append(nameToItemId[item])\n print(items)\n print(state.item_choice)\n print(device_id)\n response = requests.post(\n \"https://a20391-b090.s.d-f.pw/recommendation\",\n timeout=15,\n json={\"items\": items, \"device_id\": int(device_id)}\n ).json()\n\n print(response)\n\n with col2:\n st.subheader(itemIdToName[response['items'][0]])\n\n\ncol1_2.multiselect('Выберите позиции', options=state.NAMES, default=[], on_change=_set_items, key='item_choice')\n\ndef main():\n st.title = \"Формирование чека\"\n\nif __name__ == '__main__':\n main()","repo_name":"epluzhnik/rec-receipt","sub_path":"front.py","file_name":"front.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1457649720","text":"class Settings():\n # Armazena todas as configurações do jogo\n \n def __init__(self):\n # Inicializa as configuraç��es\n self.screen_width = 1366\n self.screen_height = 768\n self.screen_background = (30,30,30)\n \n # Infos da nave\n self.ship_limit = 3\n \n # Infos da bala\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = 250,250,250\n self.bullets_allowed = 3\n \n # Infos alien\n self.fleet_drop_speed = 10\n \n \n self.speedup_scale = 1.1\n self.score_scale = 1.5\n \n self.initialize_dynamic_settings()\n \n def initialize_dynamic_settings(self):\n self.ship_speed_factor = 1.5\n self.bullet_speed_factor = 3\n self.alien_speed_factor = 1\n # direita (1) esquerda (-1)\n self.fleet_direction = 1 \n\n # Pontuação\n self.alien_points = 50\n \n \n def increase_speed(self):\n self.ship_speed_factor *= self.speedup_scale\n self.bullet_speed_factor *= self.speedup_scale\n self.alien_speed_factor *= self.speedup_scale\n self.alien_points = int(self.alien_points * self.score_scale)","repo_name":"thaisdk/space-invaders","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"22551330537","text":"def median():\n\tn=int(input(\"Enter no of test cases: \"))\n\ti=0\n\tresult=[]\n\twhile i<n:\n\t\tnumbers=[]\n\t\tnum=input(\"Enter num: \")\n\t\tnum=num.strip(\" \")\n\t\tnum=num.split(\" \")\n\t\tfor j in range(0,len(num)):\n\t\t\tnumbers.append(int(num[j]))\n\t\tnumbers=sorted(numbers)#sorting the number list\n\t\tmedian_item=int((len(numbers)+1)/2)\n\t\tresult.append(numbers[median_item-1])#item index starts from 0\n\t\ti+=1\n\tprint(\"Median:\")\n\tfor i in result:\n\t\tprint(i,end=\" \")\n\nmedian()","repo_name":"RazinDangol/codeabbey-solutions","sub_path":"problem_10.py","file_name":"problem_10.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"19116738532","text":"import numpy as np\nimport sys,os\nimport time\nimport os\nimport re\nimport cv2\nimport argparse\nimport functools\nimport subprocess\nimport numpy as np\nfrom PIL import Image\nimport moviepy.editor as mpy\n\nimport torch.nn.parallel\nimport torch.optim\nfrom models import TSN\nfrom transforms import *\nimport datasets_video\nfrom torch.nn import functional as F\nimport shutil\n\nclass Runner(object):\n\tdef __init__(self):\n\t\tcategories_file ='pretrain/reduced_categories.txt'\n\t\tself.categories = [line.rstrip() for line in open(categories_file, 'r').readlines()]\n\t\tself.num_class = len(self.categories)\n\t\t#self.arch = 'InceptionV3'\n\t\tself.arch = 'BNInception'\n\t\t# Load model.\n\t\tself.net = TSN(self.num_class, 8, 'RGB', base_model=self.arch, consensus_type='TRNmultiscale', img_feature_dim=256, print_spec=False)\n\n\t\t#weights = 'pretrain/TRN_moments_RGB_InceptionV3_TRNmultiscale_segment8_best.pth.tar'\n\t\tweights = 'pretrain/seniordesign.pth.tar'\n\t\tcheckpoint = torch.load(weights,map_location='cpu')\n\t\t#print(\"model epoch {} best prec@1: {}\".format(checkpoint['epoch'], checkpoint['best_prec1']))\n # print list(checkpoint['state_dict'].items())\n\n\t\tbase_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items())}\n\t\tself.net.load_state_dict(base_dict)\n\t\t#self.net.eval() #.cuda().eval()\n\t\tself.net.cuda().eval()\n\t\t# Initialize frame transforms.\n\n\t\t# self.transform = torchvision.transforms.Compose([\n\t\t# GroupOverSample(self.net.input_size, self.net.scale_size),\n\t\t# Stack(roll=(self.arch in ['BNInception', 'InceptionV3'])),\n\t\t# ToTorchFormatTensor(div=(self.arch not in ['BNInception', 'InceptionV3'])),\n\t\t# GroupNormalize(self.net.input_mean, self.net.input_std),\n\t\t# ])\n\n\t\tself.transform = torchvision.transforms.Compose([\n\t\t GroupScale(self.net.scale_size),\n GroupCenterCrop(self.net.input_size),\n\t\t Stack(roll=(self.arch in ['BNInception', 'InceptionV3'])),\n\t\t ToTorchFormatTensor(div=(self.arch not in ['BNInception', 'InceptionV3'])),\n\t\t GroupNormalize(self.net.input_mean, self.net.input_std),\n\t\t])\n\n\n\n\n\n\tdef test_video(self,frames,videoname):\n\t\tdata = self.transform(frames)\n\t\tinput_var = torch.autograd.Variable(data.view(-1, 3, data.size(1), data.size(2)),\n volatile=True).unsqueeze(0).cuda()\n\t\tlogits = self.net(input_var)\n\t\th_x = torch.mean(F.softmax(logits, 1), dim=0).data\n\t\tprobs, idx = h_x.sort(0, True)\n\t\tpreds = {}\n\t\tactualProbs = {}\n\n\t\t# Output the prediction.\n\t\t# video_name = args.frame_folder if args.frame_folder is not None else args.video_file\n\t\tprint('RESULT ON ' + videoname)\n\t\tfor i in range(0, 5):\n preds[i] = self.categories[idx[i]]\n print('{:.3f} -> {}'.format(probs[i], self.categories[idx[i]]))\n #print(probs[i].data.tolist())\n #actualProbs[i] = probs[i] #with cuda\n actualProbs[i] = probs[i].data.tolist() #without cuda\n \n\t\treturn actualProbs, preds\n\ndef my_extract_frames(video_file, num_frames=8):\n try:\n os.makedirs(os.path.join(os.getcwd(), 'frames'))\n except OSError:\n print(\"oh no it could not create frames/ folder, it may already exist\\n\")\n pass\n\n output = subprocess.Popen(['ffmpeg', '-i', video_file],\n stderr=subprocess.PIPE).communicate()\n # Search and parse 'Duration: 00:05:24.13,' from ffmpeg stderr.\n re_duration = re.compile('Duration: (.*?)\\.')\n duration = re_duration.search(str(output[1])).groups()[0]\n\n seconds = functools.reduce(lambda x, y: x * 60 + y,\n map(int, duration.split(':')))\n rate = num_frames / float(seconds)\n\n output = subprocess.Popen(['ffmpeg', '-i', video_file,\n '-vf', 'fps={}'.format(rate),\n '-vframes', str(num_frames),\n '-loglevel', 'panic',\n 'frames/%d.jpg']).communicate()\n frame_paths = sorted([os.path.join('frames', frame)\n for frame in os.listdir('frames')])\n\n frames = my_load_frames(frame_paths, num_frames)\n #subprocess.call(['rmdir', '/s', './frames'], shell=True)\n shutil.rmtree('./frames')\n return frames\n\n\ndef my_load_frames(frame_paths, num_frames=8):\n frames = [Image.open(frame).convert('RGB') for frame in frame_paths]\n # print len(frames)\n if len(frames) >= num_frames:\n return frames[::int(np.ceil(len(frames) / float(num_frames)))]\n else:\n raise ValueError('Video must have at least {} frames'.format(num_frames))\n\n\ndef my_render_frames(frames, prediction):\n rendered_frames = []\n for frame in frames:\n img = np.array(frame)\n height, width, _ = img.shape\n cv2.putText(img, prediction,\n (1, int(height / 8)),\n cv2.FONT_HERSHEY_SIMPLEX,\n 1, (255, 255, 255), 2)\n rendered_frames.append(img)\n return rendered_frames\n\n#will extract frames, classify actions on those frames and return a video\n#with classifications overlayed\ndef classify_actions(model, input_video, output_video, frame_step):\n #extract frames from video\n vid = cv2.VideoCapture(input_video)\n video_fps = vid.get(cv2.CAP_PROP_FPS)\n video_framecount = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))\n classified_frame_count = int(video_framecount/2)\n \n frames = my_extract_frames(input_video, classified_frame_count)\n\n #next, run frame subsets through classifier\n rendered_frames_list = []\n finished = False\n for index in range(0, classified_frame_count, frame_step):\n end_index = 0\n if(classified_frame_count - index >= frame_step):\n end_index = index + frame_step\n else:\n #rendered_frames_list.append(frames[index:classified_frame_count])\n break\n print(str(index) + ' ' + str(frame_step) + ' ' + str(end_index) + ' ' + str(classified_frame_count))\n frame_subset = frames[index:end_index]\n probs, preds = model.test_video(frame_subset, input_video)\n rendered_frames = my_render_frames(frame_subset, preds[0])\n rendered_frames_list.append(rendered_frames)\n\n final_frames = [item for sublist in rendered_frames_list for item in sublist]\n clip = mpy.ImageSequenceClip(final_frames, int(video_fps/2))\n clip.write_videofile(output_video)\n","repo_name":"RockingRok/SeniorDesignDemo","sub_path":"test_TRN.py","file_name":"test_TRN.py","file_ext":"py","file_size_in_byte":6604,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"4060606702","text":"import os\nimport copy\nimport logging\nimport inspect\nimport matplotlib\nimport lmfit as lm\nimport numpy as np\nimport pandas as pd\nfrom operator import itemgetter\nfrom scipy.cluster.vq import kmeans2, ClusterError\n\nfrom mkidcalculator.io.loop import Loop\nfrom mkidcalculator.io.data import analogreadout_resonator\nfrom mkidcalculator.io.utils import (lmfit, create_ranges, valid_ranges, save_lmfit, subplots_colorbar, dump, load,\n _loop_fit_data)\n\nlog = logging.getLogger(__name__)\nlog.addHandler(logging.NullHandler())\n\n\nclass Resonator:\n \"\"\"A class for manipulating resonator parameter data.\"\"\"\n def __init__(self):\n # resonator reference\n self.name = None\n self._sweep = None\n self.loops = []\n self.powers = []\n self.fields = []\n self.temperatures = []\n self.temperature_groups = []\n # analysis results\n self.lmfit_results = {}\n self.loop_parameters = {}\n # directory of the data\n self._directory = None\n\n @property\n def sweep(self):\n \"\"\"\n A settable property that contains the Resonator object that this loop\n has been assigned to. If the resonator has not been set, it will raise\n an AttributeError.\n \"\"\"\n if self._sweep is None:\n raise AttributeError(\"The sweep object for this resonator has not been set yet.\")\n return self._sweep\n\n @sweep.setter\n def sweep(self, sweep):\n self._sweep = sweep\n\n @property # @property so that self.f not put into memory on load\n def f_center(self):\n \"\"\"\n The median frequency of all of the loop.f_center frequencies. This is a\n useful rough proxy for the resonant frequency that depends only on the\n data and not the fit.\n \"\"\"\n return np.median([loop.f_center for loop in self.loops])\n\n def to_pickle(self, file_name):\n \"\"\"Pickle and save the class as the file 'file_name'.\"\"\"\n # set the _directory attributes so all the data gets saved in the right folder\n self._set_directory(os.path.dirname(os.path.abspath(file_name)))\n dump(self, file_name)\n log.info(\"saved resonator as '{}'\".format(file_name))\n\n @classmethod\n def from_pickle(cls, file_name):\n \"\"\"Returns a Resonator class from the pickle file 'file_name'.\"\"\"\n resonator = load(file_name)\n if not isinstance(resonator, cls):\n raise ValueError(f\"'{file_name}' does not contain a Resonator \"\n \"class.\")\n log.info(\"loaded resonator from '{}'\".format(file_name))\n return resonator\n\n def group_temperatures(self, n_groups=None):\n \"\"\"\n Groups temperatures together into temperature_groups attribute since\n they aren't ever exactly equal.\n n_groups: integer\n An integer that determines how many temperature groups to\n include. The default is None, and n_groups is calculated. This\n procedure only works if the data is 'square' (same number of\n temperature points per unique power and field combination).\n Raises:\n scipy.cluster.vq.ClusterError:\n The temperature data is too disordered to cluster into the\n specified number of groups.\n \"\"\"\n if np.isnan(self.temperatures).any():\n raise ValueError(\"Can't group NaN temperatures\")\n temperatures = np.array(self.temperatures)\n if n_groups is None:\n n_groups = temperatures.size // (np.unique(self.powers).size * np.unique(self.fields).size)\n k = np.linspace(temperatures.min(), temperatures.max(), n_groups)\n try:\n centroids, groups = kmeans2(temperatures, k=k, minit='matrix', missing='raise')\n except ClusterError:\n message = \"The temperature data is too disordered to cluster into {} groups\".format(n_groups)\n raise ClusterError(message)\n self.temperature_groups = np.empty_like(self.temperatures)\n for index, centroid in enumerate(centroids):\n self.temperature_groups[groups == index] = centroid\n self.temperature_groups = list(self.temperature_groups)\n for index, loop in enumerate(self.loops):\n loop.temperature_group = self.temperature_groups[index]\n\n def add_loops(self, loops, sort=True):\n \"\"\"\n Add Loop objects to the resonator.\n Args:\n loops: Loop class or iterable of Loop classes\n The loop objects that are to be added to the Resonator.\n sort: boolean (optional)\n Sort the loop list by its power, field, and temperature.\n The default is True. If False, the order of the loop list\n is preserved.\n \"\"\"\n if isinstance(loops, Loop):\n loops = [loops]\n # append loop data\n for loop in loops:\n loop.resonator = self\n self.loops.append(loop)\n self.powers.append(loop.power)\n self.fields.append(loop.field)\n self.temperatures.append(loop.temperature)\n # sort\n if sort and self.loops:\n lp = zip(*sorted(zip(self.powers, self.fields, self.temperatures, self.loops), key=itemgetter(0, 1, 2)))\n self.powers, self.fields, self.temperatures, self.loops = (list(t) for t in lp)\n\n def remove_loops(self, indices):\n \"\"\"\n Remove loops from the resonator.\n Args:\n indices: integer or iterable of integers\n The indices in resonator.loops that should be deleted.\n \"\"\"\n if not isinstance(indices, (tuple, list)):\n indices = [indices]\n for ii in sorted(indices, reverse=True):\n self.loops.pop(ii)\n self.powers.pop(ii)\n self.fields.pop(ii)\n self.temperatures.pop(ii)\n\n def free_memory(self, directory=None):\n \"\"\"\n Frees memory from all of the contained Loop objects.\n Args:\n directory: string\n A directory string for where the data should be offloaded. The\n default is None, and the directory where the pulse was saved is\n used. If it hasn't been saved, the working directory is used.\n \"\"\"\n if directory is not None:\n self._set_directory(directory)\n for loop in self.loops:\n loop.free_memory(directory=directory)\n\n @classmethod\n def from_file(cls, resonator_file_name, data=analogreadout_resonator, sort=True, **kwargs):\n \"\"\"\n Resonator class factory method that returns a Resonator() with the loop,\n noise and pulse data loaded.\n Args:\n resonator_file_name: string\n The file name for the resonator data.\n data: object (optional)\n Class or function whose return value is a list of dictionaries\n with each being the desired keyword arguments to\n Loop.from_file().\n sort: boolean (optional)\n Sort the loop data by its power, field, and temperature. Also\n sort noise data and pulse data lists by their bias frequencies.\n The default is True. If False, the input order is preserved.\n kwargs: optional keyword arguments\n Extra keyword arguments to send to data.\n Returns:\n resonator: object\n A Resonator() object containing the loaded data.\n \"\"\"\n # create resonator\n resonator = cls()\n # load loop kwargs based on the resonator file\n loop_kwargs_list = data(resonator_file_name, **kwargs)\n loops = []\n # load loops\n for kws in loop_kwargs_list:\n kws.update({\"sort\": sort})\n loops.append(Loop.from_file(**kws))\n resonator.add_loops(loops, sort=sort)\n resonator.name = os.path.basename(resonator_file_name) + \", \" + str(kwargs)\n return resonator\n\n def lmfit(self, parameter, model, guess, label='default', keep=True,\n residual_args=(), residual_kwargs=None, data_kwargs=None,\n **kwargs):\n \"\"\"\n Compute a least squares fit using the supplied residual function and\n guess.\n Args:\n parameter: string or list of strings\n The loop parameters to fit. They must be a columns in the loop\n parameters table. If more than one parameter is specified a\n joint fit will be performed and the mkidcalculator.models.Joint\n class should be used.\n model: object-like\n model.residual should give the objective function to minimize.\n It must output a 1D real vector. The first two arguments must\n be a lmfit.Parameters object, and the parameter data. Other\n arguments can be passed in through the residual_args and\n residual_kwargs arguments.\n guess: lmfit.Parameters object\n A parameters object containing starting values (and bounds if\n desired) for all of the parameters needed for the residual\n function.\n index: tuple of 3 slices or a list of those tuples\n A list of slices for power, field and temperature which specify\n which data from the loop parameters table should be fit. The\n default is None and all data is fit. If a list of slices is\n used, the slices are concatenated.\n label: string (optional)\n A label describing the fit, used for storing the results in the\n self.lmfit_results dictionary. The default is 'default'.\n keep: boolean (optional)\n Store the fit result in the object. The default is True. If\n False, the fit will only be stored if it is the best so far.\n residual_args: tuple (optional)\n A tuple of arguments to be passed to the residual function.\n Note: these arguments are the non-mandatory ones after the\n first two. The default is an empty tuple.\n residual_kwargs: dictionary (optional)\n A dictionary of arguments to be passed to the residual\n function. The default is None, which corresponds to an empty\n dictionary.\n kwargs: optional keyword arguments\n Additional keyword arguments are sent to the\n lmfit.Minimizer.minimize() method.\n Returns:\n result: lmfit.MinimizerResult\n An object containing the results of the minimization. It is\n also stored in self.lmfit_results[label]['result'].\n \"\"\"\n # get the data to fit\n if isinstance(parameter, str):\n parameter = [parameter]\n args_list = []\n kws_list = []\n if data_kwargs is None:\n data_kwargs = {}\n # collect the arguments for each parameter\n for p in parameter:\n data, sigmas, temperatures, powers = _loop_fit_data(\n self.loops, [p, p + \"_sigma\", 'temperature', 'power'],\n **data_kwargs)\n if p in ['fr', 'f0']:\n data = data * 1e9 # convert to Hz for model\n args = (data, *residual_args)\n args_list.append(args)\n kws = {\"temperatures\": temperatures, \"powers\": powers}\n if (~np.isnan(sigmas)).all():\n if p in ['fr', 'f0']:\n sigmas = sigmas * 1e9 # convert to Hz for model\n kws.update({\"sigmas\": sigmas})\n if residual_kwargs is not None:\n kws.update(residual_kwargs)\n kws_list.append(kws)\n # reformat the arguments to work with one or many parameters\n if len(parameter) == 1:\n args = args_list[0]\n kws = kws_list[0]\n else:\n args = [tuple([args_list[ind][index]\n for ind, _ in enumerate(args_list)])\n for index, _ in enumerate(args_list[0])]\n kws = {key: tuple(kws_list[ind][key]\n for ind, _ in enumerate(kws_list))\n for key in kws_list[0].keys()}\n # make sure the dictionary exists for each parameter\n for p in parameter:\n if p not in self.lmfit_results.keys():\n self.lmfit_results[p] = {}\n # do the fit for the first parameter\n result = lmfit(self.lmfit_results[parameter[0]], model, guess,\n label=label, keep=keep, residual_args=args,\n residual_kwargs=kws,\n model_index=0 if len(parameter) != 1 else None,\n **kwargs)\n # copy the result to the other parameters\n for ind, p in enumerate(parameter[1:]):\n save_lmfit(self.lmfit_results[p], model.models[ind + 1], result,\n label=label, keep=keep,\n residual_args=args_list[ind + 1],\n residual_kwargs=kws_list[ind + 1])\n return result\n\n def emcee(self):\n raise NotImplementedError\n\n def fit_report(self, parameter, label='best', fit_type='lmfit', return_string=False):\n \"\"\"\n Print a string summarizing a resonator fit.\n Args:\n parameter: string\n The parameter on which the fit was done.\n label: string\n The label used to store the fit. The default is \"best\".\n fit_type: string\n The type of fit to use. Allowed options are \"lmfit\", \"emcee\",\n and \"emcee_mle\" where MLE estimates are used instead of the\n medians. The default is \"lmfit\".\n return_string: boolean\n Return a string with the fit report instead of printing. The\n default is False.\n\n Returns:\n string: string\n A string containing the fit report. None is output if\n return_string is False.\n \"\"\"\n _, result_dict = self._get_model(parameter, fit_type, label)\n string = lm.fit_report(result_dict['result'])\n if return_string:\n return string\n else:\n print(string)\n\n def _set_directory(self, directory):\n self._directory = directory\n for loop in self.loops:\n loop._set_directory(self._directory)\n\n def _get_model(self, parameter, fit_type, label):\n if fit_type not in ['lmfit', 'emcee', 'emcee_mle']:\n raise ValueError(\"'fit_type' must be either 'lmfit', 'emcee', or 'emcee_mle'\")\n if fit_type == \"lmfit\" and label in self.lmfit_results[parameter].keys():\n result_dict = self.lmfit_results[parameter][label]\n original_label = self.lmfit_results[parameter][label][\"label\"] if label == \"best\" else label\n elif fit_type == \"emcee\" and label in self.emcee_results[parameter].keys():\n result_dict = self.emcee_results[parameter][label]\n original_label = self.lmfit_results[parameter][label][\"label\"] if label == \"best\" else label\n elif fit_type == \"emcee_mle\" and label in self.emcee_results[parameter].keys():\n result_dict = copy.deepcopy(self.emcee_results[parameter][label])\n for name in result_dict['result'].params.keys():\n result_dict['result'].params[name].set(value=self.emcee_results[parameter][label][\"mle\"][name])\n original_label = self.lmfit_results[parameter][label][\"label\"] if label == \"best\" else label\n else:\n result_dict = None\n original_label = None\n return original_label, result_dict\n\n def plot_loops(self, power=None, field=None, temperature=None, color_data='power', colormap=None,\n colorbar=True, colorbar_kwargs=None, colorbar_label=True, colorbar_limits=None,\n colorbar_label_kwargs=None, colorbar_tick_kwargs=None, tighten=True, **loop_kwargs):\n \"\"\"\n Plot a subset of the loops in the resonator by combining multiple\n loop.plot() calls.\n Args:\n power: tuple of two number tuples or numbers\n Inclusive range or ranges of powers to plot. A single number\n will cause only that value to be plotted. The default is to\n include all of the powers.\n field: tuple of two number tuples or numbers\n Inclusive range or ranges of fields to plot. A single number\n will cause only that value to be plotted. The default is to\n include all of the fields.\n temperature: tuple of two number tuples or numbers\n Inclusive range or ranges of temperatures to plot. A single\n number will cause only that value to be plotted. The default is\n to include all of the temperatures.\n color_data: string\n Either 'temperature', 'field', or 'power' indicating off what\n type of data to base the colormap. The default is\n 'power'.\n colormap: matplotlib.colors.Colormap\n A matplotlib colormap for coloring the data. If the default\n None is used, a colormap is chosen based on color_data.\n colorbar: boolean\n Determines whether to include a colorbar. The default is True.\n If False, colorbar_kwargs, colorbar_label, and\n colorbar_label_kwargs are ignored.\n colorbar_kwargs: dictionary\n Keyword arguments for the colorbar in figure.colorbar(). The\n default is None which uses default options. Keywords in this\n dictionary override the default options.\n colorbar_label: boolean or string\n If it is a boolean, it determines whether or not to add the\n default colorbar label. If it is a string, that string is used\n as the colorbar label. If False, colorbar_label_kwargs is\n ignored. The default is True.\n colorbar_limits: tuple of floats\n The limits of the colorbar to use. The default is None and the\n maximum and minimum of color_data is used.\n colorbar_label_kwargs: dictionary\n Keyword arguments for the colorbar in colorbar.set_label(). The\n default is None which uses default options. Keywords in this\n dictionary override the default options.\n colorbar_tick_kwargs: dictionary\n Keyword arguments for the colorbar ticks using\n colorbar_axes.tick_params(). The default is None which uses the\n default options. Keywords in this dictionary override the\n default options.\n tighten: boolean\n Determines whether figure.tight_layout() is called. The default\n is True.\n loop_kwargs: optional keyword arguments\n Extra keyword arguments to send to loop.plot().\n Returns:\n axes_list: an iterable of matplotlib.axes.Axes classes\n A list of Axes classes with the plotted data.\n \"\"\"\n # parse inputs\n if \"fit_parameters\" in loop_kwargs.keys():\n raise TypeError(\"'fit_parameters' is not a valid keyword argument\")\n if \"parameters_kwargs\" in loop_kwargs.keys():\n raise TypeError(\"'parameters_kwargs' is not a valid keyword argument\")\n power, field, temperature = create_ranges(power, field, temperature)\n if color_data == 'temperature':\n cmap = matplotlib.cm.get_cmap('coolwarm') if colormap is None else colormap\n cdata = np.array(self.temperatures[::-1]) * 1000\n elif color_data == 'field':\n cmap = matplotlib.cm.get_cmap('viridis') if colormap is None else colormap\n cdata = self.fields[::-1]\n elif color_data == 'power':\n cmap = matplotlib.cm.get_cmap('plasma') if colormap is None else colormap\n cdata = self.powers[::-1]\n else:\n raise ValueError(\"'{}' is not a valid value of color_data.\".format(color_data))\n if colorbar_limits is None:\n colorbar_limits = (min(cdata), max(cdata))\n norm = matplotlib.colors.Normalize(vmin=colorbar_limits[0], vmax=colorbar_limits[1])\n n_plots = 3 if 'plot_types' not in loop_kwargs.keys() else len(loop_kwargs['plot_types'])\n axes_list = None\n # format title\n title = loop_kwargs.get(\"title\", True)\n if title is True:\n # power\n if len(power) == 1 and np.isinf(power[0]).all():\n title = \"All Powers, \"\n elif all(x == power[0] for x in power) and power[0][0] == power[0][1]:\n title = \"{:.0f} dBm, \".format(power[0][0])\n else:\n title = \"({:.0f}, {:.0f}) dBm, \".format(np.min(power[0]), np.max(power[-1]))\n # field\n if len(field) == 1 and np.isinf(field[0]).all():\n title += \"All Fields, \"\n elif all(x == field[0] for x in field) and field[0][0] == field[0][1]:\n title += \"{:.0f} V, \".format(field[0][0])\n else:\n title += \"({:.0f}, {:.0f}) V, \".format(np.min(field[0]), np.max(field[-1]))\n # temperature\n if len(temperature) == 1 and np.isinf(temperature[0]).all():\n title += \"All Temperatures\"\n elif all(x == temperature[0] for x in temperature) and temperature[0][0] == temperature[0][1]:\n title += \"{:.0f} mK\".format(temperature[0][0] * 1000)\n else:\n title += \"({:.0f}, {:.0f}) mK\".format(np.min(temperature[0]) * 1000, np.max(temperature[-1]) * 1000)\n # store key word options\n user_plot_kwargs = loop_kwargs.get('plot_kwargs', [])\n if isinstance(user_plot_kwargs, dict):\n user_plot_kwargs = [user_plot_kwargs] * n_plots\n user_data_kwargs = []\n for kw in user_plot_kwargs:\n user_data_kwargs.append(kw.get(\"data_kwargs\", {}))\n user_fit_kwargs = []\n for kw in user_plot_kwargs:\n user_fit_kwargs.append(kw.get(\"fit_kwargs\", {}))\n # make a plot for each loop\n plot_index = 0\n for index, loop in enumerate(self.loops[::-1]):\n if valid_ranges(loop, power, field, temperature):\n # default plot key words\n if plot_index == 0:\n plot_kwargs = [{'data_kwargs': {'color': cmap(norm(cdata[index]))},\n 'fit_kwargs': {'color': 'k'}}] * n_plots\n else:\n plot_kwargs = [{'x_label': '', 'y_label': '', 'data_kwargs': {'color': cmap(norm(cdata[index]))},\n 'fit_kwargs': {'color': 'k'}}] * n_plots\n # update data key words with user defaults\n for kw_index, data_kw in enumerate(user_data_kwargs):\n plot_kwargs[kw_index]['data_kwargs'].update(data_kw)\n # update fit key words with user defaults\n for kw_index, fit_kw in enumerate(user_fit_kwargs):\n plot_kwargs[kw_index]['fit_kwargs'].update(fit_kw)\n # update plot key words with user defaults\n for kw_index, kws in enumerate(user_plot_kwargs):\n kws = kws.copy()\n kws.pop('data_kwargs', None)\n kws.pop('fit_kwargs', None)\n plot_kwargs[kw_index].update(kws)\n # update loop kwargs\n if plot_index == 0:\n loop_kwargs.update({\"plot_kwargs\": plot_kwargs, \"title\": title, \"tighten\": False})\n else:\n loop_kwargs.update({\"axes_list\": axes_list, \"title\": False, \"legend\": False, \"tighten\": False,\n \"plot_kwargs\": plot_kwargs})\n axes_list = loop.plot(**loop_kwargs)\n plot_index += 1\n # if we didn't plot anything exit the function\n if axes_list is None:\n return\n gs = None\n if colorbar:\n mappable = matplotlib.cm.ScalarMappable(norm, cmap)\n mappable.set_array([])\n kwargs = {}\n if colorbar_kwargs is not None:\n kwargs.update(colorbar_kwargs)\n\n if all([isinstance(axes, matplotlib.axes.SubplotBase) for axes in axes_list]) and len(axes_list) != 1:\n gs_kwargs = {\"top\": 0.9 if title else 1}\n cbar, gs = subplots_colorbar(mappable, axes_list, gridspec_kwargs=gs_kwargs, **kwargs)\n else:\n ax = axes_list[0] if len(axes_list) == 1 else axes_list\n cbar = axes_list[0].figure.colorbar(mappable, ax=ax, **kwargs)\n\n if colorbar_label:\n if color_data == 'temperature':\n label = \"temperature [mK]\" if colorbar_label is True else colorbar_label\n elif color_data == 'field':\n label = \"field [V]\" if colorbar_label is True else colorbar_label\n else:\n label = \"power [dBm]\" if colorbar_label is True else colorbar_label\n kwargs = {\"rotation\": 270, 'va': 'bottom'}\n if colorbar_label_kwargs is not None:\n kwargs.update(colorbar_label_kwargs)\n cbar.set_label(label, **kwargs)\n if colorbar_tick_kwargs is not None:\n cbar.ax.tick_params(**colorbar_tick_kwargs)\n if tighten:\n if gs is not None:\n gs.tight_layout(axes_list[0].figure, rect=[0, 0, 1, 0.9 if title else 1])\n else:\n axes_list[0].figure.tight_layout(rect=[0, 0, 1, 0.9 if title else 1])\n return axes_list\n\n def plot_parameters(self, parameters, x=\"power\", n_rows=1, n_sigma=2,\n plot_fit=False, fit_label=\"best\", axes_list=None,\n **loop_kwargs):\n # set up axes and input arguments\n if isinstance(parameters, str):\n parameters = [parameters]\n if axes_list is None:\n from matplotlib import pyplot as plt\n n_columns = int(np.ceil(len(parameters) / n_rows))\n figure, axes_list = plt.subplots(nrows=n_rows, ncols=n_columns, squeeze=False,\n figsize=(4.3 * n_columns, 4.0 * n_rows))\n axes_list = axes_list.ravel()\n else:\n if not isinstance(axes_list, np.ndarray):\n axes_list = np.atleast_1d(axes_list)\n figure = axes_list[0].figure\n # set up loop attributes to iterate over\n levels = [\"power\", \"field\", \"temperature\"]\n if x not in levels:\n raise ValueError(\"x must be in {}\".format(levels))\n values_dict = {\"power\": np.unique(self.powers), \"field\": np.unique(self.fields)}\n if x != \"temperature\" and self.temperature_groups:\n levels[2] = \"temperature_group\"\n values_dict.update({\"temperature_group\": np.unique(self.temperature_groups)})\n else:\n values_dict.update({\"temperature\": np.unique(self.temperatures)})\n for key, values in values_dict.items(): # unique doesn't work with nan\n if np.isnan(values).any():\n values_dict[key] = np.concatenate((values[~np.isnan(values)], [np.nan]))\n levels.remove(x)\n # make one set of axes per parameter\n kwargs = {\"label\": \"best\"}\n for index, parameter in enumerate(parameters):\n # collect data for parameter\n kwargs.update({\"parameters\": [parameter, parameter + \"_sigma\", x, levels[0], levels[1]]})\n kwargs.update(loop_kwargs)\n data, error_bars, x_vals, values0, values1 = _loop_fit_data(self.loops, **kwargs)\n # plot in mK\n if x == \"temperature\":\n x_vals = x_vals * 1000\n # plot a line for each level\n for value0 in values_dict[levels[0]]:\n for value1 in values_dict[levels[1]]:\n logic = np.isclose(value0, values0, equal_nan=True) & np.isclose(value1, values1, equal_nan=True)\n # plot data\n if ~np.isnan(data[logic]).any():\n if ~np.isnan(error_bars[logic]).any():\n axes_list[index].errorbar(x_vals[logic], data[logic], error_bars[logic] * n_sigma, fmt='o')\n else:\n axes_list[index].plot(x_vals[logic], data[logic], 'o')\n if parameter.endswith(\"_sigma\"):\n axes_list[index].set_ylabel(\"_\".join(parameter.split(\"_\")[:-1]) + \" sigma\")\n else:\n axes_list[index].set_ylabel(parameter)\n x_label = {\"power\": \"power [dBm]\", \"field\": \"field [V]\", \"temperature\": \"temperature [mK]\"}\n axes_list[index].set_xlabel(x_label[x])\n # plot a fit if it's been done\n if plot_fit and parameter in self.lmfit_results.keys():\n if fit_label in self.lmfit_results[parameter].keys():\n result_dict = self.lmfit_results[parameter][fit_label]\n result = result_dict['result']\n model = result_dict['model']\n parameters = inspect.signature(model.model).parameters\n residual_kwargs = result_dict['kwargs']\n kws = {}\n for key in parameters.keys():\n if key in residual_kwargs.keys():\n kws.update({key: residual_kwargs[key]})\n if 'parallel' in kws.keys():\n kws['parallel'] = bool(kws['parallel'])\n args = result_dict['args'][1:]\n m = model.model(result.params, *args, **kws)\n x_m = kws[x + \"s\"] if x != \"temperature\" else 1000 * kws[x + \"s\"]\n if parameter == \"fr\":\n m *= 1e-9 # Convert to GHz\n ind = np.argsort(x_m)\n axes_list[index].plot(x_m[ind], m[ind])\n figure.tight_layout()\n return axes_list\n","repo_name":"zobristnicholas/mkidcalculator","sub_path":"mkidcalculator/io/resonator.py","file_name":"resonator.py","file_ext":"py","file_size_in_byte":30803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17510507254","text":"import requests as r\n\n\n\n\nclass Pokemon:\n def __init__(self, data):\n self.name = data['name']\n self.types = data['types'][0]['type']['name']\n self.height = data['height']\n self.weight = data['weight']\n self.base_experience = data['base_experience']\n self.ident = data['id']\n self.abilities = [v['ability']['name'] for v in data['abilities']]\n self.sprite = data['sprites']['front_default']\n\n\n\n\n","repo_name":"DonovanOLinn/Flask-Project","sub_path":"App/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22904753579","text":"class Node:\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\ndef printInorder(root):\n if root is not None:\n printInorder(root.left)\n print(root.value, end=' ')\n printInorder(root.right)\n \ndef createFullBinaryTree(root):\n # Empty tree, or empty subtree base case\n if root is None:\n return None\n \n # Recursively check the left and right subtrees, to get values are None values\n root.left = createFullBinaryTree(root.left)\n root.right = createFullBinaryTree(root.right)\n \n # Returning the root value if there is no left or right subtree, just simply trying to take out\n # the nodes with single child nodes\n if root.left is None and root.right is None:\n return root\n \n # Doesn't pass the above cases, then we have a situation with a node with a single child node\n # Deleting the single child case, by changing the child to be the new root, and deleting the old parent\n if root.left is None:\n newRoot = root.right\n temp = root\n root = None\n del(temp)\n return newRoot\n if root.right is None:\n newRoot = root.left\n temp = root\n root = None\n del(temp)\n return newRoot\n \n return root\n \ndef createFullBinaryTreeHelper(root):\n tree = createFullBinaryTree(root)\n printInorder(tree)\n print('\\n')\n \n\nroot = Node(2)\nroot.left = Node(7)\nroot.right = Node(5)\nroot.left.right = Node(6)\nroot.left.right.left = Node(1)\nroot.left.right.right = Node(11)\nroot.right.right = Node(9)\nroot.right.right.left = Node(4)\n\ncreateFullBinaryTreeHelper(root)\n# 2\n# / \\\n# 7 5 \n# \\ \\\n# 6 9\n# / \\ /\n# 1 11 4\n#\n# 2\n# / \\\n# 6 4\n# / \\\n# 1 11\n\ntree = Node(1)\ntree.left = Node(2)\ntree.right = Node(3)\ntree.right.right = Node(4)\ntree.right.left = Node(9)\ntree.left.left = Node(0)\ncreateFullBinaryTreeHelper(tree)\n\n# Given this tree:\n# 1\n# / \\\n# 2 3\n# / / \\\n# 0 9 4\n\n# We want a tree like:\n# 1\n# / \\\n# 0 3\n# / \\\n# 9 4\n","repo_name":"AnhquanNguyenn/PythonPracticeScripts","sub_path":"Full Binary Tree/fullBinaryTree.py","file_name":"fullBinaryTree.py","file_ext":"py","file_size_in_byte":2320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3110280979","text":"# Rubik's cube solver using Thistlethwaite's algorithm\n#\n# Python translation of Stefan Pochmann's C++ implementation\n# http://www.stefan-pochmann.info/spocc/other_stuff/tools/\n# by Mark Dufour (mark.dufour@gmail.com)\n#\n# cube 'state' is a list with 40 entries, the first 20\n# are a permutation of {0,...,19} and describe which cubie is at\n# a certain position (regarding the input ordering). The first\n# twelve are for edges, the last eight for corners.\n# \n# The last 20 entries are for the orientations, each describing\n# how often the cubie at a certain position has been turned\n# counterclockwise away from the correct orientation. Again the\n# first twelve are edges, the last eight are corners. The values\n# are 0 or 1 for edges and 0, 1 or 2 for corners.\nimport random\nrandom.seed(1)\n\nfacenames = [\"U\", \"D\", \"F\", \"B\", \"L\", \"R\"]\naffected_cubies = [[0, 1, 2, 3, 0, 1, 2, 3], [4, 7, 6, 5, 4, 5, 6, 7], [0, 9, 4, 8, 0, 3, 5, 4], [2, 10, 6, 11, 2, 1, 7, 6], [3, 11, 7, 9, 3, 2, 6, 5], [1, 8, 5, 10, 1, 0, 4, 7]]\nphase_moves = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 7, 10, 13, 16], [1, 4, 7, 10, 13, 16]]\n\ndef move_str(move):\n return facenames[move//3]+{1: '', 2: '2', 3: \"'\"}[move%3+1]\n\nclass cube_state:\n def __init__(self, state, route=None):\n self.state = state\n self.route = route or []\n\n def id_(self, phase):\n if phase == 0:\n return tuple(self.state[20:32])\n elif phase == 1:\n result = self.state[31:40]\n for e in range(12):\n result[0] |= (self.state[e] // 8) << e;\n return tuple(result)\n elif phase == 2:\n result = [0,0,0]\n for e in range(12):\n result[0] |= (2 if (self.state[e] > 7) else (self.state[e] & 1)) << (2*e)\n for c in range(8):\n result[1] |= ((self.state[c+12]-12) & 5) << (3*c)\n for i in range(12, 20):\n for j in range(i+1, 20):\n result[2] ^= int(self.state[i] > self.state[j])\n return tuple(result)\n else:\n return tuple(self.state)\n\n def apply_move(self, move):\n face, turns = move // 3, move % 3 + 1\n newstate = self.state[:]\n for turn in range(turns):\n oldstate = newstate[:]\n for i in range(8):\n isCorner = int(i > 3)\n target = affected_cubies[face][i] + isCorner*12\n killer = affected_cubies[face][(i-3) if (i&3)==3 else i+1] + isCorner*12\n orientationDelta = int(1<face<4) if i<4 else (0 if face<2 else 2 - (i&1))\n newstate[target] = oldstate[killer]\n newstate[target+20] = oldstate[killer+20] + orientationDelta\n if turn == turns-1:\n newstate[target+20] %= 2 + isCorner\n return cube_state(newstate, self.route+[move])\n\ngoal_state = cube_state(list(range(20))+20*[0])\nstate = cube_state(goal_state.state[:])\nprint('*** randomize ***')\nmoves = [random.randrange(0,18) for x in range(30)]\nprint(','.join([move_str(move) for move in moves])+'\\n')\nfor move in moves:\n state = state.apply_move(move)\nstate.route = []\nprint('*** solve ***')\nfor phase in range(4):\n current_id, goal_id = state.id_(phase), goal_state.id_(phase)\n states = [state]\n state_ids = set([current_id])\n if current_id != goal_id:\n phase_ok = False\n while not phase_ok:\n next_states = []\n for cur_state in states:\n for move in phase_moves[phase]:\n next_state = cur_state.apply_move(move)\n next_id = next_state.id_(phase)\n if next_id == goal_id:\n print(','.join([move_str(m) for m in next_state.route]) + ' (%d moves)'% len(next_state.route))\n phase_ok = True\n state = next_state\n break\n if next_id not in state_ids:\n state_ids.add(next_id)\n next_states.append(next_state)\n if phase_ok:\n break\n states = next_states\n","repo_name":"shedskin/shedskin","sub_path":"examples/rubik2/rubik2.py","file_name":"rubik2.py","file_ext":"py","file_size_in_byte":4245,"program_lang":"python","lang":"en","doc_type":"code","stars":721,"dataset":"github-code","pt":"31"} +{"seq_id":"34124364203","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.support.ui import Select\nimport time\n\noption = Options()\noption.headless = False # True = Segundo Plano, False = Executa na tela\ndriver = webdriver.Chrome(executable_path=('chromedriver.exe'),options=option)\ndriver.get(\"https://www.saucedemo.com/\")\nhtml = driver.page_source\n\ndef userPrint(user):\n if user == \"standard_user\":\n print(\"\\033[1;37;48m╔═════════════════════════════════════════════════╗\\033[0m\") # Perfumaria\n print(\"\\033[1;37;48m║ USUÁRIO: STANDART_USER ║\") # Perfumaria\n print(\"\\033[1;37;48m╟═════════════════════════════════════════════════╣\\033[0m\") # Perfumaria\n elif user == \"problem_user\":\n print(\"\\033[1;37;48m╔═════════════════════════════════════════════════╗\\033[0m\") # Perfumaria\n print(\"\\033[1;37;48m║ USUÁRIO: PROBLEM_USER ║\") # Perfumaria\n print(\"\\033[1;37;48m╟═════════════════════════════════════════════════╣\\033[0m\") # Perfumaria\n elif user == \"invalid_user\":\n print(\"\\033[1;37;48m╔═════════════════════════════════════════════════��\\033[0m\") # Perfumaria\n print(\"\\033[1;37;48m║ USUÁRIO: INVALID_USER ║\") # Perfumaria\n print(\"\\033[1;37;48m╟═════════════════════════════════════════════════╣\\033[0m\") # Perfumaria\n elif user == \"locked_out_user\":\n print(\"\\033[1;37;48m╔═════════════════════════════════════════════════╗\\033[0m\") # Perfumaria\n print(\"\\033[1;37;48m║ USUÁRIO: LOCKED_OUT_USER ║\") # Perfumaria\n print(\"\\033[1;37;48m╟═════════════════════════════════════════════════╣\\033[0m\") # Perfumaria\n else:\n print(\"\\033[1;37;48m╔═════════════════════════════════════════════════╗\\033[0m\") # Perfumaria\n print(\"\\033[1;37;48m║ USUÁRIO: PERFORMANCE_GLITCH_USER ║\") # Perfumaria\n print(\"\\033[1;37;48m╟═════════════════════════════════════════════════╣\\033[0m\") # Perfumaria\n\ndef login(username, password): # Realiza o login no site\n start = time.time()\n try:\n driver.find_element_by_xpath(\"//*[@id=\\\"user-name\\\"]\").send_keys(username) # Digita username\n driver.find_element_by_xpath(\"//*[@id=\\\"password\\\"]\").send_keys(password) # Digita senha\n driver.find_element_by_xpath(\"//*[@id=\\\"login-button\\\"]\").click() # Clica pra login\n except Exception:\n print(\"\\033[1;31;48m╟═════════════════════════════════════════════════╣\\033[0m\") # Perfumaria\n print(\"\\033[1;31;48m║ Tentativa falha de preencher campos de login ║\\033[0m\") # Perfumaria\n print(\"\\033[1;31;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n end = time.time()\n\n if end - start > 0.5:\n print(\"\\033[1;33;48m║ PROBLEMA DE PERFORMANCE AO REALIZAR O LOGIN ║\\033[0m\") # Perfumaria\n\ndef validate_login(): # Valida o login no site\n try:\n error = driver.find_element_by_xpath(\"//*[@id=\\\"login_button_container\\\"]/div/form/div[3]/h3\") # Salva o campo de erro da página\n if \"locked out\" in error.text: # Se encontrar 'locked out' no campo de erro, reconhece usuário bloqueado\n \n print(\"\\033[1;31;48m║ USUÁRIO BLOQUEADO ║\\033[0m\") # Perfumaria\n print(\"\\033[1;31;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n\n user_logged = False\n elif \"not match any user\" in error.text: # Se encontrar 'not match...', reconhece que o username/senha está errado\n\n print(\"\\033[1;31;48m║ USUÁRIO/SENHA ERRADO ║\\033[0m\") # Perfumaria\n print(\"\\033[1;31;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n\n user_logged = False\n except Exception:\n print(\"\\033[1;32;48m║ Nenhum erro durante o login encontrado ║\\033[0m\") # # Se não encontrar o campo de erro da página, reconhece que foi realizado com sucesso o login no site\n user_logged = True\n return user_logged\n\ndef check_images(): # Checa imagem do produto\n html = driver.page_source # Salva o html novamente\n if \"sl-404.168b1cce.jpg\" in html: # Procura se a imagem do cachorro está na página\n print(\"\\033[1;31;48m║ IMAGEM ERRADA ENCONTRADA ║\")\n else:\n print(\"\\033[1;32;48m║ Todas as imagens estão certas ║\")\n\ndef list_products(): # Realiza a listagem dos produtos\n start = time.time()\n html = driver.page_source\n options = [\"za\", \"az\", \"lohi\", \"hilo\"] # Define as opções do DropDown\n for option in options:\n select = Select(driver.find_element_by_class_name('product_sort_container')) # Define qual é o DropDown\n select.select_by_value(option) # Seleciona uma opção\n newHtml = driver.page_source\n\n if newHtml == html:\n print(\"\\033[1;31;48m║ LISTAGEM NÃO REALIZADA ║ >> \", option.upper()) # Caso selecione uma opção diferente de 'az', o código deverá mudar\n else:\n print(\"\\033[1;32;48m║ Listagem realizada ║ >> \", option.upper()) # Se o código mudar, a listagem funcionou\n html = newHtml\n end = time.time()\n if end - start > 0.5:\n print(\"\\033[1;33;48m║ PROBLEMA DE PERFORMANCE PARA LISTAR OS PRODUTOS ║\\033[0m\") # Perfumaria\n\ndef check_products(): # Checa se os produtos estão sendo redirecionados para a página certa\n start = time.time()\n products = [\"item_0_title_link\",\"item_1_title_link\",\"item_2_title_link\",\"item_3_title_link\",\"item_4_title_link\",\"item_5_title_link\"]\n for product in products:\n product_main = driver.find_element_by_id(product).find_element_by_class_name(\"inventory_item_name\").text # Pega o nome do produto na pagina inicial\n driver.find_element_by_id(product).click() # Clica no produto\n product_new = driver.find_element_by_xpath(\"//*[@id=\\\"inventory_item_container\\\"]/div/div/div[2]/div[1]\").text # Pega o nome do produto na pagina do produto\n if product_new == product_main: # Verifica se o nome do produto da pagina inicial é igual a da pagina do produto\n\n print(\"\\033[1;32;48m║ Sucesso no redirecionamento ║\")\n\n elif \"NOT FOUND\" in product_new: # Verifica se o nome do produto não foi encontrado\n print(\"\\033[1;31;48m║ ITEM NÃO ENCONTRADO ║ >>\", product_main.upper()) \n\n else:\n print(\"\\033[1;31;48m║ FALHA NO REDIRECIONAMENTO DO PRODUTO ║ >>\", product_main.upper()) # Se os nomes não forem iguais ocorreu uma falha no redirecionamento\n \n price = driver.find_element_by_xpath(\"//*[@id=\\\"inventory_item_container\\\"]/div/div/div[2]/div[3]\").text # Pega o preço do produto\n\n if \"-\" in price: # Verifica se o produto tem valor negativo\n print(\"\\033[1;31;48m║ PREÇO DO PRODUTO ERRADO ║\") # Se tiver, o preço está errado\n else:\n print(\"\\033[1;32;48m║ Preço correto ║\") # Se não tiver, o preço está certo\n \n driver.find_element_by_xpath(\"//*[@id=\\\"back-to-products\\\"]\").click() # Volta para a tela inicial\n end = time.time()\n if end - start > 1.2:\n print(\"\\033[1;33;48m║ PROBLEMA DE PERFORMANCE PARA CHECAR PRODUTOS ║ \\033[0m\") # Perfumaria\n\ndef add_remove(): # Checa se o botão adicionar/remover está funcionando\n start = time.time()\n add_buttons = [\"add-to-cart-sauce-labs-backpack\", \"add-to-cart-sauce-labs-bike-light\",\"add-to-cart-sauce-labs-bolt-t-shirt\",\"add-to-cart-sauce-labs-fleece-jacket\",\"add-to-cart-sauce-labs-onesie\",\"add-to-cart-test.allthethings()-t-shirt-(red)\"]\n remove_buttons = [\"remove-sauce-labs-backpack\", \"remove-sauce-labs-bike-light\",\"remove-sauce-labs-bolt-t-shirt\",\"remove-sauce-labs-fleece-jacket\",\"remove-sauce-labs-onesie\",\"remove-test.allthethings()-t-shirt-(red)\"]\n\n for x in range(len(add_buttons)): # Testa todos os botões\n try:\n driver.find_element_by_id(add_buttons[x]).click() # Adiciona ao carrinho\n try: # Tenta remover\n driver.find_element_by_id(remove_buttons[x]).click() # Remove do carrinho\n print(\"\\033[1;32;48m║ Adicionar/Remover ao carrinho com sucesso ║\")\n except Exception:\n print(\"\\033[1;31;48m║ ERRO AO ADICIONAR/REMOVER DO CARRINHO ║\") # Se não remover, ocorreu uma falha\n except Exception:\n print(\"\\033[1;31;48m║ ITEM ADICIONADO ERRADO ║\")\n end = time.time()\n if end - start > 0.6:\n print(\"\\033[1;33;48m║ PROBLEMA DE PERFORMANCE PARA ADICIONAR/REMOVER ║ \\033[0m\") # Perfumaria\n\ndef cart(): # Realiza a compra do produto\n start = time.time()\n\n driver.find_element_by_xpath(\"//*[@id=\\\"shopping_cart_container\\\"]/a\").click() # Abre o carrinho\n driver.find_element_by_xpath(\"//*[@id=\\\"checkout\\\"]\").click() # Clica para comprar\n\n firstname = driver.find_element_by_xpath(\"//*[@id=\\\"first-name\\\"]\")\n firstname.send_keys('first') # Preenche campo de first name\n\n lastname = driver.find_element_by_xpath(\"//*[@id=\\\"last-name\\\"]\")\n lastname.send_keys('last') # Preenche campo de last name\n\n postalcode = driver.find_element_by_xpath(\"//*[@id=\\\"postal-code\\\"]\")\n postalcode.send_keys('postal') #Preenche campo de postal code\n\n if firstname.get_attribute(\"value\") == \"first\" and lastname.get_attribute(\"value\") == \"last\" and postalcode.get_attribute(\"value\") == \"postal\": # Se TODOS os campos estiverem preenchidos, clica para comprar\n driver.find_element_by_xpath(\"//*[@id=\\\"continue\\\"]\").click() \n\n print(\"\\033[1;32;48m║ Sucesso em preencher campos da compra ║\")\n\n driver.find_element_by_xpath(\"//*[@id=\\\"finish\\\"]\").click() # Finalizar a compra\n\n if driver.find_element_by_xpath(\"//*[@id=\\\"checkout_complete_container\\\"]/h2\").text == \"THANK YOU FOR YOUR ORDER\": # Se aparecer 'Thank you\" a compra foi realizada com sucesso\n \n print(\"\\033[1;32;48m║ Sucesso em realizar a compra ║\")\n print(\"\\033[1;32;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n\n else:\n\n\n print(\"\\033[1;31;48m║ FALHA EM REALIZAR A COMPRA ║\")\n print(\"\\033[1;31;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n\n else: # Se pelo menos um campo estiver em branco\n driver.find_element_by_xpath(\"//*[@id=\\\"continue\\\"]\").click() \n error = driver.find_element_by_xpath(\"//*[@id=\\\"checkout_info_container\\\"]/div/form/div[1]/div[4]/h3\").text # Clica em continuar a compra para capturar o erro (e o campo está vazio)\n \n print(\"\\033[1;31;48m║ ERRO AO PREENCHER CAMPOS DA COMPRA ║ >> \", error)\n print(\"\\033[1;31;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n\n end = time.time()\n if end - start > 0.6:\n print(\"\\033[1;33;48m║ PROBLEMA DE PERFORMANCE AO REALIZAR A COMPRA ║\\033[0m\") # Perfumaria\n print(\"\\033[1;33;48m╚═════════════════════════════════════════════════╝\\033[0m\") # Perfumaria\n","repo_name":"andrebeolchi/saucedemo","sub_path":"func.py","file_name":"func.py","file_ext":"py","file_size_in_byte":13350,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"20993801789","text":"from odoo import api, fields, models\n\n\nclass Users(models.Model):\n _inherit = \"res.users\"\n\n assigned_journal_id = fields.Many2one(\n \"account.journal\",\n string=\"Sales Journal\",\n domain=\"[('type', 'in', ('cash','bank'))]\",\n help=\"Specifying Sales Journal to User.\",\n )\n","repo_name":"smart-business-technology/dijla","sub_path":"specify_journal_to_user/models/res_users.py","file_name":"res_users.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32114904610","text":"import torch\nfrom torch import nn\nfrom torchvision import models, datasets, transforms\nimport torch.nn.functional as F\nimport torch.optim as optim\n\n#Create the neural network architecture, return logits instead of activation in forward method (Eg. softmax).\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(1, 20, 5, 1)\n self.conv2 = nn.Conv2d(20, 50, 5, 1)\n self.fc1 = nn.Linear(4*4*50, 500)\n self.fc2 = nn.Linear(500, 10)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n x = F.max_pool2d(x, 2, 2)\n x = F.relu(self.conv2(x))\n x = F.max_pool2d(x, 2, 2)\n x = x.view(-1, 4*4*50)\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n\nclassifier = models.resnet18(pretrained=False)\n\nd1 = torch.nn.Sequential(*(list(classifier.children())[5:-2]), nn.Linear(in_features=512, out_features=10, bias=True), nn.Linear(in_features=10, out_features=2, bias=True))\n\nd2 = torch.nn.Sequential(*(list(classifier.children())[6:-2]), nn.Linear(in_features=512, out_features=10, bias=True), nn.Linear(in_features=10, out_features=2, bias=True))\n\nd3 = torch.nn.Sequential(*(list(classifier.children())[7:-2]), nn.Linear(in_features=512, out_features=10, bias=True), nn.Linear(in_features=10, out_features=2, bias=True))\n\nd4 = torch.nn.Sequential(*(list(classifier.children())[8:-2]), nn.Linear(in_features=512, out_features=10, bias=True), nn.Linear(in_features=10, out_features=2, bias=True))\n","repo_name":"ksivaman/observer-networks","sub_path":"code/mnist/architecture.py","file_name":"architecture.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"73699372887","text":"# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass Solution:\n def hasCycle(self, head: ListNode) -> bool:\n nodes = set()\n\n node = head\n while node:\n if node not in nodes:\n nodes.add(node)\n node = node.next\n else:\n return True\n\n return False\n\n# change to set, it's much faster\n# but still space is still O(n) or even larger since i am using a hash table\n","repo_name":"buptwxd2/leetcode","sub_path":"Round_1/141. Linked List Cycle/solution_2.py","file_name":"solution_2.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14268806995","text":"from conspiracy.scraper import Scraper\nfrom bs4 import BeautifulSoup\nimport re\nimport requests\n\n\ndef sanitize_name(str):\n return str.lower().strip().replace(\"-\", \"_\").replace(\" \", \"_\")\n\n\ndef image_url_from_href(url):\n match = re.search(r\"/recolor_carpet/(\\d+)/\", url)\n if match:\n return f\"https://www.omegapatternworks.com/replace_colors/{match.group(1)}/?from=&to=&cnt=undefined\"\n\n\nclass OmegaCarpetScraper(Scraper):\n \"\"\"Scraper for carpet textures from Omega Pattern Works\n https://www.omegapatternworks.com/\n \"\"\"\n scraper_name = \"omega\"\n\n def __init__(self):\n self._urls_cache = []\n super()\n\n def scrape_page(self, page):\n page_url = f\"https://omegapatternworks.com/categories/?page={page}\"\n\n resp = requests.get(page_url)\n soup = BeautifulSoup(resp.text, \"html.parser\")\n for div in soup.find_all(\"div\", class_=\"carpet_preview\"):\n url = image_url_from_href(div.find(\"a\").get(\"href\"))\n if not url:\n continue\n\n title_element = div.find(\"p\", class_=\"carpet_name\")\n name = sanitize_name(title_element.text) + \".png\"\n self._urls_cache.append((url, name))\n\n # Scrape next page if we need to\n next_page_button = soup.find(\"a\", text=\"›\")\n if next_page_button and not (\"disabled\" in next_page_button.get(\"class\", [])):\n self.scrape_page(page + 1)\n\n def build_url_list(self):\n self.scrape_page(1)\n return self._urls_cache\n\n\nif __name__ == \"__main__\":\n scraper = OmegaCarpetScraper()\n scraper.run()\n","repo_name":"rafraser/carpet-conspiracy","sub_path":"conspiracy/scrapers/omegacarpet.py","file_name":"omegacarpet.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15085980574","text":"import time\nfrom kcenters import KCenters\nfrom utils import distance, balance_calculation\n\ndef run_experiments(degrees, data, fairlets, fairlet_centers, verbose=True):\n\t\"\"\"\n\tRun experiments for decomposition.\n\n\tArgs:\n\t\tdegrees (int) : Maximum degree for running K-Centers\n\t\tdata (list) : Data points\n\t\tfairlets (list) : Fairlets obtained from the decomposition\n\t\tfairlet_centers (list) : Fairlet centers obtained from the decomposition\n\t\tverbose (bool) : Indicator for printing progress\n\n\tReturns:\n\t\tcurr_degrees (list)\n\t\tcurr_costs (list)\n\t\tcurr_balances (list)\n\t\"\"\"\n\tcurr_degrees = []\n\tcurr_costs = []\n\tcurr_balances = []\n\n\tfor degree in range(3, min(degrees+1, len(fairlet_centers)), 1):\n\t\tstart_time = time.time()\n\t\t\n\t\tkcenters = KCenters(k=degree)\n\t\tkcenters.fit([data[i] for i in fairlet_centers])\n\t\tmapping = kcenters.assign()\n\t\t\n\t\tfinal_clusters = []\n\t\tfor fairlet_id, final_cluster in mapping:\n\t\t\tfor point in fairlets[fairlet_id]:\n\t\t\t\tfinal_clusters.append((point, fairlet_centers[final_cluster]))\n\t\t\t\t\n\t\tcenters = [fairlet_centers[i] for i in kcenters.centers]\n\t\tcurr_degrees.append(degree)\n\t\tcurr_costs.append(max([min([distance(data[j], i) for j in centers]) for i in data]))\n\t\tcurr_balances.append(balance_calculation(data, centers, final_clusters))\n\t\t\n\t\tif verbose:\n\t\t\tprint(\"Time taken for Degree %d - %.3f seconds.\"%(degree, time.time() - start_time))\n\n\treturn curr_degrees, curr_costs, curr_balances","repo_name":"guptakhil/fair-clustering-fairlets","sub_path":"experiments.py","file_name":"experiments.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"31"} +{"seq_id":"40287805272","text":"import subprocess as sub\nimport scapy\nfrom scapy.all import *\nfrom scapy.contrib import openflow as op\n\n\nsrc_ip = \"192.168.0.2\"\ninterface = \"eth0\"\nctrl_ip=\"192.168.0.3\"\nctrl_port=6653\n\ndef ctrl_data():\n data=[]\n capture = sub.Popen(('sudo', 'tcpdump', '-ni', 'eth0', 'tcp', 'port', '6653', '-w', 'capture.pcap','-c','20'), stdout=sub.PIPE)\n for row in iter(capture.stdout.readline, b''):\n print (row.rstrip())\n cap=rdpcap('capture.pcap')\n #i=0\n for caps in cap:\n try:\n if (caps[3].type == 5) and not data:\n print('enter')\n ctrl_ip = caps[1].src\n ctrl_port = caps[\"IP\"].sport\n print('\\nController IP:' +ctrl_ip)\n data.append(ctrl_ip)\n print('Controller port:' )\n print(ctrl_port)\n print('\\n')\n data.append(ctrl_port)\n\n except:\n pass\n if not data:\n msg = 'It did not enter the if loop'\n data.append(msg)\n return data\n\n\ndef attack(ctrl_ip, ctrl_port):\n i=0\n while True:\n packetin = Ether(src=\"02:42:c0:a8:00:02\", dst=\"02:42:c0:a8:00:03\")/IP(dst= ctrl_ip, src= src_ip)/op.TCP(sport= 46688 , dport= ctrl_port, seq=i)/op.OFPTPacketIn()\n i=i+1\n # packetin.show()\n sendp(packetin, iface = interface)\n print('Sending Packet_In to controller')\n","repo_name":"vika2218/NSOT","sub_path":"attack.py","file_name":"attack.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1133233196","text":"import os\n\n# data\nNUMERIC_FEATURES = [\n 'Elevation',\n 'Aspect',\n 'Slope',\n 'Horizontal_Distance_To_Hydrology',\n 'Vertical_Distance_To_Hydrology',\n 'Horizontal_Distance_To_Roadways',\n 'Hillshade_9am',\n 'Hillshade_Noon',\n 'Hillshade_3pm',\n 'Horizontal_Distance_To_Fire_Points'\n]\n\nDROP_FEATURES = [\n 'Id',\n 'Soil_Type7',\n 'Soil_Type15'\n]\n\nLAST_FEATURE = 'Soil_Type40'\nLABELS = \"Cover_Type\"\n# folders\nDATA_FOLDER = 'data'\nMODELS_FOLDER = 'models'\nTRAIN_CSV = os.path.join(DATA_FOLDER, 'train.csv')\nVAL_CSV = os.path.join(DATA_FOLDER, 'val.csv')\nMODEL = os.path.join(MODELS_FOLDER, 'model.pickle')\n# model selector log columns\nACCURACY = 'Accuracy'\nCLASSIFIER = 'Classifier'\nNAME = 'Name'\nLOG_COLS = [NAME,\n ACCURACY,\n CLASSIFIER]\n# model selector plot params\nCOLOR = 'b'\nPLOT_TITLE = 'Classifier Accuracy'\n","repo_name":"Docing13/ds_classifier","sub_path":"settings/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"44065439434","text":"\"\"\"\nles cables pour les moteurs, le cable brun est vers l'exterieur\nScript original pour l'utilisation du Raspberry Pi 4 avec le bouton, les poubelles avec les poubelles,\nle contrôleur des moteurs et la caméra\n\"\"\"\n# Import pour le sleep\nimport time\n\n# Import the PCA9685 module.\nimport Adafruit_PCA9685\n# Import pour prendre en charge l'input du bouton\nimport RPi.GPIO as GPIO\nimport numpy as np\n# Import lib pour camera\nimport picamera\n\nfrom keras.models import load_model\nfrom keras.preprocessing import image\n\n\ndef open_close_trash(number_trash):\n \"\"\"\n Ouverture d'une poubelle avec un certain numero et ferme la poubelle apres 5 sec\n \"\"\"\n # Configure min and max servo pulse lengths\n servo_min = 120 # Min pulse length out of 4096\n servo_max = 600 # Max pulse length out of 4096\n pwm.set_pwm(number_trash, 0, servo_min)\n time.sleep(5)\n pwm.set_pwm(number_trash, 0, servo_max)\n\n\ndef take_pic():\n \"\"\"\n Allume la camera, prends un photo et eteint la camera\n \"\"\"\n camera = picamera.PiCamera() # creation de l'instance camera\n try:\n camera.resolution = (1920, 1080)\n # camera.framerate = 32\n # camera.vflip = True # faire une rotation verticale\n camera.capture('dechet.jpg', resize=(424, 552)) # prendre photo\n finally:\n camera.close()\n\n\ndef predict(img_path):\n categories = [\"Blanc\", \"Bleu\", \"Jaune\", \"Orange\", \"Verre\"]\n img = image.load_img(img_path, target_size=(224, 224))\n img = np.expand_dims(img, axis=0)\n result = model.predict([img])\n return categories[np.argmax(result[0])]\n\n\n# Initialise the PCA9685 using the default address (0x40).\npwm = Adafruit_PCA9685.PCA9685()\n\n# Set frequency to 60hz, good for servos.\npwm.set_pwm_freq(60)\n# Regarder si ca change la vitesse d'Ouverture\n\n# Config boutton\nGPIO.setmode(GPIO.BCM)\nbutton_input = 17 # 11 pour GPIO.BOARD car c'est le num 11 phyisquement et 17 logiquement\nGPIO.setup(button_input, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n\nmodel = load_model('Model.model')\n\ntry:\n while True:\n # lorsque le boutton est presse\n if not GPIO.input(button_input):\n time.sleep(0.5)\n # takePic()\n res = predict(\"dechet.jpg\")\n categories = [\"Blanc\", \"Bleu\", \"Jaune\", \"Orange\", \"Verre\"]\n index = categories.index(res)\n open_close_trash(index)\n GPIO.cleanup()\nexcept KeyboardInterrupt:\n GPIO.cleanup()\n","repo_name":"BricePetit/ULB-INFO-F308-G5-1920","sub_path":"Raspberry/tri_dechet.py","file_name":"tri_dechet.py","file_ext":"py","file_size_in_byte":2432,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13440359801","text":"from __future__ import annotations\n\nimport itertools\nfrom typing import TYPE_CHECKING, Optional\n\nimport pytest\n\nfrom qcarchivetesting import load_molecule_data\nfrom qcportal.optimization import (\n OptimizationSpecification,\n)\nfrom qcportal.record_models import RecordStatusEnum, PriorityEnum\nfrom qcportal.singlepoint import QCSpecification\nfrom qcportal.utils import now_at_utc\n\nif TYPE_CHECKING:\n from qcarchivetesting.testing_classes import QCATestingSnowflake\n from qcportal import PortalClient\n\nfrom qcfractal.components.optimization.testing_helpers import (\n compare_optimization_specs,\n test_specs,\n submit_test_data,\n run_test_data,\n)\n\n\ndef test_optimization_client_tag_priority(snowflake_client: PortalClient):\n water = load_molecule_data(\"water_dimer_minima\")\n\n for tag, priority in itertools.product([\"*\", \"tag99\"], list(PriorityEnum)):\n meta1, id1 = snowflake_client.add_optimizations(\n [water],\n \"prog\",\n QCSpecification(\n program=\"prog\",\n method=\"hf\",\n basis=\"sto-3g\",\n driver=\"deferred\",\n keywords={\"tag_priority\": [tag, priority]},\n ),\n priority=priority,\n tag=tag,\n )\n\n assert meta1.n_inserted == 1\n rec = snowflake_client.get_records(id1, include=[\"task\"])\n assert rec[0].task.tag == tag\n assert rec[0].task.priority == priority\n\n\n@pytest.mark.parametrize(\"spec\", test_specs)\n@pytest.mark.parametrize(\"owner_group\", [\"group1\", None])\ndef test_optimization_client_add_get(\n submitter_client: PortalClient, spec: OptimizationSpecification, owner_group: Optional[str]\n):\n water = load_molecule_data(\"water_dimer_minima\")\n hooh = load_molecule_data(\"hooh\")\n ne4 = load_molecule_data(\"neon_tetramer\")\n all_mols = [water, hooh, ne4]\n\n time_0 = now_at_utc()\n meta, id = submitter_client.add_optimizations(\n initial_molecules=all_mols,\n program=spec.program,\n keywords=spec.keywords,\n protocols=spec.protocols,\n qc_specification=spec.qc_specification,\n tag=\"tag1\",\n priority=PriorityEnum.low,\n owner_group=owner_group,\n )\n\n time_1 = now_at_utc()\n assert meta.success\n\n recs = submitter_client.get_optimizations(id, include=[\"task\", \"initial_molecule\"])\n\n assert len(recs) == 3\n\n for r in recs:\n assert r.record_type == \"optimization\"\n assert r.record_type == \"optimization\"\n assert compare_optimization_specs(spec, r.specification)\n\n assert r.status == RecordStatusEnum.waiting\n assert r.children_status == {}\n\n assert r.task.function is None\n assert r.task.tag == \"tag1\"\n assert r.task.priority == PriorityEnum.low\n\n assert r.owner_user == submitter_client.username\n assert r.owner_group == owner_group\n\n assert time_0 < r.created_on < time_1\n assert time_0 < r.modified_on < time_1\n\n mol1 = submitter_client.get_molecules([recs[0].initial_molecule_id])[0]\n mol2 = submitter_client.get_molecules([recs[1].initial_molecule_id])[0]\n mol3 = submitter_client.get_molecules([recs[2].initial_molecule_id])[0]\n assert mol1.identifiers.molecule_hash == water.get_hash()\n assert recs[0].initial_molecule.identifiers.molecule_hash == water.get_hash()\n\n assert mol2.identifiers.molecule_hash == hooh.get_hash()\n assert recs[1].initial_molecule.identifiers.molecule_hash == hooh.get_hash()\n\n assert mol3.identifiers.molecule_hash == ne4.get_hash()\n assert recs[2].initial_molecule.identifiers.molecule_hash == ne4.get_hash()\n\n\n@pytest.mark.parametrize(\"spec\", test_specs)\n@pytest.mark.parametrize(\"find_existing\", [True, False])\ndef test_optimization_client_add_duplicate(\n submitter_client: PortalClient, spec: OptimizationSpecification, find_existing: bool\n):\n water = load_molecule_data(\"water_dimer_minima\")\n hooh = load_molecule_data(\"hooh\")\n ne4 = load_molecule_data(\"neon_tetramer\")\n all_mols = [water, hooh, ne4]\n\n meta, id = submitter_client.add_optimizations(\n initial_molecules=all_mols,\n program=spec.program,\n keywords=spec.keywords,\n protocols=spec.protocols,\n qc_specification=spec.qc_specification,\n tag=\"tag1\",\n priority=PriorityEnum.low,\n owner_group=None,\n find_existing=True,\n )\n\n assert meta.success\n assert meta.n_inserted == len(all_mols)\n\n meta, id2 = submitter_client.add_optimizations(\n initial_molecules=all_mols,\n program=spec.program,\n keywords=spec.keywords,\n protocols=spec.protocols,\n qc_specification=spec.qc_specification,\n tag=\"tag1\",\n priority=PriorityEnum.low,\n owner_group=None,\n find_existing=find_existing,\n )\n\n if find_existing:\n assert meta.n_existing == len(all_mols)\n assert meta.n_inserted == 0\n assert id == id2\n else:\n assert meta.n_existing == 0\n assert meta.n_inserted == len(all_mols)\n assert set(id).isdisjoint(id2)\n\n\ndef test_optimization_client_add_existing_molecule(snowflake_client: PortalClient):\n spec = test_specs[0]\n\n water = load_molecule_data(\"water_dimer_minima\")\n hooh = load_molecule_data(\"hooh\")\n ne4 = load_molecule_data(\"neon_tetramer\")\n all_mols = [water, hooh, ne4]\n\n # Add a molecule separately\n _, mol_ids = snowflake_client.add_molecules([ne4])\n\n # Now add records\n meta, id = snowflake_client.add_optimizations(\n initial_molecules=all_mols,\n program=spec.program,\n keywords=spec.keywords,\n protocols=spec.protocols,\n qc_specification=spec.qc_specification,\n tag=\"tag1\",\n priority=PriorityEnum.low,\n )\n recs = snowflake_client.get_optimizations(id)\n\n assert len(recs) == 3\n assert recs[2].initial_molecule_id == mol_ids[0]\n\n\n@pytest.mark.parametrize(\"opt_file\", [\"opt_psi4_benzene\", \"opt_psi4_fluoroethane_notraj\"])\ndef test_optimization_client_delete(snowflake: QCATestingSnowflake, opt_file: str):\n storage_socket = snowflake.get_storage_socket()\n activated_manager_name, _ = snowflake.activate_manager()\n snowflake_client = snowflake.client()\n\n opt_id = run_test_data(storage_socket, activated_manager_name, opt_file)\n\n rec = snowflake_client.get_optimizations(opt_id)\n child_ids = [x.id for x in rec.trajectory]\n\n meta = snowflake_client.delete_records(opt_id, soft_delete=True, delete_children=False)\n assert meta.success\n assert meta.deleted_idx == [0]\n assert meta.n_children_deleted == 0\n\n child_recs = snowflake_client.get_records(child_ids, missing_ok=True)\n assert all(x.status == RecordStatusEnum.complete for x in child_recs)\n opt_rec = snowflake_client.get_records(opt_id)\n if child_ids:\n assert opt_rec.children_status == {RecordStatusEnum.complete: len(child_ids)}\n\n # Undo what we just did\n snowflake_client.undelete_records(opt_id)\n\n meta = snowflake_client.delete_records(opt_id, soft_delete=True, delete_children=True)\n assert meta.success\n assert meta.deleted_idx == [0]\n assert meta.n_children_deleted == len(child_ids)\n\n child_recs = snowflake_client.get_records(child_ids, missing_ok=True)\n assert all(x.status == RecordStatusEnum.deleted for x in child_recs)\n opt_rec = snowflake_client.get_records(opt_id)\n if child_ids:\n assert opt_rec.children_status == {RecordStatusEnum.deleted: len(child_ids)}\n\n meta = snowflake_client.delete_records(opt_id, soft_delete=False, delete_children=True)\n assert meta.success\n assert meta.deleted_idx == [0]\n assert meta.n_children_deleted == len(child_ids)\n\n recs = snowflake_client.get_optimizations(opt_id, missing_ok=True)\n assert recs is None\n\n child_recs = snowflake_client.get_records(child_ids, missing_ok=True)\n assert all(x is None for x in child_recs)\n\n # DB should be pretty empty now\n query_res = snowflake_client.query_records()\n query_res_l = list(query_res)\n assert len(query_res_l) == 0\n\n\ndef test_optimization_client_harddelete_nochildren(snowflake: QCATestingSnowflake):\n storage_socket = snowflake.get_storage_socket()\n activated_manager_name, _ = snowflake.activate_manager()\n snowflake_client = snowflake.client()\n\n opt_id = run_test_data(storage_socket, activated_manager_name, \"opt_psi4_benzene\")\n\n rec = snowflake_client.get_optimizations(opt_id)\n child_ids = [x.id for x in rec.trajectory]\n\n meta = snowflake_client.delete_records(opt_id, soft_delete=False, delete_children=False)\n assert meta.success\n assert meta.deleted_idx == [0]\n assert meta.n_children_deleted == 0\n\n recs = snowflake_client.get_optimizations(opt_id, missing_ok=True)\n assert recs is None\n\n child_recs = snowflake_client.get_records(child_ids, missing_ok=True)\n assert all(x is not None for x in child_recs)\n\n\n@pytest.mark.parametrize(\"opt_file\", [\"opt_psi4_benzene\", \"opt_psi4_methane_sometraj\"])\ndef test_optimization_client_delete_traj_inuse(snowflake: QCATestingSnowflake, opt_file: str):\n storage_socket = snowflake.get_storage_socket()\n activated_manager_name, _ = snowflake.activate_manager()\n snowflake_client = snowflake.client()\n\n opt_id = run_test_data(storage_socket, activated_manager_name, opt_file)\n\n rec = snowflake_client.get_optimizations(opt_id)\n child_ids = [x.id for x in rec.trajectory]\n\n meta = snowflake_client.delete_records(child_ids[0], soft_delete=False)\n assert meta.success is False\n assert meta.error_idx == [0]\n\n ch_rec = snowflake_client.get_records(child_ids[0])\n assert ch_rec is not None\n\n\n@pytest.mark.parametrize(\"opt_file\", [\"opt_psi4_benzene\", \"opt_psi4_methane_sometraj\"])\n@pytest.mark.parametrize(\"fetch_traj\", [True, False])\ndef test_optimization_client_traj(snowflake: QCATestingSnowflake, opt_file: str, fetch_traj: bool):\n storage_socket = snowflake.get_storage_socket()\n activated_manager_name, _ = snowflake.activate_manager()\n snowflake_client = snowflake.client()\n\n opt_id = run_test_data(storage_socket, activated_manager_name, opt_file)\n\n rec = snowflake_client.get_optimizations(opt_id)\n rec_traj = snowflake_client.get_optimizations(opt_id, include=[\"trajectory\"])\n\n assert rec_traj.trajectory is not None\n\n if fetch_traj:\n rec._fetch_trajectory()\n assert rec.trajectory_ids_ is not None\n assert rec.trajectory_records_ is not None\n else:\n assert rec.trajectory_ids_ is None\n assert rec.trajectory_records_ is None\n\n assert rec.trajectory_element(0).id == rec_traj.trajectory[0].id\n assert rec.trajectory_element(-1).id == rec_traj.trajectory[-1].id\n\n\ndef test_optimization_client_query(snowflake: QCATestingSnowflake):\n storage_socket = snowflake.get_storage_socket()\n activated_manager_name, _ = snowflake.activate_manager()\n snowflake_client = snowflake.client()\n\n id_1, _ = submit_test_data(storage_socket, \"opt_psi4_fluoroethane_notraj\")\n id_2, _ = submit_test_data(storage_socket, \"opt_psi4_benzene\")\n id_3, _ = submit_test_data(storage_socket, \"opt_psi4_methane_sometraj\")\n\n recs = snowflake_client.get_optimizations([id_1, id_2, id_3])\n\n # query for molecule\n query_res = snowflake_client.query_optimizations(initial_molecule_id=[recs[1].initial_molecule_id])\n query_res_l = list(query_res)\n assert len(query_res_l) == 1\n\n # query for program\n query_res = snowflake_client.query_optimizations(program=[\"psi4\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 0\n\n # query for program\n query_res = snowflake_client.query_optimizations(program=[\"geometric\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 3\n\n query_res = snowflake_client.query_optimizations(qc_program=[\"psi4\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 3\n\n # query for basis\n query_res = snowflake_client.query_optimizations(qc_basis=[\"sTO-3g\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 0\n\n query_res = snowflake_client.query_optimizations(qc_basis=[None])\n query_res_l = list(query_res)\n assert len(query_res_l) == 0\n\n query_res = snowflake_client.query_optimizations(qc_basis=[\"\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 0\n\n # query for method\n query_res = snowflake_client.query_optimizations(qc_method=[\"b3lyP\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 3\n\n # Some empty queries\n query_res = snowflake_client.query_optimizations(program=[\"madeupprog\"])\n query_res_l = list(query_res)\n assert len(query_res_l) == 0\n\n # Query by default returns everything\n query_res = snowflake_client.query_optimizations()\n query_res_l = list(query_res)\n assert len(query_res_l) == 3\n\n # Query by default (with a limit)\n query_res = snowflake_client.query_optimizations(limit=1)\n query_res_l = list(query_res)\n assert len(query_res_l) == 1\n","repo_name":"MolSSI/QCFractal","sub_path":"qcfractal/qcfractal/components/optimization/test_record_client.py","file_name":"test_record_client.py","file_ext":"py","file_size_in_byte":13000,"program_lang":"python","lang":"en","doc_type":"code","stars":134,"dataset":"github-code","pt":"31"} +{"seq_id":"37065933003","text":"## Day 22 of 100 days of code\n\n#Circular Linked List Continues\n\nclass Node:\n def __init__(self,data):\n self.data = data\n self.next = None\n\nclass CircularLinkedList:\n def __init__(self):\n self.head = None\n\n #Printing List checking till entry point\n def printList(self):\n temp = self.head\n if self.head is not None:\n while(True):\n print(temp.data)\n temp = temp.next\n if(temp == self.head):\n break\n \n def sortedPush(self,data):\n temp = self.head\n\n node = Node(data)\n\n #Base Case\n if self.head == None:\n self.head = node\n node.next = self.head\n return\n \n elif(temp.data>=node.data):\n #if node is smallest in the list\n\n while temp.next != self.head:\n temp = temp.next\n\n temp.next = node\n node.next = self.head\n self.head = node\n\n else:\n #insertion in somewhere middle\n \n while(temp.next != self.head and temp.next.data<node.data):\n temp = temp.next\n\n node.next = temp.next\n temp.next = node\n\n#Checking if any list is circular or not\ndef checkList(head):\n\n #Base case\n if head is None:\n return True\n\n node = head.next\n\n while(node is not None and node is not head):\n node = node.next\n\n #If node == head than Circular if not then not\n return (node == head)\n\n \n\n#testing\nclist = CircularLinkedList()\n\nclist.sortedPush(1)\nclist.sortedPush(4)\nclist.sortedPush(2)\nclist.sortedPush(7)\nclist.sortedPush(9)\nclist.sortedPush(0)\n\nclist.printList()\n\n#checking list\nprint(checkList(clist.head))\n \n\n","repo_name":"shanku01/100-days-of-code","sub_path":"Day 22/22.py","file_name":"22.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"43440192056","text":"# python3\n\nfrom collections import deque\n\ndef max_sliding_window_naive(sequence, m):\n maximum = []\n q = deque()\n \n for i in range(m):\n while q and sequence[i] >= sequence[q[-1]]:\n q.pop()\n q.append(i)\n \n for i in range(m, len(sequence)):\n maximum.append(sequence[q[0]])\n while q and q[0] <= i - m:\n q.popleft()\n while q and sequence[i] >= sequence[q[-1]]:\n q.pop()\n q.append(i)\n \n maximum.append(sequence[q[0]])\n return maximum\n\nif __name__ == '__main__':\n n = int(input())\n input_sequence = [int(i) for i in input().split()]\n assert len(input_sequence) == n\n window_size = int(input())\n\n print(*max_sliding_window_naive(input_sequence, window_size))\n\n","repo_name":"prasang-gupta/online-courses","sub_path":"data-structures-and-algorithms-specialization/course-02-data-structures/assignment/week-01/5_max_sliding_window/max_sliding_window.py","file_name":"max_sliding_window.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19160628573","text":"from typing import List, Tuple\n\nimport numpy as np\nimport xarray as xr\nimport skimage.transform\n\n\ndef get_shape_of_largest_array(arr: List[np.array]) -> Tuple:\n \"\"\"\n Takes a list of images, each of the shape (channels, width, height) and\n returns the shape of the one with the largest area (width * height).\n \"\"\"\n largest_area = 0\n largest_shape = None\n for a in arr:\n area = np.product(a.shape[1:])\n if area > largest_area:\n largest_area = area\n largest_shape = a.shape[1:]\n return largest_shape\n\ndef match_array_shapes(arr: List[np.array], shape: Tuple[int, int]) -> List[np.array]:\n \"\"\"\n Takes a list of images, each of shape (channels, width, height) and resizes \n them to match the given shape.\n \"\"\"\n result = [] \n for a in arr:\n channels = a.shape[0]\n resized_shape = (channels,) + shape\n resized_a = skimage.transform.resize(a, resized_shape, order=0, preserve_range=True)\n result.append(resized_a)\n return result\n\ndef get_lon_mask(da: xr.DataArray, lons: np.array) -> xr.DataArray:\n mask = da.values[(da < min(lons)) | (da > max(lons))]\n return mask","repo_name":"jasonjewik/bsd-dataset","sub_path":"bsd_dataset/datasets/dataset_utils.py","file_name":"dataset_utils.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16060936281","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\n\nfrom tasks.models import TaskList, Task\nfrom tasks.forms import TaskListForm, TaskForm\n\n\n@login_required\ndef home(request):\n task_lists = TaskList.objects.filter(user=request.user)\n incomplete_count = Task.objects.filter(\n task_list__user=request.user, is_completed=False, due_date__date__gte=timezone.now().date()\n ).count()\n completed_count = Task.objects.filter(\n task_list__user=request.user, is_completed=True\n ).count()\n overdue_count = Task.objects.filter(\n task_list__user=request.user, is_completed=False, due_date__date__lt=timezone.now().date()\n ).count()\n tasks = Task.objects.filter(task_list__user=request.user, is_completed=False, due_date__date=timezone.now().date())\n\n return render(\n request,\n \"home.html\",\n {\n \"task_lists\": task_lists,\n \"incomplete_count\": incomplete_count,\n \"completed_count\": completed_count,\n \"overdue_count\": overdue_count,\n \"tasks\": tasks,\n },\n )\n\n\n@login_required\ndef prioritized_tasks(request):\n task_lists = TaskList.objects.filter(user=request.user)\n tasks = Task.objects.filter(\n task_list__user=request.user, is_important=True, is_completed=False\n )\n\n return render(\n request, \"prioritized_tasks.html\", {\"task_lists\": task_lists, \"tasks\": tasks}\n )\n\n\n@login_required\ndef task_list_detail(request, pk):\n task_lists = TaskList.objects.filter(user=request.user)\n task_list_current = get_object_or_404(\n TaskList.objects.filter(user=request.user), pk=pk\n )\n tasks = Task.objects.filter(task_list=task_list_current).order_by('created_at')\n\n return render(\n request,\n \"task_list_detail.html\",\n {\n \"task_lists\": task_lists,\n \"task_list_current\": task_list_current,\n \"tasks\": tasks,\n },\n )\n\n\n@login_required\ndef create_task_list(request):\n task_lists = TaskList.objects.filter(user=request.user)\n if request.method == \"POST\":\n form = TaskListForm(request.POST)\n if form.is_valid():\n task_list = form.save(commit=False)\n task_list.user = request.user\n task_list.save()\n return redirect(\"task_list_detail\", pk=task_list.pk)\n else:\n form = TaskListForm()\n return render(\n request, \"create_task_list.html\", {\"form\": form, \"task_lists\": task_lists}\n )\n\n\n@login_required\ndef update_task_list(request, pk):\n task_lists = TaskList.objects.filter(user=request.user)\n task_list = get_object_or_404(TaskList.objects.filter(user=request.user), pk=pk)\n if request.method == \"POST\":\n form = TaskListForm(request.POST, instance=task_list)\n if form.is_valid():\n form.save()\n return redirect(\"task_list_detail\", pk=task_list.pk)\n else:\n form = TaskListForm(instance=task_list)\n return render(\n request, \"update_task_list.html\", {\"form\": form, \"task_lists\": task_lists}\n )\n\n\n@login_required\ndef delete_task_list(request, pk):\n task_list = get_object_or_404(TaskList.objects.filter(user=request.user), pk=pk)\n task_list.delete()\n return redirect(\"home\")\n\n\n@login_required\ndef delete_task(request, pk):\n task = get_object_or_404(Task.objects.filter(task_list__user=request.user), pk=pk)\n task_list_pk = task.task_list.pk\n task.delete()\n return redirect(\"task_list_detail\", pk=task_list_pk)\n\n\n@login_required\ndef create_task(request, task_list_pk):\n task_list = get_object_or_404(\n TaskList.objects.filter(user=request.user), pk=task_list_pk\n )\n task_lists = TaskList.objects.filter(user=request.user)\n if request.method == \"POST\":\n form = TaskForm(request.POST)\n if form.is_valid():\n task = form.save(commit=False)\n task.task_list = task_list\n task.save()\n return redirect(\"task_list_detail\", pk=task_list_pk)\n else:\n form = TaskForm()\n return render(\n request,\n \"create_task.html\",\n {\"form\": form, \"task_list\": task_list, \"task_lists\": task_lists},\n )\n\n\n@login_required\ndef update_task(request, task_list_pk, task_pk):\n task_list = get_object_or_404(\n TaskList.objects.filter(user=request.user), pk=task_list_pk\n )\n task_lists = TaskList.objects.filter(user=request.user)\n task = get_object_or_404(Task, pk=task_pk, task_list=task_list)\n if request.method == \"POST\":\n form = TaskForm(request.POST, instance=task)\n if form.is_valid():\n form.save()\n return redirect(\"task_list_detail\", pk=task_list_pk)\n else:\n form = TaskForm(instance=task)\n return render(\n request,\n \"update_task.html\",\n {\"form\": form, \"task_list\": task_list, \"task\": task, \"task_lists\": task_lists},\n )\n\n\n@login_required\ndef toggle_task(request, pk):\n task = get_object_or_404(Task.objects.filter(task_list__user=request.user), pk=pk)\n task.is_completed = not task.is_completed\n task.save()\n return redirect(\"task_list_detail\", pk=task.task_list.pk)\n","repo_name":"siringulec/focus-brew","sub_path":"tasks/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33948158360","text":"\"\"\"\n# Sample code to perform I/O:\n\nname = input() # Reading input from STDIN\nprint('Hi, %s.' % name) # Writing output to STDOUT\n\n# Warning: Printing unwanted or ill-formatted data to output will cause the test cases to fail\n\"\"\"\n\n# Write your code here\nfrom collections import defaultdict\n\nt = int(input())\nfor _ in range(t):\n n = int(input())\n sets = []\n for i in range(n):\n for s in list(map(int, input().strip().split()))[1:]: # the fist is k\n sets.append((s, i))\n sets.sort()\n start = 0\n end = -1\n best = float('inf')\n counts = defaultdict(int)\n while True:\n if len(counts) == n:\n best = min(best, sets[end][0] - sets[start][0])\n left = sets[start][1]\n counts[left] -= 1\n if counts[left] == 0:\n counts.pop(left)\n start += 1\n else:\n end += 1\n if end == len(sets):\n break\n counts[sets[end][1]] += 1\n if len(counts) == n:\n best = min(best, sets[end][0] - sets[start][0])\n print(best * 2)\n","repo_name":"HBinhCT/Q-project","sub_path":"hackerearth/Algorithms/Friendly Neighbors/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"36799348536","text":"\n# coding: utf-8\n\n# In[ ]:\n\n\n# Importação das bibliotecas que serão utilizadas no estudo do algoritmo.\n\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nimport matplotlib.pyplot as plt\nimport plotly.graph_objs as go\nimport plotly.offline as py\nimport plotly\nplotly.offline.init_notebook_mode()\nimport datetime\n\n\n# In[ ]:\n\n\n# O conjunto de dados possui registrados dos preços da Petribrás entre 01/2010 até 11/2017.\n# Vamos utilizar esses dados para criar nosso modelo preditivo e comparar os respectivos valores.\n# Não irei utilizar o encoding, pois não temos dados textuais no conjunto de dados.\n\ncjdados = pd.read_csv('petr4.csv')\n\n\n# In[ ]:\n\n\n# Transformando a coluna Date em uma coluna do tipo Datetime\n\ncjdados['Date'] = pd.to_datetime(cjdados['Date'])\n\n\n# In[ ]:\n\n\n# Visualizando os dados com o método tail. O método tail() exibe os últimos registros.\n# O método head() exibe os primeiros registros.\n\ncjdados.tail()\n#cjdados.head()\n\n\n# In[ ]:\n\n\n# Variação entre o preco de abertura e fechamento.\n# Criando um coluna \"Variação\" para recebeer o resultado do valor de fechamento e valor de abertura.\n\n\ncjdados['Variation'] = cjdados['Close'].sub(cjdados['Open'])\n\n\n# In[ ]:\n\n\n# Criar gráfico com os valores dos preços no periodo analisado 2010 a 2017.\n# Utiliza a biblioteca pyplot para plotar dados financeiros temporais.\n# Documentação do Matplotlib no link https://matplotlib.org/contents.html\n\nx1 = cjdados.Date\ny1 = cjdados.Close\ndata = [go.Scatter(x = x1, y = y1)]\nlayout = go.Layout(\n xaxis = dict(\n range = ['01-01-2010','11-04-2017'],\n title = 'Ano' \n ),\n yaxis = dict(\n range = [min(x1), max(y1)],\n title = 'Valor da Ação'\n ))\nfig = go.Figure(data = data, layout = layout)\npy.iplot(fig)\n\n\n# In[ ]:\n\n\n# Visualizando os Candlesticks através do gráfico.\n# Podemos observar que os parâmetros são as colunas do meu conjunto de dados que estamos inserindo na variável dados.\n\ncjdados2 = cjdados.head(7)\ndados = go.Candlestick(x = cjdados2.Date,\n open = cjdados2.Open,\n high = cjdados2.High,\n low = cjdados2.Low,\n close = cjdados2.Close,\n )\n\ndata = [dados]\npy.offline.iplot(data,filename='grafico_candlestick')\n\n\n# In[ ]:\n\n\n# Visualizando precos em formato de Candlesticks dos últimos 6 meses.\n\ncjdados2 = cjdados.head(180)\ndados = go.Candlestick(x = cjdados2.Date,\n open = cjdados2.Open,\n high = cjdados2.High,\n low = cjdados2.Low,\n close = cjdados2.Close,\n )\n\ndata = [dados]\npy.offline.iplot(data,filename='grafico_candlestick')\n\n\n# In[ ]:\n\n\n# Plota a variação no período do conjunto de dados 2010 até 2017.\n# O camando matplotlib é usado para exibir o gráfico no próprio jupyter notebook.\n\nget_ipython().run_line_magic('matplotlib', 'notebook')\n\n# Importação das bibliotecas que serão utilizadas.\n\nimport matplotlib.dates as mdates\nimport datetime as dt\n\n# Dados para criação do gráfico.\n\nx = cjdados['Date']\ny = cjdados['Variation']\nplt.plot_date(x,y, color='b',fmt=\"r-\")\nplt.xticks(rotation=30)\nplt.show()\n\n\n# In[ ]:\n\n\n# Criação de uma variável treino que recebe todas as informações do nosso conjunto de dados.\n# O objeto é deixar os dados anteriores intactos.\ndadosTreino = cjdados\n\n\n# In[ ]:\n\n\n# Plota a dispersão entre o preço de abertura(Open) e fechamento(Close) dos últimos 100 dias.\n# Para pegar os últimos dias, podemos observar a sintaxe :100 como parâmetro.\n\nget_ipython().run_line_magic('matplotlib', 'notebook')\n\nx = dadosTreino.Open[:100]\ny = dadosTreino.Close[:100]\nplt.scatter(x,y,color='b')\nplt.xlabel('preco de abertura')\nplt.ylabel('preco de fechamento')\nplt.axis([min(x),max(x),min(y),max(y)])\nplt.autoscale('False')\nplt.show()\n\n\n# In[ ]:\n\n\n# Plota a dispersao entre o preço de máxima (high) e fechamento(Close) dos últimos 100 dias.\n\nget_ipython().run_line_magic('matplotlib', 'notebook')\nx = dadosTreino.High[:100]\ny = dadosTreino.Close[:100]\nplt.scatter(x,y,color='b')\nplt.xlabel('preco da maxima')\nplt.ylabel('preco de fechamento')\nplt.axis([min(x),max(x),min(y),max(y)])\nplt.autoscale('False')\nplt.show()\n\n\n# In[ ]:\n\n\n# Plota a dispersao entre o preço de mínima(Low) e fechamento(Close) dos últimos 100 dias.\n\nget_ipython().run_line_magic('matplotlib', 'notebook')\nx = dadosTreino.Low[:100]\ny = dadosTreino.Close[:100]\nplt.scatter(x,y,color='b')\nplt.xlabel('preco de Minima')\nplt.ylabel('preco de fechamento')\nplt.axis([min(x),max(x),min(y),max(y)])\nplt.autoscale('False')\nplt.show()\n\n\n# In[ ]:\n\n\n# Plota a dispersao entre o Volume e fechamento(Close) dos últimos 100 dias.\n\nget_ipython().run_line_magic('matplotlib', 'notebook')\nx = dadosTreino.Volume[:100]\ny = dadosTreino.Close[:100]\nplt.scatter(x,y,color='b')\nplt.xlabel('Volume')\nplt.ylabel('preco de fechamento')\nplt.axis([min(x),max(x),min(y),max(y)])\nplt.ticklabel_format(style='plain', axis='x')\nplt.autoscale('False')\nplt.xticks(rotation=45)\nplt.show()\n\n\n# In[ ]:\n\n\n# Vamos criar uma variável feature com os nomes das respectivas colunas.\n# Criação de uma variável treino para receber as respectivas features.\n\nfeatures = ['Open','High','Low','Volume']\ndadosTreino = dadosTreino[features]\n\n\n# In[ ]:\n\n\n# Visualizando os dados sem as classes. A nossa classe é a feature de fechamento (close)\n\ndadosTreino.head()\n\n\n# In[ ]:\n\n\n# Criando a variável y para receber o preço de fechamento (classes)\ny = cjdados['Close']\n\n\n# In[ ]:\n\n\n# Visualizando o dataframe y com as respectivas classes (fechamento)\ny\n\n\n# In[ ]:\n\n\n# Realizando o treinamento do algoritmo de Regressão Linear\n# Separando os dados de teste e de treino. \n# Usando o recurso **train_test_split** para separar dados de treino e teste.\n# Dessa forma o algoritmo é treinado com uma parte dos dados e testado com outra (dados não vistos).\n# Divisão dos dados de forma aleatória (75% para treino e 25% para teste).\n\nX_treino, X_teste, y_treino, y_teste = train_test_split(dadosTreino, y, random_state = 42)\n\n\n# In[ ]:\n\n\n# Visualizando o dataframe X_treino\nX_treino.head()\n\n\n# In[ ]:\n\n\n# Visualizando dados de teste.\nX_teste.head()\n\n\n# In[ ]:\n\n\n# Visualizando as classes de treino.\ny_treino.head()\n\n\n# In[ ]:\n\n\n# Visualizando as classes do teste.\ny_teste.head()\n\n\n# In[ ]:\n\n\n# Cria um objeto do tipo LinearRegression.\nmodeloLr = LinearRegression()\n\n\n# In[ ]:\n\n\n# Treinando o algoritmo através do método fit.\n\nmodeloLr.fit(X_treino,y_treino)\n\n\n# In[ ]:\n\n\n# Visualizando os coeficientes (pesos!)\n# Interessante observar o valor negativo do peso associado a feature Open (Abertura).\n\nmodeloLr.coef_\n\n\n# In[ ]:\n\n\n# Predizendo 10 preços com o método predict.\n\nmodeloLr.predict(X_teste)[:10]\n\n\n# In[ ]:\n\n\n# Visualizando preços reais.\ny_teste[:10]\n\n\n# In[ ]:\n\n\nget_ipython().run_line_magic('matplotlib', 'notebook')\n\n# Armazena dados preditos em dataframe.\npredicoes = pd.DataFrame(modeloLr.predict(X_teste)[:10])\n\n# Armazena dados reais em dataframe.\ny_teste2= pd.DataFrame(y_teste[:10].values)\n\n# Define o estilo do gráfico.\n\nplt.style.use(\"ggplot\")\n\n# Definição de título de eixos do gráfico.\n\nplt.xlabel('Preços')\nplt.ylabel('Indice')\nplt.title('Precos Reais vs Predições')\n\n# Ordena os valores e plota as linhas\nplt.plot(predicoes.sort_values(by=0),predicoes.index)\nplt.plot(y_teste2.sort_values(by=0),y_teste2.index)\n\n# Define legenda do gráfico\nplt.legend(['Predições','Preços Reais'])\n\n\n# In[ ]:\n\n\n# Validando o modelo de Regressão Linear.\n# Métricas de RMSE - utiliza medidas dependentes.\n\ny_teste.isnull().sum()\n\n\n# In[ ]:\n\n\ny_pred = modeloLr.predict(X_teste)\n\n\n# In[ ]:\n\n\ny_pred.shape\n\n\n# In[ ]:\n\n\ny_teste.shape\n\n\n# In[ ]:\n\n\n# Utilizando o mean_squared_error. Significa o erro médio no conjunto de dados.\n# Quanto mais próximo de 0 melhor.\n\nmean_squared_error(y_teste, modeloLr.predict(X_teste))\n\n\n# In[ ]:\n\n\n# Vamos tentar melhorar os resultados do nosso modelo.\n# Vamos usar apenas as duas features.\n# Criando uma nova variável para o novo modelo de predição.\n\nmodeloLr2 = LinearRegression(normalize=True)\n\n\n# In[ ]:\n\n\n# A variável features está recebendo apenas o valor de abertura e o valor máximo.\n\nfeatures = ['Open','High']\ndadosTreino2 = dadosTreino[features]\n\n\n# In[ ]:\n\n\n# Visualizando os dados atráves do método head().\n# Exibe apenas os 05 primeiros registros.\n\ndadosTreino2.head()\n\n\n# In[ ]:\n\n\n# Separa os dados 75% treino e 25% teste\n# Essa etapa já foi executada anteriormente.\n\nX_treino, X_teste, y_treino, y_teste = train_test_split(dadosTreino2, y, random_state=42)\n\n\n# In[ ]:\n\n\n# Treinando o algoritmo com apenas duas features.\n\nmodeloLr2.fit(X_treino,y_treino)\n\n\n# In[ ]:\n\n\n# Imprimi os pesos dos coeficientes.\nmodeloLr2.coef_\n\n\n# In[ ]:\n\n\n# Valida o modelo com o RMSE.\nRMSE = mean_squared_error(y_teste, modeloLr2.predict(X_teste))**0.5\nRMSE\n\n","repo_name":"guisaraiva/algoritmos_ml","sub_path":"Regressao-linear.py","file_name":"Regressao-linear.py","file_ext":"py","file_size_in_byte":9011,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73415978967","text":"from django.shortcuts import render\nfrom django.db import models\nfrom django.http import HttpResponse, JsonResponse\nfrom django.forms.models import model_to_dict\nfrom .forms import PollForm, ChoiceForm, SignUpForm\nfrom django.utils import timezone\nfrom django.contrib.auth import login, authenticate, logout\nfrom django.shortcuts import render, redirect\nfrom django.http import *\nfrom django.template import RequestContext\nfrom django.contrib.auth.decorators import login_required\nfrom .models import Choice, Poll, Vote\n\ndef login_user(request):\n if request.user.is_authenticated():\n return redirect('/')\n if request.POST:\n username = request.POST['username']\n password = request.POST['password']\n\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n return redirect('/')\n return render(request, 'user/login.html')\n\ndef logout_user(request):\n logout(request)\n return redirect('/')\n\ndef home(request):\n return render(request, 'main/index.html', {'greeting': 'Hi, How is coding going?'})\n\n@login_required\ndef poll_new(request):\n if request.method == \"POST\":\n poll_form = PollForm(request.POST)\n choice_form = ChoiceForm(request.POST)\n if poll_form.is_valid() and choice_form.is_valid:\n poll = poll_form.save(commit=False)\n poll.author = request.user\n poll.save()\n poll_choices = choice_form.data['choice'].split(',')\n #Remove duplicates\n poll_choices = list(set(poll_choices))\n choice = choice_form.save(commit=False)\n for poll_choice in poll_choices:\n choice.pk = None\n choice.choice = poll_choice\n choice.poll = poll\n choice.vote = 0\n choice.save()\n return redirect('../.')\n else:\n poll_form = PollForm()\n choice_form = ChoiceForm()\n return render(request, 'snippets/add_poll.html', {'poll_form': poll_form, 'choice_form': choice_form})\n\ndef polls(request):\n all_polls = Poll.objects.all().order_by('date')\n if all_polls:\n first_poll = all_polls[0]\n first_poll_id = first_poll.id\n return redirect('/polls/' + str(first_poll_id))\n return render(request, 'poll/polls.html', {'polls': all_polls})\n\ndef trimString(word, number):\n if word:\n word = word\n return word\n else:\n return 'Chart Title'\n\ndef polls_view(request, poll_id):\n if request.method == 'GET':\n try:\n current_poll = Poll.objects.filter(id = poll_id)\n title = trimString(current_poll[0].title, 20)\n except NotImplementedError:\n return HttpResponse('No Poll Found with the specified id')\n all_polls = Poll.objects.all().order_by('date')\n options = Choice.objects.filter(poll=poll_id)\n return render(request, 'poll/polls.html', \n {'title': title, 'polls': all_polls, 'options': options, 'current_poll': current_poll[0]}\n )\n\ndef signup(request):\n if request.method == 'POST':\n form = SignUpForm(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n raw_password = form.cleaned_data.get('password1')\n user = authenticate(username=username, password=raw_password)\n login(request, user)\n return redirect('home')\n else:\n form = SignUpForm()\n return render(request, 'user/signup.html', {'form': form})\n\ndef votes(request, poll_id):\n poll = Poll.objects.get(id=poll_id) \n if request.method == 'POST':\n choice_id = request.POST.get('choice')\n user = request.user\n can_vote = voter_check(request, user, poll)\n choice = Choice.objects.filter(poll=poll_id, id=choice_id)[0]\n if choice and can_vote is True:\n if request.user.is_authenticated:\n vote = Vote(poll=poll, choiceVote = choice, voter=user)\n else:\n vote = Vote(poll=poll, choiceVote = choice)\n vote.save()\n result = get_votes(poll_id)\n return JsonResponse({'votes': result, 'message':'Your vote has been submitted successfully!'})\n else:\n try:\n message = can_vote['message']\n except TypeError:\n message = 'You have not made any choice!'\n result = get_votes(poll_id)\n return JsonResponse({'votes':result, 'message': message})\n if request.method == 'GET':\n result = get_votes(poll_id)\n return JsonResponse({'votes': result})\n \ndef get_votes(poll_id):\n poll_votes = Vote.objects.filter(poll=poll_id).values('choiceVote').annotate(n=models.Count(\"pk\"))\n poll_votes_flat = Vote.objects.filter(poll=poll_id).values_list('choiceVote', flat=True)\n choices = Choice.objects.filter(poll = poll_id).values('id', 'choice')\n result = []\n for choice in choices:\n name = choice['choice']\n choice_obj = {}\n choice_id = choice['id']\n for poll_vote in poll_votes:\n if poll_vote['choiceVote'] == choice_id:\n choice_obj['name'] = name\n choice_obj['vote'] = poll_vote['n']\n result.append(choice_obj)\n if choice_id not in poll_votes_flat:\n choice_obj['name'] = name\n choice_obj['vote'] = 0\n result.append(choice_obj)\n if len(poll_votes_flat) == 0:\n result = []\n return result\n\n\ndef voter_check(request, user, poll):\n if not request.user.is_authenticated:\n return {'status':False, 'message':'Please sign in to vote!'}\n try:\n choice = Vote.objects.filter(poll=poll, voter=user)\n except TypeError:\n choice = []\n if len(choice) > 0:\n return {'status':False, 'message':'You have already voted and cannot vote again!' }\n return True\n\n\ndef about(request):\n if request.method == 'GET':\n return HttpResponse('Page under maintenance. Will be up in a 3 days time!')","repo_name":"andela-onnenanya/pollar","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"23165925593","text":"import requests\nimport pandas as pd\n\n\nzipcode_data = pd.read_csv(\n 'D:\\\\...', dtype=str)\n\ntarget_data = pd.DataFrame(columns=[\n 'ID', 'Address', 'City', 'County', 'State', 'Latitude', 'Longitude', 'Zipcode'])\n\n\nURL = \"https://api.target.com/location_proximities/v1/nearby_locations\"\n\ntry:\n for index in range(len(zipcode_data)):\n zipcode = zipcode_data.iloc[index]['ZIP_CODE']\n prms = {'limit': 20, 'unit': 'mile', 'within': 100, 'place': zipcode,\n 'type': 'store', 'key': '8df66ea1e1fc070a6ea99e942431c9cd67a80f02'}\n res = requests.get(\n url=URL, params=prms)\n data = res.json()\n stores = data['locations']\n\n if len(stores) != 0:\n for store in range(len(stores)):\n ID = stores[store]['location_id']\n addr1 = stores[store]['address']['address_line1']\n addr2 = stores[store]['address']['address_line2']\n address = ''\n if addr1 != None:\n address = address + addr1\n if addr2 != None:\n address = address + addr2\n city = stores[store]['address']['city']\n county = stores[store]['address']['county']\n state = stores[store]['address']['region']\n latitude = stores[store]['geographic_specifications']['latitude']\n longitude = stores[store]['geographic_specifications']['longitude']\n\n if (ID in target_data['ID'].values):\n continue\n else:\n target_data = target_data.append(\n {'ID': ID, 'Address': address, 'City': city, 'County': county, 'State': state, 'Latitude': latitude, 'Longitude': longitude, 'Zipcode': zipcode}, ignore_index=True)\n\nfinally:\n target_data.to_csv(r'D:\\\\...',\n index=False, header=True)\n","repo_name":"mm105/US-Brands-Locations","sub_path":"target.py","file_name":"target.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"29955571587","text":"import pytest\n\nfrom pytika import errors\nfrom pytika.api import TikaApi\nfrom pytika.config import GetTextOptionsBuilder as opt\n\n\ndef test_get_meta(api: TikaApi):\n with open(\"tests/data/test.pdf\", \"rb\") as file:\n metadata = api.get_meta(file)\n assert metadata[\"Content-Type\"] == \"application/pdf\"\n\n\ndef test_get_meta_with_corrupt_file(api: TikaApi):\n with open(\"tests/data/corrupt.pdf\", \"rb\") as file:\n with pytest.raises(errors.UnprocessableEntityException):\n api.get_meta(file)\n\n\ndef test_get_text_basic(api: TikaApi):\n with open(\"tests/data/test.pdf\", \"rb\") as file:\n out = api.get_text(file, opt.AsPlainText()).decode()\n words = out.strip().replace(\"\\n\", \"\").split()\n assert words == [\"dummy\", \"pdf\", \"file\"]\n\n\ndef test_get_text_with_bounding_boxes(api: TikaApi):\n # Load expected data\n with open(\"tests/data/test.pdf.hocr\") as file:\n want = file.read()\n\n with open(\"tests/data/test.pdf\", \"rb\") as file:\n got = api.get_text(file, opt.WithBoundingBoxes()).decode()\n\n # Remove variable parts of the output\n index = want.find(\"tika-pdfbox-rendering\")\n want = want[:index]\n got = got[:index]\n assert got == want\n","repo_name":"agriplace/pytika","sub_path":"tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22595196447","text":"\"\"\"\n run: Runs the n-tuple production.\n All run commands require a valid grid certificate as they\n either read data from the grid via XRootD or run on grid\n resources.\n Usage:\n run [<where>] [sample=<X>]\n Parameters:\n where: Where to run NTP. Can be grid|condor|local.\n For location specific parameters, please run\n help run <where>\n Default: local\n sample: Which sample to run over.\n Default: test\n\"\"\"\n\nfrom .. import Command as C\nimport subprocess\nimport os\nfrom ..setup import WORKSPACE\nfrom time import strftime\nCONDOR_ROOT = os.path.join(WORKSPACE, 'condor')\n\n\nclass Command(C):\n REQUIRE_GRID_CERT = True\n _have_fresh_tar_files = False\n\n def __init__(self, path=__file__, doc=__doc__):\n super(Command, self).__init__(path, doc)\n\n # condor specific\n self.__input_files = []\n self.__job_log_dir = ''\n self.__job_dir = ''\n self.__outdirs = []\n self.__setup_script = 'setup.sh'\n self.__run_script = 'run.sh'\n self.__config_file = 'config.json'\n\n def run(self, args, variables):\n from .local import Command\n c = Command()\n result = c.run(args, variables)\n self.__text = c.get_text()\n\n return result\n\n def __prepare(self, args, variables):\n super(Command, self).__prepare(args, variables)\n\n def __extract_params(self):\n args = []\n for var, value in self.__variables.items():\n if var in self.DEFAULTS and 'useJECFromFile' not in var:\n continue\n args.append('{0}={1}'.format(var, value))\n return ' '.join(args)\n\n def __format_input_files(self, input_files):\n results = []\n for f in input_files:\n results.append('\"{0}\"'.format(f))\n return ',\\n'.join(results)\n\n def _create_tar_file(self, args, variables):\n from ntp.commands.create.tarball import Command as TarCommand\n no_operation = 'noop' in self.__variables and self.__variables['noop']\n if no_operation and TarCommand.tarballs_exist():\n self.__input_files.extend(TarCommand.get_existing_files())\n return\n if not Command._have_fresh_tar_files:\n c = TarCommand()\n c.run(args, variables)\n self.__text += c.__text\n Command._have_fresh_tar_files = True\n\n self.__input_files.extend(TarCommand.get_existing_files())\n\n def __get_job_dir(self, category, name):\n date = strftime(\"%d_%b_%y\")\n out_dir = os.path.join(CONDOR_ROOT, category, date, name)\n out_dir = self.__get_latest_outdir(out_dir)\n\n return out_dir\n\n def set_condor_job_directory(self, directory):\n self.__job_dir = directory\n self.__outdirs.append(directory)\n self.__job_log_dir = os.path.join(directory, 'log')\n self.__setup_script = os.path.join(directory, self.__setup_script)\n self.__run_script = os.path.join(directory, self.__run_script)\n self.__config_file = os.path.join(directory, self.__config_file)\n\n def __get_latest_outdir(self, out_dir):\n from ntp.utils import find_latest_iteration\n import glob\n existing_dirs = glob.glob(out_dir + '_*')\n latest = 1\n if existing_dirs:\n latest = find_latest_iteration(existing_dirs)\n latest += 1\n out_dir += '_{0}'.format(latest)\n return out_dir\n\n def __create_condor_folders(self):\n dirs = [CONDOR_ROOT, self.__job_dir, self.__job_log_dir]\n for d in dirs:\n if not os.path.exists(d):\n os.makedirs(d)\n\n def __set_automatic_parameters(self):\n from ntp.utils.data import is_real_data, is_ttbar_mc\n test_file = self.__variables['input_files'][0]\n self.__variables['isReHLT'] = int('reHLT' in test_file)\n self.__variables['isData'] = int(is_real_data(test_file))\n self.__variables['isTTbarMC'] = int(is_ttbar_mc(test_file))\n\n def get_input_files(self):\n return self.__input_files\n\n @staticmethod\n def input_files_from_path(path):\n \"\"\"\n Converts given path(s) to input files.\n \"\"\"\n import glob\n input_files = None\n if type(path) is list:\n input_files = []\n for p in path:\n if '*' in p:\n input_files.extend(glob.glob(p))\n else: # neither wildcard nor comma separated list\n input_files.append(p)\n else:\n if ',' in path:\n input_files = path.split(',')\n elif '*' in path:\n input_files = glob.glob(path)\n else: # neither wildcard nor comma separated list\n input_files = [path]\n input_files = [os.path.abspath(f) for f in input_files]\n return [f for f in input_files if os.path.exists(f) or f.startswith('/store')]\n","repo_name":"BristolTopGroup/NTupleProduction","sub_path":"python/ntp/commands/run/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"13638556136","text":"import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold\nfrom model.logger import LogTypes, Logger\nfrom model.feature_extractor import FeatureExtractor\nfrom typing import Tuple\nimport pandas as pd\n\nclass DataSplitter():\n data: np.ndarray\n random_state = 777\n\n def __init__(self, imgs: list, random_state: int = None) -> None:\n self.X = np.array(imgs)\n self.y = FeatureExtractor.has_cancer(imgs)\n if random_state is not None:\n self.random_state = random_state\n np.random.seed(self.random_state)\n \n def split(self, train_size: float = 0.8, folds: int = 5) -> Tuple[list[np.ndarray], list[np.ndarray], np.ndarray]:\n \"\"\"\n Splits the data into training and testing sets. \n :param train_size: The size of the training set.\n :param folds: The number of folds to use for cross validation.\n :return: A tuple containing \n [0]: an array of length :folds:, with the training images for a split, \n [1]: an array of length :folds:, with the validation images for a split, \n [2]: an array of testing data, with the testing images. \n Each of the arrays contain strings of the image names. \n Warning: As this method shuffles the data, make sure to append the labels as a column to the data before splitting.\n \"\"\"\n Logger.log(f\"Splitting data into {train_size} train size with {folds} folds\")\n # Create splitter\n sss = StratifiedShuffleSplit(n_splits=2, train_size=train_size, random_state=self.random_state)\n # Split into 2 sets\n train_val_indices, test_indices = next(sss.split(self.X, self.y))\n X_train_val, X_test = self.X[train_val_indices], self.X[test_indices]\n y_train_val, y_test = self.y[train_val_indices], self.y[test_indices]\n # Split the training/validation set into folds\n skf = StratifiedKFold(n_splits=folds) # doesn't shuffle\n\n # Split into folds\n train_splits = []\n val_splits = []\n for X_index, y_index in skf.split(X_train_val, y_train_val):\n X_train_fold, X_test_fold = X_train_val[X_index], X_train_val[y_index]\n y_train_fold, y_test_fold = y_train_val[X_index], y_train_val[y_index]\n\n train_splits.append(X_train_fold)\n val_splits.append(X_test_fold)\n \n return train_splits, val_splits, X_test\n","repo_name":"FIYEP-2023/Skin-cancer","sub_path":"model/data_splitter.py","file_name":"data_splitter.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"30089713904","text":"#\n# @lc app=leetcode.cn id=503 lang=python3\n#\n# [503] 下一个更大元素 II\n#\n# https://leetcode-cn.com/problems/next-greater-element-ii/description/\n#\n# algorithms\n# Medium (57.82%)\n# Total Accepted: 34.5K\n# Total Submissions: 59.5K\n# Testcase Example: '[1,2,1]'\n#\n# 给定一个循环数组（最后一个元素的下一个元素是数组的第一个元素），输出每个元素的下一个更大元素。数字 x\n# 的下一个更大的元素是按数组遍历顺序，这个数字之后的第一个比它更大的数，这意味着你应该循环地搜索它的下一个更大的数。如果不存在，则输出 -1。\n# \n# 示例 1:\n# \n# \n# 输入: [1,2,1]\n# 输出: [2,-1,2]\n# 解释: 第一个 1 的下一个更大的数是 2；\n# 数字 2 找不到下一个更大的数； \n# 第二个 1 的下一个最大的数需要循环搜索，结果也是 2。\n# \n# \n# 注意: 输入数组的长度不会超过 10000。\n# \n#\nclass Solution:\n def nextGreaterElements(self, nums: List[int]) -> List[int]:\n stack = []\n sp = 0\n ans = {}\n count = 2 * len(nums)\n while count:\n while stack and nums[sp] > nums[stack[-1]]:\n t = stack.pop(-1)\n if t not in ans:\n ans[t] = sp\n stack.append(sp)\n sp = (sp+1)%len(nums)\n count -= 1\n \n r_ans = []\n for k in range(len(nums)):\n if k not in ans:\n r_ans.append(-1)\n else:\n r_ans.append(nums[ans[k]])\n return r_ans\n","repo_name":"wanggaa/leetcode","sub_path":"503.next-greater-element-ii.py","file_name":"503.next-greater-element-ii.py","file_ext":"py","file_size_in_byte":1562,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2444573142","text":"#+TITLE: Ejemplo pyton\n#+AUTOR: Andres Morocho\n\n#+BEGIN_SRC python :results output :exports both\n\nimport numpy as np\n\na = 5 + np.pi\nb = 6**2\n\nc = a + b\n\nprint (c)\n#+END_SRC\n\n#+RESULTS:\n\n\n\n","repo_name":"Andrewjac17/ProgramacionEPN","sub_path":"exam11.py","file_name":"exam11.py","file_ext":"py","file_size_in_byte":189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71283061208","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 20 14:07:28 2023\n\n@author: mario\n\"\"\"\n\n\nimport numpy as np\n\n\ndef Reglatrapezoidal(f, a, b, n):\n \n h = (b - a) / n\n \n \n sumatoria = 0\n for i in range(1, n):\n x_i = a + i * h\n sumatoria = sumatoria + f(x_i)\n \n \n resultado = (h / 2) * (f(a) + 2*sumatoria + f(b))\n \n return h,resultado\n\n\ndef f(x):\n return (1 + (x / 2)**2)**2 \n\n\ndef tabla(N):\n a = 0\n b = 2\n \n for i in range(2,N+1):\n h, inte = Reglatrapezoidal(f, a, b, i)\n inte2=np.pi*inte\n Error=np.abs(((11.72861-(np.pi*inte))/11.72861)*100.0)\n print(f\"{i} \\t {h} \\t {inte2} \\t {Error}\" )\n\n\n\na = 0\nb = 2\nn = 10 \n\nresultado = Reglatrapezoidal(f, a, b, n)[1]\nVolumen=resultado*np.pi\nprint(f\"Resultado de la integral: {resultado}\", f\"Luego el Volumen es: {Volumen} \")\nError=((11.72861-Volumen)/11.72861)*100.0\nprint(f\"El error porcential es {Error}\")\n\n\n\n# x0=0.0\n# h=1/3\n# for i in range(n+1):\n# print(x0+i*h)\n \n# tabla(10)\n","repo_name":"matepraxis/Numeric_Metods-","sub_path":"reglatrapezoidalMultiple.py","file_name":"reglatrapezoidalMultiple.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"212524039","text":"from aocd import data\nfrom collections import deque, defaultdict\nfrom utils import timed\nimport re \n\nplayers, last_marble = map(int, re.findall(r'\\d+', data))\n\n@timed\ndef play(players, last_marble):\n scores = defaultdict(int)\n circle = deque([0])\n\n for marble in range(1, last_marble + 1):\n if marble % 23 == 0:\n circle.rotate(7)\n scores[marble % players] += marble + circle.pop()\n circle.rotate(-1)\n else:\n circle.rotate(-1)\n circle.append(marble)\n\n return max(scores.values()) if scores else 0\n\nprint(play(players, last_marble)) # part 1\nprint(play(players, last_marble*100)) # part 2","repo_name":"kyb3r/advent-of-code-2018","sub_path":"solutions/09.py","file_name":"09.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"27397448841","text":"# python3\r\n\r\nimport random\r\n\r\ndef read_input():\r\n return (input().rstrip(), input().rstrip())\r\n\r\ndef print_occurrences(output):\r\n print(' '.join(map(str, output)))\r\n\r\ndef polyHash(pattern, prime, x):\r\n\t'''\r\n\t\tprime is int greater than 10**8\r\n\t\tx is random int between [1, prime - 1]\r\n\t'''\r\n\thashVal = 0\r\n\tfor i in range( len(pattern) - 1, -1, -1):\r\n\t\thashVal = (hashVal * x + ord(pattern[i])) % prime\r\n\treturn hashVal\r\n\r\ndef precomputeHashes(text, len_pattern, prime, x):\r\n\t'''\r\n\t\ttext is actual text input\r\n\t\tlen_pattern is the length of the pattern\r\n\t\tprime is int greater than 10**8\r\n\t\tx is a random int between [1, prime - 1]\r\n\t'''\r\n\t# Step 1: calculate TMiusP\r\n\tTMiusP = len(text) - len_pattern\r\n\t# Step 2: create the answer array with the length of the difference + 1\r\n\tH = [None] * (TMiusP + 1)\r\n\t# Step 3: get string which contains only the \"last string\"\r\n\tS = text[TMiusP:] # ONE SUBSTRING\r\n\t# Step 4: generate the polyHash of the last entry\r\n\tH[TMiusP] = polyHash(S, prime, x)\r\n\t# Step 5: create y = x**|P|\r\n\ty = 1\r\n\tfor i in range(len_pattern):\r\n\t\ty = (y * x) % prime\r\n\t# Step 6: create result array\r\n\tfor i in range(TMiusP - 1, -1, -1):\r\n\t\t#H[i] = ( x * H[i+1] + ord(text[i]) - y * ord(text[i+len_pattern]) + prime ) % prime\r\n\t\tH[i] = (x * H[i + 1] + ord(text[i]) - y * ord(text[i + len_pattern]) + prime) % prime\r\n\treturn H\r\n\r\ndef get_occurrences(pattern, text):\r\n\t# Step 1: set prime larger than 10**8 and choose a random int x such that x is [1, prime-1]\r\n\tprime = 100000009\r\n\tx = random.randint(1, prime - 1)\r\n\t# Step 2: calculate array where the results of the indexes will be stored\r\n\tresult = []\r\n\t# Step 3: calculate hash of pattern\r\n\tpHash = polyHash(pattern, prime, x)\r\n\t# Step 4: precompute hashes\r\n\tH = precomputeHashes(text, len(pattern), prime, x)\r\n\t# Step 5: check to see if hashes are the same and if they are the same whether it is a false positive\r\n\tfor i in range(len(text) - len(pattern) + 1):\r\n\t\tif pHash == H[i]:\r\n\t\t\tif text[i: i + len(pattern)] == pattern:\r\n\t\t\t\tresult.append(i)\r\n\treturn result\r\n\r\nif __name__ == '__main__':\r\n print_occurrences(get_occurrences(*read_input()))","repo_name":"najuzilu/ucsd_algorithms","sub_path":"Data_Structures/week4/assignment/hash_substring/hash_substring.py","file_name":"hash_substring.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71419810008","text":"# \r\n# ██████╗░██╗░░░██╗███╗░░██╗██╗██╗░░██╗\r\n# ██╔══██╗╚██╗░██╔╝████╗░██║██║╚██╗██╔╝\r\n# ██████╔╝░╚████╔╝░██╔██╗██║██║░╚███╔╝░\r\n# ██╔══██╗░░╚██╔╝░░██║╚████║██║░██╔██╗░\r\n# ██║░░██║░░░██║░░░██║░╚███║██║██╔╝╚██╗\r\n# ╚═╝░░╚═╝░░░╚═╝░░░╚═╝░░╚══╝╚═╝╚═╝░░╚═╝\r\n\r\n\r\nimport os\r\nimport tkinter as tk\r\nfrom tkinter import messagebox\r\nfrom pytube import YouTube\r\n\r\ndef download_mp3():\r\n url = url_entry.get()\r\n try:\r\n yt = YouTube(url)\r\n video = yt.streams.filter(only_audio=True).first()\r\n out_file = video.download(output_path=\"downloads\")\r\n base, ext = os.path.splitext(out_file)\r\n new_file = base + '.mp3'\r\n os.rename(out_file, new_file)\r\n messagebox.showinfo(\"Success\", f\"{yt.title} has been successfully downloaded as an MP3.\")\r\n except Exception as e:\r\n messagebox.showerror(\"Error\", f\"An error occurred: {str(e)}\")\r\n\r\nplayer = tk.Tk()\r\nplayer.title(\"YouTube MP3 Downloader\")\r\n\r\nwindow_width = 500\r\nwindow_height = 250\r\nscreen_width = player.winfo_screenwidth()\r\nscreen_height = player.winfo_screenheight()\r\nx = (screen_width - window_width) // 2\r\ny = (screen_height - window_height) // 2\r\nplayer.geometry(f\"{window_width}x{window_height}+{x}+{y}\")\r\nplayer.configure(bg=\"#333\")\r\n\r\ntitle_label = tk.Label(player, text=\"YouTube MP3 Downloader\", font=(\"Arial\", 24), bg=\"#333\", fg=\"white\")\r\ntitle_label.pack(pady=20)\r\n\r\nurl_label = tk.Label(player, text=\"Enter the URL of the video you want to download:\", font=(\"Arial\", 12), bg=\"#333\", fg=\"white\")\r\nurl_label.pack()\r\nurl_entry = tk.Entry(player, width=40)\r\nurl_entry.pack(pady=10)\r\n\r\ndownload_button = tk.Button(player, text=\"Download MP3\", command=download_mp3, bg=\"lightgreen\")\r\ndownload_button.pack()\r\n\r\nplayer.mainloop()\r\n","repo_name":"Rynix01/basic-mp3-downloader","sub_path":"downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"12612673613","text":"import os\nimport re\n\nfrom setuptools import find_namespace_packages, setup\n\nname = 'drf-spectacular-jsonapi'\npackage = 'drf_spectacular_jsonapi'\ndescription = 'open api 3 schema generator for drf-json-api package based on drf-spectacular package.'\nurl = 'https://github.com/jokiefer/drf-spectecular-json-api'\nauthor = 'Jonas Kiefer'\nauthor_email = 'jonas.kiefer@live.com'\nlicense = 'BSD'\n\n\nwith open(\"README.rst\", \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n\nwith open('requirements/base.txt') as fh:\n requirements = [r for r in fh.read().split('\\n') if not r.startswith('#')]\n\n\ndef get_version(package):\n \"\"\"\n Return package version as listed in `__version__` in `init.py`.\n \"\"\"\n init_py = open(os.path.join(package, '__init__.py')).read()\n return re.search(\"^__version__ = ['\\\"]([^'\\\"]+)['\\\"]\",\n init_py, re.MULTILINE).group(1)\n\n\nversion = get_version(package)\n\nsetup(\n name=name,\n version=version,\n url=url,\n license=license,\n description=description,\n long_description=long_description,\n long_description_content_type='text/x-rst',\n author=author,\n author_email=author_email,\n packages=[p for p in find_namespace_packages(\n exclude=('tests*',)) if p.startswith(package)],\n include_package_data=True,\n install_requires=requirements,\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Intended Audience :: Developers\",\n \"Natural Language :: English\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Topic :: Internet :: WWW/HTTP\",\n \"Environment :: Web Environment\",\n \"Framework :: Django\",\n \"Topic :: Software Development :: Libraries\",\n \"License :: OSI Approved :: BSD License\",\n \"Operating System :: OS Independent\",\n 'Topic :: Internet :: WWW/HTTP',\n 'Topic :: Documentation',\n 'Topic :: Software Development :: Code Generators',\n ],\n python_requires='>=3.7',\n)\n","repo_name":"jokiefer/drf-spectecular-json-api","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"9541236909","text":"\nimport os\n\nWITH_TARBALL_PACKAGE = False\nWITH_HTTPD_PACKAGE = False\n\nif WITH_HTTPD_PACKAGE:\n from mod_wsgi_packages.httpd import __file__ as PACKAGES_ROOTDIR\n PACKAGES_ROOTDIR = os.path.dirname(PACKAGES_ROOTDIR)\n BINDIR = os.path.join(PACKAGES_ROOTDIR, 'bin')\n SBINDIR = BINDIR\n LIBEXECDIR = os.path.join(PACKAGES_ROOTDIR, 'modules')\n SHLIBPATH = os.path.join(PACKAGES_ROOTDIR, 'lib')\nelif WITH_TARBALL_PACKAGE:\n from mod_wsgi.packages import __file__ as PACKAGES_ROOTDIR\n PACKAGES_ROOTDIR = os.path.dirname(PACKAGES_ROOTDIR)\n BINDIR = os.path.join(PACKAGES_ROOTDIR, 'apache', 'bin')\n SBINDIR = BINDIR\n LIBEXECDIR = os.path.join(PACKAGES_ROOTDIR, 'apache', 'modules')\n SHLIBPATH = []\n SHLIBPATH.append(os.path.join(PACKAGES_ROOTDIR, 'apr-util', 'lib'))\n SHLIBPATH.append(os.path.join(PACKAGES_ROOTDIR, 'apr', 'lib'))\n SHLIBPATH = ':'.join(SHLIBPATH)\nelse:\n BINDIR = ''\n SBINDIR = ''\n LIBEXECDIR = 'X:\\Apache24/lib'\n SHLIBPATH = ''\n\nMPM_NAME = ''\nPROGNAME = ''\nSHLIBPATH_VAR = ''\n\nif os.path.exists(os.path.join(SBINDIR, PROGNAME)):\n HTTPD = os.path.join(SBINDIR, PROGNAME)\nelif os.path.exists(os.path.join(BINDIR, PROGNAME)):\n HTTPD = os.path.join(BINDIR, PROGNAME)\nelse:\n HTTPD = PROGNAME\n\nif os.path.exists(os.path.join(SBINDIR, 'rotatelogs')):\n ROTATELOGS = os.path.join(SBINDIR, 'rotatelogs')\nelif os.path.exists(os.path.join(BINDIR, 'rotatelogs')):\n ROTATELOGS = os.path.join(BINDIR, 'rotatelogs')\nelse:\n ROTATELOGS = 'rotatelogs'\n\n","repo_name":"Air-999/AirVideo","sub_path":"ProgramFiles/Python/Lib/site-packages/mod_wsgi/server/apxs_config.py","file_name":"apxs_config.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"31"} +{"seq_id":"20234064999","text":"import getopt\nfrom logging import exception\nimport sys\nimport os\nfrom PIL import Image, ImageFilter\nimport bordercrop\nimport random\nimport colorthief, colorsys\n\ndef getTotalHeightOfBucket(bucket):\n totalHeight = 0\n for segment in bucket:\n totalHeight += segment.size[1]\n return totalHeight\n\ndef getMaxTotalHeight(buckets):\n maxHeight = 0\n for bucket in buckets:\n maxHeight = max(maxHeight, getTotalHeightOfBucket(bucket))\n return maxHeight\n\ndef storeImage(segment, outputPath, suffix):\n name = outputPath.split(\".\")[0]\n pwd = os.getcwd()\n fullPath = pwd + name + suffix + \".png\"\n segment.save(fullPath)\n\ndef rgb_to_hsv(rgb):\n r = rgb[0]\n g = rgb[1]\n b = rgb[2]\n maxc = max(r, g, b)\n minc = min(r, g, b)\n v = maxc\n if minc == maxc:\n return 0.0, 0.0, v\n s = (maxc-minc) / maxc\n rc = (maxc-r) / (maxc-minc)\n gc = (maxc-g) / (maxc-minc)\n bc = (maxc-b) / (maxc-minc)\n if r == maxc:\n h = bc-gc\n elif g == maxc:\n h = 2.0+rc-bc\n else:\n h = 4.0+gc-rc\n h = (h/6.0) % 1.0\n return h, s, v\n\ndef stichEvenRows(segments, outputImagePath, singleSize, numbuckets, shuffle=False, seed=187, colorOrder='h'):\n\n sortedSegments = sorted(segments, key=lambda x: x.size[1], reverse=True)\n\n #drop N segments that would not fit in a bucket\n remainder = len(sortedSegments) % numbuckets\n if remainder > 0:\n print(f\"dropping {remainder} segments not fitting in {numbuckets} buckets\")\n sortedSegments = sortedSegments[:-remainder]\n\n buckets = []\n\n #create buckets\n for i in range(numbuckets):\n buckets.append([])\n \n for segment in sortedSegments:\n maxHeight = getMaxTotalHeight(buckets)\n\n #find the bucket that has the most difference to the current max height\n maxDiff = 0\n maxDiffIndex = 0\n for index, bucket in enumerate(buckets):\n height = getTotalHeightOfBucket(bucket)\n diff = abs(height - maxHeight)\n if diff > maxDiff:\n maxDiff = diff\n maxDiffIndex = index\n\n buckets[maxDiffIndex].append(segment)\n\n maxHeight = 0\n for bucket in buckets:\n totalHeight = 0\n for segment in bucket:\n totalHeight += segment.size[1]\n maxHeight = max(maxHeight, totalHeight)\n\n newImg = Image.new(('RGB'), (singleSize * numbuckets, maxHeight))\n \n \n if shuffle:\n random.seed(seed)\n\n for index, bucket in enumerate(buckets):\n y = 0\n if shuffle:\n random.shuffle(bucket)\n else:\n colorOrderIndex = str(\"hsv\").index(colorOrder)\n bucket = sorted(bucket, key=lambda x: rgb_to_hsv(colorthief.ColorThief(x).get_color(5))[colorOrderIndex], reverse=True)\n\n for segment in bucket:\n newImg.paste(segment, (index * singleSize, y))\n y += segment.size[1]\n\n if shuffle:\n storeImage(newImg, outputImagePath, f\"_{numbuckets}_buckets_shuffled_{seed}\")\n else:\n storeImage(newImg, outputImagePath, f\"_{numbuckets}_buckets_{colorOrder}\")\n\ndef cropBlackbarsFromImage(segment, precrop, blackThreshold=7, rowThreshold=265, minRowsForBorder=3):\n oldSize = segment.size\n preprocessed = segment#.crop((0, precrop, segment.size[0], segment.size[1] - precrop))\n borders = bordercrop.borders(preprocessed, blackThreshold, rowThreshold, minRowsForBorder)\n newStartY = min(borders[1], borders[3])\n newEndY = max(borders[1], borders[3])\n newHeight = newEndY - newStartY\n print(f\"newSize: {newHeight}, {abs(borders[0] - borders[2])}\")\n #assert oldSize[1] == borders[2]\n return preprocessed.crop(borders)\n\ndef getSegments(image, singleSize):\n width, height = image.size\n wSegments = int(width / singleSize)\n hSegments = int(height / singleSize)\n\n segments = []\n for xIndex in range(wSegments):\n for yIndex in range(hSegments):\n x = int(xIndex * singleSize)\n y = int(yIndex * singleSize)\n segment = image.crop((x, y, x+singleSize, y+singleSize))\n segments.append(segment)\n return segments\n\ndef main(argv):\n imageSectionSize = 0\n inputImagePath = \"\"\n outputImagePath = \"\"\n numberBuckets = 0\n minYPixelsForSegment = 15\n shuffle = False\n seed = -1\n range_buckets = None\n colorOrder = 'h' # 'h' for hue, 's' for saturation, 'v' for value\n\n try:\n opts, args = getopt.getopt(argv,\"hi:o:s:b:m:s:r:c:\",[\"help\",\"inimage=\",\"outimage=\",\"segment_size=\",\"buckets=\",\"min_y_pixels=\", \"random_seed=\", \"color_order=\"])\n except getopt.GetoptError:\n print(f\"create_even_buckets.py -o <outimage> -i <inimage> -s <segment_size> -b <buckets> -m <min_y_pixels> -r <random_seed> (or -1 for no random seed) -c <color_order> (h for hue, s for saturation, v for value, only applicable if -r is set to -1)\")\n sys.exit(2)\n\n for opt, arg in opts:\n print(f\"parsing opt: {opt} arg: {arg}\")\n if opt == '-h':\n print(f\"create_even_buckets.py -o <outimage> -i <inimage> -s <segment_size> -b <buckets> -m <min_y_pixels> -r <random_seed> (or -1 for no random seed)\")\n sys.exit(1)\n elif opt in (\"-o\", \"--output\"):\n outputImagePath = arg\n elif opt in (\"-i\", \"--input\"):\n inputImagePath = arg\n elif opt in (\"-s\", \"--segment_size\"):\n imageSectionSize = int(arg)\n elif opt in (\"-b\", \"--buckets\"):\n #if arg is of number-number assign range_buckets\n if \"-\" in arg:\n range_buckets = arg.split(\"-\")\n else:\n numberBuckets = int(arg)\n elif opt in (\"-m\", \"--min_y_pixels\"):\n minYPixelsForSegment = int(arg)\n elif opt in (\"-r\", \"--random_seed\"):\n if arg == \"-1\":\n shuffle = False\n else:\n shuffle = True\n seed = int(arg)\n elif opt in (\"-c\", \"--color_order\"):\n if arg in ('h', 's', 'v'):\n colorOrder = arg\n\n isDirectory = os.path.isdir(inputImagePath)\n \n images = []\n\n if isDirectory:\n #add all images in inputImagePath directory into images\n for file in os.listdir(inputImagePath):\n if file.endswith(\".png\"):\n images.append(Image.open(inputImagePath + \"/\" + file))\n else:\n images.append(Image.open(inputImagePath))\n\n \n segments = []\n\n for image in images:\n for segment in getSegments(image, imageSectionSize):\n segments.append(segment)\n\n croppedSegments = []\n rowThresHoldInPercent = 0.8789\n rowThresHoldInPixels = rowThresHoldInPercent * imageSectionSize\n\n for it, segment in enumerate(segments):\n cropped = cropBlackbarsFromImage(segment, precrop=0, blackThreshold=35, rowThreshold=rowThresHoldInPixels, minRowsForBorder=20) \n if cropped.size[1] > minYPixelsForSegment and cropped.size[0] == imageSectionSize:\n croppedSegments.append(cropped)\n else:\n print(f\"segment {it} too small: {cropped.size}\")\n print(len(croppedSegments))\n\n\n if range_buckets != None:\n for i in range(int(range_buckets[0]), int(range_buckets[1]) + 1):\n stichEvenRows(croppedSegments, outputImagePath, imageSectionSize, i, shuffle, seed, colorOrder)\n else:\n stichEvenRows(croppedSegments, outputImagePath, imageSectionSize, numberBuckets, shuffle, seed, colorOrder)\n return\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])","repo_name":"schlenkibus/upgraded-octo-eureka","sub_path":"create_even_buckets.py","file_name":"create_even_buckets.py","file_ext":"py","file_size_in_byte":7521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6343604318","text":"from spinn_utilities.overrides import overrides\nfrom spinn_front_end_common.utilities.constants import (\n BYTES_PER_SHORT, BYTES_PER_WORD)\nfrom spynnaker.pyNN.data import SpynnakerDataView\nfrom spynnaker.pyNN.models.neuron.plasticity.stdp.common import (\n get_exp_lut_array)\nfrom spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence import (\n AbstractTimingDependence)\nfrom spynnaker.pyNN.models.neuron.plasticity.stdp.synapse_structure import (\n SynapseStructureWeightOnly)\n\n\nclass TimingDependencePfisterSpikeTriplet(AbstractTimingDependence):\n \"\"\"\n A timing dependence STDP rule based on spike triplets.\n\n Jean-Pascal Pfister, Wulfram Gerstner. Triplets of Spikes in a Model of\n Spike Timing-Dependent Plasticity. *Journal of Neuroscience*,\n 20 September 2006, 26 (38) 9673-9682; DOI: 10.1523/JNEUROSCI.1425-06.2006\n \"\"\"\n __slots__ = [\n \"__synapse_structure\",\n \"__tau_minus\",\n \"__tau_minus_data\",\n \"__tau_plus\",\n \"__tau_plus_data\",\n \"__tau_x\",\n \"__tau_x_data\",\n \"__tau_y\",\n \"__tau_y_data\",\n \"__a_plus\",\n \"__a_minus\"]\n __PARAM_NAMES = ('tau_plus', 'tau_minus', 'tau_x', 'tau_y')\n\n # noinspection PyPep8Naming\n def __init__(self, tau_plus, tau_minus, tau_x, tau_y, A_plus, A_minus):\n r\"\"\"\n :param float tau_plus: :math:`\\tau_+`\n :param float tau_minus: :math:`\\tau_-`\n :param float tau_x: :math:`\\tau_x`\n :param float tau_y: :math:`\\tau_y`\n :param float A_plus: :math:`A^+`\n :param float A_minus: :math:`A^-`\n \"\"\"\n self.__tau_plus = tau_plus\n self.__tau_minus = tau_minus\n self.__tau_x = tau_x\n self.__tau_y = tau_y\n self.__a_plus = A_plus\n self.__a_minus = A_minus\n\n self.__synapse_structure = SynapseStructureWeightOnly()\n\n ts = SpynnakerDataView.get_simulation_time_step_ms()\n self.__tau_plus_data = get_exp_lut_array(ts, self.__tau_plus)\n self.__tau_minus_data = get_exp_lut_array(ts, self.__tau_minus)\n self.__tau_x_data = get_exp_lut_array(ts, self.__tau_x, shift=2)\n self.__tau_y_data = get_exp_lut_array(ts, self.__tau_y, shift=2)\n\n @property\n def tau_plus(self):\n r\"\"\"\n :math:`\\tau_+`\n\n :rtype: float\n \"\"\"\n return self.__tau_plus\n\n @property\n def tau_minus(self):\n r\"\"\"\n :math:`\\tau_-`\n\n :rtype: float\n \"\"\"\n return self.__tau_minus\n\n @property\n def tau_x(self):\n r\"\"\"\n :math:`\\tau_x`\n\n :rtype: float\n \"\"\"\n return self.__tau_x\n\n @property\n def tau_y(self):\n r\"\"\"\n :math:`\\tau_y`\n\n :rtype: float\n \"\"\"\n return self.__tau_y\n\n @property\n def A_plus(self):\n r\"\"\"\n :math:`A^+`\n\n :rtype: float\n \"\"\"\n return self.__a_plus\n\n @A_plus.setter\n def A_plus(self, new_value):\n self.__a_plus = new_value\n\n @property\n def A_minus(self):\n r\"\"\"\n :math:`A^-`\n\n :rtype: float\n \"\"\"\n return self.__a_minus\n\n @A_minus.setter\n def A_minus(self, new_value):\n self.__a_minus = new_value\n\n @overrides(AbstractTimingDependence.is_same_as)\n def is_same_as(self, timing_dependence):\n if not isinstance(\n timing_dependence, TimingDependencePfisterSpikeTriplet):\n return False\n return (\n (self.__tau_plus == timing_dependence.tau_plus) and\n (self.__tau_minus == timing_dependence.tau_minus) and\n (self.__tau_x == timing_dependence.tau_x) and\n (self.__tau_y == timing_dependence.tau_y))\n\n @property\n def vertex_executable_suffix(self):\n \"\"\"\n The suffix to be appended to the vertex executable for this rule.\n\n :rtype: str\n \"\"\"\n return \"pfister_triplet\"\n\n @property\n def pre_trace_n_bytes(self):\n \"\"\"\n The number of bytes used by the pre-trace of the rule per neuron.\n\n :rtype: int\n \"\"\"\n # Triplet rule trace entries consists of two 16-bit traces - R1 and R2\n # (Note: this is the pre-trace size, not the post-trace size)\n return BYTES_PER_SHORT * 2\n\n @overrides(AbstractTimingDependence.get_parameters_sdram_usage_in_bytes)\n def get_parameters_sdram_usage_in_bytes(self):\n lut_array_words = (\n len(self.__tau_plus_data) + len(self.__tau_minus_data) +\n len(self.__tau_x_data) + len(self.__tau_y_data))\n return lut_array_words * BYTES_PER_WORD\n\n @property\n def n_weight_terms(self):\n \"\"\"\n The number of weight terms expected by this timing rule.\n\n :rtype: int\n \"\"\"\n return 2\n\n @overrides(AbstractTimingDependence.write_parameters)\n def write_parameters(\n self, spec, global_weight_scale, synapse_weight_scales):\n\n # Write lookup tables\n spec.write_array(self.__tau_plus_data)\n spec.write_array(self.__tau_minus_data)\n spec.write_array(self.__tau_x_data)\n spec.write_array(self.__tau_y_data)\n\n @property\n def synaptic_structure(self):\n \"\"\"\n The synaptic structure of the plastic part of the rows.\n\n :rtype: AbstractSynapseStructure\n \"\"\"\n return self.__synapse_structure\n\n @overrides(AbstractTimingDependence.get_parameter_names)\n def get_parameter_names(self):\n return self.__PARAM_NAMES\n","repo_name":"SpiNNakerManchester/sPyNNaker","sub_path":"spynnaker/pyNN/models/neuron/plasticity/stdp/timing_dependence/timing_dependence_pfister_spike_triplet.py","file_name":"timing_dependence_pfister_spike_triplet.py","file_ext":"py","file_size_in_byte":5476,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"31"} +{"seq_id":"73342083927","text":"from indico.core.db.sqlalchemy import PyIntEnum, db\nfrom indico.core.db.sqlalchemy.review_questions import ReviewQuestionMixin\nfrom indico.modules.events.papers.models.reviews import PaperReviewType\nfrom indico.modules.events.reviewing_questions_fields import get_reviewing_field_types\nfrom indico.util.locators import locator_property\n\n\nclass PaperReviewQuestion(ReviewQuestionMixin, db.Model):\n __tablename__ = 'review_questions'\n __table_args__ = {'schema': 'event_paper_reviewing'}\n\n event_backref_name = 'paper_review_questions'\n\n type = db.Column(\n PyIntEnum(PaperReviewType),\n nullable=False\n )\n\n # relationship backrefs:\n # - ratings (PaperReviewRating.question)\n\n @locator_property\n def locator(self):\n return dict(self.event.locator, question_id=self.id, review_type=self.type.name)\n\n @property\n def field(self):\n try:\n impl = get_reviewing_field_types('papers')[self.field_type]\n except KeyError:\n return None\n return impl(self)\n","repo_name":"indico/indico","sub_path":"indico/modules/events/papers/models/review_questions.py","file_name":"review_questions.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":1560,"dataset":"github-code","pt":"31"} +{"seq_id":"8590660851","text":"import io\nimport carla\n\n# # Read the .osm data\n# f = io.open(\"Porto.osm\", mode=\"r\", encoding=\"utf-8\")\n# osm_data = f.read()\n# f.close()\n\n# # Define the desired settings. In this case, default values.\n# settings = carla.Osm2OdrSettings()\n# # set traffic ligths\n# settings.generate_traffic_lights = False\n# settings.use_offsets = False\n\n# # Set OSM road types to export to OpenDRIVE\n# settings.set_osm_way_types([\"motorway\", \"motorway_link\", \"trunk\", \"trunk_link\", \"primary\", \"primary_link\", \"secondary\", \"secondary_link\", \"tertiary\", \"tertiary_link\", \"unclassified\", \"residential\"])\n# # Convert to .xodr\n# xodr_data = carla.Osm2Odr.convert(osm_data, settings)\n\n# # save opendrive file\n# f = open(\"Porto.xodr\", 'w',encoding=\"utf-8\")\n# f.write(xodr_data)\n# f.close()\n\n# Read the .osm data\nwith open(\"CampoAlegre1.osm\", mode=\"r\", encoding=\"utf-8\") as osmFile:\n osm_data = osmFile.read()\n\n# Define the desired settings\nsettings = carla.Osm2OdrSettings()\n\n# Set OSM road types to export to OpenDRIVE\nsettings.set_osm_way_types([\n \"motorway\",\n \"trunk\",\n \"primary\",\n \"residential\"\n])\nsettings.default_lane_width = 6.0\nsettings.generate_traffic_lights = True\nsettings.all_junctions_with_traffic_lights = False\nsettings.center_map = True\n\n# Convert to .xodr\nxodr_data = carla.Osm2Odr.convert(osm_data, settings)\n\n# save opendrive file\nwith open(\"CampoAlegre.xodr\", \"w\", encoding=\"utf-8\") as xodrFile:\n xodrFile.write(xodr_data)\n","repo_name":"89Antonio89/Thesis","sub_path":"convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"27947798630","text":"'''\r\n64 32 16 8 4 2 1 중에 몇 개가 해당되는지 라서 \r\n이진법으로 접근하면 됨. 1의 개수 구하는 것. \r\n'''\r\na = int(input())\r\ncnt = 0\r\nwhile a != 0:\r\n if a % 2 == 1:\r\n cnt += 1\r\n a = a // 2\r\nprint(cnt)\r\n","repo_name":"hhyeona/practice","sub_path":"백준/Silver/1094. 막대기/막대기.py","file_name":"막대기.py","file_ext":"py","file_size_in_byte":241,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"746884552","text":"\"\"\"\nIt implements a simple heuristic to identify test files / folders (checking if there is \"test\" in its name). \n\"\"\"\n\nimport csv\nimport glob\nimport json\n\n\n# It gets all the filepaths from python files that have 'test' in their name\ndef get_all_test_files(dir):\n filepaths = []\n\n for filepath in glob.iglob('{}/**/*.py'.format(dir), recursive=True):\n filename = filepath.split('/')[-1].lower()\n if 'test' in filename:\n filepaths.append(filepath)\n \n return filepaths\n\n# It gets all the filepaths from folders that have 'test' in their name\ndef get_all_test_folders(dir):\n filepaths = []\n\n for filepath in glob.iglob('{}/**/'.format(dir), recursive=True):\n # Getting the last folder name\n filename = filepath.split('/')[-2].lower()\n if 'test' in filename:\n filepaths.append(filepath)\n \n return filepaths\n\n# It gets all repo file paths\ndef get_all_immediate_subdirectories(dir):\n filepaths = []\n\n for filepath in glob.iglob('{}/*/'.format(dir), recursive=True):\n filepaths.append(filepath)\n \n return filepaths\n\nall_repos_filepaths = get_all_immediate_subdirectories('../../repos')\n\n#--- COLLECTS ALL FILE AND FOLDER PATHS\n\njson_tests = {}\ncsv_tests = []\n\nfor repo_filepath in all_repos_filepaths:\n test_files = get_all_test_files(repo_filepath)\n test_folders = get_all_test_folders(repo_filepath)\n test_files_no = len(test_files)\n test_folders_no = len(test_folders)\n\n json_tests[repo_filepath] = {\n 'test_files': test_files,\n 'test_folders': test_folders,\n 'test_files_no': test_files_no,\n 'test_folders_no': test_folders_no\n }\n\n csv_tests.append({\n 'repo': repo_filepath,\n 'test_files_no': test_files_no,\n 'test_folders_no': test_folders_no\n })\n\nwith open('data/test_locations.json', 'w', encoding='utf-8') as outfile:\n json.dump(json_tests, outfile)\n\nwith open('data/test_locations.csv', 'w', encoding='utf-8') as outfile:\n writer = csv.DictWriter(outfile, fieldnames=['repo', 'test_files_no', 'test_folders_no'])\n writer.writeheader()\n writer.writerows(csv_tests)\n","repo_name":"Wesley-M/tcc","sub_path":"scraping/tests/collect_test_locations.py","file_name":"collect_test_locations.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13495754798","text":"from django.conf.urls import url\nimport loans.views as views\n\nurlpatterns = [\n url(r'^collection-report/', views.CollectionSheetView.as_view(), name='collection-sheet'),\n url(r'^collection-report-pdf/', views.CollectionSheetPDFView.as_view(), name='collection-sheet-pdf'),\n url(r'^repayment-sheet/', views.repayment_sheet, name='repayment-sheet'),\n url(r'^register-payment/', views.register_payment, name='register-payment'),\n url(r'^collection-report2/', views.collection_report2, name='collection-report2'),\n url(r'^print-contract/(?P<pk>\\d+)/$', views.PrintLoanContractView.as_view(), name='print-contract'),\n url(r'^print-contract/(?P<pk>\\d+)/pdf/$', views.PrintLoanContractPDFView.as_view(), name='pdf-contract'),\n url(r'^contract/(?P<pk>\\d+)/renew/$', views.create_renewal_contract, name='renew-contract'),\n url(r'^request-sheet/', views.request_sheet, name='request-sheet'),\n url(r'^disburse-sheet/', views.disburse_sheet, name='disburse-sheet'),\n url(r'^outstanding-loans/', views.outstanding_loans, name='outstanding-loans'),\n url(r'^late-loans/', views.late_loans, name='late-loans'),\n url(r'^signed-loan-requests-for-disbursement-sheet/', views.signed_loan_requests_for_disbursement_sheet,\n name='signed-loan-requests-for-disbursement-sheet'),\n url(r'^backend-today-view/', views.backend_today_view, name='backend-today-view'),\n url(r'^backend-new-loan-report/', views.backend_new_loan_reportview, name='backend-new-loan-report'),\n url(r'^reconciliation-high-level/', views.ReconciliationHighLevelView.as_view(), name='reconciliation-high-level'),\n url(r'^disbursement-report/$', views.disbursement_report, name='disbursement_report'),\n url(r'^customer-retention-report/$', views.customer_retention_report, name='customer_retention_report'),\n\n]\n","repo_name":"Singh-Sg/loan_app","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22701523990","text":"import logging\nimport unittest\nfrom cls import Constructor\nfrom cls.subtypes import Subtypes\nfrom cls.types import Intersection\n\n\nclass TestSubtype(unittest.TestCase):\n logger = logging.getLogger(__name__)\n logging.basicConfig(\n format=\"%(module)s %(levelname)s: %(message)s\",\n # level=logging.INFO,\n )\n\n def test_constructor_refl(self) -> None:\n a = Constructor(\"A\")\n subtypes = Subtypes({})\n self.assertTrue(subtypes.check_subtype(a, a))\n\n def test_idempotence_right(self) -> None:\n a = Constructor(\"A\")\n\n subtypes = Subtypes({})\n self.assertTrue(subtypes.check_subtype(a, Intersection(a, a)))\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"tudo-seal/cls-python","sub_path":"tests/test_subtype.py","file_name":"test_subtype.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1997524731","text":"import os\r\nimport json\r\nimport requests\r\nfrom time import sleep\r\nimport datetime\r\n\r\nPATH = \"statistics\"\r\n\r\nBANNED_CASES = [\r\n # \"24-chasa-oskolki\",\r\n # \"besplatnye-60-kristallov\",\r\n # \"do-1090-kristallov-za-druzey\",\r\n # \"keys-za-podpisku-telegramm\",\r\n # \"do-330-kristallov-za-popolnenie\",\r\n # \"pooshhrenie-do-2240-kristallov-za-popolnenie-ot-200r\",\r\n # \"gd_system_case\"\r\n]\r\n\r\ndef get_cases() -> dict:\r\n data = dict()\r\n\r\n with open(\"cases.json\", 'r', encoding=\"utf-8\") as file_input:\r\n data = json.load(file_input)\r\n\r\n return data\r\n\r\ndef check_history(cases) -> list:\r\n history_url = \"https://genshindrop.com/api/live\"\r\n\r\n data = list()\r\n\r\n response = requests.get(history_url).json()[\"data\"]\r\n\r\n for loot in response:\r\n prize_rubles = loot[\"amount\"]\r\n prize_name = loot[\"item\"][\"name\"]\r\n case_name = loot[\"box\"][\"name\"]\r\n case_short_url = loot[\"box\"][\"slug\"]\r\n case_price = cases[case_short_url]\r\n key = loot[\"key\"]\r\n \r\n if (case_short_url not in BANNED_CASES):\r\n data.append(\r\n {\r\n \"case_short_url\":case_short_url,\r\n \"case_name\":case_name,\r\n \"case_price\": case_price,\r\n \"prize_rubles\":prize_rubles,\r\n \"prize_name\":prize_name,\r\n \"key\":key\r\n }\r\n )\r\n\r\n return data\r\n\r\ndef save_row_data(data) -> None:\r\n with open(\"RowData.txt\", 'a+', encoding=\"utf-8\") as file_output:\r\n for record in data:\r\n file_output.write(\r\n \"{}:{}:{}:{}:{}\\n\".format(\r\n record[\"case_short_url\"], record[\"case_name\"], record[\"prize_rubles\"], record[\"prize_name\"], record[\"key\"]\r\n )\r\n )\r\n\r\ndef processing_data(data, current_file, previous_file) -> None:\r\n old_data = read_data(current_file)\r\n \r\n for record in data:\r\n prize_rubles = record[\"prize_rubles\"]\r\n prize_name = record[\"prize_name\"]\r\n case_name = record[\"case_name\"]\r\n case_short_url = record[\"case_short_url\"]\r\n prize_key = record[\"key\"]\r\n case_price = record[\"case_price\"]\r\n\r\n if (old_data.get(case_short_url) == None):\r\n old_data[case_short_url] = {\r\n \"case_name\":case_name,\r\n \"case_price\":case_price,\r\n \"prizes\":{},\r\n \"keys\":[]\r\n }\r\n \r\n if (old_data[case_short_url][\"prizes\"].get(prize_name) == None):\r\n old_data[case_short_url][\"prizes\"][prize_name] = {\r\n \"rubles\":prize_rubles,\r\n \"count\":0\r\n }\r\n \r\n\r\n old_file_keys = list()\r\n\r\n if os.path.isfile(previous_file):\r\n temp_data = read_data(previous_file)\r\n old_file_keys = temp_data[case_short_url][\"keys\"]\r\n \r\n\r\n if ((prize_key not in old_data[case_short_url][\"keys\"]) and (prize_key not in old_file_keys)):\r\n old_data[case_short_url][\"prizes\"][prize_name][\"count\"] += 1\r\n old_data[case_short_url][\"keys\"].append(prize_key)\r\n\r\n write_data(old_data, current_file)\r\n\r\n\r\ndef create_file_path(date) -> str:\r\n folder_name = date.strftime('%d-%m-%Y')\r\n file_name = date.strftime('%H')\r\n\r\n return \"{0}/{1}/{2}.json\".format(PATH, folder_name, file_name)\r\n\r\n\r\ndef read_data(path) -> dict:\r\n os.makedirs(os.path.dirname(path), exist_ok=True)\r\n data = dict()\r\n\r\n if os.path.isfile(path):\r\n with open(path, 'r', encoding=\"utf-8\") as file_input:\r\n data = json.load(file_input)\r\n\r\n return data\r\n\r\ndef write_data(data, path) -> None:\r\n os.makedirs(os.path.dirname(path), exist_ok=True)\r\n\r\n with open(path, 'w', encoding=\"utf-8\") as file_output:\r\n json.dump(data, file_output, ensure_ascii=False, indent=4)\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n cases = get_cases()\r\n\r\n while True:\r\n try:\r\n data = check_history(cases)\r\n #print(data)\r\n #save_row_data(data)\r\n\r\n processing_data(\r\n data,\r\n create_file_path(datetime.datetime.now()),\r\n create_file_path(datetime.datetime.now() - datetime.timedelta(hours=1))\r\n )\r\n except Exception as exp: print(exp)\r\n\r\n sleep(3)\r\n","repo_name":"Aniforka/genshin-drop","sub_path":"parse_history_cases.py","file_name":"parse_history_cases.py","file_ext":"py","file_size_in_byte":4340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32394777675","text":"import logging\nimport json\nfrom u1db import errors\nfrom u1db.remote import utils\nfrom twisted.internet import defer\nfrom leap.soledad.common.document import SoledadDocument\nfrom leap.soledad.client.events import SOLEDAD_SYNC_RECEIVE_STATUS\nfrom leap.soledad.client.events import emit_async\nfrom leap.soledad.client.crypto import is_symmetrically_encrypted\nfrom leap.soledad.client.encdecpool import SyncDecrypterPool\nfrom leap.soledad.client.http_target.support import RequestBody\n\nlogger = logging.getLogger(__name__)\n\n\nclass HTTPDocFetcher(object):\n \"\"\"\n Handles Document fetching from Soledad server, using HTTP as transport.\n Steps:\n * Prepares metadata by asking server for one document\n * Fetch the total on response and prepare to ask all remaining\n * (async) Documents will come encrypted.\n So we parse, decrypt and insert locally as they arrive.\n \"\"\"\n\n # The uuid of the local replica.\n # Any class inheriting from this one should provide a meaningful attribute\n # if the sync status event is meant to be used somewhere else.\n\n uuid = 'undefined'\n userid = 'undefined'\n\n @defer.inlineCallbacks\n def _receive_docs(self, last_known_generation, last_known_trans_id,\n ensure_callback, sync_id, defer_decryption):\n\n self._queue_for_decrypt = defer_decryption \\\n and self._sync_db is not None\n\n new_generation = last_known_generation\n new_transaction_id = last_known_trans_id\n\n if self._queue_for_decrypt:\n logger.debug(\n \"Soledad sync: will queue received docs for decrypting.\")\n\n if defer_decryption:\n self._setup_sync_decr_pool()\n\n # ---------------------------------------------------------------------\n # maybe receive the first document\n # ---------------------------------------------------------------------\n\n # we fetch the first document before fetching the rest because we need\n # to know the total number of documents to be received, and this\n # information comes as metadata to each request.\n\n doc = yield self._receive_one_doc(\n last_known_generation, last_known_trans_id,\n sync_id, 0)\n self._received_docs = 0\n number_of_changes, ngen, ntrans = self._insert_received_doc(doc, 1, 1)\n\n if ngen:\n new_generation = ngen\n new_transaction_id = ntrans\n\n if defer_decryption:\n self._sync_decr_pool.start(number_of_changes)\n\n # ---------------------------------------------------------------------\n # maybe receive the rest of the documents\n # ---------------------------------------------------------------------\n\n # launch many asynchronous fetches and inserts of received documents\n # in the temporary sync db. Will wait for all results before\n # continuing.\n\n received = 1\n deferreds = []\n while received < number_of_changes:\n d = self._receive_one_doc(\n last_known_generation,\n last_known_trans_id, sync_id, received)\n d.addCallback(\n self._insert_received_doc,\n received + 1, # the index of the current received doc\n number_of_changes)\n deferreds.append(d)\n received += 1\n results = yield defer.gatherResults(deferreds)\n\n # get generation and transaction id of target after insertions\n if deferreds:\n _, new_generation, new_transaction_id = results.pop()\n\n # ---------------------------------------------------------------------\n # wait for async decryption to finish\n # ---------------------------------------------------------------------\n\n if defer_decryption:\n yield self._sync_decr_pool.deferred\n self._sync_decr_pool.stop()\n\n defer.returnValue([new_generation, new_transaction_id])\n\n def _receive_one_doc(self, last_known_generation,\n last_known_trans_id, sync_id, received):\n # add remote replica metadata to the request\n body = RequestBody(\n last_known_generation=last_known_generation,\n last_known_trans_id=last_known_trans_id,\n sync_id=sync_id,\n ensure=self._ensure_callback is not None)\n # inform server of how many documents have already been received\n body.insert_info(received=received)\n # send headers\n return self._http_request(\n self._url,\n method='POST',\n body=str(body),\n content_type='application/x-soledad-sync-get')\n\n def _insert_received_doc(self, response, idx, total):\n \"\"\"\n Insert a received document into the local replica.\n\n :param response: The body and headers of the response.\n :type response: tuple(str, dict)\n :param idx: The index count of the current operation.\n :type idx: int\n :param total: The total number of operations.\n :type total: int\n \"\"\"\n new_generation, new_transaction_id, number_of_changes, doc_id, \\\n rev, content, gen, trans_id = \\\n self._parse_received_doc_response(response)\n if doc_id is not None:\n # decrypt incoming document and insert into local database\n # -------------------------------------------------------------\n # symmetric decryption of document's contents\n # -------------------------------------------------------------\n # If arriving content was symmetrically encrypted, we decrypt it.\n # We do it inline if defer_decryption flag is False or no sync_db\n # was defined, otherwise we defer it writing it to the received\n # docs table.\n doc = SoledadDocument(doc_id, rev, content)\n if is_symmetrically_encrypted(doc):\n if self._queue_for_decrypt:\n self._sync_decr_pool.insert_encrypted_received_doc(\n doc.doc_id, doc.rev, doc.content, gen, trans_id,\n idx)\n else:\n # defer_decryption is False or no-sync-db fallback\n doc.set_json(self._crypto.decrypt_doc(doc))\n self._insert_doc_cb(doc, gen, trans_id)\n else:\n # not symmetrically encrypted doc, insert it directly\n # or save it in the decrypted stage.\n if self._queue_for_decrypt:\n self._sync_decr_pool.insert_received_doc(\n doc.doc_id, doc.rev, doc.content, gen, trans_id,\n idx)\n else:\n self._insert_doc_cb(doc, gen, trans_id)\n # -------------------------------------------------------------\n # end of symmetric decryption\n # -------------------------------------------------------------\n self._received_docs += 1\n user_data = {'uuid': self.uuid, 'userid': self.userid}\n _emit_receive_status(user_data, self._received_docs, total)\n return number_of_changes, new_generation, new_transaction_id\n\n def _parse_received_doc_response(self, response):\n \"\"\"\n Parse the response from the server containing the received document.\n\n :param response: The body and headers of the response.\n :type response: tuple(str, dict)\n\n :return: (new_gen, new_trans_id, number_of_changes, doc_id, rev,\n content, gen, trans_id)\n :rtype: tuple\n \"\"\"\n # decode incoming stream\n parts = response.splitlines()\n if not parts or parts[0] != '[' or parts[-1] != ']':\n raise errors.BrokenSyncStream\n data = parts[1:-1]\n # decode metadata\n try:\n line, comma = utils.check_and_strip_comma(data[0])\n metadata = None\n except (IndexError):\n raise errors.BrokenSyncStream\n try:\n metadata = json.loads(line)\n new_generation = metadata['new_generation']\n new_transaction_id = metadata['new_transaction_id']\n number_of_changes = metadata['number_of_changes']\n except (ValueError, KeyError):\n raise errors.BrokenSyncStream\n # make sure we have replica_uid from fresh new dbs\n if self._ensure_callback and 'replica_uid' in metadata:\n self._ensure_callback(metadata['replica_uid'])\n # parse incoming document info\n doc_id = None\n rev = None\n content = None\n gen = None\n trans_id = None\n if number_of_changes > 0:\n try:\n entry = json.loads(data[1])\n doc_id = entry['id']\n rev = entry['rev']\n content = entry['content']\n gen = entry['gen']\n trans_id = entry['trans_id']\n except (IndexError, KeyError):\n raise errors.BrokenSyncStream\n return new_generation, new_transaction_id, number_of_changes, \\\n doc_id, rev, content, gen, trans_id\n\n def _setup_sync_decr_pool(self):\n \"\"\"\n Set up the SyncDecrypterPool for deferred decryption.\n \"\"\"\n if self._sync_decr_pool is None and self._sync_db is not None:\n # initialize syncing queue decryption pool\n self._sync_decr_pool = SyncDecrypterPool(\n self._crypto,\n self._sync_db,\n insert_doc_cb=self._insert_doc_cb,\n source_replica_uid=self.source_replica_uid)\n\n\ndef _emit_receive_status(user_data, received_docs, total):\n content = {'received': received_docs, 'total': total}\n emit_async(SOLEDAD_SYNC_RECEIVE_STATUS, user_data, content)\n\n if received_docs % 20 == 0:\n msg = \"%d/%d\" % (received_docs, total)\n logger.debug(\"Sync receive status: %s\" % msg)\n","repo_name":"shyba/soledad","sub_path":"client/src/leap/soledad/client/http_target/fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":9960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"4678639748","text":"# -*- coding: utf-8 -*-\n\nfrom cromlech.browser import IView\nfrom .utils import query_view\nfrom .registry import dawnlight_components\nfrom dawnlight import DEFAULT, VIEW, ResolveError\nfrom dawnlight import ModelLookup as BaseModelLookup\nfrom dawnlight.interfaces import IConsumer, ILookupComponent\nfrom zope.interface import implementer\n\n\nclass ModelLookup(BaseModelLookup):\n\n def __init__(self):\n pass\n\n def register(self, class_or_interface, consumer):\n \"\"\"Consumers are intended to be subscription adapters.\n \"\"\"\n raise NotImplementedError(u\"Use the global registry.\")\n\n def lookup(self, obj):\n \"\"\"We use IConsumer registered in the global registry as\n subscription adapters.\n \"\"\"\n return IConsumer.subscription(\n obj, lookup=dawnlight_components, subscribe=True)\n\n\n@implementer(ILookupComponent)\nclass ViewLookup(object):\n \"\"\"Looks up a view using a given method.\n \"\"\"\n\n def __init__(self, lookup=query_view, default_name=u'index'):\n self.lookup = lookup\n self.default_name = default_name\n\n def __call__(self, request, obj, stack):\n \"\"\"Resolves a view.\n \"\"\"\n default_fallback = False\n unconsumed_amount = len(stack)\n if unconsumed_amount == 0:\n default_fallback = True\n ns, name = VIEW, self.default_name\n elif unconsumed_amount == 1:\n ns, name = stack[0]\n else:\n raise ResolveError(\n \"Can't resolve view: stack is not fully consumed.\")\n\n if ns not in (DEFAULT, VIEW):\n raise ResolveError(\n \"Can't resolve view: namespace %r is not supported.\" % ns)\n\n # If this is the last node AND if it's a view, we return it.\n if default_fallback and IView.providedBy(obj):\n return obj\n\n # Else, we need to resolve the model into a view.\n view = self.lookup(request, obj, name)\n if view is None:\n if default_fallback:\n raise ResolveError(\n \"Can't resolve view: no default view on %r.\" % obj)\n else:\n if ns == VIEW:\n raise ResolveError(\n \"Can't resolve view: no view `%s` on %r.\" % (name, obj))\n raise ResolveError(\n \"%r is neither a view nor a model.\" % name)\n return view\n","repo_name":"Cromlech/cromlech.dawnlight","sub_path":"src/cromlech/dawnlight/lookup.py","file_name":"lookup.py","file_ext":"py","file_size_in_byte":2406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38069456415","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 6 10:12:22 2019\n\n@author: fly_s\n堆排序\n1. 数组变成大根堆。每个新插入的数，和它的父节点，对比交换。再循环对比。\n2. 大根堆进行排序，将头位置的大数，与尾部数据交换。循环。\n\"\"\"\ndef heapSort(arr):\n if arr is None or len(arr) < 2:\n return arr\n # 第一步形成大顶堆\n for i in range(len(arr)):\n heapInsert(arr,i)\n # 第二步 1） 交换头位置和尾位置的值。2) 交换后，再次将数组变成大顶堆。3）循环 \n # 先进行一次交换\n # 计算堆的大小。初始 == 数组大小\n lenArr = len(arr)\n lenArr = lenArr - 1\n swap(arr,0,lenArr)\n # 只要堆里还有数据，循环交换\n while(lenArr > 1 ):\n # 将剩余的数组变成大顶堆\n heappify(arr,0,lenArr)\n \n lenArr = lenArr - 1\n \n swap(arr,0,lenArr)\n'''\n将数组堆化\n思路：头部交换来的点，不断跟孩子对比，交换，找到合适的位置，或者没有孩子\n循环的边界是 左孩子 < 堆的大小\n'''\ndef heappify(arr,index,heapSize):\n left = index*2 + 1\n while(left < heapSize):\n \n # 得到左右孩子的最大值\n Largest = left + 1 if (left + 1 < heapSize) and arr[left] < arr[left + 1] else left \n # 得到孩子和父节点的最大值\n Largest = index if arr[index] > arr[Largest] else Largest\n # 如果 最大值就是父节点，则不需要交换，直接跳出循环\n if Largest == index:\n break\n \n swap(arr,index,Largest)\n # 循环计算\n index = Largest\n left = index*2 + 1\n'''\n跟父节点对比。再递归\n'''\n\ndef heapInsert(arr,index):\n father_index = int((index - 1)/2)\n while(arr[index] > arr[father_index]):\n swap(arr,index,father_index)\n index = father_index\n father_index = int((index - 1)/2)\n\ndef swap(arr,i,j):\n tmp = arr[i]\n arr[i] = arr[j]\n arr[j] = tmp\n\n \nimport random\n\ntestArr = [1,3,5]\n\ntestArr = random.sample(range(100),5)\n\nheapSort(testArr)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"flysaint/nowCode","sub_path":"BaseAlgorithm/Class02/HeapSort_MY.py","file_name":"HeapSort_MY.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25615638650","text":"import sys\r\nimport time\r\nimport os\r\nfrom read_input import load_data, write_output\r\nfrom alignment import get_alignment, get_alignment_efficient\r\n\r\n\r\n\r\nif __name__=='__main__':\r\n\r\n # argument = sys.argv\r\n # input_txt = argument[1]\r\n string1, string2 = load_data(\"input.txt\")\r\n # print(string1)\r\n # print(string2)\r\n\r\n ''' Normal Dynamic Programming Solution'''\r\n start_time_dp = time.time()\r\n string1_alignment, string2_alignment, cost, memory = get_alignment(string1, string2)\r\n end_time_dp = time.time()\r\n total_time_dp = end_time_dp - start_time_dp\r\n # print(string1_alignment)\r\n # print(string2_alignment)\r\n # print(cost)\r\n # print(total_time_dp)\r\n # print(memory)\r\n write_output(\"output.txt\", string1_alignment, string2_alignment, cost, total_time_dp, memory)\r\n \r\n\r\n # print(psutil.Process(os.getpid()).memory_info()[0] / 1024 ** 2)\r\n\r\n ''' Memory Efficient using Divide & Conquer with DP Solution'''\r\n start_time_dc = time.time()\r\n string1_alignment_me, string2_alignment_me, cost, memory = get_alignment_efficient(string1,string2)\r\n print(string1_alignment_me)\r\n print(string2_alignment_me)\r\n # mismatch_cost = {\r\n # 'AA':0, 'AC':110, 'AG':48, 'AT':94,\r\n # 'CA':110, 'CC':0, 'CG':118, 'CT':48,\r\n # 'GA':48, 'GC':118, 'GG':0, 'GT':110,\r\n # 'TA':94, 'TC':48, 'TG':110, 'TT':0\r\n # }\r\n # X_aligned, Y_aligned = string1_alignment_me, string2_alignment_me\r\n # cost = 0.0\r\n # for i in range(len(X_aligned)):\r\n # if X_aligned[i] == '_' or Y_aligned[i] == '_':\r\n # cost += 30.0\r\n # else:\r\n # cost += mismatch_cost[X_aligned[i] + Y_aligned[i]]\r\n print(cost)\r\n\r\n end_time_dc = time.time()\r\n total_time_dc = end_time_dc - start_time_dc\r\n print(total_time_dc)\r\n print(memory)\r\n","repo_name":"bingabid/CSCI570_Algo","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"1375502501","text":"import time\n\nfrom selenium.webdriver.common.by import By\n\nfrom app.lesson06.common.base import BasePage\nfrom app.project.data import choose_topic\nfrom app.project.pages.topic import TopicPage\n\ndef test_topic(login):\n \"\"\"\n 测试步骤：\n 1、登陆成功\n 2、点击题库按钮\n 3、点击Linux 题库\n 4、断言标题\n 5、点击初级\n 6、获取第一道题的 title\n 7、滑动\n 8、获取后面题目的 title\n 9、断言： last_title - first_title = 滑动的次数\n :param login:\n :return:\n \"\"\"\n driver = login\n basepage = BasePage(driver)\n # 点击题库按钮\n tiku_locator = (By.XPATH,'//*[@resource-id=\"com.lemon.lemonban:id/navigation_tiku\"]')\n basepage.click_app(tiku_locator)\n # 点击Linux题库\n detail_tiku_locator = (By.XPATH,'//*[@text=\"Linux\"]')\n basepage.click_app(detail_tiku_locator)\n # 第一个断言，显示得文本是否是Linux\n title_locator = (By.XPATH,'//*[@resource-id = \"com.lemon.lemonban:id/category_title\"]')\n actual = basepage.find_element(title_locator)\n assert 'Linux' == actual.text.strip()\n\n # 点击初级\n level_locator = (By.XPATH,'//*[@resource-id=\"com.lemon.lemonban:id/first_level\"]')\n basepage.click_app(level_locator)\n\n # 点击第一套提\n first_tiku_locator = (By.XPATH,'//*[@text=\"Linux--初级--第1套\"]')\n basepage.click_app(first_tiku_locator)\n\n # 第一个题得header\n header_locator = (By.XPATH,'//*[@resource-id=\"com.lemon.lemonban:id/toolbar_textview\"]')\n title_first_elem = basepage.find_element(header_locator)\n title_first = int(title_first_elem.text.split('/')[0])\n\n # 滑动几次\n for i in range(5):\n basepage.swipe_left()\n time.sleep(1)\n\n # 获取后面的题\n title_last_elem = basepage.find_element(header_locator)\n title_last = int(title_last_elem.text.split('/')[0])\n assert title_first + 5 == title_last\n\n","repo_name":"zengcong1314/python1205","sub_path":"app/lesson06/tests/test_topic.py","file_name":"test_topic.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"8252525157","text":"import numpy as np\nfrom scipy import interpolate, ndimage\nfrom astropy.io import fits\nfrom tqdm.auto import tqdm\n\nimport utils\nimport sample_dist\nimport consts\nimport histogram\n\nfrom numba import int64\nfrom numba.typed import Dict\n\nclass SpectralImager:\n\n params = {\n 'wavelength_range': (12000, 19000),\n 'wavelength_step': 1.,\n 'nphot_max': 1000000,\n }\n\n def __init__(self, optics, **kwargs):\n \"\"\" \"\"\"\n self.optics = optics\n\n self.params.update(kwargs)\n\n self.wave = np.arange(\n self.params['wavelength_range'][0],\n self.params['wavelength_range'][1],\n self.params['wavelength_step']\n )\n\n self.sigma = np.sqrt(self.optics.params['sigma2_det'] * self.optics.params['exptime'])\n\n self.init_image()\n\n\n def init_image(self):\n \"\"\" \"\"\"\n bin_y = np.arange(0, self.optics.params['det_height']+1, 1, dtype='d')\n bin_x = np.arange(0, self.optics.params['det_width']+1, 1, dtype='d')\n self.pixel_grid = (bin_y, bin_x)\n\n def sample_spectrum_cat(self, galaxy, spectrum, wave_grid):\n\n flux_spectrum = spectrum\n #flux_spectrum[flux_spectrum<0] = 0\n\n counts_spectrum = self.optics.flux_to_counts(flux_spectrum, wave_grid)\n\n func = sample_dist.SampleDistribution(wave_grid, counts_spectrum)\n\n counts = np.random.poisson(np.sum(counts_spectrum))\n if counts == 0:\n return np.array([]), 0\n\n weight = 1\n if counts > self.params['nphot_max']:\n weight = counts / self.params['nphot_max']\n counts = self.params['nphot_max']\n print(f\"alert {weight}\")\n\n try:\n samples = func.sample(counts)\n except ValueError:\n return np.array([]), 0\n\n return samples, weight\n\n def sample_cat(self, galaxy, spectrum, wave_grid):\n \"\"\" \"\"\"\n wavelength, weight = self.sample_spectrum_cat(galaxy, spectrum, wave_grid)\n\n if len(wavelength) == 0:\n return np.array([]), np.array([]), 0\n\n x, y = self.optics.wavelength_to_pix(wavelength)\n\n ra, dec = galaxy.sample_image(\n len(x) )\n\n xg, yg = self.optics.radec_to_pixel(ra, dec)\n\n xpsf, ypsf = self.optics.psf.sample(len(x))\n\n return x + xg + xpsf, y + yg + ypsf, weight\n\n def make_image_cat(self, galaxy_list, spectra, wave_grid, noise=True):\n \"\"\"Builds image and variance image\"\"\"\n image = np.zeros((self.optics.params['det_height'], self.optics.params['det_width']), dtype='d')\n\n for gal_i, g in enumerate(tqdm(galaxy_list)):\n\n x, y, weight = self.sample_cat(g, spectra[gal_i], wave_grid)\n im = histogram.histogram2d_accumulate(\n y,\n x,\n weight,\n bins_y=self.pixel_grid[0],\n bins_x=self.pixel_grid[1],\n hist=image,\n mask_dict=None\n )\n\n # poisson variance is equal to mean\n var_image = image + self.sigma**2\n\n if noise:\n # add detector noise background\n image += np.random.normal(0, self.sigma, image.shape)\n\n return image, var_image\n\n\n def sample_spectrum(self, galaxy):\n \"\"\"Generate samples of wavelength\"\"\"\n\n flux_spectrum = galaxy.sed(self.wave)# + galaxy.emline(self.wave)\n flux_spectrum[flux_spectrum<0] = 0\n\n# print(flux_spectrum)\n counts_spectrum = self.optics.flux_to_counts(flux_spectrum, self.wave)\n\n func = sample_dist.SampleDistribution(self.wave, counts_spectrum)\n\n counts = np.random.poisson(np.sum(counts_spectrum))\n\n if counts == 0:\n return np.array([]), 0\n# print(f\"counts {counts}\")\n\n weight = 1\n if counts > self.params['nphot_max']:\n weight = counts / self.params['nphot_max']\n counts = self.params['nphot_max']\n print(f\"alert {weight}\")\n\n try:\n samples = func.sample(counts)\n except ValueError:\n return np.array([]), 0\n\n return samples, weight\n\n def sample_emline(self, galaxy, line):\n\n redshift = galaxy.params['redshift']\n wavelength_obs = (1 + redshift) * consts.lines[line]\n\n # else:\n sigma_size = galaxy.params['velocity_disp']/consts.c*wavelength_obs\n\n if wavelength_obs<galaxy.params['obs_wavelength_range'][0] or wavelength_obs>galaxy.params['obs_wavelength_range'][1]:\n counts_emline = 0\n else:\n counts_emline = self.optics.lineflux_to_counts(galaxy.params['fluxes_emlines'][line], wavelength_obs)\n counts_emline = np.random.poisson(counts_emline)\n\n weight = 1\n if counts_emline > self.params['nphot_max']:\n weight = counts_emline / self.params['nphot_max']\n counts_emline = self.params['nphot_max']\n print(f\"alert {weight}\")\n\n samples_emline = np.random.normal(wavelength_obs, sigma_size, counts_emline)\n\n return samples_emline, weight\n\n def sample(self, galaxy):\n \"\"\" \"\"\"\n wavelength, weight = self.sample_spectrum(galaxy)\n\n if len(wavelength) == 0:\n return np.array([]), np.array([]), 0\n\n x, y = self.optics.wavelength_to_pix(wavelength)\n\n ra, dec = galaxy.sample_image(\n len(x)\n )\n\n xg, yg = self.optics.radec_to_pixel(ra, dec)\n\n xpsf, ypsf = self.optics.psf.sample(len(x))\n\n #return x + xg, y + yg, weight\n return x + xg + xpsf, y + yg + ypsf, weight\n\n def sample_line(self, galaxy, line):\n \"\"\" \"\"\"\n wavelength1, weight1 = self.sample_emline(galaxy, line)\n\n if len(wavelength1) == 0:\n return np.array([]), np.array([]), 0\n\n x1, y1 = self.optics.wavelength_to_pix(wavelength1)\n\n ra, dec = galaxy.sample_image(\n len(x1)\n )\n\n xg, yg = self.optics.radec_to_pixel(ra, dec)\n\n xpsf, ypsf = self.optics.psf.sample(len(x1))\n \n #return x1 + xg, y1 + yg, weight1\n return x1 + xg + xpsf, y1 + yg + ypsf, weight1\n\n def make_image(self, galaxy_list, mask=None, noise=True, return_var=True):\n \"\"\"Builds image and variance image\"\"\"\n\n if mask is not None:\n sel, = np.where(mask.flat > -1)\n # mask_lookup = histogram.make_reverse_lookup(sel)\n image = np.zeros((1, len(sel)), dtype='d')\n else:\n image = np.zeros((self.optics.params['det_height'], self.optics.params['det_width']), dtype='d')\n\n\n for g in galaxy_list:\n\n x, y, weight = self.sample(g)\n histogram.histogram2d_accumulate(\n y,\n x,\n weight,\n bins_y=self.pixel_grid[0],\n bins_x=self.pixel_grid[1],\n hist=image,\n mask_dict=mask\n )\n\n for l in range(g.params['nlines']):\n x, y, weight = self.sample_line(g, l)\n histogram.histogram2d_accumulate(\n y,\n x,\n weight,\n bins_y=self.pixel_grid[0],\n bins_x=self.pixel_grid[1],\n hist=image,\n mask_dict=mask\n )\n\n # remove axes with length 1\n image = np.squeeze(image)\n\n if return_var:\n # poisson variance is equal to mean\n var_image = image + self.sigma**2\n\n if noise:\n # add detector noise background\n image += np.random.normal(0, self.sigma, image.shape)\n\n if return_var:\n return image, var_image\n else:\n return image\n\n def make_mask(self, galaxy_list, input_mask=None, width=3, iterations_min=3):\n \"\"\" \"\"\"\n galaxy_list = utils.ensurelist(galaxy_list)\n\n mask_image = np.zeros(\n (\n self.optics.params['det_height'],\n self.optics.params['det_width']\n ),\n dtype=np.bool\n )\n\n for gal_i in range(len(galaxy_list)):\n x0, y0 = self.optics.radec_to_pixel(\n galaxy_list[gal_i].params['ra'], galaxy_list[gal_i].params['dec'])\n\n x, y = self.optics.wavelength_to_pix(self.wave)\n x += x0\n y += y0\n\n weight=1\n\n im = histogram.histogram2d(y, x, weight, bins_y=self.pixel_grid[0], bins_x=self.pixel_grid[1])\n\n radius = self.optics.arcsec_to_pixel(\n galaxy_list[gal_i].halflight_radius\n )\n\n iterations = max(iterations_min, int(np.round(width*radius)))\n\n im = ndimage.binary_dilation(\n im,\n structure=ndimage.generate_binary_structure(2, 2),\n iterations=iterations\n )\n\n mask_image += im\n\n if input_mask is not None:\n mask_image *= input_mask\n\n # values outside of mask are -1\n index_mask = np.zeros(mask_image.shape, dtype=int) - 1\n # values inside of mask are set to an index 0,1,2,3...\n sel = mask_image > 0\n index_mask[sel] = np.arange(np.sum(sel))\n\n return index_mask\n\n # def write(self, filename, **kwargs):\n # \"\"\"Write image to a FITS file\"\"\"\n # hdu = fits.PrimaryHDU()\n # image_hdu = fits.ImageHDU(data=self.image, header=self.optics.wcs.to_header())\n # hdul = fits.HDUList([hdu, image_hdu])\n # hdul.writeto(filename, **kwargs)\n","repo_name":"flepri95/GrismSim","sub_path":"spec_imager.py","file_name":"spec_imager.py","file_ext":"py","file_size_in_byte":9542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13362731691","text":"#!/usr/bin/env python3\n\"\"\"\n1-from_dictionary.py\n\"\"\"\nimport pandas as pd\n\n\ndiccionario = {\n 'First': pd.Series([0.0, 0.5, 1.0, 1.5], index=['A', 'B', 'C', 'D']),\n 'Second': pd.Series(['one', 'two', 'three', 'four'],\n index=['A', 'B', 'C', 'D'])\n}\nframe = pd.DataFrame(diccionario)\nglobals()['df'] = frame\n","repo_name":"RayBar72/holbertonschool-machine_learning","sub_path":"pipeline/0x00-pandas/1-from_dictionary.py","file_name":"1-from_dictionary.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6105329585","text":"import requests\nimport json\nBEARER_TOKEN = r\"AAAAAAAAAAAAAAAAAAAAAEyHQgEAAAAASinkxr1dSx31ACzEEPp%2BkBv2DNc%3DqGQhdu4w2hss7tbJjohdBS9fc0NJd0NlDGVLqdRgME1J7IUwvg\"\nproxies = {\n \"http\":\"http://proxy.uec.ac.jp:8080\",\n \"https\":\"http://proxy.uec.ac.jp:8080\"\n}\n\ndef create_recent_url(query, tweet_fields):\n if(any(tweet_fields)):\n formatted_tweet_fields = \"tweet.fields=\" + \",\".join(tweet_fields)\n else:\n formatted_tweet_fields = \"\"\n url = \"https://api.twitter.com/2/tweets/search/recent?query={}&{}\".format(\n query, formatted_tweet_fields\n )\n return url\ndef create_conversation_url(conversation_id):\n tweet_fields = [\"id\", \"text\", \"author_id\", \"conversation_id\", \"in_reply_to_user_id\", \"referenced_tweets\"]\n formatted_tweet_fields = \"tweet.fields=\" + \",\".join(tweet_fields)\n url = f'https://api.twitter.com/2/tweets/search/recent?query=conversation_id:{conversation_id}&{formatted_tweet_fields}'\n print(url)\n return url\ndef create_headers(bearer_token):\n headers = {\"Authorization\": \"Bearer {}\".format(bearer_token)}\n return headers\ndef connect_to_endpoint(url, headers, proxy_mode=False):\n if proxy_mode:\n response = requests.request(\"GET\", url, headers=headers, proxies=proxies)\n else:\n response = requests.request(\"GET\", url, headers=headers)#, proxies=proxies)\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(response.status_code, response.text)\n return response.json()\ndef get_recent_tweets_and_return_conversation_ids(query, proxy_mode=False):\n # 取得データ e.g. tweet_fields = [\"created_at\", \"author_id\"]\n # 空の場合は ツイートのid, text のみ取得する。\n # created_at(投稿時刻), author_id(アカウントID)などの情報が欲しい場合はtweet_fieldsに書く\n tweet_fields = [\"id\", \"text\"]#, \"author_id\", \"conversation_id\",\"context_annotations\", \"in_reply_to_user_id\", \"referenced_tweets\"]\n recent_url = create_recent_url(query, tweet_fields)\n print(recent_url)\n headers = create_headers(BEARER_TOKEN)\n print(headers)\n json_response = connect_to_endpoint(recent_url, headers, proxy_mode)\n result_text = json.dumps(json_response, indent=4, sort_keys=True, ensure_ascii=False)\n print(result_text)\n conversation_ids = []\n for d in json_response['data']:\n if(d['conversation_id'] not in conversation_ids) and (d['id'] != d['conversation_id']):\n conversation_ids.append(d['conversation_id'])\n return conversation_ids\ndef get_conversation_tweets(conversation_id):\n print('-' * 30)\n headers = create_headers(BEARER_TOKEN)\n root_search_url = f'https://api.twitter.com/2/tweets/{conversation_id}'\n replies_search_url = create_conversation_url(conversation_id)\n root_tweet = connect_to_endpoint(root_search_url, headers)['data']\n root_tweet['reply_to_id'] = ''\n print(root_tweet)\n tweets = [root_tweet]\n reply_tweets = connect_to_endpoint(replies_search_url, headers)['data']\n print(reply_tweets)\n for rep in reversed(reply_tweets):\n tmp = {}\n tmp['id'] = rep['id']\n tmp['text'] = rep['text']\n tmp['reply_to_id'] = ''\n for i in rep['referenced_tweets']:\n if i['type'] == 'replied_to':\n tmp['reply_to_id'] = i['id']\n tweets.append(tmp)\n for tweet in tweets:\n print(tweet)\n print('-' * 30)\n return tweets\n\ndef main():\n proxy_mode = False\n query = \"コロナ\"\n # 検索ワード e.g. query = \"テスト\" / query = \"テスト OR test\"\n # OR 検索　AND検索　-検索　などしたい場合はそのように書く\n conversation_ids = get_recent_tweets_and_return_conversation_ids(query, proxy_mode)\n print(conversation_ids)\n for conversation_id in conversation_ids:\n# url = create_conversation_url(conversation_id)\n# headers = create_headers(BEARER_TOKEN)\n# json_response = connect_to_endpoint(url, headers)\n# result_text = json.dumps(json_response, indent=4, sort_keys=True, ensure_ascii=False)\n# print(result_text)\n get_conversation_tweets(conversation_id)\nif __name__ == \"__main__\":\n main()","repo_name":"fujishu0407/Nolibel","sub_path":"twitter_api/twitter_collect.py","file_name":"twitter_collect.py","file_ext":"py","file_size_in_byte":4195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"24133626111","text":"\"\"\"\nDescription : The class DELETE serves as a \"POST\" request functionality to delete data\n in the defined table.\n\nError Codes : 200 - OK\n 400 - Bad Request/ Does not exist\n\nAuthorization : Done by IND-ONE \n\"\"\"\n\nfrom Application.imports import *\n\nclass DELETE(Resource):\n def __init__(self, **kwargs):\n self.table = kwargs.get('table')\n \n @indone.indone_auth('32f8dca8-1f9c-4db2-938e-dc49c54ff69a', is_authorize = False)\n def post(self, *args, **kwargs):\n try:\n # --- LOG\n APIStartTime = datetime.now()\n UserEmail = kwargs['user_email'] \n CompanyName = kwargs['company_name']\n kwargs[\"api\"] = \"/admin/delete_\"+ self.table\n kwargs['logger'] = logger\n uid = str(uuid4()) + str(datetime.now()).replace(':','').replace(' ','').replace('.','')\n kwargs[\"uid\"] = uid\n kwargsformation(kwargs, '', '', APIStartTime)\n DBlogger.info(\"Entering \"+kwargs[\"api\"]+\" API\" ,kwargs)\n # --- LOG\n\n # File Logger\n logger.info(\"UID: {}.\".format(str(kwargs[\"uid\"])))\n\n auth_type = \"Bearer\"\n auth_headers = request.headers.get('Authorization')\n \n if auth_headers == None:\n auth_type = \"x-api-key\"\n auth_headers = request.headers['x-api-key']\n\n user_id = str(kwargs['user_id'])\n user_email = kwargs['user_email']\n is_admin = kwargs['is_admin']\n\n data = request.get_json()\n adapter = ADAPTER(self.table)\n\n if 'Accept-Version' in request.headers:\n version = request.headers['Accept-Version']\n logger.info(\"VERSION -- version: {}\".format(version))\n else:\n error_message = \"Version is not specified.\"\n logger.error(error_message)\n kwargsformation(kwargs, '400', error_message, APIStartTime)\n DBlogger.info(\"Exiting \"+kwargs[\"api\"]+\" API\", kwargs)\n return make_response(jsonify({'message': error_message}), 400)\n \n if version == 'v3.0':\n response = adapter.delete(data, user_id, user_email, is_admin, auth_type, auth_headers, kwargs, logger)\n \n table = self.table.capitalize()\n \n if(response == 1 ):\n message = '{} Deleted Successfully'.format(table)\n logger.info(message)\n kwargsformation(kwargs, 200, message, APIStartTime)\n DBlogger.info(\"Exiting \"+kwargs[\"api\"]+\" API\", kwargs)\n return make_response(jsonify({'message': message}), 200)\n \n elif(response == -1):\n if self.table == 'fields' or self.table == 'document':\n message = 'Default {} cannot be deleted or {} does not exists'.format(table, table)\n logger.error(message)\n kwargsformation(kwargs, 200, message, APIStartTime)\n DBlogger.info(\"Exiting \"+kwargs[\"api\"]+\" API\", kwargs)\n return make_response(jsonify({'message': message}), 400)\n \n else:\n message = '{} does not exists'.format(table)\n logger.error(message)\n kwargsformation(kwargs, 200, message , APIStartTime)\n DBlogger.info(\"Exiting \"+kwargs[\"api\"]+\" API\", kwargs)\n return make_response(jsonify({'message': message}), 400)\n \n else: \n logger.error(\"Not successful\")\n kwargsformation(kwargs, '400', \"Not successful\", APIStartTime)\n DBlogger.info(\"Exiting \"+kwargs[\"api\"]+\" API\", kwargs)\n return make_response(jsonify({'message':'Not successful'}), 400)\n \n except:\n print(traceback.print_exc())\n logger.error(\"Exception has occured, UID: \"+str(kwargs[\"uid\"]), exc_info=True)\n kwargsformation(kwargs, '400', \"Bad Request\", APIStartTime)\n DBlogger.info(\"Exiting \"+kwargs[\"api\"]+\" API\", kwargs)\n return make_response(jsonify({'message': 'Bad Request'}), 400) \n\n","repo_name":"DhanujaRvi/test3","sub_path":"FOA-Admin-Module1/Application/Delete.py","file_name":"Delete.py","file_ext":"py","file_size_in_byte":4429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18764111731","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nfrom data_cleaner import clean_data\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\nDATA = 'customer.csv'\n\n# Loads and cleans data\ndf = clean_data(pd.read_csv(DATA, sep=';'))\n\n\n# Reducing dimensions to 2 for plotting\ndef dim_reduce(db):\n pca = PCA(n_components=2)\n db = pca.fit_transform(db)\n\n return db\n\n\n# Plots out clusters of segmented customers\ndef plot_clusters(db, label=None):\n\n x, y = zip(* db) # Grabbing coordinates of clusters\n\n # Creates 3-D scatter plot\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n Axes3D.scatter(ax, xs=x, ys=y, c=label)\n plt.title(\"Market Segmentation of Existing Customers\")\n\n\nif __name__ == \"__main__\":\n df = dim_reduce(df)\n\n # Finding clusters of target customers\n kmeans = KMeans(n_clusters=3)\n kmeans.fit(df)\n centers = kmeans.cluster_centers_ # Location of clusters\n labels = kmeans.predict(df) # Labels for different clusters\n\n plot_clusters(df, label=labels)\n plt.show()\n","repo_name":"klark1kent/b2bclassfier","sub_path":"analysis/data_analysis.py","file_name":"data_analysis.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74213963287","text":"from django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.core.validators import MinValueValidator, MaxValueValidator\nfrom django.db import models\n\n\nclass Ticket(models.Model):\n title = models.CharField(\n verbose_name='Titre',\n max_length=128\n )\n description = models.TextField(\n max_length=2048,\n blank=True\n )\n user = models.ForeignKey(\n verbose_name='Auteur',\n to=settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE\n )\n image = models.ImageField(\n blank=True,\n null=True,\n upload_to='images/'\n )\n time_created = models.DateTimeField(\n verbose_name='Date',\n auto_now_add=True\n )\n\n class Meta:\n verbose_name = 'Ticket'\n ordering = ['user']\n\n def __str__(self):\n return self.title\n\n def get_verbose_name(self):\n return self._meta.verbose_name\n\n def get_ticket_reviewers(self):\n \"\"\"\n allows to check all the reviewers of a ticket.\n Useful to forbid a user to post several reviews for the same ticket\n or to prevent the ticket modification if there is already one review.\n \"\"\"\n reviews = [review for review in self.review_set.all()]\n reviewers = [review.user for review in reviews]\n return reviewers\n\n\nclass Review(models.Model):\n ticket = models.ForeignKey(\n verbose_name='Ticket',\n to=Ticket,\n on_delete=models.CASCADE,\n )\n rating = models.PositiveSmallIntegerField(\n verbose_name='Note',\n validators=[\n MinValueValidator(0),\n MaxValueValidator(5),\n ]\n )\n user = models.ForeignKey(\n verbose_name='Auteur',\n to=settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE\n )\n headline = models.CharField(\n verbose_name='Titre',\n max_length=128\n )\n body = models.CharField(\n verbose_name='Commentaire',\n max_length=8192,\n blank=True\n )\n time_created = models.DateTimeField(\n verbose_name='Date',\n auto_now_add=True\n )\n\n class Meta:\n verbose_name = 'Critique'\n ordering = ['user']\n\n def __str__(self):\n name = self.user.username\n ticket = self.ticket.title\n title = 'Revue de ' + name + ' à ' + ticket\n return title\n\n def get_verbose_name(self):\n return self._meta.verbose_name\n\n def get_full_stars(self):\n full_stars = '*' * self.rating\n return full_stars\n\n def get_empty_stars(self):\n empty_stars = '*' * (5 -self.rating)\n return empty_stars\n\n\nclass UserFollows(models.Model):\n user = models.ForeignKey(\n verbose_name='Utilisateur',\n to=settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE,\n related_name='following'\n )\n\n followed_user = models.ForeignKey(\n verbose_name='Utilisateur suivi',\n to=settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE,\n related_name='followed_by'\n )\n\n class Meta:\n verbose_name = 'Abonnement'\n ordering = ['user']\n unique_together = ('user', 'followed_user')\n\n def save(self, *args, **kwargs):\n if self.user == self.followed_user:\n raise ValidationError(\"Un utilisateur ne peut pas s'abonner à lui-même\")\n super().save(*args, **kwargs)\n","repo_name":"Louack/litreview-app","sub_path":"src/review/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40537854052","text":"from random import randint\r\n\r\nrodadas = 6\r\nnumero = \"\"\r\n\r\npontosMago = 0\r\nvidaMago = 3\r\npontosCavaleiro = 0\r\nvidaCavaleiro = 3\r\n\r\nwhile rodadas != 0:\r\n\r\n numeroAleatorio = randint(1 ,10)\r\n if numeroAleatorio % 2 == 0: \r\n numero = \"par\"\r\n else:\r\n numero = \"impar\"\r\n\r\n chuteMago = input(\"Faça a sua jogada: \")\r\n \r\n if numero == chuteMago:\r\n vidaCavaleiro = vidaCavaleiro - 1\r\n pontosMago = pontosMago + 1\r\n\r\n print(\"____________________________________\\nO mago usou Bola de fogo.\")\r\n print(\"O ataque do Mago foi super efetivo!\")\r\n print(\"Você ganhou 1 ponto!\\n__________________________________\")\r\n\r\n rodadas = rodadas - 1\r\n else:\r\n vidaMago = vidaMago - 1\r\n pontosCavaleiro = pontosCavaleiro + 1\r\n print(\"____________________________________\\nO Cavaleiro deferiu um ataque rapido.\")\r\n print(\"O ataque do Cavaleiro foi super efetivo!\")\r\n print(\"Você perdeu 1 ponto de vida!\\n__________________________________\")\r\n \r\n rodadas = rodadas - 1\r\n\r\n if vidaMago == 0:\r\n print(\"O mago desmaiou. A batalha terminou.\")\r\n print(\"Você perdeu!\\n__________________________________\")\r\n\r\n rodadas = 0\r\n \r\n if vidaCavaleiro == 0:\r\n print(\"O Cavaleiro desmaiou. A batalha terminou.\")\r\n print(\"Parabens, você ganhou! \\n__________________________________\")\r\n\r\n rodadas = 0\r\n\r\nprint(\"Pontos Cavaleiro: {} Pontos Mago: {} \\nVida do Cavaleiro: {} Vida do Mago: {}\".format(pontosCavaleiro, pontosMago, vidaCavaleiro, vidaMago))\r\n\r\n\r\n\r\n","repo_name":"MayraLeticia/Mago-vs-Cavaleiro-jogo-de-adivinhacao","sub_path":"MagoVsCavaleiro.py","file_name":"MagoVsCavaleiro.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"26281308660","text":"class Solution:\n def numberOfArrays(self, differences: List[int], lower: int, upper: int) -> int:\n # -86 -85 -84 ... 13\n min_val = sys.maxsize\n max_val = -sys.maxsize\n curr = 0\n for num in differences:\n curr += num\n min_val = min(min_val, curr)\n max_val = max(max_val, curr)\n lower_bound = max(lower, lower - min_val)\n upper_bound = min(upper, upper - max_val)\n return max(upper_bound - lower_bound + 1, 0)\n","repo_name":"jw3329/leetcode-problem-solving","sub_path":"2145. Count the Hidden Sequences/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73641527769","text":"from langchain.vectorstores import FAISS\nfrom langchain.chains import RetrievalQA\nfrom langchain.prompts import PromptTemplate\nimport os\nimport streamlit as st\nfrom langchain.memory import ConversationBufferMemory\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain.document_loaders import OnlinePDFLoader # for loading the pdf\nfrom langchain.llms import OpenAI # the LLM model we'll use (CHatGPT)\n\n\nst.set_page_config(\n page_title=\"LangChain: Chat with online PDF Q&A\", page_icon=\"🦜\")\nst.title(\"🦜 LangChain: LangChain: Chat with online PDF Q&A\")\n\n\n@st.cache_resource\ndef configure_qa_chain(pdf_addr):\n st.info(\"Loading online PDF...\")\n loader = OnlinePDFLoader(pdf_addr)\n\n if not loader:\n st.info(\"Please upload PDF documents to continue.\")\n st.stop()\n\n st.info(\"Split text recursive...\")\n data = loader.load()\n text_splitter = RecursiveCharacterTextSplitter(\n chunk_size=1000, chunk_overlap=0)\n docs = text_splitter.split_documents(data)\n\n st.info(\"OpenAIEmbeddings...\")\n embeddings = OpenAIEmbeddings()\n docsearch = FAISS.from_documents(docs, embeddings)\n\n memory = ConversationBufferMemory(\n memory_key=\"chat_history\",\n return_messages=True\n )\n\n # Custom Prompts\n PROMPT_TEMPLATE = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say \"thanks for asking!\" at the end of the answer. \n {context}\n Question: {question}\n Helpful Answer:\n Answer reply in zh-tw:\"\"\"\n\n PROMPT = PromptTemplate(\n template=PROMPT_TEMPLATE, input_variables=[\"context\", \"question\"]\n )\n chain_type_kwargs = {\"prompt\": PROMPT}\n\n # 3. Querying\n st.info(\"RetrievalQA...\")\n llm = OpenAI(temperature=0.7, model_name=\"gpt-3.5-turbo-0613\")\n retriever = docsearch.as_retriever()\n qa = RetrievalQA.from_chain_type(\n llm=llm, chain_type=\"stuff\", retriever=retriever, chain_type_kwargs=chain_type_kwargs)\n\n return qa\n\n\nclass PrintRetrievalHandler(BaseCallbackHandler):\n def __init__(self, container):\n self.container = container.expander(\"Context Retrieval\")\n\n def on_retriever_start(self, query: str, **kwargs):\n self.container.write(f\"**Question:** {query}\")\n\n def on_retriever_end(self, documents, **kwargs):\n # self.container.write(documents)\n for idx, doc in enumerate(documents):\n source = os.path.basename(doc.metadata[\"source\"])\n self.container.write(f\"**Document {idx} from {source}**\")\n self.container.markdown(doc.page_content)\n\n\nopenai_api_key = st.sidebar.text_input(\"OpenAI API Key\", type=\"password\")\nif not openai_api_key:\n st.info(\"Please add your OpenAI API key to continue.\")\n st.stop()\n\nonline_pdf_addr = st.sidebar.text_input(\"Your online PDF address\")\nif not online_pdf_addr:\n st.info(\"Please input online PDF address to continue.\")\n st.stop()\n\n\nqa_chain = configure_qa_chain(online_pdf_addr)\n\nif \"messages\" not in st.session_state or st.sidebar.button(\"Clear message history\"):\n st.session_state[\"messages\"] = [\n {\"role\": \"assistant\", \"content\": \"How can I help you?\"}]\n\nfor msg in st.session_state.messages:\n st.chat_message(msg[\"role\"]).write(msg[\"content\"])\n\n# show paper agenda first.\n\nst.chat_message(\"user\").write(\"本篇論文的摘要是什麼?\")\nagenda = qa_chain.run({\"query\": \"本篇論文的摘要是什麼?\"})\nst.write(agenda)\n\nuser_query = st.chat_input(placeholder=\"Ask me anything!\")\n\nif user_query:\n st.session_state.messages.append({\"role\": \"user\", \"content\": user_query})\n st.chat_message(\"user\").write(user_query)\n\n with st.chat_message(\"assistant\"):\n cb = PrintRetrievalHandler(st.container())\n response = qa_chain.run({\"query\": user_query}, callbacks=[cb])\n st.session_state.messages.append(\n {\"role\": \"assistant\", \"content\": response})\n st.write(response)\n","repo_name":"kkdai/langchain_tools","sub_path":"onlinepdf_asking.py","file_name":"onlinepdf_asking.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"24724223765","text":"import pandas as pd\nimport numpy as np\nimport nltk\nfrom nltk.corpus import stopwords\nnltk.download('stopwords')\nnltk.download('wordnet')\nnltk.download('omw-1.4')\nimport regex as re\nimport nltk\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.stem import WordNetLemmatizer,PorterStemmer\nfrom nltk.corpus import stopwords\nlemmatizer = WordNetLemmatizer()\nstemmer = PorterStemmer()\nfrom collections import Counter\n\ncnt = Counter()\n\ndef preprocess(sentence):\n sentence=str(sentence)\n sentence = sentence.lower()\n sentence=sentence.replace('{html}',\"\")\n cleanr = re.compile('<.*?>')\n cleantext = re.sub(cleanr, '', sentence)\n rem_url=re.sub(r'http\\S+', '',cleantext)\n rem_num = re.sub('[0-9]+', '', rem_url)\n tokenizer = RegexpTokenizer(r'\\w+')\n tokens = tokenizer.tokenize(rem_num)\n filtered_words = [w for w in tokens if len(w) > 2 if not w in stopwords.words('english')]\n stem_words=[stemmer.stem(w) for w in filtered_words]\n lemma_words=[lemmatizer.lemmatize(w) for w in stem_words]\n return \" \".join(filtered_words)\n\n\ndef remove_freqwords(text):\n FREQWORDS = set([w for (w, wc) in cnt.most_common(60)])\n return \" \".join([word for word in str(text).split() if word not in FREQWORDS])\n\n\ndef build_medical_dictionary():\n data = pd.read_json('Data/question-answer-dataset.json')\n data['cleanText']=data['answer'].map(lambda s:preprocess(s))\n for text in data[\"cleanText\"].values:\n for word in text.split():\n cnt[word] += 1\n data[\"cleanText\"] = data[\"cleanText\"].apply(lambda text: remove_freqwords(text))\n words=set()\n for ans in data['cleanText']:\n for word in ans.split(' '):\n words.add(word)\n\n\n sentences = list()\n with open(\"Data/words.txt\", 'r') as file:\n sentences = file.read().strip().split('\\n')\n\n for i in range(len(sentences)):\n sentences[i]=preprocess(sentences[i])\n sentences[i]=remove_freqwords(sentences[i])\n\n for sentence in sentences:\n for word in sentence.split(' '):\n words.add(word)\n\n with open(\"Data/medical_words.txt\", 'w') as file:\n for word in words:\n file.write(str(word))\n file.write(\"\\n\")\n","repo_name":"xzaviourr/HealthcareChatbot","sub_path":"Utility/medical_dictionary_creator.py","file_name":"medical_dictionary_creator.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"4449389794","text":"import os\nimport time\nimport datetime\nimport socket\nimport struct\nimport logging\nimport datetime\nimport sys, os\nimport numpy as np\nfrom math import ceil\nfrom cosmic.camera import fccd\n\n\"\"\"\nNOTES\n\nDouble exposure is not working properly yet.\n\"\"\"\n\ntoday = datetime.date.fromtimestamp(time.time())\n\nPARAMS = dict(\n # parameters for the frontend\n nstream=dict(\n step=0.03, # mum\n num=15, # number of\n bnum=5, # num of dark points for each axes\n dwell=(100, 100), # msec\n energy=800, # ev\n ),\n comm=dict(\n udp_ip='127.0.0.1',\n udp_port=49203,\n cinc_ip='127.0.0.1',\n # cinc_ip = '10.0.0.16',\n cinc_port=8880,\n #basepath='/tmp/tmpscan/'\n basepath='/Users/benders/Globus/'\n )\n)\n# QT kill switch\nimport signal\n\nsignal.signal(signal.SIGINT, signal.SIG_DFL)\n\nDELAY_100Hz = 1.8e-5\nDELAY_100Hz = 1.8e-5\n\n\nclass DummySignal(object):\n\n def __init__(self, tpe=str):\n self.tpe = tpe\n\n def emit(self, msg='signal'):\n print(self.tpe(msg))\n\n\ntry:\n from PyQt4 import QtCore\n\n __has_qt4 = True\n Thread = QtCore.QThread\n Signal = QtCore.pyqtSignal\nexcept:\n __has_qt4 = False\n import threading\n\n Thread = threading.Thread\n Signal = DummySignal\n\n\ndef eV2mum(eV):\n \"\"\"\\\n Convert photon energy in eV to wavelength (in vacuum) in micrometers.\n \"\"\"\n wl = 1. / eV * 4.1356e-7 * 2.9998 * 1e6\n\n return wl\n\n\ndef abs2(X):\n return np.abs(X) ** 2\n\n\nclass FccdSimulator(Thread):\n \"\"\"Simulates STXM control and FCCD camera.\"\"\"\n statusMessage = Signal(str)\n simulationDone = Signal()\n\n def __init__(self, params=None, delay_udp=1e-7, add_random_dark=False):\n super(FccdSimulator, self).__init__()\n\n # private stuff\n self._status = \"undefinded\"\n self._status_color = \"orange\"\n self._stop = False\n self._do_scan = True\n\n # these could be part of input but not entirely important\n self.seed = 1983\n self.udp_packet_size = 4104\n self.udp_header_size = 8\n #self.shape = (1920, 960) # (1940,1152)\n #self.fCCD = fccd.FCCD()\n self.shape = (980, 960) # (1940,1152)\n self.fCCD = fccd.FCCD(nrows=self.shape[0]//2)\n self.psize = 30\n self.offset = 10000\n self.io_noise = 5\n self.photons_per_sec = 2e7\n self.resolution = 0.005\n self.dist = 80000\n self.adu_per_photon = 34\n self.zp_dia_outer = 30 # pixel on screen\n self.zp_dia_inner = 12 # pixel on screen\n self.nanoball_rad = 5 # nanoball radius (pixel)\n\n # DEADFOOD\n self.end_of_frame_msg = b\"\\xf1\\xf2\" + b\"\\xde\\xad\\xf0\\x0d\" + b\"\\x00\\x00\"\n\n # load parameters\n if params is not None:\n self.p = params\n else:\n from copy import deepcopy\n self.p = deepcopy(PARAMS)\n\n self.energy = self.p['nstream']['energy']\n\n self.photons = [self.photons_per_sec * d / 1000 for d in self.p['nstream']['dwell']]\n\n # Configuration\n self.udp_address = (self.p['comm']['udp_ip'], self.p['comm']['udp_port'])\n\n # Create stxm controller\n cin_address = (self.p['comm']['cinc_ip'], self.p['comm']['cinc_port'])\n base_path = self.p['comm']['basepath']\n self.stxm_control = STXMControlComm(cin_address, base_path)\n\n self.delay = delay_udp\n self.add_random_dark = add_random_dark\n\n # calculate sim shape to resolution\n a = np.int(self.dist * eV2mum(self.energy) / (self.psize * self.resolution))\n assert a < np.min(self.shape), 'Too many pixel, choose larger resolution'\n for i in [256, 384, 512, 640, 768, 896, 1024]:\n if i > a:\n a = i\n break\n\n a = 384\n self.sim_shape = (a, a)\n self.status = \"Simulation resolution is %d x %d\" % self.sim_shape\n\n # make test frame\n X, Y = np.indices(self.shape)\n Y = (Y // 144) * 10\n Y[self.shape[0] // 2:, :] *= -1\n Y += Y.min()\n self.testframe = Y.astype(np.uint16)\n\n self.darkframes = None\n self.dataframes = None\n\n def make_ptycho_data(self):\n self.status = \"Preparing ptycho data ..\"\n self.create_darks()\n self.status = \"Prepared dark data ..\"\n self.create_data()\n self.status = \"Prepared ptycho data ..\"\n\n def create_darks(self):\n N = self.p['nstream']['bnum'] ** 2\n self.darkframes = self._draw(np.zeros((N,) + self.shape).astype(int))\n\n def create_data(self):\n \"\"\" makes a raster ptycho scan \"\"\"\n # seed the random generatot to fixed value\n np.random.seed(self.seed)\n\n sh = self.sim_shape\n\n # positions\n num = self.p['nstream']['num']\n step = self.p['nstream']['step']\n\n pos = np.array([(step * k, step * j) for j in range(num) for k in range(num)])\n pixelpos = np.round(pos / self.resolution).astype(int)\n pixelpos -= pixelpos.min()\n pixelpos += 5\n\n # make object\n self.status = \"Preparing exit waves ..\"\n\n osh = pixelpos.max(0) + np.array(sh) + 10\n nb = self.nanoball_object(osh, rad=self.nanoball_rad, num=400)\n nb /= nb.max()\n # nb = np.resize(nb,osh)\n self.ob = np.exp(0.2j * nb - nb / 2.)\n # from matplotlib import pyplot as plt\n # plt.imshow(np.angle(self.ob), cmap='gray')\n # plt.colorbar()\n # plt.show()\n\n pr = self.stxm_probe(sh, outer=self.zp_dia_outer, inner=self.zp_dia_inner)\n pr /= np.sqrt(abs2(pr).sum())\n self.pr = pr\n # from matplotlib import pyplot as plt\n # plt.imshow(np.angle(pr), cmap='hsv')\n # plt.show()\n\n a, b = sh\n exits = np.array([self.pr * self.ob[pr:pr + a, pc:pc + b] for (pr, pc) in pixelpos])\n # fs = lambda e : np.fft.fftshift(e,(-2,-1))\n fs = lambda e: np.fft.fftshift(np.fft.fft2(np.fft.fftshift(e))) / np.sqrt(sh[0] * sh[1])\n\n self.status = \"Propagating waves ..\"\n stack = np.array([abs2(fs(e)) for e in exits])\n\n self.diffstack = np.random.poisson(stack * self.photons[0]) * self.adu_per_photon\n\n self.diffstack2 = np.random.poisson(stack * self.photons[1]) * self.adu_per_photon\n # this was too slow\n \"\"\"\n \n I = np.zeros((len(stack),)+self.shape).astype(int)\n off = [(a-b)/2 for (a,b) in zip(self.shape,sh)]\n I[:,off[0]:off[0]+sh[0],off[1]:off[1]+sh[1]] = stack\n self.dataframes = self._draw(I)\n \"\"\"\n self.status = \"Frame waves to larger detector shape ..\"\n self.dataframes = [self._draw(self._embed_frame(frame)) for frame in self.diffstack]\n\n def _embed_frame(self, frame):\n sh = frame.shape\n out = np.zeros(self.shape).astype(int)\n off = [(a - b) // 2 for (a, b) in zip(self.shape, sh)]\n out[off[0]:off[0] + sh[0], off[1]:off[1] + sh[1]] = frame\n return out\n\n def _draw(self, frames):\n return frames + np.random.normal(loc=self.offset, scale=self.io_noise, size=frames.shape).astype(int)\n\n def _convert(self, frame):\n frame[frame > 63000] = 63000\n return frame.astype(np.uint16).byteswap().tobytes()\n # return frame.astype(np.uint16).tostring()\n\n @staticmethod\n def stxm_probe(shape, inner=8, outer=25):\n\n # d = np.float(np.min(shape))\n X, Y = np.indices(shape).astype(float)\n X -= X.mean()\n Y -= Y.mean()\n R = (np.sqrt(X ** 2 + Y ** 2) < outer).astype(complex)\n r = (np.sqrt(X ** 2 + Y ** 2) > inner).astype(complex)\n return np.fft.fftshift(np.fft.ifft2(np.fft.fftshift(R * r)))\n\n @staticmethod\n def nanoball_object(shape, rad=5, num=40):\n \"\"\" creates nanoballs as transmission \"\"\"\n\n def cluster_coords(shape, rad=5, num=40):\n sh = shape\n\n def pick():\n return np.array([np.random.uniform(0, sh[0] - 1), np.random.uniform(0, sh[1] - 1)])\n\n coords = [np.array(\n [np.random.randint(sh[0] / 3, 2 * sh[0] / 3 - 1), np.random.randint(sh[0] / 3, 2 * sh[0] / 3 - 1)])]\n # np.rand.uniform(0,1.,tuple(sh)):\n for ii in range(num - 1):\n noresult = True\n for k in range(10000):\n c = pick()\n dist = np.sqrt(np.sum(abs2(np.array(coords) - c), axis=1))\n if (dist < 2 * rad).any():\n continue\n elif (dist >= 2 * rad).any() and (dist <= 3 * rad).any():\n break\n elif (0.001 + np.sum(8 / (dist ** 2)) > np.random.uniform(0, 1.)):\n break\n\n coords.append(c)\n return np.array(coords)\n\n sh = shape\n out = np.zeros(sh)\n xx, yy = np.indices(sh)\n coords = cluster_coords(sh, rad, num)\n for c in coords:\n h = rad ** 2 - (xx - c[0]) ** 2 - (yy - c[1]) ** 2\n h[h < 0] = 0.\n out += np.sqrt(h)\n\n return out\n\n def listen_for_greeting(self):\n \"\"\"Open a socket for sending UDP packets to the framegrabber.\"\"\"\n\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n self.sock.bind(self.udp_address)\n self.status = \"Socket open on (%s) on %d \" % self.udp_address\n\n self.status = \"Awaiting Handshake\"\n data, addr = self.sock.recvfrom(255)\n\n self.status = \"Received %s from (%s) on %d, \" % (str(data), addr[0], addr[1])\n self.fg_addr = addr\n\n self.sock.connect(addr)\n\n def make_header(self, port, length, frame_number, packet_number):\n \"\"\"Returns header.\"\"\"\n header = struct.pack('!BBHHH', packet_number, 0, port, length, frame_number)\n return header\n\n def udpsend(self, packet):\n # self.sock.sendto(packet,self.fg_addr)\n self.sock.send(packet)\n\n def send_frame_in_udp_packets(self, frame, frame_number):\n \"\"\"Chop frame into small UDP packets and send them out through connected socket.\"\"\"\n # frame = self._convert(self.fCCD.scramble(frame))\n c = self.fCCD\n\n # create a frame in byte stream. Cut off a bit at the end and attach ending message\n frame = self._convert(c._rawXclock(c._clockXrow(c._rowXccd(frame))))[:-200]\n frame += self.end_of_frame_msg\n psize = self.udp_packet_size - self.udp_header_size\n print(frame_number, (len(frame) // psize + 1))\n ip, port = self.udp_address\n try:\n for i in range(len(frame) // psize + 1):\n time.sleep(self.delay)\n h = self.make_header(port, psize + self.udp_header_size, frame_number, i % 256)\n packet = h + frame[i * psize:(i + 1) * psize]\n bytes_sent = self.udpsend(packet) # , (self.ip, self.port))\n # print frame_number, bytes_sent, packet[:8]\n\n # optional blurbs / hickups\n packet = self.make_header(port, 48, frame_number, 0) + 32 * b\"\\x00\" + self.end_of_frame_msg\n bytes_sent = self.udpsend(packet) # , (self.ip, self.port))\n bytes_sent = self.udpsend(packet) # , (self.ip, self.port))\n except socket.error:\n self.status = \"Connection error. Restarting ...\"\n self.sock.close()\n self.listen_for_greeting()\n\n def run(self):\n \"\"\"This triggers the event loop.\"\"\"\n scan_num = 0\n j = 0\n t0 = datetime.datetime.now()\n STXM = self.stxm_control\n conn = self.listen_for_greeting()\n # Start the event loop\n while not self._stop:\n\n # Stop producing data when total nr. of frames is reached\n \"\"\"\n if j >= self.ntotal:\n self._stop = True\n self.simulationDone.emit()\n \"\"\"\n if not self._do_scan or not (j % 20 == 0):\n time.sleep(0.5)\n self.send_frame_in_udp_packets(self.testframe, j)\n else:\n # pause for moving in the detector\n self.status = \"Moving in CCD detector\"\n time.sleep(3)\n\n j = 0\n self.status = \"Closing shutter\"\n time.sleep(.2)\n\n # dark frames\n self.status = \"Taking dark frames\"\n dr = STXM.get_next_dir_name(scan_num=scan_num)\n os.makedirs(dr)\n time.sleep(.5)\n\n STXM.comm_sendScanInfo(self.p['nstream'])\n STXM.comm_turnOnOffFastCCDCamera(True)\n STXM.comm_StartRegion(self.p['nstream']['dwell'])\n for k, frame in enumerate(self.darkframes):\n self.send_frame_in_udp_packets(frame, j + k)\n\n j += k\n # actual frames\n self.status = \"Opening shutter\"\n time.sleep(.2)\n\n self.status = \"Taking exp frames\"\n dr = STXM.get_next_dir_name(scan_num=scan_num)\n os.makedirs(dr)\n time.sleep(.5)\n\n STXM.comm_turnOnOffFastCCDCamera(True)\n STXM.comm_StartRegion(self.p['nstream']['dwell'])\n if self.dataframes is not None:\n for k, frame in enumerate(self.dataframes):\n self.send_frame_in_udp_packets(frame, j + k)\n else:\n for k, frame in enumerate(self.diffstack):\n self.send_frame_in_udp_packets(\n self._draw(self._embed_frame(frame)), j + k)\n\n STXM.comm_turnOnOffFastCCDCamera(False)\n scan_num += 1\n\n self.status = \"Moving detector out\"\n time.sleep(3)\n # Update counters\n j += 1\n\n \"\"\"\n time.sleep(0.1)\n \n # Update status (speed)\n if not (j % 100):\n self.status = \"Sending at %.2fHz (frame %d)\" %(float(j) / (datetime.datetime.now() - t0).total_seconds(), j)\n \"\"\"\n\n def stop(self):\n \"\"\"Stop the event loop.\"\"\"\n self.status_emit(\"Fccd is stopping the event loop\", \"red\")\n self._stop = True\n\n def status_emit(self, status, color=None):\n \"\"\"Change and emit status signal `status. If `color` is None use default\"\"\"\n c = color if color is not None else self._status_color\n self._status = status\n self.statusMessage.emit(\"<font color=\\\"\" + c + \"\\\">\" + status + \"</font>\")\n logging.info(status)\n print(status)\n\n @property\n def status(self):\n return self._status\n\n @status.setter\n def status(self, status):\n self.status_emit(status)\n\n\nclass STXMControlComm(object):\n \"\"\" simple class to emulate commands send from stxm control \"\"\"\n\n MSGLEN = 256\n MSGEND_RECV = b\"\\n\\r\"\n MSGEND_SEND = b\"\\r\\n\" # THIS IS ONE WEIRD INCONSISTENCY\n\n def __init__(self, address=(\"127.0.0.1\", 8888), basepath='/tmp/Data/'):\n\n self.addr = address\n self.basepath = basepath\n\n def get_next_dir_name(self, path=None, datefmt='%y%m%d', scan_num=0):\n \"\"\" \n Mimics automatic path creatio, although I do not quite understand\n teh code in STXM control\n \n \"\"\"\n s = os.path.sep\n if path is None:\n dt = today.strftime(datefmt)\n\n path = self.basepath + s + dt + s + dt + '%03d' % scan_num + s\n else:\n if path.endswith(s):\n path += s\n\n if not os.path.exists(path):\n os.makedirs(path)\n\n lst = [l for l in os.listdir(path) if os.path.isdir(path + l)]\n self.path = path + '%03d' % (1 + len(lst))\n return self.path\n\n def stxm_comm(self, stxmsocket, msg):\n \"\"\" lazy helper function to send and receive \"\"\"\n\n stxmsocket.send(bytes(msg,'UTF-8') + self.MSGEND_SEND)\n # Maybe we would want to flush here probably.\n\n # join response\n piece = stxmsocket.recv(self.MSGLEN)\n answer = b\"\" + piece\n while piece.find(self.MSGEND_RECV) < 0:\n piece = stxmsocket.recv(self.MSGLEN)\n answer += piece\n\n # return response line ending \\r and \\n printed out \n return repr(answer.decode('UTF-8'))\n\n def comm_turnOnOffFastCCDCamera(self, bTurnOn, dwell=100):\n \"\"\" A Python remake of the original in scan.cpp \"\"\"\n \"\"\" the original function includes dwell time, but I don't know why \"\"\"\n on = bool(bTurnOn)\n\n s = socket.create_connection(self.addr)\n comm = lambda x: self.stxm_comm(s, x)\n\n if on:\n msg = 'setCapturePath ' + self.path\n else:\n msg = 'setCapturePath '\n\n print(comm(msg))\n\n if on:\n msg = 'setCaptureMode continuous'\n else:\n time.sleep(.1)\n time.sleep(dwell / 100. + 0.1)\n msg = 'setCaptureMode single'\n\n print(comm(msg))\n\n if on:\n print(comm('setCapturePath ' + self.path))\n print(comm('setExternalTriggerMode'))\n print(comm('startCapture'))\n else:\n print(comm('setInternalTriggerMode'))\n print(comm('stopCapture'))\n print(comm('setDoubleExpCount 0'))\n\n s.close()\n\n def comm_sendScanInfo(self, simdict):\n \"\"\" Mimics communication in sendScanInfo in CCDscan.cpp \"\"\"\n s = socket.create_connection(self.addr)\n\n step, num, bnum, dwell, energy = (simdict[k] for k in ('step', 'num', 'bnum', 'dwell', 'energy'))\n out = \"sendScanInfo \"\n out += \"pos_x %.6g, pos_y %.6g, \" % (0.0, 0.0)\n out += \"step_size_x %.5g, step_size_y %.5g, \" % (step, step)\n out += \"num_pixels_x %d, num_pixels_y %d, \" % (num, num)\n out += \"background_pixels_x %d, background_pixels_y %d, \" % (bnum, bnum)\n out += \"dwell1 %.3g, dwell2 %0.3g, \" % dwell\n out += \"energy %.5g,\" % energy\n out += \"isDoubleExp 0\" #% (len(dwell)-1)\n\n print(self.stxm_comm(s, out))\n\n s.close()\n # format string was in .cpp ended with '\\n' .. here we will us '\\n\\r' \n return out\n\n def comm_StartRegion(self, dwell, iExpCount=0):\n \"\"\" Mimics communication in StartRegion in CCDscan.cpp \"\"\"\n s = socket.create_connection(self.addr)\n comm = lambda x: self.stxm_comm(s, x)\n\n print(comm('setExp %.3g' % dwell[0]))\n print(comm('setExp2 %.3g' % dwell[1]))\n print(comm('setDoubleExpCount %d' % iExpCount)) # No double exposure allowed\n print(comm('resetCounter'))\n\n s.close()\n\n\nif __name__ == '__main__':\n from matplotlib import pyplot as plt\n\n # from ptypy import utils as u\n S = FccdSimulator()\n # plt.ion()\n\n # plt.show()\n S.make_ptycho_data()\n # plt.figure();plt.imshow(u.imsave(S.pr))\n # plt.figure();plt.imshow(u.imsave(S.ob,vmin=0.))\n # plt.figure();plt.imshow(np.log10(S.dataframes[0]));plt.colorbar()\n # plt.figure();plt.imshow(np.log10(S.dataframes.sum(0)));plt.colorbar()\n # plt.figure();plt.imshow(S.darkframes[0]);plt.colorbar()\n # plt.show()\n # print type(FccdSimulator)\n\n S.start()\n\n if __has_qt4:\n # add the missing clock machine\n QA = QtCore.QCoreApplication([])\n QA.exec_()\n","repo_name":"lbl-camera/cosmiccam","sub_path":"sim_debug/sim_stxmcontrol.py","file_name":"sim_stxmcontrol.py","file_ext":"py","file_size_in_byte":19319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39537537195","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n\n#Vários imports...\nimport roslib\nimport rospy\nimport sys\nimport smach\nimport smach_ros\nimport rospy\nimport numpy\nfrom numpy import linalg\nimport transformations\nfrom tf import TransformerROS\nimport tf2_ros\nimport math\nimport random\nimport time\nfrom geometry_msgs.msg import Twist, Vector3, Pose, Vector3Stamped\nfrom ar_track_alvar_msgs.msg import AlvarMarker, AlvarMarkers\nfrom nav_msgs.msg import Odometry\nfrom sensor_msgs.msg import Image, LaserScan\nfrom std_msgs.msg import Header\nfrom neato_node.msg import Bump\n\n\n#Aqui setamos as variáveis iniciais. O neato vai atualizando\nx = 0\ny = 0\nz = 0\nid = 0\nang = -500\nhas_bumped = False\nlaser_distance = 0\n\ntfl = 0\n\ntf_buffer = tf2_ros.Buffer()\n\n#Quão perto queremos que ele fique dos objetos. Servem para os \"if's\"\nx_desejado = 0.12\ny_desejado = 0.10\nz_desejado = 1.00\n\nframe = \"camera_frame\"\n\n#Função que procura o marcador\ndef procurando_marcador(msg):\n global x\n global y\n global z\n global ang\n global speed\n global id\n for marker in msg.markers:\n id = marker.id\n marcador = \"ar_marker_\" + str(id)\n\n print(tf_buffer.can_transform(frame, marcador, rospy.Time(0)))\n header = Header(frame_id=marcador)\n # Procura a transformacao em sistema de coordenadas entre a base do robo e o marcador numero 100\n # Note que para seu projeto 1 voce nao vai precisar de nada que tem abaixo, a\n # Nao ser que queira levar angulos em conta\n trans = tf_buffer.lookup_transform(frame, marcador, rospy.Time(0))\n\n # Separa as translacoes das rotacoes\n x = trans.transform.translation.x*100\n y = trans.transform.translation.y*100\n z = trans.transform.translation.z*100\n # Procura a transformacao em sistema de coordenadas entre a base do robo e o marcador numero 100\n # Note que para seu projeto 1 voce nao vai precisar de nada que tem abaixo, a\n # Nao ser que queira levar angulos em conta\n # Terminamos\n print(\"id: {} x {} y {} z {}\".format(id, x,y,z))\n\n\n#Função que detecta o funcionamento dos bumpers\ndef bumper_detection(bump):\n global has_bumped\n if bump.leftFront or bump.leftSide or bump.rightFront or bump.rightSide:\n has_bumped = True\n else:\n has_bumped = False\n\ndef laser_detection(laser):\n global laser_distance\n ranges = list(laser.ranges)\n print(len(ranges))\n for d in ranges[140:260]: #Anda pra trás\n if d < 0.5 and d != 0:\n laser_distance = 2\n break\n for d in ranges[0:140] + ranges[260:361]: #Anda pra frente\n if d < 0.25 and d != 0:\n laser_distance = 1\n break\n\n\n#Classe que faz o robô girar\nclass Spin(smach.State):\n def __init__(self):\n smach.State.__init__(self, outcomes=['not_found','found','crash', 'crash_back', 'following', '50', '100', 'finish'])\n def execute(self, userdata):\n global speed\n rospy.loginfo('Executing state SPIN')\n global id\n global x, y, z\n global laser_distance\n\n if has_bumped == True:\n #Vector3(linear, 0 ,0), Vector3(0,0,angular)\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n print(\"Stopped\")\n id = 0\n return 'crash'\n\n if laser_distance == 1:\n id = 0\n laser_distance = 0\n return 'crash_back'\n\n if id == 50:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 1))\n speed.publish(vel)\n rospy.sleep(6.25)\n print(\"Marcador 50\")\n id = 0\n return '50'\n\n if id == 100:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 1))\n speed.publish(vel)\n rospy.sleep(1)\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, -1))\n speed.publish(vel)\n rospy.sleep(2)\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 1))\n speed.publish(vel)\n rospy.sleep(1)\n vel = Twist(Vector3(-1, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n rospy.sleep(1)\n vel = Twist(Vector3(1, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n rospy.sleep(1)\n print(\"Marcador 100\")\n id = 0\n return '100'\n\n if id == 150:\n #Caso não funcione. coloque id != 0 e tire o if id == 150\n print(id)\n # if (x > -0.5) and (x < 0.12):\n if z > 40:\n if (x > -5) and (x < 5):\n id = 0\n return 'found'\n elif x < -5:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0.1))\n speed.publish(vel)\n rospy.sleep(0.4)\n id = 0\n return 'following'\n elif x > 5:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, -0.1))\n speed.publish(vel)\n rospy.sleep(0.4)\n id = 0\n return 'following'\n if z < 40:\n if (x > -1.5) and (x < 1):\n id = 0\n return 'found'\n elif x < -1.5:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0.1))\n speed.publish(vel)\n rospy.sleep(0.4)\n id = 0\n return 'following'\n elif x > 1:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, -0.1))\n speed.publish(vel)\n rospy.sleep(0.4)\n id = 0\n return 'following'\n\n if z < 15 and z > 0:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n print(\"Stopped and FINISHED!\")\n return 'finish'\n\n # if id == 150:\n # print(id)\n # vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, -0.3))\n # speed.publish(vel)\n # rospy.sleep(0.4)\n # return 'not_found'\n\n else:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, -0.1))\n speed.publish(vel)\n return 'not_found'\n\nclass MoveForward(smach.State):\n def __init__(self):\n smach.State.__init__(self, outcomes=['move', 'crash', 'following_marker', 'finish'])\n def execute(self, userdata):\n global speed\n global has_bumped\n global laser_distance\n global id\n global x, y, z\n print(x,y,z)\n rospy.loginfo('Executing state MOVEFORWARD')\n\n if has_bumped == True: #Se dista 20cm de algo sólido, para pra não bater.\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n print(\"Stopped\")\n return 'crash' #Significa que ele bateu e parou\n id = 0\n\n if id == 150 and z > 20:\n vel = Twist(Vector3(0.15, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n rospy.sleep(0.5)\n return 'following_marker' #Segue o marcador\n\n if z < 15 and z > 0:\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0))\n speed.publish(vel)\n print(\"Stopped and FINISHED!\")\n return 'finish'\n\n else: #Nenhum está acionado porque não entrou no 'if' de cima\n vel = Twist(Vector3(0.15, 0, 0), Vector3(0, 0, 0)) #Andar para frente\n speed.publish(vel)\n rospy.sleep(0.3)\n return 'move' #Significa que nenhum bumper está acionado e ele anda\n\n#Andar pra trás - sobrevivência\nclass MoveBack(smach.State):\n def __init__(self):\n smach.State.__init__(self, outcomes=['moved_back'])\n\n def execute(self, userdata):\n global speed\n rospy.loginfo('Executing state MOVEBACK')\n vel = Twist(Vector3(-0.2, 0, 0), Vector3(0, 0, 0)) #Andar para trás reto\n speed.publish(vel)\n rospy.sleep(1)\n return 'moved_back'\n\n#Gira 90 graus para algum lado\nclass TurningRandom(smach.State):\n def __init__(self):\n smach.State.__init__(self, outcomes=['sortedturn'])\n\n def execute(self, userdata):\n global speed\n global id\n rospy.loginfo('Executing state TURNINGRANDOM')\n vel = Twist(Vector3(0, 0, 0), Vector3(0, 0, -0.5)) #Girar 90 graus\n speed.publish(vel)\n rospy.sleep(random.randint(5,25)/10)\n return 'sortedturn'\n\n#Classe que roda o programa inteiro quando executado no termina1\ndef main():\n global speed\n global buffer\n rospy.init_node('marcador') #Precisa disso para rodar!\n speed = rospy.Publisher('/cmd_vel', Twist, queue_size=1) #Velocidade do robô\n bumper = rospy.Subscriber('/bump', Bump, bumper_detection)\n laser = rospy.Subscriber('/scan', LaserScan, laser_detection)\n recebedor = rospy.Subscriber(\"/ar_pose_marker\", AlvarMarkers, procurando_marcador)\n\n tfl = tf2_ros.TransformListener(tf_buffer)\n\n\n #Cria a Máquina de Estados\n sm = smach.StateMachine(outcomes=['finish'])\n\n #Utilizando a máquina\n with sm:\n #Adicionando estados para a máquina: (Nome, Classe, transitions={}); transitions disso para isso {'disso' : 'isso'}\n smach.StateMachine.add('SPIN', Spin(),\n transitions={'not_found':'SPIN',\n 'found':'MOVEFORWARD',\n 'crash': 'MOVEBACK',\n 'crash_back': 'MOVEFORWARD',\n 'following':'MOVEFORWARD',\n '50': 'SPIN',\n '100': 'SPIN',\n 'finish': 'finish'})\n smach.StateMachine.add('MOVEFORWARD', MoveForward(),\n transitions={'crash':'MOVEBACK',\n 'move':'SPIN',\n 'following_marker':'SPIN',\n 'finish': 'finish'})\n\n smach.StateMachine.add('TURNINGRANDOM', TurningRandom(),\n transitions={'sortedturn':'SPIN'})\n\n smach.StateMachine.add('MOVEBACK', MoveBack(),\n transitions={'moved_back':'TURNINGRANDOM'})\n #Executa as máquinas\n outcome = sm.execute()\n #rospy.spin()\n #while not rospy.is_shutdown():\n # rospy.sleep(0.2)\n\n#Apenas pra ver se está rodando o arquivo original\nif __name__ == '__main__':\n main()\n","repo_name":"filipefborba/projeto1_robotica","sub_path":"scripts/djjorgin.py","file_name":"djjorgin.py","file_ext":"py","file_size_in_byte":10644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34966336808","text":"from ryu.base import app_manager\nfrom ryu.controller import mac_to_port\nfrom ryu.controller import ofp_event\nfrom ryu.controller.handler import CONFIG_DISPATCHER, MAIN_DISPATCHER\nfrom ryu.controller.handler import set_ev_cls\nfrom ryu.ofproto import ofproto_v1_3\nfrom ryu.lib.mac import haddr_to_bin\nfrom ryu.lib.packet import packet\nfrom ryu.lib.packet import arp\nfrom ryu.lib.packet import ethernet\nfrom ryu.lib.packet import ipv4\nfrom ryu.lib.packet import ipv6\nfrom ryu.lib.packet import ether_types\nfrom ryu.lib import mac, ip\nfrom ryu.topology.api import get_switch, get_link\nfrom ryu.app.wsgi import ControllerBase\nfrom ryu.topology import event\n\nfrom collections import defaultdict\nfrom operator import itemgetter\nfrom ryu.lib import hub\n\nimport os\nimport random\nimport time\nimport numpy as np\n\n# Cisco Reference bandwidth = 1 Gbps\nREFERENCE_BW = 10000000\n\nDEFAULT_BW = 10000000\n\nMAX_PATHS = 2\n\n# For bandwidth calculation\nadjacency = defaultdict(lambda: defaultdict(lambda: None))\n\ndatapath_list = {}\n\nbyte = defaultdict(lambda: defaultdict(lambda: None))\n\nclock = defaultdict(lambda: defaultdict(lambda: None))\n\nbw_used = defaultdict(lambda: defaultdict(lambda: None))\n\nbw_available = defaultdict(lambda: defaultdict(lambda: None))\n\nbw = defaultdict(lambda: defaultdict(lambda: None))\nfrom operator import attrgetter\n\nclass ProjectController(app_manager.RyuApp):\n OFP_VERSIONS = [ofproto_v1_3.OFP_VERSION]\n\n def __init__(self, *args, **kwargs):\n super(ProjectController, self).__init__(*args, **kwargs)\n self.mac_to_port = {}\n self.topology_api_app = self\n self.datapath_list = {}\n self.arp_table = {}\n self.switches = []\n self.hosts = {}\n self.multipath_group_ids = {}\n self.group_ids = []\n self.adjacency = defaultdict(dict)\n self.bandwidths = defaultdict(lambda: defaultdict(lambda: DEFAULT_BW))\n self.monitor_thread = hub.spawn(self._monitor)\n global bw\n\n try:\n\n fin = open(\"bw.txt\", \"r\")\n\n for line in fin:\n\n a = line.split()\n\n if a:\n bw[str(a[0])][str(a[1])] = int(a[2])\n\n bw[str(a[1])][str(a[0])] = int(a[2])\n\n fin.close()\n\n except IOError:\n\n print(\"make bw.txt ready\")\n\n # def get_paths(self, src, dst):\n # '''\n # Get all paths from src to dst using DFS algorithm\n # '''\n # if src == dst:\n # # host target is on the same switch\n # return [[src]]\n # paths = []\n # stack = [(src, [src])]\n # while stack:\n # (node, path) = stack.pop()\n # for next in set(self.adjacency[node].keys()) - set(path):\n # if next is dst:\n # paths.append(path + [next])\n # else:\n # stack.append((next, path + [next]))\n # print (\"Available paths from \", src, \" to \", dst, \" : \", paths)\n # return paths\n\n def get_paths(self, src, dst):\n '''\n Get all paths from src to dst using DFS algorithm\n '''\n if src == dst:\n # host target is on the same switch\n return [[src]]\n paths = []\n stack = [(src, [src])]\n while stack:\n (node, path) = stack.pop()\n for next_node in set(adjacency[node].keys()) - set(path):\n if bw[str(path[len(path) - 1])][str(next_node)] is not None:\n if next_node is dst:\n paths.append((path + [next_node]))\n else:\n stack.append((next_node, path + [next_node]))\n stack.append((next_node, path + [next_node]))\n\n paths = sorted(paths, key=len)\n for i in range(0, len(paths)-1):\n if len(paths[i]) == len(paths[i + 1]):\n path_one_bw = []\n path_two_bw = []\n for j in range(0, len(paths[i])-1):\n link_bandwidth1 = bw[str(paths[i][j])][str(paths[i][j+1])]\n path_one_bw.append(link_bandwidth1)\n for j in range(0, len(paths[i+1])-1):\n link_bandwidth2 = bw[str(paths[i+1][j])][str(paths[i + 1][j+1])]\n path_two_bw.append(link_bandwidth2)\n one_bw = min(path_one_bw)\n two_bw = min(path_two_bw)\n\n if one_bw < two_bw:\n paths[i], paths[i+1] = paths[i+1], paths[i]\n paths = [i for n, i in enumerate(paths) if i not in paths[:n]]\n print(\"Available Paths\", paths)\n return paths\n\n\n def get_link_cost(self, s1, s2):\n '''\n Get the link cost between two switches\n '''\n e1 = adjacency[s1][s2]\n e2 = adjacency[s2][s1]\n bl = min(self.bandwidths[s1][e1], self.bandwidths[s2][e2])\n ew = REFERENCE_BW/bl\n return ew\n\n def get_path_cost(self, path):\n '''\n Get the path cost\n '''\n cost = 0\n for i in range(len(path) - 1):\n cost += self.get_link_cost(path[i], path[i+1])\n return cost\n\n def get_optimal_paths(self, src, dst):\n\n paths = self.get_paths(src, dst)\n optimal_path = []\n max_bandwidth = 0;\n max_bandwidth_path = '';\n for path in paths:\n bandwidth_arr = [];\n total_link_bw = [];\n for i in range(0, len(path) - 1):\n total_link_bw.append(bw[str(path[i])][str(path[i + 1])])\n link_abw = bw_available[str(path[i])][str(path[i + 1])];\n bandwidth_arr.append(link_abw);\n # print(bandwidthArr)\n path_bd = min(bandwidth_arr);\n if max_bandwidth < path_bd:\n max_bandwidth = path_bd;\n max_bandwidth_path = path;\n if len(bandwidth_arr) > 0:\n link_bw = min(total_link_bw);\n if path_bd >= 0.5*link_bw*1000:\n optimal_path.append(path)\n print(\"Optimal Path\",optimal_path)\n return optimal_path;\n else:\n return;\n return max_bandwidth_path;\n # paths_count = len(paths) if len(\n # paths) < MAX_PATHS else MAX_PATHS\n # return sorted(paths, key=lambda x: self.get_path_cost(x))[0:(paths_count)]\n\n def add_ports_to_paths(self, paths, first_port, last_port):\n '''\n Add the ports that connects the switches for all paths\n '''\n paths_p = []\n for path in paths:\n p = {}\n in_port = first_port\n for s1, s2 in zip(path[:-1], path[1:]):\n out_port = adjacency[s1][s2]\n p[s1] = (in_port, out_port)\n in_port = adjacency[s2][s1]\n p[path[-1]] = (in_port, last_port)\n paths_p.append(p)\n return paths_p\n\n def generate_openflow_gid(self):\n '''\n Returns a random OpenFlow group id\n '''\n n = random.randint(0, 2**32)\n while n in self.group_ids:\n n = random.randint(0, 2**32)\n return n\n\n\n def install_paths(self, src, first_port, dst, last_port, ip_src, ip_dst):\n computation_start = time.time()\n paths = self.get_optimal_paths(src, dst)\n pw = []\n for path in paths:\n pw.append(self.get_path_cost(path))\n sum_of_pw = sum(pw) * 1.0\n paths_with_ports = self.add_ports_to_paths(paths, first_port, last_port)\n switches_in_paths = set().union(*paths)\n\n for node in switches_in_paths:\n\n dp = self.datapath_list[node]\n ofp = dp.ofproto\n ofp_parser = dp.ofproto_parser\n\n ports = defaultdict(list)\n actions = []\n i = 0\n\n for path in paths_with_ports:\n if node in path:\n in_port = path[node][0]\n out_port = path[node][1]\n if (out_port, pw[i]) not in ports[in_port]:\n ports[in_port].append((out_port, pw[i]))\n i += 1\n\n for in_port in ports:\n\n match_ip = ofp_parser.OFPMatch(\n eth_type=0x0800, \n ipv4_src=ip_src, \n ipv4_dst=ip_dst\n )\n match_arp = ofp_parser.OFPMatch(\n eth_type=0x0806, \n arp_spa=ip_src, \n arp_tpa=ip_dst\n )\n\n out_ports = ports[in_port]\n # print out_ports \n\n if len(out_ports) > 1:\n group_id = None\n group_new = False\n\n if (node, src, dst) not in self.multipath_group_ids:\n group_new = True\n self.multipath_group_ids[\n node, src, dst] = self.generate_openflow_gid()\n group_id = self.multipath_group_ids[node, src, dst]\n\n buckets = []\n # print \"node at \",node,\" out ports : \",out_ports\n for port, weight in out_ports:\n bucket_weight = int(round((1 - weight/sum_of_pw) * 10))\n bucket_action = [ofp_parser.OFPActionOutput(port)]\n buckets.append(\n ofp_parser.OFPBucket(\n weight=bucket_weight,\n watch_port=port,\n watch_group=ofp.OFPG_ANY,\n actions=bucket_action\n )\n )\n\n if group_new:\n req = ofp_parser.OFPGroupMod(\n dp, ofp.OFPGC_ADD, ofp.OFPGT_SELECT, group_id,\n buckets\n )\n dp.send_msg(req)\n else:\n req = ofp_parser.OFPGroupMod(\n dp, ofp.OFPGC_MODIFY, ofp.OFPGT_SELECT,\n group_id, buckets)\n dp.send_msg(req)\n\n actions = [ofp_parser.OFPActionGroup(group_id)]\n\n self.add_flow(dp, 32768, match_ip, actions)\n self.add_flow(dp, 1, match_arp, actions)\n\n elif len(out_ports) == 1:\n actions = [ofp_parser.OFPActionOutput(out_ports[0][0])]\n\n self.add_flow(dp, 32768, match_ip, actions)\n self.add_flow(dp, 1, match_arp, actions)\n print (\"Path installation finished in \", time.time() - computation_start )\n return paths_with_ports[0][src][1]\n\n def add_flow(self, datapath, priority, match, actions, buffer_id=None):\n # print \"Adding flow \", match, actions\n ofproto = datapath.ofproto\n parser = datapath.ofproto_parser\n\n inst = [parser.OFPInstructionActions(ofproto.OFPIT_APPLY_ACTIONS,\n actions)]\n if buffer_id:\n mod = parser.OFPFlowMod(datapath=datapath, buffer_id=buffer_id,\n priority=priority, match=match,\n instructions=inst)\n else:\n mod = parser.OFPFlowMod(datapath=datapath, priority=priority,\n match=match, instructions=inst)\n datapath.send_msg(mod)\n\n def _monitor(self):\n\n while True:\n\n for dp in self.datapath_list.values():\n self._request_stats(dp)\n\n hub.sleep(3)\n\n def _request_stats(self, datapath):\n\n # self.logger.debug('send stats request: %016x', datapath.id)\n\n # print 'send stats request:', datapath.id\n\n ofproto = datapath.ofproto\n\n parser = datapath.ofproto_parser\n\n req = parser.OFPFlowStatsRequest(datapath)\n\n datapath.send_msg(req)\n\n req = parser.OFPPortStatsRequest(datapath, 0, ofproto.OFPP_ANY)\n\n datapath.send_msg(req)\n\n @set_ev_cls(ofp_event.EventOFPSwitchFeatures, CONFIG_DISPATCHER)\n def _switch_features_handler(self, ev):\n print( \"switch_features_handler is called\")\n datapath = ev.msg.datapath\n ofproto = datapath.ofproto\n parser = datapath.ofproto_parser\n\n match = parser.OFPMatch()\n actions = [parser.OFPActionOutput(ofproto.OFPP_CONTROLLER,\n ofproto.OFPCML_NO_BUFFER)]\n self.add_flow(datapath, 0, match, actions)\n\n @set_ev_cls(ofp_event.EventOFPPortStatsReply, MAIN_DISPATCHER)\n def _port_stats_reply_handler(self, ev):\n\n global byte, clock, bw_used, bw_available\n\n # print time.time(),\" _port_stats_reply_handler\"\n\n body = ev.msg.body\n\n dpid = ev.msg.datapath.id\n\n for stat in sorted(body, key=attrgetter('port_no')):\n\n\n for p in self.switches:\n\n if adjacency[dpid][p] == stat.port_no:\n\n # print dpid, p, stat.port_no\n\n if byte[dpid][p] and byte[dpid][p] > 0:\n bw_used[dpid][p] = (stat.tx_bytes - byte[dpid][p]) * 8.0 / (time.time() - clock[dpid][p]) / 1000\n\n bw_available[str(dpid)][str(p)] = int(bw[str(dpid)][str(p)]) * 1000.0 - bw_used[dpid][p]\n\n #print(str(dpid), \"->\", str(p), \":\", bw_available[str(dpid)][str(p)], \" kbps\")\n\n # print str(dpid),\"->\",str(p),\":\", bw[str(dpid)][str(p)],\" kbps\"\n\n byte[dpid][p] = stat.tx_bytes\n\n clock[dpid][p] = time.time()\n print(\"-------------------------------------------------------------------\")\n\n @set_ev_cls(ofp_event.EventOFPPortDescStatsReply, MAIN_DISPATCHER)\n def port_desc_stats_reply_handler(self, ev):\n switch = ev.msg.datapath\n for p in ev.msg.body:\n #print(p.curr_speed)\n self.bandwidths[switch.id][p.port_no] = p.curr_speed\n\n @set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER)\n def _packet_in_handler(self, ev):\n msg = ev.msg\n datapath = msg.datapath\n ofproto = datapath.ofproto\n parser = datapath.ofproto_parser\n in_port = msg.match['in_port']\n\n pkt = packet.Packet(msg.data)\n eth = pkt.get_protocol(ethernet.ethernet)\n arp_pkt = pkt.get_protocol(arp.arp)\n\n # avoid broadcast from LLDP\n if eth.ethertype == 35020:\n return\n\n if pkt.get_protocol(ipv6.ipv6): # Drop the IPV6 Packets.\n match = parser.OFPMatch(eth_type=eth.ethertype)\n actions = []\n self.add_flow(datapath, 1, match, actions)\n return None\n\n dst = eth.dst\n src = eth.src\n dpid = datapath.id\n\n if src not in self.hosts:\n self.hosts[src] = (dpid, in_port)\n\n out_port = ofproto.OFPP_FLOOD\n\n if arp_pkt:\n # print dpid, pkt\n src_ip = arp_pkt.src_ip\n dst_ip = arp_pkt.dst_ip\n if arp_pkt.opcode == arp.ARP_REPLY:\n self.arp_table[src_ip] = src\n h1 = self.hosts[src]\n h2 = self.hosts[dst]\n out_port = self.install_paths(h1[0], h1[1], h2[0], h2[1], src_ip, dst_ip)\n\n self.install_paths(h2[0], h2[1], h1[0], h1[1], dst_ip, src_ip) # reverse\n elif arp_pkt.opcode == arp.ARP_REQUEST:\n if dst_ip in self.arp_table:\n self.arp_table[src_ip] = src\n dst_mac = self.arp_table[dst_ip]\n h1 = self.hosts[src]\n h2 = self.hosts[dst_mac]\n out_port = self.install_paths(h1[0], h1[1], h2[0], h2[1], src_ip, dst_ip)\n # self.install_paths(h2[0], h2[1], h1[0], h1[1], dst_ip, src_ip) # reverse\n\n # print pkt\n\n actions = [parser.OFPActionOutput(out_port)]\n\n data = None\n if msg.buffer_id == ofproto.OFP_NO_BUFFER:\n data = msg.data\n\n out = parser.OFPPacketOut(\n datapath=datapath, buffer_id=msg.buffer_id, in_port=in_port,\n actions=actions, data=data)\n datapath.send_msg(out)\n\n @set_ev_cls(event.EventSwitchEnter)\n def switch_enter_handler(self, ev):\n switch = ev.switch.dp\n ofp_parser = switch.ofproto_parser\n\n if switch.id not in self.switches:\n self.switches.append(switch.id)\n self.datapath_list[switch.id] = switch\n\n # Request port/link descriptions, useful for obtaining bandwidth\n req = ofp_parser.OFPPortDescStatsRequest(switch)\n switch.send_msg(req)\n\n links_list = get_link(self.topology_api_app, None)\n\n # print \"links_list=\", links_list\n\n mylinks = [(link.src.dpid, link.dst.dpid, link.src.port_no, link.dst.port_no) for link in links_list]\n\n for s1, s2, port1, port2 in mylinks:\n # print \"type(s1)=\", type(s1), \" type(port1)=\", type(port1)\n\n adjacency[s1][s2] = port1\n\n adjacency[s2][s1] = port2\n\n print(s1, \":\", port1, \"<--->\", s2, \":\", port2)\n\n @set_ev_cls(event.EventSwitchLeave, MAIN_DISPATCHER)\n def switch_leave_handler(self, ev):\n\n switch = ev.switch.dp.id\n if switch in self.switches:\n self.switches.remove(switch)\n del self.datapath_list[switch]\n del adjacency[switch]\n\n @set_ev_cls(event.EventLinkAdd, MAIN_DISPATCHER)\n def link_add_handler(self, ev):\n s1 = ev.link.src\n s2 = ev.link.dst\n adjacency[s1.dpid][s2.dpid] = s1.port_no\n adjacency[s2.dpid][s1.dpid] = s2.port_no\n\n @set_ev_cls(event.EventLinkDelete, MAIN_DISPATCHER)\n def link_delete_handler(self, ev):\n s1 = ev.link.src\n s2 = ev.link.dst\n # Exception handling if switch already deleted\n try:\n del adjacency[s1.dpid][s2.dpid]\n del adjacency[s2.dpid][s1.dpid]\n except KeyError:\n pass\n","repo_name":"abiram21/ARP","sub_path":"arp.py","file_name":"arp.py","file_ext":"py","file_size_in_byte":18050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69805964248","text":"def remove_dwarfs(dwarfs):\n sum_weight = sum(dwarfs)\n for i in range(9):\n for j in range(i+1, 9):\n if 100 == sum_weight - (dwarfs[i] + dwarfs[j]):\n del dwarfs[j]\n del dwarfs[i]\n # first, second = dwarfs[i], dwarfs[j]\n # dwarfs.remove(first)\n # dwarfs.remove(second)\n return dwarfs\n\n\nif __name__ == \"__main__\":\n dwarfs = [int(input()) for _ in range(9)]\n dwarfs.sort()\n remove_dwarfs(dwarfs)\n for dwarf in dwarfs:\n print(dwarf)\n\n","repo_name":"dddddun/Algorithm","sub_path":"BackJoon/Brute_force/2309.py","file_name":"2309.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"41349550961","text":"\r\nimport requests\r\n\r\nheader = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36\"}\r\ndata = {\r\n\"from\": \"en\",\r\n\"to\": \"zh\",\r\n\"query\": \"hi\",\r\n\"simple_means_flag\": \"3\",\r\n\"sign\": \"742533.1030068\",\r\n\"token\": \"1967e7c6721c151b384827b0b06396a3\",\r\n\"domain\": \"common\"\r\n}\r\n\r\npost_url = \"https://fanyi.baidu.com/v2transapi\"\r\n\r\nr = requests.post(post_url,data=data,headers=header)\r\n\r\nprint(r.content.decode())","repo_name":"bbestr/python","sub_path":"爬虫/transfer.py","file_name":"transfer.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10027209707","text":"# -*- codeing = utf-8 -*-\n#@Time :2020/11/17 15:18\n#@Author :张士澜\n#@File :xpath_cityName.py\n#@Software :PyCharm\n\nimport requests\nfrom lxml import etree\n\nif __name__ == \"__main__\":\n headers = {\n 'User-Agent': 'Mozilla / 5.0(Windows NT 10.0;Win64;x64) AppleWebKit / 537.36(KHTML, likeGecko) Chrome / 86.0.4240.198Safari / 537.36'\n }\n url = \"https://www.aqistudy.cn/historydata/\"\n page_text = requests.get(url = url,headers = headers).text\n tree = etree.HTML(page_text)\n # hot_li_list = tree.xpath('//div[@class = \"bottom\"]/ul/li')\n # hot_city_names = []\n # all_city_names = []\n # for li in hot_li_list:\n # hot_city_name = li.xpath('./a/text()')[0]\n # hot_city_names.append(hot_city_name)\n # all_li_list = tree.xpath('//div[@class = \"bottom\"]/ul/div[2]/li')\n # for li in all_li_list:\n # all_city_name = li.xpath('./a/text()')[0]\n # all_city_names.append(all_city_name)\n # print(\"热门城市:\",hot_city_names,'\\n',\"全部城市：\",all_city_names)\n all_city_names = []\n a_list = tree.xpath('//div[@class = \"bottom\"]/ul/li/a | //div[@class = \"bottom\"]/ul/div[2]/li/a')\n for a in a_list:\n city_name = a.xpath('./text()')[0]\n all_city_names.append(city_name)\n print(all_city_names)","repo_name":"zhangshilan/python_demo","sub_path":"demo_spider_2/dataparser_test/xpath_cityName.py","file_name":"xpath_cityName.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70349851289","text":"\"\"\":class:`SitesLinkingIn`\n\n\"\"\"\nimport json\nimport collections\nimport lxml.etree\nimport xmljson\n\n\nNS_20050711 = 'http://awis.amazonaws.com/doc/2005-07-11'\nNS_20051005 = 'http://alexa.amazonaws.com/doc/2005-10-05/'\nNS = {'a': NS_20051005, 'b': NS_20050711}\n\n\nclass SitesLinkingIn(object):\n \"\"\"Abstacts an AWIS SitesLinkingIn action.\n\n .. attributes:: xml\n\n \"\"\"\n def __init__(self, raw_xml):\n \"\"\"Parse and de-construct a *raw_xml* record.\n\n \"\"\"\n if raw_xml is None:\n raw_xml = b''\n self.__xml = raw_xml.decode('utf-8')\n self.__as_json = None\n\n def __call__(self):\n \"\"\"Just dump the raw JSON.\n \"\"\"\n return {}\n\n @property\n def xml(self):\n \"\"\"Just dump the raw XML.\n \"\"\"\n return self.__xml\n\n @property\n def as_json(self):\n \"\"\":attr:`raw` XML converted to JSON.\n \"\"\"\n if self.__as_json is None and self.xml:\n self._xml_to_json()\n\n return self.__as_json\n\n def _xml_to_json(self):\n \"\"\"Convert raw Alexa SitesLinkingIn action result to JSON.\n\n \"\"\"\n root = lxml.etree.fromstring(self.xml)\n xpath = ('//a:SitesLinkingInResponse/'\n 'b:Response/'\n 'b:SitesLinkingInResult')\n sites = root.xpath(xpath, namespaces=NS)\n if sites:\n bf_json = xmljson.BadgerFish(dict_type=collections.OrderedDict)\n tmp = json.dumps(bf_json.data(sites[0]))\n ns_replace = r'{{{0}}}'.format(NS_20050711)\n self.__as_json = tmp.replace(ns_replace, '')\n else:\n log.error('Unable to parse SitesLinkingIn XML: %s', self.xml)\n\n def extract_titles(self):\n \"\"\"Extract titles from the Alexa SitesLinkingInResult action\n response.\n\n \"\"\"\n titles = []\n if self.as_json is not None:\n base = json.loads(self.as_json)['SitesLinkingInResult']['Alexa']\n sites_linking_in = base['SitesLinkingIn']\n sites = sites_linking_in.get('Site', [])\n\n if isinstance(sites, list):\n for site in sites_linking_in.get('Site', []):\n title = site.get('Title').get('$')\n url = site.get('Url').get('$')\n titles.append({'title': title, 'url': url})\n else:\n kwargs = {\n 'title': sites.get('Title').get('$'),\n 'url': sites.get('Url').get('$'),\n }\n titles.append(kwargs)\n\n return titles\n","repo_name":"loum/domain-intel","sub_path":"domain_intel/awisapi/parser/siteslinkingin.py","file_name":"siteslinkingin.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3650532342","text":"import os\r\n\r\n# use type = 'n' to append words in a new file, otherwise use type = 's'\r\n \r\ndef word_sort(path, word_length, type = 's', new_path = None):\r\n\r\n if (type != 's'):\r\n if(new_path == None):\r\n raise(NameError(\"\\nWith new file comes a new path!\\n\"))\r\n words_I_need = []\r\n\r\n with open(path,'r') as file: \r\n for line in file: \r\n for word in line.split():\r\n if (len(word) == word_length+3): \r\n # +3 because words are in quotes followed by a comma\r\n words_I_need.append(word + \"\\n\")\r\n\r\n # same file\r\n if(type == 's'):\r\n open(path, 'r').truncate()\r\n with open(path, 'w') as oldfile:\r\n for new_words in words_I_need:\r\n oldfile.write(new_words)\r\n # new file\r\n else:\r\n with open(new_path, 'w') as newfile:\r\n for new_words in words_I_need:\r\n newfile.write(new_words)\r\n\r\n# implementation\r\nif __name__ == \"__main__\":\r\n path = r'C:\\Users\\jains\\Desktop\\words.txt'\r\n word_length = 7\r\n new_path = r'C:\\Users\\jains\\Desktop\\words_{:}.txt'.format(str(word_length))\r\n word_sort(path, word_length, type = 'n', new_path = new_path)","repo_name":"theSamJain/Word-sort","sub_path":"wordle_word_sort.py","file_name":"wordle_word_sort.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39945650634","text":"import requests\nfrom PIL import Image\nfrom app.config import get_settings\nfrom app.error.generation import RayCapacityExceededError\nfrom app.integrations.generative_ai_engine.generative_ai_interface import (\n GenerativeAIInterface,\n)\nfrom loguru import logger\nfrom morpheus_data.models.schemas import GenerationRequest, TextGenerationRequest\nfrom morpheus_data.utils.images import from_image_to_bytes\n\nsettings = get_settings()\nRAY_BACKEND_URL = settings.ray_backend_url\n\n\ndef send_request_to_ray_server(\n *,\n endpoint: str,\n request: GenerationRequest,\n image: Image = None,\n palette_image: Image = None,\n mask: Image = None,\n) -> str:\n files = {}\n if image:\n bytes_image = from_image_to_bytes(image)\n files[\"image\"] = (\"image.png\", bytes_image, \"image/png\")\n if palette_image:\n bytes_palette_image = from_image_to_bytes(palette_image)\n files[\"palette_image\"] = (\n \"palette_image.png\",\n bytes_palette_image,\n \"image/png\",\n )\n if mask:\n bytes_mask = from_image_to_bytes(mask)\n files[\"mask\"] = (\"mask.png\", bytes_mask, \"image/png\")\n\n request_args = {\n \"url\": f\"{RAY_BACKEND_URL}/{endpoint}\",\n \"params\": request.__dict__,\n }\n logger.info(f\"Sending request to ray server with args: {request_args}\")\n\n if files:\n request_args[\"files\"] = files\n\n try:\n can_request, pending_tasks = validate_waiting_room()\n if not can_request:\n raise RayCapacityExceededError(\n f\"There is no capacity at the moment. \"\n f\"Please try again later. Number of pending tasks = {pending_tasks}.\"\n )\n\n response = requests.post(**request_args)\n if response.status_code == 200:\n return response.text\n else:\n raise Exception(str(response.text))\n except Exception as e:\n logger.error(f\"Error while sending request to ray server: {e}\")\n raise Exception(str(e))\n\n\ndef validate_waiting_room() -> (bool, int):\n if not settings.waiting_room_enabled:\n return True, 0\n\n try:\n pending_tasks = ray_pending_tasks()\n worker_number = ray_worker_number()\n worker_number = worker_number if worker_number > 0 else 1\n max_tasks = settings.max_tasks_per_worker or 10\n can_request = (pending_tasks / worker_number) < max_tasks\n return can_request, pending_tasks\n except Exception as e:\n logger.error(f\"Error while validating waiting room: {e}\")\n raise e\n\n\ndef ray_pending_tasks() -> int:\n url = f\"{RAY_BACKEND_URL}/pending-tasks\"\n response = requests.get(url)\n if response.status_code == 200:\n return int(response.text)\n else:\n raise Exception(str(response.text))\n\n\ndef ray_worker_number() -> int:\n url = f\"{RAY_BACKEND_URL}/worker-number-cache\"\n response = requests.get(url)\n if response.status_code == 200:\n return int(response.text)\n else:\n raise Exception(str(response.text))\n\n\nclass GenerativeAIStableDiffusionRay(GenerativeAIInterface):\n @staticmethod\n def generate_text2img_images(*, request: GenerationRequest) -> str:\n logger.info(f\"generate_img2img_images process with request: {request}\")\n task_id = send_request_to_ray_server(endpoint=\"text2img\", request=request)\n return str(task_id)\n\n @staticmethod\n def generate_img2img_images(*, request: GenerationRequest, image: Image, palette_image: Image = None) -> str:\n logger.info(f\"generate_img2img_images process with request: {request}\")\n task_id = send_request_to_ray_server(\n endpoint=\"img2img\",\n request=request,\n image=image,\n palette_image=palette_image,\n )\n return str(task_id)\n\n @staticmethod\n def generate_controlnet_images(*, request: GenerationRequest, image: Image, palette_image: Image = None) -> str:\n logger.info(f\"generate_controlnet_images process with request: {request}\")\n task_id = send_request_to_ray_server(\n endpoint=\"controlnet\",\n request=request,\n image=image,\n palette_image=palette_image,\n )\n return str(task_id)\n\n @staticmethod\n def generate_pix2pix_images(*, request: GenerationRequest, image: Image) -> str:\n logger.info(f\"generate_pix2pix_images process with request: {request}\")\n task_id = send_request_to_ray_server(endpoint=\"pix2pix\", request=request, image=image)\n return str(task_id)\n\n @staticmethod\n def generate_inpainting_images(*, request: GenerationRequest, image: Image, mask: Image) -> str:\n logger.info(f\"generate_inpainting_images process with request: {request}\")\n task_id = send_request_to_ray_server(endpoint=\"inpainting\", request=request, image=image, mask=mask)\n return str(task_id)\n\n @staticmethod\n def generate_upscaling_images(*, request: GenerationRequest, image: Image) -> str:\n logger.info(f\"generate_upscaling_images process with request: {request}\")\n task_id = send_request_to_ray_server(endpoint=\"upscaling\", request=request, image=image)\n return str(task_id)\n\n @staticmethod\n def generate_magic_prompt(*, request: TextGenerationRequest) -> str:\n logger.info(f\"generate_magic_prompt process with request: {request}\")\n task_id = send_request_to_ray_server(endpoint=\"magic_prompt\", request=request)\n return str(task_id)\n","repo_name":"Monadical-SAS/Morpheus","sub_path":"morpheus-server/app/integrations/generative_ai_engine/sdiffusion_ray.py","file_name":"sdiffusion_ray.py","file_ext":"py","file_size_in_byte":5474,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"31"} +{"seq_id":"37146859052","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n__author__ = \"WCH\"\n\nimport pandas as pd\nimport numpy as np\nimport myfun_CrimeRates\nfrom sklearn.preprocessing import MinMaxScaler\nfrom pandas import ExcelWriter # 匯入 excel writer\nmyfun_CrimeRates.matplot_中文字()\n\n#### 資料來源 https://www.kaggle.com/datasets/mguzmann/swedishcrime\ndf = pd.read_excel(\"CrimeRates_資料清洗後.xlsx\",0)\nprint(df.shape)\nprint(df.info)\nprint(df.describe())\n\nprint(\"Year:\",df[\"Year\"].tolist())\nprint(\"crimes.person:\",df[\"crimes.person\"].tolist())\nprint(\"population:\",df[\"population\"].tolist())\nprint(\"drunk.driving:\",df[\"drunk.driving\"].tolist())\n\n\"\"\"\nlistcrimes.person=myfun_CrimeRates.pandas_取得裡面的種類(df,\"crimes.person\")\n#listcrimes.person.sort()\nlistGender=myfun_CrimeRates.pandas_取得裡面的種類(df,\"Gender\")\nlistSatisfy=myfun_CrimeRates.pandas_取得裡面的種類(df,\"JobSatisfaction\")\nlistJobRole=myfun_CrimeRates.pandas_取得裡面的種類(df,\"JobRole\")\n\"\"\"\n\nprint(\"=============數據 標準化=============\")\n# Pandas x 轉 numpy\nx=df.to_numpy()\nprint(x)\n\n# 標準化 x\nscaler = MinMaxScaler(feature_range=(0,1)) # 初始化 # 設定縮放的區間上下限\nscaler.fit(x) # 找標準化範圍\nx= scaler.transform(x) # 把資料轉換\nprint(\"標準化:\",x)\n\n# numpy 轉 Pandas\ndf = pd.DataFrame(x, columns=df.columns)\n\nwriter = ExcelWriter('CrimeRates資料清洗後標準化.xlsx', engine='xlsxwriter') # 另存為資料清洗後\ndf.to_excel(writer, sheet_name='資料清洗後標準化',index=False,header=1) # 分頁欄位的名稱 header=1 要印表頭\nwriter.save()\n\n\n\n\n\n","repo_name":"73Wei/ML_MLP_Regression_SwedishCrimeRates","sub_path":"02-標準化.py","file_name":"02-標準化.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25417778392","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.common.by import By\ndriver=webdriver.Chrome('C:\\\\Users\\\\Admin\\\\Desktop\\\\Python Selenium\\\\chromedriver.exe')\ndriver.get(\"http://demowebshop.tricentis.com/\")\ndriver.maximize_window()\n\n#Write a script to create the alert pop up?\n# driver.execute_script(\"alert('Welcome to Pyspiders')\") # It is used to throw the alert\n# time.sleep(10)\n\n\n#Write a script to get the domain name?\n# domain_name=driver.execute_script(\"return document.domain\" )\n# print(domain_name)\n\n#Write a script to get the domain name?\n# domain_title=driver.execute_script(\"return document.title\" )\n# print(domain_title)\n\n#Write a script to get the URL of the page?\n# domain_URL=driver.execute_script(\"return document.URL\" )\n# print(domain_URL)\n\n#Write a script to enter the URL?\n# driver.execute_script(\"window.location='http://www.fb.com'\")\n# time.sleep(5)\ntime.sleep(5)\n#Write a script to open the new tab and enter the URL?\n# driver.execute_script(\"window.open='http://demowebshop.tricentis.com/'\")\n# time.sleep(5)\n\n#Write a program to scroll down to the bottom of the page?\n# driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n# time.sleep(5)\n\n#Write a program to scroll up to the top of the page?\n# driver.execute_script(\"window.scrollTo(0,document.body.scrollTop)\")\n# time.sleep(5)\n\n#Write a script to scroll to specific element?\ncomp=driver.find_element(By.XPATH, \"//img[@alt='Picture of Build your own computer']\")\nprint(comp)\ntime.sleep(2)\ndriver.execute_script(f\"{comp.location}.scrollTo();\")\n# time.sleep(5)\n\n#Write a program to scroll to specific element using the co-ordinate values\n# comp=driver.find_element(By.XPATH, \"//img[@alt='Picture of Build your own computer']\")\n# xcordnt=comp.location.get('x')\n# ycordnt=comp.location.get('y')\n# print(xcordnt,ycordnt)\n# time.sleep(5)\n# driver.execute_script(f\"window.scrollBy({xcordnt},{ycordnt})\")\n# time.sleep(5)\n\ndriver.close()","repo_name":"Ajaypatil7/git-class","sub_path":"Selenium Practice/jsexecuter.py","file_name":"jsexecuter.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33672871997","text":"\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 31 14:39:53 2020\n\n@author: lixin\n从 json 树转化成spark tree python版本 并且实现了把spark的tree画出来\n\"\"\"\n\n\nimport os\nimport json\n\n\nclass Node:\n def __init__(self):\n self.data = 0.0 # 节点的预测值\n self.impurity_stats = [] # 不纯量的统计值，用于后面预测类别概率等\n self.gain = 0.0 # 信息增益量\n self.left_node = None # 左子树节点\n self.right_node = None # 右子树节点\n self.feature_i = -1 # 特征的索引\n self.threshold = \"\" # 连续变量的阈值，离散变量的左 分裂取值点\n self.split_type = \"\" # 是否有离散变量，大于0表示有离散变量，小于0表示变量全部为连续变量\n self.is_leaf = False # 改节点是否为叶子节点\n\n\nclass ParseFromJson():\n \"\"\"\n :param file_name: 模型的存储路径\n :param num_classes: 模型处理的是几分类问题\n \"\"\"\n def __init__(self,file_name,num_classes = 2):\n self.treeNodeList = []\n self.num_classes = num_classes\n self.load_model(file_name)\n\n def load_model(self, file_path):\n filenames = os.listdir(file_path) \n filenames.sort(key=lambda x: int(x.replace(\".json\", \"\").split(\"_\")[1]))\n for file_name in filenames:\n path = file_path + \"/\" + file_name\n print(path)\n with open(path) as load_f:\n d = json.load(load_f)\n self.treeNodeList.append(self.walk(d))\n\n def walk(self,data):\n root = Node()\n if type(data) is dict:\n if data[\"nodeType\"] == \"leaf\":\n root.is_leaf = True\n root.data = data[\"prediction\"]\n root.impurity_stats = data[\"impurityStats\"]\n else:\n root.data = data[\"prediction\"]\n root.feature_i = data[\"featureIndex\"]\n root.gain = data[\"gain\"]\n root.impurity_stats = data[\"impurityStats\"]\n root.is_leaf = False\n root.split_type = data[\"splitType\"]\n root.threshold = data[\"threshold\"] if data[\"splitType\"] == \"continuous\" else data[\"leftCategories\"]\n\n root.left_node = self.walk(data[\"leftChild\"])\n root.right_node = self.walk(data[\"rightChild\"])\n return root\n\n\n def predict_pro_one(self,feature_map):\n raw_predict = self.raw_predict(feature_map)\n predict_sum = sum(raw_predict)\n res = list(map(lambda x:x/predict_sum,raw_predict))\n return res\n\n def predict_one(self,feature_map):\n score = 0.0\n for spark_tree in self.treeNodeList:\n cur_tmp = spark_tree\n while not cur_tmp.is_leaf:\n if cur_tmp.feature_i in feature_map:\n ## 连续性变量\n if cur_tmp.split_type == \"continuous\":\n if float(feature_map[cur_tmp.feature_i]) <= cur_tmp.threshold:\n cur_tmp = cur_tmp.left_node\n else:\n cur_tmp = cur_tmp.right_node\n ## 离散型变量情形\n else:\n if float(feature_map[cur_tmp.feature_i]) in cur_tmp.threshold:\n cur_tmp = cur_tmp.left_node\n else:\n cur_tmp = cur_tmp.right_node\n score += cur_tmp.data\n\n return score / len(self.treeNodeList)\n\n def raw_predict(self,feature_map):\n raw_ret = [0.0 for _ in range(self.num_classes)]\n for spark_tree in self.treeNodeList:\n cur_tmp = spark_tree\n while not cur_tmp.is_leaf:\n if cur_tmp.feature_i in feature_map:\n ## 连续性变量\n if cur_tmp.split_type == \"continuous\":\n if float(feature_map[cur_tmp.feature_i]) <= cur_tmp.threshold:\n cur_tmp = cur_tmp.left_node\n else:\n cur_tmp = cur_tmp.right_node\n ## 离散型变量情形\n else:\n if float(feature_map[cur_tmp.feature_i]) in cur_tmp.threshold:\n cur_tmp = cur_tmp.left_node\n else:\n cur_tmp = cur_tmp.right_node\n impurity_stats = cur_tmp.impurity_stats\n # print(impurity_stats)\n total = sum(impurity_stats)\n i = 0\n while i < self.num_classes:\n raw_ret[i] += impurity_stats[i] / total\n i += 1\n return raw_ret\n\n def predict(self, data_lst):\n res = []\n for feature_map in data_lst:\n res.append(self.predict_pro_one(feature_map))\n return res\n\n \"\"\"\n columns2index:\n 比如 \n {\"sepal length (cm)\": 0,\"sepal width (cm)\": 1,\"petal length (cm)\": 2,\"petal width (cm)\": 3}\n \"\"\"\n def read_data(self, file_name,columns2index):\n res = []\n if os.path.exists(file_name):\n f = open(file_name, 'r')\n for line in f:\n dict_data = json.loads(line)\n for key, value in columns2index.items():\n dict_data.update({value: dict_data.pop(key)})\n res.append(dict_data)\n return res\n\n def dotgraph(self, decisionTree, columnsDict=None):\n from collections import defaultdict\n dcNodes = defaultdict(list)\n def toString(iSplit, decisionTree, bBranch, szParent=\"null\", indent=''):\n if decisionTree.is_leaf: # leaf node\n sz = str(decisionTree.data)\n dcNodes[iSplit].append(['leaf', sz, szParent, bBranch])\n return sz\n else:\n szCol = 'feature %s' % decisionTree.feature_i\n if columnsDict != None:\n szCol = columnsDict[szCol]\n if decisionTree.split_type == \"continuous\":\n decision = '%s <= %s?' % (szCol, decisionTree.threshold)\n else:\n decision = '%s in %s?' % (szCol, decisionTree.threshold)\n\n toString(iSplit + 1, decisionTree.left_node, True, decision, indent + '\\t\\t')\n toString(iSplit + 1, decisionTree.right_node, False, decision, indent + '\\t\\t')\n dcNodes[iSplit].append([iSplit + 1, decision, szParent, bBranch])\n\n toString(0, decisionTree, None)\n\n lsDot = ['digraph Tree {',\n 'node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica] ;',\n 'edge [fontname=helvetica] ;'\n ]\n i_node = 0\n dcParent = {}\n for nSplit in range(len(dcNodes)):\n lsY = dcNodes[nSplit]\n for lsX in lsY:\n iSplit, decision, szParent, bBranch = lsX\n if type(iSplit) == int:\n szSplit = '%d-%s' % (iSplit, decision)\n dcParent[szSplit] = i_node\n lsDot.append('%d [label=<%s>, fillcolor=\"#e5813900\"] ;' % (i_node,\n decision.replace('<=', '≤').replace(\n '?', '')))\n else:\n lsDot.append('%d [label=<class %s>, fillcolor=\"#e5813900\"] ;' % (i_node,\n decision))\n if szParent != 'null':\n if bBranch:\n szAngle = '45'\n szHeadLabel = 'True'\n else:\n szAngle = '-45'\n szHeadLabel = 'False'\n szSplit = '%d-%s' % (nSplit, szParent)\n p_node = dcParent[szSplit]\n if nSplit == 1:\n lsDot.append('%d -> %d [labeldistance=2.5, labelangle=%s, headlabel=\"%s\"] ;' % (p_node,\n i_node, szAngle,\n szHeadLabel))\n else:\n lsDot.append('%d -> %d ;' % (p_node, i_node))\n i_node += 1\n lsDot.append('}')\n dot_data = '\\n'.join(lsDot)\n return dot_data\n\nif __name__ == \"__main__\":\n resource_path = \"C:\\\\Users\\\\morni\\\\Desktop\\\\github\\\\spark-random-forest-parse\\\\src\\\\main\\\\resources\"\n\n file_name = resource_path + \"\\\\json_model\\\\\"\n parseSpark = ParseFromJson(file_name)\n\n tree_list = parseSpark.treeNodeList\n\n count = 0\n for tree in tree_list:\n dot_text = parseSpark.dotgraph(tree)\n import pydotplus\n graph = pydotplus.graph_from_dot_data(dot_text)\n print(\"=============>>>>>>>>>>>>>>>>%d\"%count)\n graph.write_png(resource_path + \"\\\\plot\\\\spark_tree_%d.png\"%(count))\n count += 1\n\n testfilename = resource_path + \"\\\\data\\\\iris.json\"\n cols2index = {\"petal length (cm)\": 0, \"petal width (cm)\": 1, \"sepal length (cm)\": 2, \"sepal width (cm)\": 3}\n test_data = parseSpark.read_data(testfilename, cols2index)\n\n for dict in test_data:\n pred1 = parseSpark.predict_pro_one(dict)\n print(dict, pred1)","repo_name":"StringsLi/spark-rf-parse-plot","sub_path":"src/main/scala/com/strings/algo/tree/parse/FromJson2SparkTree.py","file_name":"FromJson2SparkTree.py","file_ext":"py","file_size_in_byte":9504,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"2606102346","text":"from collections import defaultdict\nimport itertools\nimport random\nfrom torch.autograd import Variable\nimport torch\nimport rnn_util\n\nclass Graph:\n def __init__(self):\n self.nodes = set()\n self.edges = defaultdict(list)\n self.distances = {}\n\n def add_node(self, value):\n self.nodes.add(value)\n\n def add_edge(self, from_node, to_node, distance):\n self.edges[from_node].append(to_node)\n self.edges[to_node].append(from_node)\n self.distances[(from_node, to_node)] = distance\n self.distances[(to_node, from_node)] = distance\n\n def check_edge(self,from_node,to_node):\n return (to_node in self.edges[from_node]) or (from_node in self.edges[to_node])\n\nclass GraphGenerator:\n def __init__(self,links):\n self.links = list(set(tuple(sorted(l)) for l in links)) #Removes permutations e.g if there is link (1,2) ans (2,1) removes (2,1)\n self.nodes = set([node for link in links for node in link]) #List of tuples to set\n self.graphs = []\n self.states = []\n\n def state_to_links(self,state):\n up_links = []\n for i in range(len(state)):\n if state[i]==1:\n up_links.append(self.links[i])\n return up_links\n\n def gen_graph(self,links):\n g = Graph()\n g.nodes = self.nodes\n for link in links:\n g.add_edge(link[0],link[1],1)\n return g\n\n def get_state_graph(self):\n return list(zip(self.states,self.graphs))\n\n\n def gen_all(self):\n states = list(itertools.product([0, 1], repeat=len(self.links))) #All possibilities of links up and down. Binary list where states[i]=1 means links[i] is up\n for state in states:\n links = self.state_to_links(state)\n g = self.gen_graph(links)\n self.graphs.append(g)\n self.states.append(state)\n return self.graphs\n\ndef nx2Graph(nx,distance=1):\n g = Graph()\n g.nodes = set(nx.nodes)\n for edge in nx.edges:\n g.add_edge(edge[0],edge[1],distance)\n return g\n\ndef dijsktra(graph, initial):\n visited = {initial: 0}\n path = {}\n\n nodes = set(graph.nodes)\n\n while nodes:\n min_node = None\n for node in nodes:\n if node in visited:\n if min_node is None:\n min_node = node\n elif visited[node] < visited[min_node]:\n min_node = node\n\n if min_node is None:\n break\n\n nodes.remove(min_node)\n current_weight = visited[min_node]\n\n for edge in graph.edges[min_node]:\n weight = current_weight + graph.distances[(min_node, edge)]\n if edge not in visited or weight < visited[edge]:\n visited[edge] = weight\n path[edge] = min_node\n\n return visited, path\n\ndef path_to_node(dij,from_node,target_node,include_from=False):\n node = target_node\n path = [node]\n while True:\n node = dij[node]\n if node==from_node:\n break\n path.insert(0,node)\n if include_from:\n path.insert(0,from_node)\n return path\n\n\ndef BuildDataset(links,from_node,to_node):\n samples = []\n labels = []\n gg = GraphGenerator(links)\n gg.gen_all()\n for element in gg.get_state_graph():\n state = element[0]\n graph = element[1]\n samples.append(state)\n _,paths = dijsktra(graph,from_node)\n if to_node not in paths:\n next_hop= -1\n else:\n next_hop = path_to_node(paths,from_node,to_node)[0]\n labels.append(next_hop)\n return samples,labels\n\ndef BuildDatatasetRNN(g,nr_samples,verbose=False,torch_format=True):\n X = []\n y = []\n EOS_token = 1000\n for i in range(nr_samples):\n from_node = random.randint(0,len(g.nodes))\n n,p = dijsktra(g,from_node)\n to_node = random.randint(0,len(g.nodes))\n if to_node not in p:\n path = [989]\n else:\n path = path_to_node(p,from_node,to_node,include_from=True)\n path.append(EOS_token)\n x_path = [from_node,to_node,EOS_token]\n if torch_format:\n path = Variable(torch.LongTensor(path))\n x_path = Variable(torch.LongTensor(x_path))\n X.append(x_path)\n y.append(path)\n if i%100==0 and verbose:\n print(i/nr_samples*100)\n return X,y\n\ndef checkValidPath(g,path):\n curr_node = path[0]\n for i in range(1,len(path)):\n if path[i] in g.edges[curr_node]:\n curr_node = path[i]\n else:\n return False\n return True\n\ndef checkAccuracy(y_true,preds):\n corrects = 0\n for i in range(len(y_true)):\n if list(y_true[i])==preds[i]:\n corrects += 1\n return corrects/len(y_true)\n\ndef getPreds(encoder,decoder,X):\n preds = []\n for x in X:\n pred, _ = rnn_util.evaluate(encoder, decoder,x)\n pred = pred[:-1]\n preds.append(pred)\n return preds\n\ndef getNumberValids(g,preds):\n valids = 0\n for pred in preds:\n if checkValidPath(g,pred[:-1]):\n valids += 1\n return valids\n\ndef splitTrainTest(X,y,p=0.3):\n nr_train = int(len(X)*(1-p))\n return X[:nr_train],y[:nr_train],X[nr_train:],y[nr_train:]\n","repo_name":"joaoareis/rnn_dijkstra","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5198,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"6570301549","text":"from odoo import fields,models,api, _\n\nclass PurchaseOrderLine(models.Model):\n _inherit = 'purchase.order.line'\n\n z_po_order_date = fields.Datetime(related=\"order_id.date_order\",string=\"Date Order\")\n z_status = fields.Char('Document Status',store=True,track_visibility=\"always\",compute='_compute_status_type')\n\n\n @api.depends('qty_received','qty_invoiced','product_qty')\n def _compute_status_type(self):\n \tfor line in self:\n \t\tif line.qty_received == line.product_qty == line.qty_invoiced:\n \t\t\tline.z_status = 'GRN & Invoice Done'\n \t\tif line.product_qty == line.qty_received and line.qty_invoiced == 0:\n \t\t\tline.z_status = 'Pending for Invoice'\n \t\tif line.product_qty == line.qty_received and line.qty_invoiced != 0 and line.qty_received != line.qty_invoiced:\n \t\t\tline.z_status = 'Partial Invoice Done'\n \t\tif line.product_qty != 0 and line.qty_received == 0:\n \t\t\tline.z_status = 'Pending for GRN'\n \t\tif line.product_qty != line.qty_received and line.qty_received != 0:\n \t\t\tline.z_status = 'Partial GRN'\n\n\n\n","repo_name":"krishna11174/college","sub_path":"cn_new/odoo/custom/odoov13_26/models/purchase_order_line.py","file_name":"purchase_order_line.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18432722558","text":"import socket\nimport struct\nimport time\nimport thread\nimport sys\nimport os\nlib_path = os.path.abspath(os.path.join('../controller'))\nsys.path.append(lib_path)\nfrom nc_config import *\n\nrs = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nrs.bind((sys.argv[1], REPLY_PORT))\n\ncounter = 0\ndef counting():\n last_counter = 0\n while True:\n print (counter - last_counter), counter\n sys.stdout.flush()\n last_counter = counter\n time.sleep(1)\nthread.start_new_thread(counting, ())\n\ndef main():\n global counter\n last_count = 0\n current_time = time.time()\n while (1):\n packet_received, addr = rs.recvfrom(2048)\n op_field = int(struct.unpack(\"B\", packet_received[0])[0])\n counter = counter + 1\n continue\n \nif __name__ == \"__main__\":\n main()","repo_name":"muhe1991/p4-programs-survey","sub_path":"netchain/client/receiver.py","file_name":"receiver.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"31"} +{"seq_id":"4813064428","text":"def set_number():\n num = set()\n print('В конце введите 0 ')\n while True:\n number = int(input('Введите ��исло: '))\n num.add(number)\n if number == 0:\n num.remove(number)\n break\n return len(num)\n\n\nprint(f'Введено уникальных чисел: {set_number()}')\n","repo_name":"IlyaNik0506/synergy_edu","sub_path":"1. Start/Webinar_5/case_1.py","file_name":"case_1.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15875447238","text":"import numpy as np\nimport pandas as pd\nimport glob\nfrom pmdarima.arima import ndiffs\nfrom pandas.tseries.offsets import QuarterBegin, QuarterEnd\nfrom .hand_select import hand_select\nimport pandas_datareader.data as web\nimport xlrd, csv\nfrom openpyxl.workbook import Workbook\nfrom openpyxl.reader.excel import load_workbook, InvalidFileException\n\n\ndef set_date_as_index(df):\n df.columns = [name.lower() for name in df.columns]\n df[\"date\"] = pd.to_datetime(df[\"date\"])\n df.set_index(\"date\", inplace=True)\n return df\n\n\ndef make_float(df):\n df = df.replace(\".\", np.nan)\n df = df.astype(float)\n return df\n\n\ndef read_files(paths, fillna=True):\n csv_list = []\n xls_list = []\n\n for path in paths:\n csv_files = glob.glob(path + \"/*.csv\")\n xls_files = glob.glob(path + \"/*.xls\")\n\n for elt in csv_files:\n df = pd.read_csv(elt)\n df = set_date_as_index(df)\n df = make_float(df)\n if fillna:\n df = df.fillna(method='ffill')\n csv_list.append(df)\n\n for elt in xls_files:\n try:\n df = pd.read_excel(elt)\n df = set_date_as_index(df)\n df = make_float(df)\n if fillna:\n df = df.fillna(method='ffill')\n xls_files.append(df)\n except Exception:\n pass\n\n return csv_list, xls_list\n\n\ndef make_stationary(df):\n df = hand_select(df)\n df = df.dropna()\n columns = df.columns\n for name in columns:\n x = df[name].values\n d_kpss = ndiffs(x, test='kpss')\n d_adf = ndiffs(x, test='adf')\n d_pp = ndiffs(x, test='pp')\n d_ = max(d_kpss, d_adf, d_pp)\n if d_ > 0:\n new_name = name + '_diff' + str(d_)\n if d_ == 1:\n df[new_name] = df[name].diff()\n elif d_ == 2:\n df[new_name] = df[name].diff().diff()\n elif d_ > 2:\n raise ValueError('High order differentiation')\n else:\n raise Exception('Some thing is wrong')\n\n df = df.drop(columns=[name])\n return df\n\n\ndef open_xls_as_xlsx(filename):\n # first open using xlrd\n book = xlrd.open_workbook(filename)\n index = 0\n nrows, ncols = 0, 0\n while nrows * ncols == 0:\n sheet = book.sheet_by_index(index)\n nrows = sheet.nrows\n ncols = sheet.ncols\n index += 1\n\n # prepare a xlsx sheet\n book1 = Workbook()\n sheet1 = book1.active\n\n for row in range(1, nrows):\n for col in range(1, ncols):\n sheet1.cell(row=row, column=col).value = sheet.cell_value(row, col)\n\n return book1\n\n\ndef read_data(path, sheet=False, header='infer'):\n file_format = path.split('.')[-1]\n if 'msci' in path:\n header = 6\n if sheet is False:\n # if file_format == 'csv':\n # df = pd.read_csv(path, header=header)\n # elif file_format == 'xls':\n # df = open_xls_as_xlsx(path)\n # else:\n try:\n df = pd.read_excel(path, header=header, engine='openpyxl')\n except Exception:\n try:\n df = open_xls_as_xlsx(path)\n except Exception as e:\n try:\n df = pd.read_csv(path, header=header)\n except Exception as e:\n raise Exception(e)\n else:\n try:\n # excel_file = pd.ExcelFile(path)\n # assert sheet in excel_file.sheet_names\n # df = excel_file.parse(sheet, header=header)\n df = pd.read_excel(path, header=header, engine='openpyxl', sheet_name=sheet)\n except Exception:\n raise Exception(\"Can not read sheet\")\n\n df.columns = [name.lower() for name in df.columns]\n\n if 'year2' in df.columns:\n drop_columns = ['year2']\n else:\n drop_columns = []\n for elt in df.columns:\n if 'unnamed' in elt:\n drop_columns.append(elt)\n df.drop(columns=drop_columns, inplace=True)\n\n first_valid = df.iloc[:, 1].first_valid_index()\n last_valid = df.iloc[:, 1].last_valid_index() + 1\n df = df.iloc[first_valid:last_valid]\n df.columns = df.columns.str.replace('.', '_')\n df.columns = df.columns.str.replace(' ', '_')\n df.columns = df.columns.str.replace('__', '_')\n return df\n\n\ndef make_monthly_date(df, offset=True):\n datetime = pd.to_datetime(\n (\n df['year'].astype(int) * 100\n + df['month'].astype(int)\n ).astype(str),\n format='%Y%m'\n )\n if offset:\n datetime += pd.tseries.offsets.MonthBegin(1)\n else:\n datetime = datetime\n df['date'] = datetime\n df.drop(columns=['year', 'month'], inplace=True)\n df.set_index('date', inplace=True)\n df.columns = [elt + '_monthly' for elt in df.columns]\n return df\n\n\ndef make_quarterly_date(df, offset=True):\n df['year'] = df['year'].str.lower()\n df['year'] = df['year'].str.replace(r'(q\\d)-(\\d+)', r'\\2-\\1')\n if offset:\n # Bug that quarterbegin is March 01\n df['date'] = pd.to_datetime(df['year'])\\\n + pd.tseries.offsets.DateOffset(days=1)\\\n + pd.tseries.offsets.QuarterBegin(1, startingMonth=1)\n else:\n df['date'] = pd.to_datetime(df['year'])\n df.drop(columns=['year'], inplace=True)\n df.set_index('date', inplace=True)\n # Manually shift because of QuarterBegin bug\n df.columns = [elt + '_quarterly' for elt in df.columns]\n df = df.dropna()\n return df\n\n\ndef make_daily_date(df):\n datetime = pd.to_datetime(\n (\n df['year'].astype(int) * 10000\n + df['month'].astype(int) * 100\n + df['day'].astype(int)\n ).astype(str),\n format='%Y%m%d'\n )\n df['date'] = datetime\n df.drop(columns=['year', 'month', 'day'], inplace=True)\n df.set_index('date', inplace=True)\n df.columns = [elt + '_daily' for elt in df.columns]\n return df\n\n\n# If date of low frequency data is specified, assume It is announced\n# before the start of the market\n# If not specified, assume it is announced after the market is closed\ndef daily_data(df, freq, offset=True, fill_method='ffill'):\n drop_columns = []\n for elt in df.columns:\n if 'unnamed' in elt:\n drop_columns.append(elt)\n df.drop(columns=drop_columns, inplace=True)\n\n if freq.lower() == 'monthly':\n try:\n df = make_monthly_date(df, offset=offset)\n except Exception:\n print(\"set monthly date as index\")\n datetime = pd.to_datetime(df['date'])\n df['date'] = datetime\n df.set_index('date', inplace=True)\n df.columns = [elt + '_monthly' for elt in df.columns]\n df = make_stationary(df)\n if offset:\n daily_datetime = pd.date_range(\n df.index[0] + pd.tseries.offsets.MonthBegin(1),\n df.index[-1] + pd.tseries.offsets.MonthEnd(1),\n freq='D'\n )\n else:\n daily_datetime = pd.date_range(\n df.index[0] + pd.tseries.offsets.MonthBegin(1),\n df.index[-1] + pd.tseries.offsets.MonthEnd(1),\n freq='D'\n )\n df = df.reindex(daily_datetime, method=fill_method)\n elif freq.lower() == 'daily':\n try:\n df = make_daily_date(df)\n except Exception:\n print(\"set daily date as index\")\n datetime = pd.to_datetime(df['date'])\n df['date'] = datetime\n df.set_index('date', inplace=True)\n df.columns = [elt + '_daily' for elt in df.columns]\n df = make_stationary(df)\n daily_datetime = pd.date_range(\n df.index[0],\n df.index[-1],\n freq='D'\n )\n df = df.reindex(daily_datetime, method=fill_method)\n elif freq.lower() == 'quarterly':\n try:\n df = make_quarterly_date(df)\n except Exception:\n print(\"set quarterly date as index\")\n datetime = pd.to_datetime(df['date'])\n df['date'] = datetime\n df.set_index('date', inplace=True)\n df.columns = [elt + '_quarterly' for elt in df.columns]\n df = make_stationary(df)\n if offset:\n daily_datetime = pd.date_range(\n df.index[0] + QuarterBegin(1, startingMonth=1),\n df.index[-1] + QuarterEnd(1, startingMonth=1),\n freq='D'\n )\n else:\n daily_datetime = pd.date_range(\n df.index[0],\n df.index[-1] + pd.tseries.offsets.QuarterEnd(1),\n freq='D'\n )\n df = df.reindex(daily_datetime, method=fill_method)\n else:\n print(\"Type frequency\")\n daily_datetime = pd.date_range(\n df.index[0], df.index[-1],\n freq='D'\n )\n df = df.reindex(daily_datetime, method=fill_method)\n\n drop_columns = []\n for elt in df.columns:\n if 'unnamed' in elt:\n drop_columns.append(elt)\n df.drop(columns=drop_columns, inplace=True)\n return df\n\n\ndef get_nonfinancial():\n print('monthly epu')\n monthly_epu = read_data(\n 'https://www.policyuncertainty.com/media/All_Country_Data.xlsx'\n )\n daily_epu = daily_data(monthly_epu, 'monthly')\n daily_epu.columns = ['epu_' + elt for elt in daily_epu.columns]\n\n print('daily_infectious')\n daily_infectious = read_data(\n 'https://www.policyuncertainty.com/media/All_Infectious_EMV_Data.csv'\n )\n daily_infectious = daily_data(daily_infectious, 'daily')\n daily_infectious.columns = [\n 'daily_infectious_' + elt for elt in daily_infectious.columns]\n\n print('categorical_epu')\n categorical_epu = read_data(\n 'https://www.policyuncertainty.com/media/Categorical_EPU_Data.xlsx'\n )\n categorical_epu = daily_data(categorical_epu, 'monthly')\n categorical_epu.columns = [\n 'categorical_epu_' + elt for elt in categorical_epu.columns]\n\n # print('eurq_data')\n # eurq_data = read_data(\n # '../../data/epu/EURQ_data.xlsx',\n # sheet='EURQ'\n # )\n # eurq_data = daily_data(eurq_data, 'monthly')\n # eurq_data.columns = ['eurq_data_' + elt for elt in eurq_data.columns]\n\n # print('trade_unc')\n # trade_uncertainty_data = read_data(\n # 'https://www.policyuncertainty.com/media/Trade_Uncertainty_Data.xlsx'\n # )\n # trade_uncertainty_data = daily_data(trade_uncertainty_data, 'monthly')\n # trade_uncertainty_data.columns = [\n # 'trade_uncertainty_' + elt for elt in trade_uncertainty_data.columns\n # ]\n\n print('wpui')\n wpui_url = (\n 'https://worlduncertaintyindex.com/'\n 'wp-content/uploads/2020/07/WPUI_Data.xlsx'\n )\n wpui_data = read_data(\n wpui_url, sheet='F1', header=1\n )\n wpui_data = daily_data(wpui_data, 'quarterly')\n wpui_data.columns = [\n 'wpui_' + elt for elt in wpui_data.columns\n ]\n\n print('wui')\n wui_url = (\n 'https://worlduncertaintyindex.com/'\n 'wp-content/uploads/2020/07/WUI_Data.xlsx'\n )\n wui_data = read_data(\n wui_url, sheet='F1', header=2\n )\n wui_data = daily_data(wui_data, 'quarterly')\n wui_data.columns = [\n 'wui_' + elt for elt in wui_data.columns\n ]\n\n df_non_financial = pd.concat(\n [\n daily_epu, daily_infectious, categorical_epu,\n # eurq_data, trade_uncertainty_data,\n wpui_data, wui_data\n ], axis=1\n )\n\n print('non-financial data')\n return df_non_financial\n\n\ndef get_financial():\n print('finance data')\n\n sp500 = df = web.DataReader(\n '^GSPC', 'yahoo',\n start='1990-01-03', end='2020-08-31'\n )\n sp500.columns = [elt.lower().replace(' ', '_') for elt in sp500.columns]\n try:\n sp500.set_index('date', inplace=True)\n except Exception:\n pass\n sp500.index.name = 'date'\n sp500.index = pd.DatetimeIndex(sp500.index)\n\n print('dex jp us')\n dexjpus_url = (\n 'https://fred.stlouisfed.org/graph/fredgraph.csv?'\n 'bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&'\n 'graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars='\n 'on&txtcolor=%23444444&ts=12&tts=12&width=968&nt=0&thu=0&trc='\n '0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id='\n 'DEXJPUS&scale=left&cosd=1971-01-04&coed=2020-08-28&'\n 'line_color=%234572a7&link_values=false&line_style=solid&'\n 'mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&'\n 'fq=Daily&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&'\n 'transformation=lin&vintage_date=2020-09-01&revision_date'\n '=2020-09-01&nd=1971-01-04'\n )\n dexjpus = pd.read_csv(dexjpus_url)\n dexjpus.columns = [elt.lower() for elt in dexjpus.columns]\n dexjpus['date'] = pd.DatetimeIndex(dexjpus['date'])\n dexjpus.set_index('date', inplace=True)\n\n print('dex us eu')\n dexuseu_url = (\n 'https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor='\n '%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor='\n '%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%'\n '23444444&ts=12&tts=12&width=968&nt=0&thu=0&trc=0&show_legend='\n 'yes&show_axis_titles=yes&show_tooltip=yes&id=DEXUSEU&scale=left&'\n 'cosd=1999-01-04&coed=2020-08-28&line_color=%234572a7&link_values='\n 'false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&'\n 'oet=99999&mma=0&fml=a&fq=Daily&fam=avg&fgst=lin&fgsnd='\n '2020-02-01&line_index=1&transformation=lin&vintage_date='\n '2020-09-01&revision_date=2020-09-01&nd=1999-01-04'\n )\n dexuseu = pd.read_csv(dexuseu_url)\n dexuseu.columns = [elt.lower() for elt in dexuseu.columns]\n dexuseu['date'] = pd.DatetimeIndex(dexuseu['date'])\n dexuseu.set_index('date', inplace=True)\n\n print('dex us uk')\n dexusuk_url = (\n 'https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor='\n '%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor='\n '%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%'\n '23444444&ts=12&tts=12&width=968&nt=0&thu=0&trc=0&show_legend='\n 'yes&show_axis_titles=yes&show_tooltip=yes&id=DEXUSUK&scale='\n 'left&cosd=1971-01-04&coed=2020-08-28&line_color=%234572a7&'\n 'link_values=false&line_style=solid&mark_type=none&mw=3&lw='\n '2&ost=-99999&oet=99999&mma=0&fml=a&fq=Daily&fam=avg&fgst=lin&'\n 'fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date='\n '2020-09-01&revision_date=2020-09-01&nd=1971-01-04'\n )\n dexusuk = pd.read_csv(dexusuk_url)\n dexusuk.columns = [elt.lower() for elt in dexusuk.columns]\n dexusuk['date'] = pd.DatetimeIndex(dexusuk['date'])\n dexusuk.set_index('date', inplace=True)\n\n print('dex ch us')\n dexchus_url = (\n 'https://fred.stlouisfed.org/graph/fredgraph.csv?'\n 'bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%'\n '20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&'\n 'recession_bars=on&txtcolor=%23444444&ts=12&tts=12&'\n 'width=968&nt=0&thu=0&trc=0&show_legend=yes&show_'\n 'axis_titles=yes&show_tooltip=yes&id=DEXCHUS&scale='\n 'left&cosd=1981-01-02&coed=2020-08-28&line_color='\n '%234572a7&link_values=false&line_style=solid&'\n 'mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&'\n 'mma=0&fml=a&fq=Daily&fam=avg&fgst=lin&fgsnd=2020-02-01&'\n 'line_index=1&transformation=lin&vintage_date='\n '2020-09-01&revision_date=2020-09-01&nd=1981-01-02'\n )\n dexchus = pd.read_csv(dexchus_url)\n dexchus.columns = [elt.lower() for elt in dexchus.columns]\n dexchus['date'] = pd.DatetimeIndex(dexchus['date'])\n dexchus.set_index('date', inplace=True)\n\n df = pd.concat(\n [sp500, dexjpus, dexuseu, dexusuk, dexchus],\n axis=1, sort=False\n )\n df = make_float(df)\n df.columns = [elt.lower() for elt in df.columns]\n df = df.drop(df.tail(3).index)\n df = df.fillna(method='ffill')\n df.columns = [elt.lower() + '_daily' for elt in df.columns]\n\n df = df.dropna()\n df = make_stationary(df)\n\n print('fed_funds')\n fed_funds_url = (\n 'https://fred.stlouisfed.org/graph/fredgraph.csv?'\n 'bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&'\n 'graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&'\n 'txtcolor=%23444444&ts=12&tts=12&width=968&nt=0&thu=0&trc='\n '0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&'\n 'id=FEDFUNDS&scale=left&cosd=1954-07-01&coed=2020-07-01&'\n 'line_color=%234572a7&link_values=false&line_style=solid&'\n 'mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&'\n 'fq=Monthly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&'\n 'transformation=lin&vintage_date=2020-09-01&'\n 'revision_date=2020-09-01&nd=1954-07-01'\n )\n fed_funds = read_data(fed_funds_url)\n fed_funds = daily_data(fed_funds, 'monthly', offset=False)\n\n print('msci')\n msci_data = read_data('../data/financial_market_monthly/msciworld.xlsx')\n msci_data = msci_data.dropna()\n msci_data = daily_data(msci_data, 'monthly', offset=False)\n\n df = pd.concat([df, fed_funds, msci_data], axis=1, sort=False)\n return df\n","repo_name":"Byung-June/oil_future_forecasting","sub_path":"data_preprocessing/ml_data_preprocessing/data_reader.py","file_name":"data_reader.py","file_ext":"py","file_size_in_byte":17286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2692440421","text":"import requests\nimport json\nimport unittest\nfrom GitHubAPI1 import get_repos_and_commits\ndef get_repos_and_commits(github_id):\n # Make the API call to get the list of repositories for the given GitHub ID\n repos_url = f\"https://api.github.com/users/{github_id}/repos\"\n repos_response = requests.get(repos_url)\n\n # If the API call was successful, parse the list of repositories\n if repos_response.status_code == 200:\n repos_data = json.loads(repos_response.text)\n result = []\n\n # For each repository, make an API call to get the list of commits\n for repo in repos_data:\n repo_name = repo['name']\n commits_url = f\"https://api.github.com/repos/{github_id}/{repo_name}/commits\"\n commits_response = requests.get(commits_url)\n\n # If the API call was successful, parse the list of commits\n if commits_response.status_code == 200:\n commits_data = json.loads(commits_response.text)\n result.append((repo_name, len(commits_data)))\n else:\n print(f\"Error getting commits for repository {repo_name}: {commits_response.status_code}\")\n\n return result\n else:\n print(f\"Error getting repositories: {repos_response.status_code}\")\n return None\n\n# Example usage\ngithub_id = \"cconway1-stevens\"\nrepos_and_commits = get_repos_and_commits(github_id)\n\nif repos_and_commits is not None:\n for repo_name, commit_count in repos_and_commits:\n print(f\"Repo: {repo_name} Number of commits: {commit_count}\")\n\n# create unit test cases for the above functions\n# Path: GitHubAPI1_test.py\n\nclass TestGitHubAPI1(unittest.TestCase):\n def testIDIsString(self):\n # test to see if the input GitHub ID is a string\n ID = \"cconway1-stevens\"\n self.assertIs(type(ID), str)\n def testOutput(self):\n # test to see if the output of the given ID is correct\n output = ['Error getting commits for repository cconway1-stevens: 409',\n 'Error getting commits for repository personalsite: 409',\n 'Error getting commits for repository test-HW3: 409',\n 'Repo: Complexity Number of commits: 30',\n 'Repo: Complexity-1 Number of commits: 30',\n 'Repo: E_115-Final Number of commits: 1',\n 'Repo: HW_SSW345 Number of commits: 2',\n 'Repo: SSW345 Number of commits: 12',\n 'Repo: SSW567-HW4a Number of commits: 2',\n 'Repo: SSW_567 Number of commits: 28',\n 'Repo: stevens-ssw-567-Final Number of commits: 13',\n 'Repo: Testsite Number of commits: 1']\n\n self.assertEqual(github_user(\"cconway1-stevens\"), output)\n\n def testRepoError(self):\n # test to see if the repository exists for given ID\n ID = \"asdasdasdasd\"\n self.assertEqual(github_user(ID), \"Error obtaining repository names!\")\n def testCommitError(self):\n # test to see if the commits exist in the repository for given ID\n self.assertEqual(github_user(\"SSWSample\"), \"Error obtaining number of commits!\")\n self.assertEqual(github_user(\"cconway1-stevens\"), ['Error getting commits for repository cconway1-stevens: 409',\n 'Error getting commits for repository personalsite: 409',\n 'Error getting commits for repository test-HW3: 409',\n 'Repo: Complexity Number of commits: 30',\n 'Repo: Complexity-1 Number of commits: 30',\n 'Repo: E_115-Final Number of commits: 1',\n 'Repo: HW_SSW345 Number of commits: 2',\n 'Repo: SSW345 Number of commits: 12',\n 'Repo: SSW567-HW4a Number of commits: 2',\n 'Repo: SSW_567 Number of commits: 28',\n 'Repo: stevens-ssw-567-Final Number of commits: 13',\n 'Repo: Testsite Number of commits: 1'])\n\n","repo_name":"cconway1-stevens/SSW567-HW4a","sub_path":"GitHubAPI1.py","file_name":"GitHubAPI1.py","file_ext":"py","file_size_in_byte":3787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17255653961","text":"import os.path as osp\nfrom PIL import Image\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom . import transforms\n\n\nclass ImageNet(Dataset):\n def __init__(self, data_config):\n super().__init__()\n self.transform = transforms.create_transform(data_config.transforms)\n self.data_list = self._read_data_list(\n data_config.data_dir, data_config.data_list_path\n )\n\n @classmethod\n def _read_data_list(cls, data_dir, data_list_path):\n data_list = []\n with open(data_list_path) as data_list_file:\n for line in data_list_file:\n image_name, class_ = line.split()\n image_path = osp.join(data_dir, image_name)\n class_ = int(class_)\n data_list.append((image_path, class_))\n return data_list\n\n def __len__(self):\n return len(self.data_list)\n\n def __getitem__(self, index):\n image, class_ = self._read_example(index)\n image = self.transform(image)\n return index, image, class_\n\n def _read_example(self, index):\n image_path, class_ = self.data_list[index]\n image = Image.open(image_path)\n return image, class_\n\n","repo_name":"cmsflash/beauty-net","sub_path":"beauty/datasets/image_net.py","file_name":"image_net.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"en","doc_type":"code","stars":186,"dataset":"github-code","pt":"31"} +{"seq_id":"38662889075","text":"\nimport sys\n\ndef ne_contient_que_chiffres(chaine):\n return chaine.isdigit() # Vérifie si la chaîne ne contient que des chiffres\n\n# Vérifie le nombre d'arguments passés en ligne de commande\nif len(sys.argv) != 2:\n print(\"error\")\n sys.exit()\n\nchaine = sys.argv[1] # Récupère la chaîne de caractères passée en argument\ntry:\n if ne_contient_que_chiffres(chaine):\n print(\"true\")\n else:\n print(\"false\")\nexcept AttributeError:\n print(\"error\")","repo_name":"BrahimCodeA/eau-ok","sub_path":"eau08.py","file_name":"eau08.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19211762866","text":"from django.shortcuts import render, redirect\nfrom django.db import OperationalError\nfrom django.contrib.auth.decorators import login_required\nfrom posts.models import Post\nfrom django.contrib.auth.models import User\nfrom django.contrib import messages\nfrom .forms import UserRegisterFrom\n\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\n\nfrom djelery.celery import app\nfrom posts.tasks import user_scraping\n\n# WHAT IS GOING ON HERE:\n# 1. PROFILE 2. REGISTER PAGES\n# 2. API FOR USER FEED SCRAB (CUZ ITS USER-RELATED)\n# 3. API FOR TASK STATUS CHECK (CUZ ONLY USER-FEED UPDATING STATUS MATTERS YET)\n\n\n# Create your views here.\n@login_required\ndef profile(request):\n return render(request, 'accounts/account.html', {})\n\ndef register(request):\n if request.user.is_authenticated: \n return redirect('home')\n if request.method == 'POST':\n form = UserRegisterFrom(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n messages.success(request, f'Account for {username} has been created! You can log in!')\n return redirect('/login')\n else: \n form = UserRegisterFrom()\n return render(request, 'accounts/register.html', {'form': form})\n\n\n@api_view([\"POST\"])\n@permission_classes([IsAuthenticated])\ndef verify_credentials(request):\n '''\n Once user enters credentials on a profile page, it comes right here\n and parsing starts; if habr login successfull then \n (mailname - is mail or username depending on a server)\n '''\n mailname = request.data['mailname']\n password = request.data['password']\n # need to verify source\n source = request.data['source']\n username = request.user.username\n task = user_scraping.delay(password, mailname, source, username)\n\n return Response({\"task_id\": task.task_id}, status=status.HTTP_202_ACCEPTED)\n\n@api_view([\"POST\"])\n# @permission_classes([IsAuthenticated])\ndef task_check(request):\n '''\n AJAX requests to check the task status\n and accordingly update the UI\n '''\n try:\n print(f'task_check got: {request.data[\"task_id\"]}')\n task = app.AsyncResult(request.data['task_id'])\n task_status = task.status\n task_result = 'NOT DONE YET'\n if (task_status == 'SUCCESS'):\n task_result = task.get()\n # print(f'task_check is sending: status: {task_status}; result: {task_result}')\n # database is locked, I assume it means that celery task is using the db\n # the question is whether this thing sqlite specific or postgresql may face it as well?\n except OperationalError as err:\n return Response({'status': 'PENDING', 'result': task_result}, status=status.HTTP_200_OK)\n return Response({'status': task_status, 'result': task_result}, status=status.HTTP_200_OK)\n","repo_name":"EvgenyOcean/2020-Djelery","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22206671593","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#\n# if you do not have tensorflow,\n# run:\n# $ pip3 install tensorflow\n#\n# if you do not have pip3, run:\n# $ sudo apt install python3-pip\n#\nimport tensorflow as tf\n\n\n# In[2]:\n\n\nmnist = tf.keras.datasets.mnist\n\n\n# In[3]:\n\n\n(x_train, y_train),(x_test, y_test) = mnist.load_data()\n\n\n# In[4]:\n\n\nx_train, x_test = x_train / 255.0, x_test / 255.0\n\n\n# In[5]:\n\n\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape=(28, 28)),\n tf.keras.layers.Dense(512, activation=tf.nn.relu),\n tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n])\n\n\n# In[6]:\n\n\nmodel.compile(optimizer='adam',\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n\n# In[7]:\n\n\nmodel.fit(x_train, y_train, epochs=5)\n\n\n# In[8]:\n\n\nmodel.evaluate(x_test, y_test)\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"Azure-Samples/azure-intelligent-edge-patterns","sub_path":"machine-learning-notebooks/deploying-on-k8s/demo_notebook.py","file_name":"demo_notebook.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"31"} +{"seq_id":"12982494731","text":"\"\"\"\n=======================\nThe count_tables module\n=======================\n\nThe count_tables module contains non-cythonized (i.e. slow) functions for counts tables\n(otherwise known as contingency tables). These functions are the equivalent\nof those in the cosegregation_internal cython module, but they generally work for\nn-dimensions and are orders of magnitude slower.\n\n\"\"\"\n\n\nimport numpy as np\n\n\ndef get_transpositions(array):\n \"\"\"\n Generator that iterates through all possible transpositions of\n an n-dimensional array.\n \"\"\"\n\n axes = range(len(array.shape))\n for i in axes:\n yield tuple(axes[i:] + axes[:i])\n\n\ndef frequency_to_probability(counts_table):\n \"\"\"\n Convert a contingency table expressed in frequencies to one\n expressed in probabilities.\n \"\"\"\n\n total = counts_table.sum()\n probs_table = counts_table / float(total)\n\n return probs_table\n\n\ndef get_marginal_probabilities(probs_table):\n \"\"\"\n Get the marginal probability of each event given a\n contingency table.\n \"\"\"\n\n ind = []\n for transp in get_transpositions(probs_table):\n marginal_probs = [probs_table.transpose(transp)[1, ...].sum(),\n probs_table.transpose(transp)[0, ...].sum()]\n ind.append(marginal_probs)\n return np.array(ind)\n\n\ndef either_locus_not_detected(probs):\n \"\"\"\n Returns True if the probability of any event in a contingency\n table is 0.\n \"\"\"\n\n return bool(probs.min())\n\n\ndef cosegregation(counts_table):\n \"\"\"\n Return the co-segregation frequency of n loci given their\n contingency table.\n \"\"\"\n\n probs_table = frequency_to_probability(counts_table)\n\n if either_locus_not_detected(probs_table):\n return np.NAN\n\n return probs_table.flat[-1]\n\n\ndef expected(counts_table):\n \"\"\"\n Return the expected co-segregation probability of an arbitrary number\n of loci given their contingency table.\n \"\"\"\n\n probs_table = frequency_to_probability(counts_table)\n marginal_probs = get_marginal_probabilities(probs_table)\n\n if either_locus_not_detected(marginal_probs):\n return np.NAN\n\n exp_freqs = marginal_probs.prod(axis=0)[0]\n return exp_freqs\n\n\ndef linkage(counts_table):\n \"\"\"\n Return the linkage disequilibrium (D) for an arbitrary number of\n loci given their contingency table.\n \"\"\"\n\n probs_table = frequency_to_probability(counts_table)\n marginal_probs = get_marginal_probabilities(probs_table)\n\n if either_locus_not_detected(marginal_probs):\n return np.NAN\n\n exp_freqs = marginal_probs.prod(axis=0)[0]\n observed = probs_table.flat[-1]\n if observed == 0:\n return np.NAN\n return observed - exp_freqs\n","repo_name":"pombo-lab/gamtools","sub_path":"lib/gamtools/count_tables.py","file_name":"count_tables.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"31"} +{"seq_id":"37785989600","text":"import pytest\n\nimport src.db\nimport src.example\nfrom src.example import foo\nfrom src.example import multiply_with_constant\n\n\n# mocker is a fixture from pytest-mock, which is a thin-wrapper around\n# the patching api of the unittest.mock package\ndef test_example_pytest(mocker):\n mocker.patch( # doesn't require that we import the object before patching\n # db_write is from db.py but imported into example.py\n 'src.example.db_write',\n return_value=10\n )\n # db_write will be a MagicMock\n\n expected = 10\n actual = foo()\n assert expected == actual\n\n\n# equivalent to\ndef test_example_pytest3(mocker):\n mocker.patch.object( # requires that we import before patching\n # db_write is from db.py but imported into example.py\n src.example,\n \"db_write\",\n return_value=8 # specify directly the return value of mock\n )\n\n expected = 8\n actual = src.example.foo()\n assert expected == actual\n\n\n# equivalent to\ndef test_example_pytest2(mocker):\n def mocked_dbwrite():\n # extra computations here\n return 9\n\n mocker.patch.object(\n # db_write is from db.py but imported into example.py\n src.example,\n \"db_write\",\n new=mocked_dbwrite # if we need more than the return value\n )\n\n expected = 9\n actual = src.example.foo()\n assert expected == actual\n\n\ndef test_example_pytest_longrunning(mocker):\n def mocked_long_running_function():\n return {\"status\": 500}\n\n mocker.patch.object(\n src.example,\n \"long_api_call\",\n new=mocked_long_running_function\n )\n\n expected = 500\n actual = src.example.check_status()\n assert expected == actual\n\ndef test_compute(monkeypatch):\n monkeypatch.setattr(\"src.example.MY_CONSTANT\", 3)\n assert multiply_with_constant(6) == 18\n\n\ndef test_floats_comparison_approx_abs():\n \"\"\"\n Fails with:\n Expected :0.004 ± 1.0e-04\n Actual :0.005\n because valid ranges are (0.0039 -> 0.0041)\n \"\"\"\n assert 0.005 == pytest.approx(0.004, abs=1e-4)\n\n assert 0.004099999 == pytest.approx(0.004, abs=1e-4) # passes\n assert 0.0041 == pytest.approx(0.004, abs=1e-4) # fails\n\n assert 0.003900001 == pytest.approx(0.004, abs=1e-4) # passes\n assert 0.0039 == pytest.approx(0.004, abs=1e-4) # fails\n\ndef test_floats_comparison_approx_rel(): \n \"\"\"\n Fails with:\n Expected :0.004 ± 4.0e-06\n Actual :0.005\n because rel uses 0.004 * 0.001 = 0.000004 = 4.0e-06 as a tolerance,\n valid ranges are 0.003996 -> 0.004004\n \"\"\"\n # assert 0.005 == pytest.approx(0.004, rel=1e-3)\n assert 0.003996 == pytest.approx(0.004, rel=1e-3)\n assert 0.004004 == pytest.approx(0.004, rel=1e-3)\n\n assert 0.004002 == pytest.approx(0.004, rel=1e-4) #passes\n\n\ndef test_list():\n assert (1, 2, 3) == (3, 2, 1)\n","repo_name":"cloudflightio/python-learning-sessions","sub_path":"04_pytest_mocking/tests/test_example_pytest.py","file_name":"test_example_pytest.py","file_ext":"py","file_size_in_byte":2837,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"41323460480","text":"import os\nimport json\nimport argparse\nfrom datetime import datetime\n\nimport pandas as pd\nimport numpy as np\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import datasets, layers, optimizers, metrics, Sequential\n\nimport horovod.tensorflow as hvd\n\n##########################################################################################\n# horovod基于open-mpi技术从而实现高效率的高性能计算，主要用open-mpi解决worker（process）之间的数据传输效率问题\n# 主要的概念都源于MPI：size，rank，local_rank，allreduce，allgather和allbroad\n# 1. size，代表整个horovod有多少个process，一个GPU对应一个process\n# 2. rank，process在整个horovod环境中的ID\n# 3. local_rank，process在本机上的ID\n# 4. allreduce，先reduce各个process的数据，然后再将结果broadcast到各个process：\n# https://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/\n# 5. allgather，各个process先broadcast自己数据，然后各个process在gather这些broadcast的数据\n# https://mpitutorial.com/tutorials/mpi-scatter-gather-and-allgather/\n# 6. allbroad，process把自己的数据广播到其他processes\n# 所以，使用horovod需要遵循一下流程：\n# 参考链接：https://horovod.readthedocs.io/en/latest/summary_include.html#concepts\n# 1. 运行hvd.init()\n# 2. 把每个训练的process绑定到对应的GPU上，每个GPU对应一个process\n# 3. 根据worker的数量放大learning rate\n# 4. 用hvd.DistributedOptimizer包裹原有的optimizer，这个hvd.DistributedOptimizer会通过allgather和allreduce收集各个worker的梯度并计算均值，然后更新variables\n# 5. 使用hvd.BroadcastGlobalVariablesHook(0)将rank0上的初始化梯度广播到所有processes，以同步一个variables tensor的起点，然后进行训练\n# 6. 训练结束或者中间存储checkpoint时，在hvd.rank()==0时进行\n# 使用keras compile和不使用，在用horovod时有区别，参考下两例：\n# keras compile：https://github.com/horovod/horovod/blob/master/examples/tensorflow2_keras_mnist.py\n# 非keras compile：https://github.com/horovod/horovod/blob/master/examples/tensorflow2_mnist.py\n##########################################################################################\n\n# 设置tensorflow日志等级，0:无屏蔽，1:屏蔽info，2:屏蔽warning及以下，3:屏蔽error及以下\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n'''\n获取模型函数，通过keras layer构建一个普通的DNN\nreture：karas model对象\n'''\ndef get_model():\n \n # 使用keras的Sequential类，创建一个容器，其中定义每一层的Layer\n model = Sequential([\n layers.Dense(256, input_shape=(784,), activation=tf.nn.relu, # 增加input_shape参数，否则会有警告\n kernel_regularizer=keras.regularizers.l2(0.01)), # w:[784, 256], b:[256], 激活函数使用relu, 设置l2正则化抑制过拟合\n layers.Dense(128, activation=tf.nn.relu,\n kernel_regularizer=keras.regularizers.l2(0.01)), # w:[256, 128], b:[128], 激活函数使用relu\n layers.Dense(64, activation=tf.nn.relu,\n kernel_regularizer=keras.regularizers.l2(0.01)), # w:[128, 64], b:[64], 激活函数使用relu\n layers.Dense(32, activation=tf.nn.relu,\n kernel_regularizer=keras.regularizers.l2(0.01)), # w:[64, 32], b:[32], 激活函数使用relu\n layers.Dense(10, activation=tf.nn.softmax) # w:[64, 10], b:[10], 为了输出一个one-hot概率值，这里使用softmax\n ])\n\n # build网络，指定输入数据的格式，这个输入的shape一定要跟真实数据一致\n model.build(input_shape=[None, 28*28])\n # 打印出网络的汇总信息，每层类型和参数等\n if hvd.rank() == 0:\n model.summary()\n \n return model\n\n\n'''\n训练算法，训练多个epochs，每100个batch输出一次loss，并且每个epoch输出一次准确率\nparas：\n model：keras model对象\n train_db：keras dataset对象\n validation_db：keras dataset对象\n epoch\n batch_size\n learning_rate\nreturn：None\n'''\ndef train(model, train_db, validation_db, epochs, batch_size, learning_rate, model_dir):\n \n # 对dataset进行处理\n # 预处理传入process函数\n # shuffle用于把数据打散，参数越大打的越散\n # 设定每一个batch的大小\n train_db = train_db.map(process).shuffle(10000, seed=np.random.randint(999)).batch(batch_size)\n validation_db = validation_db.map(process).shuffle(10000, seed=np.random.randint(999)).batch(batch_size)\n\n # 获得sample数据，查看数据的shape信息，用于下一步定义网络的一些参数\n train_iter = iter(train_db)\n train_sample = next(train_iter)\n print('train dataset x shape {}, train dataset y shape {}'.format(train_sample[0].shape, train_sample[1].shape))\n\n # 第三步，learning rate随着horovod的size的增加，而扩展\n optimazer = optimizers.Adam(lr=learning_rate*hvd.size())\n \n # 设定epoch的次数\n for epoch in range(epochs):\n \n for step, (x, y) in enumerate(train_db):\n # 对input进行reshape，对应model build的input_shape\n # x = tf.reshape(x, [-1,28*28])，不需要reshape了，传进来的数据已经是[b, 784]\n # tape包裹前向运算，用于记录varibale，好计算梯度\n with tf.GradientTape() as tape:\n # 直接将x输入model，实际上调用的实例的__call__方法，输出结果为softmax后的预测数据\n softmax = model(x) \n # 对y进行onehot编码，因为y的shape是[b,],而logits的shape是[b, 10],需要将y转换成[b, 10]\n y_hot = tf.one_hot(y, depth=10)\n # 这里设置两个损失函数mse和交叉熵，如果是分类问题，推荐使用交叉熵\n # 注意，如果是使用logits计算交叉熵的化，需要指定参数from_logits=True，这里是softmax所以不需要了\n loss_mse = tf.reduce_mean(tf.losses.mean_squared_error(y_hot, softmax))\n loss_ce = tf.reduce_mean(tf.losses.categorical_crossentropy(y_hot, softmax))\n # loss_ce = tf.reduce_mean(tf.losses.categorical_crossentropy(y_hot, logits, from_logits=True))\n \n # 第四步，用hvd tape包裹之前的tape，这样可以allreduce各个process的梯度然后在同步到各个process，用于更新variables\n tape = hvd.DistributedGradientTape(tape)\n # 计算对于交叉熵的参数的梯度 \n grads = tape.gradient(loss_ce, model.trainable_variables)\n # 更新梯度，这里参数的列表直接调用model.trainable_variables\n optimazer.apply_gradients(zip(grads, model.trainable_variables))\n \n # 第五步，将初始化的varibles广播到所有的process，要确保多有的process在同一个起点开始\n if epoch==0 and step==0:\n hvd.broadcast_variables(model.variables, root_rank=0)\n hvd.broadcast_variables(optimazer.variables(), root_rank=0)\n \n # 每100个step打印一次信息\n if step % 100 ==0 and hvd.rank() == 0:\n print('epoch:{}\\t step:{}\\t loss_mse:{}\\t loss_ce:{}\\t'.format(epoch, step, float(loss_mse), float(loss_ce)))\n \n # 每个epoch计算一次准确率\n total_corrects = 0 # 统计变量\n total_number = 0\n # 针对validation dataset进行测试\n for x, y in validation_db:\n \n # 得到验证数据的预测结果\n probs = model(x)\n \n # 取最大值的索引为预测值\n preds = tf.cast(tf.argmax(probs, axis=1), dtype=tf.int32)\n \n # 累加正确的个数，和总数\n corrects = tf.equal(y, preds)\n corrects = tf.reduce_sum(tf.cast(corrects, dtype=tf.int32))\n total_corrects += corrects\n total_number += x.shape[0]\n \n # 计算测试数据集的准确率\n acc = total_corrects / total_number\n \n if hvd.rank() == 0:\n print('accuracy={};'.format(acc))\n \n # 两种保存model的方法都可以，low level：tf.saved_model.save(model, model_dir+'/'+datetime.now().strftime('%Y%m%d%H%M%S'))\n # 存储model的路径必须是数字类型的字符串\n # 第六步，只有在rank=0时才存储checkpoint或者model\n if hvd.rank() == 0:\n model.save(model_dir+'/'+datetime.now().strftime('%Y%m%d%H%M%S'))\n\n'''\n用于在train()中，对datasets进行类型转换和进行归一化处理\nparas：\n x：features\n y：labels\nreturn：x，y\n'''\ndef process(x, y):\n x = tf.cast(x, dtype=tf.float32) / 255.\n y = tf.cast(y, dtype=tf.int32)\n return x, y\n\n'''\n获取训练数据集，从train数据集路径，通过pandas解析数据，然后分割成feature和label\n最后组合成tensorflow dataset数据类型并返回\nparas：\n train_data_path：train channel在sagemaker中的路径，examples是路径下的数据文件名，这里是硬coding需要改进\nreturn：training的dataset\n'''\ndef get_train_db(train_data_path):\n \n df=pd.read_csv(train_data_path+'/examples', sep=',',header=None)\n \n x_train, y_train = df.values[:, 1:], df.values[:, 0]\n \n train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n\n return train_db\n \ndef get_validation_db(validation_data_path):\n \n df=pd.read_csv(validation_data_path+'/examples', sep=',',header=None)\n \n x_validation, y_validation = df.values[:, 1:], df.values[:, 0]\n \n validation_db = tf.data.Dataset.from_tensor_slices((x_validation, y_validation))\n\n return validation_db\n\n'''\n解析sagemaker传递给这个训练脚本的参数，然后返回这些参数，供训练算法使用\n'''\ndef parse_args():\n \n parser = argparse.ArgumentParser()\n\n # hyperparameters sent by the client are passed as command-line arguments to the script\n parser.add_argument('--epochs', type=int, default=1)\n parser.add_argument('--batch_size', type=int, default=64)\n parser.add_argument('--learning_rate', type=float, default=0.1)\n \n # data directories\n parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))\n parser.add_argument('--validation', type=str, default=os.environ.get('SM_CHANNEL_VALIDATION'))\n \n # model directory: we will use the default set by SageMaker, /opt/ml/model\n parser.add_argument('--model_dir', type=str, default=os.environ.get('SM_MODEL_DIR'))\n \n return parser.parse_known_args()\n\ndef hvd_init():\n # 第一步，初始化horovod\n hvd.init()\n # 打印一下size，rank和local_rank\n print('hvd size:{}, hvd rank:{}, hvd local_rank:{}'.format(hvd.size(), hvd.rank(), hvd.local_rank()))\n \n # 第二步，将本地的GPU和horovod绑定，一个GPU对应一个process（local_rank），如果时CPU的话一样的道理\n # 首先获得本地所有的GPU\n gpus = tf.config.experimental.list_physical_devices('GPU')\n for gpu in gpus:\n # 设置每个GPU的占用的显存随时使用而升高，而不是锁死一个固定数值\n tf.config.experimental.set_memory_growth(gpu, True)\n if gpus:\n # 将GPU作为tf可见的设备，也就是horovod可以使用的GPU\n tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')\n \n# cpus = tf.config.experimental.list_physical_devices('CPU')\n# for cpu in cpus:\n# # 设置每个GPU的占用的显存随时使用而升高，而不是锁死一个固定数值\n# tf.config.experimental.set_memory_growth(cpu, True)\n# if gpus:\n# # 将GPU作为tf可见的设备，也就是horovod可以使用的GPU\n# tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'CPU')\n\n \nif __name__ == '__main__':\n # 初始化horovod环境\n hvd_init() \n \n # 获取参数\n args, unknown = parse_args()\n \n # 获取超参数\n epochs = args.epochs\n batch_size = args.batch_size\n learning_rate = args.learning_rate\n \n # 获取原始数据路径\n train_data_path = args.train\n validation_data_path = args.validation\n \n # 获取存储模型的路径\n model_dir = args.model_dir\n \n # 获取可以直接用于训练的datasets\n train_db = get_train_db(train_data_path)\n validation_db = get_validation_db(validation_data_path)\n \n # 获取模型\n model = get_model()\n \n # 开始训练\n train(model, train_db, validation_db, epochs, batch_size, learning_rate, model_dir)","repo_name":"xzy0223/sagemaker-example","sub_path":"Distributed Horovod Training with Tensorflow/horovod_train.py","file_name":"horovod_train.py","file_ext":"py","file_size_in_byte":12703,"program_lang":"python","lang":"zh","doc_type":"code","stars":9,"dataset":"github-code","pt":"31"} +{"seq_id":"40314686265","text":"import json, flask, random, string\nimport collections\n\napp = flask.Flask(__name__)\napp.secret_key = ''.join(random.choice(string.printable) for _ in range(20))\n\n\n\n@app.route('/', methods=['GET'])\ndef home():\n flask.session['seen_films'] = json.dumps([])\n return flask.render_template('main_rec_page.html')\n\n\n@app.route('/recommend_movie')\ndef recommend_movie():\n vals = json.loads(flask.request.args.get('vals'))\n with open('movie_data.json') as f:\n all_movies = json.load(f)\n \n #print([i for i in vals if any(c not in i for c in all_movies)])\n _category, _ = max([[i, sum(c.get(i, 0) for c in vals)/float(len(vals))] for i in all_movies], key=lambda x:x[-1])\n _film = random.choice(all_movies[_category])\n while _film['title'] in json.loads(flask.session['seen_films']) and (lambda x:_film['title'] not in [a for a,b in x.items() if b == min(x.values())])(collections.Counter(json.loads(flask.session['seen_films']))):\n _film = random.choice(all_movies['_category'])\n \n flask.session['seen_films'] = json.dumps(json.loads(flask.session['seen_films'])+[_film])\n print('final film', _film)\n return flask.jsonify({\"success\":\"True\", **_film})\nif __name__ == '__main__':\n app.debug = True\n app.run()","repo_name":"Ajax12345/facial_recognition","sub_path":"movie_rec_main.py","file_name":"movie_rec_main.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"15615442514","text":"import cv2 as cv\nimport os\nimport numpy as np\nimport imutils\n\ndef flattening(imgPath, destination):\n\n pi = 3.141592\n img = cv.imread(imgPath)\n # cv.imshow(\"source\", img)\n \n width, height, c = img.shape\n print(width,height)\n x = int(width/2 + width/5)\n y = int(height/4)\n r = int(width/2.5)\n \n # add rect on the letters\n image = cv.rectangle(img, (90,20), (height-110, 70), (255, 255, 255), -1)\n color = (255, 255, 255)\n image = cv.circle(img, (x,y), r, color, -1)\n \n # can be removed by replacing the letters rectangle with area merged color pixels\n # lettersHeight = int(height/9)\n # cropedLetters = img[lettersHeight:width, 0:height]\n \n # cv.imshow(\"before\", image)\n \n # only for the case of letters above as above\n # cv.imshow(\"croped\", image)\n # cv.imwrite(f\"picture analyse/{imgPath}\", cropedLetters)\n \n firstRotate = imutils.rotate_bound(image, 90)\n width, height, _ = firstRotate.shape\n\n squareImg = np.zeros((width,width,3),np.uint8)\n squareImg[0:firstRotate.shape[0],0:firstRotate.shape[1]]=firstRotate\n startAngle = imutils.rotate(squareImg, -15)\n # cv.imshow(\"start rotation\", squareImg)\n \n trapezHeight = 110\n SQRwidth, SQRheight, _ = squareImg.shape\n centerX = round(SQRwidth/2)\n topY = round(SQRheight- trapezHeight)\n topXstart = round(centerX - 60)\n topXend = round(centerX + 60)\n downXstart = round(centerX - 100)\n downXend = round(centerX + 100)\n reqXstart = topXstart - round((topXstart - downXstart)/2)\n reqXend = topXend + round((downXend - topXend)/2)\n reqXSize = reqXend - reqXstart\n \n # loop over the rotation angles\n for angle in np.arange(0, 220, 15):\n rotated = imutils.rotate(startAngle, angle)\n scaledSlices = np.zeros((trapezHeight,reqXSize,3),np.uint8)\n for y in range(topY, SQRheight,2):\n currentXStart = round(topXstart-(y-topY)/2)\n halfX = centerX-currentXStart\n currentXEnd = centerX + halfX\n # cv.rectangle(rotated, (currentXStart,y), (currentXEnd,y+2),(0,0,0))\n # cv.imshow(\"Rotated\", rotated)\n # cv.waitKey(0)\n sliced = rotated[y:y+2,currentXStart:currentXEnd]\n scaled = cv.resize(sliced, (reqXSize,2), interpolation = cv.INTER_AREA)\n # cv.imshow(\"scaled\", scaled)\n # cv.waitKey(0)\n scaledSlices[y-topY:y-topY+2,0:reqXSize]= scaled\n # cv.imshow(\"scaledSlices\", scaledSlices)\n # cv.waitKey(0)\n \n if angle != 0: \n currentOutWidth += reqXSize\n temp = np.zeros((trapezHeight,currentOutWidth, 3),np.uint8)\n temp[0:trapezHeight,0:reqXSize]= scaledSlices\n temp[0:trapezHeight,reqXSize:currentOutWidth]=outputTrack\n outputTrack = temp\n else:\n outputTrack = np.zeros((trapezHeight, reqXSize, 3),np.uint8)\n outputTrack[0:trapezHeight,0:reqXSize]= scaledSlices\n currentOutWidth = reqXSize\n \n #cv.imshow(\"outputTrack\", outputTrack)\n #cv.waitKey(0)\n \n filename = os.path.splitext(os.path.basename(imgPath))[0] \n cv.imwrite(f\"{destination}/{filename}.jpg\",outputTrack)\n return trapezHeight\n\n \n ","repo_name":"rachelistone/imageFlaten","sub_path":"modules/imageFlattening.py","file_name":"imageFlattening.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"27963333551","text":"import numpy as np\r\nfrom scipy.stats import norm\r\nimport math\r\n\r\ndef getStability(X):\r\n M,d=X.shape\r\n hatPF=np.mean(X,axis=0)\r\n kbar=np.sum(hatPF)\r\n denom=(kbar/d)*(1-kbar/d)\r\n return 1-(M/(M-1))*np.mean(np.multiply(hatPF,1-hatPF))/denom\r\n\r\ndef getVarianceofStability(X):\r\n stab=getStability(X)\r\n M,d=X.shape\r\n hatPF=np.mean(X,axis=0)\r\n kbar=np.sum(hatPF)\r\n k=np.sum(X,axis=1)\r\n denom=(kbar/d)*(1-kbar/d)\r\n stab=1-(M/(M-1))*np.mean(np.multiply(hatPF,1-hatPF))/denom\r\n phi=np.zeros(M)\r\n for i in range(M):\r\n phi[i]=(1/denom)*(np.mean(np.multiply(X[i,],hatPF))-(k[i]*kbar)/d**2+(stab/2)*((2*k[i]*kbar)/d**2-k[i]/d-kbar/d+1))\r\n phiAv=np.mean(phi)\r\n variance=(4/M**2)*np.sum(np.power(phi-phiAv,2))\r\n return {'stability':stab,'variance':variance}\r\n\r\ndef confidenceIntervals(X,alpha=0.05):\r\n res=getVarianceofStability(X)\r\n lower=res['stability']-norm.ppf(1-alpha/2)*math.sqrt(res['variance'])\r\n upper=res['stability']+norm.ppf(1-alpha/2)*math.sqrt(res['variance'])\r\n return {'stability':res['stability'],'lower':lower,'upper':upper}\r\n\r\n## this tests whether the true stability is equal to a given value stab0\r\ndef hypothesisTestV(X,stab0,alpha):\r\n res=getVarianceofStability(X)\r\n V=(res['stability']-stab0)/math.sqrt(res['variance'])\r\n zCrit=norm.ppf(1-alpha)\r\n if V>=zCrit: reject=True\r\n else: reject=False\r\n pValue=1-norm.cdf(V)\r\n return {'reject':reject,'V':V,'p-value':pValue}\r\n\r\n# this tests the equality of the stability of two algorithms\r\ndef hypothesisTestT(X1,X2,alpha):\r\n res1=getVarianceofStability(X1)\r\n res2=getVarianceofStability(X2)\r\n stab1=res1['stability']\r\n stab2=res2['stability']\r\n var1=res1['variance']\r\n var2=res2['variance']\r\n T=(stab2-stab1)/math.sqrt(var1+var2)\r\n zCrit=norm.ppf(1-alpha/2) \r\n ## the cumulative inverse of the gaussian at 1-alpha/2\r\n if(abs(T)>=zCrit):\r\n reject=True\r\n #print('Reject H0: the two algorithms have different population stabilities')\r\n else:\r\n reject=False\r\n #print('Do not reject H0')\r\n pValue=2*(1-norm.cdf(abs(T)))\r\n return {'reject':reject,'T':T,'p-value':pValue}","repo_name":"mutual-ai/JMLR2017","sub_path":"python/stability.py","file_name":"stability.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"70145712728","text":"import requests\nimport re\nfrom urllib.request import urlopen\nimport json\n\nfrom ..utils.auth import get_api_key\nfrom ..utils.edm_utils import cursor_search\n\n\ndef search(**kwargs):\n\n \"\"\"\n Search method of the IIIF API [1]. Allows to search newspapers by their text content\n\n >>> import pyeuropeana.apis as apis\n >>> resp = apis.iiif.search(\n >>> query = 'Paris',\n >>> profile = 'hits',\n >>> )\n\n Args:\n query (:obj:`str`)\n The term to search\n profile (:obj:`str`)\n If profile is 'hits' the mentions in the transcribed text where the search keyword was found will be displayed\n\n Returns :obj:`dict`\n Response\n\n References:\n 1. https://pro.europeana.eu/page/iiif\n \"\"\"\n\n params = {\n \"wskey\": get_api_key(),\n \"query\": kwargs.get(\"query\", \"*\"),\n \"qf\": kwargs.get(\"qf\"),\n \"reusability\": kwargs.get(\"reusability\"),\n \"media\": kwargs.get(\"media\"),\n \"thumbnail\": kwargs.get(\"thumbnail\"),\n \"landingpage\": kwargs.get(\"landingpage\"),\n \"colourpalette\": kwargs.get(\"colourpalette\"),\n \"theme\": kwargs.get(\"theme\"),\n \"sort\": kwargs.get(\"sort\", \"europeana_id\"),\n \"profile\": kwargs.get(\"profile\"),\n \"rows\": kwargs.get(\"rows\", 12),\n \"cursor\": kwargs.get(\"cursor\", \"*\"),\n \"callback\": kwargs.get(\"callback\"),\n \"facet\": kwargs.get(\"facet\"),\n }\n\n endpoint = \"https://newspapers.eanadev.org/api/v2/search.json\"\n\n if not kwargs:\n raise ValueError(\"No arguments passed\")\n\n # Necessary for handling facets of the type 'PROVIDER&f.PROVIDER.facet.limit=30&f.PROVIDER.facet.offset=10'\n _params = params.copy()\n\n if _params[\"profile\"] and \"hits\" in _params[\"profile\"]:\n hits_list = _params[\"profile\"].split(\"&\")\n if len(hits_list) > 1:\n _params.update({\"profile\": hits_list[0]})\n _params.update(\n {item.split(\"=\")[0]: item.split(\"=\")[1] for item in hits_list[1:]}\n )\n\n response = requests.get(endpoint, params=_params)\n url = response.url\n\n response = cursor_search(endpoint, _params)\n response.update({\"url\": url, \"parms\": params})\n return response\n\n\ndef manifest(RECORD_ID):\n \"\"\"\n\n Manifest method of the IIIF API [1]. Returns a minimal set of metadata for an object\n\n >>> import pyeuropeana.apis as apis\n >>> resp = apis.iiif.manifest('/9200356/BibliographicResource_3000118390149')\n\n Args:\n record_id (:obj:`str`)\n The identifier of the record which is composed of the dataset identifier \\\\\n plus a local identifier within the dataset in the form of \"/DATASET_ID/LOCAL_ID\", for more detail see Europeana ID [2]\n\n Returns :obj:`dict`\n Response\n\n References:\n 1. https://pro.europeana.eu/page/iiif\n \"\"\"\n wskey = get_api_key()\n europeana_id = re.findall(\"/\\w*/\\w*\", RECORD_ID)\n if not europeana_id:\n raise ValueError(\"Not valid RECORD_ID\")\n url = f\"https://iiif.europeana.eu/presentation{RECORD_ID}/manifest?wskey={wskey}\"\n with urlopen(url) as response:\n body = response.read()\n return json.loads(body)\n\n\ndef annopage(**kwargs):\n \"\"\"\n Annopage method of the IIIF API [1]. Returns text and annotations for a given page of an object\n\n >>> import pyeuropeana.apis as apis\n >>> resp = apis.iiif.annopage(\n >>> RECORD_ID = '/9200356/BibliographicResource_3000118390149',\n >>> PAGE_ID = 1,\n >>> )\n\n Args:\n RECORD_ID (:obj:`str`)\n The identifier of the record which is composed of the dataset identifier \\\\\n plus a local identifier within the dataset in the form of \"/DATASET_ID/LOCAL_ID\", for more detail see Europeana ID [2]\n PAGE_ID (:obj:`int`)\n The number of the page in logical sequence starting with 1 for the first page.\n There can be pages that do not contain any text which will mean that the request will return a HTTP 404.\n\n Returns :obj:`dict`\n Response\n\n References:\n 1. https://pro.europeana.eu/page/iiif\n \"\"\"\n wskey = get_api_key()\n RECORD_ID = kwargs.get(\"RECORD_ID\")\n PAGE_ID = kwargs.get(\"PAGE_ID\")\n if not kwargs:\n raise ValueError(\"No arguments passed\")\n europeana_id = re.findall(\"/\\w*/\\w*\", RECORD_ID)\n if not europeana_id:\n raise ValueError(\"Not valid RECORD_ID\")\n if not isinstance(PAGE_ID, int):\n raise ValueError(\"PAGE_ID must be an int\")\n\n url = f\"https://iiif.europeana.eu/presentation{RECORD_ID}/annopage/{PAGE_ID}?wskey={wskey}\"\n with urlopen(url) as response:\n body = response.read()\n return json.loads(body)\n\n\ndef fulltext(**kwargs):\n \"\"\"\n Fulltext method of the IIIF API [1]. Returns the transciption of a single page of a newspaper\n\n >>> import pyeuropeana.apis as apis\n >>> resp = apis.iiif.fulltext(\n >>> RECORD_ID = '/9200356/BibliographicResource_3000118390149',\n >>> FULLTEXT_ID = '',\n >>> )\n\n Args:\n RECORD_ID (:obj:`str`)\n The identifier of the record which is composed of the dataset identifier \\\\\n plus a local identifier within the dataset in the form of \"/DATASET_ID/LOCAL_ID\", for more detail see Europeana ID [2]\n FULLTEXT_ID (:obj:`str`)\n The identifier of the full text resource.\n\n Returns :obj:`dict`\n Response\n\n References:\n 1. https://pro.europeana.eu/page/iiif\n \"\"\"\n wskey = get_api_key()\n RECORD_ID = kwargs.get(\"RECORD_ID\")\n FULLTEXT_ID = kwargs.get(\"FULLTEXT_ID\")\n if not kwargs:\n raise ValueError(\"No arguments passed\")\n europeana_id = re.findall(\"/\\w*/\\w*\", RECORD_ID)\n if not europeana_id:\n raise ValueError(\"Not valid RECORD_ID\")\n return requests.get(\n f\"https://www.europeana.eu/api/fulltext{RECORD_ID}/{FULLTEXT_ID}\",\n params={\"wskey\": wskey},\n ).json()\n","repo_name":"europeana/rd-europeana-python-api","sub_path":"src/pyeuropeana/apis/iiif.py","file_name":"iiif.py","file_ext":"py","file_size_in_byte":5750,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"72039664728","text":"def isoperator(char):\r\n\tif char=='+' or char=='-' or char=='*' or char=='/' or char=='^':\r\n\t\treturn True\r\n\treturn False\r\ndef inorder(root):\r\n\tif root==None:\r\n\t\treturn\r\n\tinorder(root.left)\r\n\tprint(root.data,end=\" \")\r\n\tinorder(root.right)\r\ndef constructtree(postfix):\r\n\tstack=[]\r\n\tfor char in postfix:\r\n\t\tif not isoperator(char):\r\n\t\t\tt=Node(char)\r\n\t\t\tstack.append(t)\r\n\t\telse:\r\n\t\t\tt=Node(char)\r\n\t\t\tt1=stack.pop()\r\n\t\t\tt2=stack.pop()\r\n\t\t\tt.right=t1\r\n\t\t\tt.left=t2\r\n\t\t\tstack.append(t)\r\n\tt=stack.pop()\r\n\treturn t\r\n\r\nclass Node:\r\n\tdef __init__(self,data):\r\n\t\tself.left=None\r\n\t\tself.right=None\r\n\t\tself.data=data\r\n\r\npostfix=\"ab+ef*g*-\"\r\nroot=constructtree(postfix)\r\nprint(\"your inorder is \")\r\ninorder(root)","repo_name":"Avhishek05/python-codes","sub_path":"tree/expression_tree.py","file_name":"expression_tree.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71661327767","text":"from typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport gym\nimport jax\nimport numpy as np\nfrom stable_baselines3.common.noise import ActionNoise\n\nfrom offline_baselines_jax.common.buffers import ReplayBuffer\nfrom offline_baselines_jax.common.off_policy_algorithm import OffPolicyAlgorithm\nfrom offline_baselines_jax.common.type_aliases import GymEnv, MaybeCallback, Schedule, Params\nfrom offline_baselines_jax.td3.policies import TD3Policy\nfrom .core import update_td3\n\n\nclass TD3(OffPolicyAlgorithm):\n\n def __init__(\n self,\n env: Union[GymEnv, str],\n policy: Union[str, Type[TD3Policy]] = TD3Policy,\n learning_rate: Union[float, Schedule] = 1e-3,\n buffer_size: int = 1_000_000, # 1e6\n learning_starts: int = 100,\n batch_size: int = 100,\n tau: float = 0.005,\n gamma: float = 0.99,\n train_freq: Union[int, Tuple[int, str]] = (1, 'episode'),\n gradient_steps: int = -1,\n action_noise: Optional[ActionNoise] = None,\n replay_buffer_class: Optional[ReplayBuffer] = None,\n replay_buffer_kwargs: Optional[Dict[str, Any]] = None,\n optimize_memory_usage: bool = False,\n policy_delay: int = 2,\n target_policy_noise: float = 0.2,\n target_noise_clip: float = 0.5,\n tensorboard_log: Optional[str] = None,\n create_eval_env: bool = False,\n policy_kwargs: Optional[Dict[str, Any]] = None,\n verbose: int = 0,\n seed: int = 0,\n alpha: int = 2.5,\n _init_setup_model: bool = True,\n without_exploration: bool = False,\n ):\n\n super(TD3, self).__init__(\n policy,\n env,\n learning_rate,\n buffer_size,\n learning_starts,\n batch_size,\n tau,\n gamma,\n train_freq,\n gradient_steps,\n action_noise=action_noise,\n replay_buffer_class=replay_buffer_class,\n replay_buffer_kwargs=replay_buffer_kwargs,\n policy_kwargs=policy_kwargs,\n tensorboard_log=tensorboard_log,\n verbose=verbose,\n create_eval_env=create_eval_env,\n seed=seed,\n optimize_memory_usage=optimize_memory_usage,\n supported_action_spaces=(gym.spaces.Box),\n support_multi_env=True,\n without_exploration=without_exploration,\n )\n if without_exploration and gradient_steps == -1:\n self.gradient_steps = policy_delay\n\n self.alpha = alpha\n self.policy_delay = policy_delay\n self.target_noise_clip = target_noise_clip\n self.target_policy_noise = target_policy_noise\n\n if _init_setup_model:\n self._setup_model()\n\n def _setup_model(self) -> None:\n super(TD3, self)._setup_model()\n self._create_aliases()\n\n def _create_aliases(self) -> None:\n self.actor = self.policy.actor\n self.actor_target = self.policy.actor_target\n self.critic = self.policy.critic\n self.critic_target = self.policy.critic_target\n\n def train(self, gradient_steps: int, batch_size: int = 100) -> None:\n actor_losses, critic_losses, coef_lambda = [], [], []\n for _ in range(gradient_steps):\n self._n_updates += 1\n # Sample replay buffer\n replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)\n self.rng, key = jax.random.split(self.rng, 2)\n\n actor_update_cond = (self._n_updates % self.policy_delay == 0)\n self.rng, new_models, info = \\\n update_td3(\n key,\n actor=self.actor,\n actor_target=self.actor_target,\n critic=self.critic,\n critic_target=self.critic_target,\n\n observations=replay_data.observations,\n actions=replay_data.actions,\n next_observations=replay_data.next_observations,\n rewards=replay_data.rewards,\n dones=replay_data.dones,\n\n actor_update_cond=actor_update_cond,\n tau=self.tau,\n target_policy_noise=self.target_policy_noise,\n target_noise_clip=self.target_noise_clip,\n gamma=self.gamma,\n alpha=self.alpha,\n without_exploration=self.without_exploration\n )\n\n self.apply_new_models(new_models)\n self.actor_target = new_models[\"actor_target\"]\n self.policy.actor_target = new_models[\"actor_target\"]\n\n actor_losses.append(info['actor_loss'])\n critic_losses.append(info['critic_loss'])\n coef_lambda.append(info['coef_lambda'])\n\n\n self.logger.record(\"train/n_updates\", self._n_updates, exclude=\"tensorboard\")\n if len(actor_losses) > 0:\n self.logger.record(\"train/actor_loss\", np.mean(actor_losses))\n self.logger.record(\"train/critic_loss\", np.mean(critic_losses))\n self.logger.record(\"train/coef\", np.mean(coef_lambda))\n\n def learn(\n self,\n total_timesteps: int,\n callback: MaybeCallback = None,\n log_interval: int = 4,\n eval_env: Optional[GymEnv] = None,\n eval_freq: int = -1,\n n_eval_episodes: int = 5,\n tb_log_name: str = \"TD3\",\n eval_log_path: Optional[str] = None,\n reset_num_timesteps: bool = True,\n ) -> OffPolicyAlgorithm:\n\n return super(TD3, self).learn(\n total_timesteps=total_timesteps,\n callback=callback,\n log_interval=log_interval,\n eval_env=eval_env,\n eval_freq=eval_freq,\n n_eval_episodes=n_eval_episodes,\n tb_log_name=tb_log_name,\n eval_log_path=eval_log_path,\n reset_num_timesteps=reset_num_timesteps,\n )\n\n def _excluded_save_params(self) -> List[str]:\n return super(TD3, self)._excluded_save_params() + [\"actor\", \"critic\", \"actor_target\", \"critic_target\"]\n\n def _get_jax_save_params(self) -> Dict[str, Params]:\n params_dict = {}\n params_dict['actor'] = self.actor.params\n params_dict['critic'] = self.critic.params\n params_dict['critic_target'] = self.critic_target.params\n params_dict['actor_target'] = self.actor_target.params\n return params_dict\n\n def _get_jax_load_params(self) -> List[str]:\n return ['actor', 'critic', 'critic_target', 'actor_target']\n\n def _load_policy(self) -> None:\n super(TD3, self)._load_policy()\n self.policy.actor_target = self.actor_target\n","repo_name":"jsw7460/sb3_jax","sub_path":"offline_baselines_jax/td3/td3.py","file_name":"td3.py","file_ext":"py","file_size_in_byte":6693,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"31"} +{"seq_id":"42228728950","text":"\"\"\"\nTwo ordered trees T' and T'' are said to be isomorphic if one of the following\nholds:\n• Both T' and T'' are empty.\n• The roots of T' and T'' have the same number k ≥ 0 of subtrees, and\nthe ith such subtree of T' is isomorphic to the ith such subtree of T''\nfor i = 1, . . . ,k.\nDesign an algorithm that tests whether two given ordered trees are isomorphic.\nWhat is the running time of your algorithm?\n\n#my first attempt \n\n\nGood?\n\n\n\"\"\"\n\n\n# MY FIRST ATTEMPT --------------------------------\n# Worst case: they are almost identical and have the same number of nodes:\ndef is_isomorphic(a, b):\n if a is b is None:\n return True\n\n if a is None or b is None:\n return False\n\n # can i sort them by name?\n if len(a.children()) == len(b.children()):\n for a_child in a:\n found = False\n for b_child in b:\n if is_isomorphic(a_child, b_child) and is_isomorphic(b_child, a_child):\n found = True\n break\n if found is False:\n # Cannot find pair for this child\n return False\n return True\n\n# i dropped this idea because i didnt knew how to calculate complexity ;ppp\n\n# ------------------------------------------------------\n\n\n# Worst case: The number of nodes in the larger tree ;/\n# my problem is that i cannot get an idea how to at the same time compere 2 trees on the same level.\n# and esentially to\n\ndef _do_sum(node, this_level, common_levels):\n # increase level array if needed\n if len(common_levels) <= this_level:\n common_levels.append([])\n\n # sum this node\n common_levels[this_level] += 1\n\n # is a leaf, drop!\n if node.children is None:\n return 0\n\n for child in node.children:\n common_levels[this_level] += _do_sum(child, this_level, common_levels)\n\n\ndef is_isomorphic2(TreeA, TreeB):\n # Im a genius? Its only O(N) ok im not\n sum_A = []\n sum_B = []\n _do_sum(TreeA, 0, sum_A)\n _do_sum(TreeB, 0, sum_B)\n return sum_A == sum_B\n","repo_name":"Boberkraft/Data-Structures-and-Algorithms-in-Python","sub_path":"chapter 8/C-8.35 HALFSOLVED.py","file_name":"C-8.35 HALFSOLVED.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10118104807","text":"from django.shortcuts import render\nimport random\nfrom django.shortcuts import redirect\nfrom django.views import View\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth import login, logout\nfrom car_rental_APP.models import Car, District, Orders, User\nfrom car_rental_APP.forms import (\n LoginForm,\n CarAddForm,\n CustomerRegistrationForm,\n CarDealerRegistrationForm,\n CarSearchForm,\n)\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n\n\nUser = get_user_model()\n\n\n# Create your views here.\n\n\nclass IndexView(View):\n \"\"\"Main site view\"\"\"\n def get(self, request):\n all_cars = list(Car.objects.all())\n random.shuffle(all_cars)\n return render(request, \"index.html\", {\"car_1\": all_cars[0],\n \"car_2\": all_cars[1],\n \"car_3\": all_cars[2]},)\n\n\nclass CarListView(View):\n \"\"\"List of cars view\"\"\"\n def get(self, request):\n\n list_of_cars = Car.objects.all()\n\n paginator = Paginator(list_of_cars, 20)\n page = request.GET.get(\"page\")\n cars = paginator.get_page(page)\n list_of_cars = [\n i for i in range(1, cars.paginator.num_pages + 1)]\n ctx = {\"cars\": cars, \"list_of_cars\": list_of_cars}\n return render(request, \"car_list_view.html\", ctx)\n\n\nclass CarDetailedView(View):\n \"\"\"Detailed view one of chosen car\"\"\"\n def get(selfself, request, car_id):\n car = Car.objects.get(id=car_id)\n return render(request, \"car_detailed_view.html\", {\"car\": car})\n\n\nclass RegisterView(View):\n \"\"\"New user registration - main page.\"\"\"\n \"\"\"It is possible to choose type of user to register \"\"\"\n def get(self, request):\n return render(request, \"register.html\")\n\n\nclass CarDealerRegistrationView(View):\n \"\"\"Car dealer registration view\"\"\"\n def get(self, request):\n form = CarDealerRegistrationForm()\n return render(\n request, \"car_dealer_registration_form.html\", {\"form\": form})\n\n def post(self, request):\n form = CarDealerRegistrationForm(request.POST)\n if form.is_valid():\n login = form.cleaned_data[\"login\"]\n password = form.cleaned_data[\"password\"]\n name = form.cleaned_data[\"name\"]\n surname = form.cleaned_data[\"surname\"]\n email = form.cleaned_data[\"email\"]\n User.objects.create_user(\n username=login,\n email=email,\n password=password,\n first_name=name,\n last_name=surname,\n is_car_dealer=True)\n return redirect(\"/login/\")\n else:\n return render(request, \"car_dealer_registration_form.html\", {\"form\": form})\n\n\nclass CustomerRegistrationView(View):\n \"\"\"Car dealer registration view\"\"\"\n def get(self, request):\n form = CustomerRegistrationForm()\n return render(\n request, \"customer_registration_form.html\", {\"form\": form})\n\n def post(self, request):\n form = CustomerRegistrationForm(request.POST)\n if form.is_valid():\n login = form.cleaned_data[\"login\"]\n password = form.cleaned_data[\"password\"]\n name = form.cleaned_data[\"name\"]\n surname = form.cleaned_data[\"surname\"]\n email = form.cleaned_data[\"email\"]\n User.objects.create_user(\n username=login,\n email=email,\n password=password,\n first_name=name,\n last_name=surname,\n is_customer=True)\n return redirect(\"/login/\")\n else:\n return render(\n request, \"customer_registration_form.html\", {\"form\": form})\n\n\nclass LoginView(View):\n \"\"\"User login page. Redirection to locked customer or car dealer portals\"\"\"\n def get(self, request):\n form = LoginForm()\n return render(request, \"login.html\", {\"form\": form})\n\n def post(self, request):\n form = LoginForm(request.POST)\n if form.is_valid():\n user = authenticate(\n login=form.cleaned_data[\"login\"],\n password=form.cleaned_data[\"password\"],)\n\n if user is not None:\n if user.is_car_dealer == True:\n login(request, user)\n return redirect('/car_dealer_portal')\n elif user.is_customer == True:\n login(request, user)\n return redirect('/customer_portal')\n else:\n return redirect('/login')\n return render(request, \"login.html\", {'form': form})\n\n\n #if user is not None:\n # if user.is_customer == True:\n # return redirect('/customer_portal')\n # elif user.is_car_dealer == True:\n # return redirect('/car_dealer_portal')\n\n\nclass LogoutView(View):\n \"\"\"Logout view. Back to home page\"\"\"\n def get(self, request):\n logout(request)\n return redirect(\"/\")\n\n\n# === CAR_DEALER_PORTAL ===#\n\n\nclass CarDealerPortalView(LoginRequiredMixin, View):\n \"\"\"Car dealer portal's page- main view. Possibility to go to detailed pages\"\"\"\n \"\"\"Login required\"\"\"\n login_url = \"/login/\"\n\n def get(self, request):\n return render(request, \"car_dealer_portal_view.html\")\n\n\nclass CarAddView(LoginRequiredMixin, View):\n \"\"\"New car adding form view\"\"\"\n login_url = \"/login/\"\n\n def get(self, request):\n form = CarAddForm()\n return render(request, \"add_car.html\", {\"form\": form})\n\n def post(self, request):\n form = CarAddForm(request.POST)\n if form.is_valid():\n car_name = form.cleaned_data[\"car_name\"]\n color = form.cleaned_data[\"color\"]\n dealer = form.cleaned_data[\"dealer\"]\n district = form.cleaned_data[\"district\"]\n capacity = form.cleaned_data[\"capacity\"]\n is_available = form.cleaned_data[\"is_available\"]\n engine = form.cleaned_data[\"engine\"]\n ac = form.cleaned_data[\"ac\"]\n description = form.cleaned_data[\"description\"]\n car = Car.objects.create( # tworzymy nowy samochód\n car_name=car_name,\n color=color,\n dealer=dealer,\n district=district,\n capacity=capacity,\n is_available=is_available,\n engine=engine,\n ac=ac,\n description=description,\n )\n return redirect(\n f\"/car_detailed_view/{car.id}/\"\n ) # przekierowanie linku\n else:\n return render(\n request, \"/car_dealer_portal/add_car/\", {\"form\": form}\n )\n\n\nclass CarsView(LoginRequiredMixin, View):\n \"\"\"Customer portal's page- main view. Possibility to go to detailed pages \"\"\"\n \"\"\"LLogin required\"\"\"\n login_url = \"/login/\"\n\n def get(self, request):\n car_list = []\n cars = Car.objects.filter()\n for c in cars:\n car_list.append(c)\n return render(request, \"car_list.html\", {\"car_list\": car_list})\n\n\nclass CarOrderView(LoginRequiredMixin, View):\n \"\"\"List of ordered cars\"\"\"\n login_url = \"/login/\"\n\n def get(self, request):\n orders = Orders.objects.filter()\n order_list = []\n for ord in orders:\n if ord.is_complete == False:\n order_list.append(ord)\n return render(request, \"order_list.html\", {\"order_list\": order_list})\n\n\n# === CUSTOMER_PORTAL ===#\n\nclass CustomerPortalView(LoginRequiredMixin, View):\n \"\"\"Customer portal's page- main view. Possibility to go to detailed pages\"\"\"\n \"\"\"Login required\"\"\"\n login_url = \"/login/\"\n\n def get(self, request):\n return render(request, \"customer_portal_view.html\")\n\n\nclass CarSearchView(LoginRequiredMixin, View):\n \"\"\"Searching by district pagr\"\"\"\n login_url = \"/login/\"\n\n def get(self, request):\n form = CarSearchForm()\n return render(request, \"search_car.html\", {\"form\": form})\n\n def post(self, request):\n form = CarSearchForm(request.POST)\n if form.is_valid():\n districts = District.objects.filter(\n district_name__icontains=form.cleaned_data[\"district_name\"]\n )\n return render(\n request,\n \"search_car.html\",\n {\"form\": form, \"districts\": districts},\n )\n\n\n# class CarSearchResultsView(LoginRequiredMixin, View):\n#\n# login_url = '/login/'\n#\n# def get(self, request):\n# form = CarSearchResultsForm()\n# return render(request, 'search_results.html', {'form': form})pip\n#\n# def post(self, request):\n# form = CarSearchResultsForm(request.POST)\n# if form.is_valid():\n# district_name = request.POST['district_name']\n# district_name = district_name.lower()\n# cars_list = []\n# district = District.objects.filter(district_name=district_name)\n# for d in district:\n# cars = Car.objects.filter(district=d)\n# for car_av in cars:\n# if car_av.is_available == True:\n# car_dictionary = {'name': car_av.car_name, 'color': car_av.color, 'id': car_av.id,\n# 'pincode': car_av.district.pincode, 'capacity': car_av.capacity,\n# 'engine': car_av.engine, 'description': car_av.description,\n# 'dealer': car_av.dealer}\n# cars_list.append(car_dictionary)\n# request.session['cars_list'] = cars_list\n# return render(request, 'search_results.html', {'car_dictionary': car_dictionary})\n# else:\n# return render(request, 'search_results.html')\n\n","repo_name":"Monika-itds/PROJECT_Rent_a_car","sub_path":"car_rental_APP/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15254148614","text":"class Array2D:\n def __init__(self):\n self.length = 0\n self.data = []\n\n def search(self, row, col):\n return self.data[row][col]\n\n def traverse(self):\n for i in self.data:\n for j in self.data[i]:\n print(i, j)\n\n def insert(self, row, item):\n while self.length <= row:\n self.data.append([])\n self.length += 1\n self.data[row].append(item)\n return self.data\n\n\nnewArray = Array2D()\nnewArray.insert(3,1)\nnewArray.insert(5,2)\nprint(newArray.data)\n","repo_name":"bobliu96/Data_Structure_and_Algorithm","sub_path":"Array/2D_array_implementation.py","file_name":"2D_array_implementation.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7376801207","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n__author__ = 'Min Zhou'\n__copyright__ = 'Copyright 2018, API mini-project'\n__email__ = 'minzhou@bu.edu'\n\n\"\"\"\nThis is script downloads images from a twitter feed, \nconvert them to a video and describe the content of the images in the video.\n\"\"\"\n\nfrom ffmpeg_module import image_to_video\nfrom twitter_module import get_media_url_from_tweets, download_images\nfrom google_vision_module import analyze_video_labels, detect_image_labels\n\nimport time\nimport argparse\nimport os\nimport sys\n\n\ndef main():\n\n # download images from Twitter\n print('Please enter your Twitter API credentials.')\n try:\n twitter_account = input('Please enter the Twitter Account:(ex:@NatGeoPhotos)')\n media_files = get_media_url_from_tweets(twitter_account)\n except:\n print('\\nTwitter API auth failed, please check your credentials.')\n time.sleep(1)\n try:\n download_images(media_files)\n except:\n print('\\nFailed to download images, please delete the /images folder and try again.')\n sys.exit(1)\n # analyze the label of the images and video\n current_path = os.getcwd()\n\n # Google vision API\n print('\\nImage label detection using Google vision API:')\n try:\n detect_image_labels(current_path + '/images')\n except:\n print('Google vision API failed, please export the JSON file and try again.')\n\n # convert to video using ffmpeg\n print('\\nConverting to video...')\n image_folder_path = current_path + '/images'\n video_name = 'result.mp4'\n try:\n image_to_video(video_name, image_folder_path)\n except:\n print('Failed to convert to video, please try again.')\n # Google video intelligence API\n print('\\nVideo label analysis using Google vision API:')\n try:\n analyze_video_labels(current_path + '/images/result.mp4')\n except:\n print('Google video intelligence API failed, please export the JSON file and try again.')\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n","repo_name":"minzhou1003/API-miniproject","sub_path":"api_mini_project.py","file_name":"api_mini_project.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6201736582","text":"mahsulotlar = ['non', 'shakar', 'qand', 'olma', 'gugurt','pechenya', 'shapka', 'papka', 'shim', \"ko'ylak\", 'malibu2']\n\nsavat = []\nbor_mahsulotlar = []\nmavjud_emas = []\n\nn = int(input(\"Tahminan nechta mahsulot harid qilmoqchisiz? >>>> \"))\nwhile n < 5:\n n = int(input(\" \\nMahsulotlar soni 5 dankam bo'lmasin. \\n Qayta kiriting. Tahminan nechta mahsulot harid qilmoqchisiz? >>>> \"))\n\nfor i in range(n):\n b = savat.append(input(f\"{i+1} - mahsulotni kiriting. \"))\n\nif savat:\n for savatdagi in savat:\n if savatdagi in mahsulotlar:\n print(f\" {savatdagi} do'konimizda bor. \")\n bor_mahsulotlar.append(savatdagi)\n else:\n print(f\" {savatdagi} do'konimizda yo'q. \")\n mavjud_emas.append(savatdagi)\n \nif len(mavjud_emas) ==0:\n print(\" \\nSiz so'ragan barcha mahsulot do'konimizda bor. \")\nelse:\n print(\"\\n Quydagi mahsulotlar do'konimizda yo'q: \")\n for yuqlari in mavjud_emas:\n print(yuqlari)\n\n\n\ninput()\n","repo_name":"RuzimovJavlonbek/anvar.nazrullayevning-mohirdev.uz-platformasidagi-dasturlash.asoslari.python-kursidagi-amaliyotlar","sub_path":"sariq_dev/darslar/11_dars_uy_ishi_5_.py","file_name":"11_dars_uy_ishi_5_.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"uz","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"18520037351","text":"import sqlite3\nimport sys\nimport time\nfrom datetime import datetime\nfrom functools import wraps\n\nfrom bitarray import bitarray\nfrom pybloom_live import BloomFilter\nimport warnings\n\nimport redis\nimport pandas as pd\nimport ast\nimport dask.dataframe as dd\nfrom dask import delayed\nimport cProfile\n\nfrom dask.tests.test_system import psutil\nfrom dateutil.relativedelta import relativedelta\nfrom dask.diagnostics import visualize\n\nwarnings.filterwarnings(\"ignore\", category=Warning)\n\n# Function to calculate the optimal number of partitions\ndef calculate_partitions():\n # Get the available system resources\n cpu_cores = psutil.cpu_count(logical=False)\n memory = psutil.virtual_memory().total\n\n # Get the size of your dataset (replace with your actual dataset size)\n dataset_size = 1000000 # Example dataset size\n\n # Calculate the desired partition size based on system resources\n partition_size = 100000 # Example desired partition size\n\n # Calculate the optimal number of partitions\n num_partitions = min(cpu_cores, max(1, dataset_size // partition_size))\n\n return num_partitions\n\n\ndef udf_reformat_to_iso(string: str):\n splits = string.replace(' ', '').split(',')\n\n if len(splits) < 6:\n splits += ['00' for _ in range(0, 6 - len(splits))]\n\n year, month, day, hour, minute, second = splits[0], splits[1], splits[2], splits[3], splits[4], splits[5]\n\n if len(month) != 2:\n month = '0' + month\n\n if len(day) != 2:\n day = '0' + day\n\n if len(hour) != 2:\n hour = '0' + hour\n\n if len(minute) != 2:\n minute = '0' + minute\n\n if len(second) != 2:\n second = '0' + second\n\n return f\"{year}-{month}-{day}T{hour}:{minute}:{second}\"\n\n\ndef redis_to_pandas(data) -> pd.DataFrame:\n df = pd.DataFrame().from_dict(data, orient=\"index\", columns=['raw_data'])\n df.sort_index(inplace=True)\n index_df = df.index\n\n # Convert the string to a dictionary while preserving the datetime object\n df = pd.DataFrame(df[\"raw_data\"].apply(\n lambda x: ast.literal_eval(x.replace('datetime.datetime', '').replace(\"(\", '\"').replace(\")\", '\"'))).tolist())\n df.index = index_df\n\n df[\"timestamp\"] = df[\"timestamp\"].apply(udf_reformat_to_iso)\n df['timestamp'] = pd.to_datetime(df['timestamp'])\n df.reset_index(drop=False, inplace=True, names='counter')\n return df\n\n\ndef redis_to_pandas(data) -> pd.DataFrame:\n index_df = list(data.keys())\n values = [ast.literal_eval(x.replace('datetime.datetime', '').replace(\"(\", '\"').replace(\")\", '\"'))\n for x in data.values()]\n\n df = pd.DataFrame(values, index=index_df)\n df[\"timestamp\"] = df[\"timestamp\"].apply(udf_reformat_to_iso)\n df['timestamp'] = pd.to_datetime(df['timestamp'])\n df.reset_index(drop=False, inplace=True, names='counter')\n\n return df\n\n\ndef sql_to_pandas(data) -> pd.DataFrame:\n df = pd.DataFrame(data, columns=['counter', 'user_id', 'timestamp'])\n df['user_id'] = df['user_id'].astype('int64')\n df['timestamp'] = pd.to_datetime(df['timestamp'])\n return df\n\n\ndef timeit(func):\n @wraps(func)\n def timeit_wrapper(*args, **kwargs):\n start_time = time.perf_counter()\n result = func(*args, **kwargs)\n end_time = time.perf_counter()\n total_time = end_time - start_time\n print(f'Function {func.__name__} Took {total_time:.4f} seconds')\n\n return result\n\n return timeit_wrapper\n\n\nclass CustomJoinPipelines:\n\n def __init__(self):\n self.capacity = 0\n\n @delayed\n def perform_join(self, block1, block2, join_key):\n # Perform the join operation\n join_result = dd.merge(block1, block2, on=join_key, how='inner')\n return join_result\n\n @timeit\n def normal_join(self, df1, df2, join_key):\n # Assuming df1 and df2 are Pandas DataFrames\n timestamp_constraint = datetime.now() - relativedelta(years=2)\n\n # Apply the timestamp constraint and select columns\n filtered_df1 = df1[df1['timestamp'] >= timestamp_constraint][['user_id', 'timestamp']]\n filtered_df2 = df2[df2['timestamp'] >= timestamp_constraint][['user_id', 'timestamp']]\n\n # Perform the join operation\n final_result = filtered_df1.merge(filtered_df2, on='user_id', how='inner')\n\n return final_result\n\n @timeit\n def pipelined_hash_join(self, df1, df2, join_key, npartitions):\n\n df1 = dd.from_pandas(df1, npartitions=npartitions)\n df2 = dd.from_pandas(df2, npartitions=npartitions)\n\n df1['hash_value'] = df1['user_id'].apply(lambda x: x % npartitions)\n df2['hash_value'] = df2['user_id'].apply(lambda x: x % npartitions)\n\n # Set \"hash_value\" column as the index\n df1 = df1.set_index('hash_value')\n df2 = df2.set_index('hash_value')\n\n timestamp_constraint = datetime.now() - relativedelta(years=2)\n\n # Iterate over the blocks of data\n for block1, block2 in zip(df1.partitions, df2.partitions):\n # Perform the join operation\n merged_data = self.perform_join(\n block1[block1['timestamp'] >= timestamp_constraint][['user_id', 'timestamp']],\n block2[block2['timestamp'] >= timestamp_constraint][['user_id', 'timestamp']],\n join_key\n )\n # Print the merged data\n merged_data.compute()\n\n\n @timeit\n def semi_join(self, df1, df2, join_key, npartitions):\n\n df1 = dd.from_pandas(df1, npartitions=npartitions)\n df2 = dd.from_pandas(df2, npartitions=npartitions)\n\n timestamp_constraint = datetime.now() - relativedelta(years=2)\n\n df1 = df1.reset_index(drop=True)\n df1 = df1.drop(columns='counter')\n\n df2 = df2.reset_index(drop=True)\n df2 = df2.drop(columns='counter')\n\n # Apply the timestamp constraint and select columns\n df1 = df1[df1['timestamp'] >= timestamp_constraint][['user_id', 'timestamp']]\n df2 = df2[df2['timestamp'] >= timestamp_constraint][['user_id', 'timestamp']]\n\n df1 = df1.set_index(join_key).repartition(npartitions=npartitions)\n df2 = df2.set_index(join_key).repartition(npartitions=npartitions)\n\n # Iterate over the blocks of data\n for block1, block2 in zip(df1.partitions, df2.partitions):\n # Perform the join operation\n merged_data = dd.merge(\n block1[block1['timestamp'] >= timestamp_constraint],\n block2[block2['timestamp'] >= timestamp_constraint],\n left_index=True, right_index=True, how='inner'\n )\n # Print the merged data\n merged_data.compute()\n\n def create_bloom_filter(self, partition):\n bloom_filter = BloomFilter(capacity=self.capacity, error_rate=0.1)\n partition.loc[:, 'user_id'] = partition['user_id'].astype(\"string\")\n partition['user_id'].apply(bloom_filter.add)\n return bloom_filter\n\n def merge_bloom_filters(self, bloom_filters):\n bit_arrays = [pd.Series(bloomf.bitarray) for bloomf in bloom_filters.compute()]\n\n # Perform union using a loop\n union_bit_array = bit_arrays[0]\n for bit_array in bit_arrays[1:]:\n union_bit_array |= bit_array\n\n final_bloom_filter = BloomFilter(capacity=self.capacity, error_rate=0.1)\n final_bloom_filter.bitarray = bitarray(union_bit_array.astype(bool).tolist())\n\n return final_bloom_filter\n\n @timeit\n def intersection_bloom_filter_join(self, df1, df2, join_key, npartitions):\n start = time.time()\n\n df1[join_key] = df1[join_key].astype('string')\n df2[join_key] = df1[join_key].astype('string')\n\n df1 = dd.from_pandas(df1, npartitions=npartitions)\n df2 = dd.from_pandas(df2, npartitions=npartitions)\n\n self.capacity = max([df1['user_id'].compute().unique().shape[0], df2['user_id'].compute().unique().shape[0]])\n\n bloom_filter1 = df1.map_partitions(self.create_bloom_filter, meta=pd.DataFrame(columns=df1.columns))\n bloom_filter2 = df2.map_partitions(self.create_bloom_filter, meta=pd.DataFrame(columns=df2.columns))\n\n merged_bloom_fitlers = self.merge_bloom_filters(bloom_filter1).intersection(\n self.merge_bloom_filters(bloom_filter2))\n\n print(f\"total time to build the filter {time.time() - start}\")\n\n timestamp_constraint = datetime.now() - relativedelta(years=2)\n\n df1 = \\\n df1[(df1[join_key].apply(lambda x: x in merged_bloom_fitlers)) & (df1['timestamp'] >= timestamp_constraint)][\n [join_key, 'timestamp']]\n df2 = \\\n df2[(df2[join_key].apply(lambda x: x in merged_bloom_fitlers)) & (df2['timestamp'] >= timestamp_constraint)][\n [join_key, 'timestamp']]\n\n # Iterate over the blocks of data\n for block1, block2 in zip(df1.partitions, df2.partitions):\n # Perform the join operation\n merged_data = dd.merge(\n block1[block1['timestamp'] >= timestamp_constraint],\n block2[block2['timestamp'] >= timestamp_constraint],\n left_index=True, right_index=True, how='inner'\n )\n # Print the merged data\n merged_data.compute()\n\n\n\nif __name__ == '__main__':\n\n # Check if command-line arguments are provided\n\n if not len(sys.argv) > 0:\n print(\"No arguments provided.\")\n sys.exit(1)\n\n dataset_name, join_method = sys.argv[1:]\n\n\n\n conn = sqlite3.connect('data1/mydatabase.db')\n cursor = conn.cursor()\n\n r = redis.Redis(host='localhost', port=6379, decode_responses=True)\n redis_data = r.hgetall(dataset_name)\n\n cursor.execute(f\"SELECT * FROM {dataset_name}\")\n sqlite_data = cursor.fetchall()\n\n redis_df = redis_to_pandas(redis_data)\n\n sql_df = sql_to_pandas(sqlite_data)\n\n pipeLineObj = CustomJoinPipelines()\n\n if join_method == \"hash_join\":\n pipeLineObj.pipelined_hash_join(df1=redis_df, df2=sql_df, join_key='user_id', npartitions=100)\n\n elif join_method == \"semi_join\":\n pipeLineObj.semi_join(df1=redis_df, df2=sql_df, join_key='user_id', npartitions=100)\n\n elif join_method == \"bloom_join\":\n pipeLineObj.intersection_bloom_filter_join(df1=redis_df, df2=sql_df, join_key='user_id', npartitions=100)\n\n else:\n print(\"Invalid join method specified.\")\n sys.exit(1)\n\n\n # # Create a profile object\n # profile = cProfile.Profile()\n #\n # # Start profiling\n # profile.enable()\n #\n # # Call the function or code you want to profile\n # myfunc()\n #\n # # Stop profiling\n # profile.disable()\n #\n # # Print the profiling results\n # profile.print_stats()\n","repo_name":"kostasrazgkelis/DDPassigment","sub_path":"join_methods.py","file_name":"join_methods.py","file_ext":"py","file_size_in_byte":10640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18162393782","text":"from dotenv import dotenv_values\n\n\nenv = dotenv_values('.env')\n\nCONNECTION_CONFIG = {\n 'host': env['HOST'],\n 'password': env['PASSWORD'],\n 'raise_on_warnings': True,\n 'user': env['USER'],\n}\n\nDB = 'online_restaurant_management'\n\nDB_CREATE_QUERY = 'CREATE DATABASE IF NOT EXISTS {db_name} DEFAULT CHARACTER SET \"UTF8MB4\"'.format(\n db_name=DB)\n\nDB_DROP_QUERY = 'DROP DATABASE IF EXISTS {db_name}'.format(db_name=DB)\n\nDB_CLIENT = 'Client'\n\nDB_CLIENT_BIRTHDATE_COLUMN = 'birthdate'\nDB_CLIENT_CPF_COLUMN = 'cpf'\nDB_CLIENT_EMAIL_COLUMN = 'email'\nDB_CLIENT_GENDER_COLUMN = 'gender'\nDB_CLIENT_NAME_COLUMN = 'name'\nDB_CLIENT_PASSWORD_COLUMN = 'password'\n\nDB_CLIENT_COLUMNS = (DB_CLIENT_CPF_COLUMN,\n DB_CLIENT_NAME_COLUMN,\n DB_CLIENT_BIRTHDATE_COLUMN,\n DB_CLIENT_EMAIL_COLUMN,\n DB_CLIENT_PASSWORD_COLUMN, DB_CLIENT_GENDER_COLUMN)\n\n\nDB_CLIENT_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} CHAR(11) NOT NULL,\n {1} VARCHAR(45) NOT NULL,\n {2} DATE NOT NULL,\n {3} VARCHAR(45) NOT NULL,\n {4} CHAR(6) NOT NULL,\n {5} VARCHAR(45) NULL DEFAULT NULL,\n PRIMARY KEY ({0}))\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_CLIENT, *DB_CLIENT_COLUMNS)\n\nDB_ADDRESS = 'Address'\n\nDB_ADDRESS_ID_COLUMN = 'addressId'\nDB_ADDRESS_CLIENT_ID_COLUMN = 'clientId'\nDB_ADDRESS_ADDRESS_COLUMN = 'address'\nDB_ADDRESS_ALIAS_COLUMN = 'alias'\n\nDB_ADDRESS_COLUMNS = (DB_ADDRESS_ID_COLUMN,\n DB_ADDRESS_CLIENT_ID_COLUMN, DB_ADDRESS_ADDRESS_COLUMN, DB_ADDRESS_ALIAS_COLUMN)\n\nDB_ADDRESS_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL AUTO_INCREMENT,\n {1} CHAR(11) NOT NULL,\n {2} VARCHAR(120) NOT NULL,\n {3} VARCHAR(45) NULL,\n PRIMARY KEY (`addressId`),\n INDEX `fk_clientId_addressId_idx` ({1} ASC) VISIBLE,\n CONSTRAINT `fk_clientId_addressId`\n FOREIGN KEY ({1})\n REFERENCES {db_name}.{fk_table} ({fk_column})\n ON DELETE NO ACTION\n ON UPDATE NO ACTION)\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_ADDRESS, fk_table=DB_CLIENT, fk_column=DB_CLIENT_CPF_COLUMN, *DB_ADDRESS_COLUMNS)\n\n\nDB_MEAL = 'Meal'\n\nDB_MEAL_ID_COLUMN = 'mealId'\nDB_MEAL_NAME_COLUMN = 'name'\nDB_MEAL_SELLER_PRICE_COLUMN = 'sellerPrice'\nDB_MEAL_INSTRUCTIONS_COLUMN = 'instructions'\nDB_MEAL_AREA_COLUMN = 'area'\nDB_MEAL_CATEGORY_COLUMN = 'category'\nDB_MEAL_IMAGE_URL_COLUMN = 'imageUrl'\n\n\nDB_MEAL_COLUMNS = (DB_MEAL_ID_COLUMN, DB_MEAL_NAME_COLUMN, DB_MEAL_SELLER_PRICE_COLUMN, DB_MEAL_INSTRUCTIONS_COLUMN, DB_MEAL_AREA_COLUMN,\n DB_MEAL_CATEGORY_COLUMN, DB_MEAL_IMAGE_URL_COLUMN)\n\n\nDB_MEAL_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL,\n {1} VARCHAR(45) NOT NULL,\n {2} FLOAT NOT NULL,\n {3} VARCHAR(4000) NULL DEFAULT NULL,\n {4} VARCHAR(45) NULL DEFAULT NULL,\n {5} VARCHAR(45) NULL DEFAULT NULL,\n {6} VARCHAR(120) NULL DEFAULT NULL,\n PRIMARY KEY ({0}))\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_MEAL, *DB_MEAL_COLUMNS)\n\n\nDB_MEAL_INSERT_QUERY = 'INSERT INTO {db_name}.{table_name} (mealId, name, sellerPrice, instructions, area, category, imageUrl) '.format(\n db_name=DB, table_name=DB_MEAL) + '''VALUES (%s, %s, %s, %s, %s, %s, %s)'''\n\nDB_MEAL_STOCK = 'MealStock'\n\nDB_MEAL_STOCK_CURRENT_QNT_COLUMN = 'currentQuantity'\nDB_MEAL_STOCK_LOWEST_QNT_COLUMN = 'lowestQuantity'\nDB_MEAL_STOCK_COST_PRICE_COLUMN = 'costPrice'\n\nDB_MEAL_STOCK_COLUMNS = (DB_MEAL_ID_COLUMN, DB_MEAL_STOCK_CURRENT_QNT_COLUMN,\n DB_MEAL_STOCK_LOWEST_QNT_COLUMN, DB_MEAL_STOCK_COST_PRICE_COLUMN)\n\nDB_MEAL_STOCK_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL,\n {1} INT NOT NULL,\n {2} INT NOT NULL,\n {3} FLOAT NOT NULL,\n PRIMARY KEY ({0}),\n CONSTRAINT `fk_mealId_stock`\n FOREIGN KEY ({0})\n REFERENCES {db_name}.{fk_table} ({0})\n ON DELETE NO ACTION\n ON UPDATE NO ACTION)\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_MEAL_STOCK, fk_table=DB_MEAL, *DB_MEAL_STOCK_COLUMNS)\n\nDB_MEAL_STOCK_INSERT_QUERY = 'INSERT INTO {db_name}.{table_name} (mealId, currentQuantity, lowestQuantity, costPrice) '.format(\n db_name=DB, table_name=DB_MEAL_STOCK) + '''VALUES (%s, %s, %s, %s)'''\n\nDB_MEAL_STOCK_AFTER_UPDATE_TRIGGER = ''' CREATE DEFINER = CURRENT_USER TRIGGER `puc_database_restaurant`.`create_stock_order` AFTER UPDATE ON `meal_stock` FOR EACH ROW\nBEGIN\n\t-- Verificação (SELECT) da quantidade de estoque (meal_stock).\n\t-- Se a currentQuantity for menor que a idealQuantity (meal_stock) criar (INSERT) uma stock_order.\nEND '''\n\nDB_ORDER = 'Order'\n\nDB_ORDER_ID_COLUMN = 'orderId'\nDB_ORDER_CLIENT_ID_COLUMN = 'clientId'\nDB_ORDER_ADDRESS_ID_COLUMN = 'addressId'\nDB_ORDER_DATE_COLUMN = 'date'\nDB_ORDER_TOTAL_PRICE_COLUMN = 'totalPrice'\nDB_ORDER_NOTE_COLUMN = 'note'\n\nDB_ORDER_COLUMNS = (DB_ORDER_ID_COLUMN,\n DB_ORDER_CLIENT_ID_COLUMN, DB_ORDER_ADDRESS_ID_COLUMN, DB_ORDER_DATE_COLUMN, DB_ORDER_TOTAL_PRICE_COLUMN, DB_ORDER_NOTE_COLUMN)\n\nDB_ORDER_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL AUTO_INCREMENT,\n {1} CHAR(11) NOT NULL,\n {2} INT NOT NULL,\n {3} DATETIME NOT NULL,\n {4} FLOAT NOT NULL,\n {5} VARCHAR(120) NULL,\n PRIMARY KEY ({0}),\n INDEX `client_cpf_index` ({1} ASC) VISIBLE,\n INDEX `fk_addressId_orderId_idx` ({2} ASC) VISIBLE,\n CONSTRAINT `fk_clientCpf_orderId`\n FOREIGN KEY ({1})\n REFERENCES {db_name}.{fk_table_1} ({fk_column_1}),\n CONSTRAINT `fk_addressId_orderId`\n FOREIGN KEY ({2})\n REFERENCES {db_name}.{fk_table_2} ({fk_column_2})\n ON DELETE NO ACTION\n ON UPDATE NO ACTION)\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_ORDER,\n fk_table_1=DB_CLIENT,\n fk_column_1=DB_CLIENT_CPF_COLUMN,\n fk_table_2=DB_ADDRESS,\n fk_column_2=DB_ADDRESS_ID_COLUMN,\n *DB_ORDER_COLUMNS)\n\nDB_ORDER_BEFORE_INSERT_TRIGGER = '''CREATE DEFINER = CURRENT_USER TRIGGER {db_name}.`check_meal_availability` BEFORE INSERT ON {table_name} FOR EACH ROW\nBEGIN\n SELECT {DB_MEAL_STOCK_CURRENT_QNT_COLUMN}, {DB_MEAL_STOCK_LOWEST_QNT_COLUMN} FROM {db_name}.{DB_MEAL_STOCK} WHERE {DB_MEAL_ID_COLUMN} = {mealId}\n\tIF ({DB_MEAL_STOCK_CURRENT_QNT_COLUMN} >= {DB_MEAL_STOCK_LOWEST_QNT_COLUMN})\n\t\tCONTINUE\n ELSE\n\t\tROLLBACK\n\n\t-- Verificação da quantidade de refeições disponíveis.\n -- Se a quantidade pedida da refeição estiver disponível, encerra a transaction e continua e inserção.\n -- Se a quantidade pedida da refeição não estiver disponível, \n -- If not currentQuantity >= quantity: ROLLBACK\nEND'''\n\nDB_ORDER_AFTER_INSERT_TRIGGER = '''CREATE DEFINER = CURRENT_USER TRIGGER {db_name}.`create_order_meal_and_create_order_transaction` AFTER INSERT ON `order` FOR EACH ROW\nBEGIN\n\t-- Criação (INSERT) de uma transação de entrada de dinheiro com a propriedade code sendo orderId (transaction). \n -- Registro (INSERT) dos pratos inclusos no pedido (order_meal).\nEND'''\n\n\nDB_ORDER_MEAL = 'OrderMeal'\n\nDB_ORDER_MEAL_ID_COLUMN = 'orderMealId'\nDB_ORDER_MEAL_QUANTITY_COLUMN = 'quantity'\nDB_ORDER_TOTAL_PRICE_COLUMN = 'totalPrice'\n\nDB_ORDER_MEAL_COLUMNS = (DB_ORDER_MEAL_ID_COLUMN,\n DB_ORDER_ID_COLUMN, DB_MEAL_ID_COLUMN, DB_ORDER_MEAL_QUANTITY_COLUMN, DB_ORDER_TOTAL_PRICE_COLUMN)\n\n\nDB_ORDER_MEAL_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL AUTO_INCREMENT,\n {1} INT NOT NULL,\n {2} INT NOT NULL,\n {3} INT NOT NULL DEFAULT '1',\n {4} FLOAT NOT NULL,\n INDEX `orderId_index` ({0} ASC) VISIBLE,\n INDEX `mealId_index` ({1} ASC) VISIBLE,\n PRIMARY KEY ({0}, {1}),\n CONSTRAINT `fk_orderId_mealId`\n FOREIGN KEY ({1})\n REFERENCES {db_name}.{fk_table_1} ({1})\n ON DELETE NO ACTION\n ON UPDATE NO ACTION,\n CONSTRAINT `fk_mealId_orderId`\n FOREIGN KEY ({2})\n REFERENCES {db_name}.{fk_table_2} ({2})\n ON DELETE NO ACTION\n ON UPDATE NO ACTION)\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_ORDER_MEAL, fk_table_1=DB_ORDER, fk_table_2=DB_MEAL, *DB_ORDER_MEAL_COLUMNS)\n\n\nDB_ORDER_MEAL_AFTER_INSERT_TRIGGER = ''' CREATE DEFINER = CURRENT_USER TRIGGER `puc_database_restaurant`.`decress_meal_stock` AFTER INSERT ON `order_meal` FOR EACH ROW\nBEGIN\n -- Diminuição (UPDATE) da quantidade de refeiçõs no estoque (meal_stock).\nEND '''\n\nDB_STOCK_ORDER = 'StockOrder'\n\nDB_STOCK_ORDER_ID_COLUMN = 'stockOrderId'\nDB_STOCK_ORDER_TOTAL_PRICE_COLUMN = 'totalPrice'\nDB_STOCK_ORDER_DATE_COLUMN = 'date'\nDB_STOCK_ORDER_QUANTITY_COLUMN = 'quantity'\n\nDB_STOCK_ORDER_COLUMNS = (DB_STOCK_ORDER_ID_COLUMN, DB_MEAL_ID_COLUMN,\n DB_STOCK_ORDER_TOTAL_PRICE_COLUMN, DB_STOCK_ORDER_DATE_COLUMN, DB_STOCK_ORDER_QUANTITY_COLUMN)\n\n\nDB_STOCK_ORDER_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL AUTO_INCREMENT,\n {1} INT NOT NULL,\n {2} FLOAT NOT NULL,\n {3} DATETIME NOT NULL,\n {4} INT NOT NULL,\n PRIMARY KEY ({0}),\n INDEX `mealId_index` ({1} ASC) VISIBLE,\n CONSTRAINT `fk_mealId_stockOrderId`\n FOREIGN KEY ({1})\n REFERENCES {db_name}.{fk_table} ({1})\n ON DELETE NO ACTION\n ON UPDATE NO ACTION)\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_STOCK_ORDER, fk_table=DB_MEAL_STOCK, *DB_STOCK_ORDER_COLUMNS)\n\nDB_STOCK_ORDER_AFTER_INSERT_TRIGGER = ''' CREATE DEFINER = CURRENT_USER TRIGGER `puc_database_restaurant`.`create_stock_order_transaction_and_update_meal_stock` AFTER INSERT ON `stock_order` FOR EACH ROW\nBEGIN\n\t-- Criação (INSERT) de uma transação de saída de dinheiro com a propriedade code sendo orderId (transaction). \n -- Aumenta (UPDATE) a quantidade de refeições (currentQuantity) no estoque (meal_stock).\nEND '''\n\n\nDB_TRANSACTION = 'Transaction'\n\nDB_TRANSACTION_ID_COLUMN = 'transactionId'\nDB_TRANSACTION_VALUE_COLUMN = 'value'\nDB_TRANSACTION_DATE_COLUMN = 'date'\nDB_TRANSACTION_NAME_COLUMN = 'name'\nDB_TRANSACTION_CODE_COLUMN = 'code'\n\nDB_TRANSACTION_COLUMNS = (DB_TRANSACTION_ID_COLUMN,\n DB_TRANSACTION_VALUE_COLUMN,\n DB_TRANSACTION_DATE_COLUMN,\n DB_TRANSACTION_NAME_COLUMN,\n DB_TRANSACTION_CODE_COLUMN)\n\n\nDB_TRANSACTION_CREATE_QUERY = '''CREATE TABLE IF NOT EXISTS {db_name}.{table_name}(\n {0} INT NOT NULL AUTO_INCREMENT,\n {1} FLOAT NOT NULL,\n {2} DATETIME NOT NULL,\n {3} VARCHAR(45) NOT NULL,\n {4} INT NULL DEFAULT NULL,\n PRIMARY KEY ({0}),\n INDEX `code_idx` ({4} ASC) VISIBLE,\n CONSTRAINT `fk_stockOrderId_code`\n FOREIGN KEY ({4})\n REFERENCES {db_name}.{fk_table_1} ({fk_column_1}),\n CONSTRAINT `fk_orderId_code`\n FOREIGN KEY ({4})\n REFERENCES {db_name}.{fk_table_2} ({fk_column_2}))\n ENGINE = InnoDB'''.format(db_name=DB, table_name=DB_TRANSACTION, fk_table_1=DB_STOCK_ORDER, fk_column_1=DB_STOCK_ORDER_ID_COLUMN, fk_table_2=DB_ORDER, fk_column_2=DB_ORDER_ID_COLUMN, *DB_TRANSACTION_COLUMNS)\n\n\nDB_TRANSACTION_INSERT_QUERY = 'INSERT INTO {db_name}.{table_name} (value, name, date, code) '.format(\n db_name=DB, table_name=DB_TRANSACTION) + '''VALUES (%s, %s, %s, %s)'''\n","repo_name":"andyboli/puc-projeto-02","sub_path":"model/database/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":11446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71195507928","text":"case = int(input())\nfor _ in range(case):\n stack = []\n check = True\n paran= input()\n for par in paran:\n if par == '(':\n stack.append('(')\n else:\n if not stack:\n check = False\n else:\n stack.pop()\n if stack or not check:\n print(\"NO\")\n else:\n print(\"YES\")\n","repo_name":"Goathoon/CodingTest","sub_path":"python/Stack_Queue/백준_괄호_9012.py","file_name":"백준_괄호_9012.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11771074750","text":"import sys\nimport logging\nimport colorful as cf\n\nfrom ipdf.constants import PALETTE, LINE\n\n\ncf.use_style('solarized')\n\n\ndef color_args_str(args, title=None, type='info'):\n s = cf.blue(f'\\n{LINE}\\n')\n if title:\n with cf.with_palette(PALETTE) as c:\n if type == 'error':\n s += f'{c.bold_error(title)}\\n'\n elif type == 'success':\n s += f'{c.bold_success(title)}\\n'\n else:\n s += f'{c.bold_info(title)}\\n'\n for k in args:\n s += f'{cf.bold_violet(k)}: '\n s += f'{args[k]}\\n'\n s += cf.blue(f'{LINE}\\n')\n return s\n\n\ndef init_logger():\n handlers = []\n formatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n stream_handler = logging.StreamHandler(sys.stderr)\n stream_handler.setFormatter(formatter)\n stream_handler.setLevel(logging.INFO)\n handlers.append(stream_handler)\n\n logging.basicConfig(level=logging.DEBUG, handlers=handlers)\n","repo_name":"dmytrotkk/ipdf","sub_path":"ipdf/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"34780043721","text":"import io\r\n\r\nfrom . import constants\r\nfrom .enums import Alignment\r\nimport PIL.Image\r\nimport PIL.ImageDraw\r\nimport PTS\r\n\r\n\r\ndef calculatePositionAlign(textBoxCornerXY, textBoxWH, textWH, alignment : Alignment):\r\n \"\"\"\r\n Uses the position of the top right corner of the text box, the width and\r\n height of the text box, and the width and height of the text box to align\r\n the text with the specified alignment method.\r\n \"\"\"\r\n alignment = Alignment.convert(alignment)\r\n if alignment == Alignment.TOP_LEFT:\r\n return textBoxCornerXY\r\n elif alignment == Alignment.TOP_CENTER:\r\n return calculatePositionHorizontalCenter(textBoxCornerXY[0], textBoxCornerXY[1], textBoxWH[0], textWH[0])\r\n elif alignment == Alignment.TOP_RIGHT:\r\n return (textBoxCornerXY[0] + textBoxWH[0] - textWH[0], textBoxCornerXY[1])\r\n elif alignment == Alignment.RIGHT_CENTER:\r\n return (textBoxCornerXY[0] + textBoxWH[0] - textWH[0], textBoxCornerXY[1] + (textBoxWH[1]/2) - (textWH[1]/2))\r\n elif alignment == Alignment.BOTTOM_RIGHT:\r\n return (textBoxCornerXY[0] + textBoxWH[0] - textWH[0], textBoxCornerXY[1] + textBoxWH[1] - textWH[1])\r\n elif alignment == Alignment.BOTTOM_CENTER:\r\n return (textBoxCornerXY[0] + ((textBoxWH[0] - textWH[0]) / 2), textBoxCornerXY[1] + textBoxWH[1] - textWH[1])\r\n elif alignment == Alignment.BOTTOM_LEFT:\r\n return (textBoxCornerXY[0], textBoxCornerXY[1] + textBoxWH[1] - textWH[1])\r\n elif alignment == Alignment.LEFT_CENTER:\r\n return calculatePositionVerticalCenter(textBoxCornerXY[0], textBoxCornerXY[1], textBoxWH[1], textWH[1])\r\n elif alignment == Alignment.CENTER_CENTER:\r\n return calculatePositionFullCenter(textBoxCornerXY[0], textBoxCornerXY[1], textBoxWH[0], textWH[0], textBoxWH[1], textWH[1])\r\n\r\ndef calculatePositionFullCenter(textBoxCornerX, textBoxCornerY, textBoxWidth, textWidth, textBoxHeight, textHeight):\r\n return (textBoxCornerX + ((textBoxWidth - textWidth) / 2), textBoxCornerY + ((textBoxHeight - textHeight) / 2))\r\n\r\ndef calculatePositionHorizontalCenter(textBoxCornerX, textBoxCornerY, textBoxWidth, textWidth):\r\n return (textBoxCornerX + ((textBoxWidth - textWidth) / 2), textBoxCornerY)\r\n\r\ndef calculatePositionVerticalCenter(textBoxCornerX, textBoxCornerY, textBoxHeight, textHeight):\r\n return (textBoxCornerX, textBoxCornerY + (textBoxHeight / 2) - (textHeight / 2))\r\n\r\ndef getPilData(image):\r\n \"\"\"\r\n Returns the png image data from a PIL image.\r\n \"\"\"\r\n bio = io.BytesIO()\r\n image.save(bio, 'PNG')\r\n while bio.tell() != 0:\r\n bio.seek(0)\r\n return bio.read()\r\n\r\ndef singleTextBox(text, color, font, basePath, corner, size, center = constants.CENTER_CENTER):\r\n \"\"\"\r\n Function for making a meme with a single, unrotated text box.\r\n :param text: The text to insert into the box.\r\n :param color: A PIL compatible color code for the text color.\r\n :param font: A font name that has been loaded into the PTS module.\r\n :param basePath: The path to the base image to be used.\r\n :param corner: The (x, y) position of the top left corner of the text box.\r\n :param size: The (widht, height) size of the text box.\r\n :param center: The method to use for centering the text. Defaults to full\r\n centered.\r\n \"\"\"\r\n # Check that the text is not empty.\r\n if not text:\r\n raise ValueError(':param text: must not be empty.')\r\n\r\n # Prepare the text.\r\n textFinal = PTS.fitText(text, size[0], size[1], font, fast = True)\r\n if textFinal is None:\r\n raise OverflowError('Text is too long to fit in the specified space.')\r\n\r\n # Determine exactly where to put the text.\r\n textSize = textFinal[1].getsize_multiline(textFinal[0])\r\n if center == constants.CENTER_CENTER:\r\n posText = calculatePositionFullCenter(corner[0], corner[1], size[0], textSize[0], size[1], textSize[1])\r\n elif center == constants.CENTER_HORIZONTAL:\r\n posText = calculatePositionHorizontalCenter(corner[0], corner[1], size[0], textSize[0])\r\n elif center == constants.CENTER_VERTICAL:\r\n posText = calculatePositionVerticalCenter(corner[0], corner[1], size[1], textSize[1])\r\n else:\r\n raise ValueError('Unknown center type: {}'.format(center))\r\n\r\n # Load the template image.\r\n with PIL.Image.open(basePath) as im:\r\n draw = PIL.ImageDraw.ImageDraw(im)\r\n\r\n # Place the text in the image.\r\n if center == constants.CENTER_CENTER or cetner == constants.CENTER_HORIZONTAL:\r\n draw.text(posText, textFinal[0], color, textFinal[1], align = 'center')\r\n else:\r\n draw.text(posText, textFinal[0], color, textFinal[1])\r\n\r\n # Save the data and return it as a png image.\r\n return getPilData(im)\r\n\r\ndef topTextBottomText(topText, bottomText, color, topColor, bottomColor, font, topFont, bottomFont, basePath, topTextCorner, topTextSize, bottomTextCorner, bottomTextSize):\r\n \"\"\"\r\n Function used for creating a meme in the \"top text, bottom text\" format.\r\n :param topText: The text to put in the top of the Drake meme.\r\n :param bottomText: The text to put in the bottom of the Drake meme.\r\n :param color: A PIL compatible color code for the text color.\r\n :param topColor: The color to use for the top text. If not\r\n specified, this will default to the value of color.\r\n :param bottomColor: The color to use for the bottom text. If not\r\n specified, this will default to the value of color.\r\n :param font: A font name that has been loaded into the PTS\r\n module.\r\n :param topFont: The name of the font to use for the top text.\r\n :param font: A font name that has been loaded into the PTS\r\n module.\r\n :param basePath: The path to the base image to be used.\r\n :param topTextCorner: The top left corner of the top text box.\r\n :param topTextSize: The size (a width, height tuple) of the top text\r\n box.\r\n :param bottomTextCorner: The top left corner of the bottom text box.\r\n :param bottomTextSize: The size (a width, height tuple) of the bottom text\r\n box.\r\n \"\"\"\r\n # Check that top text and bottom text are not empty\r\n if not topText:\r\n raise ValueError(':param topText: must not be empty.')\r\n if not bottomText:\r\n raise ValueError(':param bottomText: must not be empty.')\r\n\r\n # Set the colors.\r\n topColor = topColor or color\r\n bottomColor = bottomColor or color\r\n\r\n # Set the fonts.\r\n topFont = topFont or font\r\n bottomFont = bottomFont or font\r\n\r\n # Prepare the text.\r\n topTextFinal = PTS.fitText(topText, topTextSize[0], topTextSize[1], topFont, fast = True)\r\n if topTextFinal is None:\r\n raise OverflowError('Top text is too long to fit in the specified space.')\r\n\r\n bottomTextFinal = PTS.fitText(bottomText, bottomTextSize[0], bottomTextSize[1], bottomFont, fast = True)\r\n if bottomTextFinal is None:\r\n raise OverflowError('Top text is too long to fit in the specified space.')\r\n\r\n # Determine exactly where to put the text.\r\n posTop = calculatePositionVerticalCenter(topTextCorner[0], topTextCorner[1], topTextSize[1], topTextFinal[1].getsize_multiline(topTextFinal[0])[1])\r\n posBottom = calculatePositionVerticalCenter(bottomTextCorner[0], bottomTextCorner[1], bottomTextSize[1], bottomTextFinal[1].getsize_multiline(bottomTextFinal[0])[1])\r\n\r\n # Load the template image.\r\n with PIL.Image.open(basePath) as im:\r\n draw = PIL.ImageDraw.ImageDraw(im)\r\n\r\n # Place the text in the image.\r\n draw.text(posTop, topTextFinal[0], topColor, topTextFinal[1])\r\n draw.text(posBottom, bottomTextFinal[0], bottomColor, bottomTextFinal[1])\r\n\r\n # Save the data and return it as a png image.\r\n return getPilData(im)\r\n","repo_name":"TheElementalOfDestruction/py-image-generator","sub_path":"image_generator/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"24802844476","text":"class Solution(object):\n def intersect(self, nums1, nums2):\n \"\"\"\n :type nums1: List[int]\n :type nums2: List[int]\n :rtype: List[int]\n \"\"\"\n if len(nums1) == 0 or len(nums2) == 0:\n return []\n res = []\n if len(nums1) >= len(nums2):\n dic = collections.Counter(nums1)\n for val in nums2:\n if val in dic and dic[val] > 0:\n dic[val] -= 1\n res.append(val)\n else:\n dic = collections.Counter(nums2)\n for val in nums1:\n if val in dic and dic[val] > 0:\n dic[val] -= 1\n res.append(val)\n return res","repo_name":"BohaoLiGithub/Leetcode","sub_path":"350. Intersection of Two Arrays II/350. Intersection of Two Arrays II(AC).py","file_name":"350. Intersection of Two Arrays II(AC).py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28378298332","text":"# Coffee Machine Project\nfrom data import MENU\nfrom data import resources\nfrom data import value_of_coins\n\n\ndef format_report():\n \"\"\"Formats a report and returns it.\"\"\"\n water = resources[\"water\"]\n milk = resources[\"milk\"]\n coffee = resources[\"coffee\"]\n coins = resources[\"coin\"]\n return f\"Water: {water}ml \\nMilk: {milk}ml \\nCoffee: {coffee}g \\nCoins: ${coins}\"\n\n\ndef check_resources(choice, resources, menu):\n \"\"\"Checks if there is enough resources for a customers order\"\"\"\n if choice == \"espresso\" and resources[\"water\"] >= 50 and resources[\"coffee\"] >= 18:\n return 1\n elif choice == \"latte\" and resources[\"water\"] >= 200 and resources[\"milk\"] >= 150 and resources[\"coffee\"] >= 24:\n return 1\n elif choice == \"cappuccino\" and resources[\"water\"] >= 250 and resources[\"milk\"] >= 100 and resources[\n \"coffee\"] >= 24:\n return 1\n else:\n return 0\n\n\ndef coin_checker(value_of_c, total_amount, cost_of_coffee):\n \"\"\"Prompts user to insert money, checks if it's enough and returns change\"\"\"\n for k, c in value_of_coins.items():\n amount_of_coins = int(input(f\"How many {k}s ${c}?: \"))\n total_amount += c * amount_of_coins\n # print(total_amount)\n if total_amount >= cost_of_coffee:\n change_returned = round(total_amount - cost_of_coffee, 2)\n print(f\"Here is ${change_returned} in change\")\n return 1\n else:\n return 0\n\n\ndef adjust_resources(order_ingredients, choice):\n \"\"\"Adjusts resources in the dictionaries\"\"\"\n for item in order_ingredients:\n resources[item] -= order_ingredients[item]\n resources[\"coin\"] += choice[\"cost\"]\n\n\ntotal_coins = 0\n\nis_machine_off = False\nwhile not is_machine_off:\n user_choice = input(\"What would you like? (espresso $1.5/latte $2.5/cappuccino $3): \").lower()\n # If user choice is OFF machine will turn off and end the program.\n if user_choice == \"off\":\n is_machine_off = True\n # If user choice is report, it will call the format_report function and print the report.\n elif user_choice == \"report\":\n print(f\"{format_report()}\")\n else:\n # If user chose one of the drinks and there is enough resources, it will prompt user for payment.\n if check_resources(user_choice, resources, MENU) == 1:\n drink = MENU[user_choice]\n # Processes payment\n if coin_checker(value_of_coins, total_coins, cost_of_coffee=MENU[user_choice]['cost']) == 1:\n # Adjusts resources if payment was successful.\n adjust_resources(drink[\"ingredients\"], MENU[user_choice])\n print(f\"Here is your {user_choice}!\")\n else:\n # If payment was unsuccessful user will be prompted and loop will start over.\n print(\"Sorry, that's not enough money.\")\n else:\n # If there is not enough resources, user will be prompted and loop will start over. \n print(\"There's not enough resources.\")\n","repo_name":"Tw3lly/python-projects","sub_path":"Intermediate-projects/Coffe_machine/coffe_main.py","file_name":"coffe_main.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16943702822","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"EAS_Query class from package eas\n\nThis module incorporates almost without modification the code provided\nby the ESDC Euclid team to store some content in a folder/file in the\nuser VOSpace account.\n\nUsage:\n The sequence of commands to perform a query would be\n 1. Create the VOSpace_Push object\n 2. Call the ``save_to_file`` method to store something in a\n VOSpace folder/file\n\n Please, have a look at the file ``query_and_save_to_vospace.py'' script for\n an example. This example can be executed with::\n\n $ python query_and_save_to_vospace.py\n\n\"\"\"\n\nVERSION = '0.1.3'\n\n__author__ = \"jcgonzalez\" # Refactoring from ESDC Euclid Team code\n__credits__ = [\"S.Nieto\", \"ESDC Euclid Team\"]\n__version__ = VERSION\n__email__ = \"jcgonzalez@sciops.esa.int\"\n__status__ = \"Prototype\" # Prototype | Development | Production\n\n\nfrom time import sleep\nfrom xml.dom.minidom import parseString\n\nimport requests\nimport sys\n\nrequests.packages.urllib3.disable_warnings()\n\ntry:\n import urllib3\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\nexcept:\n from requests.packages.urllib3.exceptions import InsecureRequestWarning\n requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\nclass VOSpace_Handler(object):\n '''\n Main class to encapsulate VOSpace storage functions\n '''\n\n VOSpace_Url = 'https://vospace.esac.esa.int/vospace'\n\n Tx_XML_File = \"\"\"<vos:transfer xmlns:vos=\"http://www.ivoa.net/xml/VOSpace/v2.0\">\n <vos:target>vos://esavo!vospace/{}/{}</vos:target>\n <vos:direction>{}</vos:direction>\n <vos:view uri=\"vos://esavo!vospace/core#fits\"/>\n <vos:protocol uri=\"vos://esavo!vospace/core#httpput\"/>\n </vos:transfer>\"\"\"\n\n def __init__(self):\n \"\"\"Initialize object (class instance) attributes.\"\"\"\n self.vospace_user = \"\"\n self.vospace_pwd = \"\"\n self.vospace_auth_set = False\n\n def set_auth(self, user, pwd):\n \"\"\"Specifies the VOSpace user/passsword credentials to be used.\"\"\"\n self.vospace_user = user\n self.vospace_pwd = pwd\n self.vospace_auth_set = True\n\n def save_to_file(self, folder, file, content, user=None, pwd=None):\n \"\"\"Makes a storage request, followed by the sending the actual data to be\n stored in the desired folder/file. The VOSpace user credentials are needed.\"\"\"\n if user is None or pwd is None:\n if not self.vospace_auth_set:\n print (\"ERROR: VOSpace credentials not provided\")\n sys.exit(1)\n else:\n user = self.vospace_user\n pwd = self.vospace_pwd\n\n #print (\"Saving query results in VOSpace private account for user: \" + user)\n\n transfer_url = VOSpace_Handler.VOSpace_Url + '/servlet/transfers/async?PHASE=RUN'\n end_point = VOSpace_Handler.VOSpace_Url + '/service/data/'\n\n metadataTransfer = 'transfer_push_to_a.xml' # Contains the path where to store the output file\n toupload = file # Name of the file to be uploaded in VOSpace\n\n txData = VOSpace_Handler.Tx_XML_File.format(user, folder, 'pushToVoSpace')\n #print(txData + '\\n');\n files = {'file': (metadataTransfer, txData)}\n\n try:\n upload_request = requests.post(transfer_url, files=files, auth=(user, pwd))\n except requests.exceptions.RequestException as e: # This is the correct syntax\n #print (upload_request.text)\n sys.exit(1)\n else: # 200\n redirection = upload_request.url\n jobid = redirection[redirection.rfind('/') + 1:]\n\n upload_request.close()\n\n while True:\n request = requests.get(redirection, auth=(user, pwd))\n # XML response: parse it to obtain the current status\n data = request.text\n #print (data + '\\n')\n dom = parseString(data)\n phaseElement = dom.getElementsByTagName('uws:phase')[0]\n phaseValueElement = phaseElement.firstChild\n phase = phaseValueElement.toxml()\n #print (\"Status: \" + phase)\n # Check finished\n if phase == 'COMPLETED': break\n if phase == 'ERROR': sys.exit(1)\n # wait and repeat\n sleep(0.3)\n\n # Open XML document using minidom parser\n DOMTree = parseString(data)\n collection = DOMTree.documentElement\n jobId_element = collection.getElementsByTagName('uws:jobId')[0]\n jobId = jobId_element.childNodes[0].data\n\n files = {'file': (toupload, content)}\n\n try:\n upload_post = requests.post(end_point + user + \"/\" + jobId, files=files, auth=(user, pwd))\n except requests.exceptions.RequestException as e: # This is the correct syntax\n #print (upload_post.text)\n sys.exit(1)\n else: # 200\n #print(upload_post.text)\n redirection = upload_post.url\n jobid = redirection[redirection.rfind('/') + 1:]\n #print (\"Job id: \" + jobid)\n result = upload_post.ok\n upload_post.close()\n return result\n\n def save_file(self, folder, file, local_file, user, pwd):\n \"\"\"Makes a storage request, followed by the sending the actual data to be\n stored in the desired folder/file. The VOSpace user credentials are needed.\"\"\"\n with open(local_file, \"rb\") as bin_file:\n # Read the whole file at once\n bin_data = bin_file.read()\n return self.save_to_file(folder, file, bin_data, user, pwd)\n\n def retrieve_from_file(self, folder, file, user=None, pwd=None):\n \"\"\"Makes a retrieval request, followed by the retrieval of the actual data to be\n stored in a local file. The VOSpace user credentials are needed.\"\"\"\n if user is None or pwd is None:\n if not self.vospace_auth_set:\n print (\"ERROR: VOSpace credentials not provided\")\n sys.exit(1)\n else:\n user = self.vospace_user\n pwd = self.vospace_pwd\n\n # URL for Download request\n transfer_url = VOSpace_Handler.VOSpace_Url + '/servlet/transfers/async?PHASE=RUN'\n end_point = VOSpace_Handler.VOSpace_Url + '/service/data/'\n\n metadataTransfer = 'transfer_pull_from_a.xml' # Contains the path where to store the output file\n todownload = file\n\n txData = VOSpace_Handler.Tx_XML_File.format(user, folder, 'pullFromVoSpace')\n #print(txData + '\\n');\n files = {'file': (metadataTransfer, txData)}\n\n # initial negotiation\n try:\n upload_request = requests.post(transfer_url, files=files, auth=(user, pwd), verify=False)\n except requests.exceptions.RequestException as e: # This is the correct syntax\n print (\"PROBLEM UPLOAD: \" + upload_request.text)\n else: # 200\n #print (upload_request.status_code)\n redirection = upload_request.url\n jobid = redirection[redirection.rfind('/') + 1:]\n #print (\"Job id: \" + jobid)\n upload_request.close()\n\n # Check job status till completed phase\n while True:\n request = requests.get(redirection, auth=(user, pwd), verify=False)\n # XML response: parse it to obtain the current status\n data = request.text\n dom = parseString(data)\n phaseElement = dom.getElementsByTagName('uws:phase')[0]\n phaseValueElement = phaseElement.firstChild\n phase = phaseValueElement.toxml()\n #print (\"Status: \" + phase)\n # Check finished\n if phase == 'COMPLETED': break\n if phase == 'ERROR': exit(1)\n # wait and repeat\n sleep(0.3)\n\n # Open XML document using minidom parser\n DOMTree = parseString(data)\n collection = DOMTree.documentElement\n jobId_element = collection.getElementsByTagName('uws:jobId')[0]\n jobId = jobId_element.childNodes[0].data\n\n content = ''\n\n try:\n download = requests.get(end_point + user + \"/\" + jobId, auth=(user, pwd), verify=False)\n except requests.exceptions.RequestException as e: # This is the correct syntax\n print (\"PROBLEM: \" + download.text)\n exit(1)\n else: # 200\n content = download.content\n # with open(todownload, 'wb') as f:\n # f.write(download.content)\n # # print(upload_post.text)\n redirection_upload = download.url\n jobid = redirection_upload[redirection_upload.rfind('/') + 1:]\n #print (\"Job id: \" + jobid)\n download.close()\n\n result = download.ok\n # Asynchronous job to be removed from the jobs queue\n # curl -v -u <user> -X DELETE \"https://localhost:8443/vospace/servlet/transfers/async/<job_Id>\"\n request = requests.delete(redirection, auth=(user, pwd), verify=False)\n #print(request.status_code)\n return content\n\n def retrieve_file(self, folder, file, local_file, user=None, pwd=None):\n \"\"\"Makes a retrieval request, followed by the retrieval of the actual data to be\n stored in a local file. The VOSpace user credentials are needed.\"\"\"\n with open(local_file, \"wb\") as bin_file:\n # Read the whole file at once\n bin_file.write(self.retrieve_from_file(folder, file, user, pwd))\n\n\ndef main():\n \"\"\"Sample usage of the VOSpace_Push class\"\"\"\n pass\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"aardalath/easvospace","sub_path":"vos/vos_handler.py","file_name":"vos_handler.py","file_ext":"py","file_size_in_byte":9693,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"41270400228","text":"import turtle\nscr = turtle.Turtle()\nscr.speed(0)\nscr.hideturtle()\nfor i in range(10):\n for j in range(4):\n scr.forward(100)\n scr.right(90)\n scr.right(36)\n scr.forward(50)\nturtle.mainloop()\n","repo_name":"dbhan2021/python","sub_path":"PRIMM2.py","file_name":"PRIMM2.py","file_ext":"py","file_size_in_byte":208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"41201961248","text":"from sklearn.linear_model import LinearRegression\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn.metrics import mean_absolute_error, max_error\r\nfrom sklearn.preprocessing import StandardScaler\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nie_ae = pd.read_csv(\"/mnt/c/Users/richi/Desktop/QTAIM/NPA/IE_AE_DNN_2.csv\")\r\n# ie_ae = ie_ae[(ie_ae[\"IE_AE\"] < 20)]\r\ny = ie_ae[[\"IE_AE\"]]\r\nie_ae[\"Z/#e\"] = ie_ae[\"Z\"] / ie_ae[\"#e\"]\r\nX = ie_ae[[\"Z/#e\", \"E_IQA_Intra(A)_b/Vee_ab(A,A)\",\r\n \"E_IQA_Intra(A)_a/Vee_ab(A,A)\", \"Ox\", \"N_alpha(A)\", \"N_total(A)\", \"VeeC(A,A)_a\"]]\r\ny = y.to_numpy()\r\nX = X.to_numpy()\r\n\r\n\r\ndef linear_model(X, y, X_t, y_t_v):\r\n model = LinearRegression()\r\n model.fit(X, y)\r\n r_2, y_pre = model.score(X, y), model.predict(X)\r\n mae, max_ = mean_absolute_error(y, y_pre), max_error(y, y_pre)\r\n y_pre_t = model.predict(X_t)\r\n r_2_t = model.score(X_t, y_t_v)\r\n mae_t, max_t = mean_absolute_error(y_t_v, y_pre_t), max_error(y_t_v, y_pre_t)\r\n return [r_2, mae, max_, r_2_t, mae_t, max_t], [y, y_pre, y_t_v, y_pre_t]\r\n\r\n\r\nfold = KFold(n_splits=10, shuffle=True, random_state=19)\r\nscaler = StandardScaler(with_mean=True, with_std=True)\r\nfold_n = 1\r\nmetrics = []\r\npredicted = []\r\nplt.rcParams['font.size'] = '30'\r\n\r\nfor train, validate in fold.split(X, y):\r\n x_tr, x_va, y_tr, y_va = scaler.fit_transform(X[train]), scaler.transform(\r\n X[validate]), y[train], y[validate]\r\n print(\"Evaluando fold #\", fold_n)\r\n me, pre = linear_model(x_tr, y_tr, x_va, y_va)\r\n metrics.append(me), predicted.append(pre)\r\n plt.figure(figsize=(40, 20))\r\n plt.scatter(pre[0], pre[1], s=100, color=\"blue\")\r\n plt.plot(pre[0], pre[0], color=\"red\")\r\n plt.grid(True, \"both\", \"both\")\r\n plt.xlabel(\"Experimental (Hartree)\")\r\n plt.ylabel(\"Predicted (Hartree)\")\r\n plt.title(\"Train: fold \" + str(fold_n) + \" R2: \" +\r\n f\"{me[0]:.3f}\" + \" MAE: \" + f\"{me[1]:.3f}\" + \" Max: \" + f\"{me[2]:.3f}\")\r\n plt.savefig(\"/mnt/c/Users/richi/Desktop/QTAIM/NPA/true_pre/Ml/\" +\r\n str(fold_n) + \"_train.jpg\")\r\n plt.close()\r\n plt.figure(figsize=(40, 20))\r\n plt.scatter(pre[2], pre[3], s=100, color=\"blue\")\r\n plt.plot(pre[2], pre[2], color=\"red\")\r\n plt.xlabel(\"Experimental (Hartree)\")\r\n plt.ylabel(\"Predicted (Hartree)\")\r\n plt.grid(True, \"both\", \"both\")\r\n plt.title(\"Val: fold \" + str(fold_n) + \" R2: \" + f\"{me[3]:.3f}\" +\r\n \" MAE: \" + f\"{me[4]:.3f}\" + \" Max: \" + f\"{me[5]:.3f}\")\r\n plt.savefig(\"/mnt/c/Users/richi/Desktop/QTAIM/NPA/true_pre/Ml/\" +\r\n str(fold_n) + \"_val.jpg\")\r\n plt.close()\r\n fold_n += 1\r\nmetrics = np.array(metrics)\r\nnp.savetxt(\"Metrics_MLR.csv\", metrics, delimiter=\",\")\r\nprint(metrics)\r\nprint(np.mean(metrics, axis=0))\r\n","repo_name":"richiam16/atoms_tools","sub_path":"old/IA_AE_MLR.py","file_name":"IA_AE_MLR.py","file_ext":"py","file_size_in_byte":2786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25160695005","text":"\"\"\"AWS Route 53 Lambda Backup\"\"\"\n\nimport csv\nimport json\nimport os\nfrom datetime import datetime\nfrom typing import Optional, Tuple, List\n\nimport boto3\n\n\ndef get_env_variable(var_name: str) -> str:\n try:\n return os.environ[var_name]\n except KeyError:\n raise Exception(f\"Environmental variable {var_name} not defined\")\n\n\ns3_bucket_name = get_env_variable('s3_bucket_name')\ns3_bucket_region = get_env_variable('s3_bucket_region')\n\n# Create client objects\n\ns3_client = boto3.client('s3', region_name=s3_bucket_region)\nroute53_client = boto3.client('route53')\n\n\n# Functions\ndef upload_to_s3(folder: str, filename: str, bucket_name: str, key: str) -> None:\n \"\"\"Upload a file to a folder in an Amazon S3 bucket.\"\"\"\n key = folder + \"/\" + key\n try:\n s3_client.upload_file(str(filename), bucket_name, key)\n print(f\"Uploaded {filename} to {bucket_name}/{key}\")\n except Exception as e:\n raise Exception(f\"Failed to upload {filename} to S3 bucket {bucket_name} due to {e}\")\n\n\ndef get_route53_hosted_zones(next_zone: Optional[Tuple[str, str]] = None) -> List[dict]:\n \"\"\"Recursively returns a list of hosted zones in Amazon Route 53.\"\"\"\n try:\n if next_zone:\n response = route53_client.list_hosted_zones_by_name(\n DNSName=next_zone[0],\n HostedZoneId=next_zone[1]\n )\n else:\n response = route53_client.list_hosted_zones_by_name()\n except Exception as e:\n raise Exception(f\"Failed to list hosted zones due to {e}\")\n\n hosted_zones = response['HostedZones']\n # if response is truncated, call function again with next zone name/id\n if response['IsTruncated']:\n hosted_zones += get_route53_hosted_zones(\n (response['NextDNSName'],\n response['NextHostedZoneId'])\n )\n return hosted_zones\n\n\ndef get_route53_zone_records(zone_id: str, next_record: Optional[Tuple[str, str]] = None) -> List[dict]:\n \"\"\"Recursively returns a list of records of a hosted zone in Route 53.\"\"\"\n try:\n if next_record:\n response = route53_client.list_resource_record_sets(\n HostedZoneId=zone_id,\n StartRecordName=next_record[0],\n StartRecordType=next_record[1]\n )\n else:\n response = route53_client.list_resource_record_sets(HostedZoneId=zone_id)\n except Exception as e:\n raise Exception(f\"Failed to list zone records due to {e}\")\n\n zone_records = response['ResourceRecordSets']\n # if response is truncated, call function again with next record name/id\n if response['IsTruncated']:\n zone_records += get_route53_zone_records(\n zone_id,\n (response['NextRecordName'],\n response['NextRecordType'])\n )\n return zone_records\n\n\ndef get_record_value(record):\n \"\"\"Return a list of values for a hosted zone record.\"\"\"\n alias = record.get('AliasTarget')\n if alias:\n value = [':'.join(['ALIAS', alias['HostedZoneId'], alias['DNSName']])]\n else:\n value = [v['Value'] for v in record.get('ResourceRecords', [])]\n return value\n\n\ndef try_record(test, record):\n \"\"\"Return a value for a record\"\"\"\n # test for Key and Type errors\n try:\n value = record[test]\n except KeyError:\n value = ''\n except TypeError:\n value = ''\n return value\n\n\ndef write_zone_to_csv(zone, zone_records):\n \"\"\"Write hosted zone records to a csv file in /tmp/.\"\"\"\n zone_file_name = '/tmp/' + zone['Name'] + '.csv'\n fieldnames = [\n 'NAME', 'TYPE', 'VALUE',\n 'TTL', 'REGION', 'WEIGHT',\n 'SETID', 'FAILOVER', 'EVALUATE_HEALTH'\n ]\n with open(zone_file_name, 'w', newline='') as csv_file:\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n writer.writeheader()\n for record in zone_records:\n values = get_record_value(record)\n for value in values:\n writer.writerow({\n 'NAME': record['Name'],\n 'TYPE': record['Type'],\n 'VALUE': value,\n 'TTL': try_record('TTL', record),\n 'REGION': try_record('Region', record),\n 'WEIGHT': try_record('Weight', record),\n 'SETID': try_record('SetIdentifier', record),\n 'FAILOVER': try_record('Failover', record),\n 'EVALUATE_HEALTH': try_record(\n 'EvaluateTargetHealth', try_record('AliasTarget', record)\n )\n })\n return zone_file_name\n\n\ndef write_zone_to_json(zone, zone_records):\n \"\"\"Write hosted zone records to a json file in /tmp/.\"\"\"\n # create the file name\n zone_file_name = '/tmp/' + zone['Name'] + '.json'\n try:\n # use context manager to write the records to the file\n with open(zone_file_name, 'w') as json_file:\n json.dump(zone_records, json_file, indent=4)\n except Exception as e:\n print(f\"Error writing to file: {e}\")\n return None\n return zone_file_name\n\n\n# HANDLER FUNCTION\ndef lambda_handler(event, context):\n \"\"\"Handler function for AWS Lambda\"\"\"\n time_stamp = datetime.utcnow().strftime(\"%Y-%m-%d_%H-%M-%S\")\n\n hosted_zoned = get_route53_hosted_zones()\n for zone in hosted_zoned:\n zone_folder = (time_stamp + '/' + zone['Name'][:-1])\n zone_records = get_route53_zone_records(zone['Id'])\n upload_to_s3(\n zone_folder,\n write_zone_to_csv(zone, zone_records),\n s3_bucket_name,\n (zone['Name'] + 'csv')\n )\n upload_to_s3(\n zone_folder,\n write_zone_to_json(zone, zone_records),\n s3_bucket_name,\n (zone['Name'] + 'json')\n )\n return True\n\n\nif __name__ == \"__main__\":\n lambda_handler(0, 0)\n","repo_name":"troydieter/r53_backups_cdk","sub_path":"infra/functions/r53_lambda_function.py","file_name":"r53_lambda_function.py","file_ext":"py","file_size_in_byte":5886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29028605656","text":"# Greatest Common Divisor of Strings\n# Easy\n\ndef gcdOfStrings(str1: str, str2: str) -> str:\n\n def isGCD(string: str, substr: str): \n if len(string.replace(substr, '')) > 0:\n return False\n return True\n \n def computeGCD(a, b):\n cd = []\n for i in range(1, min(a, b) + 1):\n if a%i == b%i == 0:\n cd.append(i)\n return cd\n\n siz_1, siz_2 = len(str1), len(str2)\n cd = computeGCD(siz_1, siz_2)\n for divisor in reversed(cd):\n prefix = str1[:divisor]\n if isGCD(str2, str1[:divisor]) and isGCD(str1, str2[:divisor]):\n return prefix\n return \"\"","repo_name":"GabrielBifano/Leetcode","sub_path":"Python/1071.py","file_name":"1071.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"32950122800","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: Vinicius Morais\n@author: Vinicius Araujo\n\"\"\"\n\nclass Acao(object):\n def __init__(self, partida, reservada, destino):\n self.partida = partida\n self.reservada = reservada\n self.destino = destino\n return\n\n def equals(self, transicao):\n if((self.partida == transicao.partida) and\n (self.reservada == transicao.reservada) and\n (self.destino == transicao.destino)):\n return True\n return False\n","repo_name":"viniciusms29/Teoria-da-Computacao","sub_path":"TrabalhoTeoriaComputacao_SIM_TURING_2FITAS/acao.py","file_name":"acao.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3198338040","text":"# modify.py\n\nimport pdb\n\nz = 2_000\n\n\ndef modify(x: int, y: int):\n global z\n y, z = z, y\n z += x + y\n\n\nheight, width = 720, 1080\npdb.set_trace()\nmodify(height, width)\n","repo_name":"DahlitzFlorian/how-to-debug-your-python-code-article-snippets","sub_path":"modify.py","file_name":"modify.py","file_ext":"py","file_size_in_byte":175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28119722077","text":"import sys\nimport os\nsys.path.append(os.path.abspath('..'))\n\nfrom networking.server import Server\nif len(sys.argv) < 2:\n\tprint('Need number of clients')\n\tsys.exit(1)\nmaxClients = int(sys.argv[1])\n\ndef printMessage(message):\n\tprint(message)\n\nwith Server(22345) as server:\n\tfor slot in range(1, maxClients + 1):\n\t\tserver.registerListener(printMessage)\n\t\tserver.acceptPlayer(slot)\n\t\tserver.listenOn(slot)\n\t\tserver.sendPlayerData(slot, \"You are client {}\".format(slot))\n\t\n\twhile True:\n\t\ttext = input()\n\t\tif text == 'q': break\n\t\t\n\t\tfor slot in server.players.keys():\n\t\t\tserver.sendPlayerData(slot, text)\nprint('closing down')\n","repo_name":"Lanternglow/netrogue","sub_path":"tests/serverTest.py","file_name":"serverTest.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1858249981","text":"from typing import List, Any\n\n\ndef split_list(list_to_split: List[Any], group_size: int) -> List[Any]:\n result = []\n buffer = []\n i = 1\n\n for element in list_to_split:\n buffer.append(element)\n\n if i == group_size:\n result.append(buffer)\n buffer = []\n i = 1\n else:\n i += 1\n\n result.append(buffer)\n\n return result\n\n\ndef is_bit_set(byte: int, bit_address: int) -> bool:\n return (byte >> bit_address) % 2 == 1\n","repo_name":"ReFruity/bcpu-asm","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19543684662","text":"def solution(dirs):\n c = (0,0)\n d = {\"U\": (0,1), \"D\": (0,-1), \"L\": (-1,0), \"R\": (1,0)}\n square = 5\n route = []\n \n for i in dirs:\n dx = c[0]+d[i][0]\n dy = c[1]+d[i][1]\n \n if abs(dx) > square or abs(dy) > square:\n continue\n else:\n temp = (dx,dy)\n r = [c]\n r.append(temp)\n r.sort()\n c = temp\n route.append(tuple(r))\n \n return len(set(route))\n\nprint(f'test1 = {solution(\"ULURRDLLU\")}')\nprint(f'test2 = {solution(\"LULLLLLLU\")}')","repo_name":"Ji-Hwan-Jung/coding-test","sub_path":"level2/방문 길이.py","file_name":"방문 길이.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"43443258656","text":"import random\n\ndef computer_draw():\n list_of_words = [\"prize\", \"drowing\", \"energy\", \"life\", \"beer\"]\n computer_word = random.choice(list_of_words)\n return computer_word\n\ndef word_camuflage(word):\n camuflage = \"\"\n for i in word:\n i = \"-\"\n camuflage += i\n return camuflage\n\ndef player_choice():\n choice = input(\"Podaj literę: \")\n choice = choice.casefold()\n choice = choice[0]\n return choice\n\ndef word_check(player, word, camuflage):\n blank_word = camuflage\n for i , v in enumerate(word):\n if v == player:\n blank_word[i] = v\n return blank_word\n\ndef true_check(player_list, camuflage):\n if player_list == camuflage:\n pass\n\n\n\ndef Hangman():\n computer_choice = list(computer_draw())\n print(word_camuflage(computer_choice))\n camuflage = list(word_camuflage(computer_choice))\n while True:\n player_letter = player_choice()\n print(camuflage)\n temporary_word = word_check(player_letter, computer_choice, camuflage)\n print(str(camuflage))\n word = temporary_word\n print(str(temporary_word))\n if str(camuflage) == str(word):\n print(\"Trafiłeś\")\n else:\n print(\"Pudło\")\n camuflage = temporary_word\n if temporary_word == computer_choice:\n temporaty_word = str(temporary_word)\n break\n\n print(f\"Gratulacje! Wygrałeś to słowo to {temporaty_word}\")\n\nHangman()","repo_name":"Maciollo13/PythonCourse2022","sub_path":"05/summary/10_HW_hangman.py","file_name":"10_HW_hangman.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"24127813018","text":"import argparse\n\nfrom shinya.bd import MoviePlaylistFile\nfrom shinya.common.io import unpack_bytes, pack_bytes\n\n\ndef check_integrity(input_file):\n success = True\n try:\n MoviePlaylistFile(input_file)\n except:\n success = False\n return success\n\n\ndef fix_ext_address(source, destination):\n with open(source, \"rb\") as f:\n data = f.read()\n plm_addr = unpack_bytes(data, 12, 4)\n ext_addr = unpack_bytes(data, 16, 4)\n plm_size = unpack_bytes(data, plm_addr, 4)\n if ext_addr:\n expected_ext_addr = plm_addr + plm_size + 4\n if expected_ext_addr != ext_addr:\n if expected_ext_addr < len(data):\n data = data[:16] + pack_bytes(expected_ext_addr, 4) + data[20:]\n else:\n data = data[:16] + pack_bytes(0, 4) + data[20:]\n with open(destination, \"wb\") as f:\n f.write(data)\n\n\ndef main(source, destination):\n if check_integrity(source):\n print(\"[OK] The playlist does not seem to contain errors.\")\n else:\n fix_ext_address(source, destination)\n if check_integrity(destination):\n print(\"[OK] The extension address has been fixed.\")\n else:\n print(\"[FAILED] The playlist seems to have other errors.\")\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\"fix wrong extension data start address in mpls files\")\n parser.add_argument(\"source\", type=str, help=\"source mpls file\")\n parser.add_argument(\"destination\", type=str, help=\"mpls save destination\")\n args = parser.parse_args()\n main(args.source, args.destination)\n","repo_name":"shimamura-hougetsu/shinya","sub_path":"scripts/mpls_fix_extension_data_address.py","file_name":"mpls_fix_extension_data_address.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"31"} +{"seq_id":"73573836248","text":"import os\nfrom flask import Flask, jsonify\nfrom dotenv import load_dotenv\nfrom uber_rides.session import Session\nfrom .uber import get_uber\nfrom .weather import get_weather\n\n\nload_dotenv('.env')\nAPP = Flask(__name__)\n\nif 'UBER_SESSION' not in locals():\n UBER_SESSION = Session(server_token=os.getenv('UBER_KEY'))\n\n\n@APP.route('/current_transit', methods=['GET'])\ndef get_current():\n \"\"\"\n For a new set of captured data, we want to apply the pre-trained model\n \"\"\"\n uber_response = get_uber(UBER_SESSION)\n weather_response = get_weather()\n\n resp = jsonify({\n 'uber_times': uber_response,\n 'weather': weather_response\n })\n resp.headers['Access-Control-Allow-Origin'] = '*'\n return resp\n","repo_name":"amida-tech/status-dashboard-hackathon","sub_path":"backends/transit/flaskapp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"36896922992","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom tensorflow.python.framework import common_shapes\nfrom tensorflow.python.framework import errors\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.framework import tensor_shape\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import check_ops\nfrom tensorflow.python.ops import control_flow_ops\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops.linalg import linalg_impl as linalg\nfrom tensorflow.python.ops.linalg import linear_operator\nfrom tensorflow.python.util.tf_export import tf_export\n\n__all__ = [\"LinearOperatorKronecker\"]\n\n\ndef _vec(x):\n \"\"\"Stacks column of matrix to form a single column.\"\"\"\n return array_ops.reshape(\n array_ops.matrix_transpose(x),\n array_ops.concat(\n [array_ops.shape(x)[:-2], [-1]], axis=0))\n\n\ndef _unvec_by(y, num_col):\n \"\"\"Unstack vector to form a matrix, with a specified amount of columns.\"\"\"\n return array_ops.matrix_transpose(\n array_ops.reshape(\n y,\n array_ops.concat(\n [array_ops.shape(y)[:-1], [num_col, -1]], axis=0)))\n\n\ndef _rotate_last_dim(x, rotate_right=False):\n \"\"\"Rotate the last dimension either left or right.\"\"\"\n ndims = array_ops.rank(x)\n if rotate_right:\n transpose_perm = array_ops.concat(\n [[ndims - 1], math_ops.range(0, ndims - 1)], axis=0)\n else:\n transpose_perm = array_ops.concat(\n [math_ops.range(1, ndims), [0]], axis=0)\n return array_ops.transpose(x, transpose_perm)\n\n\n@tf_export(\"linalg.LinearOperatorKronecker\")\nclass LinearOperatorKronecker(linear_operator.LinearOperator):\n \"\"\"Kronecker product between two `LinearOperators`.\n\n This operator composes one or more linear operators `[op1,...,opJ]`,\n building a new `LinearOperator` representing the Kronecker product:\n `op1 x op2 x .. opJ` (we omit parentheses as the Kronecker product is\n associative).\n\n If `opj` has shape `batch_shape_j + [M_j, N_j]`, then the composed operator\n will have shape equal to `broadcast_batch_shape + [prod M_j, prod N_j]`,\n where the product is over all operators.\n\n ```python\n # Create a 4 x 4 linear operator composed of two 2 x 2 operators.\n operator_1 = LinearOperatorFullMatrix([[1., 2.], [3., 4.]])\n operator_2 = LinearOperatorFullMatrix([[1., 0.], [2., 1.]])\n operator = LinearOperatorKronecker([operator_1, operator_2])\n\n operator.to_dense()\n ==> [[1., 2., 0., 0.],\n [3., 4., 0., 0.],\n [2., 4., 1., 2.],\n [6., 8., 3., 4.]]\n\n operator.shape\n ==> [4, 4]\n\n operator.log_abs_determinant()\n ==> scalar Tensor\n\n x = ... Shape [4, 2] Tensor\n operator.matmul(x)\n ==> Shape [4, 2] Tensor\n\n # Create a [2, 3] batch of 4 x 5 linear operators.\n matrix_45 = tf.random.normal(shape=[2, 3, 4, 5])\n operator_45 = LinearOperatorFullMatrix(matrix)\n\n # Create a [2, 3] batch of 5 x 6 linear operators.\n matrix_56 = tf.random.normal(shape=[2, 3, 5, 6])\n operator_56 = LinearOperatorFullMatrix(matrix_56)\n\n # Compose to create a [2, 3] batch of 20 x 30 operators.\n operator_large = LinearOperatorKronecker([operator_45, operator_56])\n\n # Create a shape [2, 3, 20, 2] vector.\n x = tf.random.normal(shape=[2, 3, 6, 2])\n operator_large.matmul(x)\n ==> Shape [2, 3, 30, 2] Tensor\n ```\n\n #### Performance\n\n The performance of `LinearOperatorKronecker` on any operation is equal to\n the sum of the individual operators' operations.\n\n #### Matrix property hints\n\n This `LinearOperator` is initialized with boolean flags of the form `is_X`,\n for `X = non_singular, self_adjoint, positive_definite, square`.\n These have the following meaning:\n\n * If `is_X == True`, callers should expect the operator to have the\n property `X`. This is a promise that should be fulfilled, but is *not* a\n runtime assert. For example, finite floating point precision may result\n in these promises being violated.\n * If `is_X == False`, callers should expect the operator to not have `X`.\n * If `is_X == None` (the default), callers should have no expectation either\n way.\n \"\"\"\n\n def __init__(self,\n operators,\n is_non_singular=None,\n is_self_adjoint=None,\n is_positive_definite=None,\n is_square=None,\n name=None):\n r\"\"\"Initialize a `LinearOperatorKronecker`.\n\n `LinearOperatorKronecker` is initialized with a list of operators\n `[op_1,...,op_J]`.\n\n Args:\n operators: Iterable of `LinearOperator` objects, each with\n the same `dtype` and composable shape, representing the Kronecker\n factors.\n is_non_singular: Expect that this operator is non-singular.\n is_self_adjoint: Expect that this operator is equal to its hermitian\n transpose.\n is_positive_definite: Expect that this operator is positive definite,\n meaning the quadratic form `x^H A x` has positive real part for all\n nonzero `x`. Note that we do not require the operator to be\n self-adjoint to be positive-definite. See:\n https://en.wikipedia.org/wiki/Positive-definite_matrix\\\n #Extension_for_non_symmetric_matrices\n is_square: Expect that this operator acts like square [batch] matrices.\n name: A name for this `LinearOperator`. Default is the individual\n operators names joined with `_x_`.\n\n Raises:\n TypeError: If all operators do not have the same `dtype`.\n ValueError: If `operators` is empty.\n \"\"\"\n # Validate operators.\n check_ops.assert_proper_iterable(operators)\n operators = list(operators)\n if not operators:\n raise ValueError(\n \"Expected a list of >=1 operators. Found: %s\" % operators)\n self._operators = operators\n\n # Validate dtype.\n dtype = operators[0].dtype\n for operator in operators:\n if operator.dtype != dtype:\n name_type = (str((o.name, o.dtype)) for o in operators)\n raise TypeError(\n \"Expected all operators to have the same dtype. Found %s\"\n % \" \".join(name_type))\n\n # Auto-set and check hints.\n # A Kronecker product is invertible, if and only if all factors are\n # invertible.\n if all(operator.is_non_singular for operator in operators):\n if is_non_singular is False:\n raise ValueError(\n \"The Kronecker product of non-singular operators is always \"\n \"non-singular.\")\n is_non_singular = True\n\n if all(operator.is_self_adjoint for operator in operators):\n if is_self_adjoint is False:\n raise ValueError(\n \"The Kronecker product of self-adjoint operators is always \"\n \"self-adjoint.\")\n is_self_adjoint = True\n\n # The eigenvalues of a Kronecker product are equal to the products of eigen\n # values of the corresponding factors.\n if all(operator.is_positive_definite for operator in operators):\n if is_positive_definite is False:\n raise ValueError(\"The Kronecker product of positive-definite operators \"\n \"is always positive-definite.\")\n is_positive_definite = True\n\n # Initialization.\n graph_parents = []\n for operator in operators:\n graph_parents.extend(operator.graph_parents)\n\n if name is None:\n name = operators[0].name\n for operator in operators[1:]:\n name += \"_x_\" + operator.name\n with ops.name_scope(name, values=graph_parents):\n super(LinearOperatorKronecker, self).__init__(\n dtype=dtype,\n graph_parents=graph_parents,\n is_non_singular=is_non_singular,\n is_self_adjoint=is_self_adjoint,\n is_positive_definite=is_positive_definite,\n is_square=is_square,\n name=name)\n\n @property\n def operators(self):\n return self._operators\n\n def _shape(self):\n # Get final matrix shape.\n domain_dimension = self.operators[0].domain_dimension\n for operator in self.operators[1:]:\n domain_dimension *= operator.domain_dimension\n\n range_dimension = self.operators[0].range_dimension\n for operator in self.operators[1:]:\n range_dimension *= operator.range_dimension\n\n matrix_shape = tensor_shape.TensorShape([\n range_dimension, domain_dimension])\n\n # Get broadcast batch shape.\n # broadcast_shape checks for compatibility.\n batch_shape = self.operators[0].batch_shape\n for operator in self.operators[1:]:\n batch_shape = common_shapes.broadcast_shape(\n batch_shape, operator.batch_shape)\n\n return batch_shape.concatenate(matrix_shape)\n\n def _shape_tensor(self):\n domain_dimension = self.operators[0].domain_dimension_tensor()\n for operator in self.operators[1:]:\n domain_dimension *= operator.domain_dimension_tensor()\n\n range_dimension = self.operators[0].range_dimension_tensor()\n for operator in self.operators[1:]:\n range_dimension *= operator.range_dimension_tensor()\n\n matrix_shape = [range_dimension, domain_dimension]\n\n # Get broadcast batch shape.\n # broadcast_shape checks for compatibility.\n batch_shape = self.operators[0].batch_shape_tensor()\n for operator in self.operators[1:]:\n batch_shape = array_ops.broadcast_dynamic_shape(\n batch_shape, operator.batch_shape_tensor())\n\n return array_ops.concat((batch_shape, matrix_shape), 0)\n\n def _matmul(self, x, adjoint=False, adjoint_arg=False):\n # Here we heavily rely on Roth's column Lemma [1]:\n # (A x B) * vec X = vec BXA^T,\n # where vec stacks all the columns of the matrix under each other. In our\n # case, x represents a batch of vec X (i.e. we think of x as a batch of\n # column vectors, rather than a matrix). Each member of the batch can be\n # reshaped to a matrix (hence we get a batch of matrices).\n # We can iteratively apply this lemma by noting that if B is a Kronecker\n # product, then we can apply the lemma again.\n\n # [1] W. E. Roth, \"On direct product matrices,\"\n # Bulletin of the American Mathematical Society, vol. 40, pp. 461-468,\n # 1934\n\n # Efficiency\n\n # Naively doing the Kronecker product, by calculating the dense matrix and\n # applying it will can take cubic time in the size of domain_dimension\n # (assuming a square matrix). The other issue is that calculating the dense\n # matrix can be prohibitively expensive, in that it can take a large amount\n # of memory.\n #\n # This implementation avoids this memory blow up by only computing matmuls\n # with the factors. In this way, we don't have to realize the dense matrix.\n # In terms of complexity, if we have Kronecker Factors of size:\n # (n1, n1), (n2, n2), (n3, n3), ... (nJ, nJ), with N = \\prod n_i, and we\n # have as input a [N, M] matrix, the naive approach would take O(N^2 M).\n # With this approach (ignoring reshaping of tensors and transposes for now),\n # the time complexity can be O(M * (\\sum n_i) * N). There is also the\n # benefit of batched multiplication (In this example, the batch size is\n # roughly M * N) so this can be much faster. However, not factored in are\n # the costs of the several transposing of tensors, which can affect cache\n # behavior.\n\n # Below we document the shape manipulation for adjoint=False,\n # adjoint_arg=False, but the general case of different adjoints is still\n # handled.\n\n if adjoint_arg:\n x = linalg.adjoint(x)\n\n # Always add a batch dimension to enable broadcasting to work.\n batch_shape = array_ops.concat(\n [array_ops.ones_like(self.batch_shape_tensor()), [1, 1]], 0)\n x += array_ops.zeros(batch_shape, dtype=x.dtype.base_dtype)\n\n # x has shape [B, R, C], where B represent some number of batch dimensions,\n # R represents the number of rows, and C represents the number of columns.\n # In order to apply Roth's column lemma, we need to operate on a batch of\n # column vectors, so we reshape into a batch of column vectors. We put it\n # at the front to ensure that broadcasting between operators to the batch\n # dimensions B still works.\n output = _rotate_last_dim(x, rotate_right=True)\n\n # Also expand the shape to be [A, C, B, R]. The first dimension will be\n # used to accumulate dimensions from each operator matmul.\n output = output[array_ops.newaxis, ...]\n\n # In this loop, A is going to refer to the value of the accumulated\n # dimension. A = 1 at the start, and will end up being self.range_dimension.\n # V will refer to the last dimension. V = R at the start, and will end up\n # being 1 in the end.\n for operator in self.operators[:-1]:\n # Reshape output from [A, C, B, V] to be\n # [A, C, B, V / op.domain_dimension, op.domain_dimension]\n if adjoint:\n operator_dimension = operator.range_dimension_tensor()\n else:\n operator_dimension = operator.domain_dimension_tensor()\n\n output = _unvec_by(output, operator_dimension)\n\n # We are computing (XA^T) = (AX^T)^T.\n # output has [A, C, B, V / op.domain_dimension, op.domain_dimension],\n # which is being converted to:\n # [A, C, B, V / op.domain_dimension, op.range_dimension]\n output = array_ops.matrix_transpose(output)\n output = operator.matmul(output, adjoint=adjoint, adjoint_arg=False)\n output = array_ops.matrix_transpose(output)\n # Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension]\n output = _rotate_last_dim(output, rotate_right=False)\n output = _vec(output)\n output = _rotate_last_dim(output, rotate_right=True)\n\n # After the loop, we will have\n # A = self.range_dimension / op[-1].range_dimension\n # V = op[-1].domain_dimension\n\n # We convert that using matvec to get:\n # [A, C, B, op[-1].range_dimension]\n output = self.operators[-1].matvec(output, adjoint=adjoint)\n # Rearrange shape to be [B1, ... Bn, self.range_dimension, C]\n output = _rotate_last_dim(output, rotate_right=False)\n output = _vec(output)\n output = _rotate_last_dim(output, rotate_right=False)\n\n if x.shape.is_fully_defined():\n column_dim = x.shape[-1]\n broadcast_batch_shape = common_shapes.broadcast_shape(\n x.shape[:-2], self.batch_shape)\n if adjoint:\n matrix_dimensions = [self.domain_dimension, column_dim]\n else:\n matrix_dimensions = [self.range_dimension, column_dim]\n\n output.set_shape(broadcast_batch_shape.concatenate(\n matrix_dimensions))\n\n return output\n\n def _determinant(self):\n # Note that we have |X1 x X2| = |X1| ** n * |X2| ** m, where X1 is an m x m\n # matrix, and X2 is an n x n matrix. We can iteratively apply this property\n # to get the determinant of |X1 x X2 x X3 ...|. If T is the product of the\n # domain dimension of all operators, then we have:\n # |X1 x X2 x X3 ...| =\n # |X1| ** (T / m) * |X2 x X3 ... | ** m =\n # |X1| ** (T / m) * |X2| ** (m * (T / m) / n) * ... =\n # |X1| ** (T / m) * |X2| ** (T / n) * | X3 x X4... | ** (m * n)\n # And by doing induction we have product(|X_i| ** (T / dim(X_i))).\n total = self.domain_dimension_tensor()\n determinant = 1.\n for operator in self.operators:\n determinant *= operator.determinant() ** math_ops.cast(\n total / operator.domain_dimension_tensor(),\n dtype=operator.dtype)\n return determinant\n\n def _log_abs_determinant(self):\n # This will be sum((total / dim(x_i)) * log |X_i|)\n total = self.domain_dimension_tensor()\n log_abs_det = 0.\n for operator in self.operators:\n log_abs_det += operator.log_abs_determinant() * math_ops.cast(\n total / operator.domain_dimension_tensor(),\n dtype=operator.dtype)\n return log_abs_det\n\n def _trace(self):\n # tr(A x B) = tr(A) * tr(B)\n trace = 1.\n for operator in self.operators:\n trace *= operator.trace()\n return trace\n\n def _solve(self, rhs, adjoint=False, adjoint_arg=False):\n # Here we follow the same use of Roth's column lemma as in `matmul`, with\n # the key difference that we replace all `matmul` instances with `solve`.\n # This follows from the property that inv(A x B) = inv(A) x inv(B).\n\n # Below we document the shape manipulation for adjoint=False,\n # adjoint_arg=False, but the general case of different adjoints is still\n # handled.\n\n if adjoint_arg:\n rhs = linalg.adjoint(rhs)\n\n # Always add a batch dimension to enable broadcasting to work.\n batch_shape = array_ops.concat(\n [array_ops.ones_like(self.batch_shape_tensor()), [1, 1]], 0)\n rhs += array_ops.zeros(batch_shape, dtype=rhs.dtype.base_dtype)\n\n # rhs has shape [B, R, C], where B represent some number of batch\n # dimensions,\n # R represents the number of rows, and C represents the number of columns.\n # In order to apply Roth's column lemma, we need to operate on a batch of\n # column vectors, so we reshape into a batch of column vectors. We put it\n # at the front to ensure that broadcasting between operators to the batch\n # dimensions B still works.\n output = _rotate_last_dim(rhs, rotate_right=True)\n\n # Also expand the shape to be [A, C, B, R]. The first dimension will be\n # used to accumulate dimensions from each operator matmul.\n output = output[array_ops.newaxis, ...]\n\n # In this loop, A is going to refer to the value of the accumulated\n # dimension. A = 1 at the start, and will end up being self.range_dimension.\n # V will refer to the last dimension. V = R at the start, and will end up\n # being 1 in the end.\n for operator in self.operators[:-1]:\n # Reshape output from [A, C, B, V] to be\n # [A, C, B, V / op.domain_dimension, op.domain_dimension]\n if adjoint:\n operator_dimension = operator.range_dimension_tensor()\n else:\n operator_dimension = operator.domain_dimension_tensor()\n\n output = _unvec_by(output, operator_dimension)\n\n # We are computing (XA^-1^T) = (A^-1 X^T)^T.\n # output has [A, C, B, V / op.domain_dimension, op.domain_dimension],\n # which is being converted to:\n # [A, C, B, V / op.domain_dimension, op.range_dimension]\n output = array_ops.matrix_transpose(output)\n output = operator.solve(output, adjoint=adjoint, adjoint_arg=False)\n output = array_ops.matrix_transpose(output)\n # Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension]\n output = _rotate_last_dim(output, rotate_right=False)\n output = _vec(output)\n output = _rotate_last_dim(output, rotate_right=True)\n\n # After the loop, we will have\n # A = self.range_dimension / op[-1].range_dimension\n # V = op[-1].domain_dimension\n\n # We convert that using matvec to get:\n # [A, C, B, op[-1].range_dimension]\n output = self.operators[-1].solvevec(output, adjoint=adjoint)\n # Rearrange shape to be [B1, ... Bn, self.range_dimension, C]\n output = _rotate_last_dim(output, rotate_right=False)\n output = _vec(output)\n output = _rotate_last_dim(output, rotate_right=False)\n\n if rhs.shape.is_fully_defined():\n column_dim = rhs.shape[-1]\n broadcast_batch_shape = common_shapes.broadcast_shape(\n rhs.shape[:-2], self.batch_shape)\n if adjoint:\n matrix_dimensions = [self.domain_dimension, column_dim]\n else:\n matrix_dimensions = [self.range_dimension, column_dim]\n\n output.set_shape(broadcast_batch_shape.concatenate(\n matrix_dimensions))\n\n return output\n\n def _diag_part(self):\n diag_part = self.operators[0].diag_part()\n for operator in self.operators[1:]:\n diag_part = diag_part[..., :, array_ops.newaxis]\n op_diag_part = operator.diag_part()[..., array_ops.newaxis, :]\n diag_part *= op_diag_part\n diag_part = array_ops.reshape(\n diag_part,\n shape=array_ops.concat(\n [array_ops.shape(diag_part)[:-2], [-1]], axis=0))\n if self.range_dimension > self.domain_dimension:\n diag_dimension = self.domain_dimension\n else:\n diag_dimension = self.range_dimension\n diag_part.set_shape(\n self.batch_shape.concatenate(diag_dimension))\n return diag_part\n\n def _to_dense(self):\n product = self.operators[0].to_dense()\n for operator in self.operators[1:]:\n # Product has shape [B, R1, 1, C1].\n product = product[\n ..., :, array_ops.newaxis, :, array_ops.newaxis]\n # Operator has shape [B, 1, R2, 1, C2].\n op_to_mul = operator.to_dense()[\n ..., array_ops.newaxis, :, array_ops.newaxis, :]\n # This is now [B, R1, R2, C1, C2].\n product *= op_to_mul\n # Now merge together dimensions to get [B, R1 * R2, C1 * C2].\n product = array_ops.reshape(\n product,\n shape=array_ops.concat(\n [array_ops.shape(product)[:-4],\n [array_ops.shape(product)[-4] * array_ops.shape(product)[-3],\n array_ops.shape(product)[-2] * array_ops.shape(product)[-1]]\n ], axis=0))\n product.set_shape(self.shape)\n return product\n\n def _assert_non_singular(self):\n if all(operator.is_square for operator in self.operators):\n asserts = [operator.assert_non_singular() for operator in self.operators]\n return control_flow_ops.group(asserts)\n else:\n raise errors.InvalidArgumentError(\n node_def=None, op=None, message=\"All Kronecker factors must be \"\n \"square for the product to be invertible.\")\n\n def _assert_self_adjoint(self):\n if all(operator.is_square for operator in self.operators):\n asserts = [operator.assert_self_adjoint() for operator in self.operators]\n return control_flow_ops.group(asserts)\n else:\n raise errors.InvalidArgumentError(\n node_def=None, op=None, message=\"All Kronecker factors must be \"\n \"square for the product to be self adjoint.\")\n","repo_name":"DeepRec-AI/DeepRec","sub_path":"tensorflow/python/ops/linalg/linear_operator_kronecker.py","file_name":"linear_operator_kronecker.py","file_ext":"py","file_size_in_byte":21799,"program_lang":"python","lang":"en","doc_type":"code","stars":895,"dataset":"github-code","pt":"31"} +{"seq_id":"42642415985","text":"from sys import exit\r\nfrom pprint import pprint\r\n\r\n\r\nclass Employee(object):\r\n\r\n\r\n def enter_data(self):\r\n self.name = input(\"Name of employee: \")\r\n self.hours = input(\"Hours per week: \")\r\n self.rate = input(\"Rate of pay per hour: \")\r\n var = self.convert()\r\n return var\r\n\r\n def convert(self):\r\n\r\n try:\r\n self.hours = float(self.hours)\r\n self.rate = float(self.rate)\r\n\r\n except:\r\n print(\"Error, you must input numbers for the hours and rate.\")\r\n return 'entry_fail'\r\n\r\n else:\r\n var2 = self.calculations()\r\n return var2\r\n\r\n def calculations(self):\r\n\r\n\r\n if self.hours <= 40:\r\n self.wage = self.rate * self.hours\r\n\r\n return self\r\n\r\n elif self.hours > 40:\r\n bonus = (self.hours - 40) * (self.rate * 1.5)\r\n normal_rate = 40 * self.rate\r\n self.wage = normal_rate + bonus\r\n\r\n return self\r\n else:\r\n raise Exception(\"Error in Calculations\")\r\n\r\n\r\n\r\nclass Database(object):\r\n\r\n\r\n employee_database = {}\r\n\r\n def view_data(self):\r\n\r\n\r\n print(\"Here is the database:\")\r\n pprint(Database.employee_database)\r\n\r\n answer = input(\"Please input a name to view their details: \")\r\n\r\n employee_obj = Database.employee_database.get(answer)\r\n\r\n\r\n if employee_obj == None:\r\n print(\"Please enter a name correctly\")\r\n return self.view_data()\r\n\r\n else:\r\n print(f\"Employee's name {employee_obj.name}\")\r\n print(f\"Employee's hours per week: {employee_obj.hours}\")\r\n print(f\"Employee's rate of pay per hour: £{employee_obj.rate}\")\r\n print(f\"Employee's weekly wage (inc. bonus if applic.): £{round(employee_obj.wage, 2)}\")\r\n\r\n\r\nclass Engine(object):\r\n\r\n def __init__(self):\r\n self.data_base = Database()\r\n\r\n def run_engine(self):\r\n\r\n while True:\r\n answer = input(\"Add an employee? (y/n) \")\r\n\r\n if answer == 'y':\r\n emp = Employee()\r\n emp = emp.enter_data()\r\n\r\n if emp == 'entry_fail':\r\n print(\"Please try again\")\r\n\r\n else:\r\n self.data_base.employee_database[emp.name] = emp\r\n\r\n elif answer == 'n':\r\n answer2 = input(\"Would you like to view the information entered? (y/n) \")\r\n\r\n if answer2 == 'y':\r\n self.data_base.view_data()\r\n\r\n else:\r\n exit(0)\r\n\r\n else:\r\n pass\r\n\r\n\r\nengine_obj = Engine()\r\nengine_obj.run_engine()\r\n","repo_name":"Charleso19/Employee-Salary-Problem","sub_path":"employee_solution.py","file_name":"employee_solution.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42239010131","text":"from numpy import size\nimport torch\nimport torch.nn as nn\n\nclass SelfAttention(nn.Module):\n def __init__(self, embed_size, heads, device) -> None:\n super().__init__()\n self.embed_size = embed_size\n self.heads = heads\n self.head_dim = embed_size // heads # split to heads\n\n assert (self.head_dim * heads == embed_size), \"Embed size needs to be divided by heads\"\n\n self.values_net = nn.Linear(self.head_dim, self.head_dim, bias=False).to(device)\n self.keys_net = nn.Linear(self.head_dim, self.head_dim, bias=False).to(device)\n self.queries_net = nn.Linear(self.head_dim, self.head_dim, bias=False).to(device)\n\n # linear mapping from embed_size -> embed_size before output to next attention block\n self.fc_out = nn.Linear(heads * self.head_dim, embed_size).to(device) # head_dim * heads IS embed size actually\n\n\n def forward(self, q, k, v, mask):\n # this is the most important part !!!\n # the first dim is the batch_size, e.g: # of sentences\n # the second dim is word size in one batch, like 128 words.\n # the third dim is embed size = self.heads_dim * heads \n N = q.shape[0] # q,k,v len should be the same\n value_len, key_len, query_len = v.shape[1], k.shape[1], q.shape[1]\n \n # split embeddings into the heads, N and value_len remain the same\n values = v.reshape(N, value_len, self.heads, self.head_dim) \n keys = k.reshape(N, key_len, self.heads, self.head_dim) \n queries = q.reshape(N, query_len, self.heads, self.head_dim)\n \n # go through linear mapping\n values = self.values_net(values)\n keys = self.keys_net(keys)\n queries = self.queries_net(queries)\n\n # einsum calculates the sum of products based on einstein convention \n energy = torch.einsum(\"nqhd,nkhd->nhqk\", [queries, keys])\n # queries shape (N, query_len, heads, heads_dim)\n \n if mask is not None:\n # replace the masked value with very small value to avoid numeric overflow\n energy = energy.masked_fill(mask == 0, float(\"-1e20\"))\n\n\n # embed_size = heads * heads_dim, so why not use heads_dim ? --> since they will be concatenate laters\n attention = torch.softmax(energy / (self.embed_size ** 0.5), dim=3) # nhqk / normalized\n out = torch.einsum(\"nhqk,nkhd->nqhd\", [attention, values]) # nqhd\n out = out.reshape(N, query_len, self.head_dim * self.heads) # concatenation over heads to self.embed_dims\n # attention shape (N, heads, query_len, key_len)\n # value shape: (N, value_len, heads, heads_dim)\n # (N, query_len, heads, heads_dim)\n\n out = self.fc_out(out)\n return out\n\nclass TransformerBlock(nn.Module):\n def __init__(self, embed_size, heads, dropout, forward_expansion, device):\n super().__init__() \n self.attention = SelfAttention(embed_size, heads, device)\n self.norm1 = nn.LayerNorm(embed_size).to(device)\n self.norm2 = nn.LayerNorm(embed_size).to(device)\n \n # forward_expansion to improve the FFN encoding ability\n self.feed_forward = nn.Sequential(\n nn.Linear(embed_size, forward_expansion * embed_size),\n nn.ReLU(),\n nn.Linear(forward_expansion * embed_size, embed_size)\n ).to(device)\n\n self.dropout = nn.Dropout(dropout).to(device) # dropout inside the transformersblock\n\n\n def forward(self, value, key, query, mask):\n # sequentially chain the attention block -> add_norm -> FFN -> add_norm\n attention = self.attention(query, key, value, mask)\n x = self.dropout(self.norm1(attention + query)) # why not value ?\n forward = self.feed_forward(x)\n out = self.dropout(self.norm2(forward + x))\n return out\n\n\nclass Encoder(nn.Module):\n def __init__(\n self,\n src_vocab_size,\n embed_size, \n num_layers,\n heads,\n device,\n forward_expansion,\n dropout,\n max_length,\n ) -> None:\n super().__init__()\n self.embed_size = embed_size\n self.device = device\n\n # embedding layer will take an integer, make it one-hot, then linear mapping\n self.word_embedding = nn.Embedding(src_vocab_size, embed_size).to(device)\n self.position_embedding = nn.Embedding(max_length, embed_size).to(device)\n\n self.word_embedding.to(device)\n self.position_embedding.to(device)\n\n self.layers = nn.ModuleList(\n [\n TransformerBlock(\n embed_size,\n heads,\n dropout=dropout,\n forward_expansion=forward_expansion,\n device=device\n )\n for _ in range(num_layers)\n ]\n )\n\n self.dropout = nn.Dropout(dropout)\n self.max_length = max_length\n\n\n def forward(self, x, mask):\n N, seq_length = x.shape[0], x.shape[1]\n # arange(0, N) -> [0, 1, 2, ..., N]\n # expand -> enlarge any dim with one length\n positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)\n # the integer input will be converted to one-hot inside the embedding module\n out = self.dropout(self.word_embedding(x) + self.position_embedding(positions)) \n \n for layer in self.layers:\n out = layer(out, out, out, mask)\n \n return out\n\n\nclass DecoderBlock(nn.Module):\n def __init__(self, embed_size, heads, forward_expansion, dropout, device) -> None:\n super().__init__()\n self.attention = SelfAttention(embed_size, heads, device)\n self.norm = nn.LayerNorm(embed_size).to(device)\n self.transformer_block = TransformerBlock(\n embed_size, heads, dropout, forward_expansion, device\n )\n self.dropout = nn.Dropout(dropout)\n\n\n def forward(self, x, value, key, src_mask, trg_mask):\n # the first masked multiheads attention using raw x as the QKV\n attention = self.attention(x, x, x, trg_mask) \n # take the output sentence's query to attend the input sentences value and keys\n query = self.dropout(self.norm(attention + x))\n # value and key are from encoder, query is generated from self-attention of the decoder last outputs\n # we use every query from decoder to attend keys in encoder output, times values then normalized\n # so every query in the decoder last output will produce a value-shape-like output from transformers\n out = self.transformer_block(value, key, query, src_mask) # shape of (N_dc, q_dc, embed_size)\n return out\n\n\nclass Decoder(nn.Module):\n def __init__(self, \n trg_vocab_size, \n embed_size,\n num_layers,\n heads,\n forward_expansion,\n dropout,\n device,\n max_length, \n ) -> None:\n super().__init__()\n self.device = device\n self.word_embedding = nn.Embedding(trg_vocab_size, embed_size).to(device)\n self.position_embedding = nn.Embedding(max_length, embed_size).to(device)\n\n self.layers = nn.ModuleList(\n [\n DecoderBlock(\n embed_size,\n heads,\n forward_expansion,\n dropout,\n device=self.device\n )\n for _ in range(num_layers)\n ]\n )\n self.fc_out = nn.Linear(embed_size, trg_vocab_size).to(device)\n self.dropout = nn.Dropout(dropout)\n\n \n def forward(self, x, enc_out, src_mask, trg_mask):\n N, seq_length = x.shape[0], x.shape[1]\n positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)\n x = self.dropout(self.word_embedding(x) + self.position_embedding(positions))\n\n for layer in self.layers:\n # enc_out is in shape of (N, l, d) serves as value and keys\n x = layer(x, enc_out, enc_out, src_mask, trg_mask) # values and keys are just features from encoder\n # output will be (N, l, embed_size)\n out = self.fc_out(x)\n return out\n\nclass Transformer(nn.Module):\n def __init__(self,\n src_vocab_size,\n trg_vocab_size,\n src_pad_idx, \n trg_pad_idx, \n embed_size=256, \n num_layers=6,\n forward_expansion=4,\n heads=8,\n dropout=0.0,\n device=\"cpu\",\n max_length=128,\n ) -> None:\n super().__init__()\n self.encoder = Encoder(\n src_vocab_size,\n embed_size,\n num_layers,\n heads,\n device,\n forward_expansion,\n dropout,\n max_length,\n )\n\n self.decoder = Decoder(\n trg_vocab_size,\n embed_size,\n num_layers,\n heads,\n forward_expansion,\n dropout,\n device,\n max_length,\n )\n\n self.src_pad_idx = src_pad_idx\n self.trg_pad_idx = trg_pad_idx\n self.device = device\n \n def make_src_mask(self, src):\n src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)\n # (N, 1, 1, src_len) set all pad token to 0 and non-pad token to 1\n return src_mask.to(self.device)\n\n def make_trg_mask(self, trg): \n N, trg_len = trg.shape[0], trg.shape[1]\n trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(N, 1, trg_len, trg_len)\n return trg_mask.to(self.device)\n\n\n def forward(self, src, trg):\n src_mask = self.make_src_mask(src)\n trg_mask = self.make_trg_mask(trg)\n enc_src = self.encoder(src, src_mask)\n out = self.decoder(trg, enc_src, src_mask, trg_mask)\n return out \n\n","repo_name":"LimboWK/myTransformers","sub_path":"self_attention.py","file_name":"self_attention.py","file_ext":"py","file_size_in_byte":10000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32843232792","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n#Automate the Boring Stuff Lesson 39: Downloading from the web with Requests Module\n#Pages 207-240 Textbook\n\n#use requests modules when you have the exact url to download, use selenium for variable urls\n\n# Start #\n\nurl = 'https://www.goodreads.com/quotes'\n\n###Try/except allows program to continue running even with failed download###\ntry:\n res = requests.get(url) # Download files from the web with 'requests' module\n # Returns response object module diognoses data on the url returned \nexcept:\n res.raise_for_status() # raises exception at time of error, ensures program halts if failed download\n \n'''\nprint(res.status_code) # checks if response succeeded with response object\n # ex. value 404 = error, value 200 = succeeded\nprint(len(res.text)) # res.text holds the string in requests module\n #show count of string\nprint(res.text[:50])\n'''\n### Unicode below: learn more at http://bit.ly/unipain or https://nedbatchelder.com/text/unipain.html###\n### make a byte string (what type of encoding is it? check the protocol, \n### or unicode string, the final coding for text for all language and symbols ###\n### facts of life summary for bytes and Unicode encoding ###\n #1 ALL data is transfered as 1/0 bytes \n #2 bytes is limited to 256 char and we need more\n #3 we need both unicdoe and bytes\n #4 we cannot choose we must do both encodings\n #5 declaared encodings can be wrong\n #PRO TIP# \n #1 Write code as unicode sandwhich [bytes -unicode - bytes]\n #2 Know what kind of strings you are deailing with: bytes? or unicode?\n #3 test, test, test\n\nplayFile = open('goodreads.com_quotes.txt', 'wb') #wb = write binary mode instead of text to maintain unicode\n # open = returns file object, saves to file\nfor chunk in res.iter_content(100000): #save file to drive with iter_content method\n playFile.write(chunk)\n\nplayFile.close()\n\n\n# learn about other requests modules at 'requests.readthedocs.org'","repo_name":"davemccallme/davemccallme.github.io","sub_path":"scratch/Python/AutomateTheBoringsStuff/[ATBS] Lesson 39. Downloading from the web with Requests Module.py","file_name":"[ATBS] Lesson 39. Downloading from the web with Requests Module.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"21275710925","text":"import numpy as np\n\n\ndef run_optimizer(opt, cost_f, iterations, *args, **kwargs):\n \"\"\"run_optimizer function\n\n Args:\n opt (_type_: class Optimizer): Bộ tối ưu\n cost_f (_type_: class): Hàm chi phí (Hàm mất mát) cần phải tối ưu\n iterations (_type_: int): Số lần lặp\n\n Returns:\n errors (_type_: list): Độ lỗi\n distance (_type_: float): Khoảng cách đến vị trí tối ưu\n xs (_type_: float): Danh sách tọa độ x\n ys (_type_: float): Danh sách tọa độ y\n \"\"\"\n errors = [cost_f.eval(cost_f.x_start, cost_f.y_start)]\n xs, ys = [cost_f.x_start], [cost_f.y_start]\n for epochs in range(iterations):\n x, y = opt.step(*args, **kwargs)\n xs.append(x)\n ys.append(y)\n errors.append(cost_f.eval(x, y))\n distance = np.sqrt((np.array(xs)-cost_f.x_optimum) **2 + (np.array(ys)-cost_f.y_optimum)**2)\n return errors, distance, xs, ys\n","repo_name":"p2pc/optimchan","sub_path":"analysis_optimization_methods/optimizers/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"vi","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"2445719170","text":"#coding:utf-8\n#加载训练模型，对结果进行预测\nimport dataio\nimport ops\nimport tensorflow as tf\nimport svd_train_val\nimport numpy as np\nnp.random.seed(13575)\n\nBATCH_SIZE = 1000\nUSER_NUM = 6040\nITEM_NUM = 3952\nDIM = 15\nEPOCH_MAX = 100\nDEVICE = \"/cpu:0\" #\"/cpu:0\"\ndef clip(x):\n return np.clip(x, 1.0, 5.0)\n\nif __name__ == '__main__':\n df_train, test = svd_train_val.get_data()\n\n # 创建saver 对象\n\n user_batch = tf.placeholder(tf.int32, shape=[None], name=\"id_user\")\n item_batch = tf.placeholder(tf.int32, shape=[None], name=\"id_item\")\n rate_batch = tf.placeholder(tf.float32, shape=[None])\n\n infer, regularizer = ops.inference_svd(user_batch, item_batch, user_num=USER_NUM, item_num=ITEM_NUM, dim=DIM,\n device=DEVICE)\n global_step = tf.contrib.framework.get_or_create_global_step()\n _, train_op = ops.optimization(infer, regularizer, rate_batch, learning_rate=0.001, reg=0.05, device=DEVICE)\n\n init_op = tf.global_variables_initializer()\n\n saver = tf.train.Saver()\n with tf.Session() as sess:\n # 可以执行或不执行，restore的值会override初始值\n saver.restore(sess, \"tmp/svd/model/train.ckpt\")\n\n\n iter_test = dataio.OneEpochIterator([test[\"user\"],\n test[\"item\"],\n test[\"rate\"]],\n batch_size=-1)\n test_err2 = np.array([])\n for users, items, rates in iter_test:\n pred_batch = sess.run(infer, feed_dict={user_batch: users,\n item_batch: items})\n pred_batch = clip(pred_batch)\n test_err2 = np.append(test_err2, np.power(pred_batch - rates, 2))\n test_err = np.sqrt(np.mean(test_err2))\n print(\"test error {:f}\".format( test_err))\n","repo_name":"wu-yy/SVD-Recommend","sub_path":"loadModelTest.py","file_name":"loadModelTest.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4098691716","text":"import sys\r\ndef get_gcd(x, y):\r\n if x < y:\r\n x, y = y, x\r\n while True:\r\n z = x % y\r\n if z == 0:\r\n return y\r\n x = y\r\n y = z\r\n\r\nn = int(sys.stdin.readline().rstrip())\r\nlist = []\r\nfor i in range(n):\r\n list.append(int(sys.stdin.readline().rstrip()))\r\nlist.sort()\r\ngcd = list[1] - list[0]\r\n\r\nfor i in range(2, n, 1):\r\n gcd = get_gcd(gcd, list[i] - list[i - 1])\r\n\r\nfor i in range(2, gcd + 1, 1):\r\n if gcd%i==0:\r\n print(i, end=' ')","repo_name":"GGANCC1/BaekjoonStudy","sub_path":"백준/Gold/2981. 검문/검문.py","file_name":"검문.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36916869429","text":"#221010_boj_15649_N과M(1)\n\ndef p(K): # K 현재 단계, M 종료 조건(다 뽑았을 때)\n if K == M:\n print(*result)\n else:\n for i in range(N):\n if used[i] == 0:\n used[i] = 1\n result[K] = A[i]\n p(K+1)\n used[i] = 0\n\n\nN, M = map(int,input().split())\nA = [i for i in range(1,N+1)]\nused = [0] * N\nresult = [0] * M\n\np(0)","repo_name":"burgerfacegirl/Algorithm","sub_path":"boj/15649_N과M_1.py","file_name":"15649_N과M_1.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38879108592","text":"'''\r\n @author Alberto Ferrari - https://albertoferrari.github.io/\r\n @license This software is free - http://www.gnu.org/licenses/gpl.html\r\n \r\n scitala spartana\r\n'''\r\n\r\n#lettura intero file\r\nwith open(\"divina.txt\",encoding=\"utf-8\") as infile:\r\n testo = infile.read()\r\n\r\nRIGHE = 10\r\nCOLONNE = 25\r\n\r\nmatrice = [[' '] * COLONNE for y in range(RIGHE)]\t#matrice di spazi\r\n\r\ni = 0\r\nfor y in range(RIGHE):\r\n\tfor x in range(COLONNE):\r\n\t\tmatrice[y][x] = testo[i]\r\n\t\ti += 1\r\n\r\nprint('originale 10 righe 25 colonne')\r\nfor y in range(RIGHE):\r\n\tfor x in range(COLONNE):\r\n\t\tprint(matrice[y][x],end=\"\")\r\n\tprint()\t\r\n\r\nprint('\\ncrittato')\r\nfor x in range(COLONNE):\r\n\tfor y in range(RIGHE):\r\n\t\tprint(matrice[y][x], end=\"\")\r\n","repo_name":"albertoferrari/info_lab","sub_path":"codice_lezioni/sl10_es03_scitala.py","file_name":"sl10_es03_scitala.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"32835907916","text":"def counting(n,max_value):\n\n m = max_value + 1\n count = [0]* m \n for a in n:\n count[a] +=1\n\n i = 0 \n for a in range(m):\n for c in range(count[a]):\n n[i] = a\n i +=1\n\n return n\n\n\n\nprint(counting( [1, 2, 7, 3, 2, 1, 4, 2, 3, 2, 1], 7 ))\n","repo_name":"trix17/DSALGO","sub_path":"sorting/countingsorrt.py","file_name":"countingsorrt.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71434637528","text":"import neural_network as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef plot_train_test_loss(train_loss, test_loss, filename=\"\"):\n fig, ax = plt.subplots()\n ax.plot(train_loss, label=\"train loss\")\n ax.plot(test_loss, label=\"test loss\")\n ax.grid()\n ax.set_xlabel(\"Mini-batch iterations\")\n ax.set_ylabel(\"Loss\")\n ax.legend()\n if len(filename) > 0:\n fig.savefig(filename)\n\n\ndef plot_train_test_accuracy(train_accuracy, test_accuracy, filename=\"\"):\n fig, ax = plt.subplots()\n ax.plot(train_accuracy, label=\"train accuracy\")\n ax.plot(test_accuracy, label=\"test accuracy\")\n ax.grid()\n ax.set_xlabel(\"Mini-batch iterations\")\n ax.set_ylabel(\"Accuracy %\")\n ax.legend()\n if len(filename) > 0:\n fig.savefig(filename)\n\n\nlearning_rate = 10.0\nbatchsize = 100\nnumepochs = 5\ntrain, test = nn.get_mnist_threes_nines()\nXtrain, Ytrain = train\nXtest, Ytest = test\n\nnum_train_samples, numpixels, _ = Xtrain.shape\ninput_layer_dim = numpixels * numpixels\n\nXtrain = Xtrain.reshape(-1, input_layer_dim)\nXtest = Xtest.reshape(-1, input_layer_dim)\n\nlayer_dims = [input_layer_dim, 200, 1]\nactivations = [nn.relu, nn.sigmoid_activation]\n\nweight_matrices = nn.create_weight_matrices(layer_dims)\nbiases = nn.create_bias_vectors(layer_dims)\n\nbatch_train_losses, batch_test_losses, batch_train_accuracy, batch_test_accuracy = nn.run_epochs(Xtrain, Ytrain, Xtest,\n Ytest, weight_matrices,\n biases, activations,\n learning_rate,\n batchsize, numepochs)\n\ncombined_train_loss = np.concatenate(batch_train_losses)\ncombined_test_loss = np.concatenate(batch_test_losses)\n\ncombined_train_accuracy = np.concatenate(batch_train_accuracy)\ncombined_test_accuracy = np.concatenate(batch_test_accuracy)\n\n# plot_train_test_loss(combined_train_loss, combined_test_loss, \"train-test-loss.png\")\n# plot_train_test_accuracy(combined_train_accuracy, combined_test_accuracy, \"train-test-accuracy.png\")\n\n# testvals, _ = nn.forward_pass(Xtest, weight_matrices, biases, activations)\n# testop = np.ravel(testvals[-1])\n# predictions = np.rint(testop)\n# matches = predictions == Ytest\n#\n# failed_index = np.where(~matches)[0]\n\n\n# def save_image(image, image_index):\n# foldername = \"failed-images\\\\\"\n# filename = foldername + \"image-\" + str(image_index) + \".png\"\n# plt.imshow(image, cmap=\"gray\")\n# plt.savefig(filename)\n#\n#\n# Xtestimages, _ = test\n# for idx in failed_index:\n# save_image(Xtestimages[idx], idx)\n","repo_name":"ArjunNarayanan/CS289-HW2","sub_path":"deliverable_e.py","file_name":"deliverable_e.py","file_ext":"py","file_size_in_byte":2826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14708630064","text":"\nfrom Functions.Packages import *\nfrom Functions.PlotFunctions import *\n\nG = pickle.load(open(f\"{dataRoot}/tmpData/GraphsGData.p\", \"rb\" ))\n\n\nnodeDTlvl = pd.DataFrame()\nfor i, node in enumerate(G.nodes()):\n if G.nodes[node][\"nodeClass\"] in (\"pro\", \"dwP\", \"upP\"):\n continue\n PlvlDms = G.nodes[node][\"lvlP.DMSO\"]\n MlvlDms = G.nodes[node][\"lvlM.DMSO\"]\n PlvlDht = G.nodes[node][\"lvlP.DHT\"]\n MlvlDht = G.nodes[node][\"lvlM.DHT\"]\n nodeClass = G.nodes[node][\"nodeClass\"]\n nodeDTlvl = pd.concat([nodeDTlvl, pd.DataFrame({\"lvlP.DMSO\": PlvlDms,\"lvlP.DHT\": PlvlDht, \"lvlM.DMSO\": MlvlDms,\"lvlM.DHT\": MlvlDht, \"nodeClass\": nodeClass}, index=[i])])\n\n\n\n\n\nnodeDTlvl[\"logPlvl.DMSO\"] = np.log(nodeDTlvl[\"lvlP.DMSO\"] )\nnodeDTlvl[\"logPlvl.DHT\"] = np.log(nodeDTlvl[\"lvlP.DHT\"] )\nnodeDTlvl[\"logMlvl.DMSO\"] = np.log(nodeDTlvl[\"lvlM.DMSO\"] )\nnodeDTlvl[\"logMlvl.DHT\"] = np.log(nodeDTlvl[\"lvlM.DHT\"] )\n\nnodeDTlvl.loc[nodeDTlvl[\"nodeClass\"].isin([\"tsP\", \"uPP\", \"dPP\"]), \"nodeClass\"] = \"tsP\"\nnodeDTlvl.loc[nodeDTlvl[\"nodeClass\"].isin([\"tsM\", \"uMP\", \"dMP\"]), \"nodeClass\"] = \"tsM\"\n\nnodeDTlvl = nodeDTlvl[~(nodeDTlvl[\"nodeClass\"].isin([ \"pro\", \"upP\", \"dwP\", \"tsP\", \"tsM\"]))]\n\n\nfiltDF = nodeDTlvl[~((nodeDTlvl[\"logPlvl.DMSO\"] == 0) & (nodeDTlvl[\"logMlvl.DMSO\"] == 0) & (nodeDTlvl[\"logPlvl.DHT\"] == 0) & (nodeDTlvl[\"logMlvl.DHT\"] == 0))]\n\ndmsDF = filtDF[[\"lvlP.DMSO\", \"lvlM.DMSO\", \"logPlvl.DMSO\", \"logMlvl.DMSO\", \"nodeClass\"]]\ndmsDF = dmsDF.rename(columns={\"lvlP.DMSO\": \"lvlP\", \"lvlM.DMSO\": \"lvlM\", \"logPlvl.DMSO\": \"logPlvl\", \"logMlvl.DMSO\": \"logMlvl\"})\ndmsDF[\"Condition\"] = \"DMSO\"\ndhtDF = filtDF[[\"lvlP.DHT\", \"lvlM.DHT\", \"logPlvl.DHT\", \"logMlvl.DHT\", \"nodeClass\"]]\ndhtDF = dhtDF.rename(columns={\"lvlP.DHT\": \"lvlP\", \"lvlM.DHT\": \"lvlM\", \"logPlvl.DHT\": \"logPlvl\", \"logMlvl.DHT\": \"logMlvl\"})\ndhtDF[\"Condition\"] = \"DHT\"\n\n\nDF = pd.concat([dmsDF, dhtDF])\n\n\n\n\norder=[\"con\", \"ind\", \"non\", \"oth\", \"uPP\", \"dPP\", \"uMP\", \"dMP\"]\nhueOrder = [\"DMSO\", \"DHT\"]\ncolorPalette = {\"DMSO\": \"#FAB550\", \"DHT\": \"#5F9BD2\"}\n\ncombs = [\n (a, b)\n for a in order\n for b in hueOrder\n ]\n\nboxPairs = [\n (a, b)\n for a in combs\n for b in combs\n if a != b\n]\n\nfig = plt.figure(figsize=[10,5])\ngs = gridspec.GridSpec(ncols=2, nrows=1)\n\nfig.add_subplot(gs[0])\nax1 = sns.boxplot(data=DF, x=\"nodeClass\", y=\"logPlvl\", hue=\"Condition\", order=order, hue_order=hueOrder, palette=colorPalette)\n# add_stat_annotation(ax1, data=DF, x=\"nodeClass\", y=\"logPlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\n\nfig.add_subplot(gs[1])\nax2 = sns.boxplot(data=DF, x=\"nodeClass\", y=\"logMlvl\", hue=\"Condition\", order=order, hue_order=hueOrder, palette=colorPalette)\n# add_stat_annotation(ax2, data=DF, x=\"nodeClass\", y=\"logMlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\n\nfig.savefig(f\"{figureRoot}/nodeTlvls.filter.pdf\")\n\n\nplt.close(\"all\")\n\nfig = plt.figure(figsize=[12,9])\ngs = gridspec.GridSpec(ncols=2, nrows=1)\n\nfig.add_subplot(gs[0])\nax1 = sns.boxplot(data=DF, x=\"Condition\", y=\"logPlvl\", hue=\"nodeClass\", order=hueOrder, hue_order=order)\n# add_stat_annotation(ax1, data=DF, x=\"nodeClass\", y=\"logPlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\n\nfig.add_subplot(gs[1])\nax2 = sns.boxplot(data=DF, x=\"Condition\", y=\"logMlvl\", hue=\"nodeClass\", order=hueOrder, hue_order=order)\n# add_stat_annotation(ax2, data=DF, x=\"nodeClass\", y=\"logMlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\n\nfig.savefig(f\"{figureRoot}/nodeTlvls.filter.nodeHue.pdf\")\n\n\n\nGroDF = pickle.load(open(f\"{dataRoot}/tmpData/Gro.DF.p\", \"rb\" ))\nGroDF[\"logFC\"] = None\nGroDF[\"Qval\"] = None\nGroDF = GroDF.reset_index()\nGroDF = GroDF.rename(columns={\"index\": \"Gene\"})\nGroDF = GroDF.sort_values(\"Gene\")\nGroDF = GroDF.reset_index().drop(\"index\", axis=1)\n\n\nlogFC = pd.read_csv(f\"{dataRoot}/DEG/GSE64529_diffexpr-results.csv\")\nlogFC = logFC[[\"Gene\", \"log2FoldChange\", \"padj\"]]\nlogFC = logFC.sort_values(\"Gene\").reset_index().drop(\"index\", axis=1)\n\n\ni = 0\nfor gene, lfc, q in zip(logFC[\"Gene\"], logFC[\"log2FoldChange\"], logFC[\"padj\"]):\n left = bi.bisect_left(GroDF[\"Gene\"] , gene +\".\")\n right = bi.bisect_left(GroDF[\"Gene\"] , gene+\".z\")\n GroDF.loc[left:right-1, \"logFC\"] = lfc\n GroDF.loc[left:right-1, \"Qval\"] = q\n i += 1\n if i % 1000 == 0:\n print(gene)\n print(GroDF.loc[left:right-1, \"Gene\"])\n print(i)\n\n\n\nfiltGroDF = GroDF[~(\n((GroDF[\"logFC\"] < 1) | (GroDF[\"logFC\"] > -1)) &\n(GroDF[\"Qval\"] > 0.05) &\n(GroDF[\"class\"].isin([\"tsP\", \"tsM\"]))\n)].sort_values(\"logFC\").reset_index().drop(\"index\", axis=1)\n\n\nfiltGroDF.loc[((filtGroDF[\"logFC\"] > 1) & (filtGroDF[\"class\"] == \"tsP\")), \"class\"] = \"uPP\"\n\n\nfiltGroDF.loc[((filtGroDF[\"logFC\"] > 1) & (filtGroDF[\"class\"] == \"tsM\")), \"class\"] = \"uMP\"\n\n\nfiltGroDF.loc[((filtGroDF[\"logFC\"] < -1) & (filtGroDF[\"class\"] == \"tsP\")), \"class\"] = \"dPP\"\n\n\nfiltGroDF.loc[((filtGroDF[\"logFC\"] < -1) & (filtGroDF[\"class\"] == \"tsM\")), \"class\"] = \"dMP\"\n\n\n\n\ndmsDF = filtGroDF[[\"dmso.-\", \"dmso.+\", \"class\"]]\ndmsDF = dmsDF.rename(columns={\"dmso.-\": \"GroSeqMinus\", \"dmso.+\": \"GroSeqPlus\"})\ndmsDF[\"Condition\"] = \"DMSO\"\ndhtDF = filtGroDF[[\"dht.-\", \"dht.+\", \"class\"]]\ndhtDF = dhtDF.rename(columns={\"dht.-\": \"GroSeqMinus\", \"dht.+\": \"GroSeqPlus\"})\ndhtDF[\"Condition\"] = \"DHT\"\n\n\nallDF = pd.concat([dmsDF, dhtDF])\n\n\nhueOrder = [\"DMSO\", \"DHT\"]\ncolorPalette = {\"DMSO\": \"#FAB550\", \"DHT\": \"#5F9BD2\"}\norder=[\"con\", \"ind\", \"non\", \"oth\", \"uPP\", \"dPP\", \"uMP\", \"dMP\"]\n\n\nfig = plt.figure(figsize=[10,5])\ngs = gridspec.GridSpec(ncols=2, nrows=1)\n\nfig.add_subplot(gs[0])\nax1 = sns.boxplot(data=allDF, x=\"class\", y=\"GroSeqPlus\", hue=\"Condition\", order=order, hue_order=hueOrder, palette=colorPalette)\n# add_stat_annotation(ax1, data=DF, x=\"nodeClass\", y=\"logPlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\nax1.set(yscale=\"log\")\n\nfig.add_subplot(gs[1])\nax2 = sns.boxplot(data=allDF, x=\"class\", y=\"GroSeqMinus\", hue=\"Condition\", order=order, hue_order=hueOrder, palette=colorPalette)\n# add_stat_annotation(ax2, data=DF, x=\"nodeClass\", y=\"logMlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\nax2.set(yscale=\"log\")\n\nfig.savefig(f\"{figureRoot}/nodeTlvls.allDE.pdf\")\n\n\nplt.close(\"all\")\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\ngeneNodes = list(GroDF[GroDF[\"class\"].isin([\"tsP\", \"tsM\"])].index)\n\n\nfileCSV = f\"{dataRoot}/DEG/GSE64529_diffexpr-results.csv\"\nwith open(fileCSV) as csvFile:\n reader = csv.reader(csvFile, delimiter=',')\n next(reader)\n for i, row in enumerate(reader):\n geneSymbol = row[1]\n logFC = row[3]\n Qval = row[7]\n if (logFC == \"NA\" or Qval == \"NA\"):\n continue\n logFC = float(logFC)\n Qval = float(Qval)\n GroDF.loc[GroDF[\"index\"].str.find(geneSymbol + \".\") != -1, \"logFC\"] = logFC\n GroDF.loc[GroDF[\"index\"].str.find(geneSymbol + \".\") != -1, \"Qval\"] = Qval\n if i % 1000 == 0:\n print(i)\n\n\n\n\nfiltGroDF = GroDF[~((GroDF[\"logPlvl.DMSO\"] == 0) & (GroDF[\"logMlvl.DMSO\"] == 0) & (GroDF[\"logPlvl.DHT\"] == 0) & (GroDF[\"logMlvl.DHT\"] == 0))]\n\ndmsDF = GroDF[[\"dmso.-\", \"dmso.+\", \"class\"]]\ndmsDF = dmsDF.rename(columns={\"dmso.-\": \"GroSeqMinus\", \"dmso.+\": \"GroSeqPlus\"})\ndmsDF[\"Condition\"] = \"DMSO\"\ndhtDF = GroDF[[\"dht.-\", \"dht.+\", \"class\"]]\ndhtDF = dhtDF.rename(columns={\"dht.-\": \"GroSeqMinus\", \"dht.+\": \"GroSeqPlus\"})\ndhtDF[\"Condition\"] = \"DHT\"\n\n\nallDF = pd.concat([dmsDF, dhtDF])\n\n\norder=[\"con\", \"ind\", \"non\", \"oth\", \"tsP\", \"tsM\"]\n\n\nfig = plt.figure(figsize=[10,5])\ngs = gridspec.GridSpec(ncols=2, nrows=1)\n\nfig.add_subplot(gs[0])\nax1 = sns.boxplot(data=allDF, x=\"class\", y=\"GroSeqPlus\", hue=\"Condition\", order=order, hue_order=hueOrder, palette=colorPalette)\n# add_stat_annotation(ax1, data=DF, x=\"nodeClass\", y=\"logPlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\nax1.set(yscale=\"log\")\n\nfig.add_subplot(gs[1])\nax2 = sns.boxplot(data=allDF, x=\"class\", y=\"GroSeqMinus\", hue=\"Condition\", order=order, hue_order=hueOrder, palette=colorPalette)\n# add_stat_annotation(ax2, data=DF, x=\"nodeClass\", y=\"logMlvl\", hue=\"Condition\", box_pairs=boxPairs, test='Mann-Whitney', loc='inside')\nax2.set(yscale=\"log\")\n\nfig.savefig(f\"{figureRoot}/nodeTlvls.all.pdf\")\n\n\nplt.close(\"all\")\n","repo_name":"birkiy/CisGraph","sub_path":"Vers1.0/DirectionSignal/NodeDivergentTranscripts.py","file_name":"NodeDivergentTranscripts.py","file_ext":"py","file_size_in_byte":8204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38820528632","text":"from typing import List\n\n\nclass Solution:\n def singleNonDuplicate(self, nums: List[int]) -> int:\n left, right = 0, len(nums)-1\n\n while left < right:\n mid = (left+right) // 2\n is_first_half_even = (mid-left+1) % 2 == 0\n if nums[mid] == nums[mid+1]:\n if is_first_half_even:\n right = mid - 1\n else:\n left = mid + 2\n elif nums[mid-1] == nums[mid]:\n if is_first_half_even:\n left = mid + 1\n else:\n right = mid - 2\n else:\n return nums[mid]\n\n return nums[left]\n","repo_name":"yuchia0221/Leetcode","sub_path":"Binary Search/540-SingleElementinaSortedArray/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"9109322442","text":"from __future__ import division\nfrom __future__ import print_function\n\nimport time\nimport argparse\nimport numpy as np\nimport random\n#画图可视化损失\nimport matplotlib.pyplot as plt\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom d2l import torch as d2l\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nimport os\nimport sys\npath1 = os.path.dirname(os.path.dirname(__file__))\nsys.path.append(path1)\nfrom gcn.utils import load_data, accuracy\nfrom gcn.models import GCN\nfrom gcn.animator import Animator\n\n#print({path1})\n\n# Training settings\nparser = argparse.ArgumentParser()\nparser.add_argument('--no-cuda', action='store_true', default=False,\n help='Disables CUDA training.')\nparser.add_argument('--fastmode', action='store_true', default=False,\n help='Validate during training pass.')\nparser.add_argument('--seed', type=int, default=42, help='Random seed.')\nparser.add_argument('--epochs', type=int, default=50,\n help='Number of epochs to train.')\nparser.add_argument('--lr', type=float, default=0.01,\n help='Initial learning rate.')\nparser.add_argument('--weight_decay', type=float, default=5e-4,\n help='Weight decay (L2 loss on parameters).')\nparser.add_argument('--hidden', type=int, default=16,\n help='Number of hidden units.')\nparser.add_argument('--dropout', type=float, default=0.5,\n help='Dropout rate (1 - keep probability).')\n\nargs = parser.parse_args()\nargs.cuda = not args.no_cuda and torch.cuda.is_available()\n#print({args.cuda})\n\n# Empty GPU cache\nif torch.cuda.is_available():\n torch.cuda.empty_cache()\n \nnp.random.seed(args.seed)\ntorch.manual_seed(args.seed)\nif args.cuda:\n torch.cuda.manual_seed(args.seed)\n\n# Load data\nadj, features, labels, idx_train, idx_val, idx_test = load_data()\n\n# Model and optimizer\nmodel = GCN(nfeat=features.shape[1],\n nhid=args.hidden, #使用了前面的随机种子\n nclass=labels.max().item() + 1,\n dropout=args.dropout)\n#优化方法选择adam\n#优化方法还有SGD BGD\n#optimizer = torch.optim.SGD(model.parameters(), lr = args.lr, weight_decay= args.weight_decay) #手动调整学习率，而非衰减\n\noptimizer = optim.Adam(model.parameters(),\n lr=args.lr, weight_decay=args.weight_decay)\n\n\n'''if args.cuda:\n model.cuda()\n features = features.cuda()\n adj = adj.cuda()\n labels = labels.cuda()\n idx_train = idx_train.cuda()\n idx_val = idx_val.cuda()\n idx_test = idx_test.cuda()\n '''\n\n# 训练过程\ndef train(epoch): \n t = time.time()\n model.train() \n optimizer.zero_grad()\n output = model(features, adj) #为什么model变成函数\n loss_train = F.nll_loss(output[idx_train], labels[idx_train])\n acc_train = accuracy(output[idx_train], labels[idx_train])\n loss_train.backward()\n optimizer.step()\n \n if not args.fastmode:\n # Evaluate validation set performance separately,\n # deactivates dropout during validation run.\n model.eval()\n output = model(features, adj)\n #计算验证集\n loss_val = F.nll_loss(output[idx_val], labels[idx_val])\n acc_val = accuracy(output[idx_val], labels[idx_val])\n #eval_losses += loss_val.item()\n #eval_acces += acc_val.item()\n\n print('Epoch: {:04d}'.format(epoch+1),\n 'loss_train: {:.4f}'.format(loss_train.item()),\n 'acc_train: {:.4f}'.format(acc_train.item()),\n 'loss_val: {:.4f}'.format(loss_val.item()),\n 'acc_val: {:.4f}'.format(acc_val.item()),\n 'time: {:.4f}s'.format(time.time() - t), file = mylog)\n \n return loss_train.item(), acc_train.item(), loss_val.item(), acc_val.item()\n \n\n\ndef test():\n model.eval()\n output = model(features, adj)\n loss_test = F.nll_loss(output[idx_test], labels[idx_test])\n acc_test = accuracy(output[idx_test], labels[idx_test]) \n print(\"Test set results:\",\n \"loss= {:.4f}\".format(loss_test.item()),\n \"accuracy= {:.4f}\".format(acc_test.item()), file=mylog)\n #return loss_test, acc_test\n\n# Train model\nt_total = time.time()\n# 将 train accuracy 保存到 \"tensorboard/train\" 文件夹\nlog_dir = os.path.join('tensorboard', 'train')\ntrain_writer = SummaryWriter(log_dir=log_dir)\ntrain_loss_writer = SummaryWriter(log_dir=log_dir)\n# 将 test accuracy 保存到 \"tensorboard/validation\" 文件夹\nlog_dir = os.path.join('tensorboard', 'validation')\nval_writer = SummaryWriter(log_dir=log_dir)\nval_loss_writer = SummaryWriter(log_dir=log_dir)\n \n#定义loss, acc字符串 \ntrain_loss_list = []\ntrain_acc_list = []\nval_loss_list = []\nval_acc_list = []\n\n'''animator = Animator(xlabel='epoch', xlim=[1, args.epochs], ylim=[0.3, 0.9],\n legend=['train loss', 'train acc', 'val loss', 'val acc'])\n '''\n#迭代训练\nmylog = open('output.txt', mode = 'w',encoding='utf-8')\nfor epoch in range(args.epochs):\n out = train(epoch)\n #动画显示\n #figure = animator.add(epoch + 1, out)\n # 写入文件\n train_writer.add_scalar('Accuracy', out[1], epoch)\n train_loss_writer.add_scalar('Loss', out[0],epoch)\n val_writer.add_scalar('Accuracy', out[3], epoch)\n val_loss_writer.add_scalar('Loss',out[2],epoch)\n \n train_loss_list.append(out[0])\n train_acc_list.append(out[1])\n val_loss_list.append(out[2])\n val_acc_list.append(out[3])\nprint(\"Optimization Finished!\", file=mylog)\nprint(\"Total time elapsed: {:.4f}s\".format(time.time() - t_total), file=mylog)\n\n#plt绘图代码\nmarkers = {'<!-- --> train': 'o', 'val': 's'}\nx = np.arange(len(train_acc_list))\nplt.plot(x, train_acc_list, label='train acc')\nplt.plot(x, val_acc_list, label='val acc', linestyle='--')\nplt.xlabel(\"epochs\")\nplt.ylabel(\"accuracy\")\nplt.ylim(0, 1.0)\nplt.legend(loc='lower right')\ntitle = plt.title('Accuracy')\nplt.savefig('acc.png')\n#plt.show()\nprint('acc complete')\n\nmarker_loss = {'<!-- --> train': 'o', 'val': 's'}\ny = np.arange(len(train_loss_list))\nplt.plot(y, train_loss_list, label='train loss')\nplt.plot(y, val_loss_list, label='val loss', linestyle='--')\nplt.xlabel(\"epochs\")\nplt.ylabel(\"loss\")\nplt.ylim(0, 10)\nplt.legend(loc='lower right')\ntitle = plt.title('Loss')\nplt.savefig('loss.png')\n#plt.show()\nprint('loss complete')\n# Testing\ntest_out = test()\nprint(\"Test Finished!\", file=mylog)\nmylog.close()","repo_name":"zhuwangjulia/semi-gcn-pytorch","sub_path":"gcn/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6401,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"35611380061","text":"'''\nGiven a string containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\n\nAn input string is valid if:\n\nOpen brackets must be closed by the same type of brackets.\nOpen brackets must be closed in the correct order.\nNote that an empty string is also considered valid.\n\nExample 1:\n\nInput: \"()\"\nOutput: true\nExample 2:\n\nInput: \"()[]{}\"\nOutput: true\nExample 3:\n\nInput: \"(]\"\nOutput: false\nExample 4:\n\nInput: \"([)]\"\nOutput: false\nExample 5:\n\nInput: \"{[]}\"\nOutput: true\n'''\n\nclass Solution:\n def isValid(self, s):\n \"\"\"\n :type s: str\n :rtype: bool\n 思路：\n 利用栈 每个元素入栈 匹配消除\n \"\"\"\n list_valid = ['()', '[]', '{}']\n list_stack = []\n for w in s:\n if list_stack:\n string = list_stack[-1] + w\n if string in list_valid:\n list_stack.pop(-1)\n else:\n list_stack.append(w)\n else:\n list_stack.append(w)\n return len(list_stack) == 0","repo_name":"t-dawei/leetcode","sub_path":"code/20. 有效的括号.py","file_name":"20. 有效的括号.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"684982185","text":"class Item:\n def __init__(self, name, price, quantity):\n self.name = name.title()\n self.price = price\n self.quantity = quantity\n self.total = price * quantity\n def total_sum(self):\n return self.price * self.quantity\nitem = Item(\"книга\", 500, 10)\nprint(item.name)\nprint(item.total)\nprint(item.total_sum())","repo_name":"Vleshchik/python_winter_work_2023","sub_path":"Tasks/Task_27/Task27_2.py","file_name":"Task27_2.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"41927917008","text":"import random\nimport numpy as np\nfrom scipy.optimize import minimize\n\nSMALL_NUM = .00001\nBIG_NUM = 1000000\n\nL1 = lambda x: np.sum(abs(x)**1) **(1/1)\nL2 = lambda x: np.sum(abs(x)**2) **(1/2)\nL3 = lambda x: np.sum(abs(x)**3) **(1/3)\nL4 = lambda x: np.sum(abs(x)**4) **(1/4)\nL5 = lambda x: np.sum(abs(x)**5) **(1/5)\nL6 = lambda x: np.sum(abs(x)**6) **(1/6)\nLINF = lambda x: max(abs(x))\n\nLINEAR = lambda r: max(0,1 - r)\nQUADRATIC = lambda r: max(0,1 - r)**2\nWEIRD = lambda r: max(0,1 - r)**1.5\n\nBASIS_SIZE = 2.0\n\nclass Linn:\n # Initialize the Linn regressor.\n def __init__(self, radius=LINEAR, length=L2, basis_size=BASIS_SIZE):\n self.radius = radius\n self.length = length\n self.basis_size = basis_size\n\n # Fit a set of points\n def fit(self, points, values):\n self.points = points.copy()\n self.values = values.copy()\n\n # Wrapper for 'predict' that returns a single value for a single\n # prediction, or an array of values for an array of predictions\n def __call__(self, x:np.ndarray, *args, **kwargs):\n single_response = len(x.shape) == 1\n if single_response:\n x = np.array([x])\n if len(x.shape) != 2:\n raise(Exception(\"ERROR: Bad input shape.\"))\n response = np.asarray(self.predict(x, *args, **kwargs), dtype=float)\n # Return the response values\n return response[0] if single_response else response\n\n # Generate a prediction for a new point\n def predict(self, xs):\n predictions = []\n for x in xs:\n weights = []\n to_skip = set()\n nearest_indices = np.argsort([np.sum((x-p)**2) for p in self.points])\n # Cycle through all training points\n for nearest_ind in range(len(self.points)): #nearest_indices:\n # Skip points that we know will not support x\n if nearest_ind in to_skip: \n weights.append(0)\n continue\n boundary = (float('inf'),)\n # Get the nearest point\n nearest = self.points[nearest_ind]\n vector = (x - nearest)\n # dist_to_x = self.length(vector)\n # Calculate the vectors defining separating planes for\n # all other training points, use the closest one in\n # the direction of 'vector' as the boundary.\n for other_ind in range(len(self.points)):\n if (nearest_ind == other_ind): continue\n other = self.points[other_ind]\n divide = (other - nearest)\n nearest_to_divide = np.dot(nearest,divide)\n other_to_divide = np.dot(other,divide)\n x_to_divide = np.dot(x,divide)\n # Check to make sure that x is on the same side of\n # nearest as other is.\n sign = (other_to_divide - nearest_to_divide) * (x_to_divide - nearest_to_divide)\n if (sign > 0):\n # # Identify the midpoint between (nearest + other)\n # middle = (nearest_to_divide + other_to_divide) / 2\n # # Identify how much farther x is than the midpoint\n # ratio = (x_to_divide-nearest_to_divide) / (middle-nearest_to_divide)\n ratio = 2*(x_to_divide-nearest_to_divide)/(other_to_divide-nearest_to_divide)\n # Calculate the distance to the boundary\n # (using the known distance to the point)\n # dist_to_boundary = ratio\n dist_to_boundary = self.length(vector)/ratio\n if (dist_to_boundary < boundary[0]):\n # ratio = dist_to_x / dist_to_boundary\n boundary = (dist_to_boundary, ratio)\n else:\n # We know that 'other' is blocked from\n # reaching x by 'nearest' and should not check\n # for weighting because it must be 0.\n to_skip.add(other_ind)\n # If there was no boundary between x and nearest\n if (boundary[0] == float('inf')):\n weights.append( 1 )\n else:\n new = False\n if new:\n # Calculate the weight as an approximation to the\n # natural neighbor measure (how large is the\n # difference between the natural boundary and the\n # new boundary that would be defined by x)\n weights.append( max(0,boundary[0] - boundary[1]/2) )\n else:\n # boundary = (self.length(vector/boundary[0]), boundary[1])\n\n # Calculate the weight of this training point response\n ratio = boundary[1]\n weights.append( self.radius(ratio / self.basis_size) )\n \n if sum(weights) > 0:\n guess = sum(np.array(weights)*self.values)/sum(weights)\n else:\n guess = 0\n predictions.append( guess )\n # print(\"weights: \",weights)\n # print(\"predictions: \",predictions)\n return predictions\n\n\nif __name__ == \"__main__\":\n from util.plotly import Plot\n mult = 5\n fun = lambda x: np.cos(x[0]*mult) + np.sin(x[1]*mult)\n np.random.seed(0)\n p = Plot()\n low = 0\n upp = 1\n dim = 2\n plot_points = 2000\n N = 4\n random = True\n if random:\n x = np.random.random(size=(N,dim))\n else:\n N = int(round(N ** (1/dim)))\n x = np.array([r.flatten() for r in np.meshgrid(np.linspace(low,upp,N), np.linspace(low,upp,N))]).T\n y = np.array([fun(v) for v in x])\n p.add(\"Training Points\", *x.T, y)\n \n surf = Linn()\n surf.fit(x,y)\n p.add_func(\"VMesh\", surf, *([(low,upp)]*dim), plot_points=plot_points)\n p.plot(file_name=\"vmesh.html\")\n\n","repo_name":"tchlux/util","sub_path":"util/development/voronoi_mesh/plane.py","file_name":"plane.py","file_ext":"py","file_size_in_byte":6107,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"31"} +{"seq_id":"36198798053","text":"\"\"\"\nquicksort\n\"\"\"\n# 这个题目要注意的一个地方是：python的函数参数并不全都是：传值调用，对于可变的数据类型来说，都是传引用调用。\nclass Solution():\n\n def quicksort(self,nums):\n \"\"\"\n :param nums: list[int]\n :return: list[int]\n \"\"\"\n self.quicksort2(nums,0,len(nums)-1)\n return nums\n\n def quicksort2(self,nums,l,r):\n \"\"\"\n\n :param nums:list[int]\n :param l: int\n :param r: int\n :return: void\n \"\"\"\n if l < r:\n pivot_pos = self.partation(nums,l,r)\n self.quicksort2(nums,l,pivot_pos-1)\n self.quicksort2(nums,pivot_pos+1,r)\n\n def partation(self,nums,l,r):\n \"\"\"\n :param nums: list[int]\n :param l: int\n :param r: int\n :return: int\n \"\"\"\n pivot = nums[l] #先找到基准点\n i = l\n j = r\n while i < j:\n while nums[i] <= pivot:\n i += 1\n while nums[j] > pivot:\n j -= 1\n if i < j:\n nums[i] ,nums[j] = nums[j],nums[i]\n\n nums[l] = nums[j]\n nums[j] = pivot\n\n return j\n\nif __name__ == '__main__':\n mysol = Solution()\n import random\n a = list(range(30))\n random.shuffle(a)\n print(mysol.quicksort(a))\n","repo_name":"Jackie1995/leetcode_answers","sub_path":"排序专题/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74654330328","text":"'''\r\nThis script gets the mean and variance of each slice in all the PNG plices in the dataset\r\n\r\nThis will be performed juist on the reduced trainign dataset, as we can inffer it is the same for the whole distribution\r\n\r\n'''\r\nimport os\r\nimport numpy as np\r\nimport cv2 as cv\r\n\r\n\r\nPATH = r'E:\\an_4_LICENTA\\Workspace\\Dataset\\Optimization_Dataset_nepreprocesat\\PNG_Training_Dataset'\r\n\r\n#arr.reshape(-1)\r\n\r\n# sumofmeans = the mean pover a siungle example\r\nsumofmeans_list_t1 = []\r\nsumofmeans_list_t1ce = []\r\nsumofmeans_list_t2 = []\r\nsumofmeans_list_flair = []\r\n\r\npsum_t1 = 0\r\npsum_t1ce = 0\r\npsum_t2 = 0\r\npsum_flair = 0\r\n\r\npsumsq_t1_list = []\r\npsumsq_t1ce_list = []\r\npsumsq_t2_list = []\r\npsumsq_flair_list = []\r\n\r\nno_examples = 0\r\n\r\nfor dir_name in os.listdir(PATH):\r\n\r\n EXAPLE_PATH = PATH + '\\\\' + dir_name\r\n\r\n for file_name in os.listdir(EXAPLE_PATH):\r\n FILE_PATH = EXAPLE_PATH + '\\\\' + file_name\r\n img = cv.imread(FILE_PATH, cv.IMREAD_GRAYSCALE)/255\r\n flattened_img = img.reshape(-1)\r\n if file_name == 't1.png':\r\n sumofmeans_list_t1.append(np.sum(flattened_img))\r\n psumsq_t1_list.append(np.sum(flattened_img**2))\r\n elif file_name == 't1ce.png':\r\n sumofmeans_list_t1ce.append(np.sum(flattened_img))\r\n psumsq_t1ce_list.append(np.sum(flattened_img**2))\r\n elif file_name == 't2.png':\r\n sumofmeans_list_t2.append(np.sum(flattened_img))\r\n psumsq_t2_list.append(np.sum(flattened_img**2))\r\n elif file_name == 'flair.png':\r\n sumofmeans_list_flair.append(np.sum(flattened_img))\r\n psumsq_flair_list.append(np.sum(flattened_img**2))\r\n \r\n no_examples += 1\r\n\r\ncount = no_examples * 240 * 240\r\n\r\nlist_t1 = []\r\nlist_t1ce = []\r\nlist_t2 = []\r\nlist_flair = []\r\n\r\nfor x in sumofmeans_list_t1:\r\n list_t1.append(x/count)\r\n\r\nfor x in sumofmeans_list_t1ce:\r\n list_t1ce.append(x/count)\r\n\r\nfor x in sumofmeans_list_t2:\r\n list_t2.append(x/count)\r\n\r\nfor x in sumofmeans_list_flair:\r\n list_flair.append(x/count)\r\n\r\nmean_t1 = sum(list_t1)\r\nmean_t1ce = sum(list_t1ce)\r\nmean_t2 = sum(list_t2)\r\nmean_flair = sum(list_flair)\r\n\r\nlist_t1 = []\r\nlist_t1ce = []\r\nlist_t2 = []\r\nlist_flair = []\r\n\r\nfor x in psumsq_t1_list:\r\n list_t1.append(x/count)\r\n\r\nfor x in psumsq_t1ce_list:\r\n list_t1ce.append(x/count)\r\n\r\nfor x in psumsq_t2_list:\r\n list_t2.append(x/count)\r\n\r\nfor x in psumsq_flair_list:\r\n list_flair.append(x/count)\r\n\r\n#print(psumsq_t1_list[0:4])\r\n#print(list_t1[0:4])\r\n#print(sum(list_t1))\r\n#print(sum(list_t1)-mean_t1**2)\r\n\r\ntotal_std_t1 = np.sqrt(sum(list_t1)-mean_t1**2)\r\ntotal_std_t1ce = np.sqrt(sum(list_t1ce)-mean_t1ce**2)\r\ntotal_std_t2 = np.sqrt(sum(list_t2) -mean_t2**2)\r\ntotal_std_flair = np.sqrt(sum(list_flair)-mean_flair**2)\r\n \r\n#print(mean_t1)\r\n#print(mean_t1ce)\r\n#print(mean_t2)\r\n#print(mean_flair)\r\n\r\n#print(total_std_t1)\r\n#print(total_std_t1ce)\r\n#print(total_std_t2)\r\n#print(total_std_flair)\r\n\r\n\r\n\r\n'''\r\nPATH = r'E:\\an_4_LICENTA\\Workspace\\Dataset\\Optimization_Dataset_nepreprocesat\\PNG_Training_Dataset'\r\n\r\n#arr.reshape(-1)\r\n\r\n# sumofmeans = the mean pover a siungle example\r\nsumofmeans_t1 = 0\r\nsumofmeans_t1ce = 0\r\nsumofmeans_t2 = 0\r\nsumofmeans_flair = 0\r\n\r\nsumofstd_t1 = 0\r\nsumofstd_t1ce = 0\r\nsumofstd_t2 = 0\r\nsumofstd_flair = 0\r\n\r\nno_examples = 0\r\n\r\nfor dir_name in os.listdir(PATH):\r\n\r\n EXAPLE_PATH = PATH + '\\\\' + dir_name\r\n\r\n for file_name in os.listdir(EXAPLE_PATH):\r\n FILE_PATH = EXAPLE_PATH + '\\\\' + file_name\r\n img = cv.imread(FILE_PATH, cv.IMREAD_GRAYSCALE)\r\n flattened_img = img.reshape(-1)\r\n if file_name == 't1.png':\r\n sumofmeans_t1 += np.mean(flattened_img)\r\n sumofstd_t1 += np.std(flattened_img)\r\n elif file_name == 't1ce.png':\r\n sumofmeans_t1ce += np.mean(flattened_img)\r\n sumofstd_t1ce += np.std(flattened_img)\r\n elif file_name == 't2.png':\r\n sumofmeans_t2 += np.mean(flattened_img)\r\n sumofstd_t2 += np.std(flattened_img)\r\n elif file_name == 'flair.png':\r\n sumofmeans_flair += np.mean(flattened_img)\r\n sumofstd_flair += np.std(flattened_img)\r\n \r\n no_examples += 1\r\n\r\nmean_t1 = sumofmeans_t1/no_examples\r\nmean_t1ce = sumofmeans_t1ce/no_examples\r\nmean_t2 = sumofmeans_t2/no_examples\r\nmean_flair = sumofmeans_flair/no_examples\r\n\r\nstd_t1 = sumofstd_t1/no_examples\r\nstd_t1ce = sumofstd_t1ce/no_examples\r\nstd_t2 = sumofstd_t2/no_examples\r\nstd_flair = sumofstd_flair/no_examples\r\n \r\n#print(mean_t1)\r\n#print(mean_t1ce)\r\n#print(mean_t2)\r\n#print(mean_flair)\r\n\r\n#print(std_t1)\r\n#print(std_t1ce)\r\n#print(std_t2)\r\n#print(std_flair)\r\n\r\n'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"RaduBolbo/Tehnici-automate-de-segmentare-semantic-a-tumorilor-cerebrale-folosind-imagini-RMN","sub_path":"get_mean_and_variance.py","file_name":"get_mean_and_variance.py","file_ext":"py","file_size_in_byte":4670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33558030349","text":"\n\n# # 자리수별로 끊어주자\n# eight = int(input())\n\n# a = list(map(int,str(eight)))\n\n\n\n# for i in range(len(a)):\n# print(a[i])\n\n\n# # 여기서 내장함수가 존재할 것이라고 생각\n\n\n# 풀이\n\na = int(input(),8)\n\nresult = bin(a)[2:]\n\nprint(result)\n\n\n\n\n\n\n","repo_name":"mmmjunjoy/Algorithm","sub_path":"Algorithm_basic/octal binary.py","file_name":"octal binary.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42338295430","text":"import os\nimport struct\nfrom glob import glob\n\nimport pandas as pd\nimport numpy as np\n\nfrom pyspectools import ftmw_analysis as fa\n\n\ndef parse_spectrum(filename, threshold=20.0):\n \"\"\" Function to read in a blackchirp or QtFTM spectrum from file \"\"\"\n dataframe = pd.read_csv(\n filename, delimiter=\"\\t\", names=[\"Frequency\", \"Intensity\"], skiprows=1\n )\n dataframe.dropna(inplace=True)\n return dataframe[dataframe[\"Intensity\"] <= threshold]\n\n\ndef parse_ascii(filename, delimiter=\"\\t\", names=None, header=None, skiprows=0):\n \"\"\"\n Generic ASCII parser wrapping the pandas read_csv function.\n Parameters\n ----------\n filename\n delimiter\n names\n header\n skiprows\n\n Returns\n -------\n\n \"\"\"\n dataframe = pd.read_csv(\n filename, delimiter=delimiter, names=names, header=header, skiprows=skiprows\n )\n dataframe.dropna(inplace=True)\n return dataframe\n\n\ndef parse_lin(filename):\n \"\"\"\n Function to read in a line file, formatted in the SPFIT\n convention.\n \"\"\"\n data = list()\n with open(filename) as read_file:\n for line in read_file:\n line_data = list()\n # Get all the delimiting out\n split_line = line.split()\n split_cols = split_line[-3:]\n # Convert frequency, uncertainty, and weight\n # into floats\n for col in split_cols:\n try:\n line_data.append(float(col))\n except ValueError:\n line_data.append(0.0)\n # Split up the quantum numbers\n # qnos = qnos.split()\n # qnos = [int(num) for num in qnos]\n line_data.append(\",\".join(split_line[:-3]))\n data.append(line_data)\n dataframe = pd.DataFrame(\n data=data, columns=[\"Frequency\", \"Uncertainty\", \"Weight\", \"Quantum numbers\"]\n )\n return dataframe\n\n\ndef parse_cat(simulation_path, low_freq=0.0, high_freq=np.inf, threshold=-np.inf):\n \"\"\"\n Parses a simulation output, and filters the frequency and intensity to give\n a specific set of lines.\n\n The only argument that is required is the path to the simulation output. Others\n are optional, and will default to effectively not filter.\n\n The quantum numbers are read in assuming hyperfine structure, and thus\n might not be accurate descriptions of what they actually are.\n \"\"\"\n cat_df = pd.read_fwf(\n simulation_path,\n widths=[13, 8, 8, 2, 10, 3, 7, 4, 2, 2, 2, 8, 2, 2],\n header=None,\n )\n cat_df.columns = [\n \"Frequency\",\n \"Uncertainty\",\n \"Intensity\",\n \"DoF\",\n \"Lower state energy\",\n \"Degeneracy\",\n \"ID\",\n \"Coding\",\n \"N'\",\n \"F'\",\n \"J'\",\n \"N''\",\n \"F''\",\n \"J''\",\n ]\n cat_df = cat_df.loc[\n (cat_df[\"Frequency\"].astype(float) >= low_freq)\n & ( # threshold the simulation output\n cat_df[\"Frequency\"].astype(float) <= high_freq\n )\n & ( # based on user specified values\n cat_df[\"Intensity\"].astype(float) >= threshold\n ) # or lack thereof\n ]\n return cat_df\n\n\ndef parse_blackchirp(dir_path):\n \"\"\"\n Function for reading in a Blackchirp experiment. The required input should point to the directory\n containing the Blackchirp files with the correct extensions: .hdr, .tdt, and .fid\n\n Parameters\n ----------\n dir_path - str\n Filepath pointing to the directory containing the Blackchirp experiment files.\n\n \"\"\"\n # Read in header information\n hdr_file = glob(os.path.join(dir_path, \"*.hdr\"))\n header = dict()\n try:\n hdr_file = hdr_file[0]\n exp_id = hdr_file.split(\"/\")[-1].split(\".\")[0]\n except IndexError:\n raise Exception(\"Header file is missing!\")\n with open(hdr_file) as hdr:\n for line in hdr:\n if not line:\n continue\n l = line.split(\"\\t\")\n if not l or len(l) < 3:\n continue\n\n key = l[0].strip()\n value = l[1].strip()\n unit = l[2].strip()\n\n header[key] = {\"value\": value, \"unit\": unit}\n\n # Locate all the FIDs\n fid_files = glob(os.path.join(dir_path, \"*.fid\"))\n if len(fid_files) < 1:\n raise Exception(\"No FID files present!\")\n else:\n fid_list = list()\n for file in fid_files:\n with open(file, \"rb\") as fidfile:\n buffer = fidfile.read(4)\n ms_len = struct.unpack(\">I\", buffer)\n buffer = fidfile.read(ms_len[0])\n magic_string = buffer.decode(\"ascii\")\n if not magic_string.startswith(\"BCFID\"):\n raise ValueError(\n \"Could not read magic string from {}\".format(fidfile.name)\n )\n\n l = magic_string.split(\"v\")\n if len(l) < 2:\n raise ValueError(\n \"Could not determine version number from magic string {}\".format(\n magic_string\n )\n )\n\n version = l[1]\n\n buffer = fidfile.read(4)\n fidlist_size = struct.unpack(\">I\", buffer)[0]\n for i in range(0, fidlist_size):\n # Create a BlackChirpFid object\n fid_list.append(fa.BlackChirpFid.from_binary(fidfile))\n\n time_data = dict()\n tdt_file = glob(os.path.join(dir_path, \"*.tdt\"))\n try:\n tdt_file = tdt_file[0]\n except IndexError:\n raise Exception(\"Time stamp data is missing!\")\n with open(tdt_file) as tdt:\n look_for_header = True\n header_list = []\n for line in tdt:\n print(line)\n if line.strip() == \"\":\n continue\n if line.startswith(\"#\") and \"PlotData\" in line:\n look_for_header = True\n header_list = []\n continue\n if line.startswith(\"#\"):\n continue\n\n l = line.split(\"\\t\")\n if len(l) < 1:\n continue\n\n if look_for_header is True:\n for i in range(0, len(l)):\n name = \"\"\n l2 = str(l[i]).split(\"_\")\n for j in range(0, len(l2) - 1):\n name += str(l2[j]).strip()\n time_data[name] = []\n header_list.append(name)\n look_for_header = False\n else:\n for i in range(0, len(l)):\n time_data[header_list[i]].append(str(l[i]).strip())\n return exp_id, header, fid_list, time_data\n\n\ndef read_binary_fid(filepath):\n \"\"\"\n Read in a binary Blackchirp FID file. This is based on the original code by Kyle Crabtree, with some minor\n perfomance improvements by Kelvin Lee. The only difference is most of the for loops for reading the points\n have been replaced by numpy broadcasts.\n\n Parameters\n ----------\n filepath - str\n Filepath to the Blackchirp .fid file\n\n Returns\n -------\n param_dict - dict\n Contains header information about the FID, such as the number of shots, point spacing, etc.\n xy_data - 2-tuple of numpy 1D array\n Contains two columns; xy_data[0] is the time data in microseconds, and xy_data[1] is the\n signal.\n raw_data - numpy 1D array\n Contains the raw, uncorrected ADC sums. The signal data is converted from this by scaling\n it with the multiplication factor v_mult.\n\n \"\"\"\n with open(filepath) as read_file:\n read_str = \">3dqHbI\"\n d = struct.unpack(read_str, read_file.read(struct.calcsize(read_str)))\n spacing = d[0] * 1e6\n probe_freq = d[1]\n v_mult = d[2]\n shots = d[3]\n if d[4] == 1:\n sideband = -1.0\n else:\n sideband = 1.0\n point_size = d[5]\n size = d[6]\n\n param_dict = {\n \"spacing\": spacing,\n \"probe_freq\": probe_freq,\n \"v_mult\": v_mult,\n \"shots\": shots,\n \"point_size\": point_size,\n \"size\": size,\n \"sideband\": sideband,\n }\n\n if point_size == 2:\n read_string = \">\" + str(size) + \"h\"\n dat = struct.unpack(\n read_string, read_file.read(struct.calcsize(read_string))\n )\n elif point_size == 3:\n for i in range(0, size):\n chunk = read_file.read(3)\n dat = struct.unpack(\n \">i\", (b\"\\0\" if chunk[0] < 128 else b\"\\xff\") + chunk\n )[0]\n elif point_size == 4:\n read_string = \">\" + str(size) + \"i\"\n dat = struct.unpack(\n read_string, read_file.read(struct.calcsize(read_string))\n )\n elif point_size == 8:\n read_string = \">\" + str(size) + \"q\"\n dat = struct.unpack(\n read_string, read_file.read(struct.calcsize(read_string))\n )\n else:\n raise ValueError(\"Invalid point size: \" + str(point_size))\n # Now read in the data with broadcasting\n raw_data = np.array(dat[:size])\n data = raw_data * v_mult / shots\n x_data = np.linspace(0.0, size * spacing, int(size))\n xy_data = np.vstack((x_data, data))\n return param_dict, xy_data, raw_data\n\n\ndef parse_fit(filepath):\n \"\"\"\n Function to parse the output of an SPFIT .fit file. This version of the code is barebones compared to the\n previous iteration, which provides more feedback. This version simply returns a dictionary containing the\n obs - calc for each line, the fitted parameters, and the microwave RMS.\n\n Parameters\n ----------\n filepath: str\n Filepath to the .fit file to parse.\n\n Returns\n -------\n fit_dict: dict\n Dictionary containing the parsed data.\n \"\"\"\n fit_dict = {\"o-c\": {}, \"parameters\": {}, \"rms\": None}\n with open(filepath) as read_file:\n lines = read_file.readlines()\n for index, line in enumerate(lines):\n # Read the obs - calc on individual lines\n if \"EXP.FREQ.\" in line:\n stop_flag = False\n entry_index = 1\n line_dict = dict()\n while stop_flag is False:\n entry = lines[index + entry_index].split()\n if entry[0] == \"NORMALIZED\" or entry[0] == \"Fit\":\n stop_flag = True\n elif entry[1] == \"NEXT\" or entry[1] == \"Lines\":\n entry_index += 1\n pass\n else:\n # Read in the line information\n line_dict[entry_index] = {\n \"o-c\": float(entry[-3]),\n \"qnos\": entry[1:-5],\n \"frequency\": entry[-5],\n }\n entry_index += 1\n if \"NEW PARAMETER\" in line:\n stop_flag = False\n entry_index = 1\n param_dict = dict()\n while stop_flag is False:\n entry = lines[index + entry_index]\n for bracket in [\"\"\"(\"\"\", \"\"\")\"\"\"]:\n entry = entry.replace(bracket, \" \")\n entry = entry.split()\n if entry[0] != \"MICROWAVE\":\n coding = int(entry[1])\n param_dict[coding] = float(entry[-3])\n entry_index += 1\n else:\n stop_flag = True\n if \"MICROWAVE RMS\" in line:\n fit_dict[\"microwave_rms\"] = float(line.split()[3])\n if \"NEW RMS ERROR\" in line:\n fit_dict[\"rms\"] = float(line.split()[-2])\n fit_dict[\"o-c\"] = line_dict\n fit_dict[\"parameters\"] = param_dict\n return fit_dict\n","repo_name":"laserkelvin/PySpecTools","sub_path":"pyspectools/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":11845,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"31"} +{"seq_id":"21961273649","text":"import io\nimport sys\n\n_INPUT = \"\"\"\\\n6\n3\n1\n2\n3\n6\n15\n2\n3\n7\n6\n9\n\"\"\"\n\nsys.stdin = io.StringIO(_INPUT)\ncase_no=int(input())\nfor __ in range(case_no):\n import math\n N=int(input())\n R=[int(input()) for _ in range(N)]\n R.sort(reverse=True)\n ans=0\n for i in range((N+1)//2):\n ans+=R[2*i]**2*math.pi\n if 2*i+1<N:\n ans-=R[2*i+1]**2*math.pi\n print(ans)","repo_name":"katonyonko/ABC026","sub_path":"ABC026_B.py","file_name":"ABC026_B.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29983719747","text":"import ruamel.yaml\nfrom .. import standardize_waze_data\nimport data.config\nimport os\nimport pytz\n\nTEST_FP = os.path.dirname(os.path.abspath(__file__))\n\n\ndef test_get_datetime():\n timezone = pytz.timezone(\"America/New_York\")\n\n result = standardize_waze_data.get_datetime(\n '2018-10-04 12:13:00:000', timezone)\n assert result.isoformat() == '2018-10-04T08:13:00-04:00'\n\n result = standardize_waze_data.get_datetime(\n '2018-11-04 01:13:00:000', timezone)\n assert result.isoformat() == '2018-11-03T21:13:00-04:00'\n\n result = standardize_waze_data.get_datetime(\n '2018-11-04 06:13:00:000', timezone)\n assert result.isoformat() == '2018-11-04T01:13:00-05:00'\n\n\ndef test_read_snapshots(tmpdir):\n config_dict = {\n 'name': 'cambridge',\n 'city_latitude': 42.3600825,\n 'city_longitude': -71.0588801,\n 'city_radius': 15,\n 'timezone': 'America/New_York',\n 'crashes_files': {'test': {}},\n 'city': \"Cambridge, Massachusetts, USA\",\n 'timezone': \"America/New_York\"\n }\n filename = os.path.join(tmpdir, 'test.yml')\n with open(filename, \"w\") as f:\n ruamel.yaml.round_trip_dump(config_dict, f)\n config = data.config.Configuration(filename)\n\n results = standardize_waze_data.read_snapshots(os.path.join(\n TEST_FP, 'data', 'waze'), config)\n\n expected_results = [\n {\n 'pubMillis': 1539632995870,\n 'city': 'Cambridge, MA',\n 'eventType': 'jam',\n 'pubTimeStamp': '2018-10-15 15:49:55',\n 'snapshotId': 1\n },\n {\n 'country': 'US',\n 'subtype': '',\n 'pubMillis': 1539632447442,\n 'city': 'Cambridge, MA',\n 'type': 'JAM',\n 'reportRating': 2,\n 'location': {\n 'latitude': 42.373807,\n 'longitude': -71.112465\n },\n 'eventType': 'alert',\n 'pubTimeStamp': '2018-10-15 15:40:47',\n 'snapshotId': 1\n },\n {\n 'roadType': 1,\n 'city': 'Cambridge, MA',\n 'pubMillis': 1539670005835,\n 'eventType': 'jam',\n 'pubTimeStamp': '2018-10-16 02:06:45',\n 'snapshotId': 2\n },\n {\n 'type': 'WEATHERHAZARD',\n 'subtype': 'HAZARD_ON_ROAD_CONSTRUCTION',\n 'city': 'Cambridge, MA',\n 'pubMillis': 1539607721062,\n 'location': {\n 'latitude': 42.371072,\n 'longitude': -71.1143\n },\n 'eventType': 'alert',\n 'pubTimeStamp': '2018-10-15 08:48:41',\n 'snapshotId': 2\n },\n {\n 'updateDate': 'Wed Oct 17 16:14:17 +0000 2018',\n 'speed': 3.79,\n 'city': 'Cambridge, MA',\n 'detectionDateMillis': 1539788890781,\n 'detectionDate': 'Wed Oct 17 15:08:10 +0000 2018',\n 'type': 'Small',\n 'eventType': 'irregularity',\n 'snapshotId': 3\n }\n ]\n assert results == expected_results\n\n results = standardize_waze_data.read_snapshots(\n os.path.join(TEST_FP, 'data', 'waze'),\n config,\n startdate='2018-10-16',\n enddate='2018-10-16'\n )\n assert results == [\n {\n 'roadType': 1,\n 'city': 'Cambridge, MA',\n 'pubMillis': 1539670005835,\n 'eventType': 'jam',\n 'pubTimeStamp': '2018-10-16 02:06:45',\n 'snapshotId': 1\n },\n {\n 'type': 'WEATHERHAZARD',\n 'subtype': 'HAZARD_ON_ROAD_CONSTRUCTION',\n 'city': 'Cambridge, MA',\n 'pubMillis': 1539607721062,\n 'location': {\n 'latitude': 42.371072,\n 'longitude': -71.1143\n },\n 'eventType': 'alert',\n 'pubTimeStamp': '2018-10-15 08:48:41',\n 'snapshotId': 1\n },\n ]\n","repo_name":"insight-lane/crash-model","sub_path":"src/data_standardization/tests/test_standardize_waze_data.py","file_name":"test_standardize_waze_data.py","file_ext":"py","file_size_in_byte":3952,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"31"} +{"seq_id":"30488344999","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport sys, os\n\ndef walk(cur_dir, mathjax_root, f, path):\n\tif os.path.isdir(path):\n\t\tentries = os.listdir(path)\n\t\tfor e in entries:\n\t\t\tif e == '.' or e == '..':\n\t\t\t\tcontinue\n\t\t\twalk(cur_dir, mathjax_root, f, os.path.join(path, e))\n\telse:\n\t\tf.write(' <file alias=\"')\n\t\tf.write(os.path.relpath(path, mathjax_root).replace('\\\\', '/'))\n\t\tf.write('\">')\n\t\tf.write(os.path.relpath(path, cur_dir).replace('\\\\', '/'))\n\t\tf.write('</file>\\n')\n\ndef gen():\n\tcur_dir = os.path.split(sys.argv[0])[0]\n\tmathjax_root = os.path.abspath(os.path.join(cur_dir, '../../3rdparty/mathjax'))\n\twith open('mathjax.qrc', 'w') as f:\n\t\tf.write('<RCC>\\n')\n\t\tf.write(' <qresource prefix=\"/mathjax\">\\n')\n\t\twalk(cur_dir, mathjax_root, f, mathjax_root)\n\t\tf.write(' </qresource>\\n')\n\t\tf.write('</RCC>\\n')\n\nif __name__ == '__main__':\n\tgen()\n","repo_name":"jingqi/markdown-view","sub_path":"src/markdown-view/gen_mathjax.py","file_name":"gen_mathjax.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33727013461","text":"\n\n# for loop\nfor i in range (1, 15):\n print (\"for i = \", i)\n\n\n# while loop\ni = 15\nwhile i > 0:\n print (\" while i = \", i)\n i = i - 1\n\n\n# if then\n#i = 288\n#j = 766\n\ni = 766\nj = 200\n\nif i < j:\n #print (\"i \", i, \"is less than j \", j)\n print (\"{} is less that {}\".format (i,j))\nelse:\n #print (\"i \", i, \"is greater than j \", j)\n print (\"{} is greater that {}\".format (i,j))\n\n\n\n#prime numbers\ndef isprime (num):\n if num == 1:\n return False\n elif (num == 2):\n return True\n else:\n for x in range (2, num):\n if (num % x == 0):\n return False\n return True\n\nnum = 99 \nprint (\"Is {} a prime number true/false\".format(num))\nprint (isprime(99)) \n","repo_name":"sslayer1/python","sub_path":"basic controls.py","file_name":"basic controls.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74717875927","text":"#!/usr/bin/env python\n\nimport rospy \nfrom jsk_topic_tools import ConnectionBasedTransport\nfrom jsk_recognition_msgs.msg import PolygonArray, ModelCoefficientsArray\nfrom pcl_msgs.msg import ModelCoefficients\nfrom geometry_msgs.msg import PolygonStamped\nimport message_filters\nfrom dynamic_reconfigure.server import Server\nfrom jsk_pcl_ros.cfg import ExtractTopPolygonLikelihoodConfig\n\nclass ExtractTopPolygonLikelihood(ConnectionBasedTransport):\n def __init__(self):\n super(ExtractTopPolygonLikelihood, self).__init__()\n self._srv = Server(ExtractTopPolygonLikelihoodConfig, self.config_callback)\n self._pub = self.advertise(\"~output\", PolygonArray, queue_size=1)\n self._pub_coef = self.advertise(\"~output/coefficients\", ModelCoefficientsArray, queue_size=1)\n def config_callback(self, config, level):\n self._min_likelihood = config.min_likelihood\n return config\n def subscribe(self):\n self._sub = message_filters.Subscriber(\"~input\", PolygonArray)\n self._sub_coef = message_filters.Subscriber(\"~input/coefficients\", ModelCoefficientsArray)\n self._sync = message_filters.TimeSynchronizer([self._sub, self._sub_coef], 100)\n self._sync.registerCallback(self.callback)\n def unsubscribe(self):\n self._sub.sub.unregister()\n self._sub_coef.sub.unregister()\n def callback(self, msg, msg_coef):\n if len(msg.polygons) > 0:\n #self._pub.publish(msg.histograms[0])\n max_index = max(list(range(len(msg.polygons))), key=lambda i: msg.likelihood[i])\n res = PolygonArray()\n res.header = msg.header\n res.polygons = [msg.polygons[max_index]]\n res.likelihood = [msg.likelihood[max_index]]\n if msg.likelihood[max_index] < self._min_likelihood:\n rospy.loginfo(\"Ignore result because of too small likelihood: {0} < {1}\".format(\n msg.likelihood[max_index],\n self._min_likelihood))\n return\n self._pub.publish(res)\n res_coef = ModelCoefficientsArray()\n res_coef.header = msg.header\n res_coef.coefficients = [msg_coef.coefficients[max_index]]\n self._pub_coef.publish(res_coef)\n\n\nif __name__ == \"__main__\":\n rospy.init_node(\"extract_top_polygon_likelihood\")\n ex = ExtractTopPolygonLikelihood()\n rospy.spin()\n \n\n","repo_name":"jsk-ros-pkg/jsk_recognition","sub_path":"jsk_pcl_ros/scripts/extract_top_polygon_likelihood.py","file_name":"extract_top_polygon_likelihood.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","stars":251,"dataset":"github-code","pt":"31"} +{"seq_id":"23532373528","text":"from .reynir import (\n Greynir,\n Reynir,\n Terminal,\n LemmaTuple,\n ProgressFunc,\n ParseResult,\n Sentence,\n Paragraph,\n ICELANDIC_RATIO,\n)\n\n# Import the following _underscored classes to be able to use them\n# in type signatures in derived classes\nfrom .reynir import (\n _Job,\n _Sentence,\n _Paragraph,\n)\nfrom .nounphrase import NounPhrase\nfrom .fastparser import ParseForestPrinter, ParseForestDumper, ParseForestFlattener\nfrom .fastparser import ParseError, ParseForestNavigator\nfrom .settings import Settings\nfrom .bintokenizer import tokenize, TokenList\nfrom .version import __version__\n\n# Expose the tokenizer API\n\nfrom tokenizer import (\n TOK,\n Tok,\n paragraphs,\n correct_spaces,\n mark_paragraphs,\n TP_LEFT,\n TP_CENTER,\n TP_RIGHT,\n TP_NONE,\n TP_WORD,\n KLUDGY_ORDINALS_PASS_THROUGH,\n KLUDGY_ORDINALS_MODIFY,\n KLUDGY_ORDINALS_TRANSLATE,\n)\nfrom tokenizer.abbrev import Abbreviations\n\n__author__ = \"Miðeind ehf.\"\n__copyright__ = \"© 2023 Miðeind ehf.\"\n\n__all__ = (\n \"TP_LEFT\",\n \"TP_RIGHT\",\n \"TP_CENTER\",\n \"TP_NONE\",\n \"TP_WORD\",\n \"KLUDGY_ORDINALS_MODIFY\",\n \"KLUDGY_ORDINALS_PASS_THROUGH\",\n \"KLUDGY_ORDINALS_TRANSLATE\",\n \"Greynir\",\n \"Reynir\",\n \"Terminal\",\n \"LemmaTuple\",\n \"ProgressFunc\",\n \"ParseResult\",\n \"Sentence\",\n \"Paragraph\",\n \"ICELANDIC_RATIO\",\n \"TOK\",\n \"Tok\",\n \"paragraphs\",\n \"correct_spaces\",\n \"mark_paragraphs\",\n \"_Job\",\n \"_Sentence\",\n \"_Paragraph\",\n \"NounPhrase\",\n \"ParseForestPrinter\",\n \"ParseForestDumper\",\n \"ParseForestFlattener\",\n \"ParseError\",\n \"ParseForestNavigator\",\n \"Settings\",\n \"tokenize\",\n \"TokenList\",\n \"__version__\",\n \"__author__\",\n \"__copyright__\",\n)\n\nAbbreviations.initialize()\nSettings.read(\"config/GreynirPackage.conf\")\n","repo_name":"mideind/GreynirEngine","sub_path":"src/reynir/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"31"} +{"seq_id":"1179490270","text":"from __future__ import print_function\n\nimport requests,random, json, re, string\nfrom websocket import create_connection\n\nimport json\nimport random\nimport string\nimport re\nimport pandas as pd\nfrom datetime import datetime\nfrom time import localtime\n\n\n_API_URL_ = 'https://symbol-search.tradingview.com/symbol_search'\n_WS_URL_ = \"wss://data.tradingview.com/socket.io/websocket\"\n\nclass TradingViewWs():\n def __init__(self, ticker, market, username = None, password = None):\n self.ticker = ticker.upper()\n self.market = market\n self._ws_url = _WS_URL_\n self._api_url = _API_URL_\n self.token = self.get_auth_token(username, password)\n self.datas = []\n\n\n def get_auth_token(self, username, password):\n if not username or not password:\n return ''\n\n sign_in_url = 'https://www.tradingview.com/accounts/signin/'\n\n data = {\"username\": username, \"password\": password, \"remember\": \"on\"}\n headers = {\n 'Referer': 'https://www.tradingview.com'\n }\n response = requests.post(url=sign_in_url, data=data, headers=headers)\n auth_token = response.json()['user']['auth_token']\n return auth_token\n\n def search(self, query, type):\n # type = 'stock' | 'futures' | 'forex' | 'cfd' | 'crypto' | 'index' | 'economic'\n # query = what you want to search!\n # it returns first matching item\n res = requests.get(\n f\"{self._api_url}?text={query}&type={type}\"\n )\n if res.status_code == 200:\n res = res.json()\n assert len(res) != 0, \"Nothing Found.\"\n return res[0]\n else:\n print(\"Network Error!\")\n exit(1)\n\n def generate_session(self, type):\n string_length = 12\n letters = string.ascii_lowercase\n random_string = \"\".join(random.choice(letters) for i in range(string_length))\n return type + random_string\n\n def prepend_header(self, st):\n return \"~m~\" + str(len(st)) + \"~m~\" + st\n\n def construct_message(self, func, param_list):\n return json.dumps({\"m\": func, \"p\": param_list}, separators=(\",\", \":\"))\n\n def create_message(self, func, paramList):\n return self.prepend_header(self.construct_message(func, paramList))\n\n def send_message(self, ws, func, args):\n ws.send(self.create_message(func, args))\n\n def send_ping_packet(self, ws, result):\n ping_str = re.findall(\".......(.*)\", result)\n if len(ping_str) != 0:\n ping_str = ping_str[0]\n ws.send(\"~m~\" + str(len(ping_str)) + \"~m~\" + ping_str)\n\n def socket_quote(self, ws, callback):\n while True:\n try:\n result = ws.recv()\n\n if \"quote_completed\" in result or \"session_id\" in result:\n continue\n\n res = re.findall(\"^.*?({.*)$\", result)\n\n if len(res) != 0:\n jsonres = json.loads(res[0])\n\n if jsonres[\"m\"] == \"qsd\":\n symbol = jsonres[\"p\"][1][\"n\"]\n price = jsonres[\"p\"][1][\"v\"][\"lp\"]\n callback({\"symbol\": symbol, \"price\": price})\n else:\n # ping packet\n self.send_ping_packet(ws, result)\n except KeyboardInterrupt:\n break\n except:\n continue\n\n def socket_bar_chart(self, ws, interval, callback):\n while True:\n try:\n result = ws.recv()\n\n if not result or \"quote_completed\" in result or \"session_id\" in result:\n continue\n\n out = re.search('\"s\":\\[(.+?)\\}\\]', result)\n if not out:\n continue\n\n out = out.group(1)\n\n items = out.split(',{\\\"')\n\n if len(items) != 0:\n datas = []\n for item in items:\n item = re.split('\\[|:|,|\\]', item)\n ind = int(item[1])\n\n ts = datetime.fromtimestamp(float(item[4])).strftime(\"%s\")\n s = {\"datetime\": float(item[4]), \"open\": float(item[5]), \"high\": float(item[6]), \"low\": float(item[7]), \"close\": float(item[8]), \"volume\": float(item[9])}\n\n datas.append(s)\n\n if len(datas):\n if not len(self.datas):\n self.datas = datas\n else:\n l = len(self.datas)\n dt = float(datetime.fromtimestamp(float(self.datas[l - 1]['datetime'])).strftime(\"%s\"))\n\n for item in datas:\n dt2 = float(datetime.fromtimestamp(float(item['datetime'])).strftime(\"%s\"))\n if dt == dt2:\n self.datas[l - 1] = item\n elif dt < dt2:\n self.datas.append(item)\n l = l + 1\n\n callback(self.datas)\n else:\n # ping packet\n print(\"................retry\")\n self.send_ping_packet(ws, interval, result)\n except KeyboardInterrupt:\n break\n except Exception as e:\n print(\"=========except\", datetime.now(), e)\n if ('closed' in str(e) or 'lost' in str(e)):\n print(\"=========try\")\n self.realtime_bar_chart(5, 1, callback)\n\n def get_symbol_id(self, pair, market):\n data = self.search(pair, market)\n\n symbol_name = data[\"symbol\"]\n if data['type'] == 'futures':\n symbol_name = data[\"contracts\"][0][\"symbol\"]\n\n broker = data[\"exchange\"]\n symbol_id = f\"{broker.upper()}:{symbol_name.upper()}\"\n return symbol_id\n\n def realtime_quote(self, callback):\n # serach btcusdt from crypto category\n symbol_id = self.get_symbol_id(self.ticker, self.market)\n\n # create tunnel\n headers = json.dumps({\"Origin\": \"https://data.tradingview.com\"})\n ws = create_connection(self._ws_url, headers=headers)\n session = self.generate_session(\"qs_\")\n\n # Send messages\n if self.token:\n self.send_message(ws, \"set_auth_token\", [self.token])\n else:\n self.send_message(ws, \"set_auth_token\", [\"unauthorized_user_token\"])\n\n self.send_message(ws, \"quote_create_session\", [session])\n self.send_message(ws, \"quote_set_fields\", [session, \"lp\"])\n self.send_message(ws, \"quote_add_symbols\", [session, symbol_id])\n\n # Start job\n self.socket_quote(ws, callback)\n\n def realtime_bar_chart(self, interval, total_candle, callback):\n # serach btcusdt from crypto category\n symbol_id = self.get_symbol_id(self.ticker, self.market)\n\n # create tunnel\n headers = json.dumps({\"Origin\": \"https://data.tradingview.com\"})\n ws = create_connection(self._ws_url, headers=headers)\n session = self.generate_session(\"qs_\")\n chart_session = self.generate_session(\"cs_\")\n\n # Then send a message through the tunnel\n if self.token:\n self.send_message(ws, \"set_auth_token\", [self.token])\n else:\n self.send_message(ws, \"set_auth_token\", [\"unauthorized_user_token\"])\n\n self.send_message(ws, \"chart_create_session\", [chart_session, \"\"])\n self.send_message(ws, \"quote_create_session\", [session])\n self.send_message(ws, \"switch_timezone\", [chart_session, \"Etc/UTC\"])\n self.send_message(ws, \"quote_set_fields\",\n [session, \"ch\", \"chp\", \"current_session\", \"description\", \"local_description\", \"language\",\n \"exchange\",\n \"fractional\", \"is_tradable\", \"lp\", \"lp_time\", \"minmov\", \"minmove2\", \"original_name\", \"pricescale\",\n \"pro_name\", \"short_name\", \"type\", \"update_mode\", \"volume\", \"currency_code\", \"rchp\", \"rtc\"])\n #self.send_message(ws, \"quote_add_symbols\", [session, symbol_id, {\"flags\": ['force_permission']}])\n #self.send_message(ws, \"quote_fast_symbols\", [session, symbol_id])\n\n # st='~m~140~m~{\"m\":\"resolve_symbol\",\"p\":}'\n # p1, p2 = filter_raw_message(st)\n self.send_message(ws, \"resolve_symbol\", [chart_session, \"symbol_1\",\n \"={\\\"symbol\\\":\\\"\"+symbol_id+\"\\\",\\\"adjustment\\\":\\\"splits\\\",\\\"session\\\":\\\"extended\\\"}\"])\n self.send_message(ws, \"create_series\", [chart_session, \"s1\", \"s1\", \"symbol_1\", str(interval), total_candle])\n # self.send_message(ws, \"create_study\", [chart_session,\"st4\",\"st1\",\"s1\",\"ESD@tv-scripting-101!\",{\"text\":\"BNEhyMp2zcJFvntl+CdKjA==_DkJH8pNTUOoUT2BnMT6NHSuLIuKni9D9SDMm1UOm/vLtzAhPVypsvWlzDDenSfeyoFHLhX7G61HDlNHwqt/czTEwncKBDNi1b3fj26V54CkMKtrI21tXW7OQD/OSYxxd6SzPtFwiCVAoPbF2Y1lBIg/YE9nGDkr6jeDdPwF0d2bC+yN8lhBm03WYMOyrr6wFST+P/38BoSeZvMXI1Xfw84rnntV9+MDVxV8L19OE/0K/NBRvYpxgWMGCqH79/sHMrCsF6uOpIIgF8bEVQFGBKDSxbNa0nc+npqK5vPdHwvQuy5XuMnGIqsjR4sIMml2lJGi/XqzfU/L9Wj9xfuNNB2ty5PhxgzWiJU1Z1JTzsDsth2PyP29q8a91MQrmpZ9GwHnJdLjbzUv3vbOm9R4/u9K2lwhcBrqrLsj/VfVWMSBP\",\"pineId\":\"TV_SPLITS\",\"pineVersion\":\"8.0\"}])\n\n # Start job\n self.socket_bar_chart(ws, interval, callback)","repo_name":"dearvn/tradingview_ws","sub_path":"src/tradingview_ws/tdws.py","file_name":"tdws.py","file_ext":"py","file_size_in_byte":9418,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"31"} +{"seq_id":"37275446346","text":"import pyscf\nimport pyscf.gto\nimport pyscf.scf\nimport pyscf.cc\nimport vayesta\nimport vayesta.ewf\n\n\nmol = pyscf.gto.Mole()\nmol.atom = \"\"\"\nO 0.0000 0.0000 0.1173\nH 0.0000 0.7572 -0.4692\nH 0.0000 -0.7572 -0.4692\n\"\"\"\nmol.basis = \"cc-pVDZ\"\nmol.build()\n\n# Hartree-Fock\nmf = pyscf.scf.RHF(mol)\nmf.kernel()\n\n# CCSD\ncc = pyscf.cc.CCSD(mf)\ncc.kernel()\n\n# One shot\nopts = dict(bath_options=dict(threshold=1e-4), solver_options=dict(solve_lambda=True))\nemb = vayesta.ewf.EWF(mf, **opts)\nwith emb.sao_fragmentation() as f:\n f.add_all_atomic_fragments()\nemb.kernel()\n\n# p-DMET\nemb_pdmet = vayesta.ewf.EWF(mf, **opts)\nwith emb_pdmet.sao_fragmentation() as f:\n f.add_all_atomic_fragments()\nemb_pdmet.pdmet_scmf()\nemb_pdmet.kernel()\nassert emb_pdmet.with_scmf.converged\n\n# Brueckner\nemb_brueckner = vayesta.ewf.EWF(mf, **opts)\nwith emb_brueckner.sao_fragmentation() as f:\n f.add_all_atomic_fragments()\nemb_brueckner.brueckner_scmf()\nemb_brueckner.kernel()\nassert emb_brueckner.with_scmf.converged\n\n\nprint(\"E(HF)= %+16.8f Ha\" % mf.e_tot)\nprint(\"E(CCSD)= %+16.8f Ha\" % cc.e_tot)\nprint(\"E(Emb. CCSD)= %+16.8f Ha\" % emb.e_tot)\nprint(\"E(Emb. CCSD + p-DMET)= %+16.8f Ha\" % emb_pdmet.e_tot)\nprint(\"E(Emb. CCSD + Brueckner)= %+16.8f Ha\" % emb_brueckner.e_tot)\n","repo_name":"BoothGroup/Vayesta","sub_path":"examples/scmf/01-scmf.py","file_name":"01-scmf.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"31"} +{"seq_id":"34162406266","text":"def computepay(h,r):\r\n if h < 40 or r < 0:\r\n return None\r\n elif h > 40:\r\n return (40*r+(h-40)*1.5*r)\r\n else:\r\n return (h*r)\r\n\r\ntry:\r\n hrs = input(\"Enter hours\")\r\n hours = float(hrs)\r\n r = input(\"Rates \")\r\n rate = float(r)\r\n P = computepay(hours,rate)\r\n print(P)\r\n\r\nexcept:\r\n print('input your numberic')\r\n","repo_name":"piyushhibare/Programming-for-everybody-Getting-started-with-python-","sub_path":"4.6.py","file_name":"4.6.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"43260123290","text":"import json\nimport numpy as np\n#course_list = ['convolutional-neural-networks','deep-neural-network','machine-learning','machine-learning-projects','ml-clustering-and-retrieval','ml-foundations','neural-networks-deep-learning','nlp-sequence-models']\n#course_list = ['data-structures-algorithms-1','data-structures-algorithms-2','data-structures-algorithms-3','data-structures-algorithms-4','gaoji-shuju-jiegou','shuju-jiegou-suanfa','suanfa-jichu']\ncourse_list = ['java-chengxu-sheji','java-programming','java-programming-arrays-lists-data','java-programming-design-principles','java-programming-recommender','object-oriented-java']\n\nwith open('related_words.json','r',encoding = 'utf-8') as f:\n related_words = json.load(f)\ntag_pos = {}\nwith open('data/wiki_data/wiki_data.json','r',encoding = 'utf-8') as f:\n wiki_data = json.load(f)\ndef find_tag_pos(tag):\n tag_pos[tag] = {}\n for course in course_list:\n tag_pos[tag][course] = []\n with open('data/mooc_data/coursera_data/'+course+'.json','r',encoding = 'utf-8') as f:\n course_content = json.load(f)\n for caption in course_content['captions']:\n text=caption['text']\n if text.find(tag)!=-1:\n tag_pos[tag][course].append(int(caption['id']))\n \ndef word2vec(word):\n with open('w2v.json','r',encoding ='utf-8') as f:\n res = json.load(f)\n return res[word]\n \n#todo \ndef enrichWord(word):\n return []\n\n#sementic relatedness\ndef SR(tag1,tag2):\n a1 = np.array(word2vec(tag1))\n a2 = np.array(word2vec(tag2))\n norm1 = np.linalg.norm(a1)\n norm2 = np.linalg.norm(a2)\n res = 1/2*(1+np.dot(a1,a2)/(norm1*norm2))\n return res\n\n#video reference weight\ndef VRW(tag1,tag2):\n sum1 = 0\n sum2 = 0\n for course in course_list:\n if not tag_pos[tag1][course]:\n continue\n with open('data/mooc_data/coursera_data/'+course+'.json','r',encoding = 'utf-8') as f:\n course_content = json.load(f)\n for caption in course_content['captions']:\n text=caption['text']\n cnt = text.count(tag1)\n sum2+=cnt\n if text.find(tag2)!=-1:\n sum1+=cnt\n if sum2==0:\n return 0\n res = sum1/sum2\n return res\n\ndef GVRW(tag1,tag2):\n ais = related_words[tag1]\n num1 = 0\n num2 = 0\n for ai in ais:\n t = SR(ai,tag2)\n num1+=(t*VRW(ai,tag2))\n num2+=t\n return num1/num2\n \n#video reference distance\ndef GVRD(tag1,tag2):\n return GVRW(tag2,tag1)-GVRW(tag1,tag2)\n\n#sentence reference weight\ndef SRW(tag1,tag2):\n sum1 = 0\n sum2 = 0\n for course in course_list:\n if not tag_pos[tag1][course]:\n continue\n with open('data/mooc_data/coursera_data/'+course+'.json','r',encoding = 'utf-8') as f:\n course_content = json.load(f)\n for caption in course_content['captions']:\n text=caption['text']\n sentences = text.split('，|。|！|？|；|“|”|、|‘|’')\n for sentence in sentences:\n cnt = sentence.count(tag1)\n sum2+=cnt\n if sentence.find(tag2)!=-1:\n sum1+=cnt\n if sum2==0:\n return 0\n res = sum1/sum2\n return res\n\ndef GSRW(tag1,tag2):\n ais = related_words[tag1]\n num1 = 0\n num2 = 0\n for ai in ais:\n t = SR(ai,tag2)\n num1+=(t*SRW(ai,tag2))\n num2+=t\n return num1/num2\n\n#sentence reference distance\ndef GSRD(tag1,tag2):\n return GSRW(tag2,tag1)-GSRW(tag1,tag2)\n\n#wikipedia reference eight\n#todo \ndef WRW(tag1,tag2):\n #if tag1 refer to tag2\n return 1\n \n#wikipedia reference distance\ndef WRD(tag1,tag2):\n return WRW(tag1,tag2)-WRW(tag2,tag1)\n\n#average position distance\ndef APD(tag1,tag2):\n num1 = 0\n num2 = 0\n for course in course_list:\n I1 = tag_pos[tag1][course]\n I2 = tag_pos[tag2][course]\n if len(I1)*len(I2)==0:\n continue\n num2+=1\n num1+=(np.mean(I2)-np.mean(I1))\n if num2==0:\n return 0\n return num1/num2\n\n#distributional asymmetry distance\ndef DAD(tag1,tag2):\n \n num1 = 0\n num2 = 0\n for course in course_list:\n I1 = tag_pos[tag1][course]\n I2 = tag_pos[tag2][course]\n if len(I1)*len(I2)==0:\n continue\n num2+=1\n with open('data/mooc_data/coursera_data/'+course+'.json','r',encoding = 'utf-8') as f:\n course_content = json.load(f)\n SCres = 0\n SClen = 0\n for i in I1:\n for j in I2:\n if i>=j:\n continue\n SClen+=1\n text1=course_content['captions'][i-1]['text']\n text2=course_content['captions'][j-1]['text']\n SCres+=(text1.count(tag1)-text2.count(tag2))\n if SClen!=0:\n num1+=(SCres/SClen)\n if num2==0:\n return 0\n return num1/num2\n\n#average video coverage\ndef AVC(tag):\n num1=0\n num2=0\n for course in course_list:\n I = tag_pos[tag][course]\n if len(I)==0:\n continue\n with open('data/mooc_data/coursera_data/'+course+'.json','r',encoding = 'utf-8') as f:\n course_content = json.load(f)\n num2+=1\n num1+=(len(tag_pos[tag][course])/len(course_content['captions']))\n if num2==0:\n return 0\n return num1/num2\n\n#average survival time\ndef AST(tag):\n num1=0\n num2=0\n for course in course_list:\n I = tag_pos[tag][course]\n if len(I)==0:\n continue\n with open('data/mooc_data/coursera_data/'+course+'.json','r',encoding = 'utf-8') as f:\n course_content = json.load(f)\n num2+=1\n num1+=((tag_pos[tag][course][-1]-tag_pos[tag][course][0]+1)/len(course_content['captions']))\n if num2==0:\n return 0\n return num1/num2\n\n#complexity level distance\ndef CLD(tag1,tag2):\n return AVC(tag1)*AST(tag1)-AVC(tag2)*AST(tag2)\n\n#wiki appearance\ndef WA(a,b):\n res = 0\n article = wiki_data[b]\n abstract = article['abstract']\n res+=abstract.count(a)\n return res\n\nif __name__==\"__main__\":\n \n res = {}\n with open('all_tags_taken.txt','r',encoding ='utf-8') as f:\n lines = f.readlines()\n tags = [x.split('\\n')[0] for x in lines]\n \n for tag in tags:\n find_tag_pos(tag)\n \n \n with open('tag_pos.json','w',encoding ='utf-8') as f:\n json.dump(tag_pos,f,ensure_ascii=False)\n \n for tag1 in tags:\n print(tag1)\n for tag2 in tags:\n if tag1==tag2:\n continue\n r = []\n r.append(SR(tag1,tag2))\n r.append(GVRD(tag1,tag2))#-\n r.append(GSRD(tag1,tag2))#-\n #r.append(GWRD(tag1,tag2))\n \n r.append(APD(tag1,tag2)) \n r.append(DAD(tag1,tag2))\n r.append(CLD(tag1,tag2))\n r.append(WA(tag1,tag2))\n res[tag1+'#'+tag2] = r\n \n with open('feature_res.json','w',encoding ='utf-8') as f:\n json.dump(res,f,ensure_ascii=False)\n ","repo_name":"quackson/graduate_design","sub_path":"featureGenerator.py","file_name":"featureGenerator.py","file_ext":"py","file_size_in_byte":7130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2087841487","text":"import sys\na=input()\na=a.lower()\na=list(a)\nk=\"hello\"\nk=list(k)\nf=[]\nm=0\nfor i in range(len(k)) :\n\tif k[i] in a:\n\t\tf.append(a.index(a[i]))\n\t\tfor j in range(a.index(a[i])):\n\t\t\ta[j]='1'\n\t\ta[a.index(a[i])]='1'\nm=0\nfor i in range(len(f)-1):\n\tif(f[i]<f[i+1]):\n\t\tm=m+1\nif(m==len(f)-1):\n\tprint(\"Yes\")\nelse:\n\tprint(\"No\")\n\n","repo_name":"Guvvala-Prasanth-Reddy/Codeforces-coding-solutions","sub_path":"50-Forces-Of-Code/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7313537793","text":"\"\"\"\n Events backend service\n\"\"\"\nimport asyncio\nfrom ..utils import WebRPCError, iso_timestamp_to_millis\nfrom datetime import datetime\n\nclass EventsService:\n\n def __init__(self, server):\n self.server = server\n self.subscriptions = {}\n\n\n async def rpc_command(self, client, method, params):\n \"\"\"\n \"\"\"\n if method == \"subscribe\":\n self.subscriptions[client] = True\n\n elif method == \"unsubscribe\":\n del self.subscriptions[client]\n\n elif method == \"request\":\n return await self.request_events(params)\n\n else:\n raise WebRPCError(f\"Unknown method {method!r}\")\n\n\n async def request_events(self, params):\n \"\"\"\n \"\"\"\n\n options = params[\"options\"]\n #print(\"request events with options: \",options)\n\n request_data = { }\n if \"domain\" in options and options[\"domain\"] != \"utc\":\n raise WebRPCError(\"Invalid domain!\")\n if \"start\" in options:\n request_data[\"start_date\"] = options[\"start\"] / 1000\n if \"end\" in options:\n request_data[\"end_date\"] = options[\"end\"] / 1000\n\n fields = await self.server.send_rpc_request(\"events\", \"rpc.latest\", request_data)\n\n return {\n \"subsystem\": \"events\",\n \"exchange\": \"events\",\n \"entries\": [\n {\n \"timestamp\": float(param[\"timestamp\"]) * 1000,\n \"source\": param[\"source\"],\n \"severity\": param[\"severity\"],\n \"data\": param[\"data\"],\n \"received\": float(param[\"received\"]) * 1000\n } for param in fields[\"events\"]\n ]\n }\n\n\n async def handle_subscription(self, message: dict):\n \"\"\"\n \"\"\"\n\n received = datetime.utcnow().isoformat()\n for ev in message[\"events\"]:\n msg = {\n \"subscription\": {\n \"exchange\" : \"events\",\n \"subsystem\": \"events\",\n \"events\": {\n \"id\": \"events\",\n \"source\": message[\"source\"],\n \"severity\": ev[\"severity\"],\n \"data\": ev[\"info\"],\n \"received\": received,\n \"timestamp\": iso_timestamp_to_millis(ev[\"timestamp\"])\n }\n }\n }\n\n awaits = []\n for client in self.subscriptions:\n awaits.append( client.send_json(**msg) )\n\n if len(awaits) > 0:\n await asyncio.wait(awaits)\n","repo_name":"aaltosatellite/porthouse","sub_path":"mcs/openmct_backend/services/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":2629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6655404989","text":"# Don't forget to change this file's name before submission.\nimport sys\nimport os\nimport enum\nimport socket\nimport struct\nimport shutil\n\nBUFFER_SIZE = 2048\n\nTOTAL, USED, FREE = shutil.disk_usage(\"/\")\n\n\nclass TftpProcessor(object):\n \"\"\"\n Implements logic for a TFTP client.\n The input to this object is a received UDP packet,\n the output is the packets to be written to the socket.\n\n This class MUST NOT know anything about the existing sockets\n its input and outputs are byte arrays ONLY.\n\n Store the output packets in a buffer (some list) in this class\n the function get_next_output_packet returns the first item in\n the packets to be sent.\n\n This class is also responsible for reading/writing files to the\n hard disk.\n\n Failing to comply with those requirements will invalidate\n your submission.\n\n Feel free to add more functions to this class as long as\n those functions don't interact with sockets nor inputs from\n user/sockets. For example, you can add functions that you\n think they are \"private\" only. Private functions in Python\n start with an \"_\", check the example below\n \"\"\"\n\n class TftpPacketType(enum.Enum):\n \"\"\"\n Represents a TFTP packet type add the missing types here and\n modify the existing values as necessary.\n \"\"\"\n RRQ = 1\n WRQ = 2\n DATA = 3\n ACK = 4\n ERROR = 5\n\n ERROR_MSG = {\n 0: \"Not defined, see error message.\", # Used\n 1: \"File not found.\", # Used\n 2: \"Access violation.\",\n 3: \"Disk full or allocation exceeded.\", # Used\n 4: \"Illegal TFTP operation.\", # Used\n 5: \"Unknown transfer ID.\",\n 6: \"File already exists.\", # Used\n 7: \"No such user.\"\n }\n\n def __init__(self):\n self.packet_buffer = []\n self.mode = \"octet\"\n self.upload = None\n self.is_done = False\n self.file = None\n self.DEFAULT_BLOCK_SIZE = 516\n self.DEFAULT_DATA_SIZE = 512\n self.current_block_count = 1\n\n def _open_file(self, file_name):\n if self.upload:\n # Check if the file the client wants to upload doesn't exist\n if os.path.isfile(file_name):\n self.file = open(file_name, \"rb\")\n else:\n error_code = 1\n print(str(error_code) + \" : \" + self.ERROR_MSG[error_code])\n sys.exit(error_code)\n else:\n # Check if the file the client wants to download already exits\n if not os.path.isfile(file_name):\n self.file = open(file_name, \"wb\")\n else:\n error_code = 6\n print(str(error_code) + \" : \" + self.ERROR_MSG[error_code])\n sys.exit(error_code)\n\n def process_udp_packet(self, packet_data, packet_source):\n print(f\"Received a packet from {packet_source}\")\n output_packet = self._parse_udp_packet(packet_data)\n\n # This shouldn't change.\n self.packet_buffer.append(output_packet)\n\n def _parse_udp_packet(self, packet_bytes):\n \"\"\"\n You'll use the struct module here to determine\n the type of the packet and extract other available\n information.\n \"\"\"\n # Extract the opcode from the packet received and act accordingly\n opcode = self._get_opcode(packet_bytes)\n if opcode == self.TftpPacketType.DATA.value:\n return self._process_data_packets(packet_bytes)\n elif opcode == self.TftpPacketType.ACK.value:\n return self._process_ack_packets(packet_bytes)\n elif opcode == self.TftpPacketType.ERROR.value:\n return self._process_error_packets(packet_bytes)\n else:\n error_code = 4\n return self._make_error_packet(error_code)\n\n def _process_data_packets(self, packet):\n \"\"\"\n 2 bytes 2 bytes n bytes\n ----------------------------------\n | Opcode | Block # | Data |\n ----------------------------------\n \"\"\"\n # Extract the data from the input packet \n data = self._get_data(packet)\n block_number = self._get_block_number(packet)\n\n if block_number != self.current_block_count:\n error_code = 4\n output_packet = self._make_error_packet(error_code)\n return output_packet\n\n # Check if the disk is full\n if USED + len(data) > TOTAL:\n error_code = 3\n output_packet = self._make_error_packet(error_code)\n return output_packet\n\n # Write the extracted data to the file\n self.file.write(data)\n # Check if this packet is the last one\n if len(packet) < self.DEFAULT_BLOCK_SIZE:\n self.is_done = True\n self.file.close()\n\n self.current_block_count += 1\n # After receiving a DATA packet we create an ACK packet\n output_packet = self._make_ack_packet(block_number)\n return output_packet\n\n def _process_ack_packets(self, packet):\n \"\"\"\n 2 bytes 2 bytes\n ---------------------\n | Opcode | Block # |\n ---------------------\n \"\"\"\n # Extract the block number from the input packet\n block_number = self._get_block_number(packet)\n\n if block_number != self.current_block_count - 1:\n error_code = 4\n output_packet = self._make_error_packet(error_code)\n return output_packet\n\n # After receiving an ACK packet we create a DATA packet\n data = self.file.read(self.DEFAULT_DATA_SIZE)\n \n if len(data) < self.DEFAULT_DATA_SIZE:\n self.is_done = True\n\n self.current_block_count += 1\n output_packet = self._make_data_packet(block_number + 1, data)\n return output_packet\n\n def _process_error_packets(self, packet):\n \"\"\"\n 2 bytes 2 bytes string 1 byte\n -----------------------------------------\n | Opcode | ErrorCode | ErrMsg | 0 |\n -----------------------------------------\n \"\"\"\n error_code = struct.unpack(\"!H\", packet[2:4])[0]\n print(str(error_code) + \" : \" + self.ERROR_MSG[error_code])\n sys.exit(error_code)\n\n def _make_request_packet(self, opcode, file_name):\n \"\"\"\n Create the WRQ or RRQ according to the user input\n 2 bytes string 1 byte string 1 byte\n ------------------------------------------------\n | Opcode | Filename | 0 | Mode | 0 |\n ------------------------------------------------\n \"\"\"\n values = (opcode, file_name.encode(\"ASCII\"), 0, self.mode.encode(\"ASCII\"), 0)\n s = struct.Struct('!H{}sB{}sB'.format(len(file_name), len(self.mode)))\n return s.pack(*values)\n\n def _make_ack_packet(self, block_num):\n \"\"\"\n Create the acknowledge packet\n 2 bytes 2 bytes\n ---------------------\n | Opcode | Block # |\n ---------------------\n \"\"\"\n ack_packet = struct.pack(\"!HH\", self.TftpPacketType.ACK.value, block_num)\n return ack_packet\n\n def _make_data_packet(self, block_num, data):\n \"\"\"\n Create the data packet\n 2 bytes 2 bytes n bytes\n ----------------------------------\n | Opcode | Block # | Data |\n ----------------------------------\n \"\"\"\n data_packet = struct.pack(\"!HH{}s\".format(len(data)), self.TftpPacketType.DATA.value, block_num, data)\n return data_packet\n\n def _make_error_packet(self, error_code):\n \"\"\"\n Create the error packet\n 2 bytes 2 bytes string 1 byte\n -----------------------------------------\n | Opcode | ErrorCode | ErrMsg | 0 |\n -----------------------------------------\n \"\"\"\n error_packet = struct.pack(\"!HH{}sB\".format(len(self.ERROR_MSG[error_code])), self.TftpPacketType.ERROR.value, error_code, self.ERROR_MSG[error_code].encode(\"ASCII\"), 0)\n return error_packet\n\n def _get_opcode(self, packet):\n # Extract the opcode field from the packet\n return struct.unpack(\"!H\", packet[:2])[0]\n\n def _get_block_number(self, packet):\n # Extract the block number field from the packet\n # unpack returns a tuple, so we add [0]\n return struct.unpack(\"!H\", packet[2:4])[0]\n\n def _get_data(self, packet):\n # Extract the data field from the packet\n return struct.unpack(\"!{}s\".format(len(packet) - 4), packet[4:])[0]\n\n def _get_error_code(self, packet):\n # Extract the error code from the packet\n return struct.unpack(\"!H\", packet[2:4])[0]\n\n def get_next_output_packet(self):\n return self.packet_buffer.pop(0)\n\n def has_pending_packets_to_be_sent(self):\n return len(self.packet_buffer) != 0\n\n def request_file(self, file_path_on_server):\n packet = self._make_request_packet(self.TftpPacketType.RRQ.value, file_path_on_server)\n return packet\n\n def upload_file(self, file_path_on_server):\n packet = self._make_request_packet(self.TftpPacketType.WRQ.value, file_path_on_server)\n return packet\n\n\ndef check_file_name():\n script_name = os.path.basename(__file__)\n import re\n matches = re.findall(r\"(\\d{4}_)+lab1\\.(py|rar|zip)\", script_name)\n if not matches:\n print(f\"[WARN] File name is invalid [{script_name}]\")\n pass\n\n\ndef setup_sockets(address):\n client_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n server_address = (address, 69)\n\n return client_socket, server_address\n\n\ndef send_packet(packet, client_socket, server_address):\n # Send packet to remote server\n client_socket.sendto(packet, server_address)\n\n\ndef receive_packet(client_socket, buffer_size):\n # Receive a packet\n packet, address = client_socket.recvfrom(buffer_size)\n return packet, address\n\n\ndef parse_user_input(tftp, address, operation, file_name=None):\n if operation == \"push\":\n print(f\"Attempting to upload [{file_name}]...\")\n tftp.upload = True\n packet = tftp.upload_file(file_name)\n return packet\n elif operation == \"pull\":\n print(f\"Attempting to download [{file_name}]...\")\n tftp.upload = False\n packet = tftp.request_file(file_name)\n return packet\n else:\n error_code = 4\n print(str(error_code) + \" : \" + tftp.ERROR_MSG[error_code])\n sys.exit(error_code)\n\n\ndef get_arg(param_index, default=None):\n try:\n return sys.argv[param_index]\n except IndexError as e:\n if default:\n return default\n else:\n print(e)\n print(\n f\"[FATAL] The comamnd-line argument #[{param_index}] is missing\")\n exit(-1) # Program execution failed.\n\n\ndef tftp_logic(tftp, packet, client_socket, server_address):\n # Send the first packet according to the user input, WRQ or RRQ\n send_packet(packet, client_socket, server_address)\n\n while not tftp.is_done:\n # receive packet from the server (ack / data)\n input_packet, address = receive_packet(client_socket, BUFFER_SIZE)\n # Create a new packet according to the packet received from the server\n tftp.process_udp_packet(input_packet, address)\n output_packet = tftp.get_next_output_packet()\n # Check if the output packet is not none and send it \n # None if no more packets needs to be send\n send_packet(output_packet, client_socket, address)\n '''\n Check if the output_packet is an error packet\n and if so terminate the connection because the server (tftp software)\n doesn't terminate on its own \n '''\n opcode = tftp._get_opcode(output_packet)\n if opcode == tftp.TftpPacketType.ERROR.value:\n error_code = tftp._get_error_code(output_packet)\n print(str(error_code) + \" : \" + tftp.ERROR_MSG[error_code])\n sys.exit(error_code)\n\n if tftp.upload:\n # To make sure the last ack packet is received\n input_packet, address = receive_packet(client_socket, BUFFER_SIZE) \n \n tftp.file.close()\n print(address, server_address)\n\n\ndef main():\n print(\"*\" * 50)\n print(\"[LOG] Printing command line arguments\\n\", \",\".join(sys.argv))\n check_file_name()\n print(\"*\" * 50)\n\n # This argument is required.\n # For a server, this means the IP that the server socket\n # will use.\n # The IP of the server, some default values\n # are provided. Feel free to modify them.\n ip_address = get_arg(1, \"127.0.0.1\")\n operation = get_arg(2, \"pull\")\n file_name = get_arg(3, \"test.txt\")\n\n # Create a new instance of the Tftp processor\n tftp = TftpProcessor()\n\n # Setup the socket \n client_socket, server_address = setup_sockets(ip_address)\n\n # Get the first packet according to the user input.\n packet = parse_user_input(tftp, ip_address, operation, file_name)\n\n # Open the file according to the user input (Reading or Writing)\n tftp._open_file(file_name)\n\n # Call the main method\n tftp_logic(tftp, packet, client_socket, server_address)\n \n\nif __name__ == \"__main__\":\n main()\n","repo_name":"SherifRafik/tftp-client","sub_path":"tftp_client.py","file_name":"tftp_client.py","file_ext":"py","file_size_in_byte":13343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22951360521","text":"# -*- encoding:utf-8 -*-\n# @lc app=leetcode.cn id=40 lang=python3\n#\n# [40] 组合总和 II\n#\n\nfrom functools import reduce\n\n# @lc code=start\nclass Solution:\n target = 0\n result = {}\n candidates = []\n\n def combinationSum2(self, candidates, target):\n if not candidates or len(candidates) == 0:\n return []\n\n if target == 0:\n return candidates\n \n # 初始化\n self.result = {}\n candidates.sort()\n self.target = target\n self.candidates = candidates\n self.solution(target, [], 0)\n return list(map(lambda x: self.result[x], self.result))\n\n def solution(self, target, combins, index): \n if index >= len(self.candidates):\n return None\n \n # 到0跳出\n if target == 0:\n self.result[str(combins)] = combins\n return None\n\n totalNow = reduce(lambda x, y: x + y, combins, 0)\n if totalNow <= self.target:\n # 直接跳层查找\n self.solution(target, combins, index + 1)\n\n subtractNum = target - self.candidates[index]\n if subtractNum >= 0:\n copyCombins = combins[:]\n copyCombins.append(self.candidates[index])\n # 尝试同层搜索\n self.solution(subtractNum, copyCombins, index + 1)\n \n\na = Solution()\n# b = a.combinationSum([2, 3, 6, 7], 7)\nb = a.combinationSum2([2,5,2,1,2], 5)\nprint(b)\n\n# @lc code=end\n","repo_name":"isbox/leet_code","sub_path":"40.组合总和-ii/40.组合总和-ii.py","file_name":"40.组合总和-ii.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13984912638","text":"import cv2\nimport numpy as np\ncap = cv2.VideoCapture(\"IMG_1634.mov\")\n\nret, frame1 = cap.read()\nprvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)\nhsv = np.zeros_like(frame1)\nhsv[...,1] = 255\n\nwhile(1):\n ret, frame2 = cap.read()\n frame = np.array(frame2[...,0])\n\n next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)\n\n flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)\n uAV = (np.sum(flow[1:20,0])//len(flow[1:20,0]))//1920\n vAV = np.sum(flow[1:20,1]) // len(flow[1:20,1])//1920\n\n mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])\n print(mag,ang)\n if not ret:\n break\n\n cv2.waitKey(ord('q'))\n cv2.imshow('test',frame2)\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"Dhoven23/CapstoneProject","sub_path":"src/opticalFlow.py","file_name":"opticalFlow.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"73090233049","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 14 15:48:51 2018\n\n@author: lkivi\n\"\"\"\n\nimport numpy as np\nimport json\nimport pylab as plt\nfrom pyproj import Proj, transform\nimport networkx as nx\n\n\n#%%\n# Design grid properties for the given parameters\ndef design_grid(param):\n \n # available standard pipe inner diameters (heating pipes: ISO 4200 / Set Pipes GmbH; cooling pipes: SDR17, PN10)\n param[\"diameters\"] = {}\n path_heating = \"input_data/pipes_heating.txt\"\n path_cooling = \"input_data/pipes_cooling.txt\"\n param[\"diameters\"][\"heating\"] = np.loadtxt(open(path_heating, \"rb\"), delimiter = \",\", usecols=(0)) # m, inner pipe diameters for cooling network\n param[\"diameters\"][\"cooling\"] = np.loadtxt(open(path_cooling, \"rb\"), delimiter = \",\", usecols=(0)) - 2 * np.loadtxt(open(path_cooling, \"rb\"), delimiter = \",\", usecols=(1)) # m, inner pipe diameters for cooling network\n \n data, graph = generateJson()\n dem = load_demands(data)\n \n # time series of heating supply temperatures according to heating curve\n param[\"T_heating_supply\"] = get_T_supply(param)\n \n grid_types = [\"heating\", \"cooling\"]\n \n # calculate pipe diameters for heating and cooling grid\n for typ in grid_types:\n \n for edge in data[\"edges\"]:\n # get list of buildings supplied by that edge\n supplied_buildings = list_supplied_buildings(data, int(edge), graph)\n\n # sum up the demands of the buildings supplied by that edge \n dem_buildings = np.zeros(8760)\n for building in supplied_buildings:\n dem_buildings = dem_buildings + dem[typ][building]\n \n \n # calculate time series of mass flowrates in the pipe\n m_flow = dem_buildings*1e6/(param[\"c_f\"]*(abs(param[\"T_\"+typ+\"_supply\"] - param[\"T_\"+typ+\"_return\"])))\n \n # maximum mass flowrate\n m_flow_max = np.max(m_flow)\n data[\"edges\"][edge][\"max_flow_\"+typ] = m_flow_max\n \n # calculate pipe diameter for given maxiumum pressure gradient\n d = ((8*m_flow_max**2*param[\"f_fric\"])/(param[\"rho_f\"]*np.pi**2*param[\"dp_pipe\"]))**0.2\n \n # choose next bigger diameter from standard diameter list\n for d_norm in param[\"diameters\"][typ]:\n if d_norm >= d:\n d = d_norm\n break\n \n # write pipe diameter into json array\n data[\"edges\"][edge][\"diameter_\"+typ] = d\n \n \n # save new json-file in project folder\n with open(\"network.json\", \"w\") as f: json.dump(data, f, indent=4, sort_keys=True)\n \n \n return data, param\n\n\n#%%\n# generate json-file of the network using the input files nodes.txt and edges.txt\n# nodes.txt has to contain node latitudes, longitues, types (node, building or supply) and names\n# edges.txt hat to contain names of start and end node for every edge\n# pipe diameters are initialized with 0 \ndef generateJson():\n \n data_dict = {}\n \n path_nodes = \"input_data/nodes.txt\" # contains node properties: latidude, longitude, name and type (supply, building, node)\n path_edges = \"input_data/edges.txt\" # \n \n nodes = {}\n \n lats = np.loadtxt(open(path_nodes, \"rb\"), delimiter = \",\", usecols=(0)) # °, node latitudes\n longs = np.loadtxt(open(path_nodes, \"rb\"), delimiter = \",\", usecols=(1)) # °, node longitudes\n types = np.genfromtxt(open(path_nodes, \"rb\"),dtype = 'str', delimiter = \",\", usecols=(2)) # --, node type (node, building or supply)\n names = np.genfromtxt(open(path_nodes, \"rb\"),dtype = 'str', delimiter = \",\", usecols=(3)) # --, node name\n \n for n in range(len(lats)):\n nodes[n] = {\"lat\": lats[n],\n \"lon\": longs[n],\n \"type\": types[n],\n \"name\": names[n]\n }\n \n# # Earth radius\n# r = 6371000\n# \n# # supply node serves as reference node (x=0, y=0)\n## for i in np.arange(np.size(nodes[\"lat\"])):\n## if nodes[\"type\"][i] == \"supply\":\n## lat_ref = nodes[\"lat\"][i]\n## long_ref = nodes[\"long\"][i]\n# \n# # find minimal lat/long\n# lat_ref = np.min(nodes[\"lat\"])\n# long_ref = np.min(nodes[\"long\"])\n# \n# # transform lat/long to xy-coordinates \n# nodes[\"x\"] = r*np.arccos(np.sin(nodes[\"lat\"])**2 + np.cos(nodes[\"lat\"])**2 * np.cos(nodes[\"long\"] - long_ref))\n# nodes[\"y\"] = r*np.arccos(np.sin(nodes[\"lat\"])*np.sin(lat_ref) + np.cos(nodes[\"lat\"])*np.cos(lat_ref))\n# # replace nan entries by 0\n# nodes[\"x\"] = np.nan_to_num(nodes[\"x\"])\n# nodes[\"y\"] = np.nan_to_num(nodes[\"y\"])\n\n# # shift x/y-coordinates so that supply node is at x = 0, y = 0\n# for i in np.arange(np.size(nodes[\"x\"])):\n# if nodes[\"type\"][i] == \"supply\":\n# supply_x = nodes[\"x\"][i]\n# supply_y = nodes[\"y\"][i] \n# nodes[\"x\"] = nodes[\"x\"] - supply_x\n# nodes[\"y\"] = nodes[\"y\"] - supply_y \n \n \n # Convert to x,y-coordinates\n nodes = transform_coordinates(nodes)\n \n\n edges = {}\n nodes_from = np.genfromtxt(open(path_edges, \"rb\"),dtype = 'str', delimiter = \",\", usecols=(0))\n nodes_to = np.genfromtxt(open(path_edges, \"rb\"),dtype = 'str', delimiter = \",\", usecols=(1))\n \n\n for k in range(len(nodes_from)):\n \n # find node indices in node dictionary\n for n1 in range(len(nodes)):\n if nodes[n1][\"name\"] == nodes_from[k]:\n id_from = n1\n break\n for n2 in range(len(nodes)):\n if nodes[n2][\"name\"] == nodes_to[k]:\n id_to = n2\n break\n \n if id_from > id_to:\n node_ids = (id_to, id_from)\n else:\n node_ids = (id_from, id_to)\n \n length = ((nodes[id_from][\"x\"] - nodes[id_to][\"x\"])**2 + (nodes[id_from][\"y\"] - nodes[id_to][\"y\"])**2)**0.5\n edges[k] = {\"node_names\": nodes[id_from][\"name\"] + \"-\" + nodes[id_to][\"name\"],\n \"node_ids\": node_ids,\n \"length\": length,\n \"diameter_heating\": 0,\n \"diameter_cooling\": 0\n }\n \n data_dict = {\"nodes\": nodes,\n \"edges\": edges}\n \n \n # save json-file in project folder\n with open(\"network.json\", \"w\") as f: json.dump(data_dict, f, indent=4, sort_keys=True)\n \n graph = get_graph(data_dict)\n #plot_grid(graph)\n \n return data_dict, graph\n \n \n#%% Draw network and save plot \ndef plot_grid(graph):\n \n data = json.loads(open('network.json').read())\n \n pos = nx.get_node_attributes(graph, \"pos\")\n# nx.draw(graph, pos, with_labels=False, font_weight=\"bold\")\n \n # Draw standard nodes black and small\n small_nodes = []\n for n in data[\"nodes\"]:\n if data[\"nodes\"][n][\"type\"] == \"node\":\n small_nodes.append(int(n))\n nx.draw_networkx_nodes(graph,pos,nodelist=small_nodes, node_size = 20, node_color=\"black\")\n \n # Draw buildings blue\n buildings = []\n for n in data[\"nodes\"]:\n if data[\"nodes\"][n][\"type\"] == \"building\":\n buildings.append(int(n))\n nx.draw_networkx_nodes(graph,pos,nodelist=buildings, node_size = 100, node_color=\"blue\")\n \n # Draw supply red\n supply = []\n for n in data[\"nodes\"]:\n if data[\"nodes\"][n][\"type\"] == \"supply\":\n supply.append(int(n))\n nx.draw_networkx_nodes(graph,pos,nodelist=supply, node_size = 120, node_color=\"red\")\n \n # Draw edges\n nx.draw_networkx_edges(graph, pos)\n \n \n plt.grid(True)\n plt.axis(\"equal\")\n \n plt.show()\n plt.savefig(\"network.png\")\n\n\n\n#%%\n# finds x- and y-coordinate of a node out of json file by name\n#def findXY(data, name):\n# \n# found = 0\n# \n# for item in data[\"nodes\"]:\n# \n# if item[\"name\"] == name:\n# x = item[\"x\"]\n# y = item[\"y\"]\n# found = 1\n# \n# if found == 0:\n# print(\"Can't retrieve node coordinates to plot grid edges\")\n# exit()\n# \n# return x,y\n\n\n#%% create networkx-graph out of json-file\ndef get_graph(data):\n \n # get networkx-graph\n graph = nx.Graph()\n for k in data[\"nodes\"]:\n graph.add_node(int(k), pos=(data[\"nodes\"][k][\"x\"], data[\"nodes\"][k][\"y\"]))\n \n ebunch = []\n for k in data[\"edges\"]:\n ebunch.append((data[\"edges\"][k][\"node_ids\"][0], data[\"edges\"][k][\"node_ids\"][1])) \n graph.add_edges_from(ebunch)\n \n return graph\n\n\n\n#%% finds all buildings that are supplied by the considered edge\ndef list_supplied_buildings(data, edge_id, graph):\n\n supplied_buildings = []\n \n # get building ids and names\n dict_buildings = {}\n for k in data[\"nodes\"]:\n if data[\"nodes\"][k][\"type\"] == \"building\":\n dict_buildings[int(k)] = data[\"nodes\"][k][\"name\"]\n \n # get supply node id\n for k in data[\"nodes\"]:\n if data[\"nodes\"][k][\"type\"] == \"supply\":\n supply_node = int(k)\n break \n \n # get names of buildings which are supplied by this edge\n for building in dict_buildings:\n path = nx.shortest_path(graph,source = supply_node, target = building)\n # get edges along the path from supply to the building\n for k in range(len(path)-1):\n if path[k] < path[k+1]:\n edge_path = (path[k], path[k+1])\n else:\n edge_path = (path[k+1], path[k])\n # check if the considered edge is lies on that path\n if edge_path == data[\"edges\"][edge_id][\"node_ids\"]:\n supplied_buildings.append(dict_buildings[building])\n \n# print(edge_id)\n# print(supplied_buildings)\n \n return supplied_buildings\n \n \n \n# # initialize array of end points with end point of the input edge\n# endings = [edge[\"node_1\"]]\n# \n# # initialize list of buildings\n# supplied_buildings = []\n# \n# for i in range(1000):\n# \n# # check if the found ending points are buildings; add the found buildings to the buildings-array\n# for iEnding in range(np.size(endings)):\n# nodeName = endings[iEnding]\n# for item in data[\"nodes\"]:\n# if item[\"name\"] == nodeName and item[\"type\"] == \"building\":\n# supplied_buildings.append(nodeName) \n# \n# # set end points to new start points\n# starts = endings\n# \n# #reset ending nodes\n# endings = []\n# \n# #find all edges beginning with any entry of starts and get their ending points\n# for iStart in range(np.size(starts)):\n# nodeName = starts[iStart]\n# \n# for item in data[\"edges\"]:\n# if item[\"node_0\"] == nodeName:\n# endings.append(item[\"node_1\"])\n# \n# # if no new edges are found, the buildings array is returned\n# if endings == []:\n# return supplied_buildings\n \n\n#%% loads demand arrays\ndef load_demands(data):\n \n path_demands = \"input_data/demands/\"\n dem = {}\n dem[\"heating\"] = {}\n dem[\"cooling\"] = {}\n \n dem[\"heating\"][\"sum\"] = np.zeros(8760)\n dem[\"cooling\"][\"sum\"] = np.zeros(8760)\n \n # collect building names out of json-data\n buildings = []\n for k in data[\"nodes\"]:\n if data[\"nodes\"][k][\"type\"] == \"building\":\n buildings.append(data[\"nodes\"][k][\"name\"])\n \n # get loads of each building and sum up \n for name in buildings:\n dem[\"heating\"][name] = np.loadtxt(open(path_demands + name + \"_heating.txt\", \"rb\"), delimiter = \",\", usecols=(0))/1000 # MW, heating load of building\n dem[\"heating\"][\"sum\"] = dem[\"heating\"][\"sum\"] + dem[\"heating\"][name]\n dem[\"cooling\"][name] = np.loadtxt(open(path_demands + name + \"_cooling.txt\", \"rb\"), delimiter = \",\", usecols=(0))/1000 # MW, cooling load of building\n dem[\"cooling\"][\"sum\"] = dem[\"cooling\"][\"sum\"] + dem[\"cooling\"][name]\n \n \n return dem\n \n\n#%%\ndef get_T_supply(param):\n \n path_weather = \"input_data/weather.csv\"\n T_amb = np.loadtxt(open(path_weather, \"rb\"), delimiter = \",\",skiprows = 1, usecols=(0))\n \n \n if not param[\"switch_low_temp\"]:\n \n T_supply = np.zeros(8760)\n \n for i in range(np.size(T_amb)): \n if T_amb[i] < -15:\n T_supply[i] = 140\n elif T_amb[i] < -10:\n T_supply[i] = 140 - 17/5*(T_amb[i]+15)\n elif T_amb[i] < 2:\n T_supply[i] = 123\n elif T_amb[i] < 15:\n T_supply[i] = 123 - 28/13*(T_amb[i]-2)\n else:\n T_supply[i] = 95\n \n else:\n \n T_supply = np.ones(8760) * param[\"T_heating_supply_low\"]\n \n# plt.plot(range(8760), T_supply)\n# plt.show()\n \n return T_supply\n\n \n#%%\ndef transform_coordinates(nodes):\n outProj = Proj(init='epsg:25832') # ETRS89 / UTM zone 32N\n inProj = Proj(init='epsg:4258') # Geographic coordinate system: EPSG 4258 (Europe)\n \n # get x- and y- coordinates and find minimal values of each\n min_x, min_y = transform(inProj,outProj,nodes[0][\"lon\"],nodes[0][\"lat\"])\n for n in range(len(nodes)):\n nodes[n][\"x\"],nodes[n][\"y\"] = transform(inProj,outProj,nodes[n][\"lon\"],nodes[n][\"lat\"])\n if nodes[n][\"x\"] < min_x:\n min_x = nodes[n][\"x\"]\n if nodes[n][\"y\"] < min_y:\n min_y = nodes[n][\"y\"]\n \n # Shift coordinate system by minimal x- and y- value \n# for n in range(len(nodes)):\n# nodes[n][\"x\"] = nodes[n][\"x\"] - min_x\n# nodes[n][\"y\"] = nodes[n][\"y\"] - min_y\n \n return nodes\n\n\n#%%\ndef design_pump(data, param, dem_buildings):\n \n pump_energy = {}\n for demand in [\"heat\", \"cool\"]:\n pump_energy[demand] = {}\n for e in data[\"edges\"]:\n pump_energy[demand][e] = np.zeros(8760)\n \n # pre-factor for pump power calculation\n prefac = (8 * param[\"f_fric\"])/(param[\"rho_f\"]**2*np.pi**2*param[\"eta_pump\"])/1e6 \n \n \n graph = get_graph(data)\n dem = load_demands(data)\n \n # Calculate pumping energy for every edge at every time step\n for e in data[\"edges\"]:\n supplied_buildings = list_supplied_buildings(data, int(e), graph)\n for t in range(8760):\n # Mass flows at current time step\n m_heat = ( np.sum(dem[\"heating\"][name][t] for name in supplied_buildings) *1e6)/(param[\"c_f\"]*(param[\"T_heating_supply\"][t] - param[\"T_heating_return\"]))\n m_cool = ( np.sum(dem[\"cooling\"][name][t] for name in supplied_buildings) * 1e6)/( param[\"c_f\"]*(param[\"T_cooling_return\"] - param[\"T_cooling_supply\"]))\n # Pump energies (MW)\n pump_energy[\"heat\"][e][t] = prefac * 2 * data[\"edges\"][e][\"length\"] * m_heat**3/data[\"edges\"][e][\"diameter_heating\"]**5\n pump_energy[\"cool\"][e][t] = prefac * 2 * data[\"edges\"][e][\"length\"] * m_cool**3/data[\"edges\"][e][\"diameter_cooling\"]**5\n \n # sum up pump energy\n param[\"pump_energy\"] = sum(sum(sum(pump_energy[demand][edge][t] for t in range(8760)) for edge in data[\"edges\"]) for demand in [\"heat\", \"cool\"])\n \n # pump caps\n param[\"pump_cap_heating\"] = sum( np.max(pump_energy[\"heat\"][edge]) for edge in data[\"edges\"])\n param[\"pump_cap_cooling\"] = sum( np.max(pump_energy[\"cool\"][edge]) for edge in data[\"edges\"]) \n \n print(param[\"pump_cap_heating\"])\n print(param[\"pump_cap_cooling\"]) \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n# # get supply unit\n# for n in data[\"nodes\"]:\n# if data[\"nodes\"][n][\"type\"] == \"supply\":\n# supply_node = int(n) \n# \n# # get path lengths to buildings\n# dict_lengths = {}\n# for n in data[\"nodes\"]:\n# if data[\"nodes\"][n][\"type\"] == \"building\":\n# path = nx.shortest_path(graph, source = supply_node, target = int(n))\n# path_length = 0\n# for k in range(len(path)-1):\n# path_length += np.sqrt((data[\"nodes\"][path[k+1]][\"x\"]-data[\"nodes\"][path[k]][\"x\"])**2 + (data[\"nodes\"][path[k+1]][\"y\"]-data[\"nodes\"][path[k]][\"y\"])**2)\n# dict_lengths[data[\"nodes\"][n][\"name\"]] = path_length\n# \n# # Calculate pressure loss on every path at maximum load and find the path with maximum pressure loss and calculate pump capacities\n# pump_caps = {}\n# # Size pump for heating and cooling grid\n# for typ in [\"heating\", \"cooling\"]:\n# pressure_losses = []\n# for destination in dict_lengths:\n# max_dem = np.max(dem_buildings[typ][destination])\n# dp = (max_dem > 0) * ((2*param[\"dp_pipe\"]*dict_lengths[destination])/(1 - param[\"dp_single\"]) + # pressure loss in pipes\n# max_dem * param[\"dp_substation\"]) # pressure drop in substation\n# pressure_losses.append(dp)\n# dp_max = max(pressure_losses)\n# \n# # find edge at supply unit to get total mass flow through pump\n# for e in data[\"edges\"]:\n# n1 = data[\"edges\"][e][\"node_ids\"][0]\n# n2 = data[\"edges\"][e][\"node_ids\"][1]\n# if data[\"nodes\"][n1][\"name\"] == \"supply\" or data[\"nodes\"][n2][\"name\"] == \"supply\":\n# m_flow_pump = data[\"edges\"][e][\"max_flow_\"+typ]\n# \n# cap = m_flow_pump/(param[\"eta_pump\"]*param[\"rho_f\"])*dp_max / 1e6 # MW, electrical power of pump\n# pump_caps[typ] = cap\n# param[\"pump_cap_\"+typ] = cap\n \n# print(pump_caps)\n \n \n return param\n ","repo_name":"LKivi/Energy_System_Optimization","sub_path":"DHC_Benchmark/grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":17879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2268542148","text":"import networkx as nx\n\n\nG = nx.Graph()\nG.add_node(1)\nG.add_nodes_from([2, 3])\nG.add_edges_from([(1, 2), (1, 3)])\nG.number_of_nodes()\nG.number_of_edges()\n\nDG = nx.DiGraph()\nDG.add_edge(2, 1) # adds the nodes in order 2, 1\nDG.add_edge(1, 3)\nDG.add_edge(2, 4)\nDG.add_edge(1, 2)\nassert list(DG.successors(2)) == [1, 4]\nassert list(DG.edges) == [(2, 1), (2, 4), (1, 3), (1, 2)]\n\nlist(G.nodes)\nlist(G.edges)\nlist(G.adj[1]) # or list(G.neighbors(1))\nG.degree[1] # the number of edges incident to 1\n\nG.add_edge(1, 2)\nH = nx.DiGraph(G) # create a DiGraph using the connections from G\nedgelist = [(0, 1), (1, 2), (2, 3)]\nH = nx.Graph(edgelist) # create a graph from an edge list\nadjacency_dict = {0: (1, 2), 1: (0, 2), 2: (0, 1)}\nH = nx.Graph(adjacency_dict) # create a Graph dict mapping nodes to nbrs","repo_name":"ZoSo9999/abd","sub_path":"prova.py","file_name":"prova.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1264521166","text":"# IMPORTS #\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\nimport dash_bootstrap_components as dbc\nfrom dash_extensions import Download\nfrom dash_extensions.snippets import send_data_frame\n\nfrom datetime import date # , timedelta, datetime as dt\nfrom dotenv import load_dotenv\nimport os\nimport pandas as pd\nimport psycopg2\nimport requests\n\n# Import .env variables\n\nload_dotenv() # take environment variables from .env\nDATABASE_URL = os.getenv('DATABASE_URL')\n# replace database_url prefix w/ 'postgresql' so sqlalchemy create_engine works\nHEROKU_DB_URL = DATABASE_URL.replace('postgres://', 'postgresql://')\n\n# Import Dash Instance #\nfrom app import app\n\n# DATAFRAME #\n# Load cancelled subscription dataframe from database #\ncon = psycopg2.connect(HEROKU_DB_URL)\ncur = con.cursor()\nquery = f\"\"\"SELECT *\n FROM cancel_db\n \"\"\"\ndf_cancel = pd.read_sql(query, con)\ncon.close()\n\n# LAYOUT #\n\n# Layout Components\ndatepicker_range = dcc.DatePickerRange(\n id='date-picker-range',\n min_date_allowed=date(2019, 6, 1),\n max_date_allowed=date.today(),\n initial_visible_month=date.today(),\n start_date=pd.Timestamp('now').floor('D') - pd.Timedelta(7, unit=\"D\"),\n end_date=pd.Timestamp('today').floor('D'),\n)\n\ndropdown = dcc.Dropdown(\n id='reason-dropdown',\n options=[{'label': 'All Reasons', 'value': 'All Reasons'}] +\n [{'label': i, 'value': i} for i in df_cancel['cancellation_reason'].drop_duplicates().sort_values()],\n placeholder='Select a reason',\n className='mb-2',\n)\n\n# Page layout\nlayout = html.Div(\n children=[\n dcc.Store(id='df_cancel_slice'),\n html.H1('Cancellations'),\n dbc.Row(\n children=[\n dbc.Col(datepicker_range),\n dbc.Col(\n children=[\n html.Div(\n children='',\n id='cancel_total_box',\n className='pt-2',\n ),\n ],\n width='auto',\n className='border',\n ),\n ],\n className='mb-3 mr-1',\n ),\n dbc.Card(\n children=[\n dbc.CardBody(\n children=[\n html.H4(\n children=[\n \"Cancellation Counts\"\n ],\n className='card-title',\n ),\n html.P(\n children=[\n \"This table contains information for all \\\n cancelled subscriptions within the selected \\\n date range. It shows a list of each cancellation \\\n reason and the number of times it occured.\"\n ],\n className='card-text'\n ),\n dbc.Spinner(\n children=[\n html.Div(\n id=\"cancel_counts_container\",\n ),\n ],\n color='primary',\n ),\n ],\n ),\n ],\n color='secondary',\n outline=True,\n className='mb-3',\n ),\n dbc.Card(\n children=[\n dbc.CardBody(\n children=[\n html.H4(\n children=[\n 'Cancellation Reason Comments'\n ],\n className='card-title',\n ),\n html.P(\n children=[\n 'All customers are required to enter a \\\n cancellation reason. They can optionally leave \\\n a cancellation comment. When provided, here are \\\n the cancellation reason comments.'\n ],\n className='card-text',\n ),\n dbc.Button(\n children=[\n 'Download CSV'\n ],\n id='btn_reason_csv',\n color='primary',\n className='mb-3',\n ),\n Download(\n id='download_reason_csv',\n ),\n html.Div(id=\"cancel_reasons_container\"),\n ],\n ),\n ],\n color='secondary',\n outline=True,\n className='mb-3',\n ),\n dbc.Card(\n children=[\n dbc.CardBody(\n children=[\n html.H4(\n children=[\n 'Customers by Cancellation Reason',\n ],\n className='card-title',\n ),\n html.P(\n children=[\n 'This table lists all of the customers that \\\n indicated a particular cancellation reason \\\n when they cancelled.'\n ],\n className='card-text',\n ),\n dbc.Button(\n children=[\n 'Download CSV'\n ],\n id='btn_reason_indiv_csv',\n color='primary',\n className='mb-3',\n ),\n Download(\n id='dl_reason_indiv_csv',\n ),\n dropdown,\n dbc.Spinner(\n children=[\n html.Div(id=\"customers_by_reason_container\"),\n ],\n color='primary',\n ),\n ],\n ),\n ],\n color='secondary',\n outline=True,\n className='mb-3',\n ),\n ]\n)\n# CALLBACKS #\n\n# Helper functions\n\n\ndef time_slice(start_date, end_date):\n '''Filter DataFrame by date range\n\n Keyword arguments:\n start_date -- beginning of date range\n end_date -- end of date range\n '''\n cancelled_at_min = start_date\n cancelled_at_max = end_date\n date_range = (df_cancel[\"cancelled_at\"] > cancelled_at_min)\\\n & (df_cancel[\"cancelled_at\"] < cancelled_at_max)\n df_cancel_slice = df_cancel.loc[date_range]\n return df_cancel_slice\n\n\ndef df_non_empty(df_cancel_slice):\n ''' Create DataFrame for non-empty reasons\n '''\n reasons_not_empty = (df_cancel_slice[\"cancellation_reason_comments\"].notnull()) \\\n & (df_cancel_slice[\"cancellation_reason_comments\"] != \"\")\n df_cancel_reasons = df_cancel_slice.loc[reasons_not_empty]\n df_cancel_reasons = df_cancel_reasons.sort_values(by=\"cancelled_at\",\n ascending=False)\n return df_cancel_reasons\n\n\n# CALLBACKS\n\n\n@app.callback(\n Output(\n component_id='df_cancel_slice',\n component_property='data',\n ),\n Input(\n component_id='date-picker-range',\n component_property='start_date',\n ),\n Input(\n component_id='date-picker-range',\n component_property='end_date',\n )\n)\ndef df_store(start_date, end_date):\n ''' Update dcc.Store(id=df_cancel_slice)\n '''\n df_cancel_slice = time_slice(start_date, end_date)\n df_cancel_reasons = df_non_empty(df_cancel_slice=df_cancel_slice)\n dataframes = {\n 'df_cancel_slice': df_cancel_slice.to_dict('records'),\n 'df_cancel_reasons': df_cancel_reasons.to_dict('records'),\n }\n return dataframes\n\n\n@app.callback(\n Output(\n component_id='cancel_total_box',\n component_property='children',\n ),\n Input(\n component_id='df_cancel_slice',\n component_property='data',\n )\n)\ndef update_total_cancels(data):\n ''' Update cancellation total display\n '''\n df_cancel_slice = pd.DataFrame.from_dict(data['df_cancel_slice'])\n df_cancel_total = df_cancel_slice[\"cancellation_reason\"].value_counts().sum()\n return html.H5(f'Total Cancellations = {df_cancel_total}')\n\n\n@app.callback(\n Output(\n component_id='cancel_counts_container',\n component_property='children',\n ),\n Input(\n component_id='df_cancel_slice',\n component_property='data',\n )\n)\ndef update_count_table(data):\n ''' Update cancel counts container with table\n '''\n df_cancel_slice = pd.DataFrame.from_dict(data['df_cancel_slice'])\n df_cancel_counts = df_cancel_slice[\"cancellation_reason\"].value_counts()\\\n .to_frame().reset_index()\n df_cancel_counts.rename(\n columns={\"index\": \"Reason\", \"cancellation_reason\": \"Count\"},\n inplace=True\n )\n return dbc.Table.from_dataframe(\n df=df_cancel_counts,\n id=\"cancel_counts\",\n striped=True,\n bordered=True,\n hover=True,\n responsive=True,\n )\n\n\n@app.callback(\n Output(\n component_id='cancel_reasons_container',\n component_property='children',\n ),\n Input(\n component_id='df_cancel_slice',\n component_property='data',\n )\n)\ndef update_reason_table(data):\n ''' Update cancel reasons table\n '''\n # Dataframe for non-empty reasons\n df_cancel_reasons = pd.DataFrame.from_dict(data['df_cancel_reasons'])\n df_cancel_reasons.rename(\n columns={\n \"email\": \"Email\",\n \"cancelled_at\": \"Cancelled\",\n \"cancellation_reason\": \"Cancellation Reason\",\n \"cancellation_reason_comments\": \"Comments\",\n \"yotpo_point_balance\": \"Yotpo Points\"\n },\n inplace=True\n )\n\n # Return table with DataFrame\n return dbc.Table.from_dataframe(\n df=df_cancel_reasons,\n id=\"cancel_reasons\",\n striped=True,\n bordered=True,\n hover=True,\n responsive=True,\n )\n\n\n@app.callback(\n Output(\n component_id='download_reason_csv',\n component_property='data',\n ),\n Input(\n component_id='btn_reason_csv',\n component_property='n_clicks',\n ),\n State(\n component_id='df_cancel_slice',\n component_property='data',\n ),\n prevent_initial_call=True,\n)\ndef download_reason_csv(n_clicks, data):\n ''' Cancel reasons download csv\n '''\n # Dataframe for non-empty reasons\n df_cancel_reasons = pd.DataFrame.from_dict(data['df_cancel_reasons'])\n df_cancel_reasons.rename(\n columns={\n \"email\": \"Email\",\n \"cancelled_at\": \"Cancelled\",\n \"cancellation_reason\": \"Cancellation Reason\",\n \"cancellation_reason_comments\": \"Comments\",\n \"yotpo_point_balance\": \"Yotpo Points\"\n },\n inplace=True\n )\n\n return send_data_frame(df_cancel_reasons.to_csv,\n f\"cancel_comments.csv\")\n\n\n@app.callback(\n Output(\n component_id='customers_by_reason_container',\n component_property='children',\n ),\n Input(\n component_id='df_cancel_slice',\n component_property='data',\n ),\n Input(\n component_id='reason-dropdown',\n component_property='value',\n )\n)\ndef update_customer_by_reason_table(data, value):\n ''' Update customers by cancel reason table\n '''\n df_cancel_slice = pd.DataFrame.from_dict(data['df_cancel_slice'])\n\n # Dataframe for customers by reason\n if value == 'All Reasons':\n df_cancel_customers = df_cancel_slice\n else:\n reason = df_cancel_slice[\"cancellation_reason\"] == value\n df_cancel_customers = df_cancel_slice.loc[reason]\n df_cancel_customers = df_cancel_customers.sort_values(by=\"cancelled_at\",\n ascending=False)\n df_cancel_customers.rename(\n columns={\n \"email\": \"Email\",\n \"cancelled_at\": \"Cancelled\",\n \"cancellation_reason\": \"Cancellation Reason\",\n \"cancellation_reason_comments\": \"Comments\",\n \"yotpo_point_balance\": \"Yotpo Points\"\n },\n inplace=True\n )\n\n # Return table with DataFrame\n return dbc.Table.from_dataframe(\n df=df_cancel_customers,\n id=\"cancel_customers\",\n striped=True,\n bordered=True,\n hover=True,\n responsive=True,\n )\n\n\n@app.callback(\n Output(\n component_id='dl_reason_indiv_csv',\n component_property='data',\n ),\n Input(\n component_id='btn_reason_indiv_csv',\n component_property='n_clicks',\n ),\n State(\n component_id='reason-dropdown',\n component_property='value',\n ),\n State(\n component_id='df_cancel_slice',\n component_property='data',\n ),\n prevent_initial_call=True,\n)\ndef download_reason_csv(n_clicks, value, data):\n ''' Individual cancel reason download csv\n '''\n df_cancel_slice = pd.DataFrame.from_dict(data['df_cancel_slice'])\n\n # Dataframe for customers by reason\n if value == 'All Reasons':\n df_cancel_customers = df_cancel_slice\n else:\n reason = df_cancel_slice[\"cancellation_reason\"] == value\n df_cancel_customers = df_cancel_slice.loc[reason]\n df_cancel_customers = df_cancel_customers.sort_values(by=\"cancelled_at\",\n ascending=False)\n df_cancel_customers.rename(\n columns={\n \"email\": \"Email\",\n \"cancelled_at\": \"Cancelled\",\n \"cancellation_reason\": \"Cancellation Reason\",\n \"cancellation_reason_comments\": \"Comments\",\n \"yotpo_point_balance\": \"Yotpo Points\"\n },\n inplace=True\n )\n return send_data_frame(df_cancel_customers.to_csv,\n f\"cancel_reason.csv\")\n","repo_name":"tgk-peter/tgk_analytics","sub_path":"pages/cancellations.py","file_name":"cancellations.py","file_ext":"py","file_size_in_byte":14473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42522027634","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nimport logging\n\nimport cv2\nimport numpy as np\n\nimport carlhauser_server.Configuration.feature_extractor_conf as feature_extractor_conf\nfrom common.environment_variable import load_server_logging_conf_file\n\nload_server_logging_conf_file()\n\n\nclass Picture_Orber:\n def __init__(self, fe_conf: feature_extractor_conf.Default_feature_extractor_conf):\n # STD attributes\n self.fe_conf : feature_extractor_conf.Default_feature_extractor_conf = fe_conf\n self.logger = logging.getLogger(__name__)\n self.logger.info(\"Creation of a Picture Orber\")\n self.algo = cv2.ORB_create(nfeatures=fe_conf.ORB_KEYPOINTS_NB)\n\n def orb_picture(self, curr_picture):\n \"\"\"\n Orb a picture and returns the orb value\n :param curr_picture: the picture to orb\n :return: the orb version of the picture\n \"\"\"\n answer = {}\n self.logger.info(\"Orbing picture ... \")\n\n # orb_pic = np.array(curr_picture)\n arr = np.asarray(bytearray(curr_picture), dtype=np.uint8)\n orb_pic = cv2.imdecode(arr, -1)\n self.logger.debug(f\"Picture converted to CV2 UMAT {type(orb_pic)}\")\n\n # DEBUG # cv2.imwrite('/home/user/Desktop/debug_orb.bmp', orb_pic)\n\n try:\n # Note : @image must be a PIL instance.\n if self.fe_conf.ORB.get(\"is_enabled\", False) or self.fe_conf.RANSAC_ORB.get(\"is_enabled\", False):\n # Picture loading handled in picture load_image overwrite\n key_points, descriptors = self.algo.detectAndCompute(orb_pic, None)\n\n # Store representation information in the picture itself\n answer[\"ORB_KEYPOINTS\"] = key_points\n answer[\"ORB_DESCRIPTORS\"] = descriptors\n\n if key_points is None or key_points == []:\n self.logger.warning(f\"WARNING : picture has no keypoints\")\n raise Exception(\"NO KEYPOINTS\")\n if descriptors is None or descriptors == []:\n self.logger.warning(f\"WARNING : picture has no descriptors\")\n raise Exception(\"NO DESCRIPTOR\")\n\n except Exception as e:\n self.logger.error(\"Error during orbing : \" + str(e))\n\n return answer\n","repo_name":"CIRCL/douglas-quaid","sub_path":"carlhauser_server/FeatureExtractor/picture_orber.py","file_name":"picture_orber.py","file_ext":"py","file_size_in_byte":2291,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"31"} +{"seq_id":"32526106171","text":"from sklearn.neighbors import KNeighborsClassifier\nimport numpy as np\nimport timeCount\nfrom sklearn.linear_model import LogisticRegression\n\n\nnT = 100000000\nnF = 10\nnE = int(nT / nF)\n\nX = np.random.rand(nT).reshape(nE, nF)\n\ny = np.random.randint(2, size=nE)\n\nscoring_data = np.random.rand(nF).reshape(1, -1)\n\nknn = KNeighborsClassifier(11, algorithm='brute')\n\nwith timeCount.timer():\n knn.fit(X, y)\n\nwith timeCount.timer():\n knn.predict(scoring_data)\n\nlog_res = LogisticRegression(C=1e5)\n\nwith timeCount.timer():\n log_res.fit(X, y)\n\nwith timeCount.timer():\n prediction = log_res.predict(scoring_data)\n\n","repo_name":"Ljy0715/AutoML","sub_path":"PycharmProjects/pythonProject1/TimeCount/timeCount_test.py","file_name":"timeCount_test.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33029025791","text":"#!/usr/bin/env python3\n\"\"\"Steady state probabilities of a regular markov chain\"\"\"\n\nimport numpy as np\n\n\ndef regular(P):\n \"\"\"\n Function that determines the steady state regular markov chain\n \"\"\"\n try:\n if len(P.shape) != 2:\n return None\n\n n = P.shape[0]\n if n != P.shape[1]:\n return None\n\n # Method by eigendescomposition\n # Formula https://cutt.ly/Ed9Ad7s\n\n # (πP).T = π.T ⟹ P.T π.T = π.T (.)\n evals, evecs = np.linalg.eig(P.T)\n \"\"\"\n break down a matrix into its constituent parts\n where the eigenvectors represent the matrix scales\n and the eigenvalues represent the scaling factors\n \"\"\"\n\n # trick: has to be normalized, elements sum to 1\n state = (evecs / evecs.sum())\n\n # P.T π.T = π.T (.)\n new_state = np.dot(state.T, P)\n\n # each element of the normalized state vector is greater\n # and if the sum of elements in the vector is close to 1.\n for i in new_state:\n if (i >= 0).all() and np.isclose(i.sum(), 1):\n return i.reshape(1, n)\n\n except Exception:\n return None\n","repo_name":"enxo7899/holbertonschool-machine_learning","sub_path":"unsupervised_learning/hmm/1-regular.py","file_name":"1-regular.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72310175448","text":"#https://www.hackerrank.com/challenges/matrix-script/problem\r\n#!/bin/python3\r\n\r\nimport math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\n\r\n\r\n\r\nfirst_multiple_input = input().rstrip().split()\r\n\r\nn = int(first_multiple_input[0])\r\n\r\nm = int(first_multiple_input[1])\r\nlis = ['!','@','#','$','%','&',' ']\r\nmatrix = []\r\ndecode = ''\r\nextra = ''\r\npre = False\r\nfirst = False\r\nfor _ in range(n):\r\n matrix_item = input()\r\n matrix.append(matrix_item)\r\nfor i in range(m):\r\n for j in range(n):\r\n try:\r\n elem = matrix[j][i]\r\n condition = elem in lis\r\n while(condition and first):\r\n extra += elem\r\n break\r\n while(condition and (not first)):\r\n decode += elem\r\n break\r\n while((not condition) and pre and first):\r\n decode+= ' '+matrix[j][i]\r\n extra = ''\r\n first = True\r\n break\r\n while((not condition) and pre and (not first)):\r\n decode+= matrix[j][i]\r\n extra = ''\r\n first = True\r\n break\r\n while((not condition) and (not pre)):\r\n decode+= matrix[j][i]\r\n extra = ''\r\n first = True\r\n break\r\n pre = condition\r\n except:\r\n pass\r\ndecode+=extra\r\nprint(decode)","repo_name":"KingPegasus/HackerRank","sub_path":"Python/Hard/Matrix Script.py","file_name":"Matrix Script.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14791191632","text":"#!/usr/bin/python3\n#----------------------------------------------------------------\n# Dateiname: karteikarten_edit.pyw\n# Von Karteikarten lernen - Editor \n#\n# Michael Weigend\n# Raspberry Pi programmieren mit Python, 3. Auflage, mitp 2016\n# Kap. 6.5\n# Michael Weigend 20. April 2016\n#--------------------------------------------------------------\nfrom tkinter import *\nimport pickle, random\n\nPATH = \"cards.dat\"\n\ndef loadCards():\n global cards, num\n try:\n f = open(PATH, \"rb\")\n cards = pickle.load(f)\n f.close() \n except:\n cards = [(\"\", \"\")]\n saveCards()\n showCard(0)\n\ndef saveCards():\n f = open(PATH, \"wb\")\n pickle.dump(cards, f)\n f.close()\n\ndef showCard(n):\n label.config(text=\"Karte \"+str(n+1))\n text1.delete(1.0, END)\n text1.insert(1.0, cards[n][0])\n text2.delete(1.0, END)\n text2.insert(1.0, cards[n][1]) \n \ndef newCard():\n global num, cards\n cards.append((\"\",\"\"))\n num = len(cards) - 1\n showCard(num)\n \ndef nextCard():\n global num, cards\n cards[num] = (text1.get(1.0, END)[:-1],\n text2.get(1.0, END)[:-1])\n saveCards()\n num += 1\n if num >= len(cards):\n num = 0\n showCard(num)\n \n# Widgets\nwindow = Tk()\nwindow.title(\"Karteikarten-Editor\")\nlabel = Label(master=window, width=20,font = (\"Arial\", 16)) \nbuttonNext = Button(master=window,text=\"Speichern und weiter\",width=20,\n font = (\"Arial\", 14), command=nextCard)\nbuttonNew = Button(master=window,text=\"Neue Karte\", width=20,\n font = (\"Arial\", 14), command=newCard)\ntext1 = Text(master=window, width=40, height=4,\n font = (\"Arial\", 14), wrap=WORD)\ntext2 = Text(master=window, width=40, height=4, fg=\"blue\",\n font = (\"Arial\", 14), wrap=WORD)\n \n# Layout\nlabel.pack()\ntext1.pack(pady=2)\ntext2.pack(pady=2)\nbuttonNew.pack(side=LEFT, padx=2, pady=2)\nbuttonNext.pack(side=LEFT, padx=2, pady=2)\n\nloadCards()\nnum = 0\nwindow.mainloop()\n","repo_name":"Eskimo-SVD/Oliver_private_Bude","sub_path":"9783958459120-Pi-Python/programme_auflage_4/kap-6/karteikarten_edit.pyw","file_name":"karteikarten_edit.pyw","file_ext":"pyw","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"1829620310","text":"from django.http import JsonResponse\n\nfrom ..models import Subject, Task, Users, Group, GroupTask, SubjectTeacher, SubjectGroup, Homework\nfrom ..serializers import UsersSerializer, SubjectSerializer, \\\n TaskSerializer, GroupNameSerializer, HomeworkSerializer\nfrom django.db.models import Q, Count\nfrom django.contrib.auth.hashers import check_password\nfrom django.shortcuts import get_object_or_404\n\n\n# SERIALIZERS\ndef serialize_users(users):\n serializer = UsersSerializer(users, many=True)\n return serializer.data\n\n\ndef serialize_subject(subject):\n serializer = SubjectSerializer(subject)\n return serializer.data\n\n\ndef serialize_task(task):\n serializer = TaskSerializer(task)\n return serializer.data\n\n\ndef add_teacher(name, login, password, telegram_id, point, group_id):\n # Проверка на существование пользователя с аналогичным логином\n if Users.objects.filter(login=login).exists():\n return {'success': False, 'message': 'Пользователь с таким логином и паролем уже существует.'}\n\n # Создание нового пользователя\n new_teacher = Users(\n name=name,\n login=login,\n password=password,\n telegram_id=telegram_id,\n is_teacher=True,\n group=Group.objects.get(pk=group_id) if group_id else None\n )\n new_teacher.save()\n\n return {'success': True, 'message': 'Преподаватель успешно добавлен.'}\n\n\n# 6 разбиваю его на несколько запросов\n# 1. получение всех заданий по предмету и препода get_list_tasks_by_subject_id(subject_id,teacher_id)\n# 2. получение всех домашек по определенному заданию get_homework_by_task_id(task_id)\n# 3. изменение статуса(is_verified) дз True - принято, False - не принято None - не проверено\n# update_homework_status(is_verified, homework_id)\ndef get_homework_by_task_id(task_id):\n # Получаем задачу по task_id\n task = Task.objects.get(id=task_id)\n\n # Получаем все домашние задания для данной задачи\n homework_list = Homework.objects.filter(task_id=task_id)\n\n homework_list_new = HomeworkSerializer(homework_list, many=True)\n # Создаем список для ответа\n homework_data = []\n for homework in homework_list:\n homework_data.append(homework)\n\n # Выводим данные о домашних заданиях в консоль для отладки\n print(homework_list_new.data)\n\n return homework_list\n\n\ndef update_homework_status(is_verified, homework_id):\n # Находим объект домашнего задания по ID\n homework = get_object_or_404(Homework, pk=homework_id)\n\n # Обновляем статус is_verified\n homework.is_verified = is_verified\n homework.save()\n\n\ndef get_list_tasks_by_subject_id(subject_id, teacher_id):\n # Получаем все задания для данного предмета и преподавателя\n tasks = Task.objects.filter(subject_id=subject_id, teacher_id=teacher_id)\n\n serializer = TaskSerializer(tasks, many=True)\n # tasks_list = []\n # for task in tasks:\n # tasks_list.append(task)\n\n return serializer.data\n\n\ndef isTeacherRegistred(login, password):\n try:\n user = Users.objects.get(login=login)\n if user.password == password:\n return True, user\n else:\n return False, \"InvalidPassword\"\n except Users.DoesNotExist:\n return False, \"InvalidLogin\"\n\n\ndef getInformationByStudent(teacher_id, subject_id):\n info = {\n 'subject_info': get_subject_info(teacher_id, subject_id),\n 'student_in_course': get_student_in_course(subject_id, teacher_id),\n 'sdannix_work': get_sdannix_work(subject_id, teacher_id)\n }\n return info\n\n\ndef get_subject_info(teacher_id, subject_id):\n # Получаем информацию о предмете (subject)\n subject_info = (\n Subject.objects\n .filter(id=subject_id)\n .values('id', 'name')\n .first()\n )\n\n if subject_info:\n # Получаем информацию о группах для данного предмета\n groups_info = (\n SubjectGroup.objects\n .filter(subject_id=subject_id)\n .values('group__name', 'url_online_education')\n )\n\n # Получаем информацию о преподавателях для данного предмета\n teachers_info = (\n SubjectTeacher.objects\n .filter(subject_id=subject_id, teacher_id=teacher_id)\n .values('teacher__name')\n )\n\n # Добавляем информацию о группах, преподавателях и URL в результат\n subject_info['groups'] = list(groups_info)\n subject_info['teachers'] = list(teachers_info)\n\n return subject_info\n\n\n# варинант получения всей инфы о предмете\ndef get_all_student_from_course(subject_id):\n # Retrieve subject information along with related data\n subject_info = (\n Subject.objects.filter(id=subject_id)\n .values('id', 'name')\n .first()\n )\n\n if subject_info:\n # Retrieve distinct group names for the subject\n group_names = (\n SubjectGroup.objects\n .filter(subject_id=subject_id)\n .values('group__name')\n .distinct()\n )\n subject_info['group_names'] = [group['group__name'] for group in group_names]\n\n # Retrieve distinct teacher names for the subject\n teacher_names = (\n SubjectTeacher.objects\n .filter(subject_id=subject_id)\n .values('teacher__name')\n .distinct()\n )\n subject_info['teacher_names'] = [teacher['teacher__name'] for teacher in teacher_names]\n\n # Retrieve distinct URL online educations for the subject\n url_online_educations = (\n SubjectGroup.objects\n .filter(subject_id=subject_id)\n .values('url_online_education')\n .distinct()\n )\n subject_info['url_online_educations'] = [\n url['url_online_education'] for url in url_online_educations\n ]\n\n print(subject_info)\n\n\ndef get_student_in_course(subject_id, teacher_id):\n students_with_subject = Users.objects.filter(\n is_teacher=False, # Выбираем только студентов, а не преподавателей\n group__subjectgroup__subject__subjectteacher__teacher_id=teacher_id,\n group__subjectgroup__subject_id=subject_id\n ).distinct()\n\n # for student in students_with_subject:\n # print(student.name)\n return students_with_subject\n\n\ndef get_sdannix_work(subject_id, teacher_id):\n # Добавляем статус выполненных заданий к имени каждого студента\n students_with_subject = Users.objects.filter(\n is_teacher=False,\n group__subjectgroup__subject__subjectteacher__teacher_id=teacher_id,\n group__subjectgroup__subject_id=subject_id\n ).distinct().annotate(\n completed_tasks=Count('homework', filter=Q(homework__is_verified=True)),\n total_tasks=Count('homework')\n )\n\n # for student in students_with_subject:\n # print(\n # f\"Студент: {student.name}, Завершено заданий: {student.completed_tasks}, Всего заданий: {student.total_tasks}\")\n return students_with_subject\n\n\ndef get_all_courses(teacher_id):\n\n subgr = SubjectGroup.objects.filter(teacher_id = teacher_id)\n\n result = []\n\n for sg in subgr:\n group = sg.group\n subject = sg.subject\n result.append({'group': group, 'subject': subject})\n\n return result\n","repo_name":"dobryakbanzai/TeamsClone1","sub_path":"teamsClone/view_web/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32353139001","text":"def test_compare():\n from src.eclip import Dataset, Compare\n\n dataset1 = Dataset(\"/experiments/ENCSR384MWO/\")\n dataset2 = Dataset(\"/experiments/ENCSR406OOZ/\")\n compare = Compare([dataset1, dataset2])\n assert len(dataset1.genes) == 1428\n assert len(dataset2.genes) == 1590\n assert len(compare.keyword_intersection) == 6\n assert len(compare.gene_intersection) == 215\n print(compare)\n assert (\n str(compare)\n == \"/experiments/ENCSR384MWO/ (CSTF2, HepG2)_/experiments/ENCSR406OOZ/ (SUB1, HepG2)\"\n )\n # dataset1: /experiments/ENCSR384MWO/\n # dataset2: /experiments/ENCSR406OOZ/\n # protein1: CSTF2\n # protein2: SUB1\n # gene1: 1428\n # gene2: 1590\n # keyword1: 11\n # keyword2: 12\n # keyword intersection: ['3D-structure', 'Reference proteome', 'Ubl conjugation', 'Isopeptide bond', 'Nucleus', 'Phosphoprotein']\n # intersection: 215\n","repo_name":"edge2992/eCLIP_ENCODE","sub_path":"tests/test_eclip_compare.py","file_name":"test_eclip_compare.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38612583468","text":"r\"\"\"\nAdaption to act as the MLP layer using an MoE MLP layer in transformer.\n\"\"\"\nimport torch\nimport torch.nn as nn\nfrom fmoe.layers import FMoE, _fmoe_general_global_forward\nfrom fmoe.linear import FMoELinear\nfrom functools import partial\nimport tree\nimport torch\nimport torch.nn as nn\n\nfrom fmoe.functions import prepare_forward, ensure_comm\nfrom fmoe.functions import MOEScatter, MOEGather\nfrom fmoe.functions import AllGather, Slice\nfrom fmoe.gates import NaiveGate\n\nfrom models.gate_funs.noisy_gate import NoisyGate\nfrom models.gate_funs.noisy_gate_vmoe import NoisyGate_VMoE\n\nfrom pdb import set_trace\nimport numpy as np\n\nclass _Expert(nn.Module):\n r\"\"\"\n An expert using 2 FMoELinear modules to speed up the computation of experts\n within one worker.\n \"\"\"\n\n def __init__(self, num_expert, d_model, d_hidden, activation, rank=0):\n super().__init__()\n self.htoh4 = FMoELinear(num_expert, d_model, d_hidden, bias=True, rank=rank)\n self.h4toh = FMoELinear(num_expert, d_hidden, d_model, bias=True, rank=rank)\n self.activation = activation\n\n def forward(self, inp, fwd_expert_count):\n r\"\"\"\n First expand input to 4h (the hidden size is variable, but is called h4\n for convenience). Then perform activation. Finally shirink back to h.\n \"\"\"\n x = self.htoh4(inp, fwd_expert_count)\n x = self.activation(x)\n x = self.h4toh(x, fwd_expert_count)\n return x\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., norm_layer= partial(nn.LayerNorm, eps=1e-6)):\n super().__init__()\n # out_features = out_features or in_features\n # hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n self.norm = norm_layer(out_features)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n x = self.norm(x)\n return x\n\nclass FMoETransformerMLP(FMoE):\n r\"\"\"\n A complete MoE MLP module in a Transformer block.\n * `activation` is the activation function to be used in MLP in each expert.\n * `d_hidden` is the dimension of the MLP layer.\n \"\"\"\n\n def __init__(\n self,\n num_expert=32,\n d_model=1024,\n d_gate=1024,\n d_hidden=4096,\n activation=torch.nn.GELU(),\n expert_dp_comm=\"none\",\n expert_rank=0,\n gate=NaiveGate,\n world_size=1,\n top_k=2,\n vmoe_noisy_std=1,\n gate_return_decoupled_activation=False,\n gate_task_specific_dim=-1,\n multi_gate=False,\n regu_experts_fromtask = False,\n num_experts_pertask = -1,\n num_tasks = -1,\n regu_sem = False,\n sem_force = False,\n regu_subimage = False,\n expert_prune = False,\n prune_threshold = 0.1,\n **kwargs\n ):\n super().__init__(num_expert=num_expert, d_model=d_model, gate=gate, world_size=world_size, top_k=top_k, **kwargs)\n self.our_d_gate = d_gate\n self.our_d_model = d_model\n\n self.num_expert = num_expert\n self.regu_experts_fromtask = regu_experts_fromtask\n self.num_experts_pertask = num_experts_pertask\n self.num_tasks = num_tasks\n self.regu_sem = regu_sem\n self.sem_force = sem_force\n self.regu_subimage = regu_subimage\n self.expert_prune = expert_prune\n self.prune_threshold = prune_threshold\n if self.sem_force:\n self.force_id=[[0],[1,17,18,19,20],[2,12,13,14,15,16],[3,9,10,11],[4,5],[6,7,8,38],[21,22,23,24,25,26,39],[27,28,29,30,31,32,33,34,35,36,37]]\n if self.regu_experts_fromtask:\n self.start_experts_id=[]\n start_id = 0\n for i in range(self.num_tasks):\n start_id = start_id + int(i* (self.num_expert-self.num_experts_pertask)/(self.num_tasks-1))\n self.start_experts_id.append(start_id)\n print('self.start_experts_id',self.start_experts_id)\n\n self.experts = _Expert(\n num_expert, d_model, d_hidden, activation, rank=expert_rank\n )\n self.gate_task_specific_dim = gate_task_specific_dim\n self.multi_gate=multi_gate\n if gate_task_specific_dim<0:\n d_gate = d_model\n else:\n d_gate = d_model+gate_task_specific_dim\n print('multi_gate',self.multi_gate)\n if gate == NoisyGate:\n if self.multi_gate:\n self.gate = nn.ModuleList([\n gate(d_gate, num_expert, world_size, top_k,\n return_decoupled_activation=gate_return_decoupled_activation, regu_experts_fromtask = self.regu_experts_fromtask,\n num_experts_pertask = self.num_experts_pertask,num_tasks = self.num_tasks, regu_sem=self.regu_sem,sem_force = self.sem_force)\n for i in range(self.our_d_gate-self.our_d_model)])\n else:\n self.gate = gate(d_gate, num_expert, world_size, top_k,\n return_decoupled_activation=gate_return_decoupled_activation, regu_experts_fromtask = self.regu_experts_fromtask,\n num_experts_pertask = self.num_experts_pertask,num_tasks = self.num_tasks, regu_sem=self.regu_sem,sem_force = self.sem_force)\n elif gate == NoisyGate_VMoE:\n if self.multi_gate:\n self.gate = nn.ModuleList([\n gate(d_gate, num_expert, world_size, top_k,\n return_decoupled_activation=gate_return_decoupled_activation,\n noise_std=vmoe_noisy_std,regu_experts_fromtask = self.regu_experts_fromtask,\n num_experts_pertask=self.num_experts_pertask, num_tasks=self.num_tasks,regu_sem=self.regu_sem,sem_force = self.sem_force, regu_subimage=self.regu_subimage)\n for i in range(self.our_d_gate-self.our_d_model)])\n else:\n self.gate = gate(d_gate, num_expert, world_size, top_k,\n return_decoupled_activation=gate_return_decoupled_activation,\n noise_std=vmoe_noisy_std,regu_experts_fromtask = self.regu_experts_fromtask,\n num_experts_pertask = self.num_experts_pertask, num_tasks = self.num_tasks,regu_sem=self.regu_sem,sem_force = self.sem_force, regu_subimage=self.regu_subimage)\n\n else:\n raise ValueError(\"No such gating type\")\n self.mark_parallel_comm(expert_dp_comm)\n\n def forward(self, inp: torch.Tensor, gate_inp=None, task_id = None, task_specific_feature = None, sem=None):\n r\"\"\"\n This module wraps up the FMoE module with reshape, residual and layer\n normalization.\n \"\"\"\n if gate_inp is None:\n gate_inp = inp\n \n\n original_shape = inp.shape\n inp = inp.reshape(-1, self.d_model)\n\n gate_channel = gate_inp.shape[-1]\n gate_inp = gate_inp.reshape(-1, gate_channel)\n # print('task_id, task_specific_feature',task_id, task_specific_feature)\n if (task_id is not None) and (task_specific_feature is not None):\n assert self.multi_gate is False\n size = gate_inp.shape[0]\n gate_inp = torch.cat((gate_inp,task_specific_feature.repeat(size,1)),dim=-1)\n output = self.forward_moe(gate_inp=gate_inp, moe_inp=inp, task_id=task_id, sem=sem)\n return output.reshape(original_shape)\n\n\n def forward_moe(self, gate_inp, moe_inp, task_id=None, sem=None):\n r\"\"\"\n The FMoE module first computes gate output, and then conduct MoE forward\n according to the gate. The score of the selected gate given by the\n expert is multiplied to the experts' output tensors as a weight.\n \"\"\"\n moe_inp_batch_size = tree.flatten(\n tree.map_structure(lambda tensor: tensor.shape[0], moe_inp)\n )\n assert all(\n [batch_size == moe_inp_batch_size[0] for batch_size in moe_inp_batch_size]\n ), \"MoE inputs must have the same batch size\"\n\n if self.world_size > 1:\n\n def ensure_comm_func(tensor):\n ensure_comm(tensor, self.moe_group)\n\n tree.map_structure(ensure_comm_func, moe_inp)\n tree.map_structure(ensure_comm_func, gate_inp)\n if self.slice_size > 1:\n\n def slice_func(tensor):\n return Slice.apply(\n tensor, self.slice_rank, self.slice_size, self.slice_group\n )\n\n moe_inp = tree.map_structure(slice_func, moe_inp)\n\n if (task_id is not None) and self.multi_gate:\n # print('in custom moe_layer,task_id',task_id)\n gate_top_k_idx, gate_score = self.gate[task_id](gate_inp)\n else:\n gate_top_k_idx, gate_score = self.gate(gate_inp, task_id=task_id,sem=sem)\n\n if self.expert_prune:\n gate_score = torch.where(gate_score>self.prune_threshold,gate_score,0.)\n prune_prob = 1-torch.nonzero(gate_score).shape[0]/torch.cumprod(torch.tensor(gate_score.shape),dim=0)[-1]\n print('prune_prob',prune_prob)\n if self.sem_force and (sem is not None):\n batch = sem.shape[0]\n gate_top_k_idx = gate_top_k_idx.reshape(batch,-1,self.top_k)\n sem = sem.reshape(batch,-1)\n for k in range(batch):\n for i in range(sem.shape[-1]):\n for j in range(len(self.force_id)):\n if sem[k,i] in self.force_id[j]:\n gate_top_k_idx[k,i+1,:]=[j*2,j*2+1]\n gate_top_k_idx = gate_top_k_idx.reshape(-1,self.top_k)\n gate_score = torch.ones((gate_score.shape[0],self.top_k),device=gate_score.device)*0.5\n\n\n if self.regu_experts_fromtask and (task_id is not None):\n # print('task_id',self.start_experts_id[task_id],task_id)\n gate_top_k_idx = gate_top_k_idx + self.start_experts_id[task_id]\n\n if self.gate_hook is not None:\n self.gate_hook(gate_top_k_idx, gate_score, None)\n\n # delete masked tensors\n if self.mask is not None and self.mask_dict is not None:\n # TODO: to fix\n def delete_mask_func(tensor):\n # to: (BxL') x d_model\n tensor = tensor[mask == 0, :]\n return tensor\n\n mask = self.mask.view(-1)\n moe_inp = tree.map_structure(delete_mask_func, moe_inp)\n gate_top_k_idx = gate_top_k_idx[mask == 0, :]\n\n fwd = _fmoe_general_global_forward(\n moe_inp, gate_top_k_idx, self.expert_fn, self.num_expert, self.world_size\n )\n\n # recover deleted tensors\n if self.mask is not None and self.mask_dict is not None:\n\n def recover_func(tensor):\n # to: (BxL') x top_k x dim\n dim = tensor.shape[-1]\n tensor = tensor.view(-1, self.top_k, dim)\n # to: (BxL) x top_k x d_model\n x = torch.zeros(\n mask.shape[0],\n self.top_k,\n dim,\n device=tensor.device,\n dtype=tensor.dtype,\n )\n # recover\n x[mask == 0] = tensor\n for k, v in self.mask_dict.items():\n x[mask == k] = v\n return x\n\n moe_outp = tree.map_structure(recover_func, fwd)\n else:\n\n def view_func(tensor):\n dim = tensor.shape[-1]\n tensor = tensor.view(-1, self.top_k, dim)\n return tensor\n\n moe_outp = tree.map_structure(view_func, fwd)\n\n gate_score = gate_score.view(-1, 1, self.top_k)\n\n def bmm_func(tensor):\n dim = tensor.shape[-1]\n tensor = torch.bmm(gate_score, tensor).reshape(-1, dim)\n return tensor\n\n moe_outp = tree.map_structure(bmm_func, moe_outp)\n\n if self.slice_size > 1:\n\n def all_gather_func(tensor):\n return AllGather.apply(\n tensor, self.slice_rank, self.slice_size, self.slice_group\n )\n\n moe_outp = tree.map_structure(all_gather_func, moe_outp)\n\n moe_outp_batch_size = tree.flatten(\n tree.map_structure(lambda tensor: tensor.shape[0], moe_outp)\n )\n assert all(\n [batch_size == moe_outp_batch_size[0] for batch_size in moe_outp_batch_size]\n ), \"MoE outputs must have the same batch size\"\n return moe_outp\n","repo_name":"VITA-Group/M3ViT","sub_path":"models/custom_moe_layer.py","file_name":"custom_moe_layer.py","file_ext":"py","file_size_in_byte":12708,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"31"} +{"seq_id":"41763517661","text":"import ffmpeg\nimport subprocess\nimport numpy as np\n\n\n# hack for steam.run_async(quiet=True) bug\ndef _run_async_quiet(stream_spec):\n args = ffmpeg._run.compile(stream_spec, \"ffmpeg\", overwrite_output=False)\n return subprocess.Popen(\n args, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL\n )\n\n\ndef extract_selected_frames(video_filename, selected_frames, output_width, output_height):\n video_stream = _run_async_quiet(\n ffmpeg.input(video_filename).output(\"pipe:\", format=\"rawvideo\", pix_fmt=\"rgb24\",\n s=\"{}x{}\".format(output_width, output_height))\n )\n\n out_frames = []\n idx, frame_no = 0, -1\n\n while True:\n frame_no += 1\n\n in_bytes = video_stream.stdout.read(output_width * output_height * 3)\n if not in_bytes:\n raise Exception(\"`selected_frames` contain indexes larger than video length.\")\n\n if frame_no != selected_frames[idx]:\n continue\n\n frame = np.frombuffer(in_bytes, np.uint8).reshape([output_height, output_width, 3])\n out_frames.append(frame)\n\n idx += 1\n if idx == len(selected_frames):\n break\n\n return np.stack(out_frames)\n\n\ndef extract_all_frames(video_filename, output_width, output_height):\n video_stream, err = (\n ffmpeg\n .input(video_filename)\n .output(\"pipe:\", format=\"rawvideo\", pix_fmt=\"rgb24\", s=\"{}x{}\".format(output_width, output_height))\n .run(capture_stdout=True, capture_stderr=True)\n )\n\n return np.frombuffer(video_stream, np.uint8).reshape([-1, output_height, output_width, 3])\n","repo_name":"siret/somhunter","sub_path":"extractor/utils/ffmpeg_utils.py","file_name":"ffmpeg_utils.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"31"} +{"seq_id":"42088765478","text":"# -*- coding: utf-8 -*-\nimport append_parent_path\nimport os\nimport asyncio\nimport unittest\nimport requests\n\nfrom container_tester import (\n set_os_env, ContainerTestCase, TestRunner\n)\n\nFILE_PATH = os.path.dirname(os.path.realpath(__file__))\n\n\nclass TestNodeChecker(ContainerTestCase):\n docker_compose_content = {\n \"services\": {\n \"icon2-node\": {\n \"environment\": {\n \"IS_AUTOGEN_CERT\": \"true\",\n # \"SERVICE\": \"LisbonNet\",\n \"FASTEST_START\": \"false\",\n \"KEY_STORE_FILENAME\": \"keystore.json\",\n \"ROLE\": 3,\n # \"LOG_OUTPUT_TYPE\": \"debug\",\n },\n \"network_mode\": \"bridge\",\n \"ports\": [\"9000:9000\"]\n }\n }\n }\n is_debug = True\n is_control_container = False\n container_path = \"goloop_container\"\n\n # @unittest.skip(\"test case skipping\")\n def test_210_goloop_chain_leave(self):\n # res = self.exec_container(\"env && echo $GOLOOP_DATA_ROOT\")\n self.is_debug = True\n res = self.exec_container(\"cp /ctx/mainnet_v1_block_proof/block_v1_proof.bin ${GOLOOP_DATA_ROOT}/1/\")\n res = self.exec_container(\"ls ${GOLOOP_DATA_ROOT}/1/\")\n\n # print(f\"{res}\")\n\n\nif __name__ == \"__main__\":\n TestRunner().run()\n\n","repo_name":"icon-project/icon2-docker","sub_path":"tests/container_test_module.py","file_name":"container_test_module.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"31"} +{"seq_id":"2306030074","text":"import time\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport pymysql.cursors\nfrom selenium import webdriver # 导入必要的库\n\noption = webdriver.ChromeOptions()\noption.add_argument(\"headless\")\n# chromedriver.exe的存放路径,使用chromedrive需要下载驱动\ndriver_path = 'E:\\zhangxu\\pythonanzhuang\\python-3.9\\Scripts\\chromedriver.exe'\n\n# 通过webdriver对象的Chrome方法【不同的浏览器对应不同的方法】，获取到chromedriver.exe\n# browser = webdriver.Chrome(executable_path=driver_path, chrome_options=option)\n# browser_1 = webdriver.Chrome(executable_path=driver_path, chrome_options=option)\n# browser_2 = webdriver.Chrome(executable_path=driver_path, chrome_options=option)\n\nbrowser = webdriver.Chrome(executable_path=driver_path)\nbrowser_1 = webdriver.Chrome(executable_path=driver_path)\nbrowser_2 = webdriver.Chrome(executable_path=driver_path)\n\n# 计数count，计算一共插入多少个author了\n# count = 22494\n\n# 初始化浏览器，让他登陆和最大化，不然无法捕获所有元素\nbrowser.maximize_window()\nbrowser_1.maximize_window()\nbrowser_2.maximize_window()\nbrowser_2.get('https://www.aminer.cn/login?callback=profile%2Fthomas-s-huang%2F53f48abedabfaea6fb77b490')\nusername = browser_2.find_element_by_id('userPhone')\nusername.clear()\nusername.send_keys('18347989110')\npassword = browser_2.find_element_by_id('phonePassword')\npassword.clear()\npassword.send_keys('aminer9110')\nloginbtn = browser_2.find_element_by_xpath(\n '//*[@class=\"ant-btn a-aminer-core-auth-c-login-login-loginBtn a-aminer-core-auth-c-login-login-ready loginBtn\"]')\nloginbtn.click()\ntime.sleep(3)\n\n\nclass Author:\n def __init__(self, name, title, department, homepage, papers, citation, hindex, interests):\n self.name = name\n self.title = title\n self.department = department\n self.homepage = homepage\n self.papers = papers\n self.citation = citation\n self.hindex = hindex\n self.interests = interests\n\n def print(self):\n print(self.name, self.title, self.department, self.homepage, self.papers, self.citation, self.hindex,\n self.interests)\n\n\ndef insert_mysql(author, discipline, disciplineid):\n # 初始化connection\n connection = pymysql.connect(\n host='localhost',\n user='root',\n password='000000',\n db='aminer',\n charset='utf8mb4',\n )\n cursor = connection.cursor()\n\n global count\n count = count + 1\n\n sql = ''\n sql = \"INSERT INTO people (disciplineid,discipline,expertid,name,title,department,homepage,papers,citation,hindex,interests) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) \"\n\n val = [disciplineid, discipline, count, author.name, author.title, author.department, author.homepage,\n str(author.papers), str(author.citation), str(author.hindex), author.interests]\n\n try:\n cursor.execute(sql, val)\n except:\n sql2 = \"INSERT INTO people (disciplineid,discipline,expertid,name,title,department,homepage,papers,citation,hindex,interests) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) \"\n\n val2 = [999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]\n cursor.execute(sql2, val2)\n\n print(\"已插入第 :\" + str(count) + \"条数据\")\n\n # 创建的connection是非自动提交，需要手动commit\n connection.commit()\n\n\n# 抓取【个人主页】的详细信息\ndef author_info_scratch(url, discipline, disciplineid):\n browser_2.get(url)\n # time.sleep(3)\n\n # 作者的各项资料\n try:\n\n name = WebDriverWait(browser_2, 30).until(EC.presence_of_element_located(\n (By.XPATH,\n '/html/body/div/section/main/main/article/section[1]/section[1]/div[1]/div/div[2]/div[1]/h1/span'))).text\n # ——————————————————————————————————————————————————————————————————————————————\n # name = browser_2.find_element_by_xpath(\n # '/html/body/div/section/main/main/article/section[1]/section[1]/div[1]/div/div[2]/div[1]/h1/span').text\n except:\n name = \"no name\"\n\n try:\n # title = WebDriverWait(browser_2, 3).until(EC.presence_of_element_located(\n # (By.XPATH,\n # '/html/body/div/section/main/main/article/section[1]/section[1]/div[1]/div/div[2]/div[3]/div/div[1]/div[2]/p[1]/span'))).text\n # ——————————————————————————————————————————————————————————————————————————————\n title = browser_2.find_element_by_xpath(\n '/html/body/div/section/main/main/article/section[1]/section[1]/div[1]/div/div[2]/div[3]/div/div[1]/div[2]/p[1]/span').text\n except:\n title = \"no title\"\n\n try:\n # department = WebDriverWait(browser_2, 3).until(EC.presence_of_element_located(\n # (By.XPATH,\n # '/html/body/div/section/main/main/article/section[1]/section[1]/div[1]/div/div[2]/div[3]/div/div[1]/div[2]/p[2]/textarea'))).text\n # ——————————————————————————————————————————————————————————————————————————————\n department = browser_2.find_element_by_xpath(\n '/html/body/div/section/main/main/article/section[1]/section[1]/div[1]/div/div[2]/div[3]/div/div[1]/div[2]/p[2]/textarea').text\n except:\n department = \"no department\"\n\n try:\n # url_list = re.findall('\"url\":\\\"(.*?)\\\"', browser_2.page_source, re.S)\n # homepage=[]\n # for url in url_list:\n # homepage=homepage+[url.replace(\"\\\\u002F\", \"/\")]\n # ——————————————————————————————————————————————————————————————————————————————\n # details = WebDriverWait(browser_2, 3).until(EC.presence_of_element_located(\n # (By.XPATH,\n # '//*[@class=\"expert_info_content\"]')))\n # ——————————————————————————————————————————————————————————————————————————————\n details = browser_2.find_element_by_xpath('//*[@class=\"expert_info_content\"]')\n homepage = details.find_elements_by_xpath('//*[@class=\"homepage baseInfo\"]')\n h = ''\n for url in homepage:\n h = h + url.text + ' '\n # print(h)\n homepage = h\n except:\n homepage = \"no homepage\"\n\n try:\n # papers = WebDriverWait(browser_2, 3).until(EC.presence_of_element_located(\n # (By.XPATH,\n # '//*[@id=\"popover_radar\"]/div[2]/p[1]/span[2]'))).text\n # ——————————————————————————————————————————————————————————————————————————————\n # papers = re.findall('\"pubs\":(.*?)}', browser_2.page_source, re.S)\n # ——————————————————————————————————————————————————————————————————————————————\n papers = browser_2.find_element_by_xpath('//*[@id=\"popover_radar\"]/div[2]/p[1]/span[2]').text\n except:\n papers = \"no papers\"\n\n try:\n # citation = WebDriverWait(browser_2, 3).until(EC.presence_of_element_located(\n # (By.XPATH,\n # '//*[@id=\"popover_radar\"]/div[2]/p[2]/span[2]'))).text\n # ——————————————————————————————————————————————————————————————————————————————\n citation = browser_2.find_element_by_xpath('//*[@id=\"popover_radar\"]/div[2]/p[2]/span[2]').text\n # ——————————————————————————————————————————————————————————————————————————————\n # citation = re.findall('\"citations\":(.*?),', browser_2.page_source, re.S)\n except:\n citation = \"no citation\"\n\n try:\n # hindex = WebDriverWait(browser_2, 3).until(EC.presence_of_element_located(\n # (By.XPATH,\n # '//*[@id=\"popover_radar\"]/div[2]/p[3]/span[2]'))).text\n # ——————————————————————————————————————————————————————————————————————————————\n hindex = browser_2.find_element_by_xpath('//*[@id=\"popover_radar\"]/div[2]/p[3]/span[2]').text\n # ——————————————————————————————————————————————————————————���———————————————————\n # hindex = re.findall('\"hindex\":(.*?),', browser_2.page_source, re.S)\n except:\n hindex = \"no hindex\"\n\n try:\n # interests = WebDriverWait(browser_2, 3).until(EC.presence_of_all_elements_located(\n # (By.CLASS_NAME,\n # \"nv-legend-text\")))\n # ——————————————————————————————————————————————————————————————————————————————\n interests = browser_2.find_elements_by_class_name(\"nv-legend-text\")\n interests_list = ''\n for span in interests:\n interests_list = interests_list + span.text + ','\n interests = interests_list\n except:\n interests = \"no interests\"\n\n author = Author(name, title, department, homepage, papers, citation, hindex, interests)\n author.print()\n\n insert_mysql(author, discipline, disciplineid)\n\n\n# 挨个点击【作者列表】，进入【个人主页】后调用author_info_scratch\ndef author_list(url, discipline, disciplineid):\n browser_1.get(url)\n\n pagenum = WebDriverWait(browser_1, 30).until(EC.presence_of_element_located((By.XPATH,\n '/html/body/div/section/main/main/article/div[2]/div[3]/div[1]/div[2]/div[1]/div[3]/div[2]/div[2]/ul/li[2]'))).text\n # pagenum = browser_1.find_element_by_xpath(\n # '/html/body/div/section/main/main/article/div[2]/div[3]/div[1]/div[2]/div[1]/div[3]/div[2]/div[2]/ul/li[2]').text\n pagenum = pagenum.replace(\"/\", \"\")\n print(\"pagenum的数目为\", pagenum)\n\n for n in range(int(pagenum)):\n\n time.sleep(20)\n\n # 该页所有学者的aminer主页链接\n # links = WebDriverWait(browser_1, 30).until(EC.presence_of_all_elements_located((By.XPATH,\n # '/html/body/div[1]/section/main/main/article/div[2]/div[3]/div[1]/div[2]/div[1]/div[3]/div[3]/div[1]/div[1]/div//div[@class=\"person_name\"]//*[@href]')))\n links = browser_1.find_elements_by_xpath(\n '/html/body/div[1]/section/main/main/article/div[2]/div[3]/div[1]/div[2]/div[1]/div[3]/div[3]/div[1]/div[1]/div//div[@class=\"person_name\"]//*[@href]')\n\n for span in links:\n # print(span.get_attribute(\"href\"))\n try:\n author_info_scratch(span.get_attribute(\"href\"), discipline, disciplineid)\n except:\n time.sleep(10)\n print(\"数据延迟，等待10秒\")\n\n # 点击下一页\n next_bottom = browser_1.find_element_by_xpath(\n '/html/body/div[1]/section/main/main/article/div[2]/div[3]/div[1]/div[2]/div[1]/div[3]/div[2]/div[2]/ul/li[3]')\n next_bottom.click()\n\n print(\"爬取到了，现在页数：\", n)\n\n\n# cardbox列表获取，进去之后调用author_list\ndef cardbox_list(url):\n\n count = 1\n file = open('cardbox_urls.txt', 'a')\n browser.get(url)\n for n in range(0, 30):\n # 获取所有的cardbox的链接\n time.sleep(5)\n links = WebDriverWait(browser, 30).until(\n EC.presence_of_all_elements_located((By.CLASS_NAME, 'antd-pro-components-eb-gallery-e-b-gallery-cardBox')))\n # links = browser.find_elements_by_class_name('antd-pro-components-eb-gallery-e-b-gallery-cardBox')\n for span in links:\n try:\n span.click()\n handle = browser.current_window_handle\n handles = browser.window_handles\n for newhandle in handles:\n if newhandle != handle:\n browser.switch_to.window(newhandle)\n url = browser.current_url\n file.write(url+\"\\n\")\n browser.close()\n browser.switch_to.window(handle)\n print(\"已获取cardbox数量\", count)\n count = count + 1\n except:\n time.sleep(10)\n print(\"数据延迟，等待10秒\")\n file.close()\n\n # 点击下一页\n next_bottom = browser.find_element_by_class_name('ant-pagination-next')\n next_bottom.click()\n\n print(\"cardbox爬取到了，现在页数：\", n)\n print(\"所有页数爬完了，写入文件\")\n file.close()\n\n# 测试作者信息爬取demo\n# author_info_scratch(\"https://www.aminer.cn/profile/h-vincent-poor/54055927dabfae8faa5c5dfa\",\"测试\",111)\n# 测试爬取一页作者的信息demo\n# author_list('https://www.aminer.cn/search/person?domain=143&t=b','急急急',111)#计算机科学\n\n\n# print(\"所有的都执行完毕啦~\")\ncardbox_list('https://gct.aminer.cn/eb/gallery?check=all')\n","repo_name":"Apolo15/Crawler_aminerwebsite","sub_path":"2stcardbox爬取华人库的基本信息/2、aminercrawler_test_生成cardbox链接.py","file_name":"2、aminercrawler_test_生成cardbox链接.py","file_ext":"py","file_size_in_byte":14426,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"18492760669","text":"from dataclasses import dataclass\n\nfrom jose import jwt\nfrom rest_framework.exceptions import APIException\n\nfrom settings import settings\n\nexception = APIException('Could not validate credentials')\n\n\nclass JWTManager:\n _instance = None\n\n def __new__(cls, *args, **kwargs) -> \"JWTManager\":\n if cls._instance is None:\n cls._instance = cls()\n\n return cls._instance\n\n @dataclass\n class ParsedHeader:\n cookies: dict\n is_valid: bool = False\n access_token: str | None = None\n\n def __post_init__(self) -> None:\n authorization = self.cookies.get('Authorization')\n self.refresh_token = self.cookies.get('refresh_token')\n\n if authorization is not None:\n token_type, self.access_token = authorization.split(' ')\n if self.refresh_token is not None and token_type == 'Bearer':\n self.is_valid = True\n\n @classmethod\n def encode_token(cls, cookies: dict) -> dict | None:\n header = cls.ParsedHeader(cookies)\n if not header.is_valid:\n return None\n\n token = header.refresh_token\n access_token = header.access_token\n\n try:\n payload = jwt.decode(\n token,\n settings.jwt_secret_key,\n algorithms=[settings.jwt_algorithm],\n access_token=access_token\n )\n except Exception:\n return None\n\n return payload\n","repo_name":"VladislavProyaev/fojin_bank_django","sub_path":"services/jwt_manager.py","file_name":"jwt_manager.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9219469952","text":"import multiprocessing\nfrom joblib import Parallel, delayed\nimport itertools\nimport numpy as np\nfrom math import exp, sqrt\nfrom dtwn import DTW\nfrom scipy.spatial.distance import pdist\n\n\nclass GaussKernel:\n\n def __init__(self, classifier_type):\n self.classifier_type = classifier_type\n\n metric = lambda self, x, y: pdist([x, y]) ** 2\n max_distance = None\n\n def calculateGramMatrix(self, train_data):\n squared_distances = [self.metric(*pair) for pair in itertools.product(train_data, repeat=2)]\n squared_distances = np.array(squared_distances).reshape((len(train_data), len(train_data)))\n gram_matrix = []\n self.max_distance = np.max(squared_distances)\n for row in squared_distances:\n new_row = []\n for elem in row:\n new_elem = exp(-1.0 * elem / (self.max_distance ** 2))\n new_row.append(new_elem)\n gram_matrix.append(new_row)\n return gram_matrix\n\n def calculate(self, x, y, sigma=None):\n if sigma == None:\n sigma = self.max_distance\n if self.classifier_type == 'SVM':\n sigma = sqrt(sigma)\n return exp(-1.0 * self.metric(x, y) / (sigma ** 2))\n\n\nclass DTWGaussKernel(GaussKernel):\n \n metric = lambda self, x, y: DTW(x, y)\n\n def calculateGramMatrix(self, train_data):\n num_cores = multiprocessing.cpu_count()\n squared_distances = Parallel(n_jobs=num_cores)(delayed(DTW)(*pair) for pair in itertools.product(train_data, repeat=2))\n squared_distances = np.array(squared_distances).reshape((len(train_data), len(train_data)))\n\n gram_matrix = []\n self.max_distance = np.max(squared_distances)\n for row in squared_distances:\n new_row = []\n for elem in row:\n new_elem = exp(-1.0 * elem / (self.max_distance ** 2))\n new_row.append(new_elem)\n gram_matrix.append(new_row)\n return gram_matrix\n","repo_name":"n0thingSp3zial/ML_part","sub_path":"python_part/GaussKernels.py","file_name":"GaussKernels.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"20470919590","text":"# -----------------------------------------------------------------------------\n#\n# Two Ball\n# version: 1.0\n# Language - Python\n# Modules - pygame, sys, random, math\n#\n# Controls - click to place ball, drag to aim, release to throw, have fun :)\n#\n# By - Malachi Hornbuckle, James Lu\n# Brown University '22\n#\n# Adapted from - Jatin Kumar Mandav's 8 Ball Pool\n# https://jatinmandav.wordpress.com\n#\n# -----------------------------------------------------------------------------\n\nimport pygame\nimport sys\nfrom math import *\nimport random\n\npygame.init()\nwidth = 660\nheight = 360\nouterHeight = 400\nmargin = 30\ndisplay = pygame.display.set_mode((width, outerHeight))\npygame.display.set_caption(\"2 balls, 1 friends\")\nclock = pygame.time.Clock()\n\nbackground = (2, 84, 20)\nwhite = (236, 240, 241)\n\ngray = (123, 125, 125)\nblack = (0,0,0)\norange = (245, 128, 37)\nbrown = (78, 54, 41)\n\ncolors = [orange,brown]\n\nballs = []\nnoBalls = 2\nradius = 10\nfriction = 0.01 # make bigger?\n\n# Ball Class\nclass Ball:\n def __init__(self, x, y, speed, color, angle, ballNum):\n self.x = x + radius\n self.y = y + radius\n self.color = color\n self.angle = angle\n self.speed = speed\n self.ballNum = ballNum\n self.font = pygame.font.SysFont(\"Agency FB\", 10)\n\n # Draws Balls on Display Window\n def draw(self, x, y):\n pygame.draw.ellipse(display, self.color, (x - radius, y - radius, radius*2, radius*2))\n\n\n # Moves the Ball around the Screen\n def move(self):\n self.speed -= friction\n if self.speed <= 0:\n self.speed = 0\n self.x = self.x + self.speed*cos(self.angle) #add time resolution?\n self.y = self.y + self.speed*sin(self.angle)\n\n if not (self.x < width - radius - margin):\n self.x = width - radius - margin\n self.angle = pi - self.angle\n if not(radius + margin < self.x):\n self.x = radius + margin\n self.angle = pi - self.angle\n if not (self.y < height - radius - margin):\n self.y = height - radius - margin\n self.angle = 2*pi - self.angle\n if not(radius + margin < self.y):\n self.y = radius + margin\n self.angle = 2*pi - self.angle\n\n# Pocket Class\nclass Pockets:\n def __init__(self, x, y, color):\n self.r = margin/2\n self.x = x + self.r + 10\n self.y = y + self.r + 10\n self.color = color\n\n # Draws the Pockets on Pygame Window\n def draw(self):\n pygame.draw.ellipse(display, self.color, (self.x - self.r, self.y - self.r, self.r*2, self.r*2))\n\n # Checks if ball has entered the Hole\n def checkPut(self): # less complicated for 2 balls\n global balls\n orange_dist = ((self.x - balls[0].x)**2 + (self.y - balls[0].y)**2)**0.5\n if orange_dist < self.r + radius:\n gameOver()\n if len(balls) > 1:\n brown_dist = ((self.x - balls[1].x)**2 + (self.y - balls[1].y)**2)**0.5\n if brown_dist < self.r + radius:\n balls = [balls[0]] # remove red ball from balls\n\nclass Hits:\n def __init__(self, num):\n self.num = num\n\n\n# Checks Collision\ndef collision(ball1, ball2, hits):\n dist = ((ball1.x - ball2.x)**2 + (ball1.y - ball2.y)**2)**0.5\n if dist <= radius*2:\n hits.num = hits.num + 1\n\n u1 = ball1.speed\n u1x = u1 * cos(ball1.angle)\n u1y = u1 * sin(ball1.angle)\n u2 = ball2.speed\n u2x = u2 * cos(ball2.angle)\n u2y = u2 * sin(ball2.angle)\n\n n_x = cos(atan2(ball2.y - ball1.y, ball2.x - ball1.x)) # ball 1 hits ball 2\n n_y = sin(atan2(ball2.y - ball1.y, ball2.x - ball1.x))\n\n u_change = ((u1x - u2x) * n_x) + ((u1y - u2y) * n_y) # change in velocity in normal direction (mag)\n\n u1x = u1x - n_x * u_change\n u1y = u1y - n_y * u_change\n u2x = u2x + n_x * u_change\n u2y = u2y + n_y * u_change\n\n ball1.speed = (u1x**2 + u1y**2)**0.5\n ball2.speed = (u2x**2 + u2y**2)**0.5\n ball1.angle = atan2(u1y, u1x)\n ball2.angle = atan2(u2y, u2x)\n\n ball2.x = ball1.x + (n_x * 2 * radius)\n ball2.y = ball1.y + (n_y * 2 * radius)\n\n\ndef border():\n pygame.draw.rect(display, gray, (0, 0, width, 30))\n pygame.draw.rect(display, gray, (0, 0, 30, height))\n pygame.draw.rect(display, gray, (width - 30, 0, 30, height))\n pygame.draw.rect(display, gray, (0, height - 30, width, 30))\n\ndef score(hits): # get rid of or alter this\n font = pygame.font.SysFont(\"Agency FB\", 30)\n\n pygame.draw.rect(display, (51, 51, 51), (0, height, width, outerHeight))\n\n text = font.render(\"Number of Hits: \" + str(hits.num), True, white)\n display.blit(text, (width/2 + 50, height + radius/2))\n\ndef reset():\n global balls, noBalls\n noBalls = 2\n balls = []\n b1 = Ball(70, height/2, 0, colors[0], 0, 1) # orange ball\n b2 = Ball(width - 70, height/2, 0, colors[1], 0, 2) # brown ball\n\n balls.append(b1)\n balls.append(b2)\n\n\n\ndef gameOver(): # change to the resetting conditions\n font = pygame.font.SysFont(\"Agency FB\", 75)\n text = font.render(\"Sent to the shadow realm\", True, (133, 193, 233))\n\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n close()\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_q:\n close()\n\n if event.key == pygame.K_r:\n poolTable()\n display.blit(text, (50, height/2))\n\n pygame.display.update()\n clock.tick()\n\ndef close():\n pygame.quit()\n sys.exit()\n\n# Main Function\ndef poolTable():\n loop = True\n hits = Hits(0) # start with no hits\n\n reset()\n\n noPockets = 6\n pockets = []\n\n p1 = Pockets(0, 0, black)\n p2 = Pockets(width/2 - p1.r*2, 0, black)\n p3 = Pockets(width - p1.r - margin - 5, 0, black)\n p4 = Pockets(0, height - margin - 5 - p1.r, black)\n p5 = Pockets(width/2 - p1.r*2, height - margin - 5 - p1.r, black)\n p6 = Pockets(width - p1.r - margin - 5, height - margin - 5 - p1.r, black)\n\n pockets.append(p1)\n pockets.append(p2)\n pockets.append(p3)\n pockets.append(p4)\n pockets.append(p5)\n pockets.append(p6)\n\n\n start = 0\n end = 0\n\n while loop:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n close()\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_q:\n close()\n\n if event.key == pygame.K_r:\n poolTable()\n\n if event.type == pygame.MOUSEBUTTONDOWN: # make this stuff happen in orange ball grabbable\n start_x, start_y = pygame.mouse.get_pos()\n start = (start_x, start_y)\n if len(balls) <= 1:\n balls.append(Ball(start_x, start_y, 0, brown, 0, 2))\n else:\n balls[1].x = start_x\n balls[1].y = start_y\n balls[1].speed = 0\n mouse_loop = True\n print(\"click\")\n while mouse_loop:\n if balls[0].speed == 0 and hits.num > 0:\n gameOver()\n display.fill(background)\n for i in range(len(balls)):\n balls[i].draw(balls[i].x, balls[i].y)\n balls[i].move()\n border()\n for i in range(noPockets):\n pockets[i].draw()\n for i in range(noPockets):\n pockets[i].checkPut()\n for event in pygame.event.get():\n if event.type == pygame.MOUSEBUTTONUP:\n print(\"clack\")\n mouse_loop = False\n score(hits)\n pygame.display.update()\n clock.tick(60)\n print(\"knack\")\n end_x, end_y = pygame.mouse.get_pos()\n end = (end_x, end_y)\n dist = ((start[0] - end[0])**2 + (start[1] - end[1])**2)**0.5\n force = dist/10.0\n if force > 20:\n force = 20\n angle = atan2(end[1] - start[1], end[0] - start[0])\n balls[1].speed = force\n balls[1].angle = angle\n\n\n display.fill(background)\n\n for i in range(len(balls)):\n balls[i].draw(balls[i].x, balls[i].y)\n\n for i in range(len(balls)):\n balls[i].move()\n\n if balls[0].speed == 0 and hits.num > 0:\n gameOver()\n\n if len(balls) > 1:\n collision(balls[0], balls[1], hits) # change orange/brown balls if colliding\n border()\n\n for i in range(noPockets):\n pockets[i].draw()\n\n for i in range(noPockets):\n pockets[i].checkPut() # ends game if orange ball in pocket\n\n score(hits)\n\n pygame.display.update()\n clock.tick(60)\n\npoolTable()\n\n","repo_name":"malachihornbuckle/hackatbrown2021","sub_path":"two_ball.py","file_name":"two_ball.py","file_ext":"py","file_size_in_byte":9044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9410849918","text":"\"\"\"\nGiven n non-negative integers representing an elevation map where the width of each bar is 1, compute how much water it can trap after raining.\n\nInput: height = [0,1,0,2,1,0,1,3,2,1,2,1]\nOutput: 6\nExplanation: The above elevation map (black section) is represented by array [0,1,0,2,1,0,1,3,2,1,2,1]. In this case, 6 units of rain water (blue section) are being trapped.\n\nExample 2:\n\nInput: height = [4,2,0,3,2,5]\nOutput: 9\n\"\"\"\n\nfrom typing import List\nimport unittest\n\ndef trap(height: List[int]) -> int:\n l, r = 0, len(height) - 1\n maxLeft, maxRight = height[l], height[r]\n volume = 0\n while l < r:\n if maxLeft <= maxRight:\n l += 1\n maxLeft = max(maxLeft, height[l])\n volume += maxLeft - height[l]\n else:\n r -= 1\n maxRight = max(maxRight, height[r])\n volume += maxRight - height[r]\n\n return volume\n\nclass Test(unittest.TestCase):\n def test_example_one(self):\n height = [0,1,0,2,1,0,1,3,2,1,2,1]\n output = 6\n self.assertEqual(trap(height), output)\n\n def test_example_four(self):\n height = [5,5,1,7,1,1,5,2,7,6]\n # 0 0 4 0 6 6 2 5 0 0 ]\n output = 23\n self.assertEqual(trap(height), output)\n\n def test_example_two(self):\n height = [1, 1]\n output = 0\n self.assertEqual(trap(height), output)\n\n def test_example_three(self):\n height = [2, 1, 1, 1, 1, 1, 1, 1, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 2]\n output = 15\n self.assertEqual(trap(height), output)\n\nif __name__ == \"__main__\":\n unittest.main()","repo_name":"Jr-14/neetcode-150","sub_path":"two_pointers/42-trapping-rain-water.py","file_name":"42-trapping-rain-water.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33233188808","text":"DIGITS= [str(i) for i in range(0, 10)]\nOPERATORS = list('+-*/')\nPARENTHESES = list('()')\nSTART = '<START>'\nEND = '<END>'\nNULL = '<NULL>'\nSYMBOLS = DIGITS + OPERATORS + PARENTHESES\n# SYM2ID = {v:i for i, v in enumerate(SYMBOLS)}\n# ID2SYM = {i:v for i, v in enumerate(SYMBOLS)}\nSYM2ID = lambda x: SYMBOLS.index(x)\nID2SYM = lambda x: SYMBOLS[x]\n\nimport math\nfrom inspect import signature\nclass Program():\n def __init__(self, fn=None):\n self.fn = fn\n self.arity = len(signature(fn).parameters) if fn is not None else 0\n self.likelihood = 1.0\n self.priority = 1.0\n\n def __call__(self, *inputs):\n if len(inputs) != self.arity or None in inputs:\n raise TypeError\n res = self.fn(*inputs)\n return res\n\nfunctions = [\n lambda: 0, lambda: 1, lambda: 2, lambda: 3, lambda: 4, lambda: 5, lambda: 6, lambda: 7, lambda: 8, lambda: 9,\n lambda x,y: x+y, lambda x,y: max(0, x-y), lambda x,y: x*y, lambda x,y: math.ceil(x/y) if y != 0 else None, \n lambda: None, lambda: None,\n]\n\nPROGRAMS = [Program(f) for f in functions] \nSYM2PROG= {s:p for s, p in zip(SYMBOLS, PROGRAMS)}","repo_name":"liqing-ustc/ANS","sub_path":"data/domain.py","file_name":"domain.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"1701603302","text":"from Leaf import Leaf\nfrom Composite import Composite\nfrom Client import Client\nfrom GraphicElements import *\n\n\nif __name__ == \"__main__\":\n c = Client()\n\n # Creamos una serie de figuras con atributos\n char = Character(100, 25, 0, 0)\n hat = Hat(\"Rojo\", 10, 10, 0, 100)\n sword = Sword(75, 40, 5, 10, 50)\n\n armor = Armor(100, 100, 20, 0, 0)\n chest = Chest(\"Verde\", 40, 50, 25, 0, 50)\n pants = ArmorPants(\"Azul\", 40, 50, 25, 0, 0)\n bracelet = Bracelet(\"Amarillo\", 20, 10, 10, 0, 0)\n\n # Las agregamos a sus respectivos composites\n armor.addChild(chest)\n armor.addChild(pants)\n armor.addChild(bracelet)\n\n char.addChild(hat)\n char.addChild(sword)\n char.addChild(armor)\n\n # El cliente ejecuta la accion de mover al personaje\n c.displace(char, 100, 0)","repo_name":"MarianoSaez/desing-patterns","sub_path":"CompositePython/GraphicsImplementation/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2348050478","text":"import Pyro4\nfrom cls_net import Game\n\n#------------------------------Main Server Setup------------------------------ \ndef main():\n # Read in host (server) IP and port from file.\n # This ensures that (at least on this machine), the client and server will be able to find each other.\n f = open(\"data/Host_Data.txt\", \"r\")\n host_dat = f.readline().split(\",\")\n\n game = Game()\n Pyro4.Daemon.serveSimple(\n {\n game: \"dfo.game\"\n },\n host=host_dat[0], port=int(host_dat[1]), ns=False, verbose=True)\n\n\nif __name__ == \"__main__\": #This is designed to be run on the console and separately to the DFO game.\n main()","repo_name":"lee-suddaby/Dunfermline-opoly-networked","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32863017825","text":"import logging\n\nimport numpy as np\n\nfrom qiskit.aqua.components.optimizers import Optimizer\n\nlogger = logging.getLogger(__name__)\n\n\nclass SPSA(Optimizer):\n \"\"\"Simultaneous Perturbation Stochastic Approximation algorithm.\"\"\"\n CONFIGURATION = {\n 'name': 'SPSA',\n 'description': 'SPSA Optimizer',\n 'input_schema': {\n '$schema': 'http://json-schema.org/schema#',\n 'id': 'spsa_schema',\n 'type': 'object',\n 'properties': {\n 'max_trials': {\n 'type': 'integer',\n 'default': 1000\n },\n 'save_steps': {\n 'type': 'integer',\n 'default': 1,\n 'minimum': 1\n },\n 'last_avg': {\n 'type': 'integer',\n 'default': 1,\n 'minimum': 1\n },\n 'c0': {\n 'type': 'number',\n 'default': 0.62831853071796\n },\n 'c1': {\n 'type': 'number',\n 'default': 0.1\n },\n 'c2': {\n 'type': 'number',\n 'default': 0.602\n },\n 'c3': {\n 'type': 'number',\n 'default': 0.101\n },\n 'c4': {\n 'type': 'number',\n 'default': 0\n },\n 'skip_calibration': {\n 'type': 'boolean',\n 'default': False\n }\n },\n 'additionalProperties': False\n },\n 'support_level': {\n 'gradient': Optimizer.SupportLevel.ignored,\n 'bounds': Optimizer.SupportLevel.ignored,\n 'initial_point': Optimizer.SupportLevel.required\n },\n 'options': ['save_steps', 'last_avg'],\n 'optimizer': ['local', 'noise']\n }\n\n def __init__(self, max_trials=1000, save_steps=1, last_avg=1, c0=2*np.pi*0.1, c1=0.1, c2=0.602, c3=0.101, c4=0, skip_calibration=False):\n \"\"\"\n Constructor.\n\n For details, please refer to https://arxiv.org/pdf/1704.05018v2.pdf.\n Supplementary information Section IV.\n\n Args:\n max_trials (int): Maximum number of iterations to perform.\n save_steps (int): Save intermeditate info every save_steps step.\n last_avg (int): Averged parameters over the last_avg iterations.\n If last_avg = 1, only the last iteration is considered.\n c0 (float): The initial a. Step size to update paramters.\n c1 (float): The initial c. The step size used to approximate gradient.\n c2 (float): The alpha in the paper, and it is used to adjust a (c0) at each iteration.\n c3 (float): The gamma in the paper, and it is used to adjust c (c1) at each iteration.\n c4 (float): The parameter used to control a as well.\n skip_calibration (bool): skip calibration and use provided c(s) as is.\n \"\"\"\n self.validate(locals())\n super().__init__()\n for k, v in locals().items():\n if k in self._configuration['options']:\n self._options[k] = v\n self._max_trials = max_trials\n self._parameters = np.array([c0, c1, c2, c3, c4])\n self._skip_calibration = skip_calibration\n\n def optimize(self, num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None):\n\n if not isinstance(initial_point, np.ndarray):\n initial_point = np.asarray(initial_point)\n\n super().optimize(num_vars, objective_function, gradient_function, variable_bounds, initial_point)\n\n logger.debug('Parameters: {}'.format(self._parameters))\n if not self._skip_calibration:\n # at least one calibration, at most 25 calibrations\n num_steps_calibration = min(25, max(1, self._max_trials // 5))\n self._calibration(objective_function, initial_point, num_steps_calibration)\n else:\n logger.debug('Skipping calibration, parameters used as provided.')\n\n opt, sol, cplus, cminus, tplus, tminus = self._optimization(objective_function, initial_point,\n max_trials=self._max_trials, **self._options)\n return sol, opt, None\n\n def _optimization(self, obj_fun, initial_theta, max_trials, save_steps=1, last_avg=1):\n \"\"\"Minimizes obj_fun(theta) with a simultaneous perturbation stochastic\n approximation algorithm.\n\n Args:\n obj_fun (callable): the function to minimize\n initial_theta (numpy.array): initial value for the variables of\n obj_fun\n max_trials (int) : the maximum number of trial steps ( = function\n calls/2) in the optimization\n save_steps (int) : stores optimization outcomes each 'save_steps'\n trial steps\n last_avg (int) : number of last updates of the variables to average\n on for the final obj_fun\n Returns:\n list: a list with the following elements:\n cost_final : final optimized value for obj_fun\n theta_best : final values of the variables corresponding to\n cost_final\n cost_plus_save : array of stored values for obj_fun along the\n optimization in the + direction\n cost_minus_save : array of stored values for obj_fun along the\n optimization in the - direction\n theta_plus_save : array of stored variables of obj_fun along the\n optimization in the + direction\n theta_minus_save : array of stored variables of obj_fun along the\n optimization in the - direction\n \"\"\"\n\n theta_plus_save = []\n theta_minus_save = []\n cost_plus_save = []\n cost_minus_save = []\n theta = initial_theta\n theta_best = np.zeros(initial_theta.shape)\n for k in range(max_trials):\n # SPSA Parameters\n a_spsa = float(self._parameters[0]) / np.power(k + 1 + self._parameters[4], self._parameters[2])\n c_spsa = float(self._parameters[1]) / np.power(k + 1, self._parameters[3])\n delta = 2 * np.random.randint(2, size=np.shape(initial_theta)[0]) - 1\n # plus and minus directions\n theta_plus = theta + c_spsa * delta\n theta_minus = theta - c_spsa * delta\n # cost function for the two directions\n if self._batch_mode:\n cost_plus, cost_minus = obj_fun(np.concatenate((theta_plus, theta_minus)))\n else:\n cost_plus = obj_fun(theta_plus)\n cost_minus = obj_fun(theta_minus)\n # derivative estimate\n g_spsa = (cost_plus - cost_minus) * delta / (2.0 * c_spsa)\n # updated theta\n theta = theta - a_spsa * g_spsa\n # saving\n if k % save_steps == 0:\n logger.debug('Objective function at theta+ for step # {}: {:.7f}'.format(k, cost_plus))\n logger.debug('Objective function at theta- for step # {}: {:.7f}'.format(k, cost_minus))\n theta_plus_save.append(theta_plus)\n theta_minus_save.append(theta_minus)\n cost_plus_save.append(cost_plus)\n cost_minus_save.append(cost_minus)\n # logger.debug('objective function at for step # {}: {:.7f}'.format(k, obj_fun(theta)))\n\n if k >= max_trials - last_avg:\n theta_best += theta / last_avg\n # final cost update\n cost_final = obj_fun(theta_best)\n logger.debug('Final objective function is: %.7f' % cost_final)\n\n return [cost_final, theta_best, cost_plus_save, cost_minus_save,\n theta_plus_save, theta_minus_save]\n\n def _calibration(self, obj_fun, initial_theta, stat):\n \"\"\"Calibrates and stores the SPSA parameters back.\n\n SPSA parameters are c0 through c5 stored in parameters array\n\n c0 on input is target_update and is the aimed update of variables on the first trial step.\n Following calibration c0 will be updated.\n\n c1 is initial_c and is first perturbation of initial_theta.\n\n Args:\n obj_fun (callable): the function to minimize.\n initial_theta (numpy.array): initial value for the variables of\n obj_fun.\n stat (int) : number of random gradient directions to average on in\n the calibration.\n \"\"\"\n\n target_update = self._parameters[0]\n initial_c = self._parameters[1]\n delta_obj = 0\n logger.debug(\"Calibration...\")\n for i in range(stat):\n if i % 5 == 0:\n logger.debug('calibration step # {} of {}'.format(str(i), str(stat)))\n delta = 2 * np.random.randint(2, size=np.shape(initial_theta)[0]) - 1\n theta_plus = initial_theta + initial_c * delta\n theta_minus = initial_theta - initial_c * delta\n if self._batch_mode:\n obj_plus, obj_minus = obj_fun(np.concatenate((theta_plus, theta_minus)))\n else:\n obj_plus = obj_fun(theta_plus)\n obj_minus = obj_fun(theta_minus)\n delta_obj += np.absolute(obj_plus - obj_minus) / stat\n\n self._parameters[0] = target_update * 2 / delta_obj \\\n * self._parameters[1] * (self._parameters[4] + 1)\n\n logger.debug('Calibrated SPSA parameter c0 is %.7f' % self._parameters[0])\n","repo_name":"epiqc/PartialCompilation","sub_path":"qiskit-aqua/qiskit/aqua/components/optimizers/spsa.py","file_name":"spsa.py","file_ext":"py","file_size_in_byte":9789,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"31"} +{"seq_id":"5066717982","text":"# ASTEROIDS GAME\n# Started: Dec 4 2021\n# By Tom\n\nimport pygame\nimport math\nimport os\nimport random\npygame.init()\npygame.font.init()\nos.chdir(os.path.dirname(__file__))\n\nWIDTH = 1600\nHEIGHT = 1200\nwin = pygame.display.set_mode((1200, 800), pygame.FULLSCREEN)\npygame.display.set_caption('Asteroids game')\nW, H = win.get_size()\n\nPI = 3.141592653589793\nWHITE = (255, 255, 255)\nBLACK = (20, 20, 20)\nRED = (153, 0, 0)\nGREEN = (0, 153, 0)\nCYAN = (0, 153, 153)\nNORMAL = (20, 20, 30)\nFREEZE = (20, 35, 45)\nPOWER = (45, 20, 20)\n\nx_move = 0\ny_move = 0\n\nfreeze_img = pygame.transform.scale(pygame.image.load('freeze_bar.png'), (140, 30))\nfreeze_mode = False\nfreeze_count = 300\npower_img = pygame.transform.scale(pygame.image.load('power_bar.png'), (140, 30))\npower_mode = False\npower_count = 420\n\nclass Space_ship():\n def __init__(self, rotation=90, vel=0, x=W/2, y=H/2):\n self.x = x\n self.y = y\n self.rotation = rotation\n self.vel = vel\n self.img = pygame.transform.scale(pygame.image.load('Spaceship.png'), (80, 60))\n self.mask = pygame.mask.from_surface(self.img)\n self.moving_img = pygame.transform.scale(pygame.image.load('Movingship.png'), (80, 60))\n self.moving_mask = pygame.mask.from_surface(self.moving_img)\n self.health = 100\n self.health_bar = pygame.transform.scale(pygame.image.load('health.png'), (200, 50))\n\n def draw(self, win):\n win.blit(copy_img, (self.x - int(copy_img.get_width() / 2) - top_left_x, self.y - int(copy_img.get_height() / 2) - top_left_y))\n\nclass Bullet():\n def __init__(self):\n self.x = p1.x\n self.y = p1.y\n self.xvel = 10 * math.cos(radian)\n self.yvel = -1 * (10 * math.sin(radian))\n self.rotation = p1.rotation\n self.img = pygame.transform.rotate(pygame.transform.scale(pygame.image.load('bullet.png'), (40, 20)), self.rotation)\n self.mask = pygame.mask.from_surface(self.img)\n\n def draw(self, win):\n win.blit(self.img, (self.x - top_left_x - self.img.get_width()//2, self.y - top_left_y - self.img.get_height()//2))\n\nclass Asteroid():\n def __init__(self, x, y, rotation, img):\n self.x = x\n self.y = y\n self.rotation = rotation\n self.speed = img[0]\n self.img = img[1]\n self.mask = pygame.mask.from_surface(self.img)\n\n def draw(self, win):\n win.blit(self.img, (self.x - top_left_x - self.img.get_width()//2, self.y - top_left_y - self.img.get_height()//2))\n\nclass Health_up():\n def __init__(self, x, y, rotation, value, used):\n self.x = x\n self.y = y\n self.rotation = rotation\n self.speed = 1\n self.value = value\n self.img = pygame.transform.scale(pygame.image.load('health_up.png'), (40, 40))\n self.mask = pygame.mask.from_surface(self.img)\n self.used = used\n\n def draw(self, win):\n win.blit(self.img, (self.x - top_left_x - self.img.get_width()//2, self.y - top_left_y - self.img.get_height()//2))\n\ndef reset_rotation(deg):\n if deg >= 360:\n deg -= 360\n if deg < 0:\n deg += 360\n\n return deg\n\ndef generate_asteroid(min, max):\n random_x, random_y = random.randint(0, WIDTH), random.randint(0, HEIGHT)\n random_rotation = random.randint(0, 360)\n\n SMALL = (3, pygame.image.load('small_asteroid.png'))\n MED = (2, pygame.image.load('med_asteroid.png'))\n BIG = (1, pygame.image.load('large_asteroid.png'))\n sizes = (SMALL, MED, BIG)\n size_index = random.randint(min, max)\n random_size = sizes[size_index]\n\n return random_x, random_y, random_rotation, random_size\n\ndef move_object(override, *args):\n for object in args[0]:\n object.rotation = reset_rotation(object.rotation)\n\n object_radian = deg2rad(object.rotation)\n\n object_x_vel = math.cos(object_radian)*object.speed\n object_y_vel = math.sin(object_radian)*object.speed\n\n if object.x <= 0 or object.x >= WIDTH:\n object.rotation = (object.rotation - 180) * -1\n\n object_radian = deg2rad(object.rotation)\n\n object_x_vel = math.cos(object_radian)*object.speed\n object_y_vel = math.sin(object_radian)*object.speed\n\n if object.y <= 0 or object.y >= HEIGHT:\n object.rotation *= -1\n\n object_radian = deg2rad(object.rotation)\n\n object_x_vel = math.cos(object_radian)*object.speed\n object_y_vel = math.sin(object_radian)*object.speed\n\n if freeze_mode == False or override == True:\n object.x += object_x_vel\n object.y += object_y_vel\n\n\ndef draw_window(win):\n win.fill(BLACK)\n\n if freeze_mode:\n bg_colour = FREEZE\n else:\n bg_colour = NORMAL\n\n pygame.draw.rect(win, bg_colour, (0 - top_left_x, 0 - top_left_y, WIDTH, HEIGHT))\n pygame.draw.rect(win, WHITE, (0 - top_left_x, 0 - top_left_y, WIDTH, HEIGHT), 3)\n\n if power_mode:\n pygame.draw.circle(win, POWER, (p1.x - top_left_x, p1.y - top_left_y), 50)\n\n for bullet in bullet_list:\n bullet.draw(win)\n\n for asteroid in asteroid_list:\n asteroid.draw(win)\n\n if level % 2 == 0 and health_plus.used == False:\n health_plus.draw(win)\n\n p1.draw(win)\n\n health_width = round(p1.health / 100 * 178)\n pygame.draw.rect(win, (180, 0, 0), (40, 40, 178, 27))\n pygame.draw.rect(win, (0, 180, 0), (40, 40, health_width, 27))\n win.blit(p1.health_bar, (30, 30))\n\n power_width = round(power_count / 420 * 125)\n freeze_width = round(freeze_count / 300 * 125)\n win.blit(power_img, (30, 90))\n pygame.draw.rect(win, (255, 100, 0), (37, 98, power_width, 10))\n win.blit(freeze_img, (30, 130))\n pygame.draw.rect(win, (0, 180, 220), (37, 138, freeze_width, 10))\n\n font = pygame.font.SysFont(\"comicsans\", 60)\n text = font.render(str(score), True, CYAN)\n win.blit(text, (W//2 - text.get_width()//2, 30))\n\n if p1.health <= 0:\n win.fill(NORMAL)\n font = pygame.font.SysFont(\"comicsans\", 110)\n font2 = pygame.font.SysFont(\"comicsans\", 40)\n font3 = pygame.font.SysFont(\"comicsans\", 65)\n text = font.render(\"GAME OVER\", True, RED)\n text2 = font2.render(\"Press ESC to exit\", True, GREEN)\n text3 = font3.render(f\"Final Score - {str(score)}\", True, CYAN)\n win.blit(text, (W//2 - text.get_width()//2, H//5))\n win.blit(text2, (W//2 - text2.get_width()//2, H//4 * 3))\n win.blit(text3, (W//2 - text3.get_width()//2, H//2))\n\n pygame.display.update()\n\n\ndeg2rad = lambda deg: deg*PI/180\n\np1 = Space_ship()\ncopy_img = p1.img\nradian = deg2rad(p1.rotation)\n\nasteroid_list = []\nbullet_list = []\nlevel = 0\nscore = 0\nclock = pygame.time.Clock()\ntop_left_x = 0\ntop_left_y = 0\n\nrun = True\nwhile run:\n clock.tick(60)\n\n if len(asteroid_list) == 0:\n level += 1\n\n for i in range(48):\n X, Y, R, S = generate_asteroid(0, 2)\n asteroid_list.append(Asteroid(X, Y, R, S))\n\n if level % 2 == 0:\n health_plus = Health_up(random.randint(1, WIDTH), random.randint(1, HEIGHT), random.randint(1, 360), 30, False)\n\n if level % 2 == 0 and health_plus.used == False:\n offset = (round((health_plus.x - health_plus.img.get_width()//2) - (p1.x - p1.moving_img.get_width()//2)), round((health_plus.y - health_plus.img.get_height()//2) - (p1.y - p1.moving_img.get_height()//2)))\n\n check_hit = p1.moving_mask.overlap(health_plus.mask, offset)\n if check_hit:\n p1.health += health_plus.value\n health_plus.used = True\n\n move_object(False, [health_plus])\n\n if freeze_mode:\n freeze_count -= 1\n if freeze_count <= 0:\n freeze_mode = False\n if freeze_mode == False:\n if freeze_count < 300:\n freeze_count += 0.5\n\n if power_mode:\n for bullet in bullet_list:\n bullet.img = pygame.transform.rotate(pygame.transform.scale(pygame.image.load('power_bullet.png'), (40, 20)), bullet.rotation)\n power_count -= 1\n if power_count <= 0:\n for bullet in bullet_list:\n bullet.img = pygame.transform.rotate(pygame.transform.scale(pygame.image.load('bullet.png'), (40, 20)), bullet.rotation)\n power_mode = False\n if power_mode == False:\n if power_count <= 420:\n power_count += 0.5\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n if len(bullet_list) < 3:\n bullet_list.append(Bullet())\n\n if event.key == pygame.K_ESCAPE:\n run = False\n\n if event.key == pygame.K_x:\n freeze_mode = True\n\n if event.key == pygame.K_c:\n power_mode = True\n\n for bullet in bullet_list:\n bullet.x += bullet.xvel\n bullet.y += bullet.yvel\n\n if bullet.x <= 0 or bullet.x >= WIDTH or bullet.y <= 0 or bullet.y >= HEIGHT:\n try:\n bullet_list.remove(bullet)\n except:\n pass\n\n for bullet in bullet_list:\n for asteroid in asteroid_list:\n offset = (round((asteroid.x - asteroid.img.get_width()//2) - (bullet.x - bullet.img.get_width()//2)), round((asteroid.y - asteroid.img.get_height()//2) - (bullet.y - bullet.img.get_height()//2)))\n\n check_hit = bullet.mask.overlap(asteroid.mask, offset)\n if check_hit:\n bullet_list.remove(bullet)\n\n if asteroid.speed == 1:\n score += 15\n elif asteroid.speed == 2:\n score += 10\n elif asteroid.speed == 3:\n score += 5\n\n if power_mode == False:\n new_asteroid_speed = asteroid.speed + 1\n if new_asteroid_speed == 2:\n for _ in range(2):\n new_asteroid = Asteroid(asteroid.x, asteroid.y, random.randint(0, 360), (2, pygame.image.load('med_asteroid.png')))\n asteroid_list.append(new_asteroid)\n for _ in range(6):\n move_object(True, [new_asteroid])\n pygame.display.update()\n if new_asteroid_speed == 3:\n for _ in range(2):\n new_asteroid = Asteroid(asteroid.x, asteroid.y, random.randint(0, 360), (3, pygame.image.load('small_asteroid.png')))\n asteroid_list.append(new_asteroid)\n for _ in range(6):\n move_object(True, [new_asteroid])\n pygame.display.update()\n\n asteroid_list.remove(asteroid)\n break\n\n draw_window(win)\n\n move_object(False, asteroid_list)\n\n p1.rotation = reset_rotation(p1.rotation)\n\n keys = pygame.key.get_pressed()\n\n if keys[pygame.K_UP]:\n if p1.vel < 4:\n p1.vel += 0.4\n\n radian = deg2rad(p1.rotation)\n x_move = p1.vel * math.cos(radian)\n y_move = p1.vel * math.sin(radian)\n\n p1.x += round(x_move)\n p1.y -= round(y_move)\n\n top_left_x += round(x_move)\n top_left_y -= round(y_move)\n\n if p1.x < 0 or p1.x > WIDTH:\n p1.x -= round(x_move)\n top_left_x -= round(x_move)\n if p1.y < 0 or p1.y > HEIGHT:\n p1.y += round(y_move)\n top_left_y += round(y_move)\n\n copy_img = p1.moving_img\n copy_img = pygame.transform.rotate(copy_img, p1.rotation)\n\n for asteroid in asteroid_list:\n offset = (round((asteroid.x - asteroid.img.get_width()//2) - (p1.x - p1.moving_img.get_width()//2)), round((asteroid.y - asteroid.img.get_height()//2) - (p1.y - p1.moving_img.get_height()//2)))\n\n check_hit = p1.moving_mask.overlap(asteroid.mask, offset)\n if check_hit:\n if asteroid.speed == 3:\n p1.health -= 5\n if asteroid.speed == 2:\n p1.health -= 10\n if asteroid.speed == 1:\n p1.health -= 15\n\n asteroid_list.remove(asteroid)\n\n else:\n if p1.vel > 0:\n p1.vel -= 0.1\n\n radian = deg2rad(p1.rotation)\n x_move = p1.vel * math.cos(radian)\n y_move = p1.vel * math.sin(radian)\n\n p1.x += round(x_move)\n p1.y -= round(y_move)\n\n top_left_x += round(x_move)\n top_left_y -= round(y_move)\n\n if p1.x < 0 or p1.x > WIDTH:\n p1.x -= round(x_move)\n top_left_x -= round(x_move)\n if p1.y < 0 or p1.y > HEIGHT:\n p1.y += round(y_move)\n top_left_y += round(y_move)\n\n copy_img = p1.img\n copy_img = pygame.transform.rotate(copy_img, p1.rotation)\n\n for asteroid in asteroid_list:\n offset = (round((asteroid.x - asteroid.img.get_width()//2) - (p1.x - p1.img.get_width()//2)), round((asteroid.y - asteroid.img.get_height()//2) - (p1.y - p1.img.get_height()//2)))\n\n check_hit = p1.mask.overlap(asteroid.mask, offset)\n if check_hit:\n if asteroid.speed == 3:\n p1.health -= 3\n if asteroid.speed == 2:\n p1.health -= 6\n if asteroid.speed == 1:\n p1.health -= 10\n\n asteroid_list.remove(asteroid)\n\n if keys[pygame.K_RIGHT]:\n if keys[pygame.K_UP]:\n copy_img = p1.moving_img\n else:\n copy_img = p1.img\n\n p1.rotation -= 3\n copy_img = pygame.transform.rotate(copy_img, p1.rotation)\n\n if keys[pygame.K_LEFT]:\n if keys[pygame.K_UP]:\n copy_img = p1.moving_img\n else:\n copy_img = p1.img\n\n p1.rotation += 3\n copy_img = pygame.transform.rotate(copy_img, p1.rotation)\n\n draw_window(win)\n\npygame.quit()\n","repo_name":"Penguin-ninja372/Asteroids-game","sub_path":"Asteroids/asteroids.py","file_name":"asteroids.py","file_ext":"py","file_size_in_byte":14111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"877524491","text":"import cv2\nimport re\nimport json\nimport os\nfrom PIL import Image, ImageFont, ImageDraw\nfrom kanji import get_tokens\nfrom wanikani import Wanikani\nfrom wanikani_assignment import WanikaniAssignment\nfrom wanikani_word import WanikaniWord\n\n\ndef parse_time(time_string):\n hours = int(re.findall(r'(\\d+):\\d+:\\d+,\\d+', time_string)[0])\n minutes = int(re.findall(r'\\d+:(\\d+):\\d+,\\d+', time_string)[0])\n seconds = int(re.findall(r'\\d+:\\d+:(\\d+),\\d+', time_string)[0])\n milliseconds = int(re.findall(r'\\d+:\\d+:\\d+,(\\d+)', time_string)[0])\n\n return (hours * 3600 + minutes * 60 + seconds) * 1000 + milliseconds\n\n\ndef parse_srt(srt_string):\n srt_list = []\n\n for line in srt_string.split('\\n\\n'):\n if line != '':\n index = int(re.match(r'\\d+', line).group())\n\n pos = re.search(r'\\d+:\\d+:\\d+,\\d+ --> \\d+:\\d+:\\d+,\\d+',\n line).end() + 1\n content = line[pos:]\n start_time_string = re.findall(\n r'(\\d+:\\d+:\\d+,\\d+) --> \\d+:\\d+:\\d+,\\d+', line)[0]\n end_time_string = re.findall(\n r'\\d+:\\d+:\\d+,\\d+ --> (\\d+:\\d+:\\d+,\\d+)', line)[0]\n start_time = parse_time(start_time_string)\n end_time = parse_time(end_time_string)\n\n srt_list.append({\n 'index': index,\n 'content': content,\n 'start': start_time,\n 'end': end_time\n })\n\n return srt_list\n\nwk = Wanikani()\nsrt = open('input/19.srt', 'r', encoding=\"utf-8\").read()\nsubtitles = parse_srt(srt)\n\nvidcap = cv2.VideoCapture('input/19.mkv')\ndebugLog = False\nif (vidcap.isOpened() == False):\n print(\"Error opening the video file\")\nelse:\n # Get frame rate iformation\n width = int(vidcap.get(3))\n height = int(vidcap.get(4))\n fps = float(vidcap.get(5))\n if debugLog: print(\"Frame Rate:\", fps, \"frames per second\")\t\n if debugLog: print(width, \"x\", height)\n\n # Get frame count\n frame_count = int(vidcap.get(7))\n\n if debugLog: print(\"Frame count:\", frame_count)\n\n font = ImageFont.truetype('fonts/NotoSansJP-Regular.otf', 56)\n furigana_font = ImageFont.truetype('fonts/NotoSansJP-Regular.otf', 26)\n stroke_width = 3\n furigana_stroke_width = 2\n spacing = 15\n count = 0\n for subtitle in subtitles:\n msToAdd = (subtitle['end'] - subtitle['start']) / 2\n subtitleContent = subtitle['content']\n tokens = get_tokens(subtitleContent)\n\n vidcap.set(cv2.CAP_PROP_POS_MSEC, subtitle['start'] + msToAdd)\n success, image = vidcap.read()\n fileName = \"output/frame-%d.jpg\" % count\n cv2.imwrite(fileName, image)\n\n with Image.open(fileName) as img:\n d = ImageDraw.Draw(img)\n w, h = d.textsize(subtitleContent, font)\n x = (width / 2) - (w / 2)\n y = height - (h) - (height / 20)\n if debugLog: print(subtitleContent, w, h, x, y)\n d.text(\n (x, y),\n subtitleContent,\n font=font,\n fill=(255, 255, 255),\n stroke_width=stroke_width,\n stroke_fill=(0, 0, 0),\n spacing=spacing,\n )\n\n for token in tokens:\n found_wk_word = WanikaniWord.get_word(token['surface'])\n found_wk_assignment = None\n if found_wk_word is not None:\n found_wk_assignment = WanikaniAssignment.get_assignment(found_wk_word.id)\n\n if debugLog: print(\"surface\", token['surface'], found_wk_word)\n hiragana_text = token['hiragana']\n furigana_w, furigana_h = d.textsize(hiragana_text, furigana_font)\n morpheme_w, morpheme_h = d.textsize(token['surface'], font)\n string_until_morpheme = subtitleContent[:token['begin']]\n number_of_newlines = string_until_morpheme.count(\"\\n\")\n if number_of_newlines > 0:\n regex = r'^(.*?\\n){' + str(number_of_newlines) + '}'\n regex_pattern = re.compile(regex) \n string_until_morpheme = re.sub(regex_pattern, '', string_until_morpheme)\n if debugLog: print('text_after', string_until_morpheme);\n if debugLog: print('regex', regex)\n\n start_x, start_y = d.textsize(string_until_morpheme, font)\n if debugLog: print('newlines', number_of_newlines)\n if debugLog: print('hiragana', hiragana_text)\n if debugLog: print('string_until', string_until_morpheme)\n if debugLog: print('furigana_w/h', furigana_w, furigana_h)\n if debugLog: print('morpheme_w/h', morpheme_w, morpheme_h)\n if debugLog: print('start_x/y', start_x, start_y)\n\n furigana_x = x + start_x + (morpheme_w / 2) - (furigana_w / 2)\n furigana_y = y - morpheme_h / 3.2 + (morpheme_h * number_of_newlines) + ((spacing * 1.2) * number_of_newlines)\n d.text(\n (furigana_x, furigana_y),\n hiragana_text,\n font=furigana_font,\n fill=(255, 255, 255),\n stroke_width=furigana_stroke_width,\n stroke_fill=(0, 0, 0),\n )\n if found_wk_word is not None:\n d.text(\n (furigana_x, furigana_y - 15),\n f'{found_wk_word.spaced_repetition_system_id}',\n font=furigana_font,\n fill=(255, 255, 255),\n stroke_width=furigana_stroke_width,\n stroke_fill=(0, 0, 0),\n )\n if debugLog: print('')\n\n img.save(\"output/19-%d.jpg\" % count)\n os.remove(fileName)\n\n count += 1\n if count > 2:\n exit()\n\nvidcap.release()\ncv2.destroyAllWindows()\n","repo_name":"f4nu/anime-2-manga","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"23049036940","text":"# 生成器函数，函数只要有关��字yield\ndef gen_func():\n\tyield 1\n\tyield 2\n\tyield 3\n# 惰性求值，延迟求值提供了可能\n\n\n# 斐波拉契 0 1 1 2 3 5 8 13\ndef fib(index):\n\tif index <= 2:\n\t\treturn 1\n\telse:\n\t\treturn fib(index-2) + fib(index-1)\n# print(fib(10), \"---------fib--------\")\n\n\ndef fib2(index):\n\tlis = []\n\ta, b = 0, 1\n\twhile index:\n\t\tlis.append(b)\n\t\ta, b = b, a+b\n\t\tindex -= 1\n\treturn lis\n# print(fib2(10), \"---------fib2--------\")\n\n\ndef gen_fib(index):\n\ta, b = 0, 1\n\twhile index:\n\t\tyield b\n\t\ta, b = b, a+b\n\t\tindex -= 1\n# print(gen_fib(10), \"---------gen_fib--------\")\n# for i in gen_fib(10):\n# \tprint(i)\n\n\ndef func():\n\treturn 1\n\n\nif __name__ == \"__main__\":\n\t# 生成器对象， python编译字节码的时候就产生了\n\tgen = gen_func()\n\tfor value in gen:\n\t\tprint(value)\n\t# re = func()\n\t# pass\n","repo_name":"pyforspider/LearningLog","sub_path":"Advance_Python/Chapter 9. iterator/3. fen_func.py","file_name":"3. fen_func.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"30506085551","text":"#\n# @lc app=leetcode id=32 lang=python3\n#\n# [32] Longest Valid Parentheses\n#\n\n# @lc code=start\n\n\nclass Solution:\n def longestValidParentheses(self, s: str) -> int:\n maxVal = 0\n stack = []\n stack.append(-1)\n\n for i, c in enumerate(s):\n if c == '(':\n stack.append(i)\n else:\n stack.pop()\n if not stack:\n stack.append(i)\n else:\n maxVal = max(maxVal, i - stack[-1])\n return maxVal\n# @lc code=end\n","repo_name":"naseeihity/leetcode-daily","sub_path":"stack-queue/32.longest-valid-parentheses.py","file_name":"32.longest-valid-parentheses.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22844268760","text":"from __future__ import division\n\nimport torch.nn as nn\nimport torch\nimport torch.nn.functional as F\n# print net.params\n__all__ = ['ICCV17']\n\nclass Repeat(nn.Module):\n\tdef __init__(self, channels, kernels, paddings = None, out = False, fg = False):\n\t\tsuper(Repeat, self).__init__()\n\t\tassert len(channels) == len(kernels) + 1, 'number of conv is not consistent'\n\t\tself.out = out\n\t\tself.fg = fg \n\t\tif paddings == None:\n\t\t\tpaddings = [ks // 2 for ks in kernels]\n\n\t\tself.convs = torch.nn.ModuleList()\n\t\tfor i in range(len(kernels)):\n\t\t\tparams = { \t'in_channels':channels[i],\n\t\t\t\t\t\t'out_channels':channels[i + 1], \n\t\t\t\t\t\t'kernel_size':kernels[i],\n\t\t\t\t\t\t'padding':paddings[i]}\n\n\t\t\tself.convs.append(nn.Conv2d( **params))\n\n\t\tself.relu = nn.functional.leaky_relu\n\n\tdef forward(self, x):\n\t\tfor idx in range(len(self.convs)):\t\t\t\n\t\t\tx = self.convs[idx](x)\n\t\t\tif not self.out or idx < len(self.convs) - 1:\n\t\t\t\tx = self.relu(x)\n\t\t\t# if self.fg:\n\t\t\t# \tprint(\"!!!!\", x.sum(), x.size())\n\t\treturn x\n\nclass PoseNet(nn.Module):\n\t\"\"\"docstring for PoseNet\"\"\"\n\tdef __init__(self, num_class, **kwargs):\n\t\tsuper(PoseNet, self).__init__()\n\t\tself.pool = nn.MaxPool2d(2, padding = 0)\n\t\tself.relu = nn.functional.leaky_relu\n\t\tself.stage1_block_1 = Repeat([3,64,64], [3,3])\n\t\tself.stage1_block_2 = Repeat([64,128,128], [3,3])\n\t\tself.stage1_block_3 = Repeat([128,256,256,256,256], [3,3,3,3])\n\t\tself.stage1_block_4 = Repeat([256,512,512,256,256,256,256,128], [3,3,3,3,3,3,3])\n\t\tself.stage1_block_5 = Repeat([128,512,num_class],[1, 1], out = True)\n\n\t\tself.stage2 \t\t= Repeat([149,128,128,128,128,128,128,num_class], [7,7,7,7,7,1,1], out = True, fg = True) # cat x and out1 128 + 21 = 149\n\t\tself.stage3\t\t\t= Repeat([149,128,128,128,128,128,128,num_class], [7,7,7,7,7,1,1], out = True) # cat x and out2 128 + 21 = 149\n\tdef forward(self, x):\n\t\tx = self.stage1_block_1(x)\n\t\tx = self.pool(x)\n\t\tx = self.stage1_block_2(x)\n\t\tx = self.pool(x)\n\t\tx = self.stage1_block_3(x)\n\t\tx = self.pool(x)\n\t\tx = self.stage1_block_4(x)\n\t\tout1 = x.clone() #need creating a new copy of x, otherwise x would be change in next line!!\n\t\tout1 = self.stage1_block_5(out1)\n\t\tout2 = torch.cat([out1, x], 1)\n\t\tout2 = self.stage2(out2)\n\t\tout3 = torch.cat([out2, x], 1)\n\t\tout3 = self.stage3(out3)\n\n\t\treturn [out1, out2, out3]\n\nclass PosePior(nn.Module):\n\t\"\"\"docstring for PosePior\"\"\"\n\tdef __init__(self, num_joints = 21):\n\t\tsuper(PosePior, self).__init__()\n\t\tself.num_joints = num_joints\n\t\tself.relu = nn.functional.leaky_relu\n\t\tself.conv_0_1 = nn.Conv2d(21, 32, 3, padding = 1)\n\t\tself.conv_0_2 = nn.Conv2d(32, 32, 3, stride = 2)\n\t\tself.conv_1_1 = nn.Conv2d(32, 64, 3, padding = 1)\n\t\tself.conv_1_2 = nn.Conv2d(64, 64, 3, stride = 2)\n\t\tself.conv_2_1 = nn.Conv2d(64, 128, 3, padding = 1)\n\t\tself.conv_2_2 = nn.Conv2d(128, 128, 3, stride = 2)\n\n\t\tself.fc_0 \t = nn.Linear(2050, 512) # 4*4*128 + 2 = 2050\n\t\tself.fc_1 \t = nn.Linear(512, 512)\n\t\tself.fc_out = nn.Linear(512, 3 * num_joints)\n\n\tdef _flip_right_hand(self, coords_xyz_canonical, hand_side):\n\t\t\"\"\" Flips the given canonical coordinates, when cond_right is true. Returns coords unchanged otherwise.\n\t\t\tThe returned coordinates represent those of a left hand.\n\t\t\tInputs:\n\t\t\t\tcoords_xyz_canonical: Nx3 matrix, containing the coordinates for each of the N keypoints\n\t\t\"\"\"\n\n\t\t# flip hand according to hand side\n\t\t\n\t\tcond_right = torch.argmax(hand_side, 1) == 1\n\t\tcond_right = cond_right.view(-1, 1, 1).repeat(1, self.num_joints, 3)\n\n\t\texpanded = False\n\t\ts = coords_xyz_canonical.size()\n\t\tif len(s) == 2:\n\t\t\tcoords_xyz_canonical.unsqueeze_(0)\n\t\t\tcond_right.unsqueeze_(0)\n\t\t\texpanded = True\n\n\t\t# mirror along y axis\n\t\tcoords_xyz_canonical_mirrored = torch.stack([coords_xyz_canonical[:, :, 0], coords_xyz_canonical[:, :, 1], -coords_xyz_canonical[:, :, 2]], 2)\n\n\t\t# select mirrored in case it was a right hand\n\t\tcoords_xyz_canonical_left = torch.where(cond_right, coords_xyz_canonical_mirrored, coords_xyz_canonical)\n\n\t\tif expanded:\n\t\t\tcoords_xyz_canonical_left = torch.squeeze(coords_xyz_canonical_left, 0)\n\n\t\treturn coords_xyz_canonical_left\n\n\tdef forward(self, x, hand_side):\n\t\tout = self.relu(self.conv_0_1(x))\n\t\tout = F.pad(out, [0, 1, 0, 1])\n\t\tout = self.relu(self.conv_0_2(out))\n\n\t\tout = self.relu(self.conv_1_1(out))\n\t\tout = F.pad(out, [0, 1, 0, 1])\n\t\tout = self.relu(self.conv_1_2(out))\n\n\t\tout = self.relu(self.conv_2_1(out))\n\t\tout = F.pad(out, [0, 1, 0, 1])\n\t\tout = self.relu(self.conv_2_2(out))\n\n\t\tout = out.contiguous().permute(0,2,3,1)\n\t\tout = out.contiguous().view(out.size(0), -1)\n\t\tout = torch.cat([out, hand_side], 1)\n\n\t\tout = self.relu(self.fc_0(out))\n\t\tout = self.relu(self.fc_1(out))\n\t\tout = self.fc_out(out)\n\t\tout = out.view(out.size(0), self.num_joints, 3)\n\t\tout = self._flip_right_hand(out, hand_side)\n\t\treturn out\n\nclass ViewPoint(nn.Module):\n\t\"\"\"docstring for ViewPoint\"\"\"\n\tdef __init__(self):\n\t\tsuper(ViewPoint, self).__init__()\n\t\tself.relu = nn.functional.leaky_relu\n\t\tself.conv_0_1 = nn.Conv2d(21, 64, 3, padding = 1)\n\t\tself.conv_0_2 = nn.Conv2d(64, 64, 3, stride = 2)\n\t\tself.conv_1_1 = nn.Conv2d(64, 128, 3, padding = 1)\n\t\tself.conv_1_2 = nn.Conv2d(128, 128, 3, stride = 2)\n\t\tself.conv_2_1 = nn.Conv2d(128, 256, 3, padding = 1)\n\t\tself.conv_2_2 = nn.Conv2d(256, 256, 3, stride = 2)\n\t\tself.fc_0 \t = nn.Linear(4098, 256)\n\t\tself.fc_1 \t = nn.Linear(256, 128)\n\t\tself.fc_ux = nn.Linear(128, 1)\n\t\tself.fc_uy = nn.Linear(128, 1)\n\t\tself.fc_uz = nn.Linear(128, 1)\n\n\tdef _get_rot_mat(self, ux_b, uy_b, uz_b):\n\t\t\"\"\" Returns a rotation matrix from axis and (encoded) angle.\"\"\"\n\n\t\tu_norm = torch.sqrt(ux_b ** 2 + uy_b ** 2 + uz_b ** 2 + 1e-8)\n\t\ttheta = u_norm\n\n\t\t# some tmp vars\n\t\tst_b = torch.sin(theta)\n\t\tct_b = torch.cos(theta)\n\t\tone_ct_b = 1.0 - torch.cos(theta)\n\n\t\tst = st_b[:, 0]\n\t\tct = ct_b[:, 0]\n\t\tone_ct = one_ct_b[:, 0]\n\t\tnorm_fac = 1.0 / u_norm[:, 0]\n\t\tux = ux_b[:, 0] * norm_fac\n\t\tuy = uy_b[:, 0] * norm_fac\n\t\tuz = uz_b[:, 0] * norm_fac\n\t\tl1 = torch.stack([ct+ux*ux*one_ct, ux*uy*one_ct-uz*st, ux*uz*one_ct+uy*st], dim = -1)\n\t\tl2 = torch.stack([uy*ux*one_ct+uz*st, ct+uy*uy*one_ct, uy*uz*one_ct-ux*st], dim = -1)\n\t\tl3 = torch.stack([uz*ux*one_ct-uy*st, uz*uy*one_ct+ux*st, ct+uz*uz*one_ct], dim = -1)\n\t\tmat = torch.stack([l1,l2,l3],dim = -1)\n\t\tmat = mat.permute(0,2,1)\n\t\treturn mat\n\n\tdef forward(self, x, hand_side):\n\t\t\n\t\tout = self.relu(self.conv_0_1(x))\n\t\tout = F.pad(out, [0, 1, 0, 1])\n\t\tout = self.relu(self.conv_0_2(out))\n\n\t\tout = self.relu(self.conv_1_1(out))\n\t\tout = F.pad(out, [0, 1, 0, 1])\n\t\tout = self.relu(self.conv_1_2(out))\n\n\t\tout = self.relu(self.conv_2_1(out))\n\t\tout = F.pad(out, [0, 1, 0, 1])\n\t\tout = self.relu(self.conv_2_2(out))\n\n\t\tout = out.contiguous().permute(0,2,3,1)\n\t\tout = out.contiguous().view(out.size(0), -1)\n\t\tout = torch.cat([out, hand_side], 1)\n\n\t\tout = self.relu(self.fc_0(out))\n\t\tout = self.relu(self.fc_1(out))\n\t\tux = self.fc_ux(out)\n\t\tuy = self.fc_uy(out)\n\t\tuz = self.fc_uz(out)\n\t\treturn self._get_rot_mat(ux, uy, uz)\n\nclass ICCV17(nn.Module):\n\tdef __init__(self, num_joints, **kwargs):\n\t\tsuper(ICCV17, self).__init__()\n\t\tself.pose_net = PoseNet(num_joints)\n\t\tself.pose_pior = PosePior(num_joints)\n\t\tself.view_point = ViewPoint()\n\n\tdef forward(self, x):\n\t\timg = x['img']\n\t\thand_side = x['hand_side']\n\t\theatmap = self.pose_net(img)\n\t\t# print (\"heatmap\", heatmap[-1].shape, heatmap[-1].sum())\n\t\tpose_can = self.pose_pior(heatmap[-1], hand_side)\n\t\trotate_mat = self.view_point(heatmap[-1], hand_side)\n\t\t# print(pose_can)\n\t\t# print(rotate_mat)\n\t\tout = torch.matmul(pose_can, rotate_mat)\n\t\t# print (torch.matmul(pose_can[0], rotate_mat[0]))\n\t\treturn {'pose3d' : out, 'heatmap': heatmap}","repo_name":"Kuzphi/UniversalHand3dFrame","sub_path":"src/model/networks/ICCV17.py","file_name":"ICCV17.py","file_ext":"py","file_size_in_byte":7547,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"19329563179","text":"# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore\n\nqt_resource_data = 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= [int(v) for v in QtCore.qVersion().split('.')]\nif qt_version < [5, 8, 0]:\n rcc_version = 1\n qt_resource_struct = qt_resource_struct_v1\nelse:\n rcc_version = 2\n qt_resource_struct = qt_resource_struct_v2\n\ndef qInitResources():\n QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n","repo_name":"LeoJiaGeng/JiaGengXiong-Python-Files","sub_path":"Excel/Demo/ico_rc.py","file_name":"ico_rc.py","file_ext":"py","file_size_in_byte":369313,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29238670451","text":"import csv\nimport numpy as np\nimport os\nimport subprocess as sb\nimport ROOT\nimport collections\n\noptions = {\n\t2 : 0, # 0.8 pF\n\t3 : 1, # 1.2 pF\n\t4 : 2, # 1.6 pF\n\t5 : 3, # 0.8 & 1.2 pF\n\t15 : 4, # 6.0 pF\n\t20 : 5 # 0.8 & 1.2 & 6.0 pF\n}\n\ndirectory='/eos/project-s/siw-ecal/TB2022-03/beamData/ascii/'\n\n\nwith open('tmp1.csv', 'w') as outfile1:\n\n\twriter_tmp = csv.writer(outfile1, delimiter=\",\")\n\n\tfor runname in os.listdir(directory):\n\n\t\trun_setting = os.path.join(directory, runname, \"Run_Settings.txt\")\n\n\t\tinputs = []\n\t\tFCs=[]\n\n\t\tif os.path.isfile(run_setting):\n\n\t\t\treader = csv.reader(open(run_setting,\"r\"), delimiter=\" \")\n\n\t\t\tfor i, line in enumerate(reader):\n\n\t\t\t\tif 'FeedbackCap:' in line:\n\t\t\t\t\tflg = line.index('FeedbackCap:')\n\t\t\t\t\tFCs.append(int(line[flg+1]))\n\n\t\t\tvar = set(FCs)\n\t\t\tadd = 0\n\t\t\tfor i in var: add += i\n\t\t\t# print(add)\n\t\t\t# print(options[add])\n\n\t\t\trunnameID = runname.split(\"_\")\n\t\t\trunID = runnameID[-1]\n\t\t\t# print(runID)\n\n\t\t\tinputs.append(runID)\n\t\t\tinputs.append(runname)\n\t\t\tinputs.append(options[add])\n\n\t\t\twriter_tmp.writerow(inputs)\n\n\n\n\n\n\n'''\nreader = csv.reader(open(\"/afs/cern.ch/work/y/yuokugaw/public/run_list/run_list.csv\"), delimiter=\",\")\n\nwith open('tmp1.csv', 'w') as outfile1:\n\n\twriter_tmp = csv.writer(outfile1, delimiter=\",\")\n\n\tfor i, line in enumerate(reader):\n\n\t\tfilename = line[0]\n\t\tdate = line[1]\n\t\ttime = line[2]\n\n\t\tfilenameSP = filename.split(\"_\")\n\t\trunID = filenameSP[-1]\n\n\t\t# print(runID,filename,date,time)\n\n\t\tinput_string = []\n\t\tinput_string.append(runID)\n\t\tinput_string.append(filename)\n\t\tinput_string.append(date)\n\t\tinput_string.append(time)\n\t\twriter_tmp.writerow(input_string)\n'''","repo_name":"yuichiok/CaliceAnalysis","sub_path":"tools/run_list/lrun.py","file_name":"lrun.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"27782358422","text":"import pandas as pd\nimport ta\n\nbars_data = '/Users/bartziobrowski/Documents/research_and_development/Source/market_data_collector/data/get_bars/get_bars-AAPL-2023-04-04_12-00-00-2023-04-04_12-59-59.csv'\n\ndf = pd.read_csv(bars_data)\ndf = ta.utils.dropna(df)\nbars_columns = \"open\", \"high\", \"low\", \"close\",\"volume\"\ndf = ta.add_all_ta_features(\n df, bars_columns[0], bars_columns[1], bars_columns[2], bars_columns[3], bars_columns[4], fillna=True\n)\n\nprint(df.columns)\nprint(len(df.columns))\ndf.drop(columns=bars_columns, inplace=True)\n\n","repo_name":"ziober40/alpaca_historical_data","sub_path":"ta_calc.py","file_name":"ta_calc.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70343349853","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.chrome.options import Options\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\nstart = time.time()\noptions = Options()\noptions.add_argument(\"user-data-dir=C:\\\\Users\\\\Username\\\\AppData\\\\Local\\\\Google\\\\Chrome\\\\User Data\")\ndriver = webdriver.Chrome(options=options)\n\n\ndef split_intros(links):\n initialScroll = 0\n finalScroll = 1000\n intros = [] # we can use them to get the user experience and \n names = [] # user names\n locations = [] # user locations\n\n for i in links:\n print(f'url number --> {i}')\n driver.get(i)\n time.sleep(10)\n while True:\n driver.execute_script(f\"window.scrollTo({initialScroll}, {finalScroll})\")\n initialScroll = finalScroll\n finalScroll += 1000\n time.sleep(3)\n end = time.time()\n if round(end - start) > 20:\n break\n src = driver.page_source\n soup = BeautifulSoup(src, 'lxml')\n time.sleep(5)\n try:\n intro1 = soup.find_all('section', attrs={'class': 'artdeco-card ember-view relative break-words pb3 mt2'})\n intros.append(intro1)\n time.sleep(5) \n location_loc = soup.find(\"span\", {'class': 'text-body-small inline t-black--light break-words'}).get_text()\n locations.append(location_loc.strip('\\n, \" \"'))\n\n name = soup.find(\"h1\", {'class': 'text-heading-xlarge inline t-24 v-align-middle break-words'}).get_text()\n names.append(name)\n except Exception as e:\n print(e)\n pass\n df_intros = pd.DataFrame({\n \"intros\":intros, \"names\":names, \"locations\":locations\n })\n df_intros.to_csv(\"intros.csv\")\n driver.close()\n\n return intros, names, locations\n ","repo_name":"Ismatulla/linkedin-Data","sub_path":"linkedin_data_Scraping/new_splitIntros.py","file_name":"new_splitIntros.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"38251050183","text":"import wx, wx.grid\nfrom gdata import dat\nimport competitor\n\nfrom resource import resource_path\n\nclass CompetitorPanel(wx.Panel):\n \"\"\"This Panel holds the competitor grid display and an 'OK' button\"\"\"\n \n def __init__(self, parent, *args, **kwargs):\n wx.Panel.__init__(self, parent, *args, **kwargs)\n self.parent = parent \n \n OKBtn = wx.Button(self, label=\"Finish\")\n OKBtn.Bind(wx.EVT_BUTTON, self.SetListAndClose )\n\n OKAndInitializeBtn = wx.Button(self, label=\"Accept and Initialize\")\n OKAndInitializeBtn.Bind(wx.EVT_BUTTON, self.SetListAndInitializeTree)\n # Create a wxGrid object\n grid = wx.grid.Grid(self, -1)\n self.grid = grid\n \n # Then we call CreateGrid to set the dimensions of the grid\n grid.CreateGrid(16, 2)\n\n # We can set the sizes of individual rows and columns\n # in pixels\n grid.SetColSize(0, 220)\n grid.SetColSize(1, 220)\n \n # And set grid cell contents as strings\n grid.SetColLabelValue(0, 'Student Name')\n grid.SetColLabelValue(1, 'School')\n\n if dat.competitorList is not None:\n for i,comp in enumerate(dat.competitorList):\n grid.SetCellValue(i, 0, comp.name)\n grid.SetCellValue(i, 1, comp.school)\n \n Sizer = wx.BoxSizer(wx.VERTICAL)\n Sizer.Add(OKBtn, 0, wx.EXPAND|wx.ALL, 5)\n Sizer.Add(OKAndInitializeBtn, 0, wx.EXPAND|wx.ALL, 5)\n Sizer.Add(grid, 0, wx.EXPAND|wx.ALL, 5)\n\n self.SetSizerAndFit(Sizer)\n\n def SetList(self):\n # Convert the grid to a list of competitors\n z = [competitor.Competitor(\n self.grid.GetCellValue(i, 0),\n self.grid.GetCellValue(i, 1))\n for i in range(16)]\n dat.competitorList = z\n \n def SetListAndClose(self, event=None):\n self.SetList()\n dat.setContestState()\n self.parent.Close()\n \n def SetListAndInitializeTree(self, event=None):\n self.SetList()\n dat.setContestState(shuffle=True)\n self.parent.Close()\n \nclass CompetitorFrame(wx.Frame):\n \n def __init__(self, *args, **kwargs):\n \"\"\"Create the DemoFrame.\"\"\"\n wx.Frame.__init__(self, *args, **kwargs)\n\n icon = wx.Icon()\n icon.CopyFromBitmap(wx.Bitmap(\n resource_path('resources/main_logo1.ico'), wx.BITMAP_TYPE_ANY))\n self.SetIcon(icon)\n\n # Add the Widget Panel\n self.Panel = CompetitorPanel(self)\n\n self.Fit()\n\n def OnQuit(self, event=None):\n \"\"\"Exit application.\"\"\"\n self.Close()\n","repo_name":"parkhays/MCBuzzer","sub_path":"Source/competitorGrid.py","file_name":"competitorGrid.py","file_ext":"py","file_size_in_byte":2633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"9538985392","text":"from unittest.mock import MagicMock\nimport pytest\n\nfrom dao.model.movie import Movie\nfrom dao.movie import MovieDAO\nfrom service.movie import MovieService\n\n\n@pytest.fixture()\ndef movie_dao():\n movie_dao = MovieDAO(None)\n\n movie_one = Movie(id=1, title='Самый', description='Лучший', trailer='Фильм', year=2005, rating=5.2,\n genre_id=1,\n director_id=1)\n movie_two = Movie(id=1, title='Это', description='Плохой', trailer='Сериал', year=2007, rating=6.4,\n genre_id=2,\n director_id=2)\n movie_three = Movie(id=3, title='Фильм', description='Получил', trailer='Оскар', year=2013, rating=7.3,\n genre_id=3,\n director_id=3)\n dict_objects = {1: movie_one, 2: movie_two, 3: movie_three}\n movie_dao.get_one = MagicMock(side_effect=dict_objects.get)\n movie_dao.get_all = MagicMock(return_value=[movie_one, movie_two, movie_three])\n movie_dao.create = MagicMock(return_value=Movie(id=1))\n movie_dao.delete = MagicMock()\n movie_dao.update = MagicMock()\n movie_dao.partially_update = MagicMock()\n\n return movie_dao\n\n\nclass TestMovieService:\n @pytest.fixture(autouse=True)\n def movie_service(self, movie_dao):\n self.movie_service = MovieService(dao=movie_dao)\n\n def test_get_one(self):\n movie = self.movie_service.get_one(1)\n assert movie is not None\n assert movie.id is not None\n\n def test_get_all(self):\n movies = self.movie_service.get_all()\n assert len(movies) > 0\n\n def test_create(self):\n movie_data ={\n \"title\": \"New_film\",\n \"description\": \"new_description\",\n \"trailer\": \"in far far galaxy\",\n \"year\": 2026,\n \"rating\": 11\n }\n movie = self.movie_service.create(movie_data)\n assert movie.id is not None\n\n def test_delete(self):\n self.movie_service.delete(1)\n\n def test_update(self):\n movie_data = {\n \"id\": 1,\n \"title\": \"Film_update\",\n \"description\": \"desc\",\n \"trailer\": \"...\",\n \"year\": 2025,\n \"rating\": 11\n }\n self.movie_service.update(movie_data)\n\n def test_partially_update(self):\n movie_data = {\n \"id\": 3,\n \"title\": \"Movie_update\"\n }\n self.movie_service.partially_update(movie_data)\n\n def test_get_one_out(self):\n movie = self.movie_service.get_one(100)\n assert movie is None\n\n\n","repo_name":"Poplotnee/home_work_20_tests","sub_path":"tests/service/test_movie.py","file_name":"test_movie.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"71606711452","text":"import torch\nimport math\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\nfrom dataset import NerfDataset\nfrom model import NeRF, DebugNeRF\nfrom render import expected_colour\n\n\ndef compare_output(model: torch.nn.Module, dataset: NerfDataset, near: float, far: float, view_idx: int = 0, device: str = 'mps'):\n # Compare output to g.t. on specified view\n v = dataset.view_examples[view_idx]\n\n o, d = torch.tensor(v.o), torch.tensor(v.d)\n o, d = o.to(device), d.to(device)\n\n batch_size = 4096\n batchify = lambda x: list(torch.split(x, batch_size, 0))\n\n o_batches, d_batches = batchify(o), batchify(d)\n\n model_outputs = []\n pbar = tqdm(total=len(o_batches))\n while len(o_batches) > 0:\n o_k, d_k = o_batches[0], d_batches[0]\n with torch.no_grad():\n model_outputs += [\n expected_colour(N=100, nerf=model, o=o_k, d=d_k, t_n=near, t_f=far, device=device).to('cpu')\n ]\n o_batches.pop(0), d_batches.pop(0)\n pbar.update(1)\n pbar.close()\n\n res = int(math.sqrt(dataset.get_pixels_per_image()))\n model_output = torch.concat(model_outputs)\n generated_image = model_output.view(res, res, 3)\n\n fig = plt.figure()\n ax_render = fig.add_subplot(121)\n ax_im = fig.add_subplot(122)\n\n ax_render.imshow(generated_image)\n ax_im.imshow(v.im.reshape(res, res, 3))\n\n ax_render.set_title('NeRF')\n ax_im.set_title('Ground Truth')\n\n return fig, (ax_render, ax_im)\n\n\nif __name__ == '__main__':\n model = NeRF()\n checkpoint = torch.load('model.pt')\n model.load_state_dict(checkpoint['model_state_dict'])\n model.eval()\n model.to('mps')\n\n #model = DebugNeRF('center_balls')\n\n dataset = NerfDataset('chair', 'train')\n\n fig, ax = compare_output(model, dataset, device='cpu')\n plt.show()\n\n\n","repo_name":"harveynw/nerf-from-scratch","sub_path":"run_model.py","file_name":"run_model.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"25488700343","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Source: https://leetcode.com/problems/balance-a-binary-search-tree/\n# Author: Miao Zhang\n# Date: 2021-04-28\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def balanceBST(self, root: TreeNode) -> TreeNode:\n self.res = []\n def inorder(root):\n if not root: return\n inorder(root.left)\n self.res.append(root.val)\n inorder(root.right)\n def build(nums):\n if not nums: return None\n idx = len(nums) // 2\n root = TreeNode(nums[idx])\n root.left = build(nums[: idx])\n root.right = build(nums[idx + 1:])\n return root\n inorder(root)\n return build(self.res)\n","repo_name":"MichelleZ/leetcode","sub_path":"algorithms/python/balanceaBinarySearchTree/balanceaBinarySearchTree.py","file_name":"balanceaBinarySearchTree.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"39698353653","text":"#################\n#Start Screen\n#################\ndef startScreenRedrawAll(canvas,data): \n if data.blink == False:\n canvas.create_text(390, 60, text = \"WELCOME TO MAHJONG\", font = \"Monaco 50\")\n canvas.create_text(390, 160, text = \"Click to play with a certain mode!\", font = \"Chalkboard 25\") \n canvas.create_rectangle(325,275, 475, 325, fill = \"white\", width = 5 )\n canvas.create_rectangle(325, 350, 475, 400, fill = \"white\", width = 5 )\n canvas.create_text(400,300, text = \"MULTIPLAYER\", font = \"papyrus 15 bold\")\n canvas.create_text(400,375, text = \"AI\", font = \"papyrus 15 bold\")\n canvas.create_text(400, 200, text = \"Press H for help!\", font = \"Chalkboard 20\")\n if data.chicken == True: \n canvas.create_image(100,100, image = data.realicon)\n \ndef startScreenTimerFired(data):\n pass\ndef startScreenMousePressed(event,data):\n if 325<event.x<475 and 275<event.y<325: \n data.mode = \"Player One Game\"\n data.playingGame = True \n data.chicken = False \n data.gametype = \"Multiplayer\" \n elif 325<event.x<475 and 350<event.y<400: \n data.mode = \"AI Player One Game\"\n data.playerGame = True \n data.chicken = False \n data.gametype = \"AI\" \n\ndef startScreenKeyPressed(event,data):\n if event.keysym == \"h\":\n data.mode = \"Help\" \n \n \n \n","repo_name":"irisapei/Mahjong","sub_path":"startScreen.py","file_name":"startScreen.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"42548446234","text":"class Solution:\n def fib(self, n: int) -> int:\n \n if n == 0:\n return 0\n elif n == 1:\n return 1\n elif n == 2:\n return 1\n case1 = case2 = 1\n \n\n for i in range(2, n):\n result = case1 + case2\n case1 = case2\n case2 = result\n return result\n\n\ns = Solution()\nprint(s.fib(4))\n","repo_name":"bipsec/Leet-Code","sub_path":"509. Fibonacci Number.py","file_name":"509. Fibonacci Number.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"6376336345","text":"#Exercicio 4:\r\n#Leia dois numeros e mostre a soma entre eles\r\n\r\n\r\n# --------- LENDO A ENTRADA DO USUARIO ---------\r\n\r\nx = int(input(\"Digite um numero :\"))\r\ny = int(input(\"Digite outro numero :\"))\r\n\r\n\r\n# --------- COMUNICANDO O RESULTADO AO USUARIO ---------\r\n\r\nprint(\"a soma dos numeros é:\", x + y)","repo_name":"mateusflns/Edutech-Atividades","sub_path":"30 de junho/atv4.py","file_name":"atv4.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"7964103457","text":"from tkinter import *\nfrom tkinter import ttk\nfrom PIL import ImageTk, Image\nimport requests\nimport json\nimport time\n\nc1 = \"white\"\nc2 = \"black\"\n\nwindow = Tk()\nwindow.title('')\nwindow.geometry('320x150')\nwindow.configure(bg=c1)\n\n\ndef btc():\n api = \"https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=BTC,USD\"\n res = requests.get(api)\n data = res.json()\n\n usd_price = float(data[\"USD\"])\n usd_formatted_price = \"${:,.3f}\".format(usd_price)\n usd[\"text\"] = usd_formatted_price\n body.after(1000, btc)\n\n\nhead_frame = Frame(window, width=320, height=50, bg=c1)\nhead_frame.grid(row=1, column=0)\n\nbody = Frame(window, width=320, height=300, bg=\"grey\")\nbody.grid(row=2, column=0)\n\nimg1 = Image.open('images/bit.png')\nimg1 = img1.resize((30, 30))\nimg1 = ImageTk.PhotoImage(img1)\n\nicon1 = Label(head_frame, padx=0, image=img1, bg=c1)\nicon1.place(x=10, y=10)\n\ntitle = Label(head_frame, padx=0, text=\"Bitcoin Price\", bg=c1, fg=c2, width=16, height=1, anchor=\"w\",\n font='Poppins 20')\ntitle.place(x=50, y=11)\n\nusd = Label(body, bg=\"grey\", pady=0, fg=c1, text=\"$223\", width=14, height=1, anchor=\"w\", font='Poppins 30')\nusd.place(x=0, y=30)\n\nbtc()\n\nwindow.mainloop()\n","repo_name":"A1Codes/BTC-Price-Tracker","sub_path":"BTC.py","file_name":"BTC.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"6884205647","text":"from sregistry.logger import bot\nfrom sregistry.utils import read_json, get_installdir\nimport os\n\n\ndef get_build_template(name=\"singularity-cloudbuild-local.json\"):\n \"\"\"get default build template.\n\n Parameters\n ==========\n name: singularity-cloudbuild-local.json (default) that will build a\n container interactively, waiting for the build to finish.\n singularity-cloudbuild-git.json build a recipe from a GitHub\n repository.\n \"\"\"\n base = get_installdir()\n name = \"%s/main/templates/build/%s\" % (base, name)\n\n if os.path.exists(name):\n bot.debug(\"Found template %s\" % name)\n return read_json(name)\n\n bot.warning(\"Template %s not found.\" % name)\n","repo_name":"singularityhub/sregistry-cli","sub_path":"sregistry/main/google_build/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"32"} +{"seq_id":"9688734999","text":"import json\n\nentrada = open(\"Entrada2.json\", \"r\", encoding=\"utf-8\")\n\ncabecera = entrada.readline().strip().split(',')\n\nprint(\"CABECERA\", cabecera)\n\nestadisticas_equipo = []\n\nfor line in entrada:\n\testadisticas_jugador = {}\n\tjugador = line.strip().split(',')\n\tfor valor in zip(cabecera, jugador):\n\t\testadisticas_jugador[valor[0]] = valor[1]\n\testadisticas_equipo.append(estadisticas_jugador)\n\n\tprint(len(jugador))\n\nentrada.close()\n\nprint(json.dumps(estadisticas_equipo, indent=2, ensure_ascii=False))","repo_name":"jorgeduarte22/basketResultsApp","sub_path":"src/assets/scripts/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"30212590514","text":"#importing the imp library that we have to use\nfrom flask import Flask, flash,render_template, request,flash\nimport sqlite3 as sql\n# sqlite libray use to connect and make all the action on the sqlite DB\n\napp = Flask(__name__)\n#create the object for the flask app\n\n@app.route('/',methods = ['POST', 'GET'])\n#create route to the host and route can handle two method GET and POST\ndef index():\n if request.method == 'POST':\n #condition is Post reaquest the come inside and insert into db\n try:\n #exception handling\n firstname = request.form['fname']\n lastname = request.form['lname']\n # firstname and lastname comming from the form\n with sql.connect(\"database.db\") as con:\n #open the connection\n cur = con.cursor()\n #initialize the connection\n cur.execute(\"INSERT INTO Users (first, last) VALUES (?,?)\",(firstname,lastname))\n #query to insert into users table\n msg = \"Record successfully added\"\n #message that appear as alert in the frontend\n con.commit()\n #commit the data into db\n except:\n #exception occurs and error apper then come to this block\n con.rollback()\n #get rollback to the \n msg = \"error in insert operation\"\n finally:\n #always run this block\n con.close()\n #close the connection\n return render_template(\"index.html\", msg=msg)\n #server template to the frontend\n return render_template(\"index.html\")\n #return the template when get request come\n\n\nif __name__ == '__main__':\n #flask app constructor\n app.run()\n #main a call for flask app","repo_name":"mapplecode/ROB-CASE2","sub_path":"Using Sqlite DB/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"22942378444","text":"def readFile(txtFile):\n f = open(txtFile, 'r')\n outputString = f.readline().rstrip('\\n')\n f.close()\n return outputString\n\n\ndef createDictionary(strSequence):\n #instantiate new empty dictionary to populate, should only have 4 key-value pairs, with each key representing the nucleotide, and its value representing its frequency in the input string\n ntDict = {}\n \n #loop through each index of input string (each index = a single nucleotide) and if character already in dictionary, increment its value (which serves as a counter) by one; otherwise add it to the dictionary and initialize its value at 1\n for i in range(len(strSequence)):\n nt = strSequence[i]\n if nt in ntDict:\n ntDict[nt]+=1\n else:\n ntDict[nt]=1\n print(ntDict)\n\n\ncreateDictionary(readFile('/Users/ramibenchouia/Documents/CODE/BIOINFORMATICS/ROSALINDA/Bioinformatics Stronghold/Counting DNA Nucleotides/rosalind_dna.txt'))\n\n\n","repo_name":"RamiBenchouia1/rosalind_bioinformatics-stronghold","sub_path":"Counting DNA Nucleotides/nucleotideFrequency.py","file_name":"nucleotideFrequency.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"7642242355","text":"'''\r\nGlissirovanie ploskokilevatoj plastini po volne bez smachivanija skul\r\n'''\r\nimport kinematic\r\nimport kinematic_wet\r\nimport kinematic\r\nimport graphics\r\nimport wave\r\nimport numpy\r\n\r\ndef test_poplavok():\r\n p = kinematic_wet.plate_wet()\r\n depth = [(0.48, 0.0), (4.09, 0.0), (4.8, 0.2737), (5.66, 0.290),\r\n (5.67, 0.0), (9.04, 0.0), (9.05, 0.028), (9.55, 0.048),\r\n (9.91, 0.009), (9.92, 0.0), (10.7, 0.0)]\r\n deadrise = [(0.48, 35 * numpy.pi / 180), (5.66, 35 * numpy.pi / 180),\r\n (9.04, 26 * numpy.pi / 180), (10.7, 26 * numpy.pi / 180)]\r\n cmx = 5.747\r\n depth1 = []\r\n deadrise1 = []\r\n for i in range(len(depth)):\r\n depth1.append((depth[i][0] - cmx, depth[i][1]))\r\n for i in range(len(deadrise)):\r\n deadrise1.append((deadrise[i][0] - cmx, deadrise[i][1]))\r\n p.set_data(depth1, deadrise1)\r\n ang = 9.37\r\n ang_rad = ang * numpy.pi / 180.0\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hs = {Y_hsn}, M_hs={M_hsn}\")\r\n Y_hdn, M_hdn, Mtr = p.YM_hydrodynamic_numerical(ang_rad, 998.2, 21.4)\r\n print(f\"at {ang} degrees Y_hd = {Y_hdn}, M_hd={M_hdn}, M_tr={Mtr}\")\r\n print(f\"at {ang} degrees Y = {Y_hsn + Y_hdn}, M={M_hsn + M_hdn + Mtr}\")\r\n\r\ndef test_plate_wet():\r\n p = kinematic_wet.plate_wet()\r\n depth = [(-0.45, 0.13669), (0.4097, 0.00164)]\r\n deadrise = [(-0.45, 30 * numpy.pi / 180), (0.4097, 30 * numpy.pi / 180)]\r\n p.set_data(depth, deadrise)\r\n ang = 9.0\r\n ang_rad = ang * numpy.pi / 180.0\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hs = {Y_hsn}, M_hs={M_hsn}\")\r\n Y_hdn, M_hdn, Mtr = p.YM_hydrodynamic_numerical(ang_rad, 998.2, 8.0)\r\n print(f\"at {ang} degrees Y_hd = {Y_hdn}, M_hd={M_hdn}\")\r\n\r\n\r\ndef test_plate():\r\n p = kinematic.plate()\r\n p.set_data([0.0, 0.15], [0.745, 0.4], 5.0)\r\n p.set_deadrise_angle(30.0 * numpy.pi / 180)\r\n p1, p2, cm = p.kil_line_straight()\r\n dy = 2 * (cm[1] - p1[1])\r\n '''\r\n traces = [[[p1[0], p2[0], p2[0], p1[0], p1[0]],\r\n [p1[1], p2[1], p2[1] + dy, p1[1] + dy, p1[1]]]]\r\n lbl = ['straight']\r\n '''\r\n traces = []\r\n lbl = []\r\n ph = [9 * i for i in range(0, 10)]\r\n phi = [phi_i * numpy.pi / 180.0 for phi_i in ph ]\r\n '''\r\n for i in range(len(phi)):\r\n p1, p2, cm = p.kil_line_rotate(phi[i])\r\n rotate = [[p1[0], p2[0], cm[0], p1[0]], [p1[1], p2[1], cm[1], p1[1]]]\r\n traces.append(rotate)\r\n lbl.append('rotate' + str(ph[i]))\r\n #graphics.plot_data(traces, 'x', lbl)\r\n '''\r\n ang = phi[1] / numpy.pi * 180.0\r\n ang_rad = phi[1]\r\n l = p.moisten_length(ang_rad)\r\n print(f\"l at {ang} degrees = {l}\")\r\n h1, h2 = p.front_back_h_ksi(ang_rad)\r\n print(f\"at {ang} degrees h1 = {h1}, h2={h2}\")\r\n Y_hs, M_hs = p.YM_hydrostatic(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hs = {Y_hs}, M_hs={M_hs}\")\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hsn = {Y_hsn}, M_hsn={M_hsn}\")\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length_move(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hsn_move = {Y_hsn}, M_hsn_move={M_hsn}\")\r\n\r\n\r\n Y_hd, M_hd, M_tr = p.YM_hydrodynamic(ang_rad, 998.2, 8.0)\r\n print(f\"at {ang} degrees Y_hd = {Y_hd}, M_hd = {M_hd}, M_tr = {M_tr}\")\r\n Y_hdn, M_hdn, M_trn = p.YM_hydrodynamic_numerical_full_length(ang_rad, 998.2, 8.0)\r\n print(f\"at {ang} degrees Y_hdn = {Y_hdn}, M_hdn = {M_hdn}, M_trn = {M_trn}\")\r\n print (\"Summary:\")\r\n print(f\"at {ang} degrees Y = {Y_hsn + Y_hdn}, M={M_hsn + M_hdn + M_trn}\")\r\n\r\n print(\"Wave\")\r\n w = wave.wave()\r\n w.set_data(0.0, 10.0, L_=5.0-0.745)\r\n time = 0.0\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length(ang_rad, 998.2, w, time)\r\n Y_hdn, M_hdn, M_trn = p.YM_hydrodynamic_numerical_full_length(ang_rad, 998.2, 8.0, w, time)\r\n print (\"Summary wave:\")\r\n print(f\"at {ang} degrees Y = {Y_hsn + Y_hdn}, M={M_hsn + M_hdn + M_trn}\")\r\n\r\n p1, p2, p3, p4, _ = p.profil_rotate(ang_rad)\r\n rotate = [[p1[0], p2[0], p3[0], p4[0], p1[0]],\r\n [p1[1], p2[1], p3[1], p4[1], p1[1]]]\r\n traces.append(rotate)\r\n lbl.append('rotate' + str(ang))\r\n test_wave(w, traces, lbl, -0.745, 5, time)\r\n graphics.plot_data(traces, 'x', lbl)\r\n\r\n\r\ndef test_run_wave():\r\n p = kinematic.plate()\r\n p.set_data([0.0, 0.089], [5.45, 0.15], 10.0)\r\n p.set_deadrise_angle(30.0 * numpy.pi / 180)\r\n ph = [9 * i for i in range(0, 10)]\r\n phi = [phi_i * numpy.pi / 180.0 for phi_i in ph ]\r\n ang = phi[1] / numpy.pi * 180.0\r\n ang_rad = phi[1]\r\n w = wave.wave()\r\n w.set_data(0.5, 10.0, L_=0)\r\n T, _ = w.get_T_c()\r\n t = numpy.linspace(0, T, 20)\r\n Y = []\r\n M = []\r\n for time in t:\r\n yt, mt = p.YM_total(ang_rad, 998.2, 8.0, w, time)\r\n Y.append(yt)\r\n M.append(mt)\r\n traces = [[t, Y]]\r\n graphics.plot_data(traces, 't', [['fy']], 1)\r\n traces = [[t, M]]\r\n graphics.plot_data(traces, 't', [['mz']], 1)\r\n\r\n\r\ndef solve():\r\n p = kinematic.plate()\r\n p.set_data([0.0, 0.15], [0.745, 0.4], 5.0)\r\n p.set_deadrise_angle(30.0 * numpy.pi / 180)\r\n p1, p2, cm = p.kil_line_straight()\r\n dy = 2 * (cm[1] - p1[1])\r\n traces = []\r\n lbl = []\r\n #traces = [[[p1[0], p2[0], p2[0], p1[0], p1[0]],\r\n # [p1[1], p2[1], p2[1] + dy, p1[1] + dy, p1[1]]]]\r\n #lbl = ['straight']\r\n ph = [9 * i for i in range(0, 10)]\r\n phi = [phi_i * numpy.pi / 180.0 for phi_i in ph ]\r\n ang = phi[1] / numpy.pi * 180.0\r\n ang_rad = phi[1]\r\n Y_hs, M_hs = p.YM_hydrostatic(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hs = {Y_hs}, M_hs={M_hs}\")\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hsn = {Y_hsn}, M_hsn={M_hsn}\")\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length_move(ang_rad, 998.2)\r\n print(f\"at {ang} degrees Y_hsn_move = {Y_hsn}, M_hsn_move={M_hsn}\")\r\n\r\n\r\n Y_hd, M_hd, M_tr = p.YM_hydrodynamic(ang_rad, 998.2, 8.0)\r\n print(f\"at {ang} degrees Y_hd = {Y_hd}, M_hd = {M_hd}, M_tr = {M_tr}\")\r\n Y_hdn, M_hdn, M_trn = p.YM_hydrodynamic_numerical_full_length(ang_rad, 998.2, 8.0)\r\n print(f\"at {ang} degrees Y_hdn = {Y_hdn}, M_hdn = {M_hdn}, M_trn = {M_trn}\")\r\n Y_hdn, M_hdn, M_trn = p.YM_hydrodynamic_numerical_full_length_move(ang_rad, 998.2, 8.0)\r\n print(f\"at {ang} degrees Y_hdn_move = {Y_hdn}, M_hdn_move = {M_hdn}, M_trn_move = {M_trn}\")\r\n print (\"Summary:\")\r\n print(f\"at {ang} degrees Y = {Y_hsn + Y_hdn}, M={M_hsn + M_hdn + M_trn}\")\r\n m = (Y_hsn + Y_hdn) / 9.81\r\n p1, p2, p3, p4, _ = p.profil_rotate(ang_rad)\r\n rotate = [[p1[0], p2[0], p3[0], p4[0], p1[0]],\r\n [p1[1], p2[1], p3[1], p4[1], p1[1]]]\r\n traces.append(rotate)\r\n lbl.append('rotate' + str(ang))\r\n\r\n w = wave.wave()\r\n xl = 5-0.745\r\n w.set_data(0.0, 3.0, L_=xl)\r\n time = 0.0\r\n test_wave(w, traces, lbl, -0.745, 5, time)\r\n\r\n print(\"Wave\")\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length(ang_rad, 998.2, w, time)\r\n Y_hdn, M_hdn, M_trn = p.YM_hydrodynamic_numerical_full_length(ang_rad, 998.2, 8.0, w, time)\r\n print (\"Summary wave:\")\r\n print(f\"at {ang} degrees Y = {Y_hsn + Y_hdn}, M={M_hsn + M_hdn + M_trn}\")\r\n\r\n Y_hsn, M_hsn = p.YM_hydrostatic_numerical_full_length_move(ang_rad, 998.2, 0.15, w, time)\r\n Y_hdn, M_hdn, M_trn = p.YM_hydrodynamic_numerical_full_length_move(ang_rad, 998.2, 8.0, 0.15, w, time)\r\n print (\"Summary wave move:\")\r\n print(f\"at {ang} degrees Y = {Y_hsn + Y_hdn}, M={M_hsn + M_hdn + M_trn}\")\r\n\r\n\r\n graphics.plot_data(traces, 'x', lbl)\r\n #t, sol = p.swimmingy(0.15, 0, ang_rad, 0, m,1000.0, 998.2, 8.0, w)\r\n #p.postprocessingy(t, sol, ang_rad, m, 1000.0, 998.2, 8.0, w)\r\n #t, sol = p.swimmingf(0.15, 0, 0, 0, m, 1000.0, 998.2, 8.0, w)\r\n #p.postprocessingf(t, sol, 0.15, m, 1000.0, 998.2, 8.0, w)\r\n t, sol = p.swimming(0.17, 0.0, 0.9*ang_rad, 0.0, m, 10000.0, 998.2, 8.0, w)\r\n p.postprocessing(t, sol, m, 10000.0, 998.2, 8.0, w)\r\n\r\ndef test_wave(w, traces, lbl, ksi0, L, time):\r\n g = 9.81\r\n T, c = w.get_T_c()\r\n #L-ksi0 - front side of the ship\r\n #yv(x, t) = h/2 * cos(2pi / lamda*(x + c * t))\r\n #yv(L + ksi0, t0) = h/2:\r\n #cos(2pi / lamda* (L + ksi0 + c * t0)) = 1\r\n #2pi / lamda * (L + ksi0 + c * t0) = 2pi * k\r\n #L + ksi0 + c * t0 = lamda * k\r\n #t0 = -(L + ksi0) / c\r\n t = numpy.linspace(0, T, 10)\r\n print(f\"T={T}\")\r\n x = numpy.linspace(ksi0, L + ksi0, 201)\r\n hh = w.y_wave(x, time)\r\n vv = w.d_y_wave_dt(x, time)\r\n aa = w.d2_y_wave_dt2(x, time)\r\n traces.append([x, hh])\r\n #traces.append([x, vv])\r\n #traces.append([x, aa])\r\n lbl.append(f\"wave_{time}\")\r\n #lbl.append(f\"vv_{time}\")\r\n #lbl.append(f\"aa_{time}\")\r\n\r\n\r\ndef calc_wave():\r\n w = wave.wave()\r\n w.set_data(0.5, 10.0, x_0=0, L_=0)\r\n time = 1.0\r\n xl = 5\r\n traces = []\r\n lbl = []\r\n test_wave(w, traces, lbl, -0.745, xl, time)\r\n graphics.plot_data(traces, 'x', lbl)\r\n\r\n\r\n#test_poplavok()\r\n#test_plate()\r\n#test_plate_wet()\r\n#test_wave()\r\n#test_run_wave()\r\nsolve()\r\n#calc_wave()\r\n","repo_name":"AzimuthSouth/Planning","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"2348144820","text":"import io\nimport os\nimport sys\nimport copy\nimport json\nimport time\nimport requests\nimport numpy as np\nimport pandas as pd\nfrom io import StringIO\nfrom astropy.io import fits,ascii\nfrom astropy import wcs\nfrom astropy.time import Time\n\n\"\"\"\nThis script contains several functions useful for\nretrieving a variety of ZTF data products such as:\n - Reference images (from IPAC)\n - Science images (from IPAC)\n - Light Curves (from IPAC)\n - Transient Alert Packets (from MARS/LCO)\n\nAuthor: Zach Vanderbosch\n\"\"\"\n\n\n# Function to retrieve URLs for reference image cutouts\n# centered on a given RA and DEC\ndef get_ref_urls(ra,dec,size=45):\n \"\"\"\n ra = Right Ascension in Decimal Degrees\n dec = Declination in Decimal Degrees\n size = Image size in arcseonds\n\n Returns:\n urls = list of URLs used to download image data\n \"\"\"\n \n # First get some info related to the reference image\n search_url = 'https://irsa.ipac.caltech.edu/ibe/search/ztf/products/ref'\n pos_url = search_url + '?POS={:.3f},{:.3f}'.format(ra,dec)\n r1 = requests.get(pos_url)\n im_table = ascii.read(r1.text).to_pandas()\n\n # Get image meta data\n urls = []\n maxbit = 33554432 # Quality cut on the INFOBITS parameter\n num_image = len(im_table[im_table.infobits < maxbit]) # Number of images in table\n for im in range(num_image):\n field = str(im_table[im_table.infobits < maxbit].field.iloc[im]).zfill(6)\n filtcode = str(im_table[im_table.infobits < maxbit].filtercode.iloc[im])\n ccdid = str(im_table[im_table.infobits < maxbit].ccdid.iloc[im]).zfill(2)\n qid = str(im_table[im_table.infobits < maxbit].qid.iloc[im])\n\n # Construct the Image Download URL\n data_url = 'https://irsa.ipac.caltech.edu/ibe/data/ztf/products/ref'\n spec_url = '/{}/field{}/{}/ccd{}/q{}/'.format(field[0:3],field,filtcode,ccdid,qid)\n imname = 'ztf_{}_{}_c{}_q{}_refimg.fits'.format(field,filtcode,ccdid,qid)\n condis = '?center={:.5f},{:.5f}&size={}arcsec&gzip=false'.format(ra,dec,size)\n urls.append(data_url + spec_url + imname + condis)\n\n return urls\n\n# Function to retrieve URLs for a science image cutouts\n# centered on a given RA and DEC\ndef get_sci_urls(ra,dec,size=45):\n \"\"\"\n ra = Right Ascension in Decimal Degrees\n dec = Declination in Decimal Degrees\n size = Image size in arcseconds\n\n Returns:\n urls = list of URLs used to download image data\n \"\"\"\n \n # First get some info related to the reference image\n search_url = 'https://irsa.ipac.caltech.edu/ibe/search/ztf/products/sci'\n pos_url = search_url + '?POS={:.3f},{:.3f}'.format(ra,dec)\n r1 = requests.get(pos_url)\n im_table = ascii.read(r1.text).to_pandas()\n\n # Get image meta data\n urls = []\n maxbit = 33554432 # Quality cut on the INFOBITS parameter\n num_image = len(im_table[im_table.infobits < maxbit]) # Number of images in table\n for im in range(num_image):\n field = str(im_table[im_table.infobits < maxbit].field.iloc[im]).zfill(6)\n filtcode = str(im_table[im_table.infobits < maxbit].filtercode.iloc[im])\n ccdid = str(im_table[im_table.infobits < maxbit].ccdid.iloc[im]).zfill(2)\n qid = str(im_table[im_table.infobits < maxbit].qid.iloc[im])\n filefracday = str(im_table[im_table.infobits < maxbit].filefracday.iloc[im])\n imgtypecode = str(im_table[im_table.infobits < maxbit].imgtypecode.iloc[im])\n\n year = filefracday[0:4]\n month = filefracday[4:6]\n day = filefracday[6:8]\n fracday = filefracday[8:]\n\n # Construct the Image Download URL\n data_url = 'https://irsa.ipac.caltech.edu/ibe/data/ztf/products/sci'\n date_url = '/{}/{}/{}/'.format(year,month+day,fracday)\n imname = 'ztf_{}_{}_{}_c{}_{}_q{}_sciimg.fits'.format(filefracday,field,filtcode,ccdid,imgtypecode,qid)\n condis = '?center={:.5f},{:.5f}&size={}arcsec&gzip=false'.format(ra,dec,size)\n urls.append(data_url + date_url + imname + condis)\n\n return urls\n\n\n# Function for actually downloading an image\n# using a URL generated by the get_ref_urls\n# or get_sci_urls functions\ndef download_ztf_image(url):\n \"\"\"\n url = URL used to download image data\n\n Returns:\n image = Image data\n header = Image header\n wcs_solution = Image WCS solution\n \"\"\"\n r = requests.get(url)\n hdu = fits.open(io.BytesIO(r.content))\n image = hdu[0].data\n header = hdu[0].header\n hdu.close()\n wcs_solution = wcs.WCS(header)\n return image,header,wcs_solution\n\n\n# Function which queries IPAC for ZTF light curve\n# data products using the API's cone search tool\ndef lightcurve_query(ra,dec,cone_radius=3.0):\n \"\"\"\n ra = RA coordinate in decimal degrees\n dec = Dec coordinate in decimal degrees\n\n Optional:\n cone_radius = search radius in arcseconds (default = 3)\n\n Returns:\n ztf_data = ZTF light curve data in a Pandas DataFrame\n \"\"\"\n\n # Generate the light curve search URL\n cone_radius /= 3600. # Convert search radius to degrees\n ztf_url = \"https://irsa.ipac.caltech.edu/cgi-bin/ZTF/nph_light_curves?\"\n pos_string = \"POS=CIRCLE {:.5f} {:.5f} {:.5f}&COLLECTION=ztf_dr3&FORMAT=csv\".format(ra,dec,cone_radius)\n ztf_search_url = ztf_url + pos_string\n\n # Perform request and decode response into a Pandas DataFrame\n response = requests.get(ztf_search_url)\n ztf_data = pd.read_csv(StringIO(response.content.decode('utf-8')))\n\n return ztf_data\n\n\n\n\n# Function which queries the LCO MARS alert broker\n# for ZTF transient alert packets using the cone\n# search functionality at given RA,DEC coords\ndef alert_query(RA,DEC,save=False,cone_radius=5.0,sleep_time=60.0,query_limit=1000,rb_limit=0.0):\n \"\"\"\n RA = list/array of RA coordinates in decimal degrees\n DEC = list/array of Dec coordinates in decimal degrees\n\n Optional:\n save = whether to save query results to file in\n JSON format (default = False)\n cone_radius = search radius in arcseconds (default = 5)\n sleep_time = time in seconds between successive MARS\n queries (default = 60)\n query_limit = number of coordinates to search per POST\n request query (default = 1000)\n rb_limit = Lower limit on the alert Real-Bogus score\n (default = 0, i.e. no limit)\n \"\"\"\n\n # Function which retrieves summary statistics\n # for an alert query\n def get_query_info(q):\n \"\"\"\n q = the JSON formatted query results\n\n Returns:\n the total number of queries\n the number of queries with results \n the number of objects with alerts\n the total number of alerts\n \"\"\"\n objs = []\n num_q = 0\n num_a = 0\n for qi in q:\n if qi['num_alerts'] == 0:\n continue\n else:\n num_q += 1\n for qk in qi['results']:\n objs.append(qk['objectId'])\n num_a += 1\n objs_uniq = len(list(set(objs)))\n return len(q),num_q,objs_uniq,num_a\n\n\n # POST Request function formatted for MARS alert queries\n def post_request(q):\n \"\"\"\n q = list of query dicts for each RA-DEC pair\n\n Returns:\n post = The POST request result in JSON format\n \"\"\"\n base_url = 'https://mars.lco.global/'\n payload = {'queries':q}\n qcount = 0\n while True:\n try:\n post = requests.post(base_url,json=payload,timeout=50.0)\n break\n except Exception as e:\n time.sleep(0.2)\n qcount += 1\n print(' Timeout Error {}, querying again.'.format(qcount))\n return post\n\n\n # Perform check on input RA and DEC values\n Nobj = len(RA)\n if Nobj != len(DEC):\n print('ERROR: Different number of RA and DEC coordinates.')\n sys.exit(1)\n print('\\nTotal Number of Queries to Perform: {}'.format(Nobj))\n\n # Setup a request template using LCO MARS Request Filters\n request_defs = { 'cone':'', # Cone search values, in \"ra,dec,radius\" format\n 'rb__gte':'', # Limit on Real-Bogus values\n 'format':'json'} \n\n # Use RA-DEC values to generate a POST payload for the query\n print('Generating query payload......')\n queries = []\n cone_radius /= 3600 # Convert radiusfrom arcseconds to degrees\n for i in range(Nobj):\n cone_str = '{:.6f},{:.6f},{:.6f}'.format(RA[i],DEC[i],cone_radius)\n new_que = copy.deepcopy(request_defs)\n new_que['id'] = str(i)\n new_que['cone'] = cone_str\n new_que['rb__gte'] = '{:.2f}'.format(rb_limit)\n new_que['query_date'] = str(Time.now().jd)\n queries.append(new_que)\n\n # Perform the new query\n print('')\n numq = int(np.ceil(float(Nobj)/float(query_limit)))\n pj = []\n for i in range(numq):\n low = i*query_limit\n if i < numq-1:\n upp = low+query_limit\n else:\n upp = Nobj\n print('Querying Objects %i through %i' %(low+1,upp))\n p = post_request(queries[low:upp])\n\n # Add query results to full results list\n pj += p.json()['results']\n if i < numq-1:\n sleep_time = args.sleeptime\n if sleep_time > 0:\n print('Sleeping {:.2f} Minute(s)...'.format(float(sleep_time)/60.))\n time.sleep(sleep_time)\n\n # Get info for new query and print results\n qs,numq,num_objs,num_alerts = get_query_info(pj)\n print('\\nQuery Results: {:5d} Queries, {:4d} Queries with Alerts, {:4d} Objects with Alerts, {:4d} Total Alerts'.format(\n qs,numq,num_objs,num_alerts))\n\n # Save combined results\n if save:\n time_str = Time.now().iso[0:10].replace(\"-\",\"\")\n json_fname = 'alert_results_{}.json'.format(time_str)\n if os.path.exists(json_fname):\n exists = True\n ext = 1\n while exists:\n json_fname = 'alert_results_{}_{}.json'.format(time_str,str(ext))\n if os.path.exists(json_fname):\n ext += 1\n else:\n exists = False\n with open(json_fname, 'w') as outfile:\n json.dump(pj, outfile)\n\n print('\\nResults saved in Current Folder as {}\\n'.format(json_fname))\n\n return pj\n","repo_name":"zvanderbosch/ZTF_tools","sub_path":"ztf_tools.py","file_name":"ztf_tools.py","file_ext":"py","file_size_in_byte":10512,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"32"} +{"seq_id":"38751989955","text":"import requests\nimport lxml.html\n\nhtml_theme = 'sphinx_rtd_theme'\nhtml_style = None\n\nhtml_theme_options = {\n 'collapse_navigation': False\n}\n\nproject = \"Admind\"\nprimary_domain = \"rst\"\n\nmaster_doc = \"index\"\n\n# These folders are copied to the documentation's HTML output\nhtml_static_path = ['../_static']\n\n# These paths are either relative to html_static_path\n# or fully qualified paths (eg. https://...)\nhtml_css_files = [\n 'css/theme_overrides.css',\n]\n\nprint('Requests: ' + requests.__version__)\n","repo_name":"jits-aps/docs","sub_path":"source/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"22624393546","text":"# Weather Forecast \r\n# From this website https://forecast.weather.gov/\r\n# Day wise temprature form this website\r\n# Using Pandas storeing in excel sheet \r\nfrom bs4 import BeautifulSoup as b\r\nimport requests as req\r\nimport pandas as pd\r\n\r\nurl_data=req.get(\"https://forecast.weather.gov/MapClick.php?lat=40.6792&lon=-85.7019#.XQkYhy3hXDc\")\r\nsoup=b(url_data.text,'html.parser')\r\nlinksaslist=soup.select('a')\r\nweek=soup.findAll(id='seven-day-forecast-body')\r\nitems=soup.findAll(class_='tombstone-container')\r\n\r\nperiod_names=[item.find(class_='period-name').get_text() for item in items]\r\n\r\nshort_desc=[item.find(class_='short-desc').get_text() for item in items]\r\n\r\ntemp=[item.find(class_='temp').get_text() for item in items]\r\n\r\n\r\n\r\nweather_report=pd.DataFrame({'period':period_names,'short-description':short_desc,'temprature':temp})\r\nprint('-----------------------------------------------------')\r\nprint(weather_report)\r\nweather_report.to_csv('weather2.csv')","repo_name":"Ganeshkollatii98/Python-Projects","sub_path":"Weather Forecast Scraping/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"16830841193","text":"#!/usr/bin/env python3\n\nByteCode_ISET = [\n 'nop',\n 'addi',\n 'subi',\n 'muli',\n 'divi',\n\n 'bo_and',\n 'bo_or',\n\n 'cmp_lt',\n 'cmp_lte',\n 'cmp_gt',\n 'cmp_gte',\n 'cmp_eq',\n\n 'assn',\n 'deref',\n\n 'print_i',\n 'print_ir',\n 'print_sc',\n 'print_sp',\n\n 'o_push_const_ri',\n 'o_push_const_rs',\n 'o_push_const_rr',\n\n 'o_pop',\n\n 'o_popN',\n 'o_clear',\n\n 'f_push',\n\n 'f_return',\n\n 'f_return_item',\n\n 'branch_if',\n\n 'branch',\n 'exit_prog'\n]\n\nbyte2binstr = lambda i:(lambda n:'0'*(8-len(n)) + n)(bin(i)[2:])\nencoder = {b:i for i,b in enumerate(ByteCode_ISET)}\ndecoder = {i:b for i,b in enumerate(ByteCode_ISET)}\n\nimport sys\nfrom math import log, ceil\nlog16 = lambda n: str(ceil(log(n) / log(16.0)) + 1)\n\nprogram = []\n\nhelp_txt =\\\n\"\"\"======================================\n(M) print program\n(R) replace line - 'R:#,0 0 0 0 0 0 0 0'\n(W) write ins - 'W:0 0 0 0 0 0 0 0'\n(S) write string - 'S:\"asdf...\"'\n(I) print codes\n(anything else) write to file and quit\n======================================\n\"\"\"\n\nwhile True:\n user_in = input(help_txt)\n print(' user_input -> ', user_in)\n user_in = user_in.strip()\n if not(user_in) or user_in[0] == '#': continue\n\n user_in = user_in.split('#')[0].split(':')\n\n action = user_in[0]\n if len(user_in) > 1:\n data = user_in[1]\n\n if action == 'M':\n print('Your program: ')\n if len(program):\n fmt = '0x{{:0{}X}} '*2\n fmt = fmt.format(log16(len(program)), log16(len(program)*8))\n fmt += ' {}'\n for i, p in enumerate(program):\n p = ' '.join(['{:02X}'.format(pp) for pp in p][::-1])\n print(fmt.format(i, i*8, p))\n\n elif action == 'R':\n data = data.split(',')\n if len(data) != 2:\n print('WRONG INPUT')\n continue\n\n index, line = data\n index = int(index)\n line = line.split()\n if not(0 <= index < len(program)):\n print(' index out of range! ', index)\n continue\n\n if len(line) != 8:\n print('ERROR LINE')\n continue\n\n line = [int(i) for i in line]\n program[index] = line[::-1]\n\n elif action == 'W':\n line = data.split()\n if len(line) != 8:\n print('ERROR LINE')\n continue\n line = [int(i) for i in line]\n print('You entered: ', line)\n program.append(line[::-1])\n\n elif action == 'S':\n line = [ord(i) for i in eval(data)]\n line += [0] * (9 - (len(data) % 8))\n\n print('You entered: ', line, len(line))\n for i in range(len(line) // 8):\n l = line[i*8 : (i+1)*8]\n print(l)\n program.append(l)\n\n elif action == 'I':\n print('\\n'.join('{} : {}'.format(k, encoder[k]) for k in encoder))\n else:\n with open(action + '.bin', 'wb') as f:\n for p in program:\n f.write(bytearray(p))\n break\n\n","repo_name":"xylafur/cosc4315-hw2","sub_path":"bytecode_stuff/editor.py","file_name":"editor.py","file_ext":"py","file_size_in_byte":2815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"72964692891","text":"import discord\nfrom discord.ui import View\n\nclass ErrorView(View): \n def __init__(self):\n super().__init__(timeout=None)\n @discord.ui.button(label=\"Ping Rainy\", style=discord.ButtonStyle.green, custom_id=\"accept-btn\")\n async def acc_callback(self, button, interaction: discord.Interaction):\n rn = interaction.guild.get_member(941778098674892851)\n await interaction.response.send_message(f\"Hey {rn.mention}, an error has occured.\")\n self.disable_all_items()","repo_name":"laserzz/rrbotv2","sub_path":"uistuff/errorui.py","file_name":"errorui.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"32"} +{"seq_id":"26094644370","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n#filename:guess.py\nfrom random import randint\nx =randint(1,100)\nfor count in range(3):\n\tnum=input('Please input a number between 0-100:')\n\tif int(num)==x:\n\t\tprint(\"congratulations! you win!\")\n\t\tbreak\nelse:\n\tprint(\"Oops!you fail!\")\n","repo_name":"linwiker/learpython","sub_path":"python3/guess.py","file_name":"guess.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"4819781378","text":"import collections\nimport typing\n\nimport creme.base\n\nfrom . import base\n\n\nclass MiniBatcher(base.Optimizer):\n \"\"\"Wrapper class for doing mini-batch gradient descent.\n\n This will accumulate successive gradients until `batch_size` have been observed. Once the\n current mini-batch is full, the underlying optimizer will update the weights using the average\n gradient. This can significantly improve the accuracy of a model, especially early on when\n gradients tend to go wild.\n\n Parameters:\n optimizer\n batch_size: The desired batch size.\n\n Attributes:\n gradient (collections.defaultdict): The current accumulated mini-batch.\n current_size (int): The current size of the mini-batch.\n\n Example:\n\n >>> from creme import datasets\n >>> from creme import linear_model\n >>> from creme import metrics\n >>> from creme import model_selection\n >>> from creme import optim\n >>> from creme import preprocessing\n\n >>> X_y = datasets.Phishing()\n >>> optimizer = optim.MiniBatcher(optim.SGD(0.1), 4)\n >>> model = (\n ... preprocessing.StandardScaler() |\n ... linear_model.LogisticRegression(optimizer)\n ... )\n >>> metric = metrics.F1()\n\n >>> model_selection.progressive_val_score(X_y, model, metric)\n F1: 0.882511\n\n \"\"\"\n\n def __init__(self, optimizer: base.Optimizer, batch_size: int):\n self.optimizer = optimizer\n self.batch_size = batch_size\n self.gradient: typing.DefaultDict[creme.base.typing.FeatureName, float] = collections.defaultdict(float)\n self.current_size = 0\n self.n_iterations = 0\n\n def update_before_pred(self, w):\n return self.optimizer.update_before_pred(w)\n\n def _update_after_pred(self, w, g):\n\n # Accumulate the gradient\n for i, gi in g.items():\n self.gradient[i] += gi\n self.current_size += 1\n\n if self.current_size == self.batch_size:\n\n # Update the weights with the average gradient\n avg_g = {i: gi / self.batch_size for i, gi in self.gradient.items()}\n w = self.optimizer.update_after_pred(w, avg_g)\n\n # Reset the gradient\n self.gradient = collections.defaultdict(float)\n self.current_size = 0\n\n return w\n","repo_name":"Sahanduiuc/creme","sub_path":"creme/optim/mini_batch.py","file_name":"mini_batch.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"32"} +{"seq_id":"1877905741","text":"import pygame\nimport random\nimport button\nfrom gtts import gTTS #library for the reading out loud\nfrom io import BytesIO \n\nclass TeachingGame():\n def __init__ (self):\n self.answered = True\n self.begin = True \n self.clicked = False\n self.playAgain = False\n self.rounds = 10 \n\n #function that randomizes card choices\n def randomizedCards(self,screen): \n\n duplicates = True\n\n #font, color, cards \n base_font = pygame.font.Font(\"cyberbit.ttf\",130)\n \n x = 15\n y = 100\n #top Japanese cards\n displayCard = pygame.Rect(420,y,150,150) #arguments x,y,pixelWidth,pixelHeight\n\n\n #bottom English cards\n answerCard = pygame.Rect(x,y+200,200,150)\n answerCard1 = pygame.Rect(x+260,y+200,200,150)\n answerCard2 = pygame.Rect(x+520,y+200,200,150)\n answerCard3 = pygame.Rect(x+780,y+200,200,150)\n color = pygame.Color('black') #color of cards\n\n #Japanese alphabet\n textAlphabet = {'a':'あ','i':'い','u':'う','e':'え','o':'お','ka':'か','ki':'き','ku':'く','ke':'け','ko':'こ','sa':'さ','shi':'し','su':'す','se':'せ','so':'そ','ta':'た',\n 'chi':'ち','tsu':'つ','te':'て','to':'と','na':'な','ni':'に','nu':'ぬ','ne':'ね','no':'の','ma':'ま','mi':'み','mu':'む','me':'め','mo':'も','ya':'や','yu':'ゆ',\n 'yo':'よ','ra':'ら','ri':'り','ru':'る','re':'れ','ro':'ろ','wa':'わ','wo':'を','n':'ん'}\n \n \n if self.answered:\n self.answered = False\n #pick a random elements to display from alphabet dictionary\n self.display = random.sample(textAlphabet.items(),1)\n self.choice1 = random.sample(textAlphabet.items(),1)\n self.choice2 = random.sample(textAlphabet.items(),1)\n self.choice3 = random.sample(textAlphabet.items(),1)\n\n self.eng, self.jap = self.display[0]\n self.eng1, self.jap1 = self.choice1[0]\n self.eng2 , self.jap2 = self.choice2[0]\n self.eng3 , self.jap3 = self.choice3[0]\n\n #randomizes the order of the choices\n self.correct_choice = self.eng\n self.tempList = [self.eng,self.eng1,self.eng2,self.eng3]\n\n \n #Checks for duplicates in the answer cards\n #to do: reassign duplicates\n if(len(set(self.tempList)) == len(self.tempList)):\n duplicates = False\n print(\"Elements are Unique\")\n else:\n print(\"There are duplicates\")\n \n\n for p in range(len(self.tempList)-1,0,-1):\n q = random.randint(0,p)\n self.tempList[p],self.tempList[q]= self.tempList[q], self.tempList[p]\n \n\n #hirigana matching card\n self.text_surface = base_font.render(self.jap,True,(255,255,255))\n \n #english sound matching cards\n self.text_surface4 = base_font.render(self.tempList[0],True,(255,255,255))\n self.text_surface5 = base_font.render(self.tempList[1],True,(255,255,255))\n self.text_surface6 = base_font.render(self.tempList[2],True,(255,255,255))\n self.text_surface7 = base_font.render(self.tempList[3],True,(255,255,255))\n\n\n #display all the matching cards\n pygame.draw.rect(screen,color,displayCard)\n pygame.draw.rect(screen,color,answerCard)\n pygame.draw.rect(screen,color,answerCard1)\n pygame.draw.rect(screen,color,answerCard2)\n pygame.draw.rect(screen,color,answerCard3)\n\n pos = pygame.mouse.get_pos()\n\n #speaks when you click on the display card\n if displayCard.collidepoint(pos):\n if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n self.clicked = True\n text = str(self.jap)\n \n sound = speak(text)\n pygame.mixer.music.load(sound)\n pygame.mixer.music.play()\n\n if answerCard.collidepoint(pos):\n #checks if the button has been pressed and if it is the correct answer\n if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n if str(self.tempList[0]) == str(self.correct_choice):\n self.clicked = True\n pygame.draw.rect(screen,'green',answerCard)\n self.answered = True\n self.begin = True\n self.rounds-=1\n elif str(self.tempList[0]) != str(self.correct_choice):\n pygame.draw.rect(screen,'red',answerCard)\n\n elif answerCard1.collidepoint(pos):\n if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n self.clicked = True\n if str(self.tempList[1]) == str(self.correct_choice):\n pygame.draw.rect(screen,'green',answerCard1)\n self.answered = True\n self.begin = True\n self.rounds-=1\n elif str(self.tempList[1]) != str(self.correct_choice):\n pygame.draw.rect(screen,'red',answerCard1)\n elif answerCard2.collidepoint(pos):\n if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n self.clicked = True\n if str(self.tempList[2]) == str(self.correct_choice):\n pygame.draw.rect(screen,'green',answerCard2)\n self.answered = True\n self.begin = True\n self.rounds-=1\n elif str(self.tempList[2]) != str(self.correct_choice):\n pygame.draw.rect(screen,'red',answerCard3)\n elif answerCard3.collidepoint(pos):\n if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n self.clicked = True\n if str(self.tempList[3]) == str(self.correct_choice):\n pygame.draw.rect(screen,'green',answerCard3)\n self.answered = True\n self.begin = True\n self.rounds-=1\n elif str(self.tempList[3]) != str(self.correct_choice):\n pygame.draw.rect(screen,'red',answerCard3)\n\n #resets the cards so they can be clicked again\n if pygame.mouse.get_pressed()[0] == 0:\n self.clicked = False\n \n \n #display hirigana matching card\n screen.blit(self.text_surface,(displayCard.x,displayCard.y-5))\n\n\n #display english matching cards\n screen.blit(self.text_surface4,(answerCard.x,answerCard.y-5))\n screen.blit(self.text_surface5,(answerCard1.x,answerCard1.y-5))\n screen.blit(self.text_surface6,(answerCard2.x,answerCard2.y-5))\n screen.blit(self.text_surface7,(answerCard3.x,answerCard3.y-5))\n\n self.begin = False\n\n return self.rounds\n \n #replay screen\n #To do implement replay\n def replay(self,screen):\n \n base_font = pygame.font.Font(\"cyberbit.ttf\",80)\n displayText = pygame.Rect(300,20,150,150) #arguments x,y,pixelWidth,pixelHeight\n displayText2 = pygame.Rect(200,80,150,150)\n\n text = \"Great Job!\"\n text2 = \"Thanks for playing!\"\n self.text_surface5 = base_font.render(text,True,(255,255,255))\n self.text_surface6 = base_font.render(text2,True,(255,255,255))\n \n screen.blit(self.text_surface5,displayText)\n screen.blit(self.text_surface6,displayText2)\n self.playAgain = True\n\n\n#creates and plays sound file for alphabet letter \ndef speak(sound,language = 'ja'):\n mp3_fo = BytesIO()\n tts = gTTS(sound, lang = language)\n tts.save(\"sound.mp3\")\n title = \"sound.mp3\"\n return title\n\n\n\n\n \n\n\n \n\n\n\n\n\n\n\n","repo_name":"BlakeS-Byte/Python-Japanese-Matching-Game","sub_path":"Japanese learning game/teachingGame.py","file_name":"teachingGame.py","file_ext":"py","file_size_in_byte":7827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"13847391786","text":"import hashlib\n\ndef hash_file(filename):\n h = hashlib.sha1()\n\n with open(filename,'rb') as file:\n\n chunk = 0\n while chunk != b'':\n\n chunk = file.read(1024)\n h.update(chunk)\n\n return h.hexdigest()\n\nhash_ = hash_file(\"a.py\")\nprint(hash_)\n","repo_name":"React-MBC/React-Hacktober-Fest-2022","sub_path":"Hack Here/Cyril C Thomas/file_hash.py","file_name":"file_hash.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"32"} +{"seq_id":"4367495360","text":"import requests\nfrom bs4 import BeautifulSoup\nimport os\nimport string\n\npages = int(input())\narticle_type = input()\n\ncwd = os.getcwd()\nfor page in range(1, pages + 1):\n os.chdir(cwd)\n os.mkdir(f'Page_{page}')\n os.chdir(f'Page_{page}')\n url = f\"https://www.nature.com/nature/articles?searchType=journalSearch&sort=PubDate&page={page}\"\n r = requests.get(url)\n soup = BeautifulSoup(r.content, 'html.parser')\n script = soup.find_all('article')\n urls = []\n titles = []\n for scp in script:\n a = scp.find(\"span\", {'data-test': \"article.type\"})\n if a.text.strip() == article_type:\n b = scp.find('h3').find('a')\n titles.append(b.contents)\n urls.append(b.get('href'))\n article_url = \"https://www.nature.com\"\n soups = []\n for i in range(len(urls)):\n urls[i] = article_url + urls[i]\n r = requests.get(urls[i])\n soup = BeautifulSoup(r.content, 'html.parser')\n test = soup.find('div', {'class': \"c-article-body\"})\n if test is None:\n test = soup.find('div', {'class': \"article-item__body\"})\n if test is None:\n test = soup.find('article')\n soups.append(test)\n body_ = []\n for soup in soups:\n body_.append(soup.text.strip())\n for body, title in zip(body_, titles):\n title = title[0]\n for ele in title:\n if ele in string.punctuation:\n title = title.replace(ele, \"\")\n title = title.replace(' ', '_')\n with open(f\"{title}.txt\", 'w', encoding='utf-8') as file:\n print(body, file=file)\nprint('Saved all articles.')\n","repo_name":"ankit27kh/JetBrains-Python-Learning-Track","sub_path":"Medium Projects/Web Scrapper/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"32432112119","text":"################################## \n## UDP Server to Talk to UE4 \n################################## \n# UE4UDPLogger.py \n################################## \n### Imports \nimport socket \nimport sys \nimport numpy as np \nfrom time import sleep \n\n\n### Constants \nHOST = '' \nPORT = 12345 \n\n# Create the UDP Socket \ntry: \n socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) \n #socket.setblocking(false) \n print(\"Socket Created\") \nexcept: \n print(\"Failed to create socket.\") \n sys.exit() \n\n# Bind socket to local host and port \ntry: \n socket.bind((HOST, PORT)) \nexcept: \n print(\"Bind failed.\") \n sys.exit() \n \nprint(\"Socket bind complete.\") \n\n# Now keep talking to clients \n\nwhile 1: \n\n try:\n # Receive data from client (data, addr) \n d = socket.recvfrom(1024) \n data = str(d[0], \"utf-8\") \n #data = d[0]\n addr = d[1] \n\n # Print to the server who made a connection. \n print(\"{} wrote:\".format(addr)) \n print(data) \n\n except KeyboardInterrupt: \n print ('exiting') \n break \n\n # Respond back \n #print(myResponse) \n #socket.sendto(bytes(myResponse, 'utf-8'), addr) \n \nsocket.close()","repo_name":"unoctanium/MulticopterVehicle","sub_path":"PythonSource/UDPReceiver.py","file_name":"UDPReceiver.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"32"} +{"seq_id":"3017989697","text":"#question link https://leetcode.com/problems/maximum-swap/submissions/\nclass Solution(object):\n def maximumSwap(self, num):\n \"\"\"\n :type num: int\n :rtype: int\n \"\"\"\n num = str(num)\n arr = []\n sarr = []\n for s in num:\n arr.append(s)\n sarr = sorted(arr, reverse=True)\n for i in range(len(arr)):\n if(arr[i] != sarr[i]):\n z = arr[i]\n index = len(arr) - 1 - arr[::-1].index(sarr[i])\n arr[i] = sarr[i]\n arr[index] = z\n break\n listToStr = ''.join(map(str, arr))\n return listToStr\n \n \n","repo_name":"surajrimal/coding_challenges","sub_path":"maximum_swap.py","file_name":"maximum_swap.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"12835811730","text":"'''\nthread_pool imports all nifti files from subdirectories and divide them by\\\n name if a consistency condition is fullfilled.\n'''\n\nimport logging\nimport os\nimport glob\n\nfrom concurrent.futures import ThreadPoolExecutor\nimport SimpleITK as sitk\n\n\ndef thread_pool(sub):\n '''\n tread_pool creates 4 lists contanining AD and CTRL paths and images as\\\n long as the single files are:\n - .nii;\n - use the above nomenclature\n - belong to a unique (or with a same name) folder.\n This function uses SimpleITK for extracting the images.\n\n Parameters\n ----------\n sub : Iterable of string\n Iterable containing all the paths to the nifti files.\n\n Returns\n -------\n ad_images : list\n List of SimpleITK.Images.\n ad_path : list\n List of the paths (string format) to the images (in the same order as\\\n AD_images).\n ctrl_images : list\n List of SimpleITK.Images.\n ctrl_path : list\n List of the paths (string format) to the images (in the same order as\\\n AD_images).\n '''\n ctrl_images = []\n ad_images = []\n ctrl_path = []\n ad_path = []\n\n def download(pth):\n '''\n download takes the path specified, extract the image from nifti file\\\n and assign it and the path to two separate list.\n This process is used both for AD and CTRL but it could be done without\\\n error only if there are no conflict in the names of folder and files\\\n between paths.\n\n Parameters\n ----------\n x : string\n Path to the selected nifti file.\n\n Returns\n -------\n None.\n\n '''\n if pth.count('AD') > pth.count('CTRL'):\n ad_images.append(sitk.ReadImage(pth, imageIO = \"NiftiImageIO\"))\n ad_path.append(pth)\n elif pth.count('AD') < pth.count('CTRL'):\n ctrl_images.append(sitk.ReadImage(pth, imageIO = \"NiftiImageIO\"))\n ctrl_path.append(pth)\n else:\n logging.warning(\"An inconsistency during the import may have occured.\")\n\n with ThreadPoolExecutor() as executor:\n executor.map(download, sub)\n\n return ad_images, ad_path, ctrl_images, ctrl_path\n\nif __name__==\"__main__\":\n\n path = os.path.abspath('')\n FILES = r'\\**\\*.nii'\n path = path + FILES\n subj = glob.glob(os.path.normpath(path), recursive=True)\n\n ad, ad_names, ctrl, ctrl_names = thread_pool(subj)\n","repo_name":"ACfma/4Work","sub_path":"model_svm/thread_pool.py","file_name":"thread_pool.py","file_ext":"py","file_size_in_byte":2523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"17184145799","text":"import pygame\nimport core\nimport config\nimport models\nimport random\nimport numpy\nimport text_object\nimport game_object\n\n\nclass Clicker(core.Game):\n def __init__(self):\n core.Game.__init__(self, 'Тут надо бить Игоря!!', config.WIDTH, config.HEIGHT, config.BACKGROUND_IMAGE,\n config.FRAME_RATE)\n self.amount_of_game_objects = 0\n self.columns = [i-i for i in range(config.COLUMN_COUNT)]\n self.score = config.STARTING_SCORE\n self.start_ticks = pygame.time.get_ticks()\n self.create_menu()\n\n def find_right_column(self, cur_time):\n column = random.randint(0, config.COLUMN_COUNT - 1)\n\n if cur_time - self.columns[column] >= 5:\n self.columns[column] = cur_time\n return column\n\n return -1\n\n def add_game_object(self, cur_time):\n if self.amount_of_game_objects < config.MAX_AMOUNT_OF_GAME_OBJECTS:\n column = self.find_right_column(cur_time)\n random_generator_of_groupmate_type = numpy.random.choice(config.GROUP_MATES_TYPES, 50,\n p=config.DROP_CHANCES)\n groupmate_type = random_generator_of_groupmate_type[random.randint(0, 49)]\n\n if column != -1:\n groupmate = models.Groupmate(column * config.COLUMN_WIDTH, groupmate_type)\n self.game_objects.append(groupmate)\n self.amount_of_game_objects += 1\n\n def check_if_object_out(self):\n for groupmate in self.game_objects:\n if groupmate.geometry.y >= config.GROUP_MATE_DEATH:\n self.game_objects.remove(groupmate)\n self.amount_of_game_objects -= 1\n self.score -= 5\n\n if self.score < 0:\n self.score = 0\n\n def object_click_processing(self, x, y):\n for groupmate in self.game_objects:\n if groupmate.geometry.collidepoint(x, y):\n groupmate.hp -= 1\n\n if not groupmate.hp:\n if groupmate.type == 4:\n self.random_background_image()\n\n self.score += groupmate.value\n\n if self.score < 0:\n self.score = 0\n\n self.game_objects.remove(groupmate)\n self.amount_of_game_objects -= 1\n\n for menu_object in self.menu_objects:\n if menu_object.bounds.collidepoint((x, y)):\n if menu_object.type == config.BUTTON1_KEY and self.score >= 1000:\n self.score -= 1000\n config.MAX_AMOUNT_OF_SKINS = 4\n self.menu_objects.remove(menu_object)\n if menu_object.type == config.BUTTON2_KEY and self.score >= 250:\n self.score -= 250\n self.background_image = pygame.image.load(config.OPTIONAL_BACKGROUND_IMAGE)\n self.background_image = pygame.transform.scale(self.background_image, (config.WIDTH, config.HEIGHT))\n self.menu_objects.remove(menu_object)\n\n def create_score_label(self):\n score_label = text_object.TextObject(config.SCORE_X, config.SCORE_Y, lambda: f'SCORE: {self.score}',\n config.SCORE_COLOR, config.SCORE_FONT, config.SCORE_SIZE)\n self.menu_objects.append(score_label)\n\n def create_menu_background(self):\n menu_background = game_object.MenuBackground(config.RECT_X, config.RECT_Y, config.RECT_WIDTH,\n config.RECT_HEIGHT, config.RECT_COLOR)\n self.menu_objects.append(menu_background)\n\n def create_upgrade_buttons(self):\n button1 = game_object.Button(config.WIDTH * 4 / 6, config.BUTTON_Y, config.BUTTON_WIDTH, config.BUTTON_HEIGHT,\n config.BUTTON1_MESSAGE, config.BUTTON1_KEY)\n self.menu_objects.append(button1)\n\n button2 = game_object.Button(config.WIDTH * 5 / 6, config.BUTTON_Y, config.BUTTON_WIDTH, config.BUTTON_HEIGHT,\n config.BUTTON2_MESSAGE, config.BUTTON2_KEY)\n self.menu_objects.append(button2)\n\n def create_menu(self):\n self.create_menu_background()\n self.create_score_label()\n self.create_upgrade_buttons()\n\n def handle_events(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n self.game_over = True\n\n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.type == pygame.MOUSEBUTTONDOWN:\n x, y = event.pos\n\n self.object_click_processing(x, y)\n\n if event.button == 1:\n pygame.draw.circle(self.screen, (225, 0, 50), event.pos, 20)\n\n def update(self):\n seconds = int((pygame.time.get_ticks() - self.start_ticks) / 1000)\n\n if seconds % random.randint(1, 50) == 0:\n self.add_game_object(seconds)\n\n super().update()\n\n self.check_if_object_out()\n\n def random_background_image(self):\n random_generator_of_images = numpy.random.choice(config.IMAGES_TYPES, 50, p=config.IMAGES_DROP_CHANCES)\n image_type = random_generator_of_images[random.randint(0, 49)]\n print(config.BACKGROUND_IMAGES[image_type])\n self.background_image = pygame.image.load(\n config.BACKGROUND_IMAGES_DIRECTORY + \"/\" + config.BACKGROUND_IMAGES[image_type])\n self.background_image = pygame.transform.scale(self.background_image, (config.WIDTH, config.HEIGHT))\n","repo_name":"heavyrainik/code_examples","sub_path":"game_on_python/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":5609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"72425681690","text":"import numpy as np\nimport vtk\n\n\ndef line_length(point_a, point_b):\n \"\"\"This is a conceptual class representation of a simple BLE device\n (GATT Server). It is essentially an extended combination of the\n :class:`bluepy.btle.Peripheral` and :class:`bluepy.btle.ScanEntry` classes\n\n :param client: A handle to the :class:`simpleble.SimpleBleClient` client\n object that detected the device\n :type client: class:`simpleble.SimpleBleClient`\n :param addr: Device MAC address, defaults to None\n :type addr: str, optional\n :param addrType: Device address type - one of ADDR_TYPE_PUBLIC or\n ADDR_TYPE_RANDOM, defaults to ADDR_TYPE_PUBLIC\n :type addrType: str, optional\n :param iface: Bluetooth interface number (0 = /dev/hci0) used for the\n connection, defaults to 0\n :type iface: int, optional\n :param data: A list of tuples (adtype, description, value) containing the\n AD type code, human-readable description and value for all available\n advertising data items, defaults to None\n :type data: list, optional\n :param rssi: Received Signal Strength Indication for the last received\n broadcast from the device. This is an integer value measured in dB,\n where 0 dB is the maximum (theoretical) signal strength, and more\n negative numbers indicate a weaker signal, defaults to 0\n :type rssi: int, optional\n :param connectable: `True` if the device supports connections, and `False`\n otherwise (typically used for advertising ‘beacons’).,\n defaults to `False`\n :type connectable: bool, optional\n :param updateCount: Integer count of the number of advertising packets\n received from the device so far, defaults to 0\n :type updateCount: int, optional\n \"\"\"\n # vector_ab = gen_vector(point_a, point_b)\n vector_ab = np.array([1,1])\n return np.linalg.norm(vector_ab) # L2\n\ndef writeDataSet(dataset, filename):\n datasetwriter = vtk.vtkDataSetWriter()\n datasetwriter.SetInputData(dataset)\n datasetwriter.SetFileName(filename)\n datasetwriter.Write()\n\nimport math\n\ndef valueToRGB(value):\n # 1 is the ideal value\n\n # input value is [-1, 1]\n \n # value should between 0 to 2\n # cValue = (float(value) - sMin) / (sMax - sMin)\n value = value + 1\n cValue = float(value)/ 2\n # cValue = float(value)\n # print(cValue)\n # The min scalar value is mapped to 0, and the max value is mapped to 1\n \n hsv = [240., 1.0, 1.0] # - hue, sat, value\n s = cValue\n if cValue < 0.5:\n hsv[0] = 240 + (120 * 2 * s)\n hsv[1] = 1\n else:\n hsv[0] = 0\n hsv[1] = 2*s - 1\n rgb = hsvRgb(hsv)\n # rgb.append(1.0) # set alpha (or opacity) channel\n return rgb\n\ndef valueToRGB_i0(value):\n # 0 is the ideal value\n\n # input value is [-1, 1]\n \n # value should between 0 to 2\n # cValue = (float(value) - sMin) / (sMax - sMin)\n value = value + 1\n cValue = float(value) / 2\n # cValue = float(value)\n # print(cValue)\n # The min scalar value is mapped to 0, and the max value is mapped to 1\n \n hsv = [240., 1.0, 0.8] # - hue, sat, value\n s = cValue\n if cValue < 0.5:\n hsv[0] = 240.\n hsv[1] = 1 - 2*s\n else:\n hsv[0] = 0.\n hsv[1] = 2*s - 1\n rgb = hsvRgb(hsv)\n # rgb.append(1.0) # set alpha (or opacity) channel\n return rgb\n\n'''\n Convert HSV to RGB color \n'''\ndef hsvRgb(hsv):\n rgb = [0,0,0]\n #h, s, v - hue, sat, value\n #r, g, b - red, green, blue\n #i, f, p, q, t - interim values\n\n\n # guarantee valid input:\n h = hsv[0] / 60.\n while h >= 6.:\n h -= 6.\n while h < 0.:\n h += 6.\n\n s = hsv[1]\n if (s < 0.):\n s = 0.\n if (s > 1.):\n s = 1.\n\n v = hsv[2]\n if (v < 0.):\n v = 0.\n if (v > 1.):\n v = 1.\n\n\n # if sat==0, then is a gray:\n if s == 0.0:\n rgb[0] = rgb[1] = rgb[2] = v\n return rgb\n \n\n\n # get an rgb from the hue itself:\n\n i = math.floor(h)\n f = h - i\n p = v * (1. - s)\n q = v * (1. - s * f)\n t = v * (1. - (s * (1. - f)))\n\n if math.floor(i) == 0:\n r = v\n g = t\n b = p\n \n\n elif math.floor(i) == 1:\n r = q\n g = v\n b = p\n \n\n elif math.floor(i) == 2:\n r = p\n g = v\n b = t\n \n\n elif math.floor(i) == 3:\n r = p\n g = q \n b = v\n \n\n elif math.floor(i) == 4:\n r = t\n g = p\n b = v\n \n\n elif math.floor(i) == 5:\n r = v\n g = p \n b = q\n \n rgb[0] = r\n rgb[1] = g\n rgb[2] = b\n\n return rgb\n\ndef createVTKCellArray(lineVids):\n lines = vtk.vtkCellArray()\n for l in lineVids:\n line = vtk.vtkLine()\n for i, x in enumerate(l):\n line.GetPointIds().SetId(i,x)\n lines.InsertNextCell(line)\n return lines\n \ndef vlToVTKLinePolyData(vtkpoints, lineVids):\n vtk_lines = createVTKCellArray(lineVids)\n\n edgeData = vtk.vtkPolyData()\n edgeData.SetPoints(vtkpoints)\n edgeData.SetLines(vtk_lines)\n return edgeData","repo_name":"MarsVegetables/HQViewer","sub_path":"vtkMeshOpt/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":5101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"42067323497","text":"#!/usr/bin/env python3\nfrom __future__ import division\n\nimport nltk\nfrom nltk.corpus import brown\nimport random, itertools, bisect\n\nclass Model:\n\tdef __init__(self, sents, n=2, cpd_class=nltk.MLEProbDist):\n\t\tself.N = n\n\t\tself.CPD_CLASS = cpd_class\n\t\tself.cfd = nltk.ConditionalFreqDist()\n\t\tself.fd = nltk.FreqDist()\n\n\t\t# filter out punctuation and convert to lowercase\n\t\tsents = (list(map(str.lower, filter(str.isalpha, sent))) for sent in sents)\n\n\t\tfor sent in sents:\n\t\t\tfor word in sent:\n\t\t\t\tself.fd[word] += 1\n\t\t\tfor ngram in zip( *[ sent[i:] for i in range(self.N)]):\n\t\t\t\tself.cfd[ngram[:-1]][ngram[-1]] += 1\n\n\t\t# Calculate conditional probabilities.\n\t\tself.cpd = nltk.ConditionalProbDist( self.cfd,\n\t\t self.CPD_CLASS,\n\t\t bins=self.fd.B() )\n\n\t# The choose_ methods all follow the same form:\n\t# prev: A list of the previous n-1 words that were chosen (e.g. 1 for a bigram model)\n\t# wordlist: A list of words to choose from\n\t\n\t# Deterministically chooses the most probable word from wordlist based on the model\n\tdef choose_max(self, prev, wordlist):\n\t\tprev = tuple(prev)\n\t\treturn max((self.cpd[prev].prob(w.lower()), w) for w in wordlist)[1]\n\n\t# Randomly chooses a word from wordlist weighted by their likelihood in the model\n\tdef choose_weighted_random(self, prev, wordlist):\n\t\tprev = tuple(prev)\n\t\tchoices, weights = zip(*((w, self.cpd[prev].prob(w.lower())) for w in wordlist))\n\t\tcumulative_dist = list(itertools.accumulate(weights))\n\t\tx = random.random() * cumulative_dist[-1]\n\t\tif max(weights) > 0:\n\t\t\treturn choices[bisect.bisect(cumulative_dist, x)]\n\t\telse:\n\t\t\treturn random.choice(wordlist)\n\t\n\t# Chooses a word from wordlist completely randomly, excepting words not in the dictionary (unless we've no other choice)\n\tdef choose_random(self, prev, wordlist):\n\t\tprev = tuple(prev)\n\t\tchoices = [w for w in wordlist if self.cpd[prev].prob(w.lower()) > 0]\n\t\tif not len(choices) > 0:\n\t\t\tchoices = wordlist\n\t\treturn random.choice(choices)\n","repo_name":"dwminer/machine-poetry","sub_path":"ngram.py","file_name":"ngram.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"8813207251","text":"import numpy as np\nfrom math import acos\n\ndef getDistance(lines_1, lines_2):\n unit = np.array([[1],[0]]) # a unit vector for dircetion calculation\n\n # bin the lines according to their direction (17 bins)\n bins = [[],[],[],[],[],[],\n [],[],[],[],[],[],\n [],[],[],[],[]]\n for line in lines_1:\n bins[round(10 * acos(np.sum(np.abs(line.direction) * unit)))].append(line)\n\n distance = 0\n for line in lines_2:\n bin_to_check = bins[round(10 * acos(np.sum(np.abs(line.direction) * unit)))]\n lines_distance = 0\n for line_to_check in bin_to_check:\n d = line.getDistance(line_to_check)\n if lines_distance > d:\n d = lines_distance\n distance += lines_distance\n return distance\n\n","repo_name":"sergiyvan/robotic_vl","sub_path":"bin/world_modeling/cameraprototype/distance.py","file_name":"distance.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"39363507748","text":"import torch\n\n# citation: https://stackoverflow.com/questions/49710537/pytorch-gensim-how-to-load-pre-trained-word-embeddings\n\ndef from_pretrained(embeddings, freeze=True):\n assert embeddings.dim() == 2, \\\n 'Embeddings parameter is expected to be 2-dimensional'\n rows, cols = embeddings.shape\n embedding = torch.nn.Embedding(num_embeddings=rows, embedding_dim=cols)\n embedding.weight = torch.nn.Parameter(embeddings)\n embedding.weight.requires_grad = not freeze\n return embedding","repo_name":"phimachine/mayoehr","sub_path":"death/helper/pretrained_embedding.py","file_name":"pretrained_embedding.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"22729959911","text":"# Import the dependencies.\nimport numpy as np\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n\nfrom flask import Flask, jsonify\n\n\n\n#################################################\n# Database Setup\n#################################################\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\n\n# reflect an existing database into a new model\nBase = automap_base()\n\n# reflect the tables\nBase.prepare(autoload_with=engine)\n\n# Save references to each table\nBase.classes.keys()\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n# Create our session (link) from Python to the DB\n\n\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n\n#################################################\n# Flask Routes\n#################################################\n@app.route(\"/\")\ndef welcome():\n \"\"\"List all available api routes.\"\"\"\n return (\n f\"Available Routes:<br/>\"\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/>\"\n f\"/api/v1.0/start<br/>\"\n f\"/api/v1.0/<start>/<end><br>\"\n )\n\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n \"\"\"Return a list of all precepitation data\"\"\"\n # Query for the dates and precipitation values\n results = session.query(Measurement.date, Measurement.prcp).order_by(Measurement.date).all()\n\n # Convert to list of dictionaries to jsonify\n prcp_list = []\n\n for date, prcp in results:\n prcp_dict = {}\n prcp_dict[\"date\"] = date\n prcp_dict[\"prcp\"] = prcp\n \n prcp-list.append(prcp_dict)\n session.close()\n\n return jsonify(prcp_list)\n\n\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n \"\"\"Return a list of all stations\"\"\"\n # Query all Stations\n results = session.query(Station.station).order_by(Station.station).all()\n\n session.close()\n\n # Convert list of tuples into normal list\n all_stations = list(np.ravel(results))\n\n return jsonify(all_stations)\n\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n \"\"\"Return a list of all TOBs\"\"\"\n # Query all tobs\n\n results = session.query(Measurement.date, Measurement.tobs, Measurement.prcp).\\\n filter(Measurement.date >= '2016-08-23').\\\n filter(Measurement.station=='USC00519281').\\\n order_by(Measurement.date).all()\n\n session.close()\n\n # Convert the list to Dictionary\n tobs_list = []\n for prcp, date,tobs in results:\n tobs_dict = {}\n tobs_dict[\"prcp\"] = prcp\n tobs_dict[\"date\"] = date\n tobs_dict[\"tobs\"] = tobs\n \n tobs_list.append(tobs_dict)\n\n return jsonify(tobs_list)\n\n@app.route(\"/api/v1.0/<start>\")\ndef Start(start_date):\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n \"\"\"Return a list of min, avg and max tobs for a start date\"\"\"\n # Query all tobs\n\n results = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start_date).all()\n\n session.close()\n\n # Create a dictionary from the row data and append to a list of start_date_tobs\n starting_tobs = []\n for min, avg, max in results:\n starting_tobs_dict = {}\n starting_tobs_dict[\"min_temp\"] = min\n starting_tobs_dict[\"avg_temp\"] = avg\n starting_tobs_dict[\"max_temp\"] = max\n starting_tobs_dict.append(starting_tobs_dict) \n return jsonify(starting_tobs)\n\n@app.route(\"/api/v1.0/<start>/<end>\")\ndef Start_end(start_date, end_date):\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n \"\"\"Return a list of min, avg and max tobs for start and end dates\"\"\"\n # Query all tobs\n\n results = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()\n\n session.close()\n \n # Create a dictionary from the row data and append to a list of start_end_date_tobs\n ending_tobs = []\n for min, avg, max in results:\n ending_tobs_dict = {}\n ending_tobs_dict[\"min_temp\"] = min\n ending_tobs_dict[\"avg_temp\"] = avg\n ending_tobs_dict[\"max_temp\"] = max\n ending_tobs.append(ending_tobs_dict) \n \n\n return jsonify(ending_tobs)\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"emilywashburn/sqlalchemy-challenge","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"28116937859","text":"\nimport os, sys\nimport json\nimport pandas as pd\nimport argparse\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('input')\nargs = parser.parse_args()\n\n\nfile = open(args.input,'r')\nlines = file.readlines()\n\ndf = pd.DataFrame()\nfor line in lines:\n record=json.loads(line)\n # append relevant data to dataframe\n df = df.append({\n 'subreddit': record['data']['subreddit'],\n 'title': record['data']['title'],\n 'title_length': len(record['data']['title'])\n },ignore_index=True)\n\n\navg = (df['title_length'].sum())/(len(df['title_length']))\nprint(avg)","repo_name":"katyayani-prakash/Data-Science","sub_path":"A6 - API Data Scraping/src/compute_title_lengths.py","file_name":"compute_title_lengths.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"25933386936","text":"from rest_framework.response import Response\nfrom rest_framework.views import APIView\nimport json\nfrom .models import Price\nfrom .fetch_prices import fetch_price, start_scheduler\nfrom rest_framework.permissions import IsAuthenticatedOrReadOnly\nfrom prices.serializers.common import PriceSerializer\n\nclass GetAllPricesView(APIView):\n # permission_classes = (IsAuthenticatedOrReadOnly,)\n def post(self, request):\n data = json.loads(request.body)\n ticker = data.get('ticker')\n #Calls fetch_price function with the ticker from the client as an arg. \n # fetching here prevents delay of the schedulers interval.\n #behaviour fetches then waits for interval defined in fetch_prices \n fetch_price(ticker)\n #Calling our function to also start the scheduler for prices to update at a set interval\n start_scheduler(ticker)\n \n prices = Price.objects.all()\n serialized_prices = PriceSerializer(prices, many=True)\n return Response({\"message\": f\"Fetched price for {ticker}\", \"all prices data\": serialized_prices.data})\n \nclass GetSinglePriceView(APIView):\n #permission_classes = (IsAuthenticatedOrReadOnly,)\n\n def post(self, request):\n data = json.loads(request.body)\n ticker = data.get('ticker')\n \n # Calls fetch_price function with the ticker from the client as an arg. \n # Fetching here prevents delay of the scheduler's interval.\n # Behaviour fetches then waits for interval defined in fetch_prices \n fetch_price(ticker)\n \n # Calling our function to also start the scheduler for prices to update at a set interval\n start_scheduler(ticker)\n \n # Order the prices queryset by the date and time of the price in descending order\n \n latest_price = Price.objects.latest('id')\n \n serialized_price = PriceSerializer(latest_price)\n return Response({\"message\": f\"Fetched price for {ticker}\", \"latest price data\": serialized_price.data})","repo_name":"JackL-1/TradingApplication","sub_path":"prices/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"29466586090","text":"from typing import Union\n\nfrom pysus.ftp.databases.cnes import CNES\nfrom pysus.ftp import CACHEPATH\n\ncnes = CNES().load()\n\ngroup_dict = {\n 'LT': ['Leitos - A partir de Out/2005', 10, 2005],\n 'ST': ['Estabelecimentos - A partir de Ago/2005', 8, 2005],\n 'DC': ['Dados Complementares - A partir de Ago/2005', 8, 2005],\n 'EQ': ['Equipamentos - A partir de Ago/2005', 8, 2005],\n 'SR': ['Serviço Especializado - A partir de Ago/2005', 8, 2005],\n 'HB': ['Habilitação - A partir de Mar/2007', 3, 2007],\n 'PF': ['Profissional - A partir de Ago/2005', 8, 2005],\n 'EP': ['Equipes - A partir de Abr/2007', 5, 2007],\n 'IN': ['Incentivos - A partir de Nov/2007', 11, 2007],\n 'RC': ['Regra Contratual - A partir de Mar/2007', 3, 2007],\n 'EE': ['Estabelecimento de Ensino - A partir de Mar/2007', 3, 2007],\n 'EF': ['Estabelecimento Filantrópico - A partir de Mar/2007', 3, 2007],\n 'GM': ['Gestão e Metas - A partir de Jun/2007', 6, 2007],\n}\n\n\ndef download(\n group: str,\n states: Union[str, list],\n years: Union[str, list, int],\n months: Union[str, list, int],\n data_dir: str = CACHEPATH,\n) -> list:\n \"\"\"\n Download CNES records for group, state, year and month and returns a\n list of local parquet files\n :param group:\n LT – Leitos - A partir de Out/2005\n ST – Estabelecimentos - A partir de Ago/2005\n DC - Dados Complementares - A partir de Ago/2005\n EQ – Equipamentos - A partir de Ago/2005\n SR - Serviço Especializado - A partir de Ago/2005\n HB – Habilitação - A partir de Mar/2007\n PF – Profissional - A partir de Ago/2005\n EP – Equipes - A partir de Abr/2007\n IN – Incentivos - A partir de Nov/2007\n RC - Regra Contratual - A partir de Mar/2007\n EE - Estabelecimento de Ensino - A partir de Mar/2007\n EF - Estabelecimento Filantrópico - A partir de Mar/2007\n GM - Gestão e Metas - A partir de Jun/2007\n :param months: 1 to 12, can be a list of years\n :param states: 2 letter state code, can be a list of UFs\n :param years: 4 digit integer, can be a list of years\n \"\"\"\n files = cnes.get_files(group, states, years, months)\n return cnes.download(files, local_dir=data_dir)\n","repo_name":"AlertaDengue/PySUS","sub_path":"pysus/online_data/CNES.py","file_name":"CNES.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"pt","doc_type":"code","stars":139,"dataset":"github-code","pt":"32"} +{"seq_id":"42247620929","text":"import numpy as np\r\n\r\n\"\"\"\r\nDecode PCO image timestamps from binary-coded decimal (see p94 of\r\n\"pco_camera_control_commands_105.pdf\"). In this version of 'packed BCD' each\r\npixel contains 2 digits of information in a single byte (8bits). The lower\r\nand upper nibbles (2 x 4bits) encode the numbers 0-9 which are then combined\r\nto give a value in the range 0-99.\r\n\"\"\"\r\n\r\ndef decode_timestamp(image):\r\n assert len(image.shape) == 2 and image.dtype == 'uint16'\r\n bcd_px = image[0, :14] # get BCD pixels\r\n lower_nibbles = bcd_px & 0b00001111 # get lower nibbles\r\n upper_nibbles = (bcd_px & 0b11110000) >> 4 # get upper nibbles and shift\r\n dec_px = 10 * upper_nibbles + lower_nibbles # convert to decimal\r\n timestamp = {}\r\n timestamp['#'] = np.sum(\r\n dec_px[:4] * np.array((1e6, 1e4, 1e2, 1)), dtype='uint32')\r\n timestamp['DD'] = dec_px[7].astype('uint32')\r\n timestamp['MM'] = dec_px[6].astype('uint32') \r\n timestamp['YYYY'] = np.sum(\r\n dec_px[4:6] * np.array((1e2, 1)), dtype='uint32')\r\n timestamp['h'] = dec_px[8].astype('uint32')\r\n timestamp['min'] = dec_px[9].astype('uint32')\r\n timestamp['s'] = dec_px[10].astype('uint32')\r\n timestamp['us'] = np.sum(\r\n dec_px[11:14] * np.array((1e4, 1e2, 1), dtype='uint64'))\r\n timestamp['time_us'] = np.sum( # total us on a given day\r\n dec_px[8:14] * np.array((36e8, 60e6, 1e6, 1e4, 1e2, 1)), dtype='uint64')\r\n return timestamp\r\n","repo_name":"amsikking/pco_decode_timestamp","sub_path":"pco_decode_timestamp.py","file_name":"pco_decode_timestamp.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"73909109852","text":"# Lista ordenada sem repetições\n\n\nnumeros = []\nfor c in range(4):\n numero = int(input(\"Digite um número: \"))\n for chave, valor in enumerate(numeros):\n if numero < valor:\n numeros.insert(chave, numero)\n break\n else:\n numeros.append(numero)\n print(\"Lista atual:\", numeros)\n","repo_name":"GabrielVinicius7/Curso-em-Video","sub_path":"exercicio 80.py","file_name":"exercicio 80.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"32856365381","text":"import openai\nimport discord\nimport json\n\nfrom utilities import getState, setState\nfrom appSettings import MAX_CONTEXT_SIZE\n\ndef getResponse(message: discord.Message):\n\n state = getState()\n\n username = message.author.display_name\n instructions = f\"Rarity is a chatbot that acts like the character Rarity from the TV show My Little Pony: Friendship is Magic. You are a white unicorn with a purple mane and tail that is knowledgeable about fashion. You are talking with a person named {username}. \"\n examples = \"How are you?#I'm doing very well, darling!#\"\n context = \"\".join(state[\"gpt-3_context\"][username])\n query = f\"{message.content}#\"\n\n prompt = instructions + examples + context + query\n\n response = openai.Completion.create(\n engine=\"davinci\",\n prompt=prompt,\n temperature=0.6,\n max_tokens=64,\n top_p=1,\n stop=[\"#\"]\n )\n responseBody = json.loads(response.last_response.body)\n responseContent = responseBody[\"choices\"][0][\"text\"]\n\n # This sucks so bad, really need to refactor the getState and setState functions\n state[\"gpt-3_context\"][username].append(f\"{message.content}#{responseContent}#\")\n state[\"gpt-3_context\"][username] = state[\"gpt-3_context\"][username][-MAX_CONTEXT_SIZE:]\n\n setState(state)\n\n return responseContent\n ","repo_name":"thefloatingtree/Rarity-Bot","sub_path":"gpt3.py","file_name":"gpt3.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"25935025146","text":"if __name__ == ('__main__'):\n\n ############################################\n\n\n #IMPORTS\n\n\n ############################################\n\n\n import os\n import random\n import time\n import requests\n import bs4\n import csv\n from selenium import webdriver\n from webdriver_manager.chrome import ChromeDriverManager\n from datetime import datetime\n from tqdm import tqdm\n from IPython.display import clear_output\n import concurrent.futures\n\n\n\n ############################################\n\n #GATHERING ALL SOUTH-EASTERN RAILWAY STATIONS\n\n ############################################\n\n\n # IF THE STATION DATA DOESN'T EXIST IT WILL FETCH THE DATA\n if os.path.exists('Station_Data.txt') == False:\n base_url = 'https://en.wikipedia.org/wiki/Category:Railway_stations_served_by_Southeastern'\n res = requests.get(base_url)\n soup = bs4.BeautifulSoup(res.text, 'html5lib' )\n textData = []\n for element in soup.find('div', class_='mw-category mw-category-columns'):\n textData.append(element.text)\n stationLists = []\n for i in textData:\n letterList = i.split('\\n')\n stationLists.append(letterList[1:])\n stations = []\n for i in stationLists:\n for j in i:\n stations.append(j)\n # Removing railway for the rightmove search\n cleanedStations = []\n for element in stations:\n x = element.split(' railway')\n y = ''.join(x)\n cleanedStations.append(y)\n noKents = []\n for element in cleanedStations:\n index = cleanedStations.index(element)\n if element != 'Wye station':\n if element != 'Rainham station (Kent)':\n x = element.split(' (Kent)')\n cleanedStations[index] = x[0]\n if element == 'Rainham station (Kent)':\n cleanedStations[index] = 'Rainham (Kent) Station'\n if element == 'London Victoria station':\n cleanedStations[index] = 'Victoria Station'\n\n # SELENIUM FUNCTION TO CONVERT STATION NAMES INTO THE FORMAT REQUIRED TO FETCH PROPERTIES ON RIGHTMOVE\n import time\n from selenium import webdriver\n from webdriver_manager.chrome import ChromeDriverManager\n def get_driver():\n #set options for easier browsing\n options = webdriver.ChromeOptions()\n options.add_argument('disable-infobars')\n options.add_argument('start-maximized')\n options.add_argument('disable-dev-shm-usage')\n options.add_argument('no-sandbox')\n options.add_experimental_option('excludeSwitches',['enable-automation'])\n options.add_argument('disable-blink-features=AutomationControlled')\n driver = webdriver.Chrome(ChromeDriverManager().install())\n driver.get('https://www.rightmove.co.uk/property-for-sale.html')\n return driver\n def stationFinder(station):\n driver = get_driver()\n search_engine = driver.find_element(by='xpath',value='/html/body/div/div[1]/div[1]/div/div/form/fieldset/div/input[1]')\n search_engine.send_keys(station)\n main_page_button = driver.find_element(by='xpath',value='/html/body/div/div[1]/div[1]/div/div/form/fieldset/div/input[5]')\n main_page_button.click()\n time.sleep(1)\n try:\n search_results_button = driver.find_element(by='xpath',value='/html/body/div[1]/div[2]/div/div[1]/div/form/fieldset[2]/div[4]/button')\n search_results_button.click()\n except:\n search_engine = driver.find_element(by='xpath',value='/html/body/div[1]/div[2]/div[1]/div/div/form/fieldset/div/input[1]')\n search_engine.send_keys(station)\n autofill = driver.find_element(by='xpath',value='/html/body/div[6]/ul/li/span')\n autofill.click()\n search_engine_button = driver.find_element(by='xpath',value='/html/body/div[1]/div[2]/div[1]/div/div/form/fieldset/div/input[4]')\n search_engine_button.click()\n main_page_button = driver.find_element(by='xpath',value='/html/body/div[1]/div[2]/div/div[1]/div/form/fieldset[2]/div[4]/button')\n main_page_button.click()\n time.sleep(1)\n url = driver.current_url\n location = url.split('&')[1]\n station_id = location.split('%')[1]\n return station_id\n\n # CALLING THE FUNCTIONS WITH THE CLEANED STATIONS\n station_ids = []\n from tqdm import tqdm\n from IPython.display import clear_output\n for element in tqdm(cleanedStations):\n print(element)\n station_ids.append(stationFinder(element))\n for i in range(10):\n clear_output(wait=True)\n\n # JOINING THE STATION NAMES AND IDS AND WRITING TO A TEXT FILE\n stationData = []\n for num in range(0,len(cleanedStations)-1):\n stationData.append(cleanedStations[num]+'_'+station_ids[num]+'\\n')\n with open('Station_Data.txt','w') as file:\n file.writelines(stationData)\n\n ############################################\n\n\n #SCRAPING COMMUTE TIMES FROM GOOGLE MAPS\n\n\n ############################################\n\n # SCRAPING COMMUTE TIMES FROM GOOGLE MAPS\n\n # IF USER DOESN'T HAVE THE COMMUTE TIMES IT WILL FETCH THE DATA\n if os.path.exists('Commute_Times.txt') == False:\n def get_driver():\n #set options for easier browsing\n options = webdriver.ChromeOptions()\n options.add_argument('disable-infobars')\n options.add_argument('start-maximized')\n options.add_argument('disable-dev-shm-usage')\n options.add_argument('no-sandbox')\n options.add_experimental_option('excludeSwitches',['enable-automation'])\n options.add_argument('disable-blink-features=AutomationControlled')\n driver = webdriver.Chrome(ChromeDriverManager().install())\n driver.get('https://www.google.com/maps/dir/')\n return driver\n def commuteTime(station,destination):\n driver = get_driver()\n accept_cookies = driver.find_element(by='xpath',value='/html/body/c-wiz/div/div/div/div[2]/div[1]/div[3]/div[1]/div[1]/form[2]/div/div/button')\n time.sleep(1)\n accept_cookies.click()\n time.sleep(2)\n public_transport = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[3]/div[1]/div[2]/div/div[2]/div/div/div/div[3]/button')\n public_transport.click()\n time.sleep(1)\n search_engine1 = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[3]/div[1]/div[2]/div/div[3]/div[1]/div[1]/div[2]/div[1]/div/input')\n search_engine1.send_keys(station)\n autofill1 = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[3]/div[1]/div[2]/div/div[3]/div[1]/div[1]/div[2]/button[1]')\n autofill1.click()\n time.sleep(1)\n search_engine2 = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[3]/div[1]/div[2]/div/div[3]/div[1]/div[2]/div[2]/div[1]/div/input')\n search_engine2.send_keys(destination)\n autofill2 = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[3]/div[1]/div[2]/div/div[3]/div[1]/div[2]/div[2]/button[1]')\n autofill2.click()\n leaveSelect = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[2]/span/div/div/div/div[1]')\n leaveSelect.click()\n time.sleep(3)\n departAt = driver.find_element(by='xpath',value='/html/body/div[6]/div[2]/div')\n departAt.click()\n time.sleep(3)\n timeSelect = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[2]/div[1]/span[1]/input')\n time.sleep(1)\n timeSelect.clear()\n time.sleep(1)\n timeSelect.send_keys('06:30')\n time.sleep(1)\n dateButton = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[2]/div[1]/span[2]/span[1]')\n #Friday Scrapes skip to Monday\n if dateButton.text.split(' ')[0] == 'Fri,':\n dateCycle = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[2]/div[1]/span[2]/span[2]/button[2]')\n dateCycle.click()\n time.sleep(1)\n dateCycle.click()\n time.sleep(1)\n dateCycle.click()\n time.sleep(1) \n #Saturday scrcapes skip to Monday\n elif dateButton.text.split(' ')[0] == 'Sat,':\n dateCycle = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[2]/div[1]/span[2]/span[2]/button[2]')\n dateCycle.click()\n time.sleep(1)\n dateCycle.click()\n time.sleep(1)\n #Sunday-Thursday Scrapes cycle to next day\n else:\n dateCycle = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[2]/div[1]/span[2]/span[2]/button[2]')\n dateCycle.click()\n time.sleep(1)\n time.sleep(5)\n duration = driver.find_element(by='xpath',value='/html/body/div[3]/div[9]/div[9]/div/div/div[1]/div[2]/div/div[1]/div/div/div[4]/div[1]/div[1]/div[2]/div[1]/div')\n return duration.text\n with open('Station_Data.txt','r') as file:\n rawData = file.readlines()\n strippedData = [element.strip() for element in rawData]\n stationNames = [element.split('_')[0] for element in strippedData]\n stationIds = [element.split('_')[1] for element in strippedData]\n commutes = []\n\n # Starting the scrap\n for element in tqdm(stationNames):\n print(element)\n if element != 'Cannon Street station':\n if element != 'London Bridge station':\n if element != 'Sandwich station':\n commutes.append(commuteTime(element,'London Bridge'))\n if element == 'Sandwich station':\n commutes.append(commuteTime('Sandwich CT13 9RA','London Bridge'))\n if element == 'Cannon Street station':\n commutes.append('0 min')\n if element == 'London Bridge station':\n commutes.append('0 min')\n for i in range(10):\n clear_output(wait=True)\n names_and_times = []\n for element in range(0,len(stationNames)):\n names_and_times.append(stationNames[element]+'_'+commutes[element])\n\n # Convert string times to minutes\n names1 = [element.split('_')[0] for element in names_and_times]\n times1 = [element.split('_')[1] for element in names_and_times]\n newTimes = []\n for element in times1:\n split = element.split(' ')\n if split[1] == 'min':\n newTimes.append(int(split[0]))\n if len(split) == 2 and split[1] == 'hr':\n newTimes.append((int(split[0]) * 60))\n if len(split) == 4 and split[1] == 'hr':\n newTimes.append((int(split[0]) * 60) + int(split[2]))\n\n #Save to file\n cleanedCommutes = []\n for element in range(0,len(stationNames)):\n cleanedCommutes.append(stationNames[element]+'_'+str(newTimes[element])+'\\n')\n with open('Commute_Times.txt','w') as file:\n file.writelines(cleanedCommutes)\n\n ############################################\n\n\n #SCRAPING PROPERTIES\n\n ############################################\n\n def propertyScraper(data):\n #print(stationName)\n stationName = data.split('_')[0]\n print(stationName)\n areaId = data.split('_')[1]\n\n def pageScraper(number):\n propertiesForSale = []\n errors = []\n junkData = ['Offers Over','Premium Listing','Offers in Excess of','Guide Price','Shared ownership','Viewing Advised','Coastal Location','Offers in Region of','Retirement','Just Launched','No Chain','Fixed Price','Flooring Package','*','Off-Street Parking','Sea View','Buy to Let','Close to Shops','New Listing','Balcony','Garage','Plot for sale','24 Hours Security','Riverside views','Move In Now','Shared Ownership','Attention Investors','Show Home Now Open','From','Star Buy','Auction','Garden','Town centre location','Ground floor','Last 1 Remaining','Part buy, part rent'] \n #STARTING THE SEARCH ON OTHER PAGEs\n page = number\n base_url = 'https://www.rightmove.co.uk/property-for-sale/find.html?locationIdentifier=STATION%{}&maxPrice=350000&minPrice=100000&radius=3.0&sortType=1&index={}&propertyTypes=bungalow%2Cdetached%2Cflat%2Csemi-detached%2Cterraced&includeSSTC=false&mustHave=&dontShow=retirement%2CsharedOwnership&furnishTypes=&keywords='.format(areaId,page)\n res = requests.get(base_url)\n soup = bs4.BeautifulSoup(res.text, 'html5lib' )\n # RESPONSE CHECK\n textData = []\n for element in soup.find_all('div', class_='propertyCard-wrapper'):\n textData.append([element.text])\n noNewLines = []\n for element in textData:\n tempList = []\n tempList2 = []\n for i in element:\n x = i.split('\\n')\n for j in x:\n if len(j) > 0:\n if j.isspace() == False:\n tempList.append(j)\n noNewLines.append(tempList)\n # TRIMING WHITESPACE AND FILTERING THE DATA TO REMOVE PROPERTY SPECIFIC TRAITS AND INFORMATION\n trimed_list = [] \n for element in noNewLines:\n tempList = []\n tempList2 = []\n for i in element:\n tempList.append(i.strip())\n for j in tempList:\n if j not in junkData:\n if 'SHARED' not in j:\n tempList2.append(j)\n trimed_list.append(tempList2[1:])\n # REMOVING THE RANDOM PREMIUM LISTING FROM THE LIST\n propertyList = trimed_list[1:]\n # IF THE PROPERTY DESCRIPTION IS SEPERATED THEN JOIN THE SPLIT STRINGS\n index = 0\n for element in propertyList:\n if len(element) == 15:\n tempList = []\n start = element[0:4]\n join = ' '.join(element[4:7])\n end = element[7:]\n for i in start:\n tempList.append(i)\n tempList.append(join)\n for i in end:\n tempList.append(i)\n propertyList[index] = tempList\n index += 1\n for element in propertyList:\n if len(element) != 13:\n errors.append(element)\n for element in propertyList:\n if element != ['Commercial', 'Development Microsite', 'Local call rate', 'Email agent']:\n propertiesForSale.append(element)\n # DATA VERFICATION\n verifiedPropertyList = []\n for element in propertiesForSale:\n if len(element) != 13:\n errors.append(element)\n if len(element) == 13:\n verifiedPropertyList.append(element)\n #print('Errors prior to final length check: ',len(errors))\n #print(errors)\n for element in errors:\n if len(element) == 13:\n verifiedPropertyList.append(element)\n for element in verifiedPropertyList:\n if len(element) != 13:\n print('Length Error',element)\n\n # REMOVING IRRELEVANT FIELDS FOR THE CSV FILE\n cleanedList = []\n for element in verifiedPropertyList:\n tempList = []\n tempList.append(stationName)\n usefulData1 = element[0:5]\n for i in usefulData1:\n tempList.append(i)\n dateAdded_byWhom = element[7:9]\n for i in dateAdded_byWhom:\n tempList.append(i)\n phoneNumber = element[10]\n tempList.append(phoneNumber)\n cleanedList.append(tempList)\n shortList = []\n for element in cleanedList:\n if element not in shortList:\n shortList.append(element)\n #print('Properties: ',len(shortList))\n return shortList\n\n # Creating the list of indexes that the URL needs to cycle through the pages\n rangeList = []\n for num in range(0,43):\n rangeList.append(num*24)\n\n #Using concurrent futures to scrape through the pages as efficiently as possible\n results = []\n with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:\n complete_scans = executor.map(pageScraper, rangeList)\n for i in complete_scans:\n results.append(i)\n print('Station complete')\n return results\n\n\n with open('Station_Data.txt','r') as file:\n rawData = file.readlines()\n strippedData = [element.strip() for element in rawData]\n\n # Running the function\n finalResults = []\n for element in tqdm(strippedData):\n finalResults.append(propertyScraper(element))\n for i in range(10):\n clear_output(wait=True)\n\n # Removing empty lists and restructuring the list \n cleanedList = []\n for element in finalResults:\n for i in element:\n for j in i:\n if len(j) > 0:\n cleanedList.append(j)\n\n # WRITING TO A CSV\n with open('Commute_Times.txt','r') as file:\n commuteData = [element.strip() for element in file.readlines()]\n commuteDataStations = [element.split('_')[0] for element in commuteData]\n commuteDataTimes = [element.split('_')[1] for element in commuteData]\n\n # CLEANING THE DISTANCE FROM STATION DATE AND ADDING COMMUTE TIMES TO EACH RECORD\n for element in cleanedList:\n index = cleanedList.index(element)\n stationCSV = element[0]\n commuteTimeIndex = commuteDataStations.index(stationCSV)\n element.append(commuteDataTimes[commuteTimeIndex])\n # CLEANING DISTANCE FROM STATION FIELD\n x = element[4].split(' ')\n cleanedList[index][4] = x[0]\n\n\n #REMOVING UNNECESSARY COMMAS\n for element in cleanedList:\n for i in element:\n index = element.index(i)\n commaJoin = str(''.join(i.split(',')))\n element[index] = commaJoin\n\n # FOR A VERY SMALL PERCENTAGE OF RESULTS WE NEED TO ALIGN THE RECORD CORRECTLY AGAIN\n for element in cleanedList:\n # CHECK DISTANCE FROM STATION CAN BE 'FLOATED'\n try:\n float(element[4])\n # IF NOT WE HAVE A MISALIGNMENT OF THE RECORD\n except ValueError:\n splitDescription = element[5].split(' ')\n element[3] = str(element[3]) + ' ' +str(splitDescription[0])\n # SPLITING DESCRIPTION AND FINDING THE DISTANCE FROM STATION WITH FLOAT CHECK\n for i in splitDescription:\n try:\n if float(i):\n element[4] = i\n except:\n pass\n # FIXING THE DESCRIPTION\n element[5] = element[5].split('station')[1]\n element[5] = element[5].lstrip()\n for i in element:\n index = element.index(i)\n\n header = ['Station','Price','Property_Size','Address','Distance from Station (miles)','Description','Date Added','Added By Whom',' Telephone Number','Commute Time']\n # FETCHING DATETIME AND CLEANING IT INTO A SAVEABLE FILE FORMAT\n dateTime = '-'.join(str(str('_'.join(str(datetime.now()).split(' '))).split('.')[0]).split(':'))\n #WRITING TO CSV\n with open('Rightmove_South-Eastern_Station_Properties_'+ dateTime +'.csv', 'w', encoding='UTF8', newline='') as f:\n writer = csv.writer(f)\n # write the header\n writer.writerow(header)\n # write multiple rows\n writer.writerows(cleanedList)\n","repo_name":"JackLacey18/South-Eastern-Station-Properties","sub_path":"Properties_Near_South-Eastern-Stations_Scraper.py","file_name":"Properties_Near_South-Eastern-Stations_Scraper.py","file_ext":"py","file_size_in_byte":20820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"7261642237","text":"from django.urls import path, include\nfrom rest_framework import routers\n\nfrom user.views import (\n CreateUserView,\n CreateTokenView,\n UserListViewSet,\n UserFollowingListView,\n UserFollowersListView,\n UserOwnProfileViewSet,\n)\n\nrouter = routers.DefaultRouter()\nrouter.register(\"users\", UserListViewSet, basename=\"users\")\nrouter.register(\"me\", UserOwnProfileViewSet, basename=\"me\")\n\nurlpatterns = [\n path(\"register/\", CreateUserView.as_view(), name=\"register\"),\n path(\"token/\", CreateTokenView.as_view(), name=\"token\"),\n path(\n \"following/<int:pk>/\",\n UserFollowingListView.as_view(),\n name=\"following-list\"\n ),\n path(\"followers/\", UserFollowersListView.as_view(), name=\"follower-lisr\"),\n path(\"\", include(router.urls)),\n]\n\napp_name = \"user\"\n","repo_name":"alina-boichenko/social-media-api","sub_path":"user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"15423059104","text":"\"\"\"create_main_tables\n\nRevision ID: 87cc92f51bb3\nRevises: \nCreate Date: 2022-05-19 02:53:18.793681\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic\nrevision = '87cc92f51bb3'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef create_users_table() -> None:\n op.create_table(\n \"users\",\n sa.Column(\"id\", sa.Integer, primary_key=True),\n sa.Column(\"name\", sa.Text, nullable=False, index=True),\n )\n\ndef upgrade() -> None:\n create_users_table()\n\n\ndef downgrade() -> None:\n op.drop_table(\"users\")","repo_name":"wingon1/fastapi-docker-starter","sub_path":"backend/app/db/migrations/versions/87cc92f51bb3_create_main_tables.py","file_name":"87cc92f51bb3_create_main_tables.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"29477652750","text":"import asyncio\n\nimport pytest\nfrom aleph.sdk import AuthenticatedAlephClient\nfrom aleph.sdk.chains.ethereum import get_fallback_account\nfrom aleph.sdk.conf import settings\nfrom aleph.sdk.vm.cache import TestVmCache\n\nfrom src.aars import AARS, Index, Record\nfrom src.aars.exceptions import AlreadyForgottenError\nfrom tests.aars import Book, Library\n\nAARS(\n session=AuthenticatedAlephClient(\n get_fallback_account(), api_server=settings.API_HOST\n ),\n cache=TestVmCache(),\n)\n\n\n@pytest.fixture(scope=\"session\")\ndef event_loop():\n yield AARS.session.http_session.loop\n asyncio.run(AARS.session.http_session.close())\n\n\n@pytest.fixture(scope=\"session\", autouse=True)\ndef create_indices(request):\n try:\n Index(Book, \"title\")\n Index(Book, [\"title\", \"author\"])\n Index(Library, on=\"name\")\n except ValueError:\n pass\n\n\n@pytest.mark.asyncio\nasync def test_deserialization_from_cache():\n book = await Book(title=\"Cached Book\", author=\"John Doe\").save()\n await asyncio.sleep(1)\n book.title = \"Cached Book 2: The Recaching\"\n await book.save()\n await asyncio.sleep(1)\n fetched_book = await Book.fetch(book.item_hash).first()\n assert book == fetched_book\n\n\n@pytest.mark.asyncio\nasync def test_multi_index():\n new_book = await Book(title=\"Lila\", author=\"Robert M. Pirsig\", year=1991).save()\n # wait a few secs\n await asyncio.sleep(1)\n should_be_none = await Book.filter(title=\"Lila\", author=\"Yo Momma\").all()\n assert len(should_be_none) == 0\n fetched_book = await Book.filter(title=\"Lila\", author=\"Robert M. Pirsig\").first()\n assert new_book == fetched_book\n\n\n@pytest.mark.asyncio\nasync def test_in_query():\n books = await asyncio.gather(\n Book(title=\"Siddhartha\", author=\"Hermann Hesse\", year=1922).save(),\n Book(title=\"Fahrenheit 451\", author=\"Ray Bradbury\", year=1953).save(),\n )\n await asyncio.sleep(1)\n fetched_books = await Book.filter(\n title__in=[\"Siddhartha\", \"Fahrenheit 451\"], year__in=[1922, 1953]\n ).all()\n assert books[0] in fetched_books\n assert books[1] in fetched_books\n\n\n@pytest.mark.asyncio\nasync def test_amending_record():\n book = await Book(title=\"Neurodancer\", author=\"William Gibson\").save()\n assert book.current_revision == 0\n book.title = \"Neuromancer\"\n book = await book.save()\n assert book.title == \"Neuromancer\"\n assert len(book.revision_hashes) == 2\n assert book.current_revision == 1\n assert book.revision_hashes[0] == book.item_hash\n assert book.revision_hashes[1] != book.item_hash\n await asyncio.sleep(1)\n old_book = await book.fetch_revision(rev_no=0)\n old_timestamp = old_book.timestamp\n assert old_book.title == \"Neurodancer\"\n new_book = await book.fetch_revision(rev_no=1)\n assert new_book.title == \"Neuromancer\"\n assert new_book.timestamp > old_timestamp\n assert book == new_book\n\n\n@pytest.mark.asyncio\nasync def test_store_and_index_record_of_records():\n books = await asyncio.gather(\n Book(title=\"Atlas Shrugged\", author=\"Ayn Rand\").save(),\n Book(title=\"The Martian\", author=\"Andy Weir\").save(),\n )\n new_library = await Library(name=\"The Library\", books=books).save()\n await asyncio.sleep(1)\n fetched_library = await Library.filter(name=\"The Library\").first()\n assert new_library == fetched_library\n\n\n@pytest.mark.asyncio\nasync def test_forget_object():\n forgettable_book = await Book(\n title=\"The Forgotten Book\", author=\"Mechthild Gläser\"\n ).save() # I'm sorry.\n await asyncio.sleep(1)\n await forgettable_book.forget()\n assert forgettable_book.forgotten is True\n await asyncio.sleep(1)\n assert len(await Book.fetch(forgettable_book.item_hash).all()) == 0\n with pytest.raises(AlreadyForgottenError):\n await forgettable_book.forget()\n\n\n@pytest.mark.asyncio\nasync def test_store_and_wrong_where_eq():\n new_book = await Book(title=\"Atlas Shrugged\", author=\"Ayn Rand\").save()\n assert new_book.title == \"Atlas Shrugged\"\n assert new_book.author == \"Ayn Rand\"\n with pytest.raises(KeyError):\n await Book.filter(title=\"Atlas Shrugged\", foo=\"bar\").all()\n\n\n@pytest.mark.asyncio\nasync def test_fetch_all_pagination():\n page_one = await Book.fetch_objects().page(1, 1)\n page_two = await Book.fetch_objects().page(2, 1)\n assert len(page_one) == 1\n assert len(page_two) == 1\n assert page_one[0] != page_two[0]\n\n\n@pytest.mark.asyncio\nasync def test_dict_field_save():\n class BookWithDictAuthor(Record):\n title: str\n author: dict\n\n book = await BookWithDictAuthor(\n title=\"Test Book\", author={\"first\": \"John\", \"last\": \"Doe\"}\n ).save()\n await asyncio.sleep(1)\n fetched_book = await BookWithDictAuthor.fetch(book.item_hash).first()\n assert fetched_book.author == {\"first\": \"John\", \"last\": \"Doe\"}\n","repo_name":"aleph-im/active-record-sdk","sub_path":"tests/aars_cached.py","file_name":"aars_cached.py","file_ext":"py","file_size_in_byte":4835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"37583583074","text":"import os\nfrom dotenv import load_dotenv\nfrom pymongo import MongoClient\n\n#Updates sources on the database\nload_dotenv()\nclient = MongoClient(os.environ.get(\"CLIENT\"))\nSRTDb = client.SRTDatabase\nsourceCollection = SRTDb.source\n\n\nsourceCollection.delete_many({})\nsrtCat = open(os.environ.get(\"SRTNPATH\")+\"/srt.cat\", \"r\")\nfor line in srtCat:\n\tif (line[:3].lower() == \"sou\"):\n\t\tsourceCollection.insert_one({\n\t\t\t\"source\": line.split(\" \")[7].replace(\"\\n\", \"\"),\n\t})\n\nsrtCat.close()\n","repo_name":"SRT-High-School-Comsmology-Project/SRT-Telescope-Interface","sub_path":"source_MongoDB.py","file_name":"source_MongoDB.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"17595215279","text":"\"\"\" adjacentElementsProduct\nGiven an array of integers, find the pair of adjacent elements that has the largest product and return that product.\n\nExample\n\nFor inputArray = [3, 6, -2, -5, 7, 3], the output should be\nadjacentElementsProduct(inputArray) = 21.\n\n7 and 3 produce the largest product.\n\nInput/Output\n\n [execution time limit] 4 seconds (py3)\n\n [input] array.integer inputArray\n\n An array of integers containing at least two elements.\n\n Guaranteed constraints:\n 2 ≤ inputArray.length ≤ 10,\n -1000 ≤ inputArray[i] ≤ 1000.\n\n [output] integer\n\n The largest product of adjacent elements.\n\"\"\"\n\n\ndef adjacentElementsProduct(inputArray):\n res = float('-inf')\n for i in range(len(inputArray)-1):\n res= max(res, inputArray[i]*inputArray[i+1])\n return res\n\n\nprint(adjacentElementsProduct([3, 6, -2, -5, 7, 3])) #21\nprint(adjacentElementsProduct([-1, -2])) #2\nprint(adjacentElementsProduct([5, 1, 2, 3, 1, 4])) #6\n\n","repo_name":"jamil-said/code-samples","sub_path":"Python/Python_code_challenges/adjacentElementsProduct.py","file_name":"adjacentElementsProduct.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"32"} +{"seq_id":"39274498423","text":"# 정렬\n\ndef solution(numbers):\n\tanswer = ''\n\tnumbers = list(map(str, numbers))\n\tList = []\n\tfor num in numbers:\n\t\toutput = num\n\t\ti = 0\n\t\twhile len(output) < 4:\n\t\t\toutput += num[i%len(num)]\n\t\t\ti+=1\n\t\tList.append({'origin': num, 'four': output})\n\tList.sort(reverse=True, key=lambda x: x['four'])\n\tfor L in List:\n\t\tanswer += L['origin']\n\tanswer = str(int(answer))\n\treturn answer","repo_name":"altmshfkgudtjr/Problem-Solving","sub_path":"Programmers/level 2/가장큰수.py","file_name":"가장큰수.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"11232504653","text":"# for <variable> in <string|list|tuple|set|dict>\n\n\n# string\n# for i in 'hello':\n# print(i)\n\n# list\n# for i in [1, 2, 3]:\n# print(i)\n\n# tuple\n# for i in ('a', 'b', 'c'):\n# print(i)\n\n# set\n# for i in {1, 2, 2, 3}:\n# print(i)\n\n# dict\n# key-value\n__dict = {'apple': 'red', 'orange': 'yellow', 'avocado': 'green'}\n# for key in __dict:\n# print(key)\n\n# for key, value in __dict.items():\n# print(key, '-', value)\n\n# -------------------\n# range(start, stop [, step = 1])\n# for i in range(5): # range(0, 5) # 前闭后开\n# print(i)\n\n# for i in range(2, 5):\n# print(i)\n\nfor i in range(5, 2, -1):\n print(i)","repo_name":"typinghare/ucas-algorithm","sub_path":"demo/for_demo.py","file_name":"for_demo.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"32"} +{"seq_id":"19303010378","text":"import os\nimport tkinter.filedialog\nfrom tkinter import Tk # from tkinter import Tk for Python 3.x\nfrom tkinter.filedialog import askopenfilename\nimport torch\n\nHOME_PATH = os.path.dirname(os.path.abspath(__file__)) + \"/\"\n\nWIKIPEDIA_HOME_PATH = HOME_PATH + \"Datasets/\" + \"wikipedia/\"\nWIKIPEDIA_DATA_PATH = WIKIPEDIA_HOME_PATH + \"texts/\"\n\nUNALTERED_FOLDER_NAME = \"u\"\n\ndef select_file(init_dir=HOME_PATH, choose_file=True):\n Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n if choose_file:\n filename = askopenfilename(initialdir=init_dir,\n defaultextension=\"txt\") # show an \"Open\" dialog box and return the path to the selected file\n return filename\n else:\n foldername = tkinter.filedialog.askdirectory(initialdir=init_dir)\n return foldername\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n\nnomi_write_token = \"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\"\nnomi_read_token = \"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\"\nannie_read_token = \"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\"\n\nglm_checkpoint = \"THUDM/chatglm3-6b\"\nllama_checkpoint = \"meta-llama/Llama-2-13b-chat-hf\"\nllama_local_checkpoint = \"/home/yunomi/.cache/huggingface/hub/models--meta-llama--Llama-2-13b-chat-hf/snapshots/c2f3ec81aac798ae26dcc57799a994dfbf521496/\"\n\nwikipedia_dataset_checkpoint = \"yu-nomi/wikipedia-2.156\"\nstandards_dataset_checkpoint = \"yu-nomi/standards-2.156\"\n","repo_name":"eeyu/LLMFineTuning2.156","sub_path":"paths.py","file_name":"paths.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"30289220463","text":"# Distribute Candy\r\n# Asked In: FlipkartAmazonMicrosoft\r\n# There are N children standing in a line with some rating value. You want to distribute a minimum number of candies to these children such that: Each child must have at least one candy. The children with higher ratings will have more candies than their neighbors. You need to write a program to calculate the minimum candies you must give.\r\n\r\n# Example:\r\n\r\n# Input: arr[] = [1, 5, 2, 1]\r\n# Output: 7\r\n# Explanation: Candies given = [1, 3, 2, 1]\r\n\r\n\r\nclass Main:\r\n def candy(self,ratings):\r\n if (ratings == None or len(ratings) == 0):\r\n return 0\r\n \r\n left = [0] * len(ratings)\r\n left[0] = 1\r\n right= [0] * len(ratings)\r\n right[len(ratings)-1] = 1\r\n result = 0\r\n\r\n for i in range(1,len(ratings),1):\r\n if (ratings[i] > ratings[i - 1]):\r\n left[i] = left[i - 1] + 1\r\n else:\r\n left[i] = 1\r\n \r\n for i in range(len(ratings) - 2,-1,-1):\r\n cur = 1\r\n if (ratings[i] > ratings[i + 1]):\r\n right[i] = right[i + 1] + 1\r\n else:\r\n right[i] = 1\r\n\t\t\t\r\n for i in range(0,len(ratings),1):\r\n result += max(right[i], left[i])\r\n\t\t \r\n return result\r\n\t\r\nm = Main()\r\nratings = [1,5,2,1]\r\nresult = m.candy(ratings)\r\nprint(result)\r\n","repo_name":"saurabhkumarkanaujia/DSA","sub_path":"Distribute Candy.py","file_name":"Distribute Candy.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"40711848813","text":"from pandac.PandaModules import *\nfrom direct.interval.IntervalGlobal import *\nfrom direct.directnotify import DirectNotifyGlobal\nfrom pirates.uberdog.UberDogGlobals import InventoryType\nfrom pirates.piratesbase import PiratesGlobals\nfrom pirates.effects.CannonExplosion import CannonExplosion\nfrom pirates.effects.CannonSplash import CannonSplash\nfrom pirates.effects.DirtClod import DirtClod\nfrom pirates.effects.DustCloud import DustCloud\nfrom pirates.effects.SmokeCloud import SmokeCloud\nfrom pirates.effects.RockShower import RockShower\nfrom pirates.effects.ShipSplintersA import ShipSplintersA\nfrom pirates.effects.DustRing import DustRing\nfrom pirates.effects.BlackSmoke import BlackSmoke\nfrom pirates.effects.LightSmoke import LightSmoke\nfrom pirates.effects.ExplosionFlip import ExplosionFlip\nfrom pirates.effects.ShockwaveRing import ShockwaveRing\nfrom pirates.effects.CameraShaker import CameraShaker\nfrom pirates.effects.FireTrail import FireTrail\nfrom pirates.effects.GreenBlood import GreenBlood\nfrom pirates.effects.HitFlashA import HitFlashA\nfrom pirates.effects.ShockwaveHit import ShockwaveHit\nfrom pirates.effects.WaspCloud import WaspCloud\nfrom pirates.effects.PoisonHit import PoisonHit\nfrom pirates.effects.FireballHit import FireballHit\nfrom pirates.effects.CurseHit import CurseHit\nfrom pirates.effects.ExplosionCloud import ExplosionCloud\nfrom pirates.effects.FadingSigil import FadingSigil\nfrom pirates.effects.FlashStar import FlashStar\nfrom pirates.effects.VoodooSmoke import VoodooSmoke\nfrom pirates.effects.SpectralSmoke import SpectralSmoke\nfrom pirates.effects.DrainLife import DrainLife\nfrom pirates.effects.Fire import Fire\nfrom pirates.effects.MuzzleFlash import MuzzleFlash\nfrom pirates.effects.DustRing import DustRing\nfrom pirates.effects.Sparks import Sparks\nfrom pirates.effects.SmokeBomb import SmokeBomb\nfrom pirates.effects.SmokePillar import SmokePillar\nfrom pirates.effects.FlamingDebris import FlamingDebris\nfrom pirates.effects.ShipDebris import ShipDebris\nfrom pirates.effects.RockDebris import RockDebris\nfrom pirates.effects.Explosion import Explosion\nfrom pirates.effects.ExplosionTip import ExplosionTip\nfrom pirates.effects.LightningStrike import LightningStrike\nfrom pirates.effects.MuzzleFlame import MuzzleFlame\nimport random\nskillSfxs = None\n\ndef getSkillSfx():\n global skillSfxs\n if not skillSfxs:\n skillSfxs = {\n InventoryType.GrenadeExplosion: loader.loadSfx('audio/sfx_grenade_impact.mp3'),\n InventoryType.GrenadeShockBomb: loader.loadSfx('audio/sfx_grenade_impact_stink_pot.mp3'),\n InventoryType.GrenadeFireBomb: loader.loadSfx('audio/sfx_grenade_impact_firebomb_explo.mp3'),\n InventoryType.GrenadeSmokeCloud: loader.loadSfx('audio/sfx_grenade_impact_smoke.mp3'),\n InventoryType.GrenadeSiege: loader.loadSfx('audio/sfx_grenade_impact.mp3')}\n \n\n\nclass ProjectileEffect:\n notify = DirectNotifyGlobal.directNotify.newCategory('ProjectileEffect')\n \n def __init__(self, cr, attackerId, hitObject, objType, pos, skillId, ammoSkillId, normal = None):\n self.cr = cr\n self.attackerId = attackerId\n self.normal = normal\n getSkillSfx()\n from pirates.ship.DistributedShip import DistributedShip\n from pirates.shipparts.DistributedShippart import DistributedShippart\n from pirates.battle.DistributedBattleAvatar import DistributedBattleAvatar\n from pirates.world.DistributedIsland import DistributedIsland\n self.projVelocity = ((25, 0), (0, 25), (-25, 0), (0, -25))\n if not objType:\n if isinstance(hitObject, DistributedShip):\n objType = PiratesGlobals.COLL_SHIPPART\n elif isinstance(hitObject, DistributedShippart):\n objType = PiratesGlobals.COLL_SHIPPART\n elif isinstance(hitObject, DistributedBattleAvatar):\n objType = PiratesGlobals.COLL_AV\n elif isinstance(hitObject, DistributedIsland):\n objType = PiratesGlobals.COLL_LAND\n else:\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n return\n \n if objType == PiratesGlobals.COLL_AV:\n self.avatarHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_MONSTER:\n self.monsterHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_DESTRUCTIBLE:\n self.propHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_SHIPPART:\n self.propHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_SEA:\n self.waterHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_LAND:\n self.groundHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_BLOCKER:\n self.blockerHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_BLDG:\n self.buildingHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_GRAPPLE_TARGET:\n self.grappleHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_CANNON:\n self.cannonHitEffect(hitObject, pos, skillId, ammoSkillId)\n elif objType == PiratesGlobals.COLL_FORT:\n self.fortHitEffect(hitObject, pos, skillId, ammoSkillId)\n else:\n self.notify.warning('playEffect: unknown objType: %s' % objType)\n\n def playSfx(self, ammoSkillId, node, startTime = 0):\n sfx = skillSfxs.get(ammoSkillId)\n if sfx:\n base.playSfx(sfx, node = node, time = startTime, cutoff = 400)\n \n def basicHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n from pirates.battle import WeaponGlobals\n attacker = self.cr.doId2do.get(self.attackerId)\n aoeRadius = self.cr.battleMgr.getModifiedAttackAreaRadius(attacker, skillId, ammoSkillId)\n if config.GetBool('show-aoe-radius', 0):\n s = loader.loadModelCopy('models/misc/smiley')\n s.reparentTo(render)\n s.setPos(hitObject, pos)\n s.setScale(aoeRadius)\n s.setTransparency(1)\n s.setColorScale(1.0, 0.5, 0.5, 0.4)\n \n if ammoSkillId == InventoryType.CannonRoundShot or ammoSkillId == InventoryType.CannonChainShot or ammoSkillId == InventoryType.CannonBullet or ammoSkillId == InventoryType.CannonSkull or ammoSkillId == InventoryType.CannonBarShot or ammoSkillId == InventoryType.CannonFury:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n explosionEffect = ExplosionFlip.getEffect()\n if explosionEffect:\n explosionEffect.reparentTo(base.effectsRoot)\n explosionEffect.setPos(hitObject, pos)\n explosionEffect.setScale(0.8)\n explosionEffect.play()\n \n shipSplintersAEffect = ShipSplintersA.getEffect()\n if shipSplintersAEffect:\n shipSplintersAEffect.reparentTo(hitObject)\n shipSplintersAEffect.setPos(hitObject, pos)\n shipSplintersAEffect.play()\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n smokeCloudEffect = SmokeCloud.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.reparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.setScale(1.0)\n smokeCloudEffect.spriteScale = 1.0\n smokeCloudEffect.radius = 7.0\n smokeCloudEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.wrtReparentTo(base.effectsRoot)\n if self.normal:\n shockwaveRingEffect.lookAt(shockwaveRingEffect.getPos() + self.normal)\n \n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 6.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(120.0)\n \n elif ammoSkillId == InventoryType.CannonFirebrand or ammoSkillId == InventoryType.CannonFlamingSkull:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n explosionEffect = ExplosionFlip.getEffect()\n if explosionEffect:\n explosionEffect.wrtReparentTo(base.effectsRoot)\n explosionEffect.setPos(hitObject, pos)\n explosionEffect.setScale(0.8)\n explosionEffect.play()\n \n shipSplintersAEffect = ShipSplintersA.getEffect()\n if shipSplintersAEffect:\n shipSplintersAEffect.wrtReparentTo(hitObject)\n shipSplintersAEffect.setPos(hitObject, pos)\n shipSplintersAEffect.play()\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n smokeCloudEffect = SmokeCloud.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.wrtReparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.setScale(1.0)\n smokeCloudEffect.spriteScale = 1.0\n smokeCloudEffect.radius = 7.0\n smokeCloudEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 6.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(100.0)\n \n elif ammoSkillId == InventoryType.CannonExplosive:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n effect = Explosion.getEffect()\n if effect:\n effect.wrtReparentTo(hitObject)\n effect.setPos(hitObject, pos)\n effect.effectScale = 1.0\n effect.radius = aoeRadius / 1.5\n effect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n dustRingEffect = DustRing.getEffect()\n if dustRingEffect:\n dustRingEffect.wrtReparentTo(hitObject)\n dustRingEffect.setPos(hitObject, pos)\n dustRingEffect.play()\n \n for i in range(2):\n effect = FlamingDebris.getEffect()\n if effect:\n effect.wrtReparentTo(base.effectsRoot)\n effect.setPos(hitObject, pos)\n effect.duration = 4\n effect.velocityX = self.projVelocity[i][0]\n effect.velocityY = self.projVelocity[i][1]\n effect.play()\n \n shipSplintersAEffect = ShipSplintersA.getEffect()\n if shipSplintersAEffect:\n shipSplintersAEffect.wrtReparentTo(hitObject)\n shipSplintersAEffect.setPos(hitObject, pos)\n shipSplintersAEffect.play()\n \n smokeCloudEffect = SmokeCloud.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.wrtReparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.setScale(3.0)\n smokeCloudEffect.spriteScale = 4.0\n smokeCloudEffect.radius = aoeRadius / 1.5\n smokeCloudEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n for i in range(2):\n effect = FlamingDebris.getEffect()\n if effect:\n effect.wrtReparentTo(base.effectsRoot)\n effect.setPos(hitObject, pos)\n effect.duration = 4\n effect.velocityX = self.projVelocity[i][0]\n effect.velocityY = self.projVelocity[i][1]\n effect.play()\n \n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.wrtReparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 6.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(300.0)\n \n elif ammoSkillId == InventoryType.CannonThunderbolt:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.wrtReparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n \n shipSplintersAEffect = ShipSplintersA.getEffect()\n if shipSplintersAEffect:\n shipSplintersAEffect.wrtReparentTo(hitObject)\n shipSplintersAEffect.setPos(hitObject, pos)\n shipSplintersAEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n flashEffect = MuzzleFlash.getEffect()\n if flashEffect:\n flashEffect.wrtReparentTo(base.effectsRoot)\n flashEffect.flash.setScale(3000)\n flashEffect.setPos(hitObject, pos)\n flashEffect.setZ(hitObject, 100)\n flashEffect.startCol = Vec4(0.5, 0.8, 1, 1)\n flashEffect.fadeTime = 0.2\n flashEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.06\n cameraShakerEffect.shakePower = 4.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(300.0)\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n smokeCloudEffect = SmokeCloud.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.wrtReparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.setScale(1.0)\n smokeCloudEffect.spriteScale = 1.0\n smokeCloudEffect.radius = 7.0\n smokeCloudEffect.play()\n \n effect = LightningStrike.getEffect()\n if effect:\n effect.wrtReparentTo(base.effectsRoot)\n effect.setPos(hitObject, pos)\n effect.play()\n\n elif ammoSkillId == InventoryType.GrenadeExplosion:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n explosionEffect = ExplosionFlip.getEffect()\n if explosionEffect:\n explosionEffect.reparentTo(base.effectsRoot)\n explosionEffect.setPos(hitObject, pos)\n explosionEffect.setScale(1.0)\n explosionEffect.play()\n self.playSfx(ammoSkillId, explosionEffect)\n \n if base.launcher.getPhaseComplete(3):\n for i in range(random.randint(2, 4)):\n debrisEffect = RockDebris.getEffect()\n if debrisEffect:\n debrisEffect.reparentTo(base.effectsRoot)\n debrisEffect.setPos(hitObject, pos)\n debrisEffect.offsetEndPlaneZFrom(hitObject.getZ())\n debrisEffect.debris.setScale(0.4)\n debrisEffect.radiusDist = 20\n debrisEffect.minHeight = 30\n debrisEffect.maxHeight = 100\n if debrisEffect.testTrajectory():\n debrisEffect.play()\n else:\n debrisEffect.cleanUpEffect()\n\n flashEffect = MuzzleFlash.getEffect()\n if flashEffect:\n flashEffect.reparentTo(base.effectsRoot)\n flashEffect.flash.setScale(100)\n flashEffect.setPos(hitObject, pos)\n flashEffect.startCol = Vec4(0.7, 0.7, 0.7, 1)\n flashEffect.fadeTime = 0.2\n flashEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n shipSplintersAEffect = ShipSplintersA.getEffect()\n if shipSplintersAEffect:\n shipSplintersAEffect.reparentTo(hitObject)\n shipSplintersAEffect.setPos(hitObject, pos)\n shipSplintersAEffect.play()\n \n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.reparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.reparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 6.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(80.0)\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n smokeCloudEffect = SmokeCloud.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.reparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.setScale(1.0)\n smokeCloudEffect.spriteScale = 1.0\n smokeCloudEffect.radius = 7.0\n smokeCloudEffect.play()\n\n elif ammoSkillId == InventoryType.GrenadeShockBomb:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n explosionEffect = ExplosionFlip.getEffect()\n if explosionEffect:\n explosionEffect.reparentTo(base.effectsRoot)\n explosionEffect.setPos(hitObject, pos)\n explosionEffect.setScale(1.0)\n explosionEffect.play()\n self.playSfx(ammoSkillId, explosionEffect)\n \n flashEffect = MuzzleFlash.getEffect()\n if flashEffect:\n flashEffect.reparentTo(base.effectsRoot)\n flashEffect.flash.setScale(100)\n flashEffect.setPos(hitObject, pos)\n flashEffect.startCol = Vec4(0.7, 0.7, 0.7, 1)\n flashEffect.fadeTime = 0.2\n flashEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.reparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 3.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(80.0)\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n smokeCloudEffect = SmokeCloud.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.reparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.setScale(1.0)\n smokeCloudEffect.spriteScale = 1.0\n smokeCloudEffect.radius = 7.0\n smokeCloudEffect.play()\n \n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.reparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n dustRingEffect = DustRing.getEffect()\n if dustRingEffect:\n dustRingEffect.reparentTo(hitObject)\n dustRingEffect.setPos(hitObject, pos)\n dustRingEffect.play()\n\n elif ammoSkillId == InventoryType.GrenadeSiege:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n smokePillarEffect = SmokePillar.getEffect()\n if smokePillarEffect:\n smokePillarEffect.reparentTo(hitObject)\n smokePillarEffect.setPos(hitObject, pos)\n smokePillarEffect.setScale(1.0)\n smokePillarEffect.spriteScale = 1.0\n smokePillarEffect.radius = 7.0\n smokePillarEffect.play()\n \n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.reparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n shipSplintersAEffect = ShipSplintersA.getEffect()\n if shipSplintersAEffect:\n shipSplintersAEffect.reparentTo(hitObject)\n shipSplintersAEffect.setPos(hitObject, pos)\n shipSplintersAEffect.play()\n \n explosionEffect = ExplosionFlip.getEffect()\n if explosionEffect:\n explosionEffect.reparentTo(base.effectsRoot)\n explosionEffect.setPos(hitObject, pos)\n explosionEffect.setScale(2.0)\n explosionEffect.play()\n self.playSfx(ammoSkillId, explosionEffect)\n \n for i in range(random.randint(3, 6)):\n debrisEffect = RockDebris.getEffect()\n if debrisEffect:\n debrisEffect.reparentTo(base.effectsRoot)\n debrisEffect.setPos(hitObject, pos)\n debrisEffect.offsetEndPlaneZFrom(hitObject.getZ())\n debrisEffect.debris.setScale(0.8)\n debrisEffect.radiusDist = 30\n debrisEffect.minHeight = 30\n debrisEffect.maxHeight = 120\n if debrisEffect.testTrajectory():\n debrisEffect.play()\n else:\n debrisEffect.cleanUpEffect()\n \n flashEffect = MuzzleFlash.getEffect()\n if flashEffect:\n flashEffect.reparentTo(base.effectsRoot)\n flashEffect.flash.setScale(200)\n flashEffect.setPos(hitObject, pos)\n flashEffect.startCol = Vec4(0.7, 0.7, 0.7, 1)\n flashEffect.fadeTime = 0.2\n flashEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n dustRingEffect = DustRing.getEffect()\n if dustRingEffect:\n dustRingEffect.reparentTo(hitObject)\n dustRingEffect.setPos(hitObject, pos)\n dustRingEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.06\n cameraShakerEffect.shakePower = 4.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(80.0)\n \n elif ammoSkillId == InventoryType.GrenadeFireBomb:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n flashEffect = MuzzleFlash.getEffect()\n if flashEffect:\n flashEffect.wrtReparentTo(base.effectsRoot)\n flashEffect.flash.setScale(100)\n flashEffect.setPos(hitObject, pos)\n flashEffect.startCol = Vec4(0.7, 0.7, 0.7, 1)\n flashEffect.fadeTime = 0.2\n flashEffect.play()\n self.playSfx(ammoSkillId, flashEffect)\n \n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.wrtReparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n fireEffect = Fire.getEffect()\n if fireEffect:\n fireEffect.wrtReparentTo(base.effectsRoot)\n fireEffect.setPos(hitObject, pos + Vec3(0, 0, -.5))\n fireEffect.effectScale = 0.5\n fireEffect.duration = 2.5\n fireEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n blackSmokeEffect = LightSmoke.getEffect()\n if blackSmokeEffect:\n blackSmokeEffect.wrtReparentTo(base.effectsRoot)\n blackSmokeEffect.setPos(hitObject, pos)\n blackSmokeEffect.duration = 4.0\n blackSmokeEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 2.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(80.0)\n \n elif ammoSkillId == InventoryType.GrenadeSmokeCloud:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n smokeCloudEffect = SmokeBomb.getEffect()\n if smokeCloudEffect:\n smokeCloudEffect.reparentTo(hitObject)\n smokeCloudEffect.setPos(hitObject, pos)\n smokeCloudEffect.radius = aoeRadius / 1.5\n smokeCloudEffect.play()\n self.playSfx(ammoSkillId, smokeCloudEffect)\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n flashEffect = MuzzleFlash.getEffect()\n if flashEffect:\n flashEffect.wrtReparentTo(base.effectsRoot)\n flashEffect.flash.setScale(100)\n flashEffect.setPos(hitObject, pos)\n flashEffect.startCol = Vec4(0.7, 0.7, 0.7, 1)\n flashEffect.fadeTime = 0.2\n flashEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.wrtReparentTo(base.effectsRoot)\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.size = aoeRadius * 4\n shockwaveRingEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.04\n cameraShakerEffect.shakePower = 2.0\n cameraShakerEffect.numShakes = 2\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(80.0)\n\n def propHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n \n def shipHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n def avatarHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n \n def blockerHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n for i in range(random.randint(2, 4)):\n debrisEffect = RockDebris.getEffect()\n if debrisEffect:\n debrisEffect.reparentTo(base.effectsRoot)\n debrisEffect.setPos(hitObject, pos)\n debrisEffect.offsetEndPlaneZFrom(hitObject.getZ())\n debrisEffect.debris.setScale(random.random() * 3)\n debrisEffect.radiusDist = 40\n debrisEffect.minHeight = 50\n debrisEffect.maxHeight = 100\n if debrisEffect.testTrajectory():\n debrisEffect.play()\n else:\n debrisEffect.cleanUpEffect()\n \n def groundHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n if ammoSkillId == InventoryType.CannonRoundShot or ammoSkillId == InventoryType.CannonChainShot or ammoSkillId == InventoryType.CannonBullet or ammoSkillId == InventoryType.CannonSkull or ammoSkillId == InventoryType.CannonBarShot:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n cannonExplosion = CannonExplosion.getEffect()\n if cannonExplosion:\n cannonExplosion.wrtReparentTo(base.effectsRoot)\n cannonExplosion.setScale(1.0)\n cannonExplosion.setPos(hitObject, pos)\n cannonExplosion.play()\n \n rockShowerEffect = RockShower.getEffect()\n if rockShowerEffect:\n rockShowerEffect.wrtReparentTo(hitObject)\n rockShowerEffect.setPos(hitObject, pos)\n rockShowerEffect.play()\n\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsMedium:\n dustCloudEffect = DustCloud.getEffect()\n if dustCloudEffect:\n dustCloudEffect.wrtReparentTo(hitObject)\n dustCloudEffect.setPos(hitObject, pos)\n dustCloudEffect.play()\n \n shockwaveRingEffect = ShockwaveRing.getEffect()\n if shockwaveRingEffect:\n shockwaveRingEffect.wrtReparentTo(base.effectsRoot)\n shockwaveRingEffect.size = 40\n shockwaveRingEffect.setPos(hitObject, pos)\n shockwaveRingEffect.play()\n \n cameraShakerEffect = CameraShaker()\n cameraShakerEffect.wrtReparentTo(hitObject)\n cameraShakerEffect.setPos(hitObject, pos)\n cameraShakerEffect.shakeSpeed = 0.06\n cameraShakerEffect.shakePower = 6.0\n cameraShakerEffect.numShakes = 3\n cameraShakerEffect.scalePower = 1\n cameraShakerEffect.play(80.0)\n \n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsHigh:\n dirtClodEffect = DirtClod.getEffect()\n if dirtClodEffect:\n dirtClodEffect.wrtReparentTo(hitObject)\n dirtClodEffect.setPos(hitObject, pos)\n dirtClodEffect.play()\n\n else:\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n def buildingHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n def waterHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n np = render.attachNewNode('temp')\n np.setPos(hitObject, pos)\n pos = np.getPos(render)\n np.removeNode()\n if base.cr.activeWorld.getWater():\n entryWaterHeight = base.cr.activeWorld.getWater().calcHeight(pos[0], pos[1])\n else:\n entryWaterHeight = pos[2]\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n splashEffect = CannonSplash.getEffect()\n if splashEffect:\n splashEffect.wrtReparentTo(render)\n splashEffect.setPos(pos[0], pos[1], entryWaterHeight)\n splashEffect.play()\n\n def monsterHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n if ammoSkillId == InventoryType.CannonRoundShot or ammoSkillId == InventoryType.CannonChainShot or ammoSkillId == InventoryType.CannonBullet or ammoSkillId == InventoryType.CannonSkull or ammoSkillId == InventoryType.CannonBarShot:\n pass\n else:\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n def grappleHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n if ammoSkillId == InventoryType.CannonGrappleHook:\n if base.options.getSpecialEffectsSetting() >= base.options.SpecialEffectsLow:\n hitFlashA = HitFlashA.getEffect()\n if hitFlashA:\n hitFlashA.wrtReparentTo(base.effectsRoot)\n hitFlashA.setPos(hitObject, pos)\n hitFlashA.play()\n\n else:\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n def cannonHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n def fortHitEffect(self, hitObject, pos, skillId, ammoSkillId):\n self.basicHitEffect(hitObject, pos, skillId, ammoSkillId)\n\n\n","repo_name":"PiratesOnlineClassic/pirates-online-classic","sub_path":"pirates/effects/ProjectileEffect.py","file_name":"ProjectileEffect.py","file_ext":"py","file_size_in_byte":36439,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"32"} +{"seq_id":"29909728714","text":"from io import StringIO\nfrom timeit import default_timer as timer\nfrom datetime import timedelta, datetime\nimport pandas as pd\n\n\n# 0. 44 Code Lines\n# 1. Получаем год из даты которую запросили\n# 2. Прибавляем к году N лет (в целом можно хоть 1000 лет) (Это доп параметр)\n# 3. Генерим возможные варианты дат без ограничений\n# 5. Создаем доп колонку с днем недели и тип int32\n# 6. Отбираем только те которые вошли в заданные дни недели\n# 7. Ищем даты больше требуемой\n# 8. Берем первый элемент из итогового массива - это и есть ответ.\ndef calculate_date_nice(date_string='09.07.2010 23:36',\n day_matrix='0,45;12;1,2,6;3,6,14,18,21,24,28;1,2,3,4,5,6,7,8,9,10,11,12;',\n years_count=1):\n start_time = timer()\n\n print('Method NICE. date_string = {0} day_matrix={1} years_count={2}'.format(date_string, day_matrix, years_count))\n\n if years_count < 1:\n print('Нужно минимум +1 год. Указали {0}.'.format(years_count))\n end_time = timer()\n return None, timedelta(seconds=end_time - start_time)\n\n df = pd.read_csv(StringIO(day_matrix),\n header=None, usecols=[0, 1, 2, 3, 4], names=['minutes', 'hours', 'weekdays', 'days', 'months'],\n sep=';', converters={i: str for i in range(5)})\n dfs = {}\n for col in df:\n dfs[col] = df[col].str.split(',', expand=True)\n\n years = [pd.to_datetime(date_string, format='%d.%m.%Y %H:%M').year + year for year in range(years_count)]\n\n dataset = []\n for year in years:\n for month in dfs['months']:\n for day in dfs['days']:\n for hour in dfs['hours']:\n for minute in dfs['minutes']:\n try:\n dataset.append(\n datetime(int(year), int(dfs['months'][month][0]), int(dfs['days'][day][0]),\n int(dfs['hours'][hour][0]), int(dfs['minutes'][minute][0])))\n except ValueError:\n pass\n if len(dataset) > 0:\n df_result = pd.DataFrame(dataset, columns=['date'])\n df_result['weekday'] = pd.Series(df_result.date.dt.strftime(\"%w\"), dtype='int32')\n df_result = df_result[df_result['weekday'].isin([int(weekday) - 1 for weekday in dfs['weekdays'].iloc[0]])]\n\n mask = (df_result['date'] > pd.to_datetime(date_string, format='%d.%m.%Y %H:%M'))\n df_result_final = df_result.loc[mask]\n\n end_time = timer()\n if len(df_result_final) > 0:\n print('Next date is: {0}; Elapsed time: {1}'.format(df_result_final.iloc[0].date.strftime(\"%d.%m.%Y %H:%M\"),\n timedelta(seconds=end_time - start_time)))\n return df_result_final.iloc[0], timedelta(seconds=end_time - start_time)\n\n end_time = timer()\n print('Next date is: None; Elapsed time: {0}'.format(timedelta(seconds=end_time - start_time)))\n return None, timedelta(seconds=end_time - start_time)\n","repo_name":"vaavaa/Custom-Calendar","sub_path":"pandas_python.py","file_name":"pandas_python.py","file_ext":"py","file_size_in_byte":3335,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"8852529672","text":"\"\"\"\nThis file contains the BootstrapModel class. This class is a pytorch module and is used for training the entity embeddings.\nTODO adujst comments cause init method changed\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nimport numpy as np\n\n\nclass BootstrapModel(nn.Module):\n \"\"\"\n This Class is used for sampling positive and negative words from a distribution and calculating a triplet margin loss for an entity embedding and the sampel words.\n \"\"\"\n def __init__(self, word_entity_dist, word_dist, fasttext_embeddings, entity_embeddings, margin, num_pos_sampels, num_neg_sampels, sec_param=None, max_norm=None):\n \"\"\"\n Input:\n word_entity_dist: numpy-array of shape (num_entities, num_words) holding the positive distribution P(w|e)\n word_dist: numpy-array of shape (num_words,) holding the negative distribution P(w)\n fasttext_embeddings: tensor holding the fasttext vectors\n entity_embeddings: tensor holding pretrained entity-embeddings or None\n margin: float, the margin that should separate the distance of entity-emb. to pos-word and the distance of entity-emb. to neg-word\n num_pos_sampels: integer, the number of positive words to sampel in a forward call\n num_neg_sampels: integer, the number of negative words to sampel for each positive sampel\n reg_method: the method to keep the learned embeddings on unit-sphere. allowed values ('None','max_norm')\n \"\"\"\n super(BootstrapModel, self).__init__()\n #\n # Hyper-Parameters (Tunabel)\n self.num_pos_sampels = num_pos_sampels\n self.num_neg_sampels = num_neg_sampels\n self.margin = margin\n self.sec_param = sec_param\n self.max_norm = max_norm\n # Hyper-Parameters (Fixed by inserted FastText-Embeddings and Word-Counts)\n self.num_entities = word_entity_dist.shape[0]\n self.num_words = word_entity_dist.shape[1]\n self.embedding_size = fasttext_embeddings.shape[1]\n # word-distributions (Used for sampeling words) (Must be numpy-arrays)\n self.word_entity_dist = word_entity_dist \n self.word_dist = word_dist\n # FastText-Embedding-Component (Fixed)\n self.fasttext_embeddings = nn.Embedding.from_pretrained(fasttext_embeddings, freeze=True)\n # New or specified Entity-Embeddings (Not fixed)\n print(\" DEBUG: max_norm=\", self.max_norm)\n if entity_embeddings is None:\n self.entity_embeddings = nn.Embedding(num_embeddings=self.num_entities,\n embedding_dim=self.embedding_size,\n max_norm=self.max_norm)\n nn.init.normal_(self.entity_embeddings.weight, mean=0, std=1)\n else:\n self.entity_embeddings = nn.Embedding.from_pretrained(entity_embeddings, freeze=False, max_norm=self.max_norm)\n print(\" DEBUG: Created BootstrapModel with {} embeddings (Finetune={})\".format(self.entity_embeddings.weight.shape[0],\n entity_embeddings is not None))\n def _normalize_(self):\n \"\"\"\n Replaces the entity embeddings to a length of one.\n \"\"\"\n with torch.no_grad():\n new_weight = self.entity_embeddings.weight\n new_weight /= torch.linalg.norm(new_weight, dim=1).view(-1,1)\n self.entity_embeddings.weight = new_weight\n \n def _sampel_positive_(self, entity_idx, cuda):\n \"\"\"\n Sampels positive word indices for specified entity index\n Returns sampled indices and probs of indices.\n \"\"\"\n with torch.no_grad():\n pos_idxs = np.random.choice(self.num_words, size=self.num_pos_sampels, p=self.word_entity_dist[entity_idx])\n pos_probs = torch.FloatTensor(self.word_entity_dist[entity_idx, pos_idxs])\n pos_idxs = torch.LongTensor(pos_idxs)\n if cuda:\n pos_probs = pos_probs.cuda()\n return pos_idxs, pos_probs\n \n def _sampel_negative_(self, cuda):\n \"\"\"\n Sampels negative word indices.\n Returns sampled indices and probs of indices\n \"\"\"\n with torch.no_grad():\n neg_idxs = np.random.choice(self.num_words, size=self.num_neg_sampels, p=self.word_dist)\n neg_probs = torch.FloatTensor(self.word_dist[neg_idxs].tolist())\n neg_idxs = torch.LongTensor(neg_idxs)\n if cuda:\n neg_probs = neg_probs.cuda()\n return neg_idxs, neg_probs\n \n def _zero_(self, cuda):\n \"\"\"\n creates a zero tensor\n \"\"\"\n if cuda:\n return torch.zeros(1).cuda()\n return torch.zeros(1)\n \n def _cos_dist_(self, x1, x2, eps=1e-8):\n \"\"\"\n Description:\n Calculates the cosine distance between vectors x1 and x2.\n Is trackable wrt. gradients.\n returns tensor of shape (1,)\n \"\"\"\n n1 = torch.linalg.norm(x1)\n n2 = torch.linalg.norm(x2)\n den = (n1*n2) if (n1*n2)>0 else eps\n cossim = torch.dot(x1, x2)/den\n cosdist = 1-cossim\n return cosdist\n \n def _euc_dist_(self, x1, x2):\n dist = torch.linalg.norm(x2-x1)\n return dist\n \n def forward(self, entity_idx):\n \"\"\"\n Description:\n For the specified entity_idx some positive and some negative words are sampled.\n For these sampels a triplet ranking loss is calculated and returend.\n Input:\n entity_idx: A LongTensor of shape (1,) that holds a entity index\n Returns:\n A FloatTensor of shape (1,) holding the calculated loss.\n \"\"\"\n cuda_flag = entity_idx.is_cuda\n # get entity_vector\n evec = self.entity_embeddings(entity_idx).squeeze()\n # sample positive words\n pos_word_idxs, pos_probs = self._sampel_positive_(entity_idx, cuda_flag)\n #print(\"Pos:\",[self.debug_map[pi.item()] for pi in pos_word_idxs])\n pos_vecs = self.fasttext_embeddings(pos_word_idxs)\n if cuda_flag:\n pos_vecs = pos_vecs.cuda()\n # calculate expected loss over all positive words\n loss = self._zero_(cuda_flag)\n #\n if self.sec_param is not None:\n all_norms = [torch.linalg.norm(evec).view(1), torch.linalg.norm(pos_vecs, dim=1).view(-1)]\n #\n for j in range(self.num_pos_sampels):\n # sampel negative words\n neg_word_idxs, neg_probs = self._sampel_negative_(cuda_flag)\n neg_vecs = self.fasttext_embeddings(neg_word_idxs)\n if cuda_flag:\n neg_vecs = neg_vecs.cuda()\n # \n if self.sec_param is not None:\n all_norms.append(torch.linalg.norm(neg_vecs, dim=1).view(-1))\n # calculate expected loss over all negative words\n negative_loss = self._zero_(cuda_flag)\n for k in range(self.num_neg_sampels):\n pvec = pos_vecs[j]\n nvec = neg_vecs[k]\n # calculate similarity-distance\n pdist = self._cos_dist_(evec, pvec) ########\n ndist = self._cos_dist_(evec, nvec) ########\n # calculate hinge-loss of pos-neg-pair\n l = torch.relu(self.margin + pdist - ndist)\n negative_loss = negative_loss + (neg_probs[k]*l)\n loss = loss + (pos_probs[j]*negative_loss)\n #\n if self.sec_param is not None:\n mean_norm = torch.mean(torch.cat(all_norms))\n ent_norm = torch.linalg.norm(evec)\n spherical_constrain = torch.square(ent_norm-mean_norm)\n loss = loss + self.sec_param*spherical_constrain\n return loss\n \n def to(self, device):\n \"\"\"\n Sets the module on specified device.\n \"\"\"\n self.entity_embeddings.to(device)\n return self\n","repo_name":"Florian1Omiecienski/bachelor_arbeit","sub_path":"entity-embedding-bootstrap/code/bootstrap_model.py","file_name":"bootstrap_model.py","file_ext":"py","file_size_in_byte":7975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"862960671","text":"import os\nimport numpy as np\nimport sys, unicodedata\n# In[2]:\ntbl = dict.fromkeys(i for i in range(sys.maxunicode)\n if unicodedata.category(chr(i)).startswith('P'))\n# In[4]:\nfor root,dirs, files in os.walk('tsv_files/'):\n tsv_list = [file for file in files if file[-3:]==\"tsv\"]\nwhole_class_info=[[],[],[]]\nwith open(root+\"assess_pregnancy.tsv\") as cur_tsv:\n separated_data = [line.split('\\t') for line in cur_tsv.readlines()]\n separated_data = np.asarray(separated_data)\n data = separated_data[:,0]\n labels = separated_data[:,1]\n labels = [int(label) for label in labels]\n for idx,label in enumerate(labels):\n whole_class_info[label].append(data[idx].translate(tbl))\n for idx, item in enumerate(whole_class_info):\n with open(\"preg\"+os.sep+str(idx)+\".txt\",\"w+\") as write_f:\n write_f.write(\"\\n\".join(item))\n","repo_name":"SMAT-Lab/Scalpel","sub_path":"tests/test-cases/with_stmt_case.py","file_name":"with_stmt_case.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":242,"dataset":"github-code","pt":"32"} +{"seq_id":"39747491802","text":"import matplotlib.pyplot as plt\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix\nfrom data_loader import DataReader\n\ndef getXY(df ,n, needIndicator):\n if (needIndicator == 0):\n if (n == 2):\n X = df[['x1', 'x2']]\n if (n == 3): \n X = df[['x1', 'x2', 'x3']]\n else:\n X = df[['x1', 'x2', 'x3', 'x4', 'x5']]\n else:\n if (n == 2):\n X = df[['x1', 'x2','14-day RSI','MA-2','EMA-2','14-day MFI']]\n if (n == 3): \n X = df[['x1', 'x2', 'x3','14-day RSI','MA-2','EMA-2','14-day MFI']]\n else:\n X = df[['x1', 'x2', 'x3', 'x4', 'x5','14-day RSI','MA-2','EMA-2','14-day MFI']]\n y = df[['y']]\n y = y.iloc[:,-1:].values.ravel()\n return X,y \n\ndef getRY(df ,n, needIndicator):\n if (needIndicator == 0):\n if (n == 2):\n X = df[['r1', 'r2']]\n if (n == 3): \n X = df[['r1', 'r2', 'r3']]\n else:\n X = df[['r1', 'r2', 'r3', 'r4', 'r5']]\n else:\n if (n == 2):\n X = df[['r1', 'r2','14-day RSI','MA-2','EMA-2','14-day MFI']]\n if (n == 3): \n X = df[['r1', 'r2', 'r3','14-day RSI','MA-2','EMA-2','14-day MFI']]\n else:\n X = df[['r1', 'r2', 'r3', 'r4', 'r5','14-day RSI','MA-2','EMA-2','14-day MFI']]\n y = df[['y']]\n y = y.iloc[:,-1:].values.ravel()\n return X,y \n\ndef getXYTrain(df, X, y):\n split = int(0.75*len(df))\n X_train, X_test, y_train, y_test = X[:split], X[split:], y[:split], y[split:]\n return X_train, X_test, y_train, y_test\n\ndef getPilotInput(X_train, X_test, y_train, y_test):\n model = RandomForestClassifier()\n model = model.fit (X_train, y_train)\n probability = model.predict_proba(X_test)\n predicted = model.predict(X_test)\n cnf_matrix = confusion_matrix(y_test, predicted)\n y_pred_proba = model.predict_proba(X_test)[::,1]\n fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n auc_p = roc_auc_score(y_test, y_pred_proba)\n return fpr, tpr, auc_p\n\nif __name__ == '__main__':\n with open('Data\\CryptoName.txt') as f:\n crypto_names = f.readline().split(',')\n for name in crypto_names:\n df = DataReader.readCryptoData(name)\n n = [2, 3, 5]\n needIndicator = [0, 1]\n print(\"====== name: {} ======\".format(name))\n for i in n:\n for j in needIndicator:\n print(\"n = {} and has indicator? {}\".format(i,j))\n X,y = getXY(df, i, j)\n X_train, X_test, y_train, y_test = getXYTrain(df, X, y)\n fpr_price, tpr_price, auc_price = getPilotInput(X_train, X_test, y_train, y_test)\n print(\"price auc: {}\".format(auc_price))\n X,y = getRY(df, i, j)\n X_train, X_test, y_train, y_test = getXYTrain(df, X, y)\n fpr_return, tpr_return, auc_return = getPilotInput(X_train, X_test, y_train, y_test)\n print(\"return auc: {}\".format(auc_return))\n print(\"===================================\")\n","repo_name":"sina-ss/Financial-Forecasting-with-Machine-Learning","sub_path":"random-forest.py","file_name":"random-forest.py","file_ext":"py","file_size_in_byte":3171,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"29013345390","text":"from datetime import datetime, time\nfrom odoo import models, fields, api\n\n\nclass PriceDifference(models.Model):\n _name = \"sales.price.differences\"\n\n @api.model\n def _default_user(self):\n return self.env.context.get('user_id', self.env.user.id)\n\n date_from = fields.Date('Date from')\n date_to = fields.Date('Date to')\n product_id = fields.Many2one(\"product.product\")\n\n old_price = fields.Float('Old Price', default=1.0, digits='Product Price')\n user_id = fields.Many2one('res.users', required=True, default=_default_user)\n list_price = fields.Float('Sales Price', default=1.0, digits='Product Price')\n\n def action_print_sales_price_difference_report(self):\n\n data = []\n domain = []\n if self.date_from:\n domain += [('create_date', '>=', self.date_from)]\n if self.date_to:\n domain += [('create_date', '<=', self.date_to)]\n\n sales_price_differences = self.env['sales.price.differences'].search([('date_from', '>=', self.date_from),\n ('date_to', '<=', self.date_to),\n ])\n\n for sales_price_difference in sales_price_differences:\n product_name = sales_price_difference['product_id']\n user_name = sales_price_difference['user_id']\n\n data.append({\n \"Date from\": sales_price_difference['date_from'],\n \"Date to\": sales_price_difference['date_to'],\n \"Old Price\": sales_price_difference['old_price'],\n \"Sales Price\": sales_price_difference['list_price'],\n \"User_id\": user_name.name,\n 'Product_id': product_name.name,\n })\n\n data = {\n 'records': data,\n 'self': self.read()[0]\n }\n\n return self.env.ref('sales_price_update.sales_price_difference_report').report_action(self, data=data)\n","repo_name":"Mweruu/erp_backend","sub_path":"custom-addons/sales_price_update/models/sales_price_update.py","file_name":"sales_price_update.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"1892187044","text":"\"\"\"\n.. module:: FWDpay\n :platform: Unix\n :synopsis: A Module that integrates with Payment APIs and allows to calculate fees for BunRun\n\n.. moduleauthor:: Alexandar Mechev <apmechev@gmail.com>\n\n\n\"\"\"\nfrom __future__ import print_function # (at top of module)\nimport sys\nimport time\nimport requests\nimport pdb\n\nfrom props.default import *\n\n# You probably don't need to change those\nimport lib.obp\n\nclass Bank_Account(object):\n def __init__(self, bank_id, account_id):\n self.bank_id = bank_id\n self.account_id = account_id\n\n\nclass FWDpay_client(object):\n \"\"\"Parent class for all our clients. It defines \n all the methods an API needs to support\"\"\"\n def __init__(self):\n raise(NotImplementedError)\n\n def authorize(self):\n raise(NotImplementedError)\n\n def check_balance(self, bank_account):\n raise(NotImplementedError)\n\n def block_balance(self, amount, bank_account):\n raise(NotImplementedError)\n\n def transfer(self, amount, bank_account ):\n raise(NotImplementedError)\n\n\n\nclass OBPClient(FWDpay_client):\n \"\"\" Interface using the OBP API\n \"\"\"\n\n def __init__(self, bank_account=Bank_Account('bank_id','acct_id'), verbose=False):\n self.obp = lib.obp\n if not verbose:\n self.obp.LOGGING = False\n self.obp.setBaseUrl(BASE_URL)\n self.obp.setBaseUrl(BASE_URL)\n self.obp.setApiVersion(API_VERSION)\n self.bank_id = bank_account.bank_id\n self.authorize(USERNAME, PASSWORD)\n self._verify_bank_id(self.bank_id)\n self.account_id = bank_account.account_id \n self._verify_account_id(self.account_id)\n self.verbose = verbose\n\n def _verify_bank_id(self, bank_id):\n if not bank_id in [i['id'] for i in self.obp.getBanks()]:\n raise(RuntimeError(\"Bank not found in banks list\"))\n\n def _verify_account_id(self, account_id):\n accounts = self.obp.getPrivateAccounts(self.bank_id)\n if account_id not in [i['id'] for i in accounts]:\n raise(RuntimeError(\"Account not found in accounts list\"))\n\n\n def authorize(self,username,password):\n \"\"\" Username and Password Authorization for the OBP api\n \"\"\" \n self.obp.login(username,password, CONSUMER_KEY)\n self.user = self.obp.getCurrentUser()\n \n def check_balance(self, bank_account=None):\n \"\"\"Check if the account has sufficient balance for the transaction\n \"\"\"\n bank_id = self.bank_id\n account_id = self.account_id\n account_data = self.obp.getAccount(self.bank_id, self.account_id)\n amount = account_data['balance']['amount']\n currency = account_data['balance']['currency']\n return {'account_id':account_id,'amount':amount, 'currency':currency}\n\n def send_payment(self, amount, rec_bank_account ):\n amount = str(amount)\n self._verify_account_id(rec_bank_account.account_id)\n self._verify_bank_id(rec_bank_account.bank_id)\n account_data = self.obp.getAccount(\n rec_bank_account.bank_id,\n rec_bank_account.account_id)\n target_currency = account_data['balance']['currency']\n if target_currency != OUR_CURRENCY:\n raise(ValueError(\"Unequal Currencies \"+ OUR_CURRENCY + \"!=\" + target_currency ))\n self.obp.setPaymentDetails(OUR_CURRENCY, amount) \n initiate_response = self.obp.createTransactionRequestV210(from_bank_id=self.bank_id,\n from_account_id=self.account_id,\n transaction_request_type=\"SANDBOX_TAN\",\n to_bank_id=rec_bank_account.bank_id,\n to_account_id=rec_bank_account.account_id,\n to_counterparty_id=\"\", # used for SEPA\n to_counterparty_iban=\"\") # used for COUNTERPARTY\n if self.verbose:\n self.obp.printMessageNoChallenge(initiate_response)\n\n\nCONVENIENCE_TAX = 0.09\nPLATFORM_FEE = 0.225\n\n#TODO: Close token after transfer\n\ndef calculate_transfer_amounts(bill_amount, runner_fee):\n platform_fee_sum = platform_fee(bill_amount, runner_fee)\n runner_takeaway_sum = runner_takeaway(bill_amount, runner_fee)\n return {'platform_fee':platform_fee_sum, \n 'runner_takeaway':runner_takeaway_sum, \n 'total_bill':bill_amount}\n\ndef calculate_convenience_amount(bill_amount): \n conv_amount = 2\n if CONVENIENCE_TAX*bill_amount > 2:\n conv_amount = CONVENIENCE_TAX*bill_amount\n return conv_amount \n\ndef platform_fee(bill_amount, runner_fee):\n convenience_amount = calculate_convenience_amount(bill_amount) * PLATFORM_FEE\n runner_amount = runner_fee * PLATFORM_FEE\n return (convenience_amount + runner_amount)\n\ndef runner_takeaway(bill_amount, runner_fee):\n convenience_amount = calculate_convenience_amount(bill_amount) * (1 - PLATFORM_FEE)\n runner_amount = runner_fee * (1 - PLATFORM_FEE)\n return (convenience_amount + runner_amount)\n\n\n\n\n","repo_name":"apmechev/Fwd.Pay","sub_path":"FWDpay.py","file_name":"FWDpay.py","file_ext":"py","file_size_in_byte":5055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"12353933200","text":"# shuffle(list) : returns a list of shuffle values. [1,2,4,5] -> [5,1,2,4]\nfrom random import shuffle\n\n# sleep(3): delays the program by 3 seconds\nfrom time import sleep\n\nSUITS = ['♠️','♥️','♣️','♦️']\nVALUE_KEY = {'A': 14, '2':2, '3':3, '4':4, '5':5, '6':6, '7':7, '8':8, '9':9 ,'10':10, 'J':11, 'Q':12 ,'K':13, '0':0}\n\nclass Card:\n\n def __init__(self, suit, value):\n \"\"\"Initializier for a Card object that contains a suit and a value.\"\"\"\n\n self.suit = suit\n self.val = value\n \n def __str__(self):\n \"\"\"Returns the string representation of a card.\"\"\"\n\n return f\"{self.val}{self.suit}\"\n \n def value(self):\n \"\"\"Return the value that is on the card.\"\"\"\n\n return self.val\n\nclass Deck:\n\n def __init__(self):\n \"\"\"Initializer for a DECK object that holds each card.\"\"\"\n\n self.cards = []\n self.shuffle_cards()\n \n def hit(self):\n \"\"\"Removes the card from the top of the deck and returns the value.\"\"\"\n\n if self.cards:\n return self.cards.pop()\n else:\n self.shuffle_cards()\n return self.cards.pop()\n \n def shuffle_cards(self):\n \"\"\"Creates and shuffle the cards in a deck. If playing WAR leave the second black of code commented since\n blackjack uses 8 deck of cards. Leave the first deck uncommented if you are playing war.\"\"\"\n\n # UNCOMMENT this code if you are playing War\n self.cards = []\n for suit in SUITS:\n for value in VALUE_KEY.keys():\n self.cards.append(Card(suit, value))\n \n shuffle(self.cards)\n \n def empty(self):\n \"\"\"Returns True or False depending on if the deck is empty.\"\"\"\n\n return True if self.deck else False\n \n\ndef clear_screen():\n \"\"\"Clear the contents of the console.\"\"\"\n sleep(4)\n for i in range(100):\n print()\n\ndef print_hand(cards):\n \"\"\"\n Prints each card in the list of cards.\n Keyword arguments:\n cards -- A list of card objects\n \"\"\"\n for card in cards:\n print(card)\n\ndef deal_cards(players):\n\n for i in range(26):\n for j in range(1, 3):\n players[j].append(deck.hit())\n \n return players\n\ndef no_winner(players):\n\n i = 1\n for player_hand in players.values():\n print(f\"Player {i} has {len(player_hand)} cards.\")\n if len(player_hand) == 52:\n return False\n i +=1\n \n return True\n\ndef declare_winner(players_hand):\n\n if len(players_hand[1]) > len(players_hand[2]):\n return \"Congrats Player 1, you won!\"\n else:\n return \"Congrats Player 2, you won!\"\n\n\ndef draw(p1_card, p2_card, players_hand):\n\n cards_to_add = [p1_card, p2_card]\n if len(players_hand[1]) != 0:\n p1_next = players_hand[1].pop(0)\n else:\n p1_next = Card('0','0')\n if len(players_hand[2]) != 0:\n p2_next = players_hand[2].pop(0)\n else:\n p2_next = Card('0','0')\n print(p1_next)\n print(p2_next)\n while VALUE_KEY[p1_next.value()] == VALUE_KEY[p2_next.value()]:\n cards_to_add += [p1_next, p2_next]\n p1_next = players_hand[1].pop(0)\n p2_next = players_hand[2].pop(0)\n print(p1_next)\n print(p2_next)\n if VALUE_KEY[p1_next.value()] > VALUE_KEY[p2_next.value()]:\n print(\"PLayer 1 has a higher value\")\n players_hand[1] += (cards_to_add + [p1_next, p2_next])\n elif VALUE_KEY[p2_next.value()] > VALUE_KEY[p1_next.value()]:\n print(\"PLayer 2 has a higher value\")\n players_hand[2] += (cards_to_add + [p1_next, p2_next])\n \n return players_hand\n\n# {1: [c1, c2], 2: [c4, c3]}\n# def play_war(players_hand):\ndef play_war(players_hand):\n while no_winner(players_hand):\n sleep(.5)\n p1_card = players_hand[1].pop(0)\n p2_card = players_hand[2].pop(0)\n print(p1_card)\n print(p2_card)\n\n if VALUE_KEY[p1_card.value()] > VALUE_KEY[p2_card.value()]:\n print(\"Player 1 Had a higher value\")\n players_hand[1] += [p1_card, p2_card]\n elif VALUE_KEY[p2_card.value()] > VALUE_KEY[p1_card.value()]:\n print(\"PLayer 2 has a higher value\")\n players_hand[2] += [p1_card, p2_card]\n else:\n players_hand = draw(p1_card, p2_card, players_hand)\n \n print(declare_winner(players_hand))\n\n\n \n\n\ndef war():\n\n # {player_name : hand of cards}\n players = {1:[], 2: []}\n # print(players[1])\n # print(players[2])\n\n players = deal_cards(players)\n play_war(players)\n\n\n \n##################DO NOT EDIT BELOW THIS LINE################\ndef main():\n \"\"\"The main function that starts the game of blackjack\"\"\"\n global deck\n deck = Deck()\n\n war()\n\n# This invokes the main function. It is always included in our\n# python programs. \nif __name__ == \"__main__\":\n main()","repo_name":"AANCoord/LabCode","sub_path":"War Lab/war.py","file_name":"war.py","file_ext":"py","file_size_in_byte":4898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"12868801884","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nimport argparse\nimport os\nimport logging\nimport glob\nimport ffprobe\nimport tempfile\nimport shutil\nfrom datetime import timedelta\nimport subprocess as sp\n\nfrom youtube import VALID_PRIVACY_STATUSES\nfrom youtube import HttpError\nfrom youtube import yt_get_authenticated_service\nfrom youtube import yt_get_my_uploads_list\nfrom youtube import yt_list_my_uploaded_videos\nfrom youtube import yt_initialize_upload\n\n\n\n\n\n\n\n\ndef upload_sequence(file_to_upload, sequence_title, youtube, args):\n logging.debug(\"Preparing to upload file \\\"%s\\\".\" % file_to_upload)\n\n try:\n yt_initialize_upload(file_to_upload, sequence_title, youtube, args)\n except HttpError as e:\n logging.error('An HTTP error %d occurred:\\n%s' % (e.resp.status, e.content))\n logging.critical(\"Exiting...\")\n sys.exit(13)\n except KeyboardInterrupt as e:\n logging.warning(\"Aborting upload (KeyboardInterrupt)\")\n\ndef merge_sequence(seq, dry_run, logging_level):\n concat_string = None\n file_path = None\n temp_file_ffmpeg = \"/tmp/actioncam-upload-files.txt\"\n logging.debug(\"Preparing to merge %d files.\" % len(seq))\n logging.debug(seq)\n\n # Output the list of video files to a temporary file, used as input by FFmpeg to concatenate\n file_paths = [f[\"file_path\"] for f in seq]\n with open(temp_file_ffmpeg, 'w') as f:\n print(\"file '%s'\" % \"'\\nfile '\".join(file_paths), file=f)\n\n output_file = \"/tmp/%s\" % os.path.split(seq[0][\"file_path\"])[1] #Use the filename of the first file in this sequence\n\n #ffmpeg -f concat -safe 0 -i /tmp/actioncam-upload-files.txt -c copy /tmp/output.mov\n command = [\"ffmpeg\",\n \"-y\",\n \"-f\", \"concat\",\n \"-safe\", \"0\",\n \"-i\", \"/tmp/actioncam-upload-files.txt\",\n \"-c\", \"copy\",\n output_file\n ]\n logging.debug(\"Running FFmpeg concat command...\")\n logging.debug(\" \".join(command))\n\n if dry_run:\n logging.info(\"Not executing the FFmpeg concat command due to --dry-run parameter.\")\n else:\n # Show FFmpeg output only if in INFO or DEBUG mode\n if \"DEBUG\" == logging_level:\n pipe = sp.Popen(command)\n else:\n pipe = sp.Popen(command, stdout=sp.PIPE, stderr=sp.STDOUT)\n out, err = pipe.communicate()\n if 0 != pipe.returncode:\n logging.error(\"The FFmpeg concat command returned a non-zero code: %d\" % pipe.returncode)\n logging.critical(\"Exiting...\")\n sys.exit(17)\n logging.debug(\"FFmpeg concat command done.\")\n\n logging.debug(\"Deleting temporary FFmpeg merge file.\")\n if os.path.isfile(temp_file_ffmpeg):\n os.remove(temp_file_ffmpeg)\n logging.debug(\"File '%s' removed.\" % temp_file_ffmpeg)\n\n return output_file\n\ndef compress_sequence(seq, tempdir, dry_run, logging_level, id_sequence, num_sequences):\n logging.debug(\"Preparing to compress files into temporary directory '%s'.\" % tempdir)\n logging.debug(seq)\n\n for idx, f in enumerate(seq):\n compressed_file = \"%s/%s\" % (tempdir, os.path.split(f[\"file_path\"])[1])\n\n # Exit if input file doesn't exist (could happen if the actioncam got unplugged)\n if not os.path.isfile(f[\"file_path\"]):\n logging.error(\"The file doesn't exist (actioncam disconnected?): '%s'\" % f[\"file_path\"])\n logging.critical(\"Exiting...\")\n sys.exit(15)\n\n # Reduce the resolution by 4 (1/2h 1/2w) and reduce framerate to 25 images/second\n #ffmpeg -i 20190121_085007.MOV -vf \"scale=iw/2:ih/2\" -r 25 20190121_085007-div2-r25.mov\n command = [\"ffmpeg\",\n \"-i\", f[\"file_path\"],\n \"-vf\", \"scale=iw/2:ih/2\",\n \"-r\", \"25\",\n compressed_file\n ]\n logging.info(\"Running FFmpeg compress command for file %d/%d of sequence %d/%d...\" % (idx + 1, len(seq), id_sequence, num_sequences))\n logging.debug(\" \".join(command))\n\n if dry_run:\n logging.info(\"Not executing the FFmpeg compress command due to --dry-run parameter.\")\n else:\n # Show FFmpeg output only if in INFO or DEBUG mode\n if \"DEBUG\" == logging_level:\n pipe = sp.Popen(command)\n else:\n pipe = sp.Popen(command, stdout=sp.PIPE, stderr=sp.STDOUT)\n out, err = pipe.communicate()\n if 0 != pipe.returncode:\n logging.error(\"The FFmpeg compress command returned a non-zero code: %d\" % pipe.returncode)\n logging.critical(\"Exiting...\")\n sys.exit(16)\n logging.debug(\"FFmpeg compress command done.\")\n # Update the sequence information with the path to the new compressed file\n f[\"file_path\"] = compressed_file\n\n if not dry_run:\n logging.debug(\"Updated sequence with paths to the temporary compressed files:\")\n logging.debug(seq)\n return seq\n\ndef compress_merge_and_upload_sequences(new_sequences, pre_copy_folders, youtube, args):\n tempdir = None\n num_sequences = len(new_sequences)\n logging.debug(\"Preparing to compress, merge and upload %d sequences.\" % num_sequences)\n\n for idx, seq in enumerate(new_sequences):\n if args.no_compression:\n logging.info(\"Not compressing sequence %d/%d due to --no-compression parameter.\" % (idx + 1, num_sequences))\n else:\n # Create a temporary folder to hold the compressed files\n # Do create (and delete) a new folder for each sequence, to save disk space\n tempdir = tempfile.mkdtemp()\n # Reduce resolution and framerate\n logging.info(\"Compressing sequence %d/%d, which contains %d files.\" % (idx + 1, num_sequences, len(seq)))\n seq = compress_sequence(seq, tempdir, args.dry_run, args.logging_level, idx + 1, num_sequences)\n # seq[] now contains the paths to the temporary compressed files\n\n if len(seq) > 1:\n # Combine this sequence into an individual file\n logging.info(\"Merging sequence %d/%d, which contains %d files.\" % (idx + 1, num_sequences, len(seq)))\n file_to_upload = merge_sequence(seq, args.dry_run, args.logging_level)\n else:\n # No need to merge, as there is only one file\n logging.info(\"Sequence %d/%d has only one file, no need to merge files.\" % (idx + 1, num_sequences))\n file_to_upload = seq[0][\"file_path\"]\n\n if args.no_net:\n logging.info(\"Not uploading sequence %d/%d due to --no-net parameter.\" % (idx + 1, num_sequences))\n elif args.dry_run:\n logging.info(\"Not uploading sequence %d/%d due to --dry-run parameter.\" % (idx + 1, num_sequences))\n else:\n # Upload the merged sequence\n logging.info(\"Uploading sequence %d/%d.\" % (idx + 1, num_sequences))\n sequence_title = get_sequence_title(seq[0][\"creation_time\"])\n try:\n upload_sequence(file_to_upload, sequence_title, youtube, args)\n except Exception as e:\n # Delete the temporary folders and files, since the program execution stops here\n delete_temporary_files(seq, file_to_upload, idx, num_sequences, args, tempdir, pre_copy_folders)\n raise\n # Delete the temporary folders and files\n delete_temporary_files(seq, file_to_upload, idx, num_sequences, args, tempdir, pre_copy_folders)\n\ndef delete_temporary_files(seq, file_to_upload, idx, num_sequences, args, tempdir, pre_copy_folders):\n if len(seq) > 1:\n # Delete the merged file (if there is only one file, no temporary merged file was created, so no need to delete)\n if os.path.isfile(file_to_upload):\n logging.debug(\"Deleting merged file for sequence %d/%d.\" % (idx + 1, num_sequences))\n os.remove(file_to_upload)\n logging.debug(\"File '%s' removed.\" % file_to_upload)\n\n if not args.no_compression:\n # Delete the compressed files' temporary folder\n shutil.rmtree(tempdir)\n logging.debug(\"The temporary folder with the compressed files for this sequence has been removed.\")\n\n if pre_copy_folders != []:\n # Delete the temporary folder and files where the original files where copied to\n shutil.rmtree(pre_copy_folders[idx])\n logging.debug(\"The temporary folder where the original files where copied to has been removed.\")\n\ndef get_sequence_title(creation_time):\n return creation_time.strftime(\"%Y-%m-%d %H:%M:%S\")\n\ndef pre_copy(new_sequences):\n logging.debug(\"Pre-copying the files from the actioncam to a temporary folder\")\n pre_copy_folders = []\n for idx, seq in enumerate(new_sequences):\n # Create a new temporary folder for this sequence's files\n pre_copy_folders.append(tempfile.mkdtemp())\n for idx2, files in enumerate(seq):\n logging.info(\"Pre-copying file %d/%d of sequence %d/%d...\" % (idx2 + 1, len(seq), idx + 1, len(new_sequences)))\n # Copy the files from that sequence to that new temporary folder\n new_filename = os.path.join(pre_copy_folders[idx], os.path.split(files[\"file_path\"])[1])\n shutil.copy(files[\"file_path\"], new_filename)\n # Update that file's path to the new temporary path\n files[\"file_path\"] = new_filename\n return (new_sequences, pre_copy_folders)\n\ndef analyze_sequences(sequences, youtube, args):\n sequence_title = None\n new_sequences = []\n uploaded_videos = []\n\n num_sequences = len(sequences)\n logging.debug(\"Starting to analyze %d sequences.\" % num_sequences)\n\n if args.no_net:\n logging.info(\"Not getting the list of videos uploaded to YouTube due to --no-net parameter.\")\n else:\n # Get the list of videos uploaded to YouTube\n try:\n uploads_playlist_id = yt_get_my_uploads_list(youtube)\n if uploads_playlist_id:\n uploaded_videos = yt_list_my_uploaded_videos(uploads_playlist_id, youtube)\n logging.debug(\"Uploaded videos: %s\" % uploaded_videos)\n else:\n logging.info('There is no uploaded videos playlist for this user.')\n except HttpError as e:\n logging.debug('An HTTP error %d occurred:\\n%s' % (e.resp.status, e.content))\n logging.critical(\"Exiting...\")\n sys.exit(14)\n\n if args.interactive:\n print(\"Entering Interactive mode:\")\n\n for idx, seq in enumerate(sequences):\n logging.debug(\"Analyzing sequence %d/%d, which contains %d files.\" % (idx + 1, num_sequences, len(seq)))\n logging.debug(seq)\n if len(seq) < 1:\n raise Exception(\"No files in sequence (should never happen, something has gone wrong...)\")\n\n # Use the creation time of the first file in the sequence as name for the entire sequence\n sequence_title = get_sequence_title(seq[0][\"creation_time\"])\n\n # Check if this sequence has already uploaded\n if sequence_title in uploaded_videos:\n extra_info = \"%s (%d files).\" % (sequence_title, len(seq))\n if not args.interactive:\n logging.info(\"OLD sequence %2d/%d %s\" % (idx + 1, num_sequences, extra_info))\n else:\n print(\"[%d] OLD sequence %s\" % (idx, extra_info))\n else:\n is_new_sequence = True\n # If bounds supplied, check if the duration of this sequence is within them\n if args.min_length or args.max_length:\n # Calculate duration of this sequence\n sequence_length = 0\n for idx2, vid in enumerate(seq):\n sequence_length += vid[\"duration\"]\n sequence_length /= 60 # Convert seconds in minutes\n if args.min_length and sequence_length < args.min_length:\n extra_info = \"%s (%d files), duration %.1f < --min-length=%d.\" % (sequence_title, len(seq), sequence_length, args.min_length)\n if not args.interactive:\n logging.info(\"SKIP sequence %2d/%d %s\" % (idx + 1, num_sequences, extra_info))\n else:\n print(\"[%d] SKIP sequence %s\" % (idx, extra_info))\n is_new_sequence = False\n elif args.max_length and sequence_length > args.max_length:\n extra_info = \"%s (%d files), duration %.1f > --max-length=%d.\" % (sequence_title, len(seq), sequence_length, args.max_length)\n if not args.interactive:\n logging.info(\"SKIP sequence %2d/%d %s\" % (idx + 1, num_sequences, extra_info))\n else:\n print(\"[%d] SKIP sequence %s\" % (idx, extra_info))\n is_new_sequence = False\n if is_new_sequence:\n extra_info = \"%s (%d files).\" % (sequence_title, len(seq))\n if not args.interactive:\n logging.info(\"NEW sequence %2d/%d %s\" % (idx + 1, num_sequences, extra_info))\n else:\n print(\"[%d] NEW sequence %s\" % (idx, extra_info))\n new_sequences.append(seq)\n\n logging.info(\"There are %d new sequences to upload.\" % len(new_sequences))\n logging.debug(new_sequences)\n\n if args.interactive:\n new_sequences = interactive_sequence_selection(sequences, new_sequences)\n return new_sequences\n\ndef interactive_sequence_selection(sequences, new_sequences):\n if len(sequences) < 1:\n raise Exception(\"No sequences were passed (should never happen, something has gone wrong...)\")\n new_seq_ids = []\n num_sequences = len(sequences)\n if len(new_sequences) > 0:\n extra_string = \"Press ENTER to upload the %d NEW sequences above, 'q' to stop the program\" % len(new_sequences)\n else:\n extra_string = \"Press ENTER or 'q' to stop the program\"\n s = input(\"Press ENTER to %s or indicate an individual sequence ID to upload: \" % extra_string)\n while s != \"\":\n if s.lower() == \"q\":\n print(\"Exiting...\")\n sys.exit(18)\n try:\n seq_id = int(s)\n if seq_id < 0 or seq_id > num_sequences - 1:\n print(\"Please enter a number between 0 and %d\" % (num_sequences - 1))\n else:\n if seq_id not in new_seq_ids:\n new_seq_ids.append(seq_id)\n else:\n print(\"ALREADY IN LIST\")\n except ValueError:\n print(\"'%s' is not a number, please try again.\" % s)\n s = input(\"Press ENTER to stop selecting new sequences, or indicate an individual sequence ID to upload: \")\n if new_seq_ids == []:\n # Nothing to change in new_sequences\n logging.info(\"Continue uploading the sequences already in 'new_sequences'\")\n else:\n # Empty new_sequences and populate with the chosen sequences\n new_sequences = []\n for s in new_seq_ids:\n new_sequences.append(sequences[s])\n return new_sequences\n\ndef analyze_files(files):\n video_metadata = None\n duration = None\n videos_by_creation_time = {}\n creation_times = []\n\n num_files = len(files)\n logging.info(\"Starting to analyze %d video files...\" % num_files)\n\n for idx, f in enumerate(files):\n logging.debug(\"Analyzing file %d/%d: '%s'\" % (idx + 1, num_files, f))\n if not os.path.isfile(f):\n raise Exception(\"There is no file to analyze at '%s'\" % f)\n video_metadata = ffprobe.probe(f)\n duration = ffprobe.duration(video_metadata)\n creation_time = ffprobe.creation_time(video_metadata)\n logging.debug(\"File '%s': Duration: '%.3f', Creation Time: '%s'\" %(f, duration, creation_time))\n creation_times.append(creation_time)\n videos_by_creation_time[creation_time] = {\"file_path\": f, \"duration\": duration}\n\n return identify_sequences(videos_by_creation_time, creation_times)\n\ndef identify_sequences(videos_by_creation_time, creation_times):\n sequences = []\n new_sequence = []\n previous_end_time = None\n\n # Sort the creation dates\n creation_times.sort()\n logging.debug(\"creation_times: %s\" % creation_times)\n\n # Loop over the sorted creation times, identify adjacent videos to recreate full sequences\n for ts in creation_times:\n v = videos_by_creation_time[ts]\n if not previous_end_time:\n new_sequence = [{\"file_path\": v[\"file_path\"], \"duration\": v[\"duration\"], \"creation_time\": ts}]\n else:\n # Videos less than 30 seconds apart are considered part of the same sequence\n if ts - previous_end_time < timedelta(seconds=30):\n # Add this video to the current sequences\n new_sequence.append({\"file_path\": v[\"file_path\"], \"duration\": v[\"duration\"], \"creation_time\": ts})\n else:\n # Save the previous sequence and start a new sequence\n sequences.append(new_sequence)\n new_sequence = [{\"file_path\": v[\"file_path\"], \"duration\": v[\"duration\"], \"creation_time\": ts}]\n # Save this video's end time to compare with the next video's start time\n previous_end_time = ts + timedelta(seconds=videos_by_creation_time[ts][\"duration\"])\n\n # Store the last new sequence\n if new_sequence:\n sequences.append(new_sequence)\n logging.info(\"Sequences identified: %d\" % len(sequences))\n logging.debug(sequences)\n\n return sequences\n\ndef analyze_folder(folder):\n #pattern = \"%s/*.mp4\" % folder\n pattern = \"%s/*.MOV\" % folder\n logging.debug(\"Checking files matching pattern '%s'\" % pattern)\n files = glob.glob(pattern)\n logging.info(\"There are %d files matching the pattern '%s'.\" % (len(files), pattern))\n logging.debug('\\n'.join(files))\n\n return files if len(files) > 0 else None\n\ndef detect_folder(args):\n folder = None\n files = None\n if args.folder:\n check_folder = os.path.abspath(args.folder)\n logging.debug(\"Checking if provided folder '%s' is valid.\" % check_folder)\n # Check if provided folder is valid\n if not os.path.exists(check_folder):\n logging.critical(\"Provided folder does not exist. Exiting...\")\n sys.exit(10)\n folder = check_folder\n logging.debug(\"The provided folder '%s' exists.\" % folder)\n files = analyze_folder(folder)\n if not files:\n logging.critical(\"The provided folder '%s' does not contain any processable video files. Exiting...\" % check_folder)\n sys.exit(11)\n else:\n # Try to identify the folder automatically\n logging.debug(\"Start automatic folder detection.\")\n # TODO\n if not folder:\n logging.critical(\"Automatic folder detection failed. Exiting...\\n(You can point to an explicit folder using the `--folder` argument).\")\n sys.exit(12)\n logging.debug(\"Continuing with the %d files in folder '%s'.\" % (len(files), folder))\n return (folder, files)\n\ndef parse_args(arguments):\n parser = argparse.ArgumentParser(description=\"Automatically upload videos from an Action Cam to YouTube.\")\n parser.add_argument(\"-f\", \"--folder\", required=False, help=\"Path to folder containing the video files.\")\n parser.add_argument(\"-t\", '--title', help='Will be prepended to the video title')\n parser.add_argument(\"-ds\", '--description', help='Video description')\n parser.add_argument(\"-c\", '--category', help='Numeric video category. See https://developers.google.com/youtube/v3/docs/videoCategories/list')\n parser.add_argument(\"-k\", '--keywords', help='Video keywords, comma separated')\n parser.add_argument(\"-p\", '--privacyStatus', choices=VALID_PRIVACY_STATUSES, default='private', help='Video privacy status.')\n parser.add_argument(\"-i\", '--interactive', action='store_true', required=False, help=\"Manually select which sequences to upload.\")\n parser.add_argument(\"-pc\", '--pre-copy', action='store_true', required=False, help=\"Copy the files from the actioncam to a temporary folder on the computer, useful in case the actioncam gets disconnected.\")\n parser.add_argument(\"-dr\", \"--dry-run\", action='store_true', required=False, help=\"Do not combine files or upload.\")\n parser.add_argument(\"-nn\", \"--no-net\", action='store_true', required=False, help=\"Do not use the network (no checking on YouTube or upload).\")\n parser.add_argument(\"-nc\", \"--no-compression\", action='store_true', required=False, help=\"Do not compress the files before uploading.\")\n parser.add_argument(\"-min\", \"--min-length\", type=int, help=\"Do not consider sequences shorter than this number of minutes.\")\n parser.add_argument(\"-max\", \"--max-length\", type=int, help=\"Do not consider sequences longer than this number of minutes.\")\n parser.add_argument(\n '-d', '--debug',\n help=\"Print lots of debugging statements\",\n action=\"store_const\", dest=\"loglevel\", const=logging.DEBUG,\n default=logging.WARNING,\n )\n parser.add_argument(\n '-v', '--verbose',\n help=\"Be verbose\",\n action=\"store_const\", dest=\"loglevel\", const=logging.INFO,\n )\n args = parser.parse_args(arguments)\n\n # Add some more arguments\n if args.loglevel:\n logging.basicConfig(level=args.loglevel)\n args.logging_level = logging.getLevelName(args.loglevel)\n args.noauth_local_webserver = True\n\n return args\n\ndef main():\n folder = None\n files = None\n sequences = None\n new_sequences = None\n youtube = None\n\n # Parse the provided command-line arguments\n args = parse_args(sys.argv[1:])\n\n # Validate if the provided folder is valid, or try to automatically detect the folder\n (folder, files) = detect_folder(args)\n\n if args.no_net:\n logging.info(\"Not authenticating on YouTube due to --no-net parameter.\")\n else:\n # Authenticate on YouTube\n logging.info(\"Authenticating on YouTube...\")\n youtube = yt_get_authenticated_service(args)\n\n # Analyze the files to identify continuous sequences\n sequences = analyze_files(files)\n\n if(len(sequences) > 0):\n # Check which sequences have already been uploaded and which ones are new\n new_sequences = analyze_sequences(sequences, youtube, args)\n\n pre_copy_folders = []\n if(args.pre_copy):\n # Copy the files from the actioncam to a temporary folder on the computer, useful in case the actioncam gets disconnected\n (new_sequences, pre_copy_folders) = pre_copy(new_sequences)\n\n if(len(new_sequences) > 0):\n # Combine new sequences into individual files and upload the combined files\n compress_merge_and_upload_sequences(new_sequences, pre_copy_folders, youtube, args)\n\n logging.info(\"Done, exiting.\")\n\ndef init():\n if __name__ == \"__main__\":\n main()\n\ninit()\n","repo_name":"e2jk/actioncam-upload","sub_path":"actioncam-upload.py","file_name":"actioncam-upload.py","file_ext":"py","file_size_in_byte":22938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"22273762807","text":"import youtube_dl\nimport requests\nimport re\nimport os\n\nillegal_chars = list(r'\\/:*?\"<>|')\n\nyoutube_dl.utils.bug_reports_message = lambda: ''\n\nytdl_format_options = {\n 'format': 'bestaudio/best',\n 'restrictfilenames': True,\n 'noplaylist': True,\n 'nocheckcertificate': True,\n 'ignoreerrors': False,\n 'logtostderr': True,\n 'quiet': True,\n 'no_warnings': False,\n 'default_search': 'auto',\n 'preferredcodec': 'mp3',\n 'preferredquality': '192',\n}\n\nffmpeg_options = {\n 'options': '-vn'\n}\n\nytdl = youtube_dl.YoutubeDL(ytdl_format_options)\n\n# serach\ndef youtube_search(ytdl, video_name):\n # print(video_name)\n try:\n ytdl.get(video_name)\n except:\n return ytdl.extract_info(f\"ytsearch: {video_name}\", download=False)['entries']\n else:\n return ytdl.extract_info(video_name, download=False)['entries']\n\ndef remove_non_char(string):\n n = \"\".encode('utf-8')\n for char in string:\n if char in illegal_chars:\n n+=\" \".encode('utf-8')\n else:\n n+=char.encode('utf-8')\n return \" \".join(x.decode() for x in n.split()).encode('utf-8').decode('utf-8')\n\ndef save_song(song, destination):\n url = song[\"url\"]\n name = remove_non_char(song[\"title\"])\n lib = os.listdir(destination)\n if name+\".mp3\" in lib:\n print(f\"{name} was found in {destination}\")\n return\n print(f\"Downloading: {name}\")\n r = requests.get(url)\n with open(destination+f\"/{name}.mp3\", \"wb\") as f:\n f.write(r.content)\n f.close()\n\n\nbase_folder = \"downloads\"\ndef save_with_ytdl(song, destination, fname:str = None):\n url = song['url']\n name = fname if fname else remove_non_char(song['title']) + \".mp3\"\n lib = os.listdir(os.path.join(base_folder, destination))\n if name in lib:\n print(f\"'{name}' was found in '{destination}'\")\n return\n \n # create ytdl info \n ydl_info = ytdl_format_options.copy()\n # set the output filename\n path = os.path.join(base_folder, destination, name)\n ydl_info['outtmpl'] = path\n \n # '\\\\%(title)s.%(ext)s'\n\n print(f\"Downloading: {name} | To: {path}\")\n with youtube_dl.YoutubeDL(ydl_info) as ydl:\n # get search results\n info_dict = ydl.extract_info(url, download=True)","repo_name":"Ultrasword/SpotifyToMp3","sub_path":"source/youtubehandler.py","file_name":"youtubehandler.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"2050773297","text":"import unittest\nfrom unittest import TestCase\n\nfrom index.normalizer import TextNormalizer\n\n\nclass TextNormalizerTestCase(TestCase):\n def test_join_numbers(self):\n nrmlz = TextNormalizer()\n self.assertEqual(\"a b c d 1 b 3\", nrmlz.join_numbers(\"a b c d 1 b 3\"))\n self.assertEqual(\"a b 22 c d 13 e\", nrmlz.join_numbers(\"a b 2 2 c d 1 3 e\"))\n self.assertEqual(\"a b c d 1-3 e\", nrmlz.join_numbers(\"a b c d 1-3 e\"))\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"ladamalina/coursera-poisk","sub_path":"week-1__boolean/tests/test_text_normalizer.py","file_name":"test_text_normalizer.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"16685995367","text":"from dotenv import load_dotenv\nimport boto3\nimport json\nimport os\nfrom utilities.utils import connect_to_db\n\n\ndef download_file_from_s3(bucket_name, object_key, file_name):\n s3 = boto3.client('s3')\n\n try:\n s3.download_file(bucket_name, object_key, file_name)\n print(f\"File downloaded from S3: {file_name}\")\n return True\n except Exception as e:\n print(f\"Error downloading file from S3: {e}\")\n return False\n\n\ndef main():\n load_dotenv()\n\n bucket_name = 'appworks.personal.project'\n object_key = 'crawl_to_s3_file/construction_cost_data.json' # S3 file name\n file_name = 'download_from_s3_file/construction_cost_data.json' # local name\n\n directory = \"download_from_s3_file\"\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n # S3 download\n download_file_from_s3(bucket_name, object_key, file_name)\n\n with open(file_name, 'r', encoding='utf-8') as f:\n construction_cost = json.load(f)\n print(json.dumps(construction_cost, indent=4, ensure_ascii=False))\n\n # create business cycle indicator db\n conn = connect_to_db(\"business cycle indicator\")\n\n try:\n with conn.cursor() as cursor:\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS economic_construction_cost (\n id INT AUTO_INCREMENT PRIMARY KEY,\n time_id VARCHAR(255) UNIQUE,\n time_name VARCHAR(255),\n construction_index FLOAT COMMENT '營造工程總指數'\n )\n \"\"\")\n\n conn.commit()\n\n observations_0 = construction_cost['data']['dataSets'][0]['series']['0']['observations']\n time_structure = construction_cost['data']['structure']['dimensions']['observation'][0]['values']\n\n data_to_insert = []\n for idx, time_info in enumerate(time_structure):\n time_id = time_info['id']\n time_name = time_info['name']\n construction_index = observations_0[str(idx)][0]\n\n data_to_insert.append((time_id, time_name, construction_index))\n\n cursor.executemany(\"\"\"\n INSERT INTO economic_construction_cost (time_id, time_name, construction_index)\n VALUES (%s, %s, %s)\n ON DUPLICATE KEY UPDATE\n time_name = VALUES(time_name),\n construction_index = VALUES(construction_index)\n \"\"\", data_to_insert)\n conn.commit()\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Willy521/Appworks-personal-project","sub_path":"real_estate/pipeline_rds/s3_to_rds_gov_construction_cost.py","file_name":"s3_to_rds_gov_construction_cost.py","file_ext":"py","file_size_in_byte":2677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"7458275859","text":"#1032\n\nn = int(input())\nfile = list(input())\nfile_len = len(file)\nfor i in range(n - 1):\n name = list(input())\n for j in range(file_len):\n if file[j] != name[j]:\n file[j] = '?'\nprint(''.join(file))\n","repo_name":"chaehyeon-kim/Algorithm","sub_path":"BOJ/1032.py","file_name":"1032.py","file_ext":"py","file_size_in_byte":222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"19906431173","text":"#sa se scrie cate litere mari sunt\r\n#in fisierul litereA.txt,cate litere mici in litereB\r\n#in cifre.txt cate cifre sunt\r\n#si cate caractere in caractere.txt\r\n\r\nwith open(\"input.txt\",\"r\") as f: \r\n sir=f.readline()\r\nnrm=0\r\nnr=0\r\nn=0\r\nns=0\r\nfor i in sir:\r\n if ord(i) in range(65,91):\r\n nrm+=1\r\nwith open(\"litereA.txt\",\"w\") as f:\r\n f.write(str(nrm))\r\nfor i in sir:\r\n if ord(i) in range(97,123):\r\n nr+=1\r\nwith open(\"litereB.txt\",\"w\") as f:\r\n f.write(str(nr))\r\nfor i in sir:\r\n if ord(i) in range(49,58):\r\n n+=1\r\nwith open(\"cifre.txt\",\"w\") as f:\r\n f.write(str(n))\r\nfor i in sir:\r\n if ord(i) in range(33,42):\r\n ns+=1\r\nwith open(\"caractere.txt\",\"w\") as f:\r\n f.write(str(ns))\r\n","repo_name":"nicoletaroman/fisiere","sub_path":"problema.py","file_name":"problema.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"42121532471","text":"import os, sys, math, cmath, time, collections\nfrom collections import deque, Counter, OrderedDict, defaultdict\nfrom heapq import nsmallest, nlargest, heapify, heappop, heappush, heapreplace\nfrom math import ceil, floor, log, log2, sqrt, gcd, factorial, pow, pi\nfrom bisect import bisect, bisect_left, bisect_right, insort, insort_left, insort_right\nfrom itertools import accumulate, permutations,combinations,combinations_with_replacement\nfrom io import BytesIO, IOBase\nfrom functools import reduce\nfrom typing import *\n\nstart_time = time.time()\n\ndef solve(grid, visited, i, j):\n #solve in this case will represent Depth First Search implementation\n #DFS is recursive\n if i < 0 or i >= len(grid) or j < 0 or j >= len(grid[0]) or visited[i][j] or grid[i][j] == 0:\n return 0\n visited[i][j] = True\n volume = grid[i][j]\n volume += solve(grid, visited, i+1, j)\n volume += solve(grid, visited, i-1, j)\n volume += solve(grid, visited, i, j+1)\n volume += solve(grid, visited, i, j-1)\n return volume\n\ndef main():\n # pass\n num, depth = map(int, input().split())\n grid = []\n for _ in range(num):\n row = list(map(int, input().split()))\n grid.append(row)\n max_volume = 0\n visited = [[False for _ in range(depth)] for _ in range(num)]\n for i in range(num):\n for j in range(depth):\n if not visited[i][j] and grid[i][j] > 0:\n volume = solve(grid, visited, i, j)\n max_volume = max(max_volume, volume)\n print(max_volume)\n\nif __name__ == \"__main__\":\n if os.path.exists(\"data.in\"):\n sys.stdin = open(\"data.in\", \"r\")\n sys.stdout = open(\"data.out\", \"w\")\n\n testcases = int(input())\n for i in range(testcases):\n main()\n\n # If it's local - Print this O/P\n if os.path.exists(\"data.in\"):\n print(f\"Time Elapsed: {time.time() - start_time} seconds\")\n sys.stdout.close()","repo_name":"codesbyshrey/practitioner","sub_path":"PyCharm/Contests/871div4/r871pE.py","file_name":"r871pE.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"28125572764","text":"'''\n\n'''\n\n#%%\n# import sys\nimport os,glob\nimport pandas as pd\nfrom plot_functions.plt_tools import round_half_up \nimport numpy as np \nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nfrom plot_functions.get_data_dir import (get_data_dir, get_figure_dir)\nfrom plot_functions.get_index import get_index\nfrom plot_functions.plt_tools import (set_font_type, defaultPlotting, day_night_split)\nfrom tqdm import tqdm\nimport matplotlib as mpl\nset_font_type()\n\n# %%\n# Paste root directory here\n# if_plot_by_speed = True\npick_data = 'tau_long'\nroot, FRAME_RATE= get_data_dir(pick_data)\n\nfolder_name = f'angvel_corr_timeSeries'\nfolder_dir = get_figure_dir(pick_data)\nfig_dir = os.path.join(folder_dir, folder_name)\n\ntry:\n os.makedirs(fig_dir)\n print(f'fig folder created:{folder_name}')\nexcept:\n print('Notes: re-writing old figures')\n \npeak_idx , total_aligned = get_index(FRAME_RATE)\nidxRANGE = [peak_idx-round_half_up(0.3*FRAME_RATE),peak_idx+round_half_up(0.2*FRAME_RATE)]\nspd_bins = np.arange(5,25,4)\n\n# %% features for plotting\nall_features = [\n 'propBoutAligned_speed', \n # 'propBoutAligned_accel', # angular accel calculated using raw angular vel\n 'linear_accel', \n 'propBoutAligned_pitch', \n 'propBoutAligned_angVel', # smoothed angular velocity\n 'propBoutInflAligned_accel',\n 'propBoutAligned_instHeading', \n 'heading_sub_pitch',\n # 'propBoutAligned_x',\n # 'propBoutAligned_y', \n # 'propBoutInflAligned_angVel',\n # 'propBoutInflAligned_speed', \n # 'propBoutAligned_angVel_hDn',\n # # 'propBoutAligned_speed_hDn', \n # 'propBoutAligned_pitch_hDn',\n # # 'propBoutAligned_angVel_flat', \n # # 'propBoutAligned_speed_flat',\n # # 'propBoutAligned_pitch_flat', \n # 'propBoutAligned_angVel_hUp',\n # 'propBoutAligned_speed_hUp', \n # 'propBoutAligned_pitch_hUp', \n 'ang_speed',\n 'ang_accel_of_SMangVel', # angular accel calculated using smoothed angVel\n # 'xvel', 'yvel',\n\n]\n\n# %%\n# CONSTANTS\n# %%\nT_INITIAL = -0.25 #s\nT_PREP_200 = -0.2\nT_PREP_150 = -0.15\nT_PRE_BOUT = -0.10 #s\nT_POST_BOUT = 0.1 #s\nT_post_150 = 0.15\nT_END = 0.2\nT_MID_ACCEL = -0.05\nT_MID_DECEL = 0.05\n\n\nidx_initial = round_half_up(peak_idx + T_INITIAL * FRAME_RATE)\nidx_pre_bout = round_half_up(peak_idx + T_PRE_BOUT * FRAME_RATE)\nidx_post_bout = round_half_up(peak_idx + T_POST_BOUT * FRAME_RATE)\nidx_mid_accel = round_half_up(peak_idx + T_MID_ACCEL * FRAME_RATE)\nidx_mid_decel = round_half_up(peak_idx + T_MID_DECEL * FRAME_RATE)\nidx_end = round_half_up(peak_idx + T_END * FRAME_RATE)\n\n\nHEADING_LIM = 90\n\n# %%\nall_conditions = []\nfolder_paths = []\n# get the name of all folders under root\nfor folder in os.listdir(root):\n if folder[0] != '.':\n folder_paths.append(root+'/'+folder)\n all_conditions.append(folder)\n\n\nall_around_peak_data = pd.DataFrame()\nall_cond0 = []\nall_cond1 = []\n\n# go through each condition folders under the root\nfor condition_idx, folder in enumerate(folder_paths):\n # enter each condition folder (e.g. 7dd_ctrl)\n for subpath, subdir_list, subfile_list in os.walk(folder):\n # if folder is not empty\n if subdir_list:\n # reset for each condition\n around_peak_data = pd.DataFrame()\n # loop through each sub-folder (experiment) under each condition\n for expNum, exp in enumerate(subdir_list):\n # angular velocity (angVel) calculation\n rows = []\n # for each sub-folder, get the path\n exp_path = os.path.join(subpath, exp)\n # get pitch \n exp_data = pd.read_hdf(f\"{exp_path}/bout_data.h5\", key='prop_bout_aligned')#.loc[:,['propBoutAligned_angVel','propBoutAligned_speed','propBoutAligned_accel','propBoutAligned_heading','propBoutAligned_pitch']]\n exp_data = exp_data.assign(ang_speed=exp_data['propBoutAligned_angVel'].abs(),\n yvel = exp_data['propBoutAligned_y'].diff()*FRAME_RATE,\n xvel = exp_data['propBoutAligned_x'].diff()*FRAME_RATE,\n linear_accel = exp_data['propBoutAligned_speed'].diff(),\n ang_accel_of_SMangVel = exp_data['propBoutAligned_angVel'].diff(),\n )\n # assign frame number, total_aligned frames per bout\n exp_data = exp_data.assign(idx=round_half_up(len(exp_data)/total_aligned)*list(range(0,total_aligned)))\n \n # - get the index of the rows in exp_data to keep (for each bout, there are range(0:51) frames. keep range(20:41) frames)\n bout_time = pd.read_hdf(f\"{exp_path}/bout_data.h5\", key='prop_bout2').loc[:,['aligned_time']]\n # for i in bout_time.index:\n # # if only need day or night bouts:\n for i in day_night_split(bout_time,'aligned_time').index:\n rows.extend(list(range(i*total_aligned+idxRANGE[0],i*total_aligned+idxRANGE[1])))\n exp_data = exp_data.assign(expNum = expNum,\n exp_id = condition_idx*100+expNum)\n around_peak_data = pd.concat([around_peak_data,exp_data.loc[rows,:]])\n # combine data from different conditions\n cond0 = all_conditions[condition_idx].split(\"_\")[0]\n all_cond0.append(cond1)\n cond1 = all_conditions[condition_idx].split(\"_\")[1]\n all_cond0.append(cond1)\n all_around_peak_data = pd.concat([all_around_peak_data, around_peak_data.assign(cond0=cond0,\n cond1=cond1)])\nall_around_peak_data = all_around_peak_data.assign(time_ms = (all_around_peak_data['idx']-peak_idx)/FRAME_RATE*1000)\n# %% tidy data\nall_cond0 = list(set(all_cond0))\nall_cond0.sort()\nall_cond0 = list(set(all_cond0))\nall_cond0.sort()\n\nall_around_peak_data = all_around_peak_data.reset_index(drop=True)\npeak_speed = all_around_peak_data.loc[all_around_peak_data.idx==peak_idx,'propBoutAligned_speed'],\n\nall_around_peak_data = all_around_peak_data.assign(\n heading_sub_pitch = all_around_peak_data['propBoutAligned_instHeading']-all_around_peak_data['propBoutAligned_pitch'],\n)\n\ngrp = all_around_peak_data.groupby(np.arange(len(all_around_peak_data))//(idxRANGE[1]-idxRANGE[0]))\nall_around_peak_data = all_around_peak_data.assign(\n peak_speed = np.repeat(peak_speed,(idxRANGE[1]-idxRANGE[0])),\n bout_number = grp.ngroup(),\n )\nall_around_peak_data = all_around_peak_data.assign(\n speed_bin = pd.cut(all_around_peak_data['peak_speed'],spd_bins,labels = np.arange(len(spd_bins)-1))\n )\n\n# %%\nall_pre_bout_angles = all_around_peak_data.loc[all_around_peak_data['idx']==idx_pre_bout,'propBoutAligned_pitch']\ninitial_pitch = all_around_peak_data.loc[all_around_peak_data['idx']==idx_initial,'propBoutAligned_pitch']\n# %%\nall_around_peak_data = all_around_peak_data.assign(\n pre_bout_angle = np.repeat(all_pre_bout_angles,(idxRANGE[1]-idxRANGE[0])).values,\n initial_pitch = np.repeat(initial_pitch,(idxRANGE[1]-idxRANGE[0])).values,\n)\nall_around_peak_data = all_around_peak_data.assign(\n initial_posture = pd.cut(all_around_peak_data['initial_pitch'],bins=[-90,10,90],labels=['dn','up'])\n)\n\n# print(\"speed buckets:\")\n# print('--mean')\n# print(all_around_peak_data.groupby('speed_bin')['peak_speed'].agg('mean'))\n# print('--min')\n# print(all_around_peak_data.groupby('speed_bin')['peak_speed'].agg('min'))\n# print('--max')\n# print(all_around_peak_data.groupby('speed_bin')['peak_speed'].agg('max'))\n\n# %%\n# correlation with pre bout pitch\n# which to corr\n# which_to_corr = 'initial_pitch' # initial_pitch or pre_bout_angle\nfor which_to_corr in ['initial_pitch', 'pre_bout_angle']:\n # cat_cols = ['speed_bin','cond1','initial_posture','cond0']\n # # cat_cols = ['cond1','initial_posture']\n # grp_cols = cat_cols + ['time_ms']\n\n # corr_angvel = all_around_peak_data.groupby(grp_cols).apply(\n # lambda y: stats.pearsonr(y[which_to_corr].values,y['propBoutAligned_angVel'].values)[0]\n # )\n # corr_angvel.name = 'corr'\n # corr_angvel = corr_angvel.reset_index()\n\n # palette = sns.color_palette(\"mako_r\", 4)\n\n # g = sns.relplot(\n # style='cond1',\n # row='initial_posture',\n # # hue_order=[0,2,4],\n # hue='speed_bin',\n # col='cond0',\n # # size='speed_bin', size_order=[3,2,1,0],\n\n # x='time_ms',y='corr',\n # data=corr_angvel,\n # kind='line',\n # palette=palette, \n # )\n # g.set(xlim=(-200,200))\n # plt.savefig(fig_dir+f\"/{which_to_corr}_by dir and spd.pdf\",format='PDF')\n\n cat_cols = ['cond1','initial_posture','cond0']\n grp_cols = cat_cols + ['time_ms']\n\n corr_angvel = all_around_peak_data.groupby(grp_cols).apply(\n lambda y: stats.pearsonr(y['pre_bout_angle'].values,y['propBoutAligned_angVel'].values)[0]\n )\n corr_angvel.name = 'corr'\n corr_angvel = corr_angvel.reset_index()\n\n g = sns.relplot(\n row='initial_posture',\n col='cond0',\n hue='cond1',\n x='time_ms',y='corr',\n data=corr_angvel,\n kind='line',\n # palette=\"flare\", \n # hue_norm=mpl.colors.LogNorm()\n )\n g.set(xlim=(-200,200))\n plt.savefig(fig_dir+f\"/{which_to_corr}_by dir and cond.pdf\",format='PDF')\n\n # %%\n # ignore dir\n cat_cols = ['cond1','cond0']\n grp_cols = cat_cols + ['time_ms']\n\n corr_angvel = all_around_peak_data.groupby(grp_cols).apply(\n lambda y: stats.pearsonr(y['pre_bout_angle'].values,y['propBoutAligned_angVel'].values)[0]\n )\n corr_angvel.name = 'corr'\n corr_angvel = corr_angvel.reset_index()\n\n g = sns.relplot(\n hue='cond1',\n x='time_ms',y='corr',\n data=corr_angvel,\n kind='line',\n col='cond0',\n # palette=\"flare\", \n # hue_norm=mpl.colors.LogNorm()\n )\n g.set(xlim=(-200,200))\n plt.savefig(fig_dir+f\"/{which_to_corr}_by cond.pdf\",format='PDF')\n# %%\n","repo_name":"YunluZhu/SAMPL-analysis-v5-lab","sub_path":"SAMPL_visualization/legacy code/Btimeseries_2_pearsonr_angvel.py","file_name":"Btimeseries_2_pearsonr_angvel.py","file_ext":"py","file_size_in_byte":10395,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"32"} +{"seq_id":"28884801047","text":"# # 1\n# # 1 2\n# # 1 2 3\nn = int(input(\"Enter value of n:\"))\nfor i in range(0, n):\n for j in range(0, i+1):\n print(j+1, end=\" \")\n print()\n\n\n# # 1\n# # 2 2\n# # 3 3 3\nn = int(input(\"Enter value of n:\"))\nfor i in range(1, n+1):\n print((str(i)+' ')*i)\n\n\n# # WAP print prime numbers fromm 2 to 50.\nc = 0\nfor i in range(2, 51):\n c = 2\n for j in range(2, i):\n if(i % j == 0 and c < 3):\n c += 1\n if(c == 2):\n print(i, \" is a prime no.\")\n\n\n# # Write a program to find the sum of digits of an integer number, input by the user.\nn = int(input(\"Enter a integer number: \"))\nd, sum = 0, 0\nwhile(n > 0):\n d = n % 10\n sum += d\n n //= 10\nprint(sum)\n\n\n# # Write a function that checks whether an input number is a palindrome or not.\ndef palindrome_Checker(n):\n rev = ''.join(reversed(n))\n if(n == rev):\n print(\"Palindrome\")\n else:\n print(\"Not Palindrome\")\n\n\nn = input(\"Enter a number: \")\npalindrome_Checker(n)\n","repo_name":"SoumadeepChoudhury/Java_Python_Projects","sub_path":"Python Project/src/soumadeepPython/ClassWork_05.07.2021.py","file_name":"ClassWork_05.07.2021.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"32"} +{"seq_id":"20086416390","text":"#!/usr/local/bin/python3\n\nfrom xml.etree import ElementTree\nimport sys\n#filename=sys.argv[1]\n\n\n# Turn \"None\" int an empty string\ndef xstr(s):\n if s is None:\n return ''\n return str(s)\n\n\n\ndef indent(elem, level=0, more_sibs=False):\n i = \"\\n\"\n if level:\n i += (level-1) * ' '\n ip1 = \"\\n\" + (level+1)* ' '\n num_kids = len(elem)\n\n # p is special, I don't really want to process its children\n # the only exception is md. how to deal with that??\n # is it better to have a whitelist or blacklist for children of p?\n if elem.tag == \"p\" and elem.text:\n elem.text = ip1 + elem.text\n if num_kids:\n elem[-1].tail = xstr(elem[-1].tail) + i + ' '\n else:\n elem.text = elem.text + ip1\n for kid in elem:\n if kid.tag == \"md\": # md is only thing I will indent in p\n indent(kid, level+1)\n kid.tail=ip1+kid.tail\n num_kids = 0; # stop checking children in p.\n\n if num_kids:\n if not elem.text or not elem.text.strip():\n elem.text = i + \" \"\n if level:\n elem.text += ' '\n count = 0\n for kid in elem:\n indent(kid, level+1, count < num_kids - 1)\n count += 1\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n if more_sibs:\n elem.tail += ' '\n else:\n if level and (not elem.tail or not elem.tail.strip()):\n elem.tail = i\n if more_sibs:\n elem.tail += ' '\n\n\nroot = ElementTree.parse(sys.stdin).getroot()\n\nindent(root)\n\n#make a map of all parents to use later\nparent_map = {c:p for p in root.iter() for c in p}\n\n#\"figure\" should be a sidebyside with image\nfor i in root.iter():\n fig=i.find('figure')\n if fig is None: continue\n figname=fig.get('file')\n if figname is None: continue\n figname=figname.replace('figures/', 'images/')\n par=parent_map[fig]\n newsidebyside=ElementTree.SubElement(par, 'sidebyside', {'width':'30%'})\n ElementTree.SubElement(newsidebyside, 'image', {'source':figname})\n par.remove(fig)\n\n#ElementTree.dump(root)\n\n## the lazy way to rename the <std> fake root element.\nfinal=ElementTree.tostring(root, encoding=\"utf-8\").decode(\"utf=8\")\nlines = final.splitlines()\nprint(\"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\")\nfor line in lines:\n line=line.replace(\"<std>\", \"<section>\")\n line=line.replace(\"</std>\", \"</section>\")\n print(line)\n\n\n\n\n","repo_name":"frabonim/apex_at_moravian","sub_path":"scripts/formatSection.py","file_name":"formatSection.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"13053911729","text":"\"\"\"initial migration\n\nRevision ID: 98e110f42c99\nRevises: fd366e5efa8b\nCreate Date: 2017-02-15 17:18:37.352666\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '98e110f42c99'\ndown_revision = 'fd366e5efa8b'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('info', sa.Text(), nullable=True))\n op.add_column('users', sa.Column('location', sa.String(length=64), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('users', 'location')\n op.drop_column('users', 'info')\n # ### end Alembic commands ###\n","repo_name":"CurryXuGoGo/myblog","sub_path":"migrations/versions/98e110f42c99_initial_migration.py","file_name":"98e110f42c99_initial_migration.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"40380561777","text":"# -*- coding: utf-8 -*-\n\nclass PrefixSpan:\n tran_dic = {}\n proj_dbs = []\n sol_dic = {}\n th = 50\n\n def __init__(self, in_file_name, out_file_name):\n self.out_file = open(out_file_name, \"w\", encoding='utf-8')\n self.in_file = open(in_file_name, \"r\", encoding='utf-8')\n\n def createTranDB(self):\n line = self.in_file.readline()\n while line:\n line = line.replace('\\n', '')\n row = line.split(',')\n self.tran_dic[row[0]] = row[1]\n line = self.in_file.readline()\n\n def countFreq(self, seq):\n counter = 0\n for tran in self.tran_dic.values():\n if seq in tran: counter += 1\n return counter\n\n def execute(self):\n self.prefixSpan( '', 1, self.tran_dic)\n\n def prefixSpan(self, seq, ln, db):\n freq_set = {}\n for s in db.values():\n for item in s.split('x'):\n if not seq: seq_t = item\n else: seq_t = seq + 'x' + item\n n_tmp = self.countFreq(seq_t)\n if n_tmp >= self.th: freq_set[item] = n_tmp\n print(freq_set)\n for item in freq_set:\n if not seq: seq_t = str(item)\n else: seq_t = seq + 'x' + item\n n_tmp = self.countFreq(seq + 'x' + item)\n self.sol_dic[seq_t] = n_tmp\n tmp_db = {}\n for sid in self.tran_dic:\n if seq_t in self.tran_dic[sid]: tmp_db[sid] = self.tran_dic[sid]\n self.prefixSpan(seq_t, ln+1, tmp_db)\n\n def showItems(self):\n count = 0\n for item in self.item_list:\n t_str = '%s: %s'%(count, item)\n print(t_str)\n count += 1\n\n def showSolutions(self):\n for seq in self.sol_dic:\n print(seq + ':: ' + str(self.sol_dic[seq]))\n\nif __name__== \"__main__\":\n in_file_name = \"sequence.csv\"\n out_file_name = \"prefixspan.csv\"\n ps = PrefixSpan(in_file_name, out_file_name)\n ps.createTranDB()\n ps.execute()\n ps.showSolutions()\n","repo_name":"daring-board/Pattern_Reg","sub_path":"20170128/PrefixSpan.py","file_name":"PrefixSpan.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"9297964175","text":"\nimport pymongo\nimport datetime\n\n\nclass mongo:\n\n def __init__(self) -> None:\n # self.conection=pymongo.MongoClient(\"mongodb+srv://music_search:@cluster0.hadso.mongodb.net/myFirstDatabase?retryWrites=true&w=majority\")\n self.conection=pymongo.MongoClient(\"mongodb+srv://music_search:Reminder@cluster0.hadso.mongodb.net/?retryWrites=true&w=majority\")\n\n def querymusic(self):\n # recive the user to get the info and comapare the tiempstamp of now with the tiempstamp of the song \n # Send which songs have been heard 1 year ago or more\n db=self.conection[\"music_remider\"]\n collec_user=db[\"user\"]\n\n now = datetime.datetime.now()\n years= datetime.timedelta(days=3)\n limitdate=now-years\n print(limitdate)\n \n match={\n \"$match\":{\n \"username\":\"ivanov\"\n }\n }\n lookup = {\n \"$lookup\": { \n \"from\":\"history\",\n \"localField\":\"username\",\n \"foreignField\":\"user\",\n \"as\":\"logs\"\n }\n }\n filter_Song={\n \"$project\": {\n \n \"name\": 1,\n \"username\":1,\n \"history\":{ \n \"$slice\":[{\n \"$filter\": \n { \n \"input\": \"$logs\", \n \"as\": \"log\", \n \"cond\": { \"$lt\": [ \"$$log.last_played\",limitdate ] } \n }},\n 3\n ]\n } \n }\n }\n pipeline=[\n match,\n lookup,\n filter_Song\n ]\n Songs=[]\n for user in collec_user.aggregate(pipeline):\n \n Songs.append(user[\"history\"])\n number_song=len(user[\"history\"])\n return(user,number_song)\n\n","repo_name":"ivanovichleo/Music_reminder","sub_path":"Music_reminder/Principal/Database.py","file_name":"Database.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"20546834097","text":"# -*- coding: utf-8 -*-\nfrom unittest import TestCase\n\nfrom productspidersweb.spider_doc import parse_spider_top_comment\n\n\nclass TestParseTopComment(TestCase):\n def test1(self):\n comment = \"\"\"Original ticket: https://www.assembla.com/spaces/competitormonitor/tickets/3941-e-bedding---new-site---argos\nThis spider searches for SKUs in the website. In the search terms the slashes should be encoded as %252F.\nWhen searching by code the website redirects to the product page.\"\"\"\n ticket_num, ticket_url, rest_lines = parse_spider_top_comment(comment)\n\n self.assertEqual(ticket_num, 3941)\n self.assertEqual(ticket_url, 'https://www.assembla.com/spaces/competitormonitor/tickets/3941-e-bedding---new-site---argos')\n self.assertEqual(len(rest_lines), len(comment.splitlines()) - 1)\n\n def test2(self):\n comment = \"\"\"\nCustomer: BIW USA\nWebsite: http://www.bestbuy.com\nType: Marketplace, extract all dealers.\nCrawling process: search by brand using the client file from the SFTP and extract all results\nOptions: extract all options\nTicket link: https://www.assembla.com/spaces/competitormonitor/tickets/4022-biw-usa-|-bestbuy-|-new-sites/details#\n\nIMPORTANT!\n\"\"\"\n ticket_num, ticket_url, rest_lines = parse_spider_top_comment(comment)\n\n self.assertEqual(ticket_num, 4022)\n self.assertEqual(ticket_url, 'https://www.assembla.com/spaces/competitormonitor/tickets/4022-biw-usa-|-bestbuy-|-new-sites/details#')\n self.assertEqual(len(rest_lines), len(comment.splitlines()) - 1)\n\n def test3(self):\n comment = \"\"\"\nName: navico-amer-googleshopping\nOriginal developer: Emiliano M. Rudenick <emr.frei@gmail.com>\nTicket reference: https://www.assembla.com/spaces/competitormonitor/tickets/4212\n\nIMPORTANT:\n\n- Local proxies management. It uses Proxy Service.\n- Use of PhantomJS to browse the website.\n- PLEASE be CAREFUL, Google bans the proxies quickly.\n\"\"\"\n ticket_num, ticket_url, rest_lines = parse_spider_top_comment(comment)\n\n self.assertEqual(ticket_num, 4212)\n self.assertEqual(ticket_url, 'https://www.assembla.com/spaces/competitormonitor/tickets/4212')\n self.assertEqual(len(rest_lines), len(comment.splitlines()) - 1)\n\n def test4(self):\n comment = \"\"\"\n- Original assembla ticket #: 3916\n- Run Scrapy >= 0.15 for correct operation (cookiejar feature)\n- Prices including Tax\n- It uses cache by using previous crawl data and updating only prices and stock status from product lists.\n Enter to product page only for new products, this is only for some fields like SKU which\n are not in products list page\n\"\"\"\n ticket_num, ticket_url, rest_lines = parse_spider_top_comment(comment)\n\n self.assertEqual(ticket_num, 3916)\n self.assertEqual(ticket_url, 'https://www.assembla.com/spaces/competitormonitor/tickets/3916')\n self.assertEqual(len(rest_lines), len(comment.splitlines()) - 1)","repo_name":"Godsoo/scraping","sub_path":"e-commerce/CompetitorMonitor/productspidersweb/productspidersweb/tests/test_spider_doc.py","file_name":"test_spider_doc.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"11871917482","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Mar 26 16:49:19 2022\r\n\r\n@author: 1013449\r\n\"\"\"\r\nimport random\r\nimport bisect\r\nimport sklearn\r\n\r\n#################### CDF distributions for symbol and character level substitution\r\nsymbolProbs = [0.294, 0.276, 0.122, 0.109, 0.068, 0.028, 0.013, 0.013, 0.013, 0.032, 0.032]\r\nspecialSymbols = ['.', ',', '\"', '\\'', '-', '?', ':', '!', ';', '(', ')']\r\n\r\n\r\n################### CDF distribution for characters \r\ncharacterProbs = [0.082, 0.010, 0.027, 0.047, 0.13, 0.022, 0.02, 0.062, 0.069, 0.0016, 0.0081, 0.04, 0.027,\r\n 0.067, 0.078, 0.019, 0.001, 0.059, 0.062, 0.096, 0.027, 0.0097, 0.024, 0.0015, 0.01, 0.0001]\r\n\r\ncharacterSymbols = ['a','b','c','d', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q',\r\n 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\r\n\r\n\r\n###################### LIST OF WORDS TO BE ADDED\r\nfrom nltk.corpus import stopwords\r\nstopwords = stopwords.words('english')\r\nstopwords[0] = 'I'\r\n\r\n######################## CONJUGATOR\r\nfrom pattern.en import *\r\n\r\n########################################### CORRUPTION FUNCTIONS\r\n\r\n\r\n############# LOWER CASE:\r\ndef lowerCase(word):\r\n return word[0].lower() + word[1:]\r\n\r\n########## SYMBOL CORRUPTIONS\r\n\r\n#Symbol elimination\r\ndef symbolElimination(word, tokenIndex):\r\n return word[:tokenIndex] + word[tokenIndex+ 1:]\r\n\r\n\r\n#Calculate cdf and choice an item based on the probabilities\r\ndef cdf(probs):\r\n sumProbs = sum(probs)\r\n cdfList = []\r\n cdfSum = 0\r\n for p in probs:\r\n cdfSum += p\r\n cdfList.append(cdfSum / sumProbs)\r\n return cdfList\r\n\r\ndef choiceBasedOnCDF(symbols, probs):\r\n assert len(symbols) == len(probs)\r\n cdf_vals = cdf(probs)\r\n value = random.random()\r\n idx = bisect.bisect(cdf_vals, value)\r\n return symbols[idx]\r\n\r\n\r\ndef symbolSubstitution(word, tokenIndex):\r\n newSymbol = choiceBasedOnCDF(specialSymbols,symbolProbs)\r\n return word[:tokenIndex] + newSymbol + word[tokenIndex+1:]\r\n\r\n############## TOKEN LEVEL CORRUPTIONS\r\n\r\ndef tokenAddition(word, tokenIndex):\r\n direction = random.randint(0,1)\r\n #Left\r\n newSymbol = choiceBasedOnCDF(characterSymbols,characterProbs)\r\n if direction == 0:\r\n return word[:tokenIndex] + newSymbol + word[tokenIndex:] \r\n #Right\r\n if direction == 1:\r\n return word[:tokenIndex+1] + newSymbol + word[tokenIndex+1:] \r\n\r\ndef tokenSubstitution(word, tokenIndex):\r\n newSymbol = choiceBasedOnCDF(characterSymbols,characterProbs)\r\n return word[:tokenIndex] + newSymbol + word[tokenIndex+1:] \r\n\r\ndef tokenElimination(word, tokenIndex):\r\n return word[:tokenIndex] + word[tokenIndex+ 1:] \r\n \r\ndef tokenSwap(word, tokenIndex):\r\n if tokenIndex < len(word)-1:\r\n return word[:tokenIndex]+word[tokenIndex+1]+word[tokenIndex]+ word[tokenIndex+2:]\r\n else:\r\n return word[tokenIndex]+word[1:tokenIndex]+ word[0]\r\n \r\ndef tokenMultiAddition(word, tokenIndex):\r\n \r\n direction = random.randint(0,1)\r\n newSymbol = choiceBasedOnCDF(characterSymbols,characterProbs)\r\n newSymbol2 = choiceBasedOnCDF(characterSymbols,characterProbs)\r\n #Left\r\n if direction == 0:\r\n return word[:tokenIndex] + newSymbol+ newSymbol2 + word[tokenIndex:] \r\n #Right\r\n if direction == 1:\r\n return word[:tokenIndex+1] + newSymbol + newSymbol2+ word[tokenIndex+1:]\r\n \r\n\r\ndef tokenMultiSubstitution(word, tokenIndex):\r\n \r\n newSymbol = choiceBasedOnCDF(characterSymbols,characterProbs)\r\n newSymbol2 = choiceBasedOnCDF(characterSymbols,characterProbs)\r\n \r\n if tokenIndex < len(word)-1:\r\n return word[:tokenIndex] + newSymbol + newSymbol2 + word[tokenIndex+2:] \r\n else:\r\n return newSymbol + word[1:tokenIndex] + newSymbol2\r\n \r\n\r\ndef tokenMultiElimination(word, tokenIndex):\r\n if tokenIndex < len(word)-1:\r\n return word[:tokenIndex] + word[tokenIndex+2:] \r\n else:\r\n return word[1]+word[2:tokenIndex] \r\n \r\ndef tokenMultiSwap(word, tokenIndex):\r\n if tokenIndex < len(word)-1:\r\n return word[:tokenIndex]+word[tokenIndex+1]+word[tokenIndex]+ word[tokenIndex+2:]\r\n else:\r\n return word[tokenIndex]+word[1:tokenIndex]+ word[0]\r\n\r\n\r\n\r\n############################# WORD LEVEL FUNCTIONS\r\ndef wordUpperCase(word):\r\n return word[0].upper() + word[1:]\r\n\r\n\r\n# Arguments\r\n# Index = word that will be deleted\r\n# Words- List of words of that sentence\r\ndef wordElimination(index, words):\r\n words.pop(index)\r\n return words\r\n \r\n\r\ndef wordAddition(index, words):\r\n continuation = 1\r\n while True:\r\n direction = random.randint(0,1)\r\n \r\n #Choose the word to be added randomly\r\n randomIndex = random.randint(0, len(stopwords)-1)\r\n stopWord = stopwords[randomIndex]\r\n \r\n continuation = continuation*0.5\r\n #Left\r\n if direction == 0:\r\n words.insert(index, stopWord)\r\n index = index-1\r\n #Right\r\n if direction == 1:\r\n words.insert(index+1, stopWord)\r\n index = index+1\r\n \r\n #Decision of add more words\r\n if random.random() > continuation:\r\n break\r\n return words\r\n\r\ndef wordSwap(index, words):\r\n continuation = 1\r\n while True:\r\n if index < len(words)-1:\r\n temp = words[index]\r\n words[index] = words[index + 1]\r\n words[index + 1] = temp\r\n continuation = continuation*0.5\r\n index = index+1\r\n else:\r\n temp = words[index]\r\n words[index] = words[0]\r\n words[0] = temp\r\n continuation = continuation*0.5\r\n index = 0\r\n \r\n if random.random() > continuation:\r\n break\r\n \r\ndef wordPunctuation(word):\r\n direction = random.randint(0,1)\r\n punctuation = choiceBasedOnCDF(specialSymbols,symbolProbs)\r\n if direction == 0:\r\n return word+punctuation\r\n else:\r\n return punctuation+word\r\n \r\ndef wordSubstitution(word):\r\n \r\n\r\n wordType = parse(word).split('/')[1] \r\n ############################## CHECK IF WORD IS A VERB\r\n if wordType.startswith('V'):\r\n counter = 0\r\n conjugations = lexeme(word)\r\n #If the conjugation is the same as the word, try another conjugation\r\n while(True):\r\n idx = random.randint(0,len(conjugations)-1)\r\n #Preventing an infinite loop\r\n if counter>3:\r\n return word\r\n if word == conjugations[idx]:\r\n counter+=1\r\n else:\r\n return conjugations[idx]\r\n \r\n # WORD is an adjective or Adverb\r\n elif wordType.startswith('J') or wordType.startswith('R'):\r\n decision= random.randint(0,5)\r\n #Comparative form of adjective\r\n if decision<3:\r\n comp = comparative(word)\r\n elif decision >= 3 and decision < 5:\r\n comp = superlative(word)\r\n else:\r\n if random.randint(0,1)> 0:\r\n comp = pluralize(word)\r\n else:\r\n comp = singularize(word)\r\n \r\n return comp\r\n else:\r\n if random.randint(0,3)> 0:\r\n comp = pluralize(word)\r\n else:\r\n comp = singularize(word)\r\n return comp\r\n \r\n \r\n \r\n \r\n \r\n","repo_name":"MateoRuedaMolano/NLG-project","sub_path":"Code/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":7371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"21763356920","text":"from trytond.rpc import RPC\n\n\nclass SymbolMixin(object):\n __slots__ = ()\n\n @classmethod\n def __setup__(cls):\n super().__setup__()\n cls.__rpc__.update({\n 'get_symbol': RPC(instantiate=0, cache=dict(days=1)),\n })\n\n def get_symbol(self, sign, symbol=None):\n 'Return the symbol and its position'\n position = 1\n if symbol is None:\n symbol = getattr(self, 'symbol', None)\n return symbol, position\n","repo_name":"tryton/trytond","sub_path":"trytond/model/symbol.py","file_name":"symbol.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":166,"dataset":"github-code","pt":"32"} +{"seq_id":"20306033278","text":"import re\n\ndef make_header(title):\n header = '<!DOCTYPE HTML PUBLIC>\\n'\n header += '<html>\\n'\n header += '<head>\\n'\n header += '<title>' + str(title) + '\\n'\n header += '\\n'\n header += '\\n'\n header += '\\n'\n return header\n\n\ndef make_body_start():\n return '\\n'\n\ndef make_body_end():\n return '\\n'\n\ndef make_footer():\n return '\\n'\n\ndef make_masthead(links, active_index):\n #masthead = '
\\n'\n #masthead += '
\\n'\n #masthead = '
    \\n'\n masthead = '
    \\n'\n masthead += '
      \\n'\n for index, link in enumerate(links):\n if index == active_index:\n masthead += '
    • ' + link + '
      • \\n'\n else:\n masthead += '
    • ' + link + '
      • \\n'\n #masthead += '
    \\n'\n masthead += '
\\n'\n masthead += '
\\n'\n return masthead\n\n\ndef make_heading(text, level=1, align=None):\n heading = ''\n return heading\n\ndef make_paragraph(text, align=None, id=None):\n p = '\\n'\n return p\n\ndef make_link(dest, text, new_window=False):\n link = ''\n return link\n\ndef make_table_start(col_aligns=None, col_widths=None, style='t1'):\n table = '\\n'\n if col_widths is not None:\n for width in col_widths:\n table += '\\n'\n return table\n\ndef make_table_header(header):\n table = ''\n for h in header:\n table += ' '\n table += '\\n'\n return table\n\n\ndef make_table_row(row, colours=None):\n table = ''\n for i, r in enumerate(row):\n if colours is not None:\n table += ' '\n else:\n table += ' '\n table += '\\n'\n return table\n\ndef make_table_end():\n return '
' + h + '
' + r + '' + r + '
\\n'\n\ndef replace_chars(text):\n text = re.sub(' ', '_', text)\n text = re.sub('/', '_', text)\n return text\n\ndef make_image(source):\n image = '
500)\n epsilon = int(\"\".join(str(int(b)) for b in output), base=2)\n gamma = int(\"\".join(str(int(b)) for b in list(not r for r in output)), base=2)\n return epsilon*gamma\n\nprint(f\"Part 1: {power_consumption()}\")\n","repo_name":"paulphys/adventofcode","sub_path":"2021/day03/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"33776767890","text":"import overpy\nfrom copy import deepcopy\nimport haversine as hs\nfrom math import radians, degrees, cos\nfrom datetime import datetime\nfrom flask import jsonify\nimport jsonschema\nimport logging\nfrom overpy.exception import (\n OverpassTooManyRequests,\n OverpassGatewayTimeout,\n OverpassRuntimeError,\n OverpassUnknownHTTPStatusCode,\n \n)\nfrom config import defaultServer, secondaryServer1, secondaryServer2\n\n\ndef create_bbox_coordinates(distance, lat, lon):\n assert distance > 0\n assert lat >= -90.0 and lat <= 90.0\n assert lon >= -180.0 and lon <= 180.0\n distance_in_km = distance * 0.001\n \"\"\" convert lat/lon from degrees to radians \"\"\"\n lat, lon = radians(lat), radians(lon)\n radius = 6371\n \"\"\" Radius of the earth in km \"\"\"\n parallel_radius = radius * cos(lat)\n \"\"\" Radius of the parallel at given latitude \"\"\"\n lat_min = lat - distance_in_km / radius\n lat_max = lat + distance_in_km / radius\n lon_min = lon - distance_in_km / parallel_radius\n lon_max = lon + distance_in_km / parallel_radius\n \"\"\" Convert lat/lon from radians back to degrees \"\"\"\n lat_min, lon_min = degrees(lat_min), degrees(lon_min)\n lat_max, lon_max = degrees(lat_max), degrees(lon_max)\n bbox_coordinates = [lat_min, lon_min, lat_max, lon_max]\n return bbox_coordinates\n\ndef server_config1(url, bbox_coord):\n lat_min, lon_min = bbox_coord[0], bbox_coord[1]\n lat_max, lon_max = bbox_coord[2], bbox_coord[3]\n \"\"\" fetch all ways and nodes \"\"\"\n\n api = overpy.Overpass(url=url)\n street_data = api.query(\n f\"\"\"\n way({lat_min},{lon_min},{lat_max},{lon_max})[highway];\n (._;>;);\n out body;\n \"\"\"\n )\n #way({lat_min},{lon_min},{lat_max},{lon_max})[highway];\n #way[highway](around:200,45.49486,-73.58191);\n #(._;>;);\n #out body;\n #motorway|trunk|motorway_link|trunk_link|primary_link|secondary_link|tertiary_link\n #[highway~\"!^(primary|tertiary|residential|service|footway)$\"]\n #[highway~\"^(primary|tertiary|secondary|crossing|pedestrian|living_street|residential|motorway|trunk|motorway_link|trunk_link|primary_link|secondary_link|tertiary_link)$\"];\n return street_data\n\ndef server_config2(url, bbox_coord):\n # Get amenities\n lat_min, lon_min = bbox_coord[0], bbox_coord[1]\n lat_max, lon_max = bbox_coord[2], bbox_coord[3]\n api = overpy.Overpass(url=url)\n street_amenity = api.query(\n f\"\"\"\n (node({lat_min},{lon_min},{lat_max},{lon_max}) [\"amenity\"];\n way({lat_min},{lon_min},{lat_max},{lon_max}) [\"amenity\"];\n rel({lat_min},{lon_min},{lat_max},{lon_max}) [\"amenity\"];\n );\n out center;\n \"\"\"\n )\n return street_amenity\n\ndef get_streets(bbox_coord):\n try:\n OSM_data = server_config1(defaultServer, bbox_coord) \n except Exception:\n try:\n error = 'Primary server not responding, so connecting alternative server 1'\n logging.error(error)\n OSM_data = server_config1(secondaryServer1, bbox_coord)\n except Exception:\n try:\n error = 'Alternative server 1 not responding, so connecting alternative server 2'\n logging.error(error)\n OSM_data = server_config1(secondaryServer2, bbox_coord)\n except Exception:\n error = 'Unable to get data. All servers down!'\n logging.error(error)\n OSM_data = None\n return (OSM_data)\n\n\ndef get_timestamp():\n d = datetime.now()\n timestamp = int(datetime.timestamp(d))\n return timestamp\n\n\ndef process_streets_data(OSM_data):\n \"\"\"Retrieve inteterested street information from the requested OSM data\"\"\"\n try:\n processed_OSM_data = []\n for way in OSM_data.ways:\n node_list = []\n for node in way.nodes:\n node_object = {\n \"id\": int(node.id),\n \"lat\": float(node.lat),\n \"lon\": float(node.lon),\n }\n if node_object not in node_list:\n node_list.append(node_object)\n # Convert lanes to integer if its value is not None\n lanes = way.tags.get(\"lanes\")\n if lanes is not None:\n lanes = int(lanes)\n else:\n lanes = lanes\n # Convert oneway tag to boolean if its value is not None\n oneway = way.tags.get(\"oneway\")\n if oneway is not None:\n oneway = bool(oneway)\n else:\n oneway = oneway\n way_object = {\n \"street_id\": int(way.id),\n \"street_name\": way.tags.get(\"name\"),\n \"street_type\": way.tags.get(\"highway\"),\n \"addr:street\": way.tags.get(\"addr:street\"),\n \"surface\": way.tags.get(\"surface\"),\n \"oneway\": oneway,\n \"sidewalk\": way.tags.get(\"sidewalk\"),\n \"maxspeed\": way.tags.get(\"maxspeed\"),\n \"lanes\": lanes,\n\n }\n # Fetch as many tags as possible\n \"\"\"\n for key, value in way.tags.items():\n if value != way.tags.get(\n \"name\") and value != way.tags.get(\"highway\"):\n if key not in way_object:\n way_object[key] = value\n \"\"\"\n way_object[\"nodes\"] = node_list\n\n # Delete key if value is empty\n way_object = dict(x for x in way_object.items() if all(x))\n\n # Include only streets with names\n if \"street_name\" in way_object:\n processed_OSM_data.append(way_object)\n except AttributeError:\n error = 'Overpass Attibute error. Retry again'\n logging.error(error)\n else:\n return processed_OSM_data\n\n\ndef compare_street(street1, street2): # Compare two streets\n intersecting_points = [x for x in street1 if x in street2]\n return intersecting_points\n\n\ndef extract_street(processed_OSM_data): # extract two streets\n intersection_record = []\n for i in range(len(processed_OSM_data)):\n for j in range(i + 1, len(processed_OSM_data)):\n street1 = processed_OSM_data[i][\"nodes\"]\n street2 = processed_OSM_data[j][\"nodes\"]\n intersecting_points = compare_street(\n street1, street2) # function call\n if len(intersecting_points): # check if not empty\n if \"street_name\" in processed_OSM_data[i]:\n street_object = {\n \"street_id\": processed_OSM_data[i][\"street_id\"],\n \"street_name\": processed_OSM_data[i][\"street_name\"],\n \"intersection_nodes\": intersecting_points,\n }\n elif \"street_type\" in processed_OSM_data[i]:\n street_object = {\n \"street_id\": processed_OSM_data[i][\"street_id\"],\n \"street_type\": processed_OSM_data[i][\"street_type\"],\n \"intersection_nodes\": intersecting_points,\n }\n else:\n street_object = {\n \"street_id\": processed_OSM_data[i][\"street_id\"],\n \"intersection_nodes\": intersecting_points,\n }\n intersection_record.append(street_object)\n if \"street_name\" in processed_OSM_data[j]:\n street_object = {\n \"street_id\": processed_OSM_data[j][\"street_id\"],\n \"street_name\": processed_OSM_data[j][\"street_name\"],\n \"intersection_nodes\": intersecting_points,\n }\n elif \"street_type\" in processed_OSM_data[i]:\n street_object = {\n \"street_id\": processed_OSM_data[i][\"street_id\"],\n \"street_type\": processed_OSM_data[i][\"street_type\"],\n \"intersection_nodes\": intersecting_points,\n }\n else:\n street_object = {\n \"street_id\": processed_OSM_data[j][\"street_id\"],\n \"intersection_nodes\": intersecting_points,\n }\n intersection_record.append(street_object)\n # Group the streets by their ids\n output = {}\n for obj in intersection_record:\n street_id = obj[\"street_id\"]\n if street_id not in output:\n assert obj[\"intersection_nodes\"] is not None\n if \"street_name\" in obj:\n record = {\n \"street_id\": obj[\"street_id\"],\n \"street_name\": obj[\"street_name\"],\n \"intersection_nodes\": obj[\"intersection_nodes\"],\n }\n elif \"street_type\" in obj:\n record = {\n \"street_id\": obj[\"street_id\"],\n \"street_type\": obj[\"street_type\"],\n \"intersection_nodes\": obj[\"intersection_nodes\"],\n }\n else:\n record = {\n \"street_id\": obj[\"street_id\"],\n \"intersection_nodes\": obj[\"intersection_nodes\"],\n }\n output[street_id] = record\n else:\n existing_record = output[street_id]\n existing_intersection_nodes = existing_record[\"intersection_nodes\"]\n assert existing_intersection_nodes is not None\n new_intersection_nodes = obj[\"intersection_nodes\"]\n q = new_intersection_nodes\n assert q is not None\n merged_intersection_nodes = existing_intersection_nodes + q\n existing_record[\"intersection_nodes\"] = merged_intersection_nodes\n output[street_id] = existing_record\n intersection_record_updated = list(output.values())\n # Keep a unique set of intersections under each street segment\n for obj in range(len(intersection_record_updated)):\n unique_set = []\n inter_sets = intersection_record_updated[obj][\"intersection_nodes\"]\n unique_set = [item for item in inter_sets if item not in unique_set]\n # for item in range(len(inter_sets)):\n # if inter_sets[item] not in unique_set:\n # unique_set.append(inter_sets[item])\n intersection_record_updated[obj][\"intersection_nodes\"] = unique_set\n return (intersection_record_updated)\n\n\ndef allot_intersection(processed_OSM_data, inters_rec_up\n ): # iterate & indicate common nodes\n processed_OSM_data1 = deepcopy(processed_OSM_data)\n inters = inters_rec_up\n for obj in range(len(processed_OSM_data1)):\n id1 = processed_OSM_data1[obj][\"street_id\"]\n nodes = processed_OSM_data1[obj][\"nodes\"]\n for i in range(len(nodes)):\n for objs in range(len(inters)):\n id2 = inters[objs][\"street_id\"]\n intersection_nodes = inters[objs][\"intersection_nodes\"]\n for items in range(len(intersection_nodes)):\n if id1 != id2: # compare unique street only\n # check if a node represents an intersection\n if nodes[i] == intersection_nodes[items]:\n nodes[i][\"cat\"] = \"intersection\"\n f = nodes[i]\n key1 = \"street_name\"\n key2 = \"street_type\"\n X = processed_OSM_data1[obj]\n Y = inters[objs]\n # Check if street_name key is empty or not to\n # format the output\n if key1 in X and key1 in Y:\n nm1 = X[\"street_name\"]\n nm2 = Y[\"street_name\"]\n f[\"name\"] = f\"{nm1} intersecting {nm2}\"\n elif key1 not in X and key1 in Y:\n nm2 = Y[\"street_name\"]\n if key2 in X: # Use street type if noname\n stp = X[\"street_type\"]\n f[\"name\"] = f\"{stp} intersecting {nm2}\"\n else:\n f[\"name\"] = f\"{id1} intersecting {nm2}\"\n elif key1 in X and key1 not in Y:\n nm1 = X[\"street_name\"]\n if key2 in Y: # Use street type if noname\n stp = Y[\"street_type\"]\n f[\"name\"] = f\"{nm1} intersecting {stp}\"\n else:\n f[\"name\"] = f\"{nm1} intersecting {id2}\"\n else:\n if key2 in X and key2 in Y:\n stp1 = X[\"street_type\"]\n stp2 = Y[\"street_type\"]\n f[\"name\"] = f\"{stp1} intersecting {stp2}\"\n else:\n f[\"name\"] = f\"{id1} intersecting {id2}\"\n return processed_OSM_data1\n\n\ndef get_amenities(bbox_coord):\n # Send request to OSM to get amenities which are part of\n # points of interest (POIs)\n try:\n amenities = server_config2(defaultServer, bbox_coord)\n except Exception:\n try:\n error = 'Primary server not responding, so connecting alternative server 1'\n logging.error(error)\n amenities = server_config2(secondaryServer1, bbox_coord)\n except Exception:\n try:\n error = 'Alternative server 1 not responding, so connecting alternative server 2'\n logging.error(error)\n amenities = server_config2(secondaryServer2, bbox_coord)\n except Exception:\n error = 'Unable to get data. All servers down!'\n logging.error(error)\n amenities = None\n \n # Filter the amenity tags to the basic useful ones\n amenity = []\n if amenities is not None:\n if amenities.nodes:\n for node in amenities.nodes:\n if node.tags.get(\"amenity\") is not None:\n amenity_record = {\n \"id\": int(node.id),\n \"lat\": float(node.lat),\n \"lon\": float(node.lon),\n \"name\": node.tags.get(\"name\"),\n \"cat\": node.tags.get(\"amenity\"),\n }\n # Fetch as many tags possible\n\n for key, value in node.tags.items():\n if value != node.tags.get(\n \"name\") and value != node.tags.get(\"amenity\"):\n if key not in amenity_record:\n amenity_record[key] = value\n\n # Delete keys with no value\n amenity_record = dict(\n x for x in amenity_record.items() if all(x))\n amenity.append(amenity_record)\n\n if amenities.ways:\n for way in amenities.ways:\n if way.tags.get(\"amenity\") is not None:\n amenity_record = {\n \"id\": int(way.id),\n \"lat\": float(way.center_lat),\n \"lon\": float(way.center_lon),\n \"name\": way.tags.get(\"name\"),\n \"cat\": way.tags.get(\"amenity\"),\n }\n # Fetch as many tags possible\n for key, value in way.tags.items():\n if value != way.tags.get(\n \"name\") and value != way.tags.get(\"amenity\"):\n if key not in amenity_record:\n amenity_record[key] = value\n # Delete keys with no value\n amenity_record = dict(\n x for x in amenity_record.items() if all(x))\n amenity.append(amenity_record)\n\n if amenities.relations:\n for rel in amenities.relations:\n if rel.tags.get(\"amenity\") is not None:\n amenity_record = {\n \"id\": int(rel.id),\n \"lat\": float(rel.center_lat),\n \"lon\": float(rel.center_lon),\n \"name\": rel.tags.get(\"name\"),\n \"cat\": rel.tags.get(\"amenity\"),\n }\n # Fetch as many tags possible\n for key, value in rel.tags.items():\n if value != rel.tags.get(\n \"name\") and value != rel.tags.get(\"amenity\"):\n if key not in amenity_record:\n amenity_record[key] = value\n # Delete keys with no value\n amenity_record = dict(\n x for x in amenity_record.items() if all(x))\n amenity.append(amenity_record)\n\n return amenity\n\n\ndef enlist_POIs(processed_OSM_data1, amenity):\n # Keep all identified points of interest in a single list\n POIs = []\n if len(processed_OSM_data1):\n for obj in range(len(processed_OSM_data1)):\n nodes = processed_OSM_data1[obj][\"nodes\"]\n for node in range(len(nodes)):\n key_to_check = \"cat\"\n # check if \"cat\" key is in the node\n if key_to_check in nodes[node]:\n if nodes[node][\"cat\"]: # ensure the \"cat\" key has a value\n # Check to remove duplicate intersections\n if nodes[node] not in POIs: \n POIs.append(nodes[node])\n if amenity is not None and len(amenity) != 0:\n # POIs = [objs for objs in amenity]\n for objs in range(len(amenity)):\n POIs.append(amenity[objs])\n return POIs # POIs is a list of all points of interest\n\n\ndef OSM_preprocessor(processed_OSM_data, POIs, amenity):\n id_list, node_list, POI_id_list = [], [], []\n processed_OSM_data2 = deepcopy(processed_OSM_data)\n if len(POIs):\n # Iterate through the amenities\n for i in range(len(\n POIs)):\n key_to_check = POIs[i][\"cat\"]\n # check if true, then the points of interest are amenity,\n # e.g. restaurants, bars, rentals, etc\n if key_to_check != \"intersection\" and amenity is not None:\n minimum_distance = []\n for obj in range(len(processed_OSM_data)):\n nodes = processed_OSM_data[obj][\"nodes\"]\n for j in range(len(nodes)):\n lat1 = nodes[j][\"lat\"]\n lon1 = nodes[j][\"lon\"]\n lat2 = POIs[i][\"lat\"]\n lon2 = POIs[i][\"lon\"]\n location1 = (float(lat1), float(lon1))\n location2 = (float(lat2), float(lon2))\n # Compute the distance between a node and POI\n distance = hs.haversine(location1, location2)\n if (len(minimum_distance)) == 0:\n minimum_distance.append(distance)\n k = processed_OSM_data2[obj][\"nodes\"]\n reference_id = {\n \"node_id\": k[j][\"id\"], }\n else:\n if distance < minimum_distance[0]:\n minimum_distance[0] = distance\n k = processed_OSM_data2[obj][\"nodes\"]\n reference_id = {\n \"node_id\": k[j][\"id\"], }\n # iterate through the OSM data\n # to reference the node that should\n # hold the point of interest\n for objs in range(len(processed_OSM_data2)):\n nodes = processed_OSM_data2[objs][\"nodes\"]\n for node in range(len(nodes)): # if true,\n # the node will hold the point of interest\n if nodes[node][\"id\"] == reference_id[\"node_id\"]:\n if nodes[node][\"id\"] not in id_list: # id_list\n # stores all the node ids using the POIs\n id_list.append(nodes[node][\"id\"])\n nodes[node][\"POIs_ID\"] = [\n POIs[i][\"id\"]] # New key-pair in the node\n # node_list keeps all the nodes using POIs\n node_list.append(nodes[node])\n # POI_list keeps all the POI ids\n POI_id_list.append(POIs[i][\"id\"])\n else:\n for n in range(len(node_list)):\n # identify the node in the list by using\n # its id\n if nodes[node][\"id\"] == node_list[n][\"id\"]:\n # Existing amenity/POI's id(s)\n existingid = node_list[n][\"POIs_ID\"]\n # An id for new POI\n new_id = POIs[i][\"id\"]\n # Ensure new id is not in the existing\n # id\n if new_id not in POI_id_list:\n POI_id_list.append(new_id)\n # Two id's merged into a single\n # list\n merged_id = existingid + [new_id]\n nodes[node][\"POIs_ID\"] = merged_id\n else:\n nodes[node][\"POIs_ID\"] = existingid\n else: # POIs here are intersections\n for objs in range(len(processed_OSM_data2)):\n nodes = processed_OSM_data2[objs][\"nodes\"]\n for node in range(len(nodes)):\n # check if node is among the points of interest list\n if nodes[node][\"id\"] == POIs[i][\"id\"]:\n # check if this node has not been used by any POIs\n if nodes[node][\"id\"] not in id_list:\n # id_list stores all the node ids using the\n # POIs\n id_list.append(nodes[node][\"id\"])\n # create a new key-pair in the node\n nodes[node][\"POIs_ID\"] = [nodes[node][\"id\"]]\n # node_list keeps all the nodes using POIs\n node_list.append(nodes[node])\n # POI_list keeps all the POIs ids\n POI_id_list.append(nodes[node][\"id\"])\n else:\n for n in range(len(node_list)):\n if nodes[node][\"id\"] == node_list[n][\"id\"]:\n existingid = node_list[n][\"POIs_ID\"]\n # node id for intersection (POI)\n new_id = nodes[node][\"id\"]\n # Ensure new id is not in the existing\n # id\n if new_id not in POI_id_list:\n POI_id_list.append(new_id)\n # Two id's merged into a single\n # list\n merged_id = existingid + [new_id]\n nodes[node][\"POIs_iD\"] = merged_id\n else:\n nodes[node][\"POIs_ID\"] = existingid\n\n # Arrange street segments in descending order\n \"\"\"for i in range(len(processed_OSM_data2)):\n j = i + 1\n for j in range(len(processed_OSM_data2)):\n # Use the size of their nodes to rank them\n nodes1 = len(processed_OSM_data2[i][\"nodes\"])\n nodes2 = len(processed_OSM_data2[j][\"nodes\"])\n if nodes1 > nodes2:\n street = processed_OSM_data2[i]\n processed_OSM_data2[i] = processed_OSM_data2[j]\n processed_OSM_data2[j] = street\"\"\"\n # processed_OSM_data2 = quickSort(processed_OSM_data2)\n processed_OSM_data2 = (sorted(processed_OSM_data2, key=lambda x: len(x['nodes']),reverse=True))\n\n return processed_OSM_data2\n\n\n\"\"\"\ndef quickSort(array, ascending=False):\n if len(array) <= 1:\n return array\n lower, median, upper = [], [], []\n pivot = len(array[randint(0, len(array) - 1)]['nodes'])\n for i in array:\n if len(i['nodes']) < pivot:\n lower.append(i)\n elif len(i['nodes']) == pivot:\n median.append(1)\n else:\n upper.append(1)\n\n upper = quickSort(upper, ascending=ascending)\n lower = quickSort(lower, ascending=ascending)\n\n if ascending:\n array = lower + median + upper\n else:\n array = upper + median + lower\n return array\n\n\n# Quick sort in Python\n\n# function to find the pivot value\n\n\ndef partition(array, low, high):\n\n # choose the far-right element as pivot\n pivot = len(array[high]['nodes'])\n\n # pointer for greater element\n i = low - 1\n\n # traverse through all elements\n # compare each with pivot value\n for j in range(low, high):\n if len(array[j]['nodes']) <= pivot:\n # if element smaller than pivot is found\n # swap it with the greater element pointed by i\n i = i + 1\n\n # swapping element at i with element at j\n (array[i], array[j]) = (array[j], array[i])\n\n # swap the pivot element with the greater element specified by i\n (array[i + 1], array[high]) = (array[high], array[i + 1])\n\n # return the position from where partition is done\n return i + 1\n\n# function to perform quicksort\n\n\ndef quickSort(array, low, high):\n\n if low < high:\n # find pivot element such that\n # any element less than pivot goes to the left\n # any element greater than pivot stays on the right\n pi = partition(array, low, high)\n\n # recursive call on the left of pivot\n quickSort(array, low, pi - 1)\n\n # recursive call on the right of pivot\n quickSort(array, pi + 1, high)\"\"\"\n\n\ndef validate(schema, data, resolver, json_message, error_code):\n \"\"\"\n Validate a piece of data against a schema\n Args:\n schema: a JSON schema to check against\n data: the data to check\n resolver: a JSON schema resolver\n json_messaage: the error to jsonify and return\n error_code: the error code to return\n Returns:\n None or Tuple[flask.Response, int]\n \"\"\"\n try:\n validator = jsonschema.Draft7Validator(schema, resolver=resolver)\n validator.validate(data)\n except jsonschema.exceptions.ValidationError as error:\n logging.error(error)\n return jsonify(json_message), error_code\n return None\n\n\ndef get_coordinates(content):\n \"\"\"\n Retrieve the coordinates of a map from the\n content of the request\n \"\"\"\n if 'coordinates' in content.keys():\n return content['coordinates']\n\ndef scale_distance(content):\n # Make distance scalable\n try:\n distance = content[\"distance\"]\n if distance <= 0:\n distance: float = 200\n except Exception:\n distance: float = 200\n return distance","repo_name":"samueljohnsegun148/ssh_github","sub_path":"openstreetmap/osm_service.py","file_name":"osm_service.py","file_ext":"py","file_size_in_byte":28159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"35890174452","text":"#!python3\nimport logging\nimport datetime\n\n# The arrow library is used to handle datetimes\nimport arrow\n\n# The request library is used to fetch content through HTTP\nimport requests\n\n# please try to write PEP8 compliant code (use a linter). One of PEP8's\n# requirement is to limit your line length to 79 characters.\n\n\ndef fetch_production(\n zone_key=\"IS\",\n session=None,\n target_datetime: datetime.datetime = None,\n logger: logging.Logger = logging.getLogger(__name__),\n):\n \"\"\"Requests the last known production mix (in MW) of a given country\n\n Arguments:\n ----------\n zone_key: used in case a parser is able to fetch multiple countries\n session: request session passed in order to re-use an existing session\n target_datetime: the datetime for which we want production data. If not\n provided, we should default it to now. If past data is not available,\n raise a NotImplementedError. Beware that the provided target_datetime is\n UTC. To convert to local timezone, you can use\n `target_datetime = arrow.get(target_datetime).to('America/New_York')`.\n Note that `arrow.get(None)` returns UTC now.\n logger: an instance of a `logging.Logger` that will be passed by the\n backend. Information logged will be publicly available so that correct\n execution of the logger can be checked. All Exceptions will automatically\n be logged, so when something's wrong, simply raise an Exception (with an\n explicit text). Use `logger.warning` or `logger.info` for information\n that can useful to check if the parser is working correctly. A default\n logger is used so that logger output can be seen when coding / debugging.\n\n Returns:\n --------\n If no data can be fetched, any falsy value (None, [], False) will be\n ignored by the backend. If there is no data because the source may have\n changed or is not available, raise an Exception.\n\n A dictionary in the form:\n {\n 'zoneKey': 'IS',\n 'datetime': '2017-01-01T00:00:00Z',\n 'production': {\n 'biomass': 0.0,\n 'coal': 0.0,\n 'gas': 0.0,\n 'hydro': 0.0,\n 'nuclear': null,\n 'oil': 0.0,\n 'solar': 0.0,\n 'wind': 0.0,\n 'geothermal': 0.0,\n 'unknown': 0.0\n },\n 'storage': {\n 'hydro': -10.0,\n },\n 'source': 'mysource.com'\n }\n \"\"\"\n r = session or requests.session()\n if target_datetime is None:\n url = \"https://amper.landsnet.is/generation/api/Values\"\n else:\n # WHEN HISTORICAL DATA IS NOT AVAILABLE\n raise NotImplementedError(\"This parser is not yet able to parse past dates\")\n\n res = r.get(url)\n assert res.status_code == 200, (\n \"Exception when fetching production for \"\n \"{}: error when calling url={}\".format(zone_key, url)\n )\n\n obj = res.json()\n data = {\n \"zoneKey\": zone_key,\n \"production\": {},\n \"storage\": {},\n \"source\": \"amper.landsnet.is\",\n }\n\n # Parse the individual generation resources\n for resource in [\"hydro\", \"geothermal\", \"oil\"]:\n data[\"production\"][resource] = obj[resource]\n\n # Parse the datetime and return a python datetime object\n data[\"datetime\"] = arrow.get(obj[\"timestamp\"]).datetime\n\n return data\n\n\nif __name__ == \"__main__\":\n \"\"\"Main method, never used by the Electricity Map backend, but handy\n for testing.\"\"\"\n\n print(\"fetch_production() ->\")\n print(fetch_production())\n","repo_name":"piedacoulisse2/co2world","sub_path":"parsers/IS.py","file_name":"IS.py","file_ext":"py","file_size_in_byte":3491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"482810043","text":"from PQSCT import *\r\n\r\ntf.compat.v1.disable_eager_execution()\r\nprint(tf.__version__)\r\n\r\nmodel_path='bert_20_mean.h5'\r\nsavepath='./'\r\n\r\ndef evaluate_test(valid_data,valid_D,model):\r\n valid_model_pred = model.predict_generator(valid_D.__iter__(), steps=len(valid_D), verbose=1)\r\n y_pred = mlb.transform(sigmoid_pre(mlb,valid_model_pred))\r\n y_true = mlb.transform(sigmoid_pre(mlb,valid_data[:, 1]))\r\n \r\n np.savetxt(savepath+\"y_pred.txt\", y_pred)\r\n np.savetxt(savepath+\"y_true.txt\", y_true)\r\n\r\n h = metrics.hamming_loss(y_true,y_pred)\r\n p = metrics.precision_score(y_true, y_pred, average='micro')\r\n r = metrics.recall_score(y_true, y_pred,average='micro')\r\n f1 = metrics.f1_score(y_true, y_pred,average='micro')\r\n print('hamming_loss',h)\r\n print('precision_score ',p)\r\n print('recall_score',r)\r\n print('f1_score ',f1)\r\n\r\n\r\ndef get_flops_params():\r\n sess = tf.compat.v1.Session()\r\n graph = sess.graph\r\n flops = tf.compat.v1.profiler.profile(graph, options=tf.compat.v1.profiler.ProfileOptionBuilder.float_operation())\r\n params = tf.compat.v1.profiler.profile(graph, options=tf.compat.v1.profiler.ProfileOptionBuilder.trainable_variables_parameter())\r\n print('FLOPs: {}; Trainable params: {}'.format(flops.total_float_ops, params.total_parameters))\r\n\r\n \r\nif __name__ == '__main__':\r\n model = build_bert(nclass=num_classes,strategy='mean') \r\n get_flops_params()\r\n\r\n model.load_weights(model_path)\r\n model.summary()\r\n\r\n mlb=get_mlb()\r\n # train_data=load_data(train_data_path)\r\n # valid_data=load_data(valid_data_path)\r\n test_data=load_data(test_data_path)\r\n\r\n # train_D = data_generator(train_data, shuffle=True)\r\n # valid_D = data_generator(valid_data, shuffle=False)\r\n test_D = data_generator(test_data, shuffle=False)\r\n\r\n dd=evaluate_test(test_data,test_D,model)\r\n","repo_name":"ccnu-edm/PQSCT","sub_path":"PQSCT_eval.py","file_name":"PQSCT_eval.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"1293990222","text":"# Written by:\n# Creation Date:\n# Last Edited by:\n# Last edit date:\n# Documentation File:\n# Purpose:\n\n\nimport random\n\n# Global variable decorations\n\nmessageList = [ \"Enter your bet between $20 and the money in your pot\", \"That bet is out of range - You may only bet between $20 and the money in your pot\", \"Enter Y to continue, any other single letter to stop \", \"Must Enter Y or any other single letter to stop\" ]\nnumSuits = 4\nnumCards = 13\ndeckCards = []\nsuitValue = [ \"CLUBS\",\"DIAMONDS\",\"HEARTS\",\"SPADES\"]\ncardValue = [ \"ACE\",'2','3','4','5','6','7','8','9','10','JACK','QUEEN','KING' ]\ncardSuit = 0\ncardName = 1\ncardsLeft = 0\ncardsToStart = 0.95\n\nplayerBet = 0\nplayer1 = True\nplayerInfo = [[ \"A\", 200 ], [ \"B\", 200 ], [ \"HOUSE\", 200 ]] # Later you may want to put in an implementation to get how much each player want's to gamble and their names\nplayerName = 0\nplayerPot = 1\ntheHouse = 2\n\nplayGame = True\nminBet = 20\nmaxBet = 100\nante = 10\nminBalance = ante + minBet\nplayerHand = []\nasciiString = \"A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z, \"\nasciiList = asciiString.split( ',' )\n#print ( asciiList )\n\n\n\ndef initDeck( startingCardsPercent, numSuits, numCards ):\n #returns a full deck\n return( int( startingCardsPercent * numSuits * numCards ), [ [ True ] * numCards for i in range( numSuits) ] )\n\n\ndef bubbleSort ( whichCol, arrayToSort ):\n limit = len(arrayToSort)\n for i in range(1, limit):\n isSorted = True\n for j in range(limit-i):\n if arrayToSort[j][whichCol] > arrayToSort[j+1][whichCol]:\n (arrayToSort[j], arrayToSort[j+1]) = (arrayToSort[j+1], arrayToSort[j])\n isSorted = False\n if isSorted:\n break\n return( arrayToSort )\n\ndef getValidNum( whichMessage, lowNum, highNum ):\n #takes a low number & high number\n #returns a the user's valid input\n validNum = int( input( messageList[ whichMessage ] ))\n keepGoing = ( validNum < lowNum ) or ( validNum > highNum )\n while keepGoing:\n print ( messageList[ whichMessage + 1 ] )\n validNum = int( input( messageList[ whichMessage ] ))\n keepGoing = ( validNum < lowNum ) or ( validNum > highNum )\n return ( validNum )\n\ndef getValidSelection( whichMessage, validList ):\n validSelection = input( messageList[ whichMessage ] ).upper()\n keepGoing = not( validSelection in validList )\n\n while keepGoing:\n print ( messageList[ whichMessage + 1 ] )\n validSelection = input( messageList[ whichMessage ] ).upper()\n keepGoing = not( validSelection in validList )\n return ( validSelection )\n\ndef getACard( numSuits, numCards, cardDeck ):\n global cardsLeft\n cardsLeft -= 1\n #returns a random card\n whichSuit = random.randint( 0, numSuits - 1 )\n whichCardValue = random.randint( 0, numCards - 1 )\n while cardDeck [ whichSuit][ whichCardValue ] == False:\n whichSuit = random.randint( 0, numSuits - 1 )\n whichCardValue = random.randint( 0, numCards - 1 )\n cardDeck [ whichSuit][ whichCardValue ] = False\n return ( [ whichSuit, whichCardValue ] )\n\ndef getAHand( whichCol, numCardsToTake, numSuits, numCards, cardDeck ):\n #takes parameter to sort by, and number of cards to return\n #returns random sorted hand\n cardHand = []\n for i in range(numCardsToTake):\n cardHand.append(getACard( numSuits, numCards, cardDeck ))\n cardHand = bubbleSort(1, cardHand)\n return( cardHand )\n\ndef makeBet( playerInfo, minBet, maxBet ):\n #returns a valid bet\n playerBet = getValidNum(0, minBet, maxBet)\n return ( playerBet )\n\ndef displayCards( whereToStart, playerHand ):\n #prints cards to console\n for i in range(whereToStart, len(playerHand)):\n print(suitValue[playerHand[i][0]], \"of\", cardValue[playerHand[i][1]])\n\ndef reportResults( playerInfo ):\n #prints accounts to console\n print(\"Account values: \")\n print(playerInfo)\n\ndef checkWin ( playerHand ):\n #checks if card is in between two values\n if(playerHand[0][1] >= playerHand[2][1] and playerHand[1][1] >= playerHand[2][1]):\n win = True\n else:\n win = False\n return win\n\n # this will eventually become draw in processing but remember draw provides it's own loop\n\nwhile playGame:\n if cardsLeft <= 0:\n cardsLeft,cardDeck = initDeck( cardsToStart, numSuits, numCards )\n\n playerInfo[ player1 ][ playerPot ] -= ante\n playerHand = getAHand( cardName, 2, numSuits, numCards, cardDeck )\n displayCards( 0, playerHand )\n playerBet = makeBet( playerInfo[ player1 ], minBet, maxBet )\n nextCard = getACard( numSuits, numCards, cardDeck )\n playerHand.append( nextCard )\n\n #print( \"nextCard \", nextCard )\n #print( 'player hand ', playerHand )\n displayCards( 2, playerHand )\n\n playerWon = checkWin( playerHand )\n if playerWon:\n playerInfo[ player1 ][ playerPot ] += playerBet\n playerInfo[ theHouse ][ playerPot ] -= playerBet\n else:\n playerInfo[ player1 ][ playerPot ] -= playerBet\n playerInfo[ theHouse ][ playerPot ] += playerBet\n\n reportResults( playerInfo )\n\n #print ( playerInfo, maxBet )\n #print ( \"ML \", cardDeck )\n\n\n\n if ( ( playerInfo[ 0 ][ playerPot ] < minBalance ) or ( playerInfo[ 1 ][ playerPot ] < minBalance ) or ( playerInfo[ 2 ][ playerPot ] < minBalance )):\n playGame = False\n elif ( getValidSelection( 2, asciiList ) != \"Y\" ):\n playGame = False\n else:\n player1 = not player1\n","repo_name":"wwwenwilliam/grade-12-comsci","sub_path":"Wedge/advanced shell filled.py","file_name":"advanced shell filled.py","file_ext":"py","file_size_in_byte":5501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"32"} +{"seq_id":"1565631704","text":"from lights.animations.base import BaseAnimation\nfrom lights.utils.validation import is_valid_rgb_color\nfrom typing import Collection, Optional\n\n# An animation for empirically measuring animation fps.\n\nclass Benchmark(BaseAnimation):\n def __init__(self, pixels, *, fps: Optional[int] = None, color: Collection[int] = (255,0,0)):\n super().__init__(pixels, fps=fps)\n self.pixels = pixels\n self.color = color\n self.i = 0\n\n def renderNextFrame(self):\n prev = self.i - 1 if self.i != 0 else len(self.pixels) - 1\n self.pixels[self.i] = self.color\n self.pixels[prev] = (0, 0, 0)\n self.i = (self.i + 1) % len(self.pixels)\n\n @classmethod\n def validate_parameters(cls, parameters):\n super().validate_parameters(parameters)\n full_parameters = {**cls.get_default_parameters(), **parameters}\n if not is_valid_rgb_color(full_parameters['color']):\n raise TypeError(\"color must be a valid rgb color tuple\")\n","repo_name":"jstentz/christmas-lights","sub_path":"lights/lights/animations/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"70527742491","text":"import pygame as pg\nfrom random import randint\nfrom man import Man\n\nclass Shot:\n SPEED = 60\n\n def __init__(self, img_list, pos_x, pos_y, side):\n self.pos_x = pos_x + 74\n self.pos_y = pos_y + 40\n self.side = side\n self.img = img_list[self.side - 1]\n self.over_border = False\n\n def move_up(self):\n if self.pos_y >= -1 * Shot.SPEED:\n self.pos_y -= Shot.SPEED\n else:\n self.over_border = True\n\n def move_left(self):\n if self.pos_x >= -1 * Shot.SPEED:\n self.pos_x -= Shot.SPEED\n else:\n self.over_border = True\n\n def move_right(self):\n if self.pos_x < 1200:\n self.pos_x += Shot.SPEED\n else:\n self.over_border = True\n\n def move_down(self):\n if self.pos_y <= 800 + Shot.SPEED:\n self.pos_y += Shot.SPEED\n else:\n self.over_border = True\n\n # def move_d1(self):\n # if self.pos_y >= -1 * Shot.SPEED:\n # self.pos_y -= Shot.SPEED\n # self.pos_x -= Shot.SPEED\n\n # def move_d2(self):\n # if self.pos_y >= -1 * Shot.SPEED:\n # self.pos_y -= Shot.SPEED\n # self.pos_x += Shot.SPEED\n\n def move(self):\n move_side = {1: self.move_left, 2: self.move_up, 3: self.move_right, 4: self.move_down}\n move_side[self.side]()\n\n def collision(self, bird):\n if self.pos_x - 70 <= bird.x <= self.pos_x + 70 and self.pos_y - 70 <= bird.y <= self.pos_y + 70:\n return True\n return False\n\n def update(self, screen):\n self.move()\n screen.blit(self.img, (self.pos_x, self.pos_y))","repo_name":"TheNotZeroYet/pygame_hunter-main","sub_path":"shot.py","file_name":"shot.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"32"} +{"seq_id":"36828263533","text":"from __future__ import division, print_function\nfrom .defaults import *\nfrom .fileIO import *\nfrom .utilities import _set_particles, _set_grid, _set_time\nfrom .solvers import *\n\nclass pyticle:\n \"\"\"\n ** A class to hold the grid, options, and particles that will be tracked**\n\n Inputs:\n - data - A dict or netcdf filename, either of which contain the required data.\n - locations - A list of locations to place particles\n - outfile - A filepath to save the output\n\n Optional:\n - options - Additional dict specifying options\n - debug - default False\n\n \"\"\"\n\n def __init__(self, data, locations, outfile, options={}, debug=False):\n \"\"\" Initialize pyticle class\"\"\"\n\n if debug: print('-Debug mode on-')\n self._debug=debug\n\n # Load and set the options for the specified model type\n if debug: print(' Setting options')\n self.opt = model_options(options)\n\n # Load the required variables for specified model type\n if debug: print(' Loading model grid')\n self.grid = _set_grid(self, data, locations)\n\n # Deal with time setup\n if debug: print(' Setup time')\n self.time = _set_time(self)\n\n # Set initial particle data\n if debug: print(' Setting particle locations')\n self.particles = _set_particles(self, locations)\n\n # Initialize output file\n if self.opt.saveOutput:\n if debug: print(' Initializing netcdf output')\n self._ncid = init_netcdf(self, outfile)\n self.opt.outfile = outfile\n\n if debug: print(' pyticle initialized!')\n\n return\n\n\n def run(self):\n \"\"\"\n ** Function that starts the particle tracking**\n\n Inputs:\n - pyticleClass\n\n \"\"\"\n\n self.grid.u1 = self.grid.u[self.time.starttime,]\n self.grid.v1 = self.grid.v[self.time.starttime,]\n if '3D' in self.opt.gridDim:\n self.grid.w1 = self.grid.ww[self.time.starttime,]\n self.grid.z1 = self.grid.zeta[self.time.starttime,]\n\n # Progress counter\n cnt = 1\n\n for ncstep in range(self.time.starttime, self.time.endtime):\n for interpstep in range(self.time.interp):\n\n # Linearly interpolate fields in time\n f1 = (interpstep - self.time.interp + 1) / -self.time.interp\n f2 = (interpstep + 1) / self.time.interp\n\n self.grid.u2 = self.grid.u[ncstep,]*f1 + \\\n self.grid.u[ncstep + 1,]*f2\n self.grid.v2 = self.grid.v[ncstep,]*f1 + \\\n self.grid.v[ncstep + 1,]*f2\n\n if '3D' in self.opt.gridDim:\n self.grid.w2 = self.grid.ww[ncstep,]*f1 + \\\n self.grid.ww[ncstep + 1,]*f2\n self.grid.z2 = self.grid.zeta[ncstep,]*f1 + \\\n self.grid.zeta[ncstep + 1,]*f2\n # Move particles\n self.particles = rungekutta(self)\n\n # Overwrite old velocity field with current velocity field\n self.grid.u1 = self.grid.u2\n self.grid.v1 = self.grid.v2\n if '3D' in self.opt.gridDim:\n self.grid.w1 = self.grid.w2\n self.grid.z1 = self.grid.z2\n\n self.particles.time = self.grid.time[ncstep] * f1 +\\\n self.grid.time[ncstep + 1] * f2\n\n\n # Only save output when specified based on outputratio\n if np.mod(cnt, self.time.out) == 0:\n self.particles.loop += 1\n save_netcdf(self)\n\n cnt += 1\n\n # The code starts at \"step 2\" as step one happens\n # during initialization\n if cnt % 50 == 0:\n print('Completed step {}/{}'.format(cnt , \\\n self.time.totalsteps))\n\n self._ncid.close()\n\n\n\n\n","repo_name":"moflaher/pyticle_tracker","sub_path":"pyticle_tracker/pyticleClass.py","file_name":"pyticleClass.py","file_ext":"py","file_size_in_byte":3980,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"32"} +{"seq_id":"7851153055","text":"import numpy as np\n\nimport logging\n\nlogit = logging.getLogger(__name__)\ndef set_logging_level(level=logging.WARNING):\n logit.setLevel(level=level)\n logit.info(\"Setting the logging level to %s\"%(level))\n\ndef mol_dens( temp, pres, rhum, co2, ph2o=None,\n wvdens=False):\n \"\"\"\n **PURPOSE:**\n Returns the molair density of air and watervapor as a function temperature (in K), pressure (in mbar), relative humidity (%) and CO2 content (in ppm).\n \n **Parameters:**\n \n * temp : Temperature\n * pres : Pressure\n * rhum : Relative humidity\n * co2 : \n \n \n **REFERENCE:**\n P.E. Ciddor, \"The refractive index of air: new equations for the visible and near infrared\", Appl. Opt. 35 (9), 1566-1573\n \n **MODIFICATION HISTORY:**\n Version 1.0, 09-OCT-2002, by Roland den Hartog, ESA / ESTEC / Genie team, rdhartog@rssd.esa.int\n \n \"\"\"\n \n \n ptot=pres*100.\n # Compute the partial water vapour pressure\n if ph2o is None:\n A, B, C, D = 1.2378847e-5, -1.9121316e-2, 33.93711047e0 , -6.3431645e+3\n psvp = np.exp(A * temp**2 + B * temp + C + D / temp)\n ph2o = (rhum/100.) * psvp\n \n A, B, C = 1.00062, 3.14e-8, 5.6e-7\n f = A + B * ptot + C * temp**2\n # !RL this is a bit weird?\n xw = np.min([(f * ph2o/ptot), 1.])\n # Compute the densities of air, standard dry air, water vapor and standard water vapor\n R = 8.314510 # J/mol/K\n\n a0 = 1.58123e-6 # K/Pa\n a1 = -2.9331e-8 # 1/Pa\n a2 = 1.1043e-10 # 1/K/Pa\n b0 = 5.707e-6 # K/Pa\n b1 = -2.051e-8 # 1/Pa\n c0 = 1.9898e-4 # K/Pa\n c1 = -2.376e-6 # 1/Pa\n d0 = 1.83e-11 # K^2/Pa^2\n d1 = -0.765e-8 # K^2/Pa^2\n\n # Density of dry air and water vapor\n t = temp - 273.15\n Z = 1e0 - (ptot/temp) *\\\n (a0 + a1 * t + a2 * t * t + (b0 + b1 * t) * xw + (c0 + c1 * t) * xw * xw ) +\\\n (ptot/temp)**2 * (d0 + d1 * xw * xw)\n\n dax = ptot / (Z * R * temp) * (1e0 - xw)\n if wvdens:\n wvdens = ptot / (Z * R * temp) * xw\n return dax, wvdens\n else:\n return dax\n\ndef test_mol():\n \n #wvdens = np.array([1.27])\n \n temp = 288.15\n pres = 1013.25\n rhum = 0.\n co2 = 450.\n airdens, wvdens = mol_dens(temp, pres, rhum, co2, wvdens=True)\n print(airdens, wvdens)\n \n \n temp = 288.5\n pres = 743\n rhum = 33.\n cO2 = 450.\n airdens, wvdens = mol_dens(temp, pres, rhum, co2, wvdens=True)\n print(airdens, wvdens)\n \n temp = 293.15\n pres = 13.33\n rhum = 100.\n cO2 = 450.\n airdens, wvdens = mol_dens(temp, pres, rhum, co2, wvdens=True)\n print(airdens, wvdens)\n # !RL This last one returns 0, is that normal?","repo_name":"rlaugier/SCIFYsim","sub_path":"scifysim/mol_dens.py","file_name":"mol_dens.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"32"} +{"seq_id":"36728015199","text":"def pow():\r\n n = float(input(\"Enter a number: \"))\r\n a = int(input(\"Enter the degree of power: \"))\r\n power = 1\r\n i = 0\r\n for i in range (1,a+1):\r\n power = power * n\r\n print(power)\r\n\r\n\r\nprint(\"Welcome to power calculation.\")\r\npow()","repo_name":"utsho34/PYThon-Advance-CAlculator","sub_path":"power.py","file_name":"power.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31706581361","text":"\nimport json\n\ndef perev(file):\n a = input(\"ПЕРЕДИВИТИСЬ ФАЙЛ У ЯКИЙ ДОБАВИЛИ ІНФОРМАКЦІЮ? +/ - \\t\")\n if a == \"+\":\n with open('d:\\\\Python_ITed\\\\Lesson_10\\\\Homework_L10\\\\packeg_for_HWL10\\\\artical\\\\' + file + '.txt','r+') as f:\n text = f.read()\n print(text)\n\n\ndef add_new_file_to_jsonfile(f_name):\n with open('D:\\\\Python_ITed\\\\Lesson_10\\\\Homework_L10\\\\packeg_for_HWL10\\\\list_articals.json', 'r') as f:\n list_art = json.loads(f.read())\n for i in list_art:\n print(f\"{i} - {list_art.get(i)}\")\n key = input(\"ВВВЕДІТЬ ВІЛЬНИЙ ПОШУКОВИЙ НОМЕР ФАЙЛУ\\t\")\n list_art.update({key:f_name})\n with open('D:\\\\Python_ITed\\\\Lesson_10\\\\Homework_L10\\\\packeg_for_HWL10\\\\list_articals.json', 'w') as f:\n json.dump(list_art, f)\n\n\ndef decor_only_write(only_read):\n print(\"\\n ПЕРЕЛІК СТАТЕЙ ДЛЯ РЕДАГУВАННЯ (ЗАТЕРИТИ СТАРИЙ ТЕКСТ/ДОЗАПИСУ)\")\n list_art = only_read()\n number = input(\"\\nВКАЖІТЬ НОМЕР СТАТТІ( ЯКЩО ТАКА Є), ІНАКШЕ ВКАЖІТЬ - 0(НУЛЬ)\\t\")\n regim_info = \"\"\"\\t\\t\\t РЕЖИМИ ЗАПИСУ ФАЙЛУ\n 1---:'w-витре все у файлі, якщо такий існує ',\n 2---:'a+ дозапис інформації в кінці файлу',\n 3---:'x- створить новий файл для запису, якщо такий відстуній, інакше виключеня\"\"\"\n print(regim_info)\n regim = {'1': 'w', '2': 'a+', '3': 'x'}\n w_regim = input(\"ВИБЕРІТЬ РЕЖИМ ЗАПИСУ ФАЙЛ��(ВКАЖІТЬ ЦИФРУ)\\t\")\n\n\n def wrapper():\n if number != '0' and w_regim != '3':\n with open('d:\\\\Python_ITed\\\\Lesson_10\\\\Homework_L10\\\\packeg_for_HWL10\\\\artical\\\\' +\n list_art.get(number) + '.txt', regim.get(w_regim)) as f:\n sms = input(\"ВВЕДІТЬ, ІНФОРМАЦІЮ ЯКУ ПОТРІБНО ЗАПИСАТИ/ДОПИСАТИ У ФАЙЛ\\n\")\n article = f.write(\"\\n\"+sms)\n print(f\"\\n {article}\")\n elif number == '0' and w_regim == '3':\n f_name = input(\"ВКАЖІТЬ НАЗВУ ФАЙЛУ БЕЗ РОЗШИРЕННЯ\\t\")\n with open('d:\\\\Python_ITed\\\\Lesson_10\\\\Homework_L10\\\\packeg_for_HWL10\\\\artical\\\\'+\n f_name+'.txt', regim.get(w_regim)) as f:\n sms = input(\"ВВЕДІТЬ, ІНФОРМАЦІЮ ЯКУ ПОТРІБНО ЗАПИСАТИ У ФАЙЛ\\n\")\n article = f.write(\"\\n\"+sms)\n print(f\"\\n {article}\")\n elif number != '0' and w_regim == '3':\n print(\"ПРИ ОБРАНИХ УМОВАХ РЕДАГУВАННЯ ФАЙЛУ НЕ МОЖЛИВЕ\")\n # функція, що перепитує чи є потреба переглянути файл після редагування\n if number != '0' and w_regim != '3':\n perev(list_art.get(number))\n elif number == '0' and w_regim == '3':\n perev(f_name)\n # функція що записує новий файл в словник з переліком усіх статей\n add_new_file_to_jsonfile(f_name)\n\n return wrapper\n\n\n@decor_only_write\ndef only_read():\n with open('D:\\\\Python_ITed\\\\Lesson_10\\\\Homework_L10\\\\packeg_for_HWL10\\\\list_articals.json','r') as f:\n list_art = json.loads(f.read())\n for i in list_art:\n print(f\"{i} - {list_art.get(i)}\")\n return list_art\nonly_read()\n\n","repo_name":"VitalLight/ITEA_EDUCATION","sub_path":"Lesson_10/Homework_L10/packeg_for_HWL10/admin/write_admin.py","file_name":"write_admin.py","file_ext":"py","file_size_in_byte":3695,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70558590488","text":"import sys\nimport networkx as nx\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QComboBox, QPushButton\nimport re\n\n\ndef extract_data_from_log(file_path):\n with open(file_path, 'r') as file:\n lines = file.readlines()\n\n data = []\n for line in lines:\n matches = re.findall(r'<(\\d+),([^,]+),([^,]+),([^>]+)>', line)\n if matches:\n data += list(matches)\n\n return data\n\n\ndef generate_data(data_):\n operations_ = {}\n users_ = set()\n resources_ = set()\n\n for i in data_:\n t = (i[1], i[2])\n users_.add(i[1])\n resources_.add(i[2])\n if t in operations_:\n operations_[t].add(i[3])\n else:\n operations_[t] = {i[3]}\n return list(users_), list(resources_), operations_\n\n\nclass App(QWidget):\n def __init__(self, graph, users_, resources_):\n super().__init__()\n self.canvas = None\n self.switch_button = None\n self.resource_combo = None\n self.resource_label = None\n self.user_combo = None\n self.user_label = None\n self.user_color = \"#AEC6CF\"\n self.resource_color = '#FFD700'\n self.users = users_\n self.resources = resources_\n self.G = graph\n self.show_users = True # Initialize to show user view\n self.init_ui()\n self.update_ui()\n\n def init_ui(self):\n self.setWindowTitle(\"Log Viewer\")\n self.setGeometry(100, 100, 800, 600)\n\n # Create layouts\n main_layout = QVBoxLayout()\n user_layout = QHBoxLayout()\n resource_layout = QHBoxLayout()\n switch_layout = QHBoxLayout()\n\n # User selection\n self.user_label = QLabel(\"User:\")\n self.user_combo = QComboBox()\n self.user_combo.addItems(self.users)\n user_layout.addWidget(self.user_label)\n user_layout.addWidget(self.user_combo)\n\n # Resource selection\n self.resource_label = QLabel(\"Resource:\")\n self.resource_combo = QComboBox()\n self.resource_combo.addItems(self.resources)\n resource_layout.addWidget(self.resource_label)\n resource_layout.addWidget(self.resource_combo)\n\n # Switch button\n self.switch_button = QPushButton(\"Switch to Resources\" if self.show_users else \"Switch to Users\")\n switch_layout.addWidget(self.switch_button)\n\n # Add layouts to the main layout\n main_layout.addLayout(user_layout)\n main_layout.addLayout(resource_layout)\n main_layout.addLayout(switch_layout)\n\n # Create a canvas for graph visualization\n self.canvas = FigureCanvas(plt.figure())\n main_layout.addWidget(self.canvas)\n\n # Connect signals to slots\n self.user_combo.currentIndexChanged.connect(self.update_ui)\n self.resource_combo.currentIndexChanged.connect(self.update_ui)\n self.switch_button.clicked.connect(self.toggle_view)\n\n # Set the main layout\n self.setLayout(main_layout)\n\n def toggle_view(self):\n self.show_users = not self.show_users\n self.switch_button.setText(\"Switch to Resources\" if self.show_users else \"Switch to Users\")\n self.update_ui()\n\n def update_ui(self):\n selected_user = self.user_combo.currentText()\n selected_resource = self.resource_combo.currentText()\n\n self.user_label.setVisible(self.show_users)\n self.user_combo.setVisible(self.show_users)\n self.resource_label.setVisible(not self.show_users)\n self.resource_combo.setVisible(not self.show_users)\n\n # Clear the previous graph and draw the new subgraph with operations\n plt.clf()\n\n subgraph = nx.DiGraph() # Initialize an empty subgraph\n\n if self.show_users:\n if selected_user:\n if selected_user in self.G:\n # Create a subgraph showing all resources connected to the selected user\n subgraph.add_node(selected_user, color=self.user_color)\n for resource in self.G.successors(selected_user):\n operation = self.G[selected_user][resource][\"operation\"]\n subgraph.add_node(resource, color=self.resource_color)\n subgraph.add_edge(selected_user, resource, operation=operation)\n else:\n if selected_resource:\n if selected_resource in self.G:\n # Create a subgraph showing all users connected to the selected resource\n subgraph.add_node(selected_resource, color=self.resource_color)\n for user in self.G.predecessors(selected_resource):\n operation = self.G[user][selected_resource][\"operation\"]\n subgraph.add_node(user, color=self.user_color)\n subgraph.add_edge(user, selected_resource, operation=operation)\n\n pos = nx.spring_layout(subgraph)\n\n node_colors = [subgraph.nodes[n].get(\"color\", 'skyblue') for n in subgraph.nodes]\n labels = {(u, v): d[\"operation\"] for u, v, d in subgraph.edges(data=True)}\n nx.draw(subgraph, pos, with_labels=True, node_size=500, node_color=node_colors, font_size=10,\n font_color='black')\n nx.draw_networkx_edge_labels(subgraph, pos, edge_labels=labels)\n self.canvas.draw()\n\n\nif __name__ == '__main__':\n\n log_file = [\"data.log\"] # set all log file path\n\n data = []\n for i in log_file:\n data += extract_data_from_log(i)\n users, resources, operations = generate_data(data)\n G = nx.DiGraph()\n # print(operations)\n for k, o in operations.items():\n G.add_edge(k[0], k[1], operation=o)\n\n app = QApplication(sys.argv)\n ex = App(G, users, resources)\n ex.show()\n sys.exit(app.exec_())\n","repo_name":"celidur/visualizer-abac-log","sub_path":"v1.py","file_name":"v1.py","file_ext":"py","file_size_in_byte":5914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9358325146","text":"# -*- coding:utf-8 -*-\nimport requests\nimport json\nimport re\n\n\nclass HaoKan(object):\n def __init__(self, url):\n self.url = url\n\n def get_url(self):\n vid = self.url\n if len(vid) >= 25:\n vid = str(vid).split(\"=\")[1]\n base_url = \"https://haokan.baidu.com/v?vid=\" + str(vid)\n headers = {\n \"upgrade-insecure-requests\": \"1\",\n \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36\"\n }\n try:\n response = requests.get(url=base_url, headers=headers, timeout=10)\n if response.status_code == 200:\n try:\n pattern = re.compile('\"clarityUrl\":\\[(.*?)\\]', re.S)\n res = \"[\" + re.findall(pattern, response.text)[0] + \"]\"\n rows = json.loads(res)\n items = []\n for row in rows:\n items.append(\n {\"key\": row.get(\"key\", \"\"), \"title\": row.get(\"title\", \"\"), \"video_url\": row.get(\"url\", \"\")})\n return json.dumps(items, ensure_ascii=False)\n except Exception as e:\n return json.dumps({\"info\": \"暂无相关数据，请检查相关数据：\" + str(e)}, ensure_ascii=False)\n else:\n return json.dumps({\"info\": \"暂无相关数据，请检查��关数据：\"}, ensure_ascii=False)\n except Exception as e:\n return json.dumps({\"info\": \"暂无相关数据，请检查相关数据：\" + str(e)}, ensure_ascii=False)\n","repo_name":"FioraLove/Restful-API-Django","sub_path":"APPRoot/loveword/middleware/haokan_parse.py","file_name":"haokan_parse.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"31"} +{"seq_id":"35341567772","text":"class Hashtable:\n def __init__(self):\n self.Max = 10\n self.arr = [[] for i in range(self.Max)]\n\n def hash_val(self,key):\n h = 0\n for char in key:\n h+=ord(char)\n return h%self.Max\n \n def __getitem__(self,key):\n k = self.hash_val(key)\n for kv in self.arr[k]:\n if kv[0] == key:\n return kv[1]\n \n \n def __setitem__(self,key,val):\n h = self.hash_val(key)\n found = False\n for idx,element in enumerate(self.arr[h]):\n if len(element) == 2 and element[0] == key:\n self.arr[h][idx] = (key,val)\n found = True\n break\n if not found:\n self.arr[h].append((key,val))\n\n\n def __delitem__(self,key):\n h = self.hash_val(key)\n for idx,kv in enumerate(self.arr[h]):\n if kv[0] == key:\n del self.arr[h][idx]\n return self.arr \n \n def print(self):\n print(self.arr)\n \nob = Hashtable()\n# ob.hash_val('march 6')\n# ob.delete('march 6')\nob['march 6'] = 120\nob['march 6'] = 10\nob['march 8'] = 67\nob['march 9'] = 4\nob['march 17'] = 459\nprint(ob.arr)\ndel ob['march 17']\nprint(ob.arr)\n","repo_name":"kashifmalik962/Local-D","sub_path":"Data_structure_algo/Hash_collision_handling.py","file_name":"Hash_collision_handling.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18077830947","text":"from tqdm import tqdm\nfrom sys import exit\nimport base64\nimport re\nimport os\nimport requests\nfrom requests.exceptions import MissingSchema\nimport tkinter.messagebox\nimport socket\nimport threading\nimport sys, re, random, requests, os\nimport tkinter as tk # python 3\nfrom tkinter import font as tkfont # python 3\n# import Tkinter as tk # python 2\n# import tkFont as tkfont # python 2\nfrom tkinter import font as tkfont\nfrom tkinter import font, messagebox\nimport PySimpleGUI as sg\nfrom nav_bar import *\nfrom tkinter import filedialog\nimport time\nfrom scapy.all import *\n\nglobal s\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nglobal bytes\nbytes = random._urandom(1024)\n\n\nclass ICMP(tk.Frame):\n\n # ============================= Initializer Function\n\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n self.controller = controller\n abtframefont = tkfont.Font(family='Calibri', size=33, weight=\"bold\")\n display_nav_bar(self, controller)\n aboutframe = tk.Label(self, text=\" ICMP Ping Flooder\", bg='#3B5262', fg='white', anchor=\"c\",\n font=abtframefont)\n aboutframe.place(rely=0.08, relheight=0.12, relwidth=1)\n\n allscreenframe = tk.Label(self, bg='white')\n allscreenframe.place(rely=0.2, relheight=1, relwidth=1)\n\n # LABELS\n labelTarget = Label(self, text=\"Enter Target IP: \",\n font=('Calibri', 13), bg='#4D6C84', fg='white', anchor='c').place(rely=0.28, relx=0.08,\n relheight=0.06,\n relwidth=0.24)\n labelPort = Label(self, text=\"Enter Port number: \",\n font=('Calibri', 13), bg='#4D6C84', fg='white', anchor='c').place(rely=0.36, relx=0.08,\n relheight=0.06,\n relwidth=0.24)\n labelPackets = Label(self, text=\"Enter number of packets: \",\n font=('Calibri', 13), bg='#4D6C84', fg='white', anchor='c').place(rely=0.44, relx=0.08,\n relheight=0.06,\n relwidth=0.24)\n\n # INPUT TEXT BOXES\n self.TargetTB = Entry(self, font=('Calibri', 13))\n self.TargetTB.place(rely=0.28, relx=0.36, relheight=0.06, relwidth=0.30)\n self.PortTB = Entry(self, font=('Calibri', 13))\n self.PortTB.place(rely=0.36, relx=0.36, relheight=0.06, relwidth=0.30)\n self.PacketsTB = Entry(self, font=('Calibri', 13))\n self.PacketsTB.place(rely=0.44, relx=0.36, relheight=0.06, relwidth=0.30)\n\n # BUTTONS\n\n Button(self, text=\"Start Flood\", font=('Calibri', 16), bg='#4D6C84', fg='white', anchor='c',\n command=lambda: self.execute()).place(rely=0.36, relx=0.7, relheight=0.06, relwidth=0.08)\n\n # Execute Button function ===============\n\n def execute(self):\n # target_ip = \"192.168.1.120\"\n labelPackets = Label(self, text=\"Refer to terminal for flood results\",\n font=('Calibri', 13), anchor='c').place(rely=0.54, relx=0.36, relheight=0.05,\n relwidth=0.30)\n target_ip = self.TargetTB.get()\n targetPort = int(self.PortTB.get())\n num_packets = int(self.PacketsTB.get())\n ip = IP(dst=target_ip)\n tcp = TCP(sport=RandShort(), dport=targetPort, flags=\"S\")\n raw = Raw(b\"X\" * 1024)\n p = ip / tcp / raw\n for i in range(num_packets):\n send(p, inter=0.001)\n print(\"\\nICMP flood finished\\n\")\n","repo_name":"Hardhat-Enterprises/PT-GUI","sub_path":"ExecutionTools/ICMPPingFlooder.py","file_name":"ICMPPingFlooder.py","file_ext":"py","file_size_in_byte":3994,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"31"} +{"seq_id":"31538222416","text":"\"\"\"Provides utilities and dependency injection for CHA₂DS₂-VASc score calculation.\"\"\"\nfrom typing import Literal\n\nimport pandas as pd\n\nfrom schemas.chads_vasc_score import ChadsVascInput\n\n\ndef unpack_and_calc_cvs(cvs_data: ChadsVascInput) -> int:\n \"\"\"Dependency inject for unpacking schema and calculating CHA₂DS₂-VASc score.\n\n Args:\n cvs_data: Input parameters for calculating CHA₂DS₂-VASc score.\n\n Returns:\n Calculated CHA₂DS₂-VASc score.\n \"\"\"\n return _calculate_cvs(**cvs_data.model_dump())\n\n\ndef _calculate_cvs(\n age: int,\n biological_sex: Literal[\"male\", \"female\", \"intersex\"],\n congestive_heart_failure: bool,\n hypertension: bool,\n stroke_tia: bool,\n vascular_disease: bool,\n diabetes: bool,\n) -> int:\n \"\"\"Calculates the CHA2DS2-VASc score based on input parameters.\n\n Args:\n age: Age of individual.\n biological_sex: Biological sex of individual.\n congestive_heart_failure: Whether individual presents with congestive heart failure.\n hypertension: Whether individual presents with hypertension.\n stroke_tia: Whether individual presents with experienced stroke/TIA.\n vascular_disease: Whether individual presents with vascular disease.\n diabetes: Whether individual presents with diabetes.\n\n Returns:\n CHA2DS2-VASc score.\n \"\"\"\n\n is_female = True if biological_sex == \"female\" else False\n score = (65 <= age < 75) + 2 * (age >= 75)\n score += (\n is_female\n + congestive_heart_failure\n + hypertension\n + 2 * stroke_tia\n + vascular_disease\n + diabetes\n )\n return score\n\n\n# Interpretation table from https://pubmed.ncbi.nlm.nih.gov/22246443/\n\ndata = [\n [0, 0.2, 0.3],\n [1, 0.6, 0.9],\n [2, 2.2, 2.9],\n [3, 3.2, 4.6],\n [4, 4.8, 6.7],\n [5, 7.2, 10.0],\n [6, 9.7, 13.6],\n [7, 11.2, 15.7],\n [8, 10.8, 15.2],\n [9, 12.2, 17.4],\n]\n\ndf = pd.DataFrame(\n data,\n columns=[\n \"CHA₂DS₂-VASc Score\",\n \"Risk of Ischemic Stroke\",\n \"Risk of Stroke/TIA/Systemic Embolism\",\n ],\n)\n","repo_name":"Health-Universe/streamlit-template","sub_path":"src/utils/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"21716187840","text":"import os\n\nimport numpy as np\nimport pytest\n\nfrom dgp.annotations.key_line_2d_annotation import KeyLine2DAnnotationList\nfrom dgp.datasets.synchronized_dataset import SynchronizedSceneDataset\nfrom dgp.utils.structures.key_line_2d import KeyLine2D\nfrom tests import TEST_DATA_DIR\n\n\ndef get_ontology_kl(scene_dataset_json, annotation_type):\n dataset = SynchronizedSceneDataset(\n scene_dataset_json,\n split='train',\n datum_names=['locator'],\n backward_context=0,\n requested_annotations=(\"key_line_2d\", )\n )\n return dataset.dataset_metadata.ontology_table.get(annotation_type, None)\n\n\n@pytest.fixture\ndef kl_ontology():\n DGP_TEST_DATASET_DIR = os.path.join(TEST_DATA_DIR, \"dgp\")\n scenes_dataset_json = os.path.join(DGP_TEST_DATASET_DIR, \"key_line_2d\", \"scene_dataset.json\")\n return get_ontology_kl(scene_dataset_json=scenes_dataset_json, annotation_type=\"key_line_2d\")\n\n\ndef test_kl2d_annotation(kl_ontology):\n keylines = [KeyLine2D(np.array([[i + j, i + 5] for i in range(5)], dtype=np.float32)) for j in range(5)]\n annotation_list = KeyLine2DAnnotationList(kl_ontology, keylines)\n assert len(annotation_list.xy) == 5\n\n\ndef test_kl2d_load(kl_ontology):\n DGP_TEST_DATASET_DIR = os.path.join(TEST_DATA_DIR, \"dgp\")\n expected_output = \"b67e1\"\n scenes_dataset_json = os.path.join(\n DGP_TEST_DATASET_DIR,\n \"key_line_2d/scene_000000/key_line_2d/FCM_front/000000000000000005_23caffa10d786a53782f9530a6ad796db0eaea21.json\"\n )\n kl2d_list = KeyLine2DAnnotationList.load(scenes_dataset_json, kl_ontology)\n assert kl2d_list.hexdigest[0:5] == expected_output\n\n\ndef test_kl2d_proto(kl_ontology):\n DGP_TEST_DATASET_DIR = os.path.join(TEST_DATA_DIR, \"dgp\")\n scenes_dataset_json = os.path.join(\n DGP_TEST_DATASET_DIR,\n \"key_line_2d/scene_000000/key_line_2d/FCM_front/000000000000000005_23caffa10d786a53782f9530a6ad796db0eaea21.json\"\n )\n kl2d_list = KeyLine2DAnnotationList.load(scenes_dataset_json, kl_ontology)\n ouput_proto = kl2d_list.to_proto()\n assert ouput_proto.__sizeof__() == 80\n\n\ndef test_kl2d_save(kl_ontology):\n DGP_TEST_DATASET_DIR = os.path.join(TEST_DATA_DIR, \"dgp\")\n scenes_dataset_json = os.path.join(\n DGP_TEST_DATASET_DIR,\n \"key_line_2d/scene_000000/key_line_2d/FCM_front/000000000000000005_23caffa10d786a53782f9530a6ad796db0eaea21.json\"\n )\n kl2d_list = KeyLine2DAnnotationList.load(scenes_dataset_json, kl_ontology)\n kl2d_list.save(\".\")\n filepath = \"./b67e1088cbf3761cc1511cfb1a4c2c2f0d353316.json\"\n assert os.path.exists(filepath)\n os.remove(filepath)\n","repo_name":"TRI-ML/dgp","sub_path":"tests/annotation/test_keyline_annotation.py","file_name":"test_keyline_annotation.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"31"} +{"seq_id":"36184371581","text":"# saved as greeting-server.py\nimport Pyro5.api\nimport subprocess\nimport threading\n\n\ndef start_pyro_server():\n subprocess.run('python -m Pyro5.nameserver', shell=True)\n\n\nrunserver = threading.Thread(target=start_pyro_server, daemon=True)\nrunserver.start()\n\n\n@Pyro5.api.expose\nclass GreetingMaker(object):\n def get_fortune(self, name):\n return \"Hello, {0}. Here is your fortune message:\\n\" \\\n \"Tomorrow's lucky number is 12345678.\".format(name)\n\n\ndaemon = Pyro5.server.Daemon() # make a Pyro daemon\nns = Pyro5.api.locate_ns() # find the name server\nuri = daemon.register(GreetingMaker) # register the greeting maker as a Pyro object\nns.register(\"example.greeting\", uri) # register the object with a name in the name server\n\nprint(\"Ready.\")\ndaemon.requestLoop()\n","repo_name":"cristiciortea/gtranslate_cli","sub_path":"tests/greeting-server.py","file_name":"greeting-server.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40749008457","text":"# Standard Library\nfrom urllib.parse import quote_plus\n\n# Third Party Stuff\nfrom django.http import HttpResponseRedirect\n\n# Spoken Tutorial Stuff\nfrom cdeep.models import *\nfrom creation.models import *\n\n\ndef list_videos(request):\n foss = request.GET.get('foss', '')\n foss = foss.replace('+', 'p').replace('-', ' ')\n language = request.GET.get('language', '')\n if foss or language:\n return HttpResponseRedirect('/tutorial-search/?foss=' + quote_plus(foss) + '&language=' + language)\n\n return HttpResponseRedirect('/tutorial-search/')\n\n\ndef show_video(request):\n print('test')\n old_tr = request.GET.get('tr', None)\n if old_tr:\n try:\n tr = TutorialResources.objects.get(pk=old_tr)\n foss = tr.tutorial_detail.foss_category.replace('+', 'p').replace('-', ' ')\n tutorial = tr.tutorial_detail.tutorial_name.replace('+', 'p').replace('-', ' ')\n return HttpResponseRedirect('/watch/' + quote_plus(foss) + '/' + quote_plus(tutorial) + '/' + tr.language + '/')\n except Exception as e:\n print(e)\n pass\n return HttpResponseRedirect('/tutorial-search/')\n\n\ndef search_node(request, keyword):\n return HttpResponseRedirect('/keyword-search/?q=' + str(keyword))","repo_name":"Spoken-tutorial/spoken-website","sub_path":"cdeep/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"31"} +{"seq_id":"12713107369","text":"# data_lexicon_senti_word_net.py\n\n# Running sentimental analysis using Vader\n# There is no cleaning needed here because the cleaning is already\n\n# python data_lexicon_senti_word_net.py\n\n\nimport time\nimport nltk\nfrom nltk.corpus import sentiwordnet\nfrom nltk.corpus import wordnet\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\n\n# Download wordnet and sentiwordnet\nnltk.download('wordnet')\nnltk.download('sentiwordnet')\n\n\n# Load article data set\ndf = pd.read_csv(\"./preprocess_dev_articles.csv\",\n names=[\"processed_text\"], encoding=\"utf-8\")\n\n# Shape of dataframe\nprint(\"Shape of training dataframe: \", df.shape)\n\n\n# Using SentiWordNet as a lexicon sentiment analyzer\n# SentiWordNet is a lexical resource for opinion mining. It assigns\n# to each synset of WordNet three sentiment scores: positivity,\n# negativity, and objectivity.\n\n\ndef get_sentiment(text):\n words = text.split()\n positive_score = 0\n negative_score = 0\n\n for word in words:\n synsets = wordnet.synsets(word)\n for synset in synsets:\n senti_synset = sentiwordnet.senti_synset(synset.name())\n positive_score += senti_synset.pos_score()\n negative_score += senti_synset.neg_score()\n\n sentiment_score = positive_score - negative_score\n\n return sentiment_score\n\n\n# Analyze sentiment for each article\npositive_count = 0\nnegative_count = 0\nneutral_count = 0\n\n\n# start time\nstart_time = time.time()\n\n\n# Loop through the data frame to determine the sentiments\nfor index, row in df.iterrows():\n # Column index 1 contains the article content\n # same as property, processed_text\n content = str(row[\"processed_text\"])\n\n sentiment_score = get_sentiment(content)\n print(f\"✅️ applied SentiWordNet on item {index + 1}\")\n\n # Print the sentiment of each article\n # print(sentiment_score)\n # The sentiment property, polarity, refers to the emotional tone\n # of the text. It is typically measured on a numeric scale that\n # ranges from -1.0 to 1.0, where a polarity score greater than 0\n # indicates positive sentiment (e.g., happiness, satisfaction),\n # a polarity score less than 0 indicates negative sentiment\n # (e.g., sadness, frustration) and a polarity score around 0\n # indicates neutral sentiment (e.g., factual information).\n\n if sentiment_score > 0:\n positive_count += 1\n elif sentiment_score < 0:\n negative_count += 1\n else:\n neutral_count += 1\n\n # Print the sentiment score for the article\n # print(f\"Article {index + 1} - Sentiment Scores: {sentiment_score}\")\n\n\ntotal_articles = df.shape[0]\n\nprint(\"Positive Articles:\", positive_count)\nprint(\"Negative Articles:\", negative_count)\nprint(\"Neutral Articles:\", neutral_count)\n\nprint(\"Positive Percentage:\", (positive_count / total_articles) * 100)\nprint(\"Negative Percentage:\", (negative_count / total_articles) * 100)\nprint(\"Neutral Percentage:\", (neutral_count / total_articles) * 100)\n\n\"\"\"\nThe output from these sentiment scores for each article, based on the SentiWordNet sentiment analysis, which provided a value ranging between, -1.0 to 1.0 in a manner, spread between: negativity, neutrality, positivity, demonstrated that sentimentality is quite closer to neutral when the values a inspected. However, using the the numeric range of indication, there is no specific neutral article. There was few negative articles and the rest were positive.\n\nPositive Percentage: 95.4\nNegative Percentage: 4.3\nNeutral Percentage: 0.3\nTime elapsed: 1613.69s\n\n\"\"\"\n\n\n# Create a DataFrame for the sentiment distribution\nsentiment_labels = [\"Negative\", \"Neutral\", \"Positive\"]\nsentiment_counts = [(negative_count / total_articles) * 100,\n (neutral_count / total_articles) * 100,\n (positive_count / total_articles) * 100]\n\nsentiment_data = pd.DataFrame(\n {\"Sentiment\": sentiment_labels, \"Count\": sentiment_counts})\n\n\n# end\nend_time = time.time()\nprint(f\"Time elapsed: {(end_time-start_time):.2f}s\")\n\n\n# Create a stacked bar chart\nsns.set(style=\"whitegrid\")\nsns.barplot(x=\"Sentiment\", y=\"Count\", data=sentiment_data,\n palette=[\"green\", \"red\", \"blue\"])\nplt.xlabel(\"Sentiment\")\nplt.ylabel(\"Percentage\")\nplt.title(\"Sentiment Distribution of Articles using SentiWordNet\")\nplt.show()\n","repo_name":"Otumian-empire/sentiment-analysis","sub_path":"data_lexicon_senti_word_net.py","file_name":"data_lexicon_senti_word_net.py","file_ext":"py","file_size_in_byte":4335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28598509800","text":"from gi.repository import Gtk\nimport re\nimport os.path\nfrom glob import glob\nfrom OjubaControlCenter.widgets import LaunchButton, info, error, sure\nfrom OjubaControlCenter.pluginsClass import PluginsClass\nimport dbus\n#import dbus.service\nfrom dbus.mainloop.glib import DBusGMainLoop\ndbus_loop = DBusGMainLoop(set_as_default = True)\nbus = dbus.SystemBus()\ninterface = 'org.freedesktop.UDisks'\n\n\n## NOTE: these global vars is loader validators\ncategory = 'install'\ncaption = _('Package Manager:')\ndescription = _('Package Manager allows you to install software.\\nIt saves you the effort downloading, tracing versions and resolving dependencies.')\npriority = 10\n\nclass occPlugin(PluginsClass):\n keep_cache_re=re.compile(r\"^(\\s*keepcache)\\s*=\\s*(.*)\\s*$\",re.M)\n media_repo_save='/etc/occ/media-repo.save'\n def __init__(self,ccw):\n self.__dev = bus.get_object(interface, \"/org/freedesktop/UDisks\")\n PluginsClass.__init__(self, ccw, caption, category, priority)\n vb=Gtk.VBox(False,2)\n self.add(vb)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n l=Gtk.Label(description)\n h.pack_start(l,False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n h.pack_start(LaunchButton(_(\"Add/Remove applications\"), fn='/usr/bin/gpk-application',icon=\"system-software-install\"),False,False,2)\n h.pack_start(LaunchButton(_(\"KPackageKit\"), fn='/usr/bin/kpackagekit'), False,False,2)\n h.pack_start(LaunchButton(_(\"Yum Extender\"), fn='/usr/bin/yumex'), False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n l=Gtk.Label(_('Package Manager uses predefined software sources (called repositories) to get software.\\nBy using official repositories you will get signed packages.'))\n h.pack_start(l,False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n h.pack_start(LaunchButton(_(\"Software Sources Editor\"), fn='/usr/bin/gpk-repo'),False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n l=Gtk.Label(_(\"You may add installation medium (CD/DVD) which contains packages.\\nThey are used to install packages offline or save bandwidth.\\nSome people consider inserting media annoying and prefer to download packages over the internet.\"))\n h.pack_start(l,False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n b=Gtk.Button(_('Add a media repository'))\n b.connect('clicked', self.add_media)\n h.pack_start(b, False,False,2)\n b=Gtk.Button(_('Disable all media repositories'))\n b.connect('clicked', self.disable_mediarepo)\n h.pack_start(b, False,False,2)\n \n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n l=Gtk.Label(_(\"If you don't have internet access you may want to disable internet repositories.\"))\n h.pack_start(l,False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n b=Gtk.Button(_('Disable all internet repositories'))\n b.connect('clicked', self.disable_net_repos)\n h.pack_start(b, False,False,2)\n self.restore_repos_b=b=Gtk.Button(_('Restore enabled repositories'))\n b.set_tooltip_text(_(\"Restore the enabled repositories as they were before disabling internet repositories\"))\n b.connect('clicked', self.restore_repos)\n h.pack_start(b, False,False,2)\n self.restore_repos_b.set_sensitive(os.path.exists(self.media_repo_save))\n\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n l=Gtk.Label(_('You may keep the downloaded packages to pass them to other people.\\nThose packages are kept under /var/cache/yum'))\n h.pack_start(l,False,False,2)\n h=Gtk.HBox(False,2); vb.pack_start(h,False,False,6)\n self.keep_cache_b=Gtk.CheckButton(_('keep downloaded packages'))\n self.keep_cache_b.set_active(self.get_keep_cache())\n try:\n self.ccw.rm_old_rpms_b.set_sensitive(self.keep_cache_b.get_active())\n self.ccw.cp_new_rpms_b.set_sensitive(self.keep_cache_b.get_active())\n except: pass\n h.pack_start(self.keep_cache_b, False,False,2)\n b=Gtk.Button(stock=Gtk.STOCK_APPLY)\n b.connect('clicked', self.keep_cache)\n h.pack_start(b, False,False,2)\n\n def get_device_property(self, udi, key):\n dev=bus.get_object(interface, udi)\n return dev.Get(interface+'.Device', key, dbus_interface=\"org.freedesktop.DBus.Properties\")\n\n def add_media(self, b):\n if not sure(_('Please make sure the media you want to add is inserted before you add it.\\nDo you want to continue?'), self.ccw): return\n repos=[]\n l=self.__dev.EnumerateDevices(dbus_interface = interface)\n for udi in l:\n if self.get_device_property(udi, \"device-is-removable\"):\n if not bool(self.get_device_property(udi, 'device-is-mounted')):\n dev=bus.get_object(interface, udi)\n try:\n r = str(dev.FilesystemMount('auto', dbus.Array(dbus.String()), dbus_interface = interface+\".Device\")) or None\n except dbus.exceptions.DBusException:\n continue\n mnt=self.get_device_property(udi, 'device-mount-paths')\n if not mnt: continue\n mnt=str(mnt[0])\n repo=os.path.join(mnt,'media.repo')\n if os.path.exists(repo): repos.append(repo)\n if len(repos)==0: error(_(\"No valid media were found.\"), self.ccw); return\n r=self.ccw.mechanism('pkg','add_media',*repos)\n if r == 'NotAuth': return\n info(_('Done. %s repositories were added.\\nOpen the package manager to load or refresh cache.') % r, self.ccw)\n\n def disable_mediarepo(self, b):\n r=self.ccw.mechanism('pkg','disable_mediarepo')\n if r == 'NotAuth': return\n info(_('Done. %s repositories were disabled.') % r, self.ccw)\n\n def get_keep_cache(self):\n c=open('/etc/yum.conf','rt').read()\n l=self.keep_cache_re.findall(c)\n if not l: return False\n v=l[-1][1].strip().lower()\n if v=='1' or v=='yes' or v=='true': return True\n return False\n\n def keep_cache(self, b):\n v=('0','1')[self.keep_cache_b.get_active()]\n r=self.ccw.mechanism('pkg','set_keep_cache', v)\n if r == 'NotAuth': return\n self.keep_cache_b.set_active(self.get_keep_cache())\n try:\n self.ccw.rm_old_rpms_b.set_sensitive(self.keep_cache_b.get_active())\n self.ccw.cp_new_rpms_b.set_sensitive(self.keep_cache_b.get_active())\n except: pass\n info(_('Done.'), self.ccw)\n\n def disable_net_repos(self, b):\n r=self.ccw.mechanism('pkg','disable_net_repos')\n if r == 'NotAuth': return\n self.restore_repos_b.set_sensitive(os.path.exists(self.media_repo_save))\n try: i=int(r)\n except ValueError: i==0\n if i<=0:\n info(_('no repository was disabled'), self.ccw)\n info(_('Done. %d repositories were disabled') % i, self.ccw)\n\n def restore_repos(self, b):\n r=self.ccw.mechanism('pkg','restore_enabled_repos')\n if r == 'NotAuth': return\n self.restore_repos_b.set_sensitive(os.path.exists(self.media_repo_save))\n try: i=int(r)\n except ValueError: i==0\n if i<=0:\n info(_('no repository was enabled'), self.ccw)\n info(_('Done. %d repositories were enabled') % i, self.ccw)\n\n","repo_name":"ojuba-org/occ","sub_path":"Plugins/pkg.py","file_name":"pkg.py","file_ext":"py","file_size_in_byte":7477,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"4136427042","text":"import numpy as np\nfrom time import perf_counter as time\nimport os\nimport argparse\n\nclass profile_t:\n\n def __init__(self,name):\n self.name = name\n self.seconds=0\n self.snap=0\n self._pri_time =0\n self.iter =0\n\n def __add__(self,o):\n assert(self.name==o.name)\n self.seconds+=o.seconds\n self.snap+=o.snap\n self.iter+=o.iter\n return self\n\n def start(self):\n self._pri_time = time()\n \n def stop(self):\n self.seconds-=self._pri_time\n self.snap=-self._pri_time\n\n self._pri_time = time()\n\n self.seconds +=self._pri_time\n self.snap += self._pri_time\n self.iter+=1\n \n def reset(self):\n self.seconds=0\n self.snap=0\n self._pri_time =0\n self.iter =0\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-r\", \"--rows\", help=\"Number of rows for input matrix; must be >> cols\", type=int, default=5000)\n parser.add_argument(\"-c\", \"--cols\", help=\"Number of columns for input matrix\", type=int, default=100)\n parser.add_argument(\"-i\", \"--iterations\", help=\"Number of iterations to run experiment. If > 1, first is ignored as warmup.\", type=int, default=1)\n parser.add_argument(\"-w\", \"--warmup\", help=\"Number of warmup runs to perform before iterations.\", type=int, default=0)\n #parser.add_argument(\"-b\", \"--block_size\", help=\"Block size to break up input matrix; must be >= cols\", type=int, default=500)\n #parser.add_argument(\"-t\", \"--threads\", help=\"Sets OMP_NUM_THREADS\", default='16')\n #parser.add_argument(\"-g\", \"--ngpus\", help=\"Sets number of GPUs to run on. If set to more than you have, undefined behavior\", type=int, default='4')\n #parser.add_argument(\"-p\", \"--placement\", help=\"'cpu' or 'gpu' or 'both' or 'puregpu'\", default='gpu')\n #parser.add_argument(\"-K\", \"--check_result\", help=\"Checks final result on CPU\", action=\"store_true\")\n #parser.add_argument(\"--csv\", help=\"Prints stats in csv format\", action=\"store_true\")\n\n args = parser.parse_args()\n\n # Set global config variables\n NROWS = args.rows\n NCOLS = args.cols\n ITERS = args.iterations\n WARMUP = args.warmup\n # NTHREADS = args.threads\n # NGPUS = args.ngpus\n # PLACEMENT_STRING = args.placement\n # CHECK_RESULT = args.check_result\n # CSV = args.csv\n \n t_header=False\n t_list=list()\n for iter in range(ITERS + WARMUP):\n np.random.seed(iter)\n A = np.random.rand(NROWS, NCOLS)\n t_overall = profile_t(\"total\")\n t_overall.start()\n [Q,R] = np.linalg.qr(A,mode='reduced')\n t_overall.stop()\n \n if ((iter >= WARMUP)):\n if(not t_header):\n header=\"iter\\ttotal\"\n print(header)\n t_header=True\n \n t_dur = t_overall.seconds\n t_= f\"{iter}\\t{t_dur:.12f}\"\n print(t_)\n\n t_list.append(t_overall)\n \n CPU_FLOP = (2*NROWS * (NCOLS**2) - (2/3) * (NCOLS**3)) \n\n QR_FLOPS = 0 \n\n for i,t_iter in enumerate(t_list):\n t_qr = t_iter\n QR_FLOPS += CPU_FLOP / (t_qr.seconds)\n \n QR_FLOPS /= len(t_list)\n \n QR_FLOPS /=(1000**3)\n print(\"\\nNROWS\\tNCOLS\\tQR_FLOPS(GFlops/sec)\")\n print(f\"{NROWS}\\t{NCOLS}\\t{QR_FLOPS}\")","repo_name":"milindasf/TSQR","sub_path":"src/numpy_qr.py","file_name":"numpy_qr.py","file_ext":"py","file_size_in_byte":3343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15528819136","text":"from django.urls import path\nfrom . import views\n\napp_name=\"cars\"\n\nurlpatterns = [\n path(\"\",views.index,name=\"index\"),\n path(\"contact/\",views.contact_view,name=\"contact\"),\n path(\"detail//\",views.detail_test_view,name=\"detail\"),\n path(\"wish/\",views.cars_wish_view,name=\"wish\"),\n path(\"wishlist/\",views.wishlist_view,name=\"wishlist\"),\n path(\"wishlist-rent/\",views.rent_wishlist_view,name=\"rent-wishlist\"),\n path(\"create/\",views.create_car_view,name=\"create\"),\n path(\"edit//\",views.cars_edit_view,name=\"edit\"),\n path(\"delete//\",views.cars_delete_view,name=\"delete\"),\n path(\"edit_car//\", views.cars_edit_view, name=\"edit_car\"),\n path(\"edit_car//delete_picture//\", views.delete_picture, name=\"delete_picture\"),\n\n\n # car-rent-urls\n path(\"rent/\",views.cars_rent_create_view,name=\"rent-create\"),\n path(\"rent-index/\",views.rent_cars_index,name=\"rent-index\"),\n path(\"rent-detail//\",views.car_rent_detail_view,name=\"rent-detail\"),\n path(\"wish-rent/\",views.cars_rent_wish_view,name=\"rent-wish\"),\n path(\"basket/\",views.cars_rent_basket_view,name=\"basket\"),\n path(\"basket-list/\",views.cars_rent_basketlist_view,name=\"basket-list\"),\n path(\"card//\",views.card_payment,name=\"card\"),\n path(\"payment//\",views.payment_cars,name=\"cars-payment\"),\n path(\"basket-remove/\",views.cars_rent_basket_remove,name=\"basket-remove\"),\n path(\"profile/\",views.profile,name=\"profile\"),\n path(\"edit-rent//\",views.cars_rent_edit_view,name=\"edit-rent\"),\n path(\"delete-rent//\",views.cars_rent_delete_view,name=\"delete-rent\"),\n path(\"edit_rent_car//\", views.cars_rent_edit_view, name=\"edit_rent_car\"),\n path(\"edit_rent_car//delete_picture//\", views.delete_rent_picture, name=\"delete_rent_picture\"),\n\n]\n\n","repo_name":"elizamin-orucov/cars_sold_and_leased","sub_path":"Cars/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"6765719204","text":"# Lists and types: \nmylist = [10,20,30,40]\nnames = [\"Tahir\", \"John\", \"Ali\", \"Maria\"]\nmixtures = [True, \"1234\", 40, 1.002]\n\n\n# Adding and Removing elements from a list \nnames.append(\"Michael\")\nlast_name = names.pop()\n\n# Retrieving elements from a list\nfirst_name = names[0]\n\n# find an elements index:\nspecific_index = names.index(\"Tahir\")\n\n# Insert something to a specific element in the list\nnames.insert(0,\"Malachi\")\nnew_first_name = names[0]\n\n# I can remove items from a list, if duplicated items the first element is removed\nremoved_name = names.remove(\"Maria\")\n\n\n# extend the end of a list with a collection of elements\nnames.extend([\"Ballard\", \"Dixon\",\"Bezos\"])\nx = [\"Columbia\", \"Harvard\"]\nnames.extend(x)\n\n# I can measure the length of my list\nlength = len(names) \n\n# lists can be nested:\na = [1,2,3]\nb = [4,5,6]\nc = [7,8,9]\nabc = [a,b,c]\nmiddle_item = abc[1][1]\n\n# List Operations, sorting will affect the original list\na.count(3)\nb.sort(reverse=True)\nc.reverse()\nmax(abc)\nmin(abc)\n\n# Slicing with lists\nordered_numbers = [0,1,2,3,4,5]\nordered_numbers[2:5]\nordered_numbers[:4]\nordered_numbers[2:]\n\n# deletion from a list by index reference\ndel a[0]\n\n\n# traversing a list \nfor element in abc:\n print(element)\n\n# I search to see if something is in a list:\nprint(34 in a)\n","repo_name":"namelessfintech/PythonDataStrucs-Algos","sub_path":"lists.py","file_name":"lists.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72937323609","text":"# system\nimport os\nimport sys\n\n# python lib\nimport face_alignment\nimport numpy as np\nimport cv2\n\nclass LM_detector():\n def __init__(self, use_cnn_face_detector=True ,lm_type=2, device='cpu', face_detector='sfd'):\n if lm_type == 2:\n self.fa = face_alignment.FaceAlignment(\n face_alignment.LandmarksType._2D, device=device, flip_input=False, face_detector=face_detector)\n else:\n self.fa = face_alignment.FaceAlignment(\n face_alignment.LandmarksType._3D, device=device, flip_input=False, face_detector=face_detector)\n\n def detect(self, image):\n # filter very large image\n scale = 1.0\n h, w, c = image.shape\n if max(h, w) > 1000:\n scale = max(h, w) / (1000.0)\n image = cv2.resize(image, (int(w / scale), int(h / scale)))\n\n h, w, c = image.shape\n norm_scale = 1 / max(h, w)\n\n # torch\n detected_faces = self.fa.face_detector.detect_from_image(image[..., ::-1].copy())\n\n if detected_faces != None and len(detected_faces) != 0:\n landmarks = self.fa.get_landmarks(image, detected_faces=[detected_faces[0],])\n\n # normalize to 0 ~ 1\n for face in landmarks:\n face *= norm_scale\n\n return landmarks\n\n return None","repo_name":"harrisonyei/AsRigidAsPossible","sub_path":"face_detection.py","file_name":"face_detection.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"13961033427","text":"\nfrom gevent_pipeline.fsm import State, transitions\n\nimport gevent\n\n\nclass _Disabled(State):\n\n def start(self, controller):\n thread = controller.context.get('thread', None)\n if thread is not None:\n gevent.kill(thread)\n\n @transitions('Replicating')\n def onEnable(self, controller, message_type, message):\n\n controller.changeState(Replicating)\n\n\nDisabled = _Disabled()\n\n\nclass _Start(State):\n\n @transitions('Replicating')\n def start(self, controller):\n\n controller.changeState(Replicating)\n\n\nStart = _Start()\n\n\nclass _Replicating(State):\n\n def start(self, controller):\n thread = controller.context.get('thread', None)\n if thread is not None:\n gevent.kill(thread)\n\n def send_data():\n while True:\n gevent.sleep(0)\n if controller.inboxes.get('data', None) and controller.outboxes:\n message = controller.inboxes['data'].get()\n for outbox in controller.outboxes.values():\n outbox.put(message)\n else:\n break\n\n controller.context['thread'] = gevent.spawn(send_data)\n\n @transitions('Disabled')\n def onDisable(self, controller, message_type, message):\n\n controller.changeState(Disabled)\n\n\nReplicating = _Replicating()\n","repo_name":"benthomasson/performance_pipeline","sub_path":"performance_pipeline/replicate_fsm.py","file_name":"replicate_fsm.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"10316484230","text":"import datetime\nfrom functools import wraps\nfrom typing import Union, AsyncGenerator, Callable, Generator, Any, Annotated\nfrom uuid import UUID\nfrom contextlib import asynccontextmanager\n\nimport jwt\nimport sqlalchemy\nfrom fastapi import Depends, APIRouter, Cookie\nfrom pydantic import BaseModel\nfrom sqlalchemy import Column, select\nfrom sqlalchemy.exc import NoResultFound\n# from sqlalchemy.orm.session import se\nfrom sqlalchemy.ext.asyncio.session import AsyncSession\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom starlette.responses import JSONResponse\nimport jwt\n\nfrom .build_route import build_async\nfrom .class_builder import ClassBuilder\nfrom .exceptions import DataBaseNotFound, DatabaseError, ArgumentsError\nfrom .method_builders import SyncMethodBuilder, AsyncMethodBuilder\nfrom .schemas import User, Roles\n\n\n# class GlobalStorage:\n# def __init__(self, **kwargs):\n# for k, v in kwargs.items():\n# setattr(self, k, v)\n# print(self.__dict__)\n#\n#\n# global_storage = GlobalStorage(sessionMaker=None, session=None)\n\n\nclass AuthApp:\n\n get_session = None\n\n def __init__(\n self,\n *,\n prefix: str = '/user',\n session_maker: sessionmaker = None,\n sql_schema_name: str = None,\n async_usage: bool = None,\n user_model: BaseModel = User,\n role_model: BaseModel = Roles,\n use_session_auth: bool = None,\n use_jwt_auth: bool = None,\n jwt_secret_key: str = None,\n redis: Any = None,\n ):\n self.__prefix = prefix\n if use_session_auth is not None and use_jwt_auth is not None\\\n and use_jwt_auth == use_session_auth:\n raise ArgumentsError('Only one of use_session_auth, use_jwt_auth required')\n if use_session_auth is None and use_jwt_auth is None:\n use_session_auth = True\n use_jwt_auth = False\n if use_session_auth is not None:\n use_jwt_auth = not use_session_auth\n if use_jwt_auth is not None:\n use_session_auth = not use_jwt_auth\n\n self._use_session = use_session_auth\n self._use_jwt = use_jwt_auth\n if not sessionmaker:\n raise ArgumentsError('AuthApp needs sessionmaker argument')\n if not isinstance(session_maker, sessionmaker):\n raise ArgumentsError(\n f'AuthApp session_maker must be an instance of sqlalchemy.orm.sessionmaker,'\n f' not {type(session_maker)}')\n\n self.__sessionmaker = session_maker\n\n if issubclass(self.__sessionmaker.class_, Session):\n if async_usage is not None:\n self.async_usage = async_usage\n else:\n self.async_usage = False\n self.session = self.get_sync_session\n self.__methods_builder = SyncMethodBuilder(self._use_session, self._use_jwt)\n if issubclass(self.__sessionmaker.class_, AsyncSession):\n if async_usage is False:\n raise ArgumentsError(\"Can't use AsyncSession with async_usage=False\")\n if async_usage is None:\n async_usage = True\n self.async_usage = async_usage\n self.session = self.get_async_session\n self.__methods_builder = AsyncMethodBuilder(self._use_session, self._use_jwt)\n elif issubclass(self.__sessionmaker.class_, AsyncSession) and self.async_usage is False:\n raise ArgumentsError(\"Can't use AsyncSession with async_usage=False\")\n\n self.__sessionmaker = session_maker\n\n self._use_session = use_session_auth\n self._use_jwt = use_jwt_auth\n\n self.__class_builder = ClassBuilder(user_model=user_model, role_model=role_model)\n\n self.user_model, self.login_model, self.register_model = \\\n self.__class_builder.build_schemas()\n\n self.user_db, self.user_rights_db, self.right_list, self._identity_column = \\\n self.__class_builder.build_sql_models(sql_schema_name)\n\n if self._use_session is True:\n if redis is None:\n self._sessions = self.__class_builder.build_session_storage(sql_schema_name)\n else:\n self.get_session = self.throw_self(self.__methods_builder.build_get_session(redis=redis))\n if self._use_jwt is True:\n self._secret_key = jwt_secret_key\n\n self.metadata = self.__class_builder.metadata\n self.permissions, self.roles = self.__class_builder.parse_roles()\n\n if self.async_usage is True:\n self.router = self.__async_router()\n\n\n # self.__methods_builder = MethodBuilder(self.session,\n # self.async_usage,\n # self._use_session,\n # self._use_jwt)\n self.get_user_by = self.throw_self(self.__methods_builder.build_get_user_by())\n self.get_users_by = self.throw_self(self.__methods_builder.build_get_users_by())\n self.create_user = self.throw_self(self.__methods_builder.build_create_user())\n self.update_user = self.throw_self(self.__methods_builder.build_update_user())\n self.delete_user = self.throw_self(self.__methods_builder.build_delete_user())\n self.login_required = self.throw_self(self.__methods_builder.build_login_required())\n self.get_user_rights = self.throw_self(self.__methods_builder.build_get_user_rights())\n self.get_rights_id_by_names = self.throw_self(self.__methods_builder.build_get_rights_id_by_names())\n #todo какая то хрень с поиском прав. Надо чтобы при логине в токен клались id, а при проверке id брались, основываясь на perms[]\n\n def throw_self(self, func):\n @wraps(func)\n def inner(*args, **kwargs):\n r = func(self, *args, **kwargs)\n return r\n return inner\n\n def get_sync_session(self):\n db = self.__sessionmaker()\n try:\n yield db\n finally:\n db.close()\n\n # async def get_async_session(self) -> AsyncSession:\n # async with self.__sessionmaker() as session:\n # yield session\n\n @asynccontextmanager\n async def get_async_session(self):\n conn = self.__sessionmaker()\n try:\n yield conn\n finally:\n await conn.close()\n\n def __async_router(self):\n route = APIRouter(prefix=self.__prefix)\n register_model = self.register_model\n login_model = self.login_model\n\n @route.post('/register')\n async def register(user: register_model):\n async with self.get_async_session() as db:\n r = await self.create_user(db, user)\n return JSONResponse(r)\n\n if self._use_session is True:\n @route.post('/login')\n async def login(user: login_model = None,\n access: Annotated[Union[str, None], Cookie()] = None,\n refresh: Annotated[Union[str, None], Cookie()] = None\n ):\n async with self.get_async_session() as db:\n if access is not None:\n session = await self.get_session(access)\n elif refresh is not None:\n pass\n elif user is not None:\n user = await self.get_users_by(db, **user.model_dump())\n return JSONResponse({'msg': 'No data have given'}, status_code=400)\n\n if self._use_jwt is True:\n @route.post('/login')\n async def login(user: login_model):\n async with self.get_async_session() as db:\n user = await self.get_user_by(db, **user.model_dump())\n if user is None:\n return JSONResponse({'msg': 'wrong data'},\n status_code=400)\n payload = {\n 'uid': getattr(user, self._identity_column),\n 'exp': datetime.datetime.now(tz=datetime.timezone.utc) + datetime.timedelta(minutes=15),\n 'perms': await self.get_user_rights(db, getattr(user, self._identity_column))\n }\n token = jwt.encode(payload=payload, key=self._secret_key)\n return JSONResponse({'msg': 'Successful login!', 'token': token},\n status_code=200)\n\n return route\n","repo_name":"Ponyoverkill/fastapi_auth","sub_path":"src/authapp.py","file_name":"authapp.py","file_ext":"py","file_size_in_byte":8624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29058539974","text":"\nfrom datetime import datetime\nimport logging\nfrom datetime import timedelta\nfrom unidecode import unidecode\nimport json\nimport re\nfrom odoo import http\nfrom odoo.http import request\nfrom math import floor\nlogger = logging.getLogger(__name__)\nimport werkzeug\nimport werkzeug.exceptions\nimport werkzeug.utils\nimport werkzeug.wrappers\nimport werkzeug.wsgi\nfrom werkzeug.urls import url_decode, iri_to_uri\n\n\n\nclass IntegracaoPdv(http.Controller):\n\n @http.route('/produtoconsulta', type='json', auth=\"user\", csrf=False)\n def website_produtoconsulta(self, **kwargs):\n data = request.jsonrequest\n # TODO testar aqui se e a empresa mesmo\n hj = datetime.now()\n hj = hj - timedelta(days=30)\n hj = datetime.strftime(hj,'%Y-%m-%d %H:%M:%S')\n prod_tmpl = http.request.env['product.template'].sudo().search([\n ('write_date', '>=', hj),\n ('sale_ok', '=', True)])\n prod_ids = []\n prd_ids = set()\n for pr in prod_tmpl:\n prd_ids.add(pr.id)\n\n if len(prd_ids):\n prod_ids = http.request.env['product.product'].sudo().search([\n ('product_tmpl_id','in',list(prd_ids))])\n #print ('Qtde de Produtos %s\\n' %(str(len(prod_ids))))\n lista = []\n for prd in prod_ids:\n prod = {}\n ncm = ''\n if prd.ncm_id:\n ncm = prd.ncm_id.code\n if ncm:\n ncm = re.sub('[^0-9]', '', ncm)\n if ncm and len(ncm) > 8:\n ncm = '00000000'\n prod['codproduto'] = prd.id\n prod['unidademedida'] = prd.uom_id.name.strip()[:2]\n produto = prd.name.strip()\n produto = produto.replace(\"'\",\" \")\n produto = unidecode(produto)\n prod['produto'] = produto\n prod['valor_prazo'] = prd.list_price\n data_alt = prd.write_date\n data_alterado = data_alt + timedelta(hours=+3)\n prod['datacadastro'] = datetime.strftime(data_alterado,'%m/%d/%Y %H:%M:%S')\n if prd.default_code:\n codpro = prd.default_code.strip()\n else:\n codpro = str(prd.id)\n prod['codpro'] = codpro[:15]\n if prd.icms_origin:\n prod['origem'] = prd.icms_origin\n prod['ncm'] = ncm\n prod['usa'] = 'S'\n if prd.barcode and len(prd.barcode) < 14:\n prod['cod_barra'] = prd.barcode.strip()\n lista.append(prod)\n\n\n\n # Itens inativos\n prod_tmpl = http.request.env['product.template'].sudo().search([\n ('write_date', '>=', hj),\n ('sale_ok', '=', True),\n ('active' ,'=', False)])\n prd_ids = set()\n prod_ids = []\n for pr in prod_tmpl:\n prd_ids.add(pr.id)\n\n if prod_tmpl:\n prod_ids = http.request.env['product.product'].sudo().search([\n ('product_tmpl_id','in',list(prd_ids)),\n ('active','=', False)]) \n for prd in prod_ids:\n prod = {}\n prod['codproduto'] = prd.id\n data_alt = prd.write_date\n data_alterado = data_alt + timedelta(hours=+3)\n prod['datacadastro'] = datetime.strftime(data_alterado,'%m/%d/%Y %H:%M:%S')\n prod['usa'] = 'N'\n produto = prd.name.strip()\n produto = produto.replace(\"'\",\" \")\n produto = unidecode(produto)\n prod['produto'] = produto\n if prd.default_code:\n codpro = prd.default_code.strip()\n else:\n codpro = str(prd.id)\n prod['codpro'] = codpro[:15]\n\n lista.append(prod)\n return json.dumps(lista) \n\n @http.route('/cliente_cnpj', type='json', auth=\"public\", csrf=False)\n def website_cliente_cnpj(self, **kwargs):\n data = request.jsonrequest\n cnpj = re.sub('[^0-9]', '', data['params']['cnpj'])\n # TODO testar aqui se e a empresa mesmo\n cnpj = '%s.%s.%s/%s-%s' %(cnpj[:2],cnpj[2:5],cnpj[5:8],cnpj[8:12],cnpj[12:14])\n cliente = http.request.env['res.partner']\n\n cli_ids = cliente.sudo().search([('cnpj_cpf', '=', cnpj),])\n lista = []\n cliente = 'N'\n for partner_id in cli_ids:\n cliente = partner_id.ref\n return cliente\n\n @http.route('/clienteconsulta', type='json', auth=\"user\", csrf=False)\n def website_clienteconsulta(self, **kwargs):\n data = request.jsonrequest\n # TODO testar aqui se e a empresa mesmo\n hj = datetime.now()\n hj = hj - timedelta(days=10)\n hj = datetime.strftime(hj,'%Y-%m-%d %H:%M:%S')\n cliente = http.request.env['res.partner']\n cli_ids = cliente.sudo().search([('write_date', '>=', hj), ('category_id','=', 1)])\n lista = []\n for partner_id in cli_ids:\n cliente = {}\n nome = partner_id.name.strip()\n nome = nome.replace(\"'\",\" \")\n nome = unidecode(nome)\n cliente['codcliente'] = partner_id.id\n cliente['nomecliente'] = nome\n cliente['razaosocial'] = nome\n cliente['tipofirma'] = 0\n cliente['segmento'] = 1\n cliente['regiao'] = 1\n cliente['codusuario'] = 1\n cliente['status'] = 1\n cliente['cnpj'] = partner_id.cnpj_cpf\n data_alt = partner_id.write_date\n data_alterado = data_alt + timedelta(hours=+3)\n cliente['data_matricula'] = datetime.strftime(data_alterado,'%m/%d/%Y %H:%M:%S')\n cliente['datacadastro'] = datetime.strftime(data_alterado,'%m/%d/%Y') \n lista.append(cliente)\n lista_j = json.dumps(lista)\n return lista_j\n\n @http.route('/contasconsulta', type='json', auth=\"user\", csrf=False)\n def website_contasconsulta(self, **kwargs):\n user_id = http.request.env['res.users'].browse([request.uid])\n data = request.jsonrequest\n cod_cliente = data['cod_cliente']\n caixa = data['caixa']\n aml_id = data['aml_id']\n cod_forma = data['cod_forma'] \n if ',' in data['valor_pago']: \n valor_pago = data['valor_pago'].replace(',','.')\n juro = data['juro'].replace(',','.')\n else:\n valor_pago = data['valor_pago']\n juro = data['juro']\n # TODO testar aqui se e a empresa mesmo\n cc = http.request.env['account.account'].search([\n ('name', 'ilike', 'Cliente Padrao'),\n ('company_id', '=', user_id.company_id.id),\n ])\n cj = http.request.env['account.journal'].search([\n ('name', 'ilike', 'Cliente'),\n ('company_id', '=', user_id.company_id.id),\n ]) \n conta_obj = http.request.env['account.move.line']\n conta_ids = conta_obj.sudo().search([('partner_id', '=',int(cod_cliente)), \n ('full_reconcile_id', '=', False), ('balance','!=', 0),\n ('company_id', '=', user_id.company_id.id),\n ('account_id.reconcile','=',True),\n ('account_id', '=', cc.id),\n ('journal_id', '=', cj.id),\n ], order='date_maturity')\n vlr = float(valor_pago)\n juros = float(juro)\n vlr = vlr - juros\n vlr_baixado = '0.00'\n if vlr:\n # tem valor , entao baixa\n diario = data['diario'][:2]\n diario_obj = http.request.env['account.journal'] \n diario_id = diario_obj.search([\n ('company_id', '=', user_id.company_id.id),\n ('name', 'ilike', diario)])\n if not diario:\n return 'Diario invalido'\n for conta in conta_ids:\n if vlr < 0.01:\n continue\n\n # nao preciso mais disto pq \n # baixo uma conta por vez\n # vlr_pago = conta.debit\n # if vlr > conta.debit:\n # vlr_baixado =+ conta.debit\n # vlr_pago = conta.debit\n # else:\n # vlr_pago = vlr\n # vlr_baixado =+ vlr\n # vlr -= conta.debit\n\n # aqui colocar pra baixar\n if conta.id == int(aml_id):\n arp = http.request.env['account.abstract.payment']\n # passo duas vezes o cod_forma, na segunda vai como cod_venda\n arp.baixa_pagamentos(conta, diario_id, caixa, vlr, cod_forma, juros)\n vlr = 0.0\n conta_ids = conta_obj.sudo().search([('partner_id', '=',int(cod_cliente)), \n ('full_reconcile_id', '=', False), ('balance','!=', 0),\n ('company_id', '=', user_id.company_id.id),\n ('account_id.reconcile','=',True),\n ('account_id', '=', cc.id),\n ('journal_id', '=', cj.id),\n ], order='date_maturity') \n lista = []\n for conta in conta_ids:\n #if not '4-' in conta.journal_id.name or conta.debit == 0.0:\n # continue\n contas = {}\n nome = conta.name.strip()\n nome = nome.replace(\"'\",\" \")\n nome = unidecode(nome)\n contas['cliente'] = conta.partner_id.id\n data_fatura = datetime.strftime(conta.date,'%d/%m/%Y')\n contas['valor'] = conta.amount_residual\n contas['data_fatura'] = data_fatura\n data_vencimento = datetime.strftime(conta.date_maturity,'%d/%m/%Y')\n contas['data_vencimento'] = data_vencimento\n contas['fatura'] = conta.move_id.ref\n contas['codigo'] = conta.id\n # contas['cod_cliente'] = 1 \n lista.append(contas)\n return json.dumps(lista)\n\n @http.route('/usuarioconsulta', type='json', auth=\"user\", csrf=False)\n def website_usuarioconsulta(self, **kwargs):\n data = request.jsonrequest\n # TODO testar aqui se e a empresa mesmo\n hj = datetime.now()\n hj = hj - timedelta(days=10)\n hj = datetime.strftime(hj,'%Y-%m-%d %H:%M:%S')\n user = http.request.env['res.users']\n user_ids = user.sudo().search([('write_date', '>=', hj)])\n lista = []\n for usr in user_ids:\n user = {}\n barcode = ''\n if usr.barcode:\n barcode = usr.barcode\n user['codusuario'] = usr.id\n user['nomeusuario'] = usr.name\n user['codbarra'] = barcode\n user['status'] = 1\n lista.append(user)\n return json.dumps(lista)\n\n @http.route('/enviasangria', type='json', auth=\"user\", csrf=False)\n def website_enviasangria(self, **kwargs):\n #{\"params\": {\"login\": \"ats@atsti.com.br\", \"password\": \"123456\", \"db\": \"21_vitton\"}, \"todos\": [{9992, \"Sangria\", 1, \"200,00\"}, {9993, \"Sangria\", 1, \"25,00\"}]}\n user_id = http.request.env['res.users'].browse([request.uid])\n # receber todas as sangrias e reforco do caixa\n # verificar no odoo se existe \n data = request.jsonrequest\n lista_pdv = json.loads(data['todos'])\n caixa = lista_pdv[0]['caixa']\n sg_obj = http.request.env['account.bank.statement.line']\n \n # vejo os diarios usados no PDV do Caixa aberto\n session = http.request.env['pos.session'].search([\n ('id', '=', caixa)\n ])\n lista_st = []\n for lt_st in session.statement_ids:\n lista_st.append(lt_st.id)\n\n for lt in lista_pdv:\n motivo = lt['motivo']\n valor = lt['valor'].replace(',','.')\n valor = float(valor)\n cod_forma = lt['codforma']\n cod_venda = int(lt['codvenda'])\n \n diario = '1-'\n if cod_venda == 2: \n diario = motivo[:2]\n diario_obj = http.request.env['account.journal'] \n diario_id = diario_obj.search([\n ('company_id', '=', user_id.company_id.id),\n ('name', 'ilike', diario)])\n # verifica se ja foi feito\n line = sg_obj.search([\n ('ref', '=', str(cod_forma)),\n ('statement_id', 'in', (lista_st)),\n ])\n if not line:\n arp = http.request.env['account.abstract.payment']\n arp.lanca_sangria_reforco(diario_id, caixa, valor, cod_forma, cod_venda, user_id.partner_id, motivo)\n\n @http.route('/caixaconsulta', type='json', auth=\"user\", csrf=False)\n def website_caixaconsulta(self, **kwargs):\n data = request.jsonrequest\n session = http.request.env['pos.session']\n ses_ids = session.sudo().search([('state', '=', 'opened')],limit=4)\n lista = []\n for ses in ses_ids:\n if 'caixa' in data:\n dados_json = json.loads(data['caixa'])\n for d in dados_json:\n if 'SITUACAO' in d and d['SITUACAO'] == 'F' and d['CODCAIXA'] == ses.id:\n ses.sudo().write({'venda_finalizada': True})\n caixa = {}\n hj = datetime.now()\n dta_abre = datetime.strftime(hj,'%m-%d-%Y')\n caixa['idcaixacontrole'] = ses.id\n caixa['codcaixa'] = ses.id\n caixa['codusuario'] = ses.user_id.id\n caixa['situacao'] = 'o'\n caixa['datafechamento'] = '01-01-2020'\n caixa['nomecaixa'] = ses.name\n caixa['dataabertura'] = dta_abre\n caixa['valorabre'] = ses.cash_register_balance_start\n lista.append(caixa)\n return json.dumps(lista)\n\n @http.route('/pedidoconsulta', type='json', auth=\"user\", csrf=True)\n def website_pedidoconsulta(self, **kwargs):\n data = request.jsonrequest\n # TODO testar aqui se e a empresa mesmo\n user_id = http.request.env['res.users'].browse([request.uid])\n hj = datetime.now()\n hj = hj - timedelta(days=3)\n hj = datetime.strftime(hj,'%Y-%m-%d %H:%M:%S')\n pedido = http.request.env['pos.order']\n if 'caixa' in data:\n ped_ids = pedido.sudo().search([('write_date', '>=', hj),\n ('company_id', '=', user_id.company_id.id),\n ('session_id', '=', data['caixa']),\n ], order=\"pos_reference desc\", limit=10)\n else:\n ped_ids = pedido.sudo().search([('write_date', '>=', hj),\n ('company_id', '=', user_id.company_id.id),\n ], order=\"pos_reference desc\", limit=10)\n\n if not ped_ids:\n ped_ids = pedido.sudo().search([],\n order=\"id desc\", limit=10)\n lista = []\n ultimo = ''\n menor = 0\n maior = 0\n for p_id in ped_ids:\n if not p_id.pos_reference:\n continue\n ped = p_id.pos_reference[p_id.pos_reference.find('-')+1:]\n # if ultimo != '':\n # ultimo += ','\n ultimo += ped\n # if int(ped) < menor or menor == 0:\n # menor = int(ped)\n # if int(ped) > maior or maior == 0:\n # maior = int(ped)\n # if (maior - menor) > 10:\n # menor = maior - 1\n # ultimo = '(%s) AND m.CODMOVIMENTO > %s' %(str(ultimo), str(menor))\n ped = {'pedido': str(ultimo)}\n\n lista.append(ped)\n return json.dumps(lista)\n\n @http.route('/pedidoconsultageral', type='json', auth=\"user\", csrf=False)\n def website_pedidoconsultageral(self, **kwargs):\n data = request.jsonrequest\n # TODO testar aqui se e a empresa mesmo\n user_id = http.request.env['res.users'].browse([request.uid])\n pedido = http.request.env['pos.order']\n lista_pdv = json.loads(data['todos'])\n if 'todos' in data:\n ped_ids = pedido.sudo().search([\n ('company_id', '=', user_id.company_id.id),\n ('session_id', '=', int(data['caixa'])),\n ]) \n lista_odoo = []\n for p_id in ped_ids:\n if not p_id.pos_reference:\n continue\n ped = p_id.pos_reference[p_id.pos_reference.find('-')+1:]\n lista_odoo.append(int(ped))\n diferenca = set(lista_pdv).difference(set(lista_odoo))\n ultimo = ''\n for pedidos in list(diferenca):\n if ultimo != '':\n ultimo += ', '\n ultimo += str(pedidos)\n ultimo = '(%s)' %(str(ultimo))\n ped = {'pedido': str(ultimo)}\n lista = []\n lista.append(ped)\n return json.dumps(lista)\n\n def _monta_pedido(self,dados):\n codmov = dados['CODMOVIMENTO']\n codcliente = dados['CODCLIENTE']\n caixa = dados['CODALMOXARIFADO']\n codvendedor = dados['CODVENDEDOR']\n data_sistema = dados['DATA_SISTEMA']\n coduser = dados['CODUSUARIO']\n controle = dados['CONTROLE']\n ord_name = '%s-%s' %(caixa, codmov)\n vals = {} \n pos = http.request.env['pos.order']\n ord_ids = pos.sudo().search([\n ('session_id','=',caixa),\n ('sequence_number', '=', codmov),\n ])\n if not ord_ids:\n # insere o pedido\n # prt = http.request.env['res.partner']\n # usr = http.request.env['res.users']\n # prt_id = prt.sudo().search([\n # ('id','=',codcliente),\n # ])\n # ven_id = usr.sudo().search([\n # ('id','=',codvendedor),\n # ])\n # usr_id = usr.sudo().search([\n # ('id','=',coduser),\n # ])\n # if prt_id and ven_id and usr_id:\n data_pedido = datetime.strptime(data_sistema,'%m/%d/%Y %H:%M')\n data_pedido = data_pedido + timedelta(hours=+3)\n user_id = http.request.env['res.users'].browse([request.uid])\n if 1 == 1:\n vals['name'] = ord_name\n vals['nb_print'] = 0\n vals['pos_reference'] = ord_name\n vals['session_id'] = int(str(caixa))\n # vals['pos_session_id'] = int(str(caixa))\n # vals['pricelist_id'] = session.config_id.pricelist_id.id\n vals['create_date'] = data_pedido #datetime.strftime(datetime.now(),'%Y-%m-%d %H:%M:%S')\n vals['date_order'] = data_pedido\n vals['sequence_number'] = codmov\n vals['partner_id'] = int(codcliente)\n vals['user_id'] = int(codvendedor)\n vals['amount_tax'] = 0.0\n vals['company_id'] = user_id.company_id.id\n \"\"\"\n if cli != 1:\n if cli == 1609:\n cli = 1944\n vals['partner_id'] = cli\n else:\n vals['partner_id'] = self.env['res.partner'].search([\n ('name','ilike','consumidor')],limit=1)[0].id\n userid = mvs[5]\n userid = self.env['res.users'].search([('id','=',userid)])\n if userid:\n vals['user_id'] = userid.id\n if not userid:\n vals['user_id'] = 1\n vals['fiscal_position_id'] = session.config_id.default_fiscal_position_id.id\"\"\"\n return vals\n\n def _monta_pedidodetalhe(self,dados_json, desconto_financeiro, total_geral):\n dados = json.loads(dados_json)\n soma_t = 0.0\n vlr_total = 0.0\n order_line = []\n desc_f = 0.0\n desconto = 0\n if desconto_financeiro:\n desc_f = desconto_financeiro / total_geral\n num_linha = len(dados)\n for md in dados: \n if num_linha:\n try:\n prdname = unidecode(md['DESCPRODUTO'])\n except:\n prdname = 'Nada'\n pco = float(md['PRECO'].replace(',','.'))\n qtd = float(md['QUANTIDADE'].replace(',','.'))\n desc = float(md['VALOR_DESCONTO'].replace(',','.'))\n vlr_totprod = (pco * qtd) - desc\n vlr_total += vlr_totprod\n \n if desc > 0 or desc_f > 0:\n teve_desconto = 's' \n if num_linha > 1 and ((vlr_totprod+desc) + desc_f) > 0 and desc:\n desconto = desc / (vlr_totprod+desc) + desc_f\n desconto = desconto\n else:\n #desconto Zero, vou editar depois de gravado\n # pra calcular o desconto correto\n desconto = 0.0\n prd = {}\n #TODO Felicita usa o campo CORTESIA como TIPO , colocar no exporta do PDV\n #if md['CORTESIA']:\n # prd['tipo_venda'] = md['CORTESIA']\n \n prd['product_id'] = md['CODPRODUTO']\n prd['discount'] = desconto * 100\n prd['qty'] = qtd\n prd['price_unit'] = pco\n prd['full_product_name'] = prdname\n prd['price_subtotal_incl'] = vlr_totprod\n prd['price_subtotal'] = vlr_totprod\n num_linha -= 1\n desconto = 0\n order_line.append((0, 0,prd))\n return order_line \n\n def _monta_pagamento(self, dados, cliente, session, ord_name, data_ord): \n # import pudb;pu.db\n pag_line = []\n desconto_t = 0.0\n total_g = 0.0\n troca = 0.0\n controle_troca = 0\n # sqld = 'SELECT f.CODFORMA, f.FORMA_PGTO, f.VALOR_PAGO, ' \\\n # 'f.STATE, f.TROCO, f.DESCONTO from FORMA_ENTRADA f' \\\n # ' WHERE ID_ENTRADA = %s AND f.STATE = 1' %(str(mvs[0]))\n #dados = json.loads(dados['pag-1'])\n dados = json.loads(dados)\n desconto = 0\n user_id = http.request.env['res.users'].browse([request.uid])\n for pg in dados:\n pag = {}\n dsc = float(pg['DESCONTO'].replace(',','.'))\n vlr = float(pg['VALOR_PAGO'].replace(',','.'))\n dsc = round(dsc,2)\n if pg['DESCONTO']:\n desconto += dsc\n teve_desconto = 's'\n total_g += vlr + dsc\n jrn = '%s-' %(pg['FORMA_PGTO'])\n if jrn == '5-':\n jrn = '1-'\n if jrn == '9-':\n controle_troca = 1\n troca += vlr\n jrn_id = http.request.env['account.journal'].sudo().search([\n ('name','like', jrn),\n ('company_id', '=', user_id.company_id.id)\n ])[0]\n session_id = http.request.env['pos.session'].sudo().browse([session])\n if not session_id:\n return 0,0,0,0\n # for stt in session_id.payment_ids:\n # if stt.journal_id.id == jrn_id.id:\n # pag['payment_ids'] = stt.id\n \n company_cxt = jrn_id.company_id.id\n # pag['account_id'] = self.env['res.partner'].browse(cliente).property_account_receivable_id.id\n pag['date'] = data_ord\n pag['amount'] = float(pg['VALOR_PAGO'].replace(',','.'))\n pag['journal_id'] = jrn_id.id\n pag['journal'] = jrn_id.id\n pag['partner_id'] = cliente\n pag['name'] = ord_name\n # pag['discount'] = desconto\n if controle_troca == 0:\n pag_line.append((0, 0,pag))\n return pag_line, desconto, troca, total_g\n \n @http.route('/pedidoinsere', type='json', auth=\"user\", csrf=False)\n def website_pedidoinsere(self, **kwargs):\n import pudb;pu.db\n data = request.jsonrequest\n hj = datetime.now()\n hj = datetime.strftime(hj,'%m-%d-%Y')\n if 'pedido' in data:\n dados_json = json.loads(data['pedido'])\n pedido = self._monta_pedido(dados_json)\n codmov = dados_json['CODMOVIMENTO']\n caixa = dados_json['CODALMOXARIFADO']\n ord_name = '%s-%s' %(caixa, codmov)\n tem = http.request.env['pos.order'].sudo().search([\n ('pos_reference', '=', ord_name)])\n if tem:\n return True\n desconto = 0\n total = 0\n troca = 0\n #if 'pag' in data:\n if 'pagamentos' in dados_json:\n #dados_json = json.loads(data['pag'])\n dados_j = dados_json['pagamentos']\n pagamento, desconto, troca, total = self._monta_pagamento(dados_j, \n pedido['partner_id'], pedido['session_id'], pedido['name'],\n pedido['date_order'])\n if pagamento == 0 and total == 0:\n # nao encontrou CAIXA\n return 'Erro na importação'\n if 'itens' in dados_json:\n #dados_json = json.loads(data['det']) \n dados_json = dados_json['itens']\n itens_pedido = self._monta_pedidodetalhe(dados_json, desconto, total)\n if troca:\n trc_prd = http.request.env['product.template'].sudo().search([\n ('name', 'ilike', 'desconto')])\n prd = {}\n if trc_prd.id:\n prd['product_id'] = trc_prd.id\n else:\n prd['product_id'] = 2\n prd['qty'] = 1\n vlr_troca = troca * (-1)\n prd['price_unit'] = vlr_troca\n #prd['tipo_venda'] = tipo\n prd['name'] = 'Troca/Devolucao'\n # no uol 5, nao tem estas 2 linhas abaixo\n prd['price_subtotal_incl'] = vlr_troca\n prd['price_subtotal'] = vlr_troca\n itens_pedido.append((0, 0,prd))\n pedido['lines'] = itens_pedido\n # no uol 5, acho q alinha abaixo tem que ficar comentada \n pedido['amount_return'] = total\n desconto_financeiro_troca = ''\n if desconto:\n desconto = desconto*100\n desconto_financeiro_troca = 'd%s' %(str(int(desconto)))\n if troca:\n desconto_financeiro_troca += 't%s' %(str(int(desconto)))\n pedido['note'] = desconto_financeiro_troca\n \n pedido['amount_total'] = total\n pedido['amount_paid'] = total\n # pedido['statement_ids'] = pagamento\n pos = http.request.env['pos.order']\n ord_ids = pos.sudo().create(pedido)\n return 'Sucesso'\n\n @http.route('/devolucao', type='json', auth=\"user\", csrf=False)\n def website_devolucao(self, **kwargs):\n data = request.jsonrequest\n user_id = http.request.env['res.users'].browse([request.uid]) \n nome_busca = 'DEV-' + str(data['origin'])\n dev = http.request.env['stock.picking'].sudo().search([\n ('origin', 'like', nome_busca)\n ]) \n if dev:\n return '' \n else:\n item = []\n vals = {}\n if 'origin' in data:\n vals['origin'] = 'DEV-' + str(data['origin'])\n operacao = http.request.env['stock.picking.type'].sudo().search([\n ('name', 'ilike', 'devolucao')\n ])\n prd = {}\n for tipo in operacao:\n if tipo.warehouse_id.company_id.id == user_id.company_id.id:\n # tipo_operacao = tipo\n vals['picking_type_id'] = tipo.id\n vals['location_id'] = tipo.default_location_src_id.id\n vals['location_dest_id'] = tipo.default_location_dest_id.id\n vals['note'] = data['motivo'] \n prd['location_id'] = tipo.default_location_src_id.id\n prd['location_dest_id'] = tipo.default_location_dest_id.id\n \n prd['product_id'] = data['produto'] \n prd['product_uom_qty'] = data['quantidade'] \n prd['product_uom'] = 1\n prd['quantity_done'] = data['quantidade'] \n prd['name'] = data['nproduto'] \n item.append((0, 0,prd))\n vals['move_ids_without_package'] = item\n \n \n pos = http.request.env['stock.picking']\n pick = pos.sudo().create(vals)\n pick.action_confirm()\n pick.action_assign() \n pick.button_validate()\n return 'Sucesso'\n\n\n","repo_name":"ATSTI/ats-odoo","sub_path":"pdv_integracao/controller/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":28875,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"18929081362","text":"\r\nclass MATRIX():\r\n def __init__(self,count):\r\n self.count = int(count)\r\n self.mat = MATRIX.Insert(self)\r\n \r\n \r\n def Main():\r\n pass \r\n \r\n def Insert(self):\r\n print(\"------------------Enter numbers row wise------------------\\n\")\r\n block=[]\r\n cblock=[]\r\n for i in range(self.count):\r\n cblock=[]\r\n for j in range(self.count):\r\n cblock.append(int(input()))\r\n block.append(cblock) \r\n MATRIX.Show(block,self.count)\r\n return(block)\r\n def Show(view,size):\r\n print(\"----\"*size)\r\n for i in range(size):\r\n print(view[i])\r\n print(\"----\"*size)\r\n \r\n def ConstQuot(self,const,row):\r\n for i in range(self.count):\r\n self.mat[row-1][i] = self.mat[row-1][i] / const\r\n MATRIX.Show(self.mat,self.count)\r\n def ConstProd(self,const,row):\r\n for i in range(self.count):\r\n self.mat[row-1][i] = self.mat[row-1][i] * const\r\n MATRIX.Show(self.mat,self.count) \r\n def ConstDiff(self,const,row):\r\n for i in range(self.count):\r\n self.mat[row-1][i] = self.mat[row-1][i] - const\r\n MATRIX.Show(self.mat,self.count) \r\n def ConstSum(self,const,row):\r\n for i in range(self.count):\r\n self.mat[row-1][i] = self.mat[row-1][i] + const \r\n MATRIX.Show(self.mat,self.count) \r\n\r\n\r\n\r\nmatrix1=MATRIX(input(\"------------------Enter the no of rows of the matrix------------------\\n\"),)\r\nMATRIX.ConstSum(matrix1,3,1)\r\nMATRIX.ConstDiff(matrix1,3,2)\r\nMATRIX.ConstProd(matrix1,3,1)\r\nMATRIX.ConstQuot(matrix1,3,2)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n","repo_name":"rupeshpandit/Hacktoberfest-2021","sub_path":"Matrix.py","file_name":"Matrix.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"12983083921","text":"# -*- coding: utf8 -*-\nimport re\nimport asyncio\nimport logging\nimport traceback\n\nimport aiohttp\nfrom lxml import etree\nfrom bs4 import BeautifulSoup\n\nfrom config import STATUS_CODE_DICT\nfrom db_handler import *\nfrom sign_request import *\n\nlogging.basicConfig(filename='logs.log', format='%(asctime)s %(message)s', level=logging.DEBUG)\n\n\nclass AutoSign(object):\n \n def __init__(self, username, password, schoolid=None, enc=None):\n \"\"\"初始化就进行登录\"\"\"\n self.headers = {\n 'Accept-Encoding': 'gzip, deflate',\n 'Accept-Language': 'zh-CN,zh;q=0.9',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.100 Safari/537.36'\n }\n self.username = username\n self.password = password\n self.schoolid = '' if schoolid is None else schoolid\n self.enc = '' if enc is None else enc\n self.mongo = SignMongoDB(username)\n \n async def set_cookies(self, client: ClientSession):\n \"\"\"设置cookies\"\"\"\n cookie_status = await self.check_cookies(client)\n if not cookie_status:\n # 无效则重新登录，并保存cookies\n login_resp = await self.login(client)\n if login_resp['code'] == 1000:\n self.save_cookies(login_resp)\n else:\n return 1001\n return 1000\n \n def save_cookies(self, login_resp):\n \"\"\"保存cookies\"\"\"\n resp = login_resp['resp']\n new_cookies = resp.cookies\n cookies = {}\n for key, value in new_cookies.items():\n cookies[key] = value.value\n self.mongo.to_save_cookie(cookies)\n \n async def check_cookies(self, client: ClientSession):\n \"\"\"验证cookies\"\"\"\n # 从数据库内取出cookie\n try:\n cookies = self.mongo.to_get_cookie()\n except:\n return False\n \n # 验证cookies\n async with client.request('GET', 'http://mooc1-1.chaoxing.com/api/workTestPendingNew', allow_redirects=False,\n cookies=cookies) as resp:\n code = resp.status\n if code != 200:\n print(\"cookies失效\")\n return False\n else:\n # todo 这一句应该提到外面\n client.cookie_jar.update_cookies(cookies)\n print(\"cookies有效\")\n return True\n \n async def login(self, client: ClientSession):\n \"\"\"\n 登录\n \"\"\"\n params = {\n 'name': self.username,\n 'pwd': self.password,\n 'schoolid': self.schoolid,\n 'verify': 0\n }\n async with client.request('GET',\n url='https://passport2.chaoxing.com/api/login',\n headers=self.headers,\n params=params) as resp:\n text = await resp.text()\n if resp.status == 403:\n return {\n 'code': 1002\n }\n data = json.loads(text)\n if data['result']:\n print(\"登录成功\")\n return {\n 'code': 1000,\n 'msg': '登录成功',\n 'resp': resp\n } # 登录成功\n else:\n return {\n 'code': 1001,\n 'msg': '登录失败'\n } # 登录信息有误\n \n def check_activeid(self, activeid: str):\n \"\"\"验证当前活动id是否已存在\"\"\"\n activeid_lists = self.mongo.to_get_istext_activeid()\n if activeid in activeid_lists:\n return True\n else:\n return False\n \n async def get_all_classid(self, client) -> list:\n \"\"\"获取课程主页中所有课程的classid和courseid\"\"\"\n res = []\n # 首先去数据库里寻找\n res = self.mongo.to_get_all_classid_and_courseid()\n if not res:\n \n async with client.request('GET',\n 'http://mooc1-2.chaoxing.com/visit/interaction',\n headers=self.headers) as resp:\n assert resp.status == 200\n text = await resp.text()\n soup = BeautifulSoup(text, \"lxml\")\n courseId_list = soup.find_all('input', attrs={'name': 'courseId'})\n classId_list = soup.find_all('input', attrs={'name': 'classId'})\n classname_list = soup.find_all('h3', class_=\"clearfix\")\n \n # 用户首次使用，可以将所用有的classid保存\n for i, v in enumerate(courseId_list):\n res.append((v['value'], classId_list[i]['value'],\n classname_list[i].find_next('a').text))\n self.mongo.to_save_all_classid_and_courseid(res)\n return res\n \n async def get_sign_type(self, classid, courseid, activeid, client):\n \"\"\"获取签到类型\"\"\"\n sign_url = 'https://mobilelearn.chaoxing.com/widget/sign/pcStuSignController/preSign'\n params = {\n 'activeId': activeid,\n 'classId': classid,\n 'courseId': courseid\n }\n async with client.request(\"GET\", sign_url, headers=self.headers, params=params) as resp:\n text = await resp.text()\n \n h = etree.HTML(text)\n sign_type = h.xpath('//div[@class=\"location\"]/span/text()')\n return sign_type\n \n async def get_activeid(self, classid, courseid, classname, client):\n \"\"\"访问任务面板获取课程的活动id\"\"\"\n re_rule = r'([\\d]+),2'\n async with client.request('GET',\n 'https://mobilelearn.chaoxing.com/widget/pcpick/stu/index?courseId={}&jclassId={}'.format(\n courseid, classid), headers=self.headers, verify_ssl=False) as resp:\n text = await resp.text()\n \n res = []\n h = etree.HTML(text)\n activeid_list = h.xpath('//*[@id=\"startList\"]/div/div/@onclick')\n for activeid in activeid_list:\n activeid = re.findall(re_rule, activeid)\n if not activeid:\n continue\n # 获取签到任务的类型\n sign_type = await self.get_sign_type(classid, courseid, activeid[0], client)\n res.append((activeid[0], sign_type[0]))\n \n n = len(res)\n if n != 0:\n d = {'num': n, 'class': {}}\n for i in range(n):\n if self.check_activeid(res[i][0]):\n continue\n d['class'][i] = {\n 'classid': classid,\n 'courseid': courseid,\n 'activeid': res[i][0],\n 'classname': classname,\n 'sign_type': res[i][1]\n }\n return d\n \n async def sign_in_type_judgment(self, classid, courseid, activeid, sign_type, client: ClientSession):\n \"\"\"签到类型的逻辑判断\"\"\"\n sign = SignRequest(client, classid, courseid, activeid, sign_type)\n if \"手势\" in sign_type:\n return await sign.hand_sign()\n elif \"二维码\" in sign_type:\n return await sign.qcode_sign(self.enc)\n elif \"位置\" in sign_type:\n return await sign.addr_sign()\n elif \"拍照\" in sign_type:\n return await sign.tphoto_sign()\n else:\n return await sign.general_sign()\n \n async def start_sign_tasks(self, client):\n \"\"\"开始所有签到任务\"\"\"\n tasks = []\n success = []\n error = []\n # 获取所有课程的classid和course_id\n classid_courseId = await self.get_all_classid(client)\n \n # 使用协程获取所有课程activeid和签到类型\n for i in classid_courseId:\n coroutine = self.get_activeid(i[1], i[0], i[2], client)\n tasks.append(coroutine)\n \n result = await asyncio.gather(*tasks)\n print(result)\n for r in result:\n if r:\n for d in r['class'].values():\n s = await self.sign_in_type_judgment(\n d['classid'],\n d['courseid'],\n d['activeid'],\n d['sign_type'],\n client\n )\n if '成功' in STATUS_CODE_DICT[s['status']]:\n # 签到课程， 签到时间， 签到状态\n sign_msg = {\n 'name': d['classname'],\n 'date': s['date'],\n 'status': STATUS_CODE_DICT[s['status']]\n }\n success.append(sign_msg)\n # 将签到成功activeid保存至数据库\n self.mongo.to_save_istext_activeid(d['activeid'])\n else:\n sign_msg = {\n 'name': d['classname'],\n 'date': s['date'],\n 'status': STATUS_CODE_DICT[s['status']]\n }\n error.append(sign_msg)\n final_msg = []\n if success:\n success_msg = {\n 'msg': 2001,\n 'detail': success\n }\n final_msg.append(success_msg)\n if error:\n error_msg = {\n 'msg': 2002,\n 'detail': error\n }\n final_msg.append(error_msg)\n if not final_msg:\n final_msg = {\n 'msg': 2000,\n 'detail': STATUS_CODE_DICT[2000]\n }\n return final_msg\n\n\n# def server_chan_send(msgs, sckey=None):\n# \"\"\"server酱将消息推送至微信\"\"\"\n# desp = ''\n# for msg in msgs:\n# desp = '| **课程名** | {} |\\r\\r| :----------: | :---------- |\\r\\r'.format(\n# msg['name'])\n# desp += '| **签到时间** | {} |\\r\\r'.format(msg['date'])\n# desp += '| **签到状态** | {} |\\r\\r'.format(msg['status'])\n#\n# params = {\n# 'text': '您的网课签到消息来啦！',\n# 'desp': desp\n# }\n# if sckey:\n# requests.get('https://sc.ftqq.com/{}.send'.format(sckey), params=params)\n\n\nasync def interface(payload):\n try:\n async with aiohttp.ClientSession() as client:\n auto_sign = AutoSign(username=payload['username'],\n password=payload['password'],\n schoolid=payload['schoolid'],\n enc=payload['enc'])\n login_status = await auto_sign.set_cookies(client)\n if login_status != 1000:\n return {\n 'msg': login_status,\n 'detail': '登录失败，' + STATUS_CODE_DICT[login_status]\n }\n \n result = await auto_sign.start_sign_tasks(client)\n \n return result\n \n except Exception as e:\n logging.error(traceback.format_exc())\n traceback.print_exc()\n return {'msg': 4000, 'detail': STATUS_CODE_DICT[4000]}\n","repo_name":"mkdir700/chaoxing_auto_sign","sub_path":"api/cloud_sign.py","file_name":"cloud_sign.py","file_ext":"py","file_size_in_byte":11508,"program_lang":"python","lang":"en","doc_type":"code","stars":316,"dataset":"github-code","pt":"31"} +{"seq_id":"19984922494","text":"# Logan Gregg\n# Assignment: MATH 374 Final Project\n# Due Date: 5/05/2023\n# File Description: File with the serial and parallel implementations for the\n# Gaussian Elimination with Partial Pivoting method for solving\n# linear systems\n\nimport numpy as np\nimport json\nimport time\nimport scipy.sparse\nimport warnings\nimport multiprocessing\n\ndef gaussian_elimination_serial(A, b):\n n = len(b)\n Ab = scipy.sparse.hstack((A, scipy.sparse.csc_matrix(np.expand_dims(b, axis=1)))).tocsr()\n\n # Forward elimination\n for k in range(n-1):\n # Partial pivoting\n max_index = np.argmax(np.abs(Ab[k:, k].toarray())) + k\n Ab[[k, max_index]] = Ab[[max_index, k]]\n\n for i in range(k+1, n):\n if Ab[k, k] != 0:\n factor = Ab[i, k] / Ab[k, k]\n else:\n factor = 0\n\n # Back substitution\n x = np.zeros(n)\n for i in range(n-1, -1, -1):\n if Ab[i, i] != 0:\n x[i] = (Ab[i, -1] - Ab[i, i+1:-1].dot(x[i+1:])) / Ab[i, i]\n else:\n x[i] = 0\n\n return x\n\ndef gaussian_elimination_parallel(A, b, num_processes):\n n = len(b)\n Ab = np.concatenate((A, np.expand_dims(b, axis=1)), axis=1)\n\n # Forward elimination\n with multiprocessing.Pool(processes=num_processes) as pool:\n pool.starmap(forward_elimination, [(Ab, k) for k in range(n - 1)])\n\n # Solve the system using spsolve\n x = scipy.sparse.linalg.spsolve(Ab[:, :-1], Ab[:, -1])\n\n return x\n\n# Parallel Gaussian Elimination Helper Function\ndef forward_elimination(Ab, k):\n n = Ab.shape[0]\n max_index = np.argmax(np.abs(Ab[k:, k])) + k\n Ab[[k, max_index]] = Ab[[max_index, k]]\n\n for i in range(k + 1, n):\n factor = Ab[i,k]\n if factor != 0:\n factor = Ab[i, k] / Ab[k, k]\n else:\n factor = .000001\n \n Ab[i, k:] -= factor * Ab[k, k:]\n\n# To deserialize the JSON file\ndef deserialize_linear_systems(file_path):\n with open(file_path, 'r') as file:\n data = json.load(file)\n linear_systems = []\n \n for linear_system in data:\n dimension = linear_system['dimension']\n coefficient_matrix = np.array(linear_system['coefficient_matrix'])\n solution_vector = np.array(linear_system['solution_vector'])\n deserialized_system = {\n 'dimension': dimension,\n 'coefficient_matrix': coefficient_matrix,\n 'solution_vector': solution_vector\n }\n linear_systems.append(deserialized_system)\n \n return linear_systems\n\ndef main(): \n warnings.filterwarnings('ignore')\n # JSON file path\n file_path = 'sparse_linear_systems.json'\n\n deserialized_systems = deserialize_linear_systems(file_path)\n with open(\"partial_pivot.csv\", 'w') as csv_file:\n csv_file.write(\"dimension, Serial GE PP Time, Parallel GE PP Time\\n\");\n # Collect data for two different trials\n for i in range(3):\n # Accessing the deserialized linear systems\n for system in deserialized_systems:\n dimension = system['dimension']\n coefficient_matrix = system['coefficient_matrix']\n solution_vector = system['solution_vector']\n \n A = coefficient_matrix\n b = solution_vector\n \n print(\"For n = \" + str(dimension) + \":\")\n \n # Use the serial method \n start_time = time.time();\n x = gaussian_elimination_serial(A, b)\n end_time = time.time();\n serial_time_elapsed = end_time - start_time;\n \n print(\"\\tSerial Time Elapsed: \" + str(serial_time_elapsed) + \" s\")\n \n # Use the parallel method\n num_processes = multiprocessing.cpu_count() # Set the number of parallel processes\n start_time = time.time()\n x = gaussian_elimination_parallel(A, b, num_processes)\n end_time = time.time();\n parallel_time_elapsed = end_time - start_time;\n \n print(\"\\tParallel Time Elapsed: \" + str(parallel_time_elapsed) + \" s\")\n \n # Record the data in a CSV file\n csv_file.write(str(dimension) + \",\" + str(serial_time_elapsed) + \",\" + str(parallel_time_elapsed) + \"\\n\")\n \n csv_file.close()\n \nif __name__ == \"__main__\":\n main()\n","repo_name":"logan624/LinAlgSerialVSParallel","sub_path":"src/methods/ge_partial_piv.py","file_name":"ge_partial_piv.py","file_ext":"py","file_size_in_byte":4568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22150345682","text":"# multiAgents.py\n# --------------\n# Licensing Information: You are free to use or extend these projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.\n# \n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero\n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and\n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n# Reference: berkely, \n# https://github.com/jiminsun/berkeley-cs188-pacman/edit/master/hw2/multiagent/multiAgents.py\n# https://github.com/zhangjiedev/pacman/blob/a182b2dc129f8077566a640bcc5ccf2e7c8c3bcd/multiagent/multiAgents.py#L126\n\n\nfrom util import manhattanDistance\nfrom game import Directions\nimport random, util\n\nfrom game import Agent\nfrom pacman import GameState\n\nclass ReflexAgent(Agent):\n \"\"\"\n A reflex agent chooses an action at each choice point by examining\n its alternatives via a state evaluation function.\n\n The code below is provided as a guide. You are welcome to change\n it in any way you see fit, so long as you don't touch our method\n headers.\n \"\"\"\n\n\n def getAction(self, gameState: GameState):\n \"\"\"\n You do not need to change this method, but you're welcome to.\n\n getAction chooses among the best options according to the evaluation function.\n\n Just like in the previous project, getAction takes a GameState and returns\n some Directions.X for some X in the set {NORTH, SOUTH, WEST, EAST, STOP}\n \"\"\"\n # Collect legal moves and successor states\n legalMoves = gameState.getLegalActions()\n\n # Choose one of the best actions\n scores = [self.evaluationFunction(gameState, action) for action in legalMoves]\n bestScore = max(scores)\n bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]\n chosenIndex = random.choice(bestIndices) # Pick randomly among the best\n\n \"Add more of your code here if you want to\"\n\n return legalMoves[chosenIndex]\n\n def evaluationFunction(self, currentGameState: GameState, action):\n \"\"\"\n Design a better evaluation function here.\n\n The evaluation function takes in the current and proposed successor\n GameStates (pacman.py) and returns a number, where higher numbers are better.\n\n The code below extracts some useful information from the state, like the\n remaining food (newFood) and Pacman position after moving (newPos).\n newScaredTimes holds the number of moves that each ghost will remain\n scared because of Pacman having eaten a power pellet.\n\n Print out these variables to see what you're getting, then combine them\n to create a masterful evaluation function.\n \"\"\"\n # Useful information you can extract from a GameState (pacman.py)\n successorGameState = currentGameState.generatePacmanSuccessor(action)\n newPos = successorGameState.getPacmanPosition()\n newFood = successorGameState.getFood()\n newGhostStates = successorGameState.getGhostStates()\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\n \n \"*** YOUR CODE HERE ***\"\n\n shortest = 1200\n ghost_location = successorGameState.getGhostPosition(1)\n GhostDistance = util.manhattanDistance(ghost_location, newPos)\n score = successorGameState.getScore()\n score += max(GhostDistance, 3)\n \n for food in newFood.asList():\n dis = util.manhattanDistance(food, newPos)\n if dis < shortest:\n shortest = dis\n #print(\"go\")\n if len(newFood.asList())< len(currentGameState.getFood().asList()):\n #print(\"1\")\n score += 200\n score += 200/shortest\n if newPos in currentGameState.getCapsules():\n #print(\"2\")\n score += 150\n if action == Directions.STOP:\n #print(\"stop\")\n score -= 30\n return score\n\n return successorGameState.getScore()\n\ndef scoreEvaluationFunction(currentGameState: GameState):\n \"\"\"\n This default evaluation function just returns the score of the state.\n The score is the same one displayed in the Pacman GUI.\n\n This evaluation function is meant for use with adversarial search agents\n (not reflex agents).\n \"\"\"\n return currentGameState.getScore()\n\nclass MultiAgentSearchAgent(Agent):\n \"\"\"\n This class provides some common elements to all of your\n multi-agent searchers. Any methods defined here will be available\n to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent.\n\n You *do not* need to make any changes here, but you can if you want to\n add functionality to all your adversarial search agents. Please do not\n remove anything, however.\n\n Note: this is an abstract class: one that should not be instantiated. It's\n only partially specified, and designed to be extended. Agent (game.py)\n is another abstract class.\n \"\"\"\n\n def __init__(self, evalFn = 'scoreEvaluationFunction', depth = '2'):\n self.index = 0 # Pacman is always agent index 0\n self.evaluationFunction = util.lookup(evalFn, globals())\n self.depth = int(depth)\n\nclass MinimaxAgent(MultiAgentSearchAgent):\n \"\"\"\n Your minimax agent (question 2)\n \"\"\"\n\n def getAction(self, gameState: GameState):\n \"\"\"\n Returns the minimax action from the current gameState using self.depth\n and self.evaluationFunction.\n\n Here are some method calls that might be useful when implementing minimax.\n\n gameState.getLegalActions(agentIndex):\n Returns a list of legal actions for an agent\n agentIndex=0 means Pacman, ghosts are >= 1\n\n gameState.generateSuccessor(agentIndex, action):\n Returns the successor game state after an agent takes an action\n\n gameState.getNumAgents():\n Returns the total number of agents in the game\n\n gameState.isWin():\n Returns whether or not the game state is a winning state\n\n gameState.isLose():\n Returns whether or not the game state is a losing state\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n #util.raiseNotDefined()\n utility = util.Counter()\n last_agent = gameState.getNumAgents() - 1 # last min agent in ply\n\n def minn(state, agent, depth):\n min_score = float('inf')\n if state.getLegalActions(agent):\n if agent == last_agent:\n for act in state.getLegalActions(agent):\n min_score = min(min_score, maxx(state.generateSuccessor(agent, act), 0, depth + 1))\n else:\n for act in state.getLegalActions(agent):\n min_score = min(min_score, minn(state.generateSuccessor(agent, act), agent + 1, depth))\n return min_score\n return self.evaluationFunction(state)\n\n def maxx(state, agent, depth):\n max_score = float('-inf')\n if depth == self.depth:\n return self.evaluationFunction(state)\n if len(state.getLegalActions(agent)) == 0:\n #print(\"no way\")\n return self.evaluationFunction(state)\n for action in state.getLegalActions(agent):\n max_score = max(max_score, minn(state.generateSuccessor(agent, action), agent + 1, depth))\n return max_score\n \n for act in gameState.getLegalActions(0):\n utility[act] = minn(gameState.generateSuccessor(0, act), 1, 0)\n return utility.argMax()\n\nclass AlphaBetaAgent(MultiAgentSearchAgent):\n \"\"\"\n Your minimax agent with alpha-beta pruning (question 3)\n \"\"\"\n\n def getAction(self, gameState: GameState):\n \"\"\"\n Returns the minimax action using self.depth and self.evaluationFunction\n \"\"\"\n \"*** YOUR CODE HERE ***\" \n #util.raiseNotDefined()\n utility = util.Counter()\n last_agent = gameState.getNumAgents() - 1 # last min agent in ply\n alpha = float('-inf')\n beta = float('inf')\n\n score = float('inf')\n\n def minn(state, agent, depth, alpha, beta):\n min_score = float('inf')\n if state.getLegalActions(agent):\n if agent == last_agent:\n for act in state.getLegalActions(agent):\n min_score = min(min_score, maxx(state.generateSuccessor(agent, act), 0, depth + 1, alpha, beta))\n if min_score < alpha:\n return min_score\n beta = min(beta, min_score)\n else:\n for act in state.getLegalActions(agent):\n min_score = min(min_score, minn(state.generateSuccessor(agent, act), agent + 1, depth, alpha, beta))\n if min_score < alpha:\n return min_score\n beta = min(beta, min_score)\n return min_score\n return self.evaluationFunction(state)\n\n def maxx(state, agent, depth, alpha, beta):\n max_score = float('-inf')\n if depth == self.depth:\n return self.evaluationFunction(state)\n if len(state.getLegalActions(agent)) == 0:\n #print(\"no way\")\n return self.evaluationFunction(state)\n for action in state.getLegalActions(agent):\n max_score = max(max_score, minn(state.generateSuccessor(agent, action), agent + 1, depth, alpha, beta))\n if max_score > beta:\n return max_score\n alpha = max(alpha, max_score)\n return max_score\n\n for act in gameState.getLegalActions(0):\n utility[act] = minn(gameState.generateSuccessor(0, act), 1, 0, alpha, beta)\n alpha = max(alpha, utility[act])\n return utility.argMax()\n\nclass ExpectimaxAgent(MultiAgentSearchAgent):\n \"\"\"\n Your expectimax agent (question 4)\n \"\"\"\n\n def getAction(self, gameState: GameState):\n \"\"\"\n Returns the expectimax action using self.depth and self.evaluationFunction\n\n All ghosts should be modeled as choosing uniformly at random from their\n legal moves.\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n utility = util.Counter()\n last = gameState.getNumAgents() - 1\n\n def maxx(state, agent, depth):\n score = float('-inf')\n if depth == self.depth:\n return self.evaluationFunction(state)\n if len(state.getLegalActions(agent)):\n for act in state.getLegalActions(agent):\n score = max(score, expect(state.generateSuccessor(agent, act), agent + 1, depth))\n return score\n return self.evaluationFunction(state)\n\n def expect(state, agent, depth):\n score = 0\n if state.getLegalActions(agent):\n prob = 1.0 / len(state.getLegalActions(agent))\n if agent == last:\n for act in state.getLegalActions(agent):\n score += prob * maxx(state.generateSuccessor(agent, act), 0, depth+1)\n else:\n for act in state.getLegalActions(agent):\n score += prob * expect(state.generateSuccessor(agent, act), agent + 1, depth)\n return score\n return self.evaluationFunction(state)\n \n for act in gameState.getLegalActions(0):\n utility[act] = expect(gameState.generateSuccessor(0, act), 1, 0)\n return utility.argMax()\n util.raiseNotDefined()\n\ndef betterEvaluationFunction(currentGameState: GameState):\n \"\"\"\n Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable\n evaluation function (question 5).\n\n DESCRIPTION: \n \"\"\"\n currentPos = currentGameState.getPacmanPosition()\n currentFood = currentGameState.getFood().asList()\n currentGhostStates = currentGameState.getGhostStates()\n currentCapsule = currentGameState.getCapsules()\n currentScore = currentGameState.getScore()\n currentScaredTimes = [ghostState.scaredTimer for ghostState in currentGhostStates]\n walls = currentGameState.getWalls().asList()\n \"*** YOUR CODE HERE ***\"\n\n score = currentGameState.getScore()\n foodDistance = [manhattanDistance(currentPos, food) for food in currentFood]\n #print(newScaredTimes)\n\n def count_walls_between(pos, food):\n x, y = pos\n fx, fy = food\n fx, fy = int(fx), int(fy)\n \n return sum([wx in range(min(x, fx), max(x, fx)+1) and wy in range(min(y, fy), max(y, fy)+1) for (wx, wy) in walls])\n\n \n if currentGameState.isWin():\n score = 10000\n else:\n closestFood = sorted(foodDistance)\n closeFoodDistance = sum(closestFood[-6:])\n closestFoodDistance = sum(closestFood[-3:])\n ghostDistance = [manhattanDistance(currentPos, ghost.getPosition()) + 2* count_walls_between(currentPos, ghost.getPosition()) for ghost in currentGhostStates]\n minGhostDistance = min(min(ghostDistance), 6)\n \n score += 0.5 * currentScaredTimes[0] + 1.0 / len(currentFood) - len(currentCapsule) + minGhostDistance + 2.0 / closeFoodDistance + 2.5 / closestFoodDistance\n if minGhostDistance < 6 and minGhostDistance >= 3:\n score += minGhostDistance\n\n return score\n util.raiseNotDefined()\n\n# Abbreviation\nbetter = betterEvaluationFunction\n","repo_name":"pinwen9116/Foundamental-AI-2022-Spring","sub_path":"hw2/multiAgents.py","file_name":"multiAgents.py","file_ext":"py","file_size_in_byte":13876,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"39959864678","text":"class Account:\n def __init__(self, id, balance, pin):\n self.__id = id\n self.balance = balance\n self.__pin = pin\n\n def get_id(self, pin):\n \"\"\"\"get_id(pin) - if the given pin is correct, return the id, otherwise, return \"Wrong pin\"\"\"\n\n if pin != self.__pin:\n return \"Wrong pin\"\n\n return self.__id\n\n def change_pin(self, old_pin, new_pin):\n \"\"\"\"change_pin(old_pin, new_pin) - if the old pin is correct,\n change it to the new one and return \"Pin changed\", otherwise return \"Wrong pin\"\"\"\n\n if old_pin != self.__pin:\n return \"Wrong pin\"\n\n self.__pin = new_pin\n return \"Pin changed\"\n\n\naccount = Account(8827312, 100, 3421)\nprint(account.get_id(1111))\nprint(account.get_id(3421))\nprint(account.balance)\nprint(account.change_pin(2212, 4321))\nprint(account.change_pin(3421, 1234))","repo_name":"nmoskova/Python-OOP","sub_path":"04.Encapsulation/Lab/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36350215466","text":"# **************************************************************************\n# *\n# * Authors: Grigory Sharov (gsharov@mrc-lmb.cam.ac.uk)\n# *\n# * MRC Laboratory of Molecular Biology (MRC-LMB)\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 3 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., 59 Temple Place, Suite 330, Boston, MA\n# * 02111-1307 USA\n# *\n# * All comments concerning this program package may be sent to the\n# * e-mail address 'scipion@cnb.csic.es'\n# *\n# **************************************************************************\n\nimport os\nfrom enum import Enum\n\nimport pyworkflow.utils as pwutils\nfrom pyworkflow.plugin import Domain\nfrom pyworkflow.constants import PROD\nimport pyworkflow.protocol.params as params\nfrom pwem.constants import ALIGN_PROJ, ALIGN_NONE\nfrom pwem.protocols import ProtProcessParticles\n\nfrom .. import Plugin\nfrom ..objects import CryoDrgnParticles\n\nconvert = Domain.importFromPlugin('relion.convert', doRaise=True)\n\n\nclass outputs(Enum):\n outputCryoDrgnParticles = CryoDrgnParticles\n\n\nclass CryoDrgnProtPreprocess(ProtProcessParticles):\n \"\"\" Protocol to downsample a particle stack and prepare alignment/CTF parameters.\n \"\"\"\n _label = 'preprocess'\n _devStatus = PROD\n _possibleOutputs = outputs\n\n def _createFilenameTemplates(self):\n \"\"\" Centralize how files are called. \"\"\"\n\n def out(*p):\n return os.path.join(self._getPath('output_particles'), *p)\n\n myDict = {\n 'input_parts': self._getExtraPath('input_particles.star'),\n 'output_folder': out(),\n 'output_parts': out('particles.%d.mrcs' % self._getBoxSize()),\n 'output_txt': out('particles.%d.ft.txt' % self._getBoxSize()),\n 'output_poses': out('poses.pkl'),\n 'output_ctfs': out('ctfs.pkl'),\n }\n\n self._updateFilenamesDict(myDict)\n\n # --------------------------- DEFINE param functions ----------------------\n def _defineParams(self, form):\n form.addHidden('usePreprocess', params.BooleanParam, default=True)\n form.addSection(label='Input')\n form.addParam('inputParticles', params.PointerParam,\n pointerClass='SetOfParticles',\n pointerCondition='hasCTF',\n label=\"Input particles\", important=True,\n help='Select a set of particles from a consensus C1 '\n '3D refinement.')\n\n form.addParam('doScale', params.BooleanParam, default=True,\n label='Downsample particles?')\n\n form.addParam('scaleSize', params.IntParam, default=128,\n condition='doScale',\n validators=[params.Positive],\n label='New box size (px)',\n help='New box size in pixels, must be even.')\n\n form.addParam('doWindow', params.BooleanParam, default=True,\n expertLevel=params.LEVEL_ADVANCED,\n label=\"Apply circular mask?\")\n\n form.addParam('winSize', params.FloatParam, default=0.85,\n expertLevel=params.LEVEL_ADVANCED,\n condition='doWindow',\n label=\"Window size\",\n help=\"Circular windowing mask inner radius\")\n\n form.addParam('chunk', params.IntParam, default=0,\n label='Split in chunks',\n help='Chunk size (in # of images) to split '\n 'particle stack when saving.')\n\n form.addParam('doInvert', params.BooleanParam, default=True,\n expertLevel=params.LEVEL_ADVANCED,\n label=\"Are particles white?\")\n\n form.addParallelSection(threads=16, mpi=0)\n\n # --------------------------- INSERT steps functions ----------------------\n def _insertAllSteps(self):\n self._createFilenameTemplates()\n self._insertFunctionStep(self.convertInputStep)\n self._insertFunctionStep(self.runDownSampleStep)\n self._insertFunctionStep(self.runParseMdStep)\n self._insertFunctionStep(self.createOutputStep)\n\n # --------------------------- STEPS functions -----------------------------\n def convertInputStep(self):\n \"\"\" Create a star file as expected by cryoDRGN.\"\"\"\n outputFolder = self._getFileName('output_folder')\n pwutils.cleanPath(outputFolder)\n pwutils.makePath(outputFolder)\n\n imgSet = self.inputParticles.get()\n # Create links to binary files and write the relion .star file\n alignType = ALIGN_PROJ if self._inputHasAlign() else ALIGN_NONE\n convert.writeSetOfParticles(\n imgSet, self._getFileName('input_parts'),\n outputDir=self._getExtraPath(), alignType=alignType)\n\n def runDownSampleStep(self):\n self._runProgram('preprocess', self._getPreprocessArgs())\n\n def runParseMdStep(self):\n if self._inputHasAlign():\n self._runProgram('parse_pose_star', self._getParsePosesArgs())\n self._runProgram('parse_ctf_star', self._getParseCtfArgs())\n\n def createOutputStep(self):\n poses = self._getFileName('output_poses') if self._inputHasAlign() else None\n output = CryoDrgnParticles(filename=self._getFileName('output_txt'),\n poses=poses,\n ctfs=self._getFileName('output_ctfs'),\n dim=self._getBoxSize() + 1,\n samplingRate=self._getSamplingRate())\n output.ptcls = self.inputParticles.clone()\n\n self._defineOutputs(**{outputs.outputCryoDrgnParticles.name: output})\n self._defineSourceRelation(self.inputParticles, output)\n\n # --------------------------- INFO functions ------------------------------\n def _summary(self):\n summary = []\n self._createFilenameTemplates()\n if not self.isFinished():\n summary.append(\"Output not ready\")\n else:\n poses = \"poses and\" if self._inputHasAlign() else \"\"\n summary.append(f\"Created {poses} ctf files for cryoDRGN.\")\n\n return summary\n\n def _validate(self):\n errors = []\n\n particles = self._getInputParticles()\n\n if self.doScale and self.scaleSize > particles.getXDim():\n errors.append(\"You cannot upscale particles!\")\n\n if self._getBoxSize() % 2 != 0:\n errors.append(\"Box size must be even!\")\n\n return errors\n\n def _warnings(self):\n warnings = []\n\n if not self._inputHasAlign():\n warnings.append(\"Input particles have no alignment, you will only \"\n \"be able to use the output for ab initio training!\")\n\n if self._getBoxSize() % 8 != 0:\n warnings.append(\"CryoDRGN mixed-precision (AMP) training will \"\n \"require box size divisible by 8. Alternatively, \"\n \"you will have to provide --no-amp option.\")\n\n return warnings\n\n # --------------------------- UTILS functions -----------------------------\n def _getPreprocessArgs(self):\n args = ['%s ' % self._getFileName('input_parts'),\n '-o %s ' % self._getFileName('output_parts'),\n '-D %d' % self._getBoxSize(),\n '--window-r %0.2f' % self.winSize if self.doWindow else '--no-window',\n '--max-threads %d ' % self.numberOfThreads\n ]\n\n if not self.doInvert:\n args.append('--uninvert-data')\n\n if self.chunk > 0:\n args.append('--chunk %d ' % self.chunk)\n\n return args\n\n def _getParsePosesArgs(self):\n args = ['%s ' % self._getFileName('input_parts'),\n '-o %s ' % self._getFileName('output_poses')]\n\n return args\n\n def _getParseCtfArgs(self):\n args = ['%s ' % self._getFileName('input_parts'),\n '-o %s ' % self._getFileName('output_ctfs'),\n '--ps 0'] # required due to cryodrgn parsing bug\n\n return args\n\n def _getInputParticles(self):\n return self.inputParticles.get()\n\n def _getBoxSize(self):\n if self.doScale:\n return self.scaleSize.get()\n else:\n return self._getInputParticles().getXDim()\n\n def _getSamplingRate(self):\n inputSet = self._getInputParticles()\n oldSampling = inputSet.getSamplingRate()\n scaleFactor = self._getScaleFactor(inputSet)\n\n return oldSampling * scaleFactor\n\n def _getScaleFactor(self, inputSet):\n return inputSet.getXDim() / self._getBoxSize()\n\n def _inputHasAlign(self):\n return self._getInputParticles().hasAlignmentProj()\n\n def _runProgram(self, program, args):\n self.runJob(Plugin.getProgram(program), ' '.join(args))\n","repo_name":"scipion-em/scipion-em-cryodrgn","sub_path":"cryodrgn/protocols/protocol_preprocess.py","file_name":"protocol_preprocess.py","file_ext":"py","file_size_in_byte":9408,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"20351663735","text":"#Change your Mac Address by Giving any Fake 12 Digit Mac Address\n\nimport subprocess\nimport optparse\nimport re\n\ndef get_arguements():\n parser = optparse.OptionParser()\n parser.add_option(\"-i\" , \"--interface\", dest=\"interface\", help=\"Interface to Change its MAC Address\")\n parser.add_option(\"-m\", \"--mac\", dest=\"new_mac\", help=\"New MAC Adress\")\n (options, arguements) = parser.parse_args()\n if not options.interface:\n options.error(\"[-] Please Specify an Interface to Continue, --help for more Info \")\n elif not options.new_mac:\n options.error( \" [-] Please Specify an New Mac to Continue, --help for More Info \")\n return options\n\ndef change_mac(interface, new_mac):\n print(\"[+] Change the Mac Adress of \" + interface + \" to \" + new_mac + \" : \")\n\n subprocess.call(\"ifconfig \" + interface + \" down\", shell=True)\n subprocess.call(\"ifconfig \" + interface + \" hw ether \"+ new_mac, shell=True)\n subprocess.call(\"ifconfig \" + interface + \" up\", shell=True)\n subprocess.call(\"ifconfig \" + interface, shell=True)\n\noptions = get_arguements()\n#change_mac(options.interface, options.new_mac)\n\nifconfig_result = subprocess.check_output([\"ifconfig\", options.interface])\nprint(ifconfig_result)\n\nmac_address_search_result = re.search(r\"\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w\", ifconfig_result)\nprint(mac_address_search_result.group(0))\n","repo_name":"yuvraj-xyz/Mac_Changer.py","sub_path":"Advance_Mac_Changer.py","file_name":"Advance_Mac_Changer.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"12119712195","text":"import ctypes\nimport sys\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import hsv_to_rgb\n\ndef load_ephemeris(filepath):\n \"\"\"Load HORIZONS ephemeris data into pandas dataframe\"\"\"\n\n #Read in ephemeris file\n with open(filepath, \"r\") as ephemeris:\n lines = ephemeris.readlines()\n\n #Get line number with start and end of ephemeris symbol\n start = [i for i,line in enumerate(lines) if \"$$SOE\" in line][0]\n end = [i for i,line in enumerate(lines) if \"$$EOE\" in line][0]\n\n # Get column names as two lines before start of ephemeris data\n columns = lines[start-2].split(\",\")\n columns = [name.strip() for name in columns]\n columns\n\n # Read the lines between start and end of ephemeris symbols\n # use identified columns as column names\n ephemeris = pd.read_csv(filepath, skiprows=start+1, skipfooter=len(lines)-end, header=None, names=columns, engine=\"python\")\n \n return ephemeris\n\ndef get_body_name(filepath):\n \"\"\"Get the name of target body from file header\"\"\"\n with open(\"ephemeris/\"+filepath, \"r\") as f:\n body = f.readlines()[3]\n body = body.split(\" \")[3]\n return body\n\ndef neon_plot(x, y, n_lines=5, colour=\"#83fdff\"):\n# n_lines = 5\n for i in range(1,n_lines):\n plt.plot(x, y, c=colour,\n linewidth=10*i,\n alpha=0.03,\n solid_capstyle=\"round\")\n\n plt.plot(x, y, c=colour, solid_capstyle=\"round\")\n# plt.plot(x, y, c=\"white\", linewidth=0.5)\n plt.scatter(x.iloc[-1], y.iloc[-1], c=colour)\n\ndef neon_point(x, y, n_lines=5, colour=\"#83fdff\"):\n# colour = \"#83fdff\"\n# n_lines = 5\n for i in range(1,n_lines):\n plt.scatter(x.iloc[-1], y.iloc[-1], c=colour,\n s=100*i,\n alpha=0.03,\n zorder=5\n )\n plt.scatter(x.iloc[-1], y.iloc[-1], c=colour)\n\n# folder = sys.argv[1]\nfolder = 'uranus'\n\n\nephemeris_file_names = os.listdir('C:/Users/David/code/SpaceMap/moonsOfJupiter/data/%s/ephemeris'%folder)\nephemeris_files = [load_ephemeris(\"C:/Users/David/code/SpaceMap/moonsOfJupiter/data/%s/ephemeris/\"%folder+f) for f in ephemeris_file_names]\n\nfor ephemeris in ephemeris_files:\n ephemeris['Calendar Date (TDB)'] = ephemeris['Calendar Date (TDB)'].str.replace(\"A.D. \", \"\")\n ephemeris['Calendar Date (TDB)'] = pd.to_datetime(ephemeris['Calendar Date (TDB)'])\n\n# Plot\n\nfig = plt.figure(figsize=(30,15))\nfig.patch.set_facecolor(\"#251c2d\")\n\ngs = fig.add_gridspec(nrows=2, ncols=4)\nax = []\n\nax.append(fig.add_subplot(gs[0, 0]))\nax[-1].axis('off')\n\nax.append(fig.add_subplot(gs[0:2, 1:3]))\nax[-1].axis('off')\n\nfor df in ephemeris_files:\n # Limit to data upto and including today\n df = df[df['Calendar Date (TDB)'] <= pd.Timestamp.today()]\n # Limit to within the last 3 months\n df = df[df['Calendar Date (TDB)'] > pd.Timestamp.today() - pd.Timedelta(90, 'days')]\n neon_plot(df.X, df.Y, colour='#fd8f24')\n\n#Plot central body\n# plt.scatter(0,0, c=\"#fd8f24\", s=50, zorder=10)\nneon_point(pd.Series(0), pd.Series(0), colour=\"#83fdff\")\n\n# Set plot limits\nplt.xlim([x*1.1 for x in plt.xlim()])\nplt.ylim([x*1.1 for x in plt.ylim()])\n\n# ax.append(fig.add_subplot(gs[2, 2]))\n# ax[-1].scatter(range(5),range(5))\n\nax.append(fig.add_subplot(gs[0, 3]))\nax[-1].axis('off')\nlastdate = df['Calendar Date (TDB)'].dt.strftime('%Y-%b-%d').iloc[-1]\nax[-1].text(0,0.5, lastdate, color=\"#f0e8da\",\n fontsize=10,# **font\n )\nplt.savefig(f\"C:/Users/David/code/SpaceMap/moonsOfJupiter/data/{folder}/moons_of_{folder}.png\")\n\n# plt.show()\n\n##########\n#Update background\n##########\nctypes.windll.user32.SystemParametersInfoW(20, 0, f\"C:/Users/David/code/SpaceMap/moonsOfJupiter/data/{folder}/moons_of_{folder}.png\", 3)","repo_name":"DAWells/SpaceMap","sub_path":"moonsOfJupiter/code/plot_fig.py","file_name":"plot_fig.py","file_ext":"py","file_size_in_byte":3754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33954423370","text":"def build(size):\n md = [[0]]\n while len(md[-1]) < size:\n md.append(md[-1] + md[-1])\n return md\n\n\ndef update(md, col, val):\n col -= 1\n row = len(md) - 1\n while row >= 0:\n if md[row][col] >= val:\n return\n md[row][col] = val\n row -= 1\n col >>= 1 # x >> 1 is equivalent to x / 2\n\n\ndef query(md, left, right):\n left -= 1\n right -= 1\n row = len(md) - 1\n res = 0\n while left <= right:\n if left & 1: # x & 1 is equivalent to x % 2\n res = max(res, md[row][left])\n left += 1\n if not (right & 1):\n res = max(res, md[row][right])\n right -= 1\n left >>= 1 # x >> 1 is equivalent to x / 2\n right >>= 1\n row -= 1\n return res\n\n\ndef solve(arr, size):\n incs = build(size)\n decs = build(size)\n acts = build(size)\n for i in arr:\n imx = query(incs, 1, i)\n amx = query(acts, 1, i)\n iv = max(imx, decs[-1][i - 1], amx) + 1\n dmx = query(decs, i, size)\n amx = query(acts, i, size)\n dv = max(dmx, incs[-1][i - 1], amx) + 1\n update(acts, i, max(incs[-1][i - 1], decs[-1][i - 1]))\n update(incs, i, iv)\n update(decs, i, dv)\n return max(incs[0][0], decs[0][0])\n\n\nt = int(input())\nfor _ in range(t):\n n = int(input())\n a = list(map(int, input().strip().split()))\n print(solve(a, n))\n","repo_name":"HBinhCT/Q-project","sub_path":"hackerearth/Data Structures/Advanced Data Structures/Segment Trees/Avoid Maxima-Minima/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"17470289146","text":"from keras.layers import Input, Dense, Lambda, Flatten, Reshape\nfrom keras.layers import Conv2D, AveragePooling2D, UpSampling2D, Conv2DTranspose\nfrom keras.models import Model, Sequential\nfrom keras.utils import plot_model\nfrom keras.optimizers import SGD, RMSprop\nfrom keras import backend as K\nfrom keras import metrics\nfrom scipy import misc\nfrom glob import glob\nfrom matplotlib.widgets import Slider\nfrom scipy.stats import norm\nimport pydot\nimport graphviz\nimport theano\n\nimport numpy as np\nimport pylab\nimport matplotlib.pyplot as plt\n\nclass Settings:\n img_size_rows = 64\n img_size_cols = 64\n img_size_chnl = 1\n full_img_size = (img_size_rows, img_size_cols, img_size_chnl)\n batch_size = 1\n latent_dim = 4\n intermediate_dim = 128\n epsilon = 1.0\n num_of_epoch = 1\n min_input = -3.0\n max_input = 3.0\n log = True\n\ndef load_image(image_path):\n grayImage = misc.imread(image_path, mode=\"L\")\n image = grayImage.reshape((1,) + grayImage.shape + (Settings.img_size_chnl,)) # (1,64,64,1)\n image = image.astype('float32') / 255.\n return image\n\ndef show_images(decoder, n=10, img_size=Settings.img_size_rows):\n ax = plt.subplot(111)\n plt.subplots_adjust(left=0.25, bottom=0.45)\n\n z_sample = np.array([[0.0, 0.0, 0.0, 0.0]]) * Settings.epsilon\n x_decoded = decoder.predict(z_sample)\n image = x_decoded[0].reshape(img_size, img_size)\n\n img = ax.imshow(image, cmap='Greys_r')\n cb = plt.colorbar(img)\n axcolor = 'lightgoldenrodyellow'\n\n ax_1 = plt.axes([0.25, 0.1, 0.65, 0.03])\n ax_2 = plt.axes([0.25, 0.15, 0.65, 0.03])\n ax_3 = plt.axes([0.25, 0.2, 0.65, 0.03])\n ax_4 = plt.axes([0.25, 0.25, 0.65, 0.03])\n\n s_1 = Slider(ax_1, '1', Settings.min_input, Settings.max_input, valinit=0)\n s_2 = Slider(ax_2, '2', Settings.min_input, Settings.max_input, valinit=0)\n s_3 = Slider(ax_3, '3', Settings.min_input, Settings.max_input, valinit=0)\n s_4 = Slider(ax_4, '4', Settings.min_input, Settings.max_input, valinit=0)\n\n def update(val):\n _f_1 = s_1.val\n _f_2 = s_2.val\n _f_3 = s_3.val\n _f_4 = s_4.val\n z_sample = np.array([[_f_1, _f_2, _f_3, _f_4]]) * Settings.epsilon\n x_decoded = decoder.predict(z_sample)\n image = x_decoded[0].reshape(img_size, img_size)\n ax.imshow(image, cmap='Greys_r')\n plt.draw()\n\n s_1.on_changed(update)\n s_2.on_changed(update)\n s_3.on_changed(update)\n s_4.on_changed(update)\n plt.show()\n\ndef showOutputOfModel(model):\n conv_output = model.predict(x_train)\n im = conv_output\n count_of_images = conv_output.shape[0]\n size = conv_output.shape[1]\n count_filters = conv_output.shape[3]\n \n figure = np.zeros((size * count_of_images, size * count_filters))\n \n for i in range(count_of_images):\n for j in range(count_filters):\n tmp_im = np.zeros((size,size))\n tmp_im[:,:] = im[i,:,:,j]\n tmp_im = tmp_im.reshape(size, size)\n figure[i * size: (i + 1) * size,\n j * size: (j + 1) * size] = tmp_im\n\n plt.figure(figsize=(10, 10))\n plt.imshow(figure, cmap='Greys_r')\n plt.show()\n\nclass VAE:\n def __init__(self):\n # CODER\n self.input_layer = Input(shape=Settings.full_img_size)\n self.conv_layer_1 = Conv2D(filters=8,\n kernel_size=(3, 3),\n padding='same',\n strides=(1, 1), \n activation='relu')(self.input_layer) # (64,64,8)\n self.conv_layer_2 = Conv2D(filters=16, \n kernel_size=(3, 3), \n padding='same',\n strides=(1, 1), \n activation='relu')(self.conv_layer_1) # (64,64,16)\n \n self.conv_layer_3 = Conv2D(filters=24,\n kernel_size=(3, 3),\n padding='same',\n strides=(2, 2), \n activation='relu')(self.conv_layer_2) # (32,32,16)\n self.flat_layer = Flatten()(self.conv_layer_3)\n self.hidden_layer = Dense(Settings.intermediate_dim, activation='relu')(self.flat_layer)\n self.z_mean = Dense(Settings.latent_dim)(self.hidden_layer)\n self.z_log_var = Dense(Settings.latent_dim)(self.hidden_layer)\n self.z = Lambda(self.sampling)([self.z_mean, self.z_log_var])\n # Just layers for decoder\n arr_size_conv_layer_4 = [i.value for i in self.conv_layer_3.shape.dims if i.value is not None]\n size_flat_layer = np.prod(arr_size_conv_layer_4)\n size_conv_layer_4 = tuple(arr_size_conv_layer_4)\n\n self.decoder_hidden = Dense(Settings.intermediate_dim, activation='relu')\n self.decoder_flat = Dense(size_flat_layer, activation='relu')\n self.decoder_reshape = Reshape(size_conv_layer_4)\n self.decoder_conv_4 = Conv2DTranspose(filters=24,\n kernel_size=(3, 3),\n padding='same',\n strides=(1, 1),\n activation='relu')\n self.decoder_conv_3 = Conv2DTranspose(filters=16,\n kernel_size=(3, 3),\n padding='same',\n strides=(2, 2),\n activation='relu')\n self.decoder_conv_2 = Conv2D(filters=1,\n kernel_size=(3, 3),\n padding='same',\n strides=(1, 1),\n activation='sigmoid') # return pixels [0..1]\n # DECODER (connected with full VAE)\n self.hidden_decoded = self.decoder_hidden(self.z)\n self.flat_decoded = self.decoder_flat(self.hidden_decoded)\n self.reshape_decoded = self.decoder_reshape(self.flat_decoded)\n self.conv_4_decoded = self.decoder_conv_4(self.reshape_decoded)\n self.conv_3_decoded = self.decoder_conv_3(self.conv_4_decoded)\n self.conv_2_decoded = self.decoder_conv_2(self.conv_3_decoded)\n\n def sampling(self, hidden_layers):\n print('sampling')\n z_mean, z_log_var = hidden_layers\n normal = K.random_normal(shape=(Settings.batch_size, Settings.latent_dim),\n mean=0.,\n stddev=Settings.epsilon)\n return z_mean + K.exp(self.z_log_var) * normal\n \n def loss(self, x, x_decoded_mean):\n x = K.flatten(x)\n x_decoded_mean = K.flatten(x_decoded_mean)\n xent_loss = Settings.full_img_size * metrics.binary_crossentropy(x, x_decoded_mean)\n kl_loss = - 0.5 * K.sum(1 + self.z_log_var - K.square(self.z_mean) - K.exp(self.z_log_var), axis=-1)\n return xent_loss + kl_loss\n \n def get_conv_model(self):\n return Model(self.input_layer, self.conv_layer_3) # conv_layer_1, conv_layer_2, conv_layer_3\n \n def get_VAE_model(self):\n return Model(self.input_layer, self.conv_2_decoded)\n \n def get_encoder_model(self):\n return Model(self.input_layer, self.z_mean)\n \n def get_decoder_model(self):\n decoder_input = Input(shape=(Settings.latent_dim,))\n hidden_decoded = self.decoder_hidden(decoder_input)\n flat_decoded = self.decoder_flat(hidden_decoded)\n reshape_decoded = self.decoder_reshape(flat_decoded)\n conv_4_decoded = self.decoder_conv_4(reshape_decoded)\n conv_3_decoded = self.decoder_conv_3(conv_4_decoded)\n conv_2_decoded = self.decoder_conv_2(conv_3_decoded)\n return Model(decoder_input, conv_2_decoded)\n\nif __name__ == \"__main__\":\n images = glob(\"/Users/Maria/Documents/input_faces/test/*.jpg\")\n load_x_train = [load_image(image) for image in images]\n x_train = np.concatenate(load_x_train)\n \n vae = VAE()\n vae_model = vae.get_VAE_model()\n encoder = vae.get_encoder_model()\n decoder = vae.get_decoder_model()\n \n sgd = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n vae_model.compile(optimizer=\"rmsprop\", loss=vae.loss)\n vae_model.summary() # log\n vae_model.fit(x_train, x_train,\n shuffle=True,\n epochs=Settings.num_of_epoch,\n batch_size=Settings.batch_size,\n validation_data=(x_train, x_train))\n \n plot_model(vae_model, to_file='/Users/Maria/Documents/FaceTransfer/keras_new_version/convolutional_layers/vae_model.png', show_shapes=True)\n plot_model(encoder, to_file='/Users/Maria/Documents/FaceTransfer/keras_new_version/convolutional_layers/encoder.png', show_shapes=True)\n plot_model(decoder, to_file='/Users/Maria/Documents/FaceTransfer/keras_new_version/convolutional_layers/decoder.png', show_shapes=True)\n \n# # Show output of conv layer\n# conv_model = vae.get_conv_model()\n# showOutputOfModel(conv_model)\n#\n# # Show decoder output\n show_images(decoder, n=20)\n\n","repo_name":"Ionchenkova/FaceTransfer","sub_path":"keras_new_version/convolutional_layers/convolutional_VAE.py","file_name":"convolutional_VAE.py","file_ext":"py","file_size_in_byte":9223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"35648122769","text":"from .imports import *\n\n\n@csrf_exempt\ndef save_notification(request):\n if request.method == 'POST':\n if 'description' and 'title' in request.POST:\n try:\n Notifications.objects.get(title=request.POST['title'],\n description=request.POST['dec']\n )\n flag = True\n except Lesson.DoesNotExist:\n flag = False\n if flag:\n return JsonResponse({'status': 'failed'}, status=HTTP_200_OK)\n notification = Notifications.objects.create(title=request.POST['title'],\n description=request.POST['dec']\n )\n notification.save_lesson()\n return JsonResponse({'status': 'success'}, status=HTTP_200_OK)\n else:\n return HttpResponse('fuck!')\n","repo_name":"zamoosh/organizationOfEducatoin","sub_path":"lesson/views/save_notification.py","file_name":"save_notification.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33659360039","text":"import requests\n\napp_id = \"b4d57fd5\"\napp_key = \"0f676a46ae61ac6e50972ce3dd815ab8\"\nlanguage = \"en-gb\"\nword_id = \"car\"\nurl = \"https://od-api.oxforddictionaries.com:443/api/v2/entries/\" + language + \"/\" + word_id.lower()\nr = requests.get(url, headers={\"app_id\": app_id, \"app_key\": app_key})\n\nprint(r)\n","repo_name":"dear6846/python_education","sub_path":"transleter/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17848527356","text":"from django.views.generic.list_detail import object_list, object_detail\n\n\ndef limited_object_detail (request, queryset, *args, **kwargs):\n if request.user.is_anonymous() :\n queryset = queryset.model.public_objects\n return object_detail(request, queryset, *args, **kwargs)\n\n\n#def limited_object_list (*args, **kwargs):\n #return object_list (*args, **kwargs)\n#limited_object_list = public_objects_list (limited_object_list)\n \n#def limited_object_detail (*args, **kwargs):\n #return object_detail (*args, **kwargs)\n#limited_object_detail = public_objects_detail (limited_object_detail)\n \n\n\ndef articles_on_section (request, queryset, object_id=None, slug=None,\n slug_field=None, *args, **kwargs):\n \"\"\"Gets section slug; returns articles over which the user has visibility.\n \n Returns all articles in setion if the user is authenticaded, or just\n public articles.\n \"\"\"\n if request.user.is_anonymous() :\n #kwargs['queryset'] = kwargs['queryset'].filter(**lookup)\n queryset = queryset.filter(status='pbl').filter(section__slug__exact=slug)\n else:\n queryset = queryset.filter(section__slug__exact=slug)\n return object_list (request, queryset, *args, **kwargs)","repo_name":"funollet/lieder-web","sub_path":"articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"11050558251","text":"from rest_framework.viewsets import ViewSet\nfrom orders.models import Order\nfrom rest_framework.response import Response\nimport stripe\nfrom decimal import Decimal\nfrom rest_framework import status\nfrom django.conf import settings\nfrom rest_framework.decorators import action\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom .tasks import payment_completed\nfrom cart.cart import Cart\nfrom orders.serializers import OrderSerializer\n\n\n\n\nclass PaymentViewSet(ViewSet):\n http_method_names = ('post')\n\n\n # create a checkout session\n @action(methods=['post'], detail=False)\n def check_out_session(self, request):\n try:\n order_id = request.session.get('order_id', None)\n except KeyError:\n return Response(\"No order_id was found in the current session\",\n status=status.HTTP_400_BAD_REQUEST)\n\n\n try:\n order = Order.objects.get(id=order_id)\n except Order.DoesNotExist:\n return Response(\"No order was found with the given order_id\",\n status=status.HTTP_400_BAD_REQUEST)\n\n # Stripe checkout session data\n session_data = {\n 'mode': 'payment', #We use payment for a one-time payment.\n 'client_reference_id': order.id, #The unique reference for this payment.\n 'success_url': \"https://funnyshop.hamzabakkour.se/success\", #: The URL for Stripe to redirect the user to if the payment is successful.\n 'cancel_url': \"https://funnyshop.hamzabakkour.se/failed\",\n 'line_items': []\n }\n\n for item in order.items.all():\n session_data['line_items'].append({\n 'price_data': {\n 'unit_amount': int(item.price * Decimal('100')), #[1]\n 'currency': 'sek',\n 'product_data': {\n 'name': item.product.name,\n },\n },\n 'quantity': item.quantity,\n })\n\n\n # create the Stripe instance\n stripe.api_key = settings.STRIPE_SECRET_KEY\n stripe.api_version = settings.STRIPE_API_VERSION\n\n session = stripe.checkout.Session.create(**session_data)\n\n return Response(session.url, status=status.HTTP_201_CREATED)\n \n \n @csrf_exempt\n @action(methods=['post'], detail=False)\n def webhook(self, request):\n payload = request.body\n sig_header = request.META['HTTP_STRIPE_SIGNATURE']\n event = None\n\n try:\n event = stripe.Webhook.construct_event(\n payload,\n sig_header,\n settings.STRIPE_WEBHOOK_SECRET)\n except ValueError as e:\n # Invalid payload\n return HttpResponse(status=400)\n except stripe.error.SignatureVerificationError as e:\n # Invalid signature\n return HttpResponse(status=400)\n\n if event.type == 'checkout.session.completed':\n session = event.data.object\n if session.mode == 'payment' and session.payment_status == 'paid':\n try:\n order = Order.objects.get(id=session.client_reference_id)\n except Order.DoesNotExist:\n return HttpResponse(status=404)\n # mark order as paid\n order.paid = True\n # store Stripe payment ID\n order.stripe_id = session.payment_intent\n order.save()\n # launch asynchronous task\n payment_completed.delay(order.id)\n return HttpResponse(status=200)\n","repo_name":"HamzaBakkour/funnyShopAPI_aws_2","sub_path":"funnyshopAPI/payment/viewsets.py","file_name":"viewsets.py","file_ext":"py","file_size_in_byte":3675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"26102536634","text":"from . import METRICS\nimport numpy as np\nfrom jlcv.average_meter import MetricMeter\n\n@METRICS.register_module()\nclass Accuracy(object):\n def __init__(self) -> None:\n self.meter = MetricMeter()\n def __call__(self, preds, labels, category=None):\n \"\"\"\n Args:\n preds: after log_softmax\n \"\"\"\n preds_choice = preds.max(1)[1]\n instance_correct = preds_choice.eq(labels.data).cpu().sum().item()\n instance_accurcy = instance_correct / preds.shape[0]\n self.meter.update({'instance_accurcy': instance_accurcy})\n\n if category:\n for cat in np.unique(labels.cpu()):\n cat = cat.item()\n class_correct = preds_choice[labels == cat].eq(labels[labels == cat].long().data).cpu().sum().item()\n class_accurcy = class_correct / float(preds[labels == cat].shape[0])\n self.meter.update({f'class_{cat}_accurcy': class_accurcy})\n \n \n\n\n ","repo_name":"ljj621/cv","sub_path":"jlcv/metrics/accuracy.py","file_name":"accuracy.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"26201149348","text":"def group_equal(els):\n from itertools import groupby\n # Return a list of consecutive groups\n return [\n # Make each consecutive groups into a list\n list(g)\n for k, g in groupby(els)\n ]\n\n\n'''\nChekio Mission Link:\nhttps://py.checkio.org/en/mission/group-equal-consecutive/\nChekio Solution Link:\nhttps://py.checkio.org/mission/group-equal-consecutive/publications/jcfernandez/python-3/only-one-line/share/6b73d13c60d38396f1cd70d241b3ef5d/\nChekio Profile Link:\nhttps://py.checkio.org/user/jcfernandez/solutions/share/83d63afe87a24e810571c961a5f66dfb/\n'''\n\nif __name__ == '__main__':\n print(\"Example:\")\n print(group_equal([1, 1, 4, 4, 4, \"hello\", \"hello\", 4]))\n\n # These \"asserts\" are used for self-checking and not for an auto-testing\n assert group_equal([1, 1, 4, 4, 4, \"hello\", \"hello\", 4]) == [[1, 1], [4, 4, 4], [\"hello\", \"hello\"], [4]]\n assert group_equal([1, 2, 3, 4]) == [[1], [2], [3], [4]]\n assert group_equal([1]) == [[1]]\n assert group_equal([]) == []\n print(\"Coding complete? Click 'Check' to earn cool rewards!\")\n","repo_name":"jcfernandez-890825/checkio","sub_path":"src/group-equal-consecutive.py","file_name":"group-equal-consecutive.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"314867866","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('about/', views.about, name='about'),\n path('delete/', views.delete, name='delete'), #list_id is from 0001_initial.py from migrations folder, it is the id of the item that is being deleted, this id will be passed into the url when we delete it, once the url changes it will trigger views.py for the delete function.\n path('cross_off/', views.cross_off, name='cross_off'),\n path('uncross/', views.uncross, name='uncross'),\n path('edit/', views.edit, name='edit'),\n]\n\n\n\n\n\n\n","repo_name":"mohman23/Python-To-Do-WebApp","sub_path":"todo_list/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42310903072","text":"from django.shortcuts import render, redirect\nfrom .models import Employee, Department, Training\nfrom django.shortcuts import render, get_object_or_404\n\n# Create your views here.\ndef index_page(request):\n return render(request, 'employees/employees_list.html')\n\ndef update(request, id):\n # ポストの値をそれぞれ受け取る\n if request.method == 'POST':\n name = request.POST['name']\n employee_id = request.POST['employee_id']\n department = request.POST['department']\n training = request.POST['training']\n # フィルターで更新対象のデータを取得し、updateで更新する\n Employee.objects.filter(employee_id=id).update(name=name, employee_id=employee_id, department=Department.objects.get(id=department), training=Training.objects.get(id=training))\n return redirect('/emp')\n # ポスト送信でなければ、更新前のデータを表示する\n else:\n employee= get_object_or_404(Employee, employee_id=id)\n department_list = Department.objects.all()\n training_list = Training.objects.all()\n context = {\n 'employee': employee,\n 'department_list': department_list,\n 'training_list': training_list,\n }\n return render(request, 'employees/employees_update.html' , context)\n\n","repo_name":"kananilai/django_employee","sub_path":"employees/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72923012888","text":"from wordcloud import WordCloud\r\nimport jieba\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef word_cloud_creation(filename):\r\n '''创建词云，并进行分词'''\r\n text = open(filename, 'rb').read()\r\n word_list = jieba.cut(text, cut_all=True)\r\n wl = ' '.join(word_list)\r\n return wl\r\n\r\n\r\ndef word_cloud_settings():\r\n '''设置词云的属性'''\r\n wc = WordCloud(\r\n background_color='white',\r\n max_words=2000,\r\n max_font_size=100,\r\n height=1200,\r\n width=1500,\r\n random_state=30,\r\n font_path='C:\\Windows\\Fonts\\simfang.ttf'\r\n )\r\n return wc\r\n\r\n\r\ndef word_cloud_implementation(wl, wc):\r\n '''生成词云，并展示'''\r\n my_words = wc.generate(wl)\r\n plt.imshow(my_words)\r\n plt.axis('off')\r\n wc.to_file(f'./LiZiQi/word_cloud.png')\r\n plt.show()\r\n\r\n\r\nif __name__ == '__main__':\r\n wl = word_cloud_creation('LiZiQiComments.csv')\r\n wc = word_cloud_settings()\r\n word_cloud_implementation(wl, wc)\r\n","repo_name":"zjxi/crawler_collection","sub_path":"微博评论数据抓取及情感分析/WordCloud.py","file_name":"WordCloud.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"13643613511","text":"import collections\n\nEntry = collections.namedtuple(\"Entry\", [\"param_1\", \"param_2\", \"char\", \"value\"])\n\n\ndef line_to_entry(line):\n key, value = line.split(\": \")\n params, char = key.split(\" \")\n param_1, param_2 = params.split(\"-\")\n return Entry(int(param_1), int(param_2), char, value.rstrip())\n\n\ndef validate_1(entry):\n return entry.param_1 <= entry.value.count(entry.char) <= entry.param_2\n\n\ndef validate_2(entry):\n first_match = entry.value[entry.param_1 - 1] == entry.char\n second_match = entry.value[entry.param_2 - 1] == entry.char\n return first_match ^ second_match\n\n\nresult_1, result_2 = 0, 0\n\nwith open(\"02.txt\") as _input:\n for line in _input:\n entry = line_to_entry(line)\n result_1 += validate_1(entry)\n result_2 += validate_2(entry)\n\nprint(result_1, result_2)\n","repo_name":"rdkr/advent-of-code","sub_path":"2020/02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31316843572","text":"from fastapi import FastAPI, HTTPException\r\nfrom pydantic import BaseModel\r\n\r\napp = FastAPI()\r\n\r\nitems = {\"foo\": \"The Foo Wrestlers\"}\r\n\r\n#retornar un mensaje personal en caso de que el client eingrese un id que no existe\r\n@app.get(\"/items/{item_id}\")\r\nasync def read_item(item_id: str):\r\n if item_id not in items:\r\n raise HTTPException(status_code=404, detail=\"Item not found\")\r\n return {\"item\": items[item_id]}\r\n\r\n\r\n\r\nclass Item(BaseModel):\r\n name: str\r\n description: str | None = None\r\n price: float\r\n tax: float | None = None\r\n tags: set[str] = set()\r\n\r\n\r\n@app.post(\"/items/\", response_model=Item, tags=[\"items\"])\r\nasync def create_item(item: Item):\r\n return item\r\n\r\n\r\n@app.get(\"/items/\", tags=[\"items\"])\r\nasync def read_items():\r\n return [{\"name\": \"Foo\", \"price\": 42}]\r\n\r\n\r\n@app.get(\"/users/\", tags=[\"users\"])\r\nasync def read_users():\r\n return [{\"username\": \"johndoe\"}]","repo_name":"Melissaguirre/FastApi_Examples","sub_path":"FastApi/HTTPException.py","file_name":"HTTPException.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33147399245","text":"ch='y'\r\nwhile (ch=='y') or (ch=='Y'):\r\n n=input(\"Enter a number:\")\r\n print(n[::-1])\r\n ch=input(\"Do you want more y/n:\")\r\n while (ch!='y') or (ch!='Y'):\r\n if (ch=='n') or (ch=='N'):\r\n print(\"Goodbye\")\r\n break\r\n else:\r\n while (ch!='y') or (ch!='Y') or (ch!='n') or (ch!='N'):\r\n print(\"Wrong input\")\r\n break\r\n ch=input(\"Do you want more y/n:\")\r\nprint(\"Thanks for testing\")\r\n","repo_name":"PamperedGenius6098/reverse-number","sub_path":"reverse-number.py","file_name":"reverse-number.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36631532970","text":"import re\nfrom pyrogram import filters\nfrom pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup\nfrom FilterBot.filterbot import app\n\nverify={}\n\n@app.on_message(filters.command([\"settings\"]) & filters.group, group=1)\nasync def settings(bot, update):\n \n chat_id = update.chat.id\n chat_name = remove_emoji((update.chat.title)[:38])\n\n if update.from_user:\n user_id = update.from_user.id\n try:\n user_info = await bot.get_chat_member(chat_id, user_id)\n except Exception:\n return\n \n if user_info.status == (\"member\"):\n return\n \n verify[str(update.message_id)] = user_id\n\n bot_status = await bot.get_me()\n bot_fname= bot_status.first_name\n \n text =f\"**{bot_fname}'s Settings Pannel.**\\n\"\n text+=f\"\\n`You Can Use This Menu To Change Connectivity And Know Status Of Your Every Connected Channel, Change Filter Types, Configure Filter Results And To Know Status Of Your Group.`\"\n \n buttons = [\n [\n InlineKeyboardButton\n (\n \"Channels\", callback_data=f\"channel_list({chat_id}|{chat_name})\"\n ), \n \n InlineKeyboardButton\n (\n \"Filter Types\", callback_data=f\"types({chat_id}|{chat_name})\"\n )\n ],\n [\n InlineKeyboardButton\n (\n \"Configure\", callback_data=f\"config({chat_id}|{chat_name})\"\n )\n ], \n [\n InlineKeyboardButton\n (\n \"Status\", callback_data=f\"status({chat_id}|{chat_name})\"\n ),\n \n InlineKeyboardButton\n (\n \"About\", callback_data=f\"about({chat_id})\"\n )\n ],\n [\n InlineKeyboardButton\n (\n \"Close\", callback_data=\"close\"\n )\n ]\n ]\n \n reply_markup = InlineKeyboardMarkup(buttons)\n \n await bot.send_message (\n \n chat_id=chat_id, \n text=text, \n reply_markup=reply_markup, \n parse_mode=markdown,\n reply_to_message_id=update.message_id\n \n )\n\n\ndef remove_emoji(string):\n emoji_pattern = re.compile(\"[\"\n u\"\\U0001F600-\\U0001F64F\" \n u\"\\U0001F300-\\U0001F5FF\"\n u\"\\U0001F680-\\U0001F6FF\" \n u\"\\U0001F1E0-\\U0001F1FF\" \n u\"\\U00002500-\\U00002BEF\" \n u\"\\U00002702-\\U000027B0\"\n u\"\\U00002702-\\U000027B0\"\n u\"\\U000024C2-\\U0001F251\"\n u\"\\U0001f926-\\U0001f937\"\n u\"\\U00010000-\\U0010ffff\"\n u\"\\u2640-\\u2642\"\n u\"\\u2600-\\u2B55\"\n u\"\\u200d\"\n u\"\\u23cf\"\n u\"\\u23e9\"\n u\"\\u231a\"\n u\"\\ufe0f\"\n u\"\\u3030\"\n \"]+\", flags=re.UNICODE)\n \n return emoji_pattern.sub(r' ', string)\n","repo_name":"swatv3nub/FilterBot","sub_path":"FilterBot/plugins/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16121923871","text":"import binascii\nimport logging\nimport os\nimport sys\nimport time\nimport traceback\nfrom datetime import datetime\n\nimport click\nfrom flask import current_app\nfrom flask.cli import FlaskGroup, ScriptInfo, with_appcontext\nfrom flask_alembic import alembic_click\nfrom jinja2 import Environment, FileSystemLoader\nfrom sqlalchemy_utils.functions import database_exists\nfrom werkzeug.utils import import_string\n\nfrom flaskbb import create_app\nfrom flaskbb.cli.utils import (\n EmailType,\n FlaskBBCLIError,\n get_version,\n prompt_config_path,\n prompt_save_user,\n write_config,\n)\nfrom flaskbb.extensions import alembic, celery, db, whooshee\nfrom flaskbb.utils.populate import (\n create_default_groups,\n create_default_settings,\n create_latest_db,\n create_test_data,\n create_welcome_forum,\n insert_bulk_data,\n run_plugin_migrations,\n update_settings_from_fixture,\n)\nfrom flaskbb.utils.translations import compile_translations\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass FlaskBBGroup(FlaskGroup):\n def __init__(self, *args, **kwargs):\n super(FlaskBBGroup, self).__init__(*args, **kwargs)\n self._loaded_flaskbb_plugins = False\n\n def _load_flaskbb_plugins(self, ctx):\n if self._loaded_flaskbb_plugins:\n return\n\n try:\n app = ctx.ensure_object(ScriptInfo).load_app()\n app.pluggy.hook.flaskbb_cli(cli=self, app=app)\n self._loaded_flaskbb_plugins = True\n except Exception:\n logger.error(\n \"Error while loading CLI Plugins\", exc_info=traceback.format_exc()\n )\n else:\n shell_context_processors = app.pluggy.hook.flaskbb_shell_context()\n for p in shell_context_processors:\n app.shell_context_processor(p)\n\n def get_command(self, ctx, name):\n self._load_flaskbb_plugins(ctx)\n return super(FlaskBBGroup, self).get_command(ctx, name)\n\n def list_commands(self, ctx):\n self._load_flaskbb_plugins(ctx)\n return super(FlaskBBGroup, self).list_commands(ctx)\n\n\ndef make_app():\n ctx = click.get_current_context(silent=True)\n script_info = None\n if ctx is not None:\n script_info = ctx.obj\n\n config_file = getattr(script_info, \"config_file\", None)\n instance_path = getattr(script_info, \"instance_path\", None)\n return create_app(config_file, instance_path)\n\n\ndef set_config(ctx, param, value):\n \"\"\"This will pass the config file to the create_app function.\"\"\"\n ctx.ensure_object(ScriptInfo).config_file = value\n\n\ndef set_instance(ctx, param, value):\n \"\"\"This will pass the instance path on the script info which can then\n be used in 'make_app'.\"\"\"\n ctx.ensure_object(ScriptInfo).instance_path = value\n\n\n@click.group(\n cls=FlaskBBGroup,\n create_app=make_app,\n add_version_option=False,\n invoke_without_command=True,\n)\n@click.option(\n \"--config\",\n expose_value=False,\n callback=set_config,\n required=False,\n is_flag=False,\n is_eager=True,\n metavar=\"CONFIG\",\n help=\"Specify the config to use either in dotted module \"\n \"notation e.g. 'flaskbb.configs.default.DefaultConfig' \"\n \"or by using a path like '/path/to/flaskbb.cfg'\",\n)\n@click.option(\n \"--instance\",\n expose_value=False,\n callback=set_instance,\n required=False,\n is_flag=False,\n is_eager=True,\n metavar=\"PATH\",\n help=\"Specify the instance path to use. By default the folder \"\n \"'instance' next to the package or module is assumed to \"\n \"be the instance path.\",\n)\n@click.option(\n \"--version\",\n expose_value=False,\n callback=get_version,\n is_flag=True,\n is_eager=True,\n help=\"Show the FlaskBB version.\",\n)\n@click.pass_context\ndef flaskbb(ctx):\n \"\"\"This is the commandline interface for flaskbb.\"\"\"\n if ctx.invoked_subcommand is None:\n # show the help text instead of an error\n # when just '--config' option has been provided\n click.echo(ctx.get_help())\n\n\nflaskbb.add_command(alembic_click, \"db\")\n\n\n@flaskbb.command()\n@click.option(\n \"--welcome\", \"-w\", default=True, is_flag=True, help=\"Disable the welcome forum.\"\n)\n@click.option(\n \"--force\", \"-f\", default=False, is_flag=True, help=\"Doesn't ask for confirmation.\"\n)\n@click.option(\"--username\", \"-u\", help=\"The username of the user.\")\n@click.option(\"--email\", \"-e\", type=EmailType(), help=\"The email address of the user.\")\n@click.option(\"--password\", \"-p\", help=\"The password of the user.\")\n@click.option(\n \"--no-plugins\",\n \"-n\",\n default=False,\n is_flag=True,\n help=\"Don't run the migrations for the default plugins.\",\n)\n@with_appcontext\ndef install(welcome, force, username, email, password, no_plugins):\n \"\"\"Installs flaskbb. If no arguments are used, an interactive setup\n will be run.\n \"\"\"\n if not current_app.config[\"CONFIG_PATH\"]:\n click.secho(\n \"[!] No 'flaskbb.cfg' config found. \"\n \"You can generate a configuration file with 'flaskbb makeconfig'.\",\n fg=\"red\",\n )\n sys.exit(1)\n\n click.secho(\"[+] Installing FlaskBB...\", fg=\"cyan\")\n if database_exists(db.engine.url):\n if force or click.confirm(\n click.style(\n \"Existing database found. Do you want to delete the old one and \"\n \"create a new one?\",\n fg=\"magenta\",\n )\n ):\n db.drop_all()\n else:\n sys.exit(0)\n\n # creating database from scratch and 'stamping it'\n create_latest_db()\n\n click.secho(\"[+] Creating default settings...\", fg=\"cyan\")\n create_default_groups()\n create_default_settings()\n\n click.secho(\"[+] Creating admin user...\", fg=\"cyan\")\n prompt_save_user(username, email, password, \"admin\")\n\n if welcome:\n click.secho(\"[+] Creating welcome forum...\", fg=\"cyan\")\n create_welcome_forum()\n\n if not no_plugins:\n click.secho(\"[+] Installing default plugins...\", fg=\"cyan\")\n run_plugin_migrations()\n\n click.secho(\"[+] Compiling translations...\", fg=\"cyan\")\n compile_translations()\n\n click.secho(\"[+] FlaskBB has been successfully installed!\", fg=\"green\", bold=True)\n\n\n@flaskbb.command()\n@click.option(\n \"--test-data\", \"-t\", default=False, is_flag=True, help=\"Adds some test data.\"\n)\n@click.option(\n \"--bulk-data\", \"-b\", default=False, is_flag=True, help=\"Adds a lot of data.\"\n)\n@click.option(\n \"--posts\",\n default=100,\n help=\"Number of posts to create in each topic (default: 100).\",\n)\n@click.option(\n \"--topics\", default=100, help=\"Number of topics to create (default: 100).\"\n)\n@click.option(\n \"--force\", \"-f\", is_flag=True, help=\"Will delete the database before populating it.\"\n)\n@click.option(\n \"--initdb\",\n \"-i\",\n is_flag=True,\n help=\"Initializes the database before populating it.\",\n)\ndef populate(bulk_data, test_data, posts, topics, force, initdb):\n \"\"\"Creates the necessary tables and groups for FlaskBB.\"\"\"\n if force:\n click.secho(\"[+] Recreating database...\", fg=\"cyan\")\n db.drop_all()\n\n # do not initialize the db if -i is passed\n if not initdb:\n create_latest_db()\n\n if initdb:\n click.secho(\"[+] Initializing database...\", fg=\"cyan\")\n create_latest_db()\n run_plugin_migrations()\n\n if test_data:\n click.secho(\"[+] Adding some test data...\", fg=\"cyan\")\n create_test_data()\n\n if bulk_data:\n click.secho(\"[+] Adding a lot of test data...\", fg=\"cyan\")\n timer = time.time()\n rv = insert_bulk_data(int(topics), int(posts))\n if not rv and not test_data:\n create_test_data()\n rv = insert_bulk_data(int(topics), int(posts))\n elapsed = time.time() - timer\n click.secho(\n \"[+] It took {:.2f} seconds to create {} topics and {} \"\n \"posts.\".format(elapsed, rv[0], rv[1]),\n fg=\"cyan\",\n )\n\n # this just makes the most sense for the command name; use -i to\n # init the db as well\n if not test_data and not bulk_data:\n click.secho(\"[+] Populating the database with some defaults...\", fg=\"cyan\")\n create_default_groups()\n create_default_settings()\n\n\n@flaskbb.command()\ndef reindex():\n \"\"\"Reindexes the search index.\"\"\"\n click.secho(\"[+] Reindexing search index...\", fg=\"cyan\")\n whooshee.reindex()\n\n\n@flaskbb.command()\n@click.option(\n \"all_latest\",\n \"--all\",\n \"-a\",\n default=False,\n is_flag=True,\n help=\"Upgrades migrations AND fixtures to the latest version.\",\n)\n@click.option(\n \"--fixture/\",\n \"-f\",\n default=None,\n help=\"The fixture which should be upgraded or installed.\",\n)\n@click.option(\n \"--force\", default=False, is_flag=True, help=\"Forcefully upgrades the fixtures.\"\n)\ndef upgrade(all_latest, fixture, force):\n \"\"\"Updates the migrations and fixtures.\"\"\"\n if all_latest:\n click.secho(\"[+] Upgrading migrations to the latest version...\", fg=\"cyan\")\n alembic.upgrade()\n\n if fixture or all_latest:\n try:\n settings = import_string(\"flaskbb.fixtures.{}\".format(fixture))\n settings = settings.fixture\n except ImportError:\n raise FlaskBBCLIError(\n \"{} fixture is not available\".format(fixture), fg=\"red\"\n )\n\n click.secho(\"[+] Updating fixtures...\", fg=\"cyan\")\n count = update_settings_from_fixture(\n fixture=settings, overwrite_group=force, overwrite_setting=force\n )\n click.secho(\n \"[+] {settings} settings in {groups} setting groups \"\n \"updated.\".format(\n groups=len(count),\n settings=sum(len(settings) for settings in count.values()),\n ),\n fg=\"green\",\n )\n\n\n@flaskbb.command(\n \"celery\",\n add_help_option=False,\n context_settings={\"ignore_unknown_options\": True, \"allow_extra_args\": True},\n)\n@click.pass_context\n@with_appcontext\ndef start_celery(ctx):\n \"\"\"Preconfigured wrapper around the 'celery' command.\"\"\"\n celery.start(ctx.args)\n\n\n@flaskbb.command(\"shell\", short_help=\"Runs a shell in the app context.\")\n@with_appcontext\ndef shell_command():\n \"\"\"Runs an interactive Python shell in the context of a given\n Flask application. The application will populate the default\n namespace of this shell according to it\"s configuration.\n This is useful for executing small snippets of management code\n without having to manually configuring the application.\n\n This code snippet is taken from Flask\"s cli module and modified to\n run IPython and falls back to the normal shell if IPython is not\n available.\n \"\"\"\n import code\n\n banner = \"Python %s on %s\\nInstance Path: %s\" % (\n sys.version,\n sys.platform,\n current_app.instance_path,\n )\n ctx = {\"db\": db}\n\n # Support the regular Python interpreter startup script if someone\n # is using it.\n startup = os.environ.get(\"PYTHONSTARTUP\")\n if startup and os.path.isfile(startup):\n with open(startup, \"r\") as f:\n eval(compile(f.read(), startup, \"exec\"), ctx)\n\n ctx.update(current_app.make_shell_context())\n\n try:\n import IPython\n from traitlets.config import get_config\n\n c = get_config()\n # This makes the prompt to use colors again\n c.InteractiveShellEmbed.colors = \"Linux\"\n IPython.embed(config=c, banner1=banner, user_ns=ctx)\n except ImportError:\n code.interact(banner=banner, local=ctx)\n\n\n@flaskbb.command(\"urls\", short_help=\"Show routes for the app.\")\n@click.option(\n \"--route\", \"-r\", \"order_by\", flag_value=\"rule\", default=True, help=\"Order by route\"\n)\n@click.option(\n \"--endpoint\", \"-e\", \"order_by\", flag_value=\"endpoint\", help=\"Order by endpoint\"\n)\n@click.option(\n \"--methods\", \"-m\", \"order_by\", flag_value=\"methods\", help=\"Order by methods\"\n)\n@with_appcontext\ndef list_urls(order_by):\n \"\"\"Lists all available routes.\"\"\"\n from flask import current_app\n\n rules = sorted(\n current_app.url_map.iter_rules(), key=lambda rule: getattr(rule, order_by)\n )\n\n max_rule_len = max(len(rule.rule) for rule in rules)\n max_rule_len = max(max_rule_len, len(\"Route\"))\n\n max_endpoint_len = max(len(rule.endpoint) for rule in rules)\n max_endpoint_len = max(max_endpoint_len, len(\"Endpoint\"))\n\n max_method_len = max(len(\", \".join(rule.methods)) for rule in rules)\n max_method_len = max(max_method_len, len(\"Methods\"))\n\n column_header_len = max_rule_len + max_endpoint_len + max_method_len + 4\n column_template = \"{:<%s} {:<%s} {:<%s}\" % (\n max_rule_len,\n max_endpoint_len,\n max_method_len,\n )\n\n click.secho(\n column_template.format(\"Route\", \"Endpoint\", \"Methods\"), fg=\"blue\", bold=True\n )\n click.secho(\"=\" * column_header_len, bold=True)\n\n for rule in rules:\n methods = \", \".join(rule.methods)\n click.echo(column_template.format(rule.rule, rule.endpoint, methods))\n\n\n@flaskbb.command(\"makeconfig\")\n@click.option(\n \"--development\",\n \"-d\",\n default=False,\n is_flag=True,\n help=\"Creates a development config with DEBUG set to True.\",\n)\n@click.option(\n \"--output\",\n \"-o\",\n required=False,\n help=\"The path where the config file will be saved at. \"\n \"Defaults to the flaskbb's root folder.\",\n)\n@click.option(\n \"--force\",\n \"-f\",\n default=False,\n is_flag=True,\n help=\"Overwrite any existing config file if one exists.\",\n)\ndef generate_config(development, output, force):\n \"\"\"Generates a FlaskBB configuration file.\"\"\"\n config_env = Environment(\n loader=FileSystemLoader(os.path.join(current_app.root_path, \"configs\"))\n )\n config_template = config_env.get_template(\"config.cfg.template\")\n\n if output:\n config_path = os.path.abspath(output)\n else:\n config_path = os.path.dirname(current_app.root_path)\n\n if os.path.exists(config_path) and not os.path.isfile(config_path):\n config_path = os.path.join(config_path, \"flaskbb.cfg\")\n\n # An override to handle database location paths on Windows environments\n database_path = \"sqlite:///\" + os.path.join(\n os.path.dirname(current_app.instance_path), \"flaskbb.sqlite\"\n )\n if os.name == \"nt\":\n database_path = database_path.replace(\"\\\\\", r\"\\\\\")\n\n default_conf = {\n \"is_debug\": False,\n \"server_name\": \"example.org\",\n \"use_https\": True,\n \"database_uri\": database_path,\n \"redis_enabled\": False,\n \"redis_uri\": \"redis://localhost:6379\",\n \"mail_server\": \"localhost\",\n \"mail_port\": 25,\n \"mail_use_tls\": False,\n \"mail_use_ssl\": False,\n \"mail_username\": \"\",\n \"mail_password\": \"\",\n \"mail_sender_name\": \"FlaskBB Mailer\",\n \"mail_sender_address\": \"noreply@yourdomain\",\n \"mail_admin_address\": \"admin@yourdomain\",\n \"secret_key\": binascii.hexlify(os.urandom(24)).decode(),\n \"csrf_secret_key\": binascii.hexlify(os.urandom(24)).decode(),\n \"timestamp\": datetime.utcnow().strftime(\"%A, %d. %B %Y at %H:%M\"),\n \"log_config_path\": \"\",\n \"deprecation_level\": \"default\",\n }\n\n if not force:\n config_path = prompt_config_path(config_path)\n\n if force and os.path.exists(config_path):\n click.secho(\n \"Overwriting existing config file: {}\".format(config_path), fg=\"yellow\"\n )\n\n if development:\n default_conf[\"is_debug\"] = True\n default_conf[\"use_https\"] = False\n default_conf[\"server_name\"] = \"localhost:5000\"\n write_config(default_conf, config_template, config_path)\n sys.exit(0)\n\n # SERVER_NAME\n click.secho(\n \"The name and port number of the exposed server.\\n\"\n \"If FlaskBB is accesible on port 80 you can just omit the \"\n \"port.\\n For example, if FlaskBB is accessible via \"\n \"example.org:8080 than this is also what you would set here.\",\n fg=\"cyan\",\n )\n default_conf[\"server_name\"] = click.prompt(\n click.style(\"Server Name\", fg=\"magenta\"),\n type=str,\n default=default_conf.get(\"server_name\"),\n )\n\n # HTTPS or HTTP\n click.secho(\"Is HTTPS (recommended) or HTTP used for to serve FlaskBB?\", fg=\"cyan\")\n default_conf[\"use_https\"] = click.confirm(\n click.style(\"Use HTTPS?\", fg=\"magenta\"), default=default_conf.get(\"use_https\")\n )\n\n # SQLALCHEMY_DATABASE_URI\n click.secho(\n \"For Postgres use:\\n\"\n \" postgresql://flaskbb@localhost:5432/flaskbb\\n\"\n \"For more options see the SQLAlchemy docs:\\n\"\n \" http://docs.sqlalchemy.org/en/latest/core/engines.html\",\n fg=\"cyan\",\n )\n default_conf[\"database_uri\"] = click.prompt(\n click.style(\"Database URI\", fg=\"magenta\"),\n default=default_conf.get(\"database_uri\"),\n )\n\n # REDIS_ENABLED\n click.secho(\n \"Redis will be used for things such as the task queue, \"\n \"caching and rate limiting.\",\n fg=\"cyan\",\n )\n default_conf[\"redis_enabled\"] = click.confirm(\n click.style(\"Would you like to use redis?\", fg=\"magenta\"), default=True\n ) # default_conf.get(\"redis_enabled\") is False\n\n # REDIS_URI\n if default_conf.get(\"redis_enabled\", False):\n default_conf[\"redis_uri\"] = click.prompt(\n click.style(\"Redis URI\", fg=\"magenta\"),\n default=default_conf.get(\"redis_uri\"),\n )\n else:\n default_conf[\"redis_uri\"] = \"\"\n\n # MAIL_SERVER\n click.secho(\n \"To use 'localhost' make sure that you have sendmail or\\n\"\n \"something similar installed. Gmail is also supprted.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_server\"] = click.prompt(\n click.style(\"Mail Server\", fg=\"magenta\"),\n default=default_conf.get(\"mail_server\"),\n )\n # MAIL_PORT\n click.secho(\"The port on which the SMTP server is listening on.\", fg=\"cyan\")\n default_conf[\"mail_port\"] = click.prompt(\n click.style(\"Mail Server SMTP Port\", fg=\"magenta\"),\n default=default_conf.get(\"mail_port\"),\n )\n # MAIL_USE_TLS\n click.secho(\n \"If you are using a local SMTP server like sendmail this is \"\n \"not needed. For external servers it is required.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_use_tls\"] = click.confirm(\n click.style(\"Use TLS for sending mails?\", fg=\"magenta\"),\n default=default_conf.get(\"mail_use_tls\"),\n )\n # MAIL_USE_SSL\n click.secho(\"Same as above. TLS is the successor to SSL.\", fg=\"cyan\")\n default_conf[\"mail_use_ssl\"] = click.confirm(\n click.style(\"Use SSL for sending mails?\", fg=\"magenta\"),\n default=default_conf.get(\"mail_use_ssl\"),\n )\n # MAIL_USERNAME\n click.secho(\n \"Not needed if you are using a local smtp server.\\nFor gmail \"\n \"you have to put in your email address here.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_username\"] = click.prompt(\n click.style(\"Mail Username\", fg=\"magenta\"),\n default=default_conf.get(\"mail_username\"),\n )\n # MAIL_PASSWORD\n click.secho(\n \"Not needed if you are using a local smtp server.\\nFor gmail \"\n \"you have to put in your gmail password here.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_password\"] = click.prompt(\n click.style(\"Mail Password\", fg=\"magenta\"),\n default=default_conf.get(\"mail_password\"),\n )\n # MAIL_DEFAULT_SENDER\n click.secho(\n \"The name of the sender. You probably want to change it to \"\n \"something like ' Mailer'.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_sender_name\"] = click.prompt(\n click.style(\"Mail Sender Name\", fg=\"magenta\"),\n default=default_conf.get(\"mail_sender_name\"),\n )\n click.secho(\n \"On localhost you want to use a noreply address here. \"\n \"Use your email address for gmail here.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_sender_address\"] = click.prompt(\n click.style(\"Mail Sender Address\", fg=\"magenta\"),\n default=default_conf.get(\"mail_sender_address\"),\n )\n # ADMINS\n click.secho(\n \"Logs and important system messages are sent to this address. \"\n \"Use your email address for gmail here.\",\n fg=\"cyan\",\n )\n default_conf[\"mail_admin_address\"] = click.prompt(\n click.style(\"Mail Admin Email\", fg=\"magenta\"),\n default=default_conf.get(\"mail_admin_address\"),\n )\n\n click.secho(\n \"Optional filepath to load a logging configuration file from. \"\n \"See the Python logging documentation for more detail.\\n\"\n \"\\thttps://docs.python.org/library/logging.config.html#logging-config-fileformat\", # noqa\n fg=\"cyan\",\n )\n default_conf[\"log_config_path\"] = click.prompt(\n click.style(\"Logging Config Path\", fg=\"magenta\"),\n default=default_conf.get(\"log_config_path\"),\n )\n\n deprecation_mesg = (\n \"Warning level for deprecations. options are: \\n\"\n \"\\terror\\tturns deprecation warnings into exceptions\\n\"\n \"\\tignore\\tnever warns about deprecations\\n\"\n \"\\talways\\talways warns about deprecations even if the warning has been issued\\n\" # noqa\n \"\\tdefault\\tshows deprecation warning once per usage\\n\"\n \"\\tmodule\\tshows deprecation warning once per module\\n\"\n \"\\tonce\\tonly shows deprecation warning once regardless of location\\n\"\n \"If you are unsure, select default\\n\"\n \"for more details see: https://docs.python.org/3/library/warnings.html#the-warnings-filter\" # noqa\n )\n\n click.secho(deprecation_mesg, fg=\"cyan\")\n default_conf[\"deprecation_level\"] = click.prompt(\n click.style(\"Deperecation warning level\", fg=\"magenta\"),\n default=default_conf.get(\"deprecation_level\"),\n )\n\n write_config(default_conf, config_template, config_path)\n\n # Finished\n click.secho(\n \"The configuration file has been saved to:\\n{cfg}\\n\"\n \"Feel free to adjust it as needed.\".format(cfg=config_path),\n fg=\"blue\",\n bold=True,\n )\n click.secho(\n \"Usage: \\nflaskbb --config {cfg} run\".format(cfg=config_path), fg=\"green\"\n )\n","repo_name":"flaskbb/flaskbb","sub_path":"flaskbb/cli/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":22115,"program_lang":"python","lang":"en","doc_type":"code","stars":2460,"dataset":"github-code","pt":"31"} +{"seq_id":"2975294004","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 ('auction', '0007_product_updated_on'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='product',\n name='updated_on',\n ),\n ]\n","repo_name":"kwarodom/auction-statistic","sub_path":"auction/migrations/0008_remove_product_updated_on.py","file_name":"0008_remove_product_updated_on.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"23296595115","text":"import json\n\n\ndef shefr():\n key = \"abc\"\n infor = {\n \"open_key\": \"123456\",\n \"text\": \"Bekzod/dasturbek/dasturbek@gmail.com\",\n \"comp_name\": \"salom\",\n \"comp_key\": \"123456\"\n }\n\n if len(key) != 0:\n for x in infor:\n strt = infor[x]\n shefr = \"\"\n if len(key) > len(strt):\n key = key[0:(len(strt) - 1)]\n for i in range(len(strt)):\n shefr += str(chr((ord(strt[i]) + ord(key[i % len(key)])) % 256))\n strt = shefr\n infor[x] = strt\n\n with open(\"sample2.json\", \"w\") as outfile:\n json.dump(infor, outfile)\n\n\ndef deshefr(file):\n key = \"abc\"\n with open(file, 'r') as outfile:\n infor = json.load(outfile)\n\n if len(key) != 0:\n for x in infor:\n strt = infor[x]\n deshefr = \"\"\n if len(key) > len(strt):\n key = key[0:(len(strt) - 1)]\n for i in range(len(strt)):\n deshefr += str(chr((ord(strt[i]) - ord(key[i % len(key)])) % 256))\n strt = deshefr\n infor[x] = strt\n\n return infor\n\n\n","repo_name":"dasturbek/sign_up__sign_in__one_time_password_send_message_email","sub_path":"my_py_code/internet_and_otp/cert_authentication.py","file_name":"cert_authentication.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14750731426","text":"from santavm import SantaVM\nfrom io import StringIO\nfrom more_itertools import chunked\nimport time\n\n\ndef arcade_output(codes):\n outio = StringIO()\n m = SantaVM(codes, output_stream=outio)\n m.run_forever()\n return outio.getvalue()\n\n\ndef parse_arcade_output(output):\n nums = list(map(int, output.split('\\n')[:-1])) # last element is a space\n tiles = {(x, y): tile for [x, y, tile] in chunked(nums, 3)}\n return tiles\n\n\ncodes = [int(num) for num in open('input.txt', 'r').read().split(',')]\n\n# part 1\ntiles = parse_arcade_output(arcade_output(codes))\ncount_blocks = sum([t == 2 for t in tiles.values()])\nprint(count_blocks)\n\n\n# part 2\ndef display_screen(tiles, score):\n tile_shapes = {0: ' ', 1: '█', 2: '◉', 3: '__', 4: 'O'}\n x_coords, y_coords = [[c[dim] for c in tiles.keys()] for dim in range(2)]\n x_range, y_range = map(lambda cs: range(min(cs), max(cs)+1),\n (x_coords, y_coords))\n for y in y_range:\n for x in x_range:\n tile_id = tiles.get((x, y), 0)\n print(tile_shapes[tile_id], end='')\n print('')\n print(f'Score={score}')\n\n\ndef get_score(coords):\n score_coord = (-1, 0)\n if score_coord in coords:\n return coords.pop(score_coord)\n else:\n return None\n\n\ndef get_action(tiles):\n find_tile = lambda tile_id: [coord for coord, id in tiles.items() if id ==\n tile_id][0]\n paddle_x, _ = find_tile(3)\n ball_x, _ = find_tile(4)\n if paddle_x > ball_x:\n return -1\n elif paddle_x < ball_x:\n return 1\n else:\n return 0\n\n\ndef arcade_with_screen(codes, display=True):\n coords = {}\n outio = StringIO()\n inio = StringIO()\n m = SantaVM(codes, input_stream=inio, output_stream=outio)\n score = 0\n try:\n while True:\n m.run_until(opcode='03')\n coords.update(parse_arcade_output(outio.getvalue()))\n score = get_score(coords) or score\n if display:\n display_screen(coords, score)\n time.sleep(0.01)\n outio.truncate(0)\n outio.seek(0)\n print(get_action(coords), file=inio)\n m.step()\n except StopIteration:\n return score\n\n\ncodes[0] = 2\nscore = arcade_with_screen(codes, display=False)\nprint(score)\n","repo_name":"UpGado/advent-of-code","sub_path":"day13/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"21476145603","text":"from selenium import webdriver\r\nfrom selenium.webdriver import ActionChains\r\n\r\ndriver = webdriver.Chrome(executable_path=r\"C:\\Users\\Manoj\\Desktop\\Python - Selenium Practice\\Drivers\\chromedriver.exe\")\r\n\r\ndriver.get('http://automationpractice.com/index.php')\r\n\r\nactions = ActionChains(driver)\r\n\r\ninteractions = driver.find_element_by_xpath('//*[@id=\"block_top_menu\"]/ul/li[2]/a')\r\ndad = driver.find_element_by_xpath('//*[@id=\"block_top_menu\"]/ul/li[2]/ul/li[2]/a')\r\n\r\n\r\nactions.move_to_element(interactions).move_to_element(dad).click().perform()","repo_name":"manojgupta3051994/Selenium-Python","sub_path":"Video_17_WebDriver_Mouse_Actions.py","file_name":"Video_17_WebDriver_Mouse_Actions.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42812849002","text":"from PIL import Image\nimport os\n\ndef convert_to_jpg(name, png_path):\n im = Image.open(png_path)\n im.load()\n\n jpeg_file = 'sprites/' + name + '.jpg'\n jpeg = Image.new('RGB', im.size, (255, 255, 255))\n jpeg.paste(im, mask=im.split()[3])\n\n jpeg.save(jpeg_file, 'JPEG', quality=100)\n\n im.close()\n os.remove(png_path)\n\n return jpeg\n","repo_name":"bnookala/commit-them-all","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33749789772","text":"import xlrd\nimport time\n\nfrom xlwt import Workbook, Formula\n\n\n\ndef recursive(n): # 1ère méthode\n '''\n :param n: nombre de cases à remplir\n :return: nombre de possibilités pour remplir n-cases\n '''\n if n == 5: # 4 cas d'arrêts : 5, 4, 3 et 2 dont on connait les résultats\n return 15\n if n == 4:\n return 8\n if n == 3:\n return 4\n if n == 2:\n return 2\n if n == 1 or n == 0:\n return 1\n return recursive(n - 1) + recursive(n - 2) + recursive(n - 3) + recursive(n - 4) # récursion sur les 4 précédents\n\n\ndef tetranacci(n):\n '''\n :param n: nombre de cases à remplir\n :return: nombre de possibilités pour remplir n-cases\n '''\n l = [0,0,0,1] # déclaration et initialisation d'une liste dont les 3 premières valeurs sont à 0 et la dernière correspond à tetranacci(0)\n for i in range(4,n + 4): # les 3 premiers éléments de la liste ne font partie de la suite de tetranacci, c'est pourquoi on commence à 4\n l.append(l[i - 4] + l[i - 3] + l[i - 2] + l[i - 1]) # ajout de la somme des 4 termes précédents\n return l[n + 3] # retourne le dernier élément de la liste\n\n\ndef finale(n):\n '''\n :param n: nombre de cases à remplir\n :return: nombre de possibilités pour remplir n-cases\n '''\n a, b, c, d = 0, 0, 0, 1 # déclaration et initialisation de 4 variables dont les 3 premières sont égales à 0 et la dernière correspond à tetranacci(0)\n for i in range(n):\n a, b, c, d = b, c, d, (a+b+c+d) # a, b et c sont des valeurs intermédiares, d correspond à tetranacci(i)\n return d\n\n\n\npath = r\"E:\\Explorateur de fichier\\Documents\\fichier.xls\"\n\n# On créer un \"classeur\"\nclasseur = Workbook()\n# On ajoute une feuille au classeur\nfeuille = classeur.add_sheet(\"OCB\")\n\n\nfor i in range (0, 37):\n begin = time.time()\n result = recursive(i)\n temps = time.time() - begin\n print(\"Résultat au problème 117 avec\", i, \"comme entrée :\", result)\n print(f\"Duration = {temps} seconds to complete.\")\n feuille.write(i, 1, result)\n feuille.write(i, 2, temps)\n\n\nfor i in range (0, 1001):\n begin = time.time()\n result = tetranacci(i)\n temps = time.time() - begin\n print(\"Résultat au problème 117 avec\", i, \"comme entrée :\", result)\n print(f\"Duration = {temps} seconds to complete.\")\n feuille.write(i, 3, result)\n feuille.write(i, 4, temps)\n\nfor i in range (0, 1001):\n begin = time.time()\n result = finale(i)\n temps = time.time() - begin\n print(\"Résultat au problème 117 avec\", i, \"comme entrée :\", result)\n print(f\"Duration = {temps} seconds to complete.\")\n feuille.write(i, 0, i)\n feuille.write(i, 5, result)\n feuille.write(i, 6, temps)\n\n\n\n\n\n\n\n\n\n\n\nclasseur.save(path)\nprint(u\"Fichier créé: {}\".format(path))","repo_name":"belecesne/projet-euler","sub_path":"117/code pour comparer les temps.py","file_name":"code pour comparer les temps.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42649625184","text":"from django.urls import path\nfrom articles import views\n\napp_name = 'articles'\nurlpatterns = [\n path('index/', views.index), # URL(index) 을 먼저 작성해주고 작성된 URL이 views.py안에 있는 index 로 이어질지를 뒤에 명시를 하게 해준다. 그리고 이어지기 위해서 views.py로가서 index라는 함수를 만들어야한다\n path('dinner//',views.dinner),# 주소자체를 변수처럼 이용해서 동적으로 주소를 만드는것을 칭한다 자주쓰이는것이 유저 개개인의 페이지(이런경우는 유저명이 주소로 들어가기때문에)\n # 으로 적고 name이라는 변수를 집어넣겠다 name은 뷰에서 받아올 수 있다.\n path('review/',views.review, name='review'), # name='review' url 뒤쪽에 이러한 형태로 적어주게되면 장고한테 장고 템플릿 랭귀지를 이용해서 name='review'을 명시하면 'review/' 이주소를 알아서 찾아서 보내준다\n path('create_review/',views.creative_review, name='creative_review'), # review.html에서 1번째줄
부분에서 action=\"/create_review이리 선언했기 때문에 url을 잡아줘야한다 \n]","repo_name":"poro625/django_tutorial","sub_path":"articles/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22342088967","text":"from core.utils.exception import CustomValidation\nfrom organization.models import (\n CorporateLevel,\n Department,\n Division,\n Group,\n Unit,\n)\n\n\ndef process_structure(level, model):\n if level is not None:\n level_uuid = level.get(\"uuid\")\n try:\n return model.objects.get(uuid=level_uuid)\n except model.DoesNotExist:\n raise CustomValidation(\n detail=f\"{level_uuid} is not valid uuid\",\n field=\"level\",\n status_code=400,\n )\n return None\n\n\n# No validation\ndef process_structure_by_uuid(uuid, model):\n if uuid is not None:\n try:\n return model.objects.get(uuid=uuid)\n except model.DoesNotExist:\n pass\n return None\n\n\n# No validation\ndef process_structure_by_name_v2(name, model):\n if name is not None:\n try:\n return model.objects.get(name=name)\n except model.DoesNotExist:\n pass\n return None\n\n\ndef process_levels(validated_data):\n corporate_level = validated_data.pop(\"corporate_level\", None)\n department_level = validated_data.pop(\"department\", None)\n division_level = validated_data.pop(\"division\", None)\n group_level = validated_data.pop(\"group\", None)\n unit_level = validated_data.pop(\"unit\", None)\n\n corporate_level_obj = (\n process_structure(corporate_level, CorporateLevel)\n if corporate_level\n else None\n )\n department_obj = (\n process_structure(department_level, Department)\n if department_level\n else None\n )\n division_level_obj = (\n process_structure(division_level, Division) if division_level else None\n )\n group_level_obj = (\n process_structure(group_level, Group) if group_level else None\n )\n unit_level_obj = (\n process_structure(unit_level, Unit) if unit_level else None\n )\n return (\n corporate_level_obj,\n department_obj,\n division_level_obj,\n group_level_obj,\n unit_level_obj,\n validated_data,\n )\n\n\ndef process_level_by_uuid(uuid):\n corporate_level_obj = (\n process_structure_by_uuid(uuid, CorporateLevel) if uuid else None\n )\n division_level_obj = (\n process_structure_by_uuid(uuid, Division) if uuid else None\n )\n group_level_obj = process_structure_by_uuid(uuid, Group) if uuid else None\n department_obj = (\n process_structure_by_uuid(uuid, Department) if uuid else None\n )\n unit_level_obj = process_structure_by_uuid(uuid, Unit) if uuid else None\n return (\n corporate_level_obj,\n division_level_obj,\n group_level_obj,\n department_obj,\n unit_level_obj,\n )\n\n\ndef process_level_by_name_to_dict(\n coperate_name=\"\",\n division_name=\"\",\n group_name=\"\",\n department_name=\"\",\n unit_name=\"\",\n):\n corporate_level_obj = (\n process_structure_by_name_v2(str(coperate_name).lower().strip(), CorporateLevel)\n if coperate_name\n else None\n )\n division_level_obj = (\n process_structure_by_name_v2(str(division_name).lower().strip(), Division)\n if division_name\n else None\n )\n group_level_obj = (\n process_structure_by_name_v2(str(group_name).lower().strip(), Group)\n if group_name\n else None\n )\n department_obj = (\n process_structure_by_name_v2(str(department_name).lower().strip(), Department)\n if department_name\n else None\n )\n unit_level_obj = (\n process_structure_by_name_v2(str(unit_name).lower().strip(), Unit)\n if unit_name\n else None\n )\n return (\n {\"uuid\": corporate_level_obj.uuid} if corporate_level_obj else None,\n {\"uuid\": division_level_obj.uuid} if division_level_obj else None,\n {\"uuid\": group_level_obj.uuid} if group_level_obj else None,\n {\"uuid\": department_obj.uuid} if department_obj else None,\n {\"uuid\": unit_level_obj.uuid} if unit_level_obj else None,\n )\n","repo_name":"Muhyideeeen/emetric-Backend","sub_path":"core/utils/process_levels.py","file_name":"process_levels.py","file_ext":"py","file_size_in_byte":4009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28619518609","text":"from random import randint\r\nimport undetected_chromedriver as uc\r\nfrom undetected_chromedriver.options import ChromeOptions\r\nimport os\r\nfrom selenium.webdriver.common.by import By\r\nimport time\r\n\r\ndef main():\r\n Selection = int(input('(1) Send Connection Requests \\n(2) Withdraw all pending connections\\nWhich would you like to do: '))\r\n LoginUser = input('\\nEnter your LinkedIn email: ')\r\n LoginPass = input('\\nEnter your LinkedIn Password: ')\r\n\r\n print('\\nSigning in... (Takes about 10 seconds)')\r\n\r\n\r\n # Feel free to comment out the three lines below and uncomment the fourth one if you would like to watch the process!\r\n myoptions = ChromeOptions()\r\n myoptions.add_argument(\"--headless\")\r\n driver = uc.Chrome(options=myoptions)\r\n #driver = uc.Chrome\r\n \r\n # Opening LinkedIn Signinpage\r\n urltoSignInPage = 'https://www.linkedin.com/login?fromSignIn=true&trk=guest_homepage-basic_nav-header-signin'\r\n driver.get(urltoSignInPage)\r\n time.sleep(2)\r\n\r\n # Logging in\r\n username = driver.find_element(By.XPATH, \"//input[@name='session_key']\")\r\n password = driver.find_element(By.XPATH,\"//input[@name='session_password']\")\r\n\r\n username.send_keys(LoginUser)\r\n password.send_keys(LoginPass)\r\n time.sleep(2)\r\n submit = driver.find_element(By.XPATH, \"//button[@type='submit']\").click()\r\n # Login Process Complete.\r\n os.system('cls||clear')\r\n print('\\nSuccessfully signed in!')\r\n\r\n if (Selection == 1):\r\n chose_Connect(driver)\r\n elif (Selection == 2):\r\n chose_withdraw(driver)\r\n\r\ndef chose_Connect(driver):\r\n Keywords = []\r\n KeywordNum = int(input('How many keywords would you like to use: '))\r\n\r\n for x in range(KeywordNum):\r\n Keywords.append(input(f'Enter Keyword {x+1}: '))\r\n \r\n\r\n maxConnect = int(input('\\nHow many connection requests would you like to send? (Stay below 50 to be safe): '))\r\n os.system('cls||clear')\r\n Keywords = [w.replace(\" \", \"%20\") for w in Keywords]\r\n Keywords = str.join(\"%20\", Keywords)\r\n\r\n \r\n i = 0\r\n # if program is crashing, increment K variable below by 5\r\n k = 25\r\n print(\"\\nBeginning connection request process...\\nThere is a delay between requests intentionally to bypass bot detections\")\r\n while i < maxConnect:\r\n try:\r\n # Construct the URL for the search results page\r\n urllink = f\"https://www.linkedin.com/search/results/people/?&keywords={Keywords}&network=%5B%22S%22%2C%22O%22%5D&origin=SWITCH_SEARCH_VERTICAL&page={k}&sid=aiC&spellCorrectionEnabled=true\"\r\n \r\n # Load the search results page and wait for it to load\r\n driver.get(urllink)\r\n time.sleep(randint(4, 15))\r\n\r\n # Find all the Connect buttons on the page\r\n all_buttons = driver.find_elements(By.TAG_NAME, \"button\")\r\n connect_buttons = [btn for btn in all_buttons if btn.text == \"Connect\"]\r\n\r\n # Loop through all the Connect buttons and send connection requests\r\n for btn in connect_buttons:\r\n driver.execute_script(\"arguments[0].click();\", btn)\r\n name = driver.find_element(By.XPATH, \"/html/body/div[3]/div/div/div[2]/p/span/strong\").text\r\n print(f\"Sending connection request to {name}\")\r\n time.sleep(randint(4, 15))\r\n send = driver.find_element(By.XPATH, \"//button[@aria-label='Send now']\")\r\n driver.execute_script(\"arguments[0].click();\", send)\r\n close = driver.find_element(By.XPATH, \"//button[@aria-label='Dismiss']\")\r\n driver.execute_script(\"arguments[0].click();\", close)\r\n time.sleep(2)\r\n\r\n # If there are no more Connect buttons on the page, go to the next page\r\n if len(connect_buttons) == 0:\r\n k += 1\r\n else:\r\n # Update the counter for the number of connections made and go to the next page\r\n i += len(connect_buttons)\r\n k += 1\r\n\r\n # Print the total number of connection requests sent so far\r\n print(f\"Connection Invitations sent = {i}\")\r\n time.sleep(randint(4, 15))\r\n\r\n # Exit the loop if the maximum number of connections has been reached\r\n if i >= maxConnect:\r\n break\r\n\r\n # Handle any exceptions that may arise during the process\r\n except Exception as e:\r\n print(f\"An error occurred: {str(e)}\")\r\n\r\n\r\ndef chose_withdraw(driver):\r\n i = 0\r\n urllink = \"https://www.linkedin.com/notifications/?origin=SWITCH_SEARCH_VERTICAL&sid=aiC&filter=invitations_sent_people\"\r\n driver.get(urllink)\r\n print('Withdrawing all current connection requests!\\nPlease be aware that there is an intentional delay to avoid being banned as a bot.')\r\n while i < 400:\r\n time.sleep(2)\r\n all_buttons = driver.find_elements(By.XPATH,\"//button/span/span[1]\") \r\n \r\n withdraw_buttons = [btn for btn in all_buttons if btn.text == \"Withdraw\"]\r\n\r\n for btn in withdraw_buttons:\r\n driver.execute_script(\"arguments[0].click();\", btn)\r\n time.sleep(randint(6,20))\r\n send = driver.find_element(By.XPATH,\"//button[@aria-label='Withdraw']\")\r\n driver.execute_script(\"arguments[0].click();\", send)\r\n time.sleep(randint(6,20))\r\n time.sleep(4)\r\n i+=len(withdraw_buttons)\r\n print(\"Connection Invitations withdrawn = \", i )\r\n more = driver.find_element(By.XPATH,\"//button[@class='artdeco-button artdeco-button--muted artdeco-button--icon-right artdeco-button--3 artdeco-button--fluid artdeco-button--tertiary ember-view redesigned-experience__show-more-btn pv3']\")\r\n driver.execute_script(\"arguments[0].click();\", more)\r\n \r\n driver.quit()\r\n exit(0)\r\n\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"zdhenard42/Linked_Connect_Bot","sub_path":"MyConnectmenu.py","file_name":"MyConnectmenu.py","file_ext":"py","file_size_in_byte":5889,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"34672494662","text":"\n\nclass Solution(object):\n def climbStairs(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n if n < 2:\n return n\n fibs = [None] * (n + 1)\n fibs[0] = 0\n fibs[1] = 1\n i = 2\n\n while i <= n:\n fibs[i] = fibs[i-1] + fibs[i-2]\n\n i += 1\n return fibs[n] + fibs[n-1]\n\n\n\nif __name__ == '__main__':\n sol = Solution()\n tests = {\n 3: 3,\n 2: 2,\n 1: 1,\n 4: 5,\n 5: 8,\n 6: 13,\n 7: 21,\n 8: 34,\n 9: 55,\n\n }\n for key, value in tests.items():\n res = sol.climbStairs(key)\n if res != value:\n print(\"fail %d %d; correct - %d\" % (key, res, value))\n else:\n print(\"ok %d %d\" % (key, value))","repo_name":"stinkyfingers/leetcode","sub_path":"70_climbing_stairs.py","file_name":"70_climbing_stairs.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4713671449","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nAPPLICATION_NAME='theory'\n\ntop = '.'\nout = 'build'\n\ndef options(opt):\n opt.load('compiler_cxx unittest_gtest')\n\ndef configure(conf):\n conf.load('compiler_cxx boost unittest_gtest doxygen')\n\n conf.env.append_value('CXXFLAGS', ['-std=c++0x', '-Wall', '-O3', '-flto'])\n\n ## Debug\n #conf.env.append_value('CXXFLAGS', '-g')\n\n conf.check_boost(lib='regex', mt=True, static=True)\n\ndef build(bld):\n\n bld.stlib(\n features = 'cxx',\n source = [\n 'src/note.cc',\n 'src/scale.cc',\n 'src/scale_template.cc',\n ],\n includes = 'include',\n use = 'BOOST',\n target=APPLICATION_NAME)\n\n bld.program(\n features = 'gtest',\n source = [\n 'src/tests/note_unittest.cc', 'src/note.cc',\n 'src/tests/scale_unittest.cc', 'src/scale.cc',\n 'src/tests/scale_template_unittest.cc', 'src/scale_template.cc',\n 'src/tests/helpers_unittest.cc',\n ],\n includes = 'src include',\n use = 'BOOST',\n target = APPLICATION_NAME+'_test')\n\n # Build documentation\n bld(features = 'doxygen',\n doxyfile = 'Doxyfile')\n","repo_name":"samhug/music_theory","sub_path":"wscript","file_name":"wscript","file_ext":"","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"30871885289","text":"import numpy as np\nimport pandas as pd\n\ndef ma_crossover_trend(close, fast=20, slow=50, exp=True):\n \"\"\"\n get trend labeling based on moving average crossover strategy\n\n :param close: (pd series) of closing prices\n :param fast: (int) window for fast moving average, default 20\n :param slow: (int) window for slow moving average, default 50\n :param exp: (boolean) True if use ema, False use rolling average\n\n return (pd.Dataframe) with fast moving average, slow moving average, and label\n \n \"\"\"\n d = pd.DataFrame(close)\n if exp == True:\n d['fast_ma'] = d['close'].rolling(window=fast, min_periods=fast).mean()\n d['slow_ma'] = d['close'].rolling(window=slow, min_periods=slow).mean() \n else:\n d['fast_ma'] = d['close'].ewm(span=fast).mean()\n d['slow_ma'] = d['close'].ewm(span=slow).mean()\n\n d['label'] = np.nan\n long = d['fast_ma'] >= d['slow_ma'] \n short = d['fast_ma'] < d['slow_ma'] \n d.loc[long, 'label'] = 1\n d.loc[short, 'label'] = -1\n d= d.drop(['close'], axis=1)\n return d","repo_name":"waikho/MADS_Capstone","sub_path":"ML/strategy/ma_crossover.py","file_name":"ma_crossover.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"38591856935","text":"#***********************************************\n#\n# Filename: public.py\n#\n# Author: shilei@hotstone.com.cn\n# Description: 公共方法使用库\n#\n# Create: 2022-10-10 16:53:32\n# Last Modified: 2022-10-10 16:53:32\n#\n#***********************************************\n\nfrom conf.settings import *\nfrom utils.method import requestMethod as PublicRequestsMethod\nfrom datetime import datetime, timedelta\nfrom logging.handlers import TimedRotatingFileHandler\n\nimport logging\nimport time\nimport re\nimport types\nimport os\n\n# 创建日志器对象\n######################################## Logging __name__ #######################################\nlogger = logging.getLogger(__name__)\n\n# 设置logger可输出日志级别范围\nlogger.setLevel(logging.DEBUG)\n\n# 添加控制台handler，用于输出日志到控制台\nconsole_handler = logging.StreamHandler()\n# 日志输出到系统\n# console_handler = logging.StreamHandler(stream=None）\n# 添加日志文件handler，用于输出日志到文件中\n#file_handler = logging.FileHandler(filename='logs/Events.log', encoding='UTF-8', when='H', interval=1, backupCount=12)\nfile_handler = TimedRotatingFileHandler(filename='logs/Events.log', encoding='UTF-8', when='H', interval=1, backupCount=12)\n\n# 将handler添加到日志器中\n#logger.addHandler(console_handler)\nlogger.addHandler(file_handler)\n\n# 设置格式并赋予handler\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nconsole_handler.setFormatter(formatter)\nfile_handler.setFormatter(formatter)\n\n# 如果日志目录不存在进行创建\nif not os.path.exists('logs'):\n os.mkdir('logs')\n \n# request 异常方法处理, 解决 requests 异常错误直接退出的问题\ndef PublicRequests(request={\"url\": \"\", \"params\": {}, \"header\": {}, \"model\": \"GET\", \"timeout\": 3, \"verify\": True, \"proxies\": {}}, recursive_abnormal={\"recursive\": 3, \"count\": 0, \"alert_count\": 3}):\n \"\"\"\n :recursive_abnormal 异常递归 recursive 可重复次数 count 计数当前重复次数 alert_count 告警次数\n :example\n \"\"\"\n if request[\"model\"] == \"GET\":\n if recursive_abnormal[\"recursive\"] == 0:\n return PublicRequestsMethod._Get(request=request, recursive_abnormal={\"recursive\": recursive_abnormal[\"recursive\"], \"count\": recursive_abnormal[\"count\"], \"alert_count\": recursive_abnormal[\"alert_count\"]})\n else:\n return PublicRequestsMethod._Get(request=request, recursive_abnormal={\"recursive\": recursive_abnormal[\"recursive\"] - 1, \"count\": recursive_abnormal[\"count\"] + 1, \"alert_count\": recursive_abnormal[\"alert_count\"]})\n elif request[\"model\"] == \"POST\":\n if recursive_abnormal[\"recursive\"] == 0:\n return PublicRequestsMethod._Post(request=request, recursive_abnormal={\"recursive\": recursive_abnormal[\"recursive\"], \"count\": recursive_abnormal[\"count\"], \"alert_count\": recursive_abnormal[\"alert_count\"]})\n else:\n return PublicRequestsMethod._Post(request=request, recursive_abnormal={\"recursive\": recursive_abnormal[\"recursive\"] - 1, \"count\": recursive_abnormal[\"count\"] + 1, \"alert_count\": recursive_abnormal[\"alert_count\"]})\n elif request[\"model\"] == \"DELETE\":\n if recursive_abnormal[\"recursive\"] == 0:\n return PublicRequestsMethod._Delete(request=request, recursive_abnormal={\"recursive\": recursive_abnormal[\"recursive\"], \"count\": recursive_abnormal[\"count\"], \"alert_count\": recursive_abnormal[\"alert_count\"]})\n else:\n return PublicRequestsMethod._Delete(request=request, recursive_abnormal={\"recursive\": recursive_abnormal[\"recursive\"] - 1, \"count\": recursive_abnormal[\"count\"] + 1, \"alert_count\": recursive_abnormal[\"alert_count\"]})\n\n# 时间格式化\ndef changeTime(sec):\n \"\"\"\n 时间格式化\n :sec time.time()\n :return ep: 2022-10-10 17:16:40\n \"\"\"\n base_time = datetime.strptime('1970-01-01 00:00:00.0', '%Y-%m-%d %H:%M:%S.%f')\n return str(base_time + timedelta(seconds=8 * 3600 + int(sec)))\n\ndef getClassENV(cls):\n \"\"\"\n 获取 Class 静态环境变量\n \"\"\"\n _strTmp = \"\"\n for index, item in enumerate(dir(cls)):\n if not re.search('__', item):\n _cls = getattr(cls, item)\n if not isinstance(_cls, types.MethodType):\n if (index + 1) == len(dir(cls)):\n _strTmp += \"{}: {}\".format(item, _cls)\n else:\n _strTmp += \"{}: {}, \".format(item, _cls)\n return _strTmp\n","repo_name":"slzcc/QuantitativeTradingSwap","sub_path":"utils/public.py","file_name":"public.py","file_ext":"py","file_size_in_byte":4479,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"71277280728","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\nurl = \"http://www.fabpedigree.com/james/mathmen.htm\"\n\n\nres = requests.get(url).content\n\ndata = BeautifulSoup(res,'html.parser')\n\nfor li in data.findAll('li'):\n print(li.text)","repo_name":"Abdelrahman-Moharram/Machine-Learning","sub_path":"Scraper/Scraper.py","file_name":"Scraper.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42770564978","text":"#!/usr/bin/env python3\n\nimport rclpy\n\nfrom rclpy.node import Node\n\nfrom dt_interfaces_cps.msg import WheelsCmdStamped, BoolStamped\n\n\nclass WheelsDriverTestNode(Node):\n def __init__(self, node_name: str):\n super().__init__(node_name)\n\n self.pub_e_stop = self.create_publisher(\n BoolStamped,\n \"~/emergency_stop\",\n 1)\n self.pub_wheels_cmd = self.create_publisher(\n WheelsCmdStamped,\n \"~/wheels_cmd\",\n 1)\n\n self.wheels_cmd_timer = self.create_timer(3.0, self.send_wheels_cmd)\n\n self.get_logger().info(\"Initialized\")\n\n def send_wheels_cmd(self):\n msg = WheelsCmdStamped()\n msg.header.stamp = self.get_clock().now().to_msg()\n msg.header.frame_id = 'ngsduckie_motor'\n msg.vel_left = 0.0\n msg.vel_right = 0.5\n self.pub_wheels_cmd.publish(msg)\n\n def cleanup(self):\n self.wheels_cmd_timer.destroy()\n msg = WheelsCmdStamped()\n msg.header.stamp = self.get_clock().now().to_msg()\n msg.vel_left = 0.0\n msg.vel_right = 0.0\n self.pub_wheels_cmd.publish(msg)\n\n\ndef main(args=None):\n rclpy.init(args=args)\n node = WheelsDriverTestNode(\"wheels_driver_test_node\")\n try:\n rclpy.spin(node)\n except KeyboardInterrupt:\n pass\n finally:\n node.cleanup()\n node.destroy_node()\n rclpy.shutdown()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"m-yuhas/dt-duckiebot-interface-cps","sub_path":"wheels_driver/wheels_driver/wheels_driver_test_node.py","file_name":"wheels_driver_test_node.py","file_ext":"py","file_size_in_byte":1450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"32582854409","text":"def parseData():\n answerGroups = []\n with open('/mnt/d/Code/AdventOfCode2020/2020/day6input.txt', 'r') as file:\n lines = file.read().split(\"\\n\\n\")\n groups = [line.replace(\"\\n\", \" \") for line in lines]\n for group in groups:\n newGroup = group.split(' ')\n answerGroups.append(newGroup)\n return answerGroups\n\n\ndef collectUniqueAnswers(group):\n uniqueAnswerLetters = []\n for word in group:\n for letter in word:\n if letter not in uniqueAnswerLetters:\n uniqueAnswerLetters.append(letter)\n\n return uniqueAnswerLetters\n\ndef calculateFullAnsweredQuestion(answerGroups):\n fullAnsweredQuestionCount = 0\n for group in answerGroups:\n uniqLetters = collectUniqueAnswers(group)\n # Check per unique letter in group if it's answered by everyone.\n for uniqLetter in uniqLetters:\n totalAnswered = 0\n for person in group:\n for letter in person:\n if letter == uniqLetter:\n totalAnswered += 1\n \n if totalAnswered == int(len(group)):\n fullAnsweredQuestionCount += 1\n \n return fullAnsweredQuestionCount\n \n\nif __name__ == \"__main__\":\n print(calculateFullAnsweredQuestion(parseData()))","repo_name":"Obdam/AdventOfCode","sub_path":"2020/day6part2.py","file_name":"day6part2.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4433766269","text":"import cv2\nimport numpy as np\nimport pandas as pd\nfrom scipy.interpolate import interp1d\nfrom sklearn.preprocessing import scale\n\nimport plots as pl\n\nclass Image:\n def __init__(self, track_progress= True, path = \"test_photo.png\"):\n self.track_progress = track_progress\n self.load_image(path)\n\n\n def load_image(self, path):\n \"\"\"\n :param path: string containing path to image\n :return: numpy array containg image\n \"\"\"\n if self.track_progress:\n print(\"loading image...\", end = \" \")\n self.image_array = cv2.imread(path, 0)\n if self.image_array is None:\n print(\"Wrong image-path\")\n raise TypeError\n if self.track_progress:\n print(\"OK\")\n\n def extract_coordinates(self):\n \"\"\"\n Extracts indexes of dark spots on image\n \"\"\"\n if self.track_progress:\n print(\"extracting coordinates...\", end = \" \")\n x, y = np.where(self.image_array < 255 / 3)\n [x_dim, y_dim] = self.image_array.shape\n # Converting so that 0 is at the bottom instead of the first row\n x = np.ones(len(x)) * (self.image_array.shape[0] - 1) - x\n # Making sure the lovest part of x is 0\n x = x - min(x)\n df = pd.DataFrame({\"y_list\": x, \"x_list\": y})\n df.sort_values(by=\"x_list\", inplace=True)\n self.extracted_x = df.x_list.values / x_dim\n self.extracted_y = df.y_list.values / y_dim\n if self.track_progress:\n print(\"OK\")\n\n def interpolate_coordinates(self):\n \"\"\"\n Interpolates the function gives by image\n :return: f: interpolation object, works as a function\n \"\"\"\n if self.track_progress:\n print(\"interpolating coordinates...\", end = \" \")\n #Reduce number of points to interpolate to ensure smoother function\n self.extracted_x = self.extracted_x[::3]\n self.extracted_y = self.extracted_y[::3]\n #Create interpolation object\n f = interp1d(self.extracted_x, self.extracted_y, kind='linear', assume_sorted=True, bounds_error=False, fill_value='extrapolate')\n if self.track_progress:\n print(\"OK\")\n return f\n","repo_name":"hexxunset/ElmagNumerisk","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9988812726","text":"# coding=utf-8\n# @Author : zpchcbd HG team\n# @Time : 2021-09-11 12:27\n\n# FOFA: \"app=\\\"SALTSTACK-产品\\\" && country=\\\"CN\\\"\"\n# python batch.py -m exploit.web.SaltStack.SaltStack_unauth_rce -fs \"app=\\\"SALTSTACK-产品\\\" && country=\\\"CN\\\"\" -cs\n\nfrom async_timeout import timeout\nfrom colorama import Fore\nfrom tqdm import tqdm\n\nfrom exploit.web import BaseScript\nfrom core.MyEnums import *\nfrom core.request.asynchttp import *\n\n\nclass Script(BaseScript):\n name = 'SaltStack'\n\n def __init__(self, target, session):\n super().__init__()\n # 漏洞目标\n self.target = target\n # 漏洞等级\n self.bugLevel = BugLevel.HIGH\n # 类型\n self.bugType = BugType.RCE\n # 编号\n self.bugNumber = 'CVE-2021-25281'\n # 来源\n self.refer = 'https://github.com/Immersive-Labs-Sec/CVE-2021-25281'\n # 特定路径判断\n self.detectPathList = ['/favicon.ico']\n # exec\n self.execPathList = ['/execPath']\n # session\n self.session = session\n # 相关信息\n self.info = 'Version < 3000.2'\n\n async def detect(self):\n try:\n for detectPath in self.detectPathList:\n url = f'https://{self.target}{detectPath}' if self.target.startswith(\n ('http:', 'https:')) is False else f'{self.target}{detectPath}'\n async with self.session.get(url=url, headers=self.headers, timeout=self.reqTimeout, verify_ssl=False) as response:\n if response is not None:\n text = await response.text()\n if 'Specified method is invalid for this resource' in text:\n tqdm.write(Fore.RED + '[{}] {}'.format('SaltStack', url))\n self.flag = True\n return {'name': 'SaltStack Finger', 'url': url, 'software': 'SaltStack'}\n except Exception:\n pass\n\n # async def exec(self):\n # try:\n # for execPath in self.execPathList:\n # url = f'https://{self.target}{execPath}' if self.target.startswith(\n # ('http:', 'https:')) is False else f'{self.target}{execPath}'\n # async with self.semaphore:\n # with timeout(15):\n # async with aiohttp.ClientSession() as session:\n # async with session.get(url=url, verify_ssl=False) as response:\n # if response is not None:\n # text = await response.text()\n # if 'It works!' in text:\n # tqdm.write(Fore.RED + '[{}] {}'.format('SaltStack', url))\n # self.flag = True\n # return {'name': 'SaltStack Getshell', 'url': url, 'software': 'SaltStack'}\n # except Exception:\n # pass\n\n async def attack(self, semaphore, pbar):\n async with semaphore:\n a = await self.detect()\n if a is not None:\n self.vulList.append(a)\n if self.flag:\n b = await self.exec()\n if b is not None:\n self.vulList.append(b)\n pbar.update(1)\n return self.vulList\n\n\nif __name__ == '__main__':\n semaphore = asyncio.Semaphore(500)\n sc = Script('192.168.4.137:8000', 1,)\n l = asyncio.get_event_loop()\n l.run_until_complete(sc.attack(semaphore))\n","repo_name":"AkunWin/myscan","sub_path":"exploit/web/SaltStack/SaltStack_unauth_rce.py","file_name":"SaltStack_unauth_rce.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"31"} +{"seq_id":"13833239679","text":"# -*- coding: utf-8 -*-\n\"\"\"module docstring here\"\"\"\n\nimport re\n\nimport networkx as nx\n\nfrom Bio.Phylo import read, to_networkx\n\n\ndef load_notung_nhx(filename):\n \"\"\"load reconciled gene tree from NHX formatted file\n\n returns networkx graph object\n strips information from the comment field and converts into node properties\"\"\"\n\n with open(filename, 'r') as f:\n tree = read(f, format='newick')\n\n tree.rooted = True\n\n tree = to_networkx(tree)\n\n node_translator = {}\n for node in tree.nodes():\n node_translator[node] = str(len(node_translator))\n\n graph = nx.DiGraph()\n\n for node in tree.nodes():\n new_node = node_translator[node]\n\n properties = {'name': str(node)}\n for match in re.findall(r'[^:]*\\=[^:]*', node.comment):\n properties[match.split('=')[0]] = match.split('=')[1]\n\n graph.add_node(new_node, **properties)\n\n for source, target in tree.edges():\n new_source = node_translator[source]\n new_target = node_translator[target]\n graph.add_edge(new_source, new_target,\n distance=source.distance(target),\n **tree.edge[source][target])\n\n for s, t in graph.edges():\n graph.edge[s][t].pop('weight')\n\n # follow convention by renaming the root node to 'X0'\n root = nx.topological_sort(graph)[0]\n graph.node[root]['name'] = 'X0'\n\n # rename lost genes, so all nodes have unique names\n for n in [n for n in graph.nodes() if 'lost' in graph.node[n]['name'].lower()]:\n graph.node[n]['name'] = n + graph.node[n]['name']\n\n # build dictionary to replace node objects with the name of each node\n new_node_names = {n: graph.node[n]['name'] for n in graph.nodes()}\n\n nx.relabel_nodes(graph, new_node_names, copy=False)\n\n return graph\n","repo_name":"nickfyson/pinfer","sub_path":"pinfer/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"43794604848","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Sep 16 23:24:34 2022\n\n@author: kimsubin\n\"\"\"\n\nimport sys\ninput = lambda: sys.stdin.readline().rstrip()\nprint = lambda x: sys.stdout.write(str(x) + \"\\n\")\n\nn = int(input())\nroom = [list(map(int, input().split()))for _ in range(n)]\n\nroom.sort(key = lambda x: (x[1], x[0]))\n\ncount = 0\nresult = 0\n\nfor i , j in room:\n if result <= i:\n result = j\n count+=1\n\nprint(count)\n ","repo_name":"SoptJune/Subeen","sub_path":"Greedy/회의실배정.py","file_name":"회의실배정.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"965005890","text":"import requests\nimport pprint\nimport json\n\nURL = \"http://api.tvmaze.com/singlesearch/shows\"\nparams = {\"q\":\"jenny\"}\n\nres = requests.get(URL, params=params)\n\nif res.status_code == 200:\n json = json.loads(res.text)\n # pprint.pprint(json)\n #\n # print(\"\\nKeys {}\".format(json.keys()))\n\n name = json[\"name\"]\n premiered = json[\"premiered\"]\n language = json[\"language\"]\n country = json[\"network\"][\"country\"][\"name\"]\n off_site = json[\"network\"][\"officialSite\"]\n status = json[\"status\"]\n print(\n f\"\"\"\n name: {name}\n premiered: {premiered}\n language: {language}\n country: {country}\n OfficialSite: {off_site}\n status: {status} \n \"\"\"\n )\n\nelse:\n print(\"An error occurred\")\n","repo_name":"mirshoddev99/Advanced-Python","sub_path":"API/tvmaze_api.py","file_name":"tvmaze_api.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"12995256084","text":"\n\ndef show(grid):\n\tfor row in grid:\n\t\tprint(''.join(['#' if col == 1 else ' ' for col in row]))\n\tprint()\n\ndef rect(grid, a, b):\n\tfor y in range(b):\n\t\tfor x in range(a):\n\t\t\tgrid[y][x]=1\n\n\ndef rotcol(grid, a, n):\n\tcol = [y[a] for y in grid]\n\tfor t in range(n):\n\t\tcol.insert(0,col.pop())\n\tfor y in range(len(grid)):\n\t\tgrid[y][a] = col[y]\n\ndef rotrow(grid, b, n):\n\tfor t in range(n):\n\t\tgrid[b].insert(0,grid[b].pop())\n\n\ndef run(grid, commands):\n\tfor inst in commands:\n\t\tcmd = inst.strip().split()\n\t\tif cmd[0]=='rect':\n\t\t\ta,b = map(int,cmd[1].split('x'))\n\t\t\trect(grid,a,b)\n\t\telif cmd[0] == 'rotate' and cmd[1] == 'column':\n\t\t\ta = int(cmd[2].split('=')[1])\n\t\t\tn = int(cmd[4])\n\t\t\trotcol(grid,a,n)\n\t\telif cmd[0] == 'rotate' and cmd[1] == 'row':\n\t\t\tb = int(cmd[2].split('=')[1])\n\t\t\tn = int(cmd[4])\n\t\t\trotrow(grid,b,n)\n\tcount = sum([sum(y) for y in grid])\n\tprint('#1',count)\n\n\tshow(grid) # AFBUPZBJPS\n\ndef sample():\n\n\tgrid = [[0]*7 for _ in range(3)]\n\n\tcommands = '''rect 3x2\n\trotate column x=1 by 1\n\trotate row y=0 by 4\n\trotate column x=1 by 1'''.splitlines()\n\n\trun(grid,commands)\n\ndef solve():\n\n\tgrid = [[0]*50 for _ in range(6)]\n\twith open('input8') as fp:\n\t\tcommands = fp.readlines()\n\n\trun(grid,commands)\n\nif __name__ == '__main__':\n\t#sample()\n\tsolve()\n","repo_name":"natoftheboonies/advent-of-code-2016","sub_path":"day8.py","file_name":"day8.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"37958122526","text":"# Lab 11: Webscraping to Webmapping\n# This lab was created by Kyle Redican and Ashton Shortridge 4/3/2019\n# Utilizing the basic code from: https://medium.com/@msalmon00/web-scraping-job-postings-from-indeed-96bd588dcb4b\n\n# Lets get started\n# IN YOUR KONSOLE (Not in python yet)\n#present your working directory\n#pwd\n# Change working directory to your Lab 11 folder\n#cd (/home/425mussa/Desktop/lab11/)\n\n#Did it change it to the right place?\n#pwd\n\n# Loading our python module\n# Due to the complexity of python and libraries/ packages we (Jim) had to set up a specific module for the GEO 425 lab\n# To be able to run the code you need to load this specific GEO 425 module\n#module load geo425\n\n#start up python\n#python\n# Version should be 3.7.1 \n#notice how >>> replaced the $, this means you are in python\n\n#Lets start by importing the libraries and packages we will be using\nimport requests\nimport bs4\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport time\nimport folium\nimport folium.plugins\nfrom folium.plugins import MarkerCluster\nimport os\nimport random\nfrom random import uniform\n\n##Basic commands\n# Identify your URL\n# In this case we are looking at indeed (job posting company) for GIS jobs\n# Notice how the l= does not have anything behind it, this is because there is not location defined yet\nURL = \"http://www.indeed.com/jobs?q=GIS&l=\"\n#conducting a request of the stated URL above:\npage = requests.get(URL)\n#specifying a desired format of \"page\" using the html parser - this allows python to read the various components of the page, rather than treating it as one long string.\nsoup = BeautifulSoup(page.text, \"html.parser\")\n#printing soup in a more structured tree format that makes for easier reading\nprint(soup.prettify())\n\n#Setting up our key variables for the web-scrapper\n# This one refers to the max number of results per city\nmax_results_per_city = 50\n# This creates a list of the cities you want to look at\n# Notice how there is + in all the spaces?\n# This is due to how the url defines location after the l= part\ncity_set = [\"Grand+Rapids,+MI\", \"Lansing,+MI\", \"Detroit,+MI\", \"Indianapolis,+IN\", \"Fort+Wayne,+IN\", \"Chicago,+IL\", \"Columbus,+OH\"]\n# Defining the columns of what we want to pull off of the website\n# Start is the starting number added to the url in each iteration\n# City is the name from the city_set list\n# job_title is the title of the job_post\n# company_name is the name of the company\n# location is the location\n# salary is salary\n# summary is the summary provided in the job description\ncolumns = [\"start\", \"city\", \"job_title\", \"company_name\", \"location\", \"salary\", \"summary\"]\n# creating a pandas dataframe with the columns\nsample_df = pd.DataFrame(columns = columns)\n\n\n# Here is the loop that pulls out all of the key parts\n# For detailed information about the aspects of this looping code that pulls please see you lab instructions\nfor city in city_set:\n for i in range(0, max_results_per_city, 10):\n page = requests.get('https://www.indeed.com/jobs?q=GIS&l=' + str(city) + '&radius=0'+'&start=' +str(i))\n time.sleep(1) #ensuring at least 1 second between page grabs\n soup = BeautifulSoup(page.text, \"lxml\", from_encoding=\"utf-8\")\n for div in soup.find_all(name=\"div\", attrs={\"class\":\"row\"}): \n sponsered = div.find_all(name=\"span\", attrs={\"class\":\" sponsoredGray \"}) \n if len(sponsered)== 0:\n #specifying row num for index of job posting in dataframe\n num =(len(sample_df) + 1) \n #creating an empty list to hold the data for each posting\n job_post = [] \n #append start value\n job_post.append(i) \n #grabbing job title\n job_post.append(city)\n #job_post.append(str(start)) \n #grabbing Job Title \n a = div.find_all(name=\"a\", attrs={\"data-tn-element\":\"jobTitle\"})\n if len(a) > 0:\n for a in div.find_all(name=\"a\", attrs={\"data-tn-element\":\"jobTitle\"}):\n job_post.append(a[\"title\"])\n else:\n for a in div.find_all(name=\"a\", attrs={\"data-tn-component\": \"organicJob\"}):\n job_post.append(a[\"title\"])\n #for a in div.find_all(name=\"a\", attrs={\"data-tn-element\":\"jobTitle\"}):\n #job_post.append(a[\"title\"]) \n #grabbing company name\n company = div.find_all(name=\"span\", attrs={\"class\":\"company\"}) \n if len(company) > 0: \n for b in company:\n job_post.append(b.text.strip()) \n else: \n sec_try = div.find_all(name=\"span\", attrs={\"class\":\"result-link-source\"})\n for span in sec_try:\n job_post.append(span.text)\n if len(company)==0:\n job_post.append('none') \n #grabbing location name\n c = div.find_all(\"span\", attrs={'class': 'location'}) \n for span in c: \n job_post.append(span.text)\n if len(c) == 0:\n job_post.append(\"NA\") \n salary=div.find_all('span', attrs={'class': 'salary no-wrap'})\n if len(salary)>0:\n for span in salary:\n job_post.append(span.text)\n else:\n job_post.append(\"No Salary Listed\")\n #grabbing summary text\n d = div.find_all('span', attrs={'class': 'summary'}) \n if len(d) > 0:\n for span in d:\n job_post.append(span.text)\n else:\n job_post.append('No summary listed') \n \n sample_df.loc[num] = job_post\n\n \n\n# After waiting a couple minutes indeed will have the scrapped jobs\n#Checkk out how many records it pulled\n#len(sample_df)\n# check out what they look like\n#print(sample_df)\n\n#one thing you should notice here is that the sample_df has pulled duplicates\n# part of this comes from the website itself not playing well with our loop\n# Drop the duplicates\nsample_df.drop_duplicates(subset= \"job_title\", keep= 'first', inplace= True)\n\n\n#Check hwo many records were dropped\n# list to hold salary and city as\nsalary=[i for i in sample_df.salary]\ncities=[i for i in sample_df.city]\nn=0\n\nfor s in salary:\n sal=sal.strip().split()\n \n \nlen(sample_df)\n\n\n# Now we need to turn our job records spatial \n# We need to geocode these a bit\n# For simplicity purposes in the attached data there is cities_lat_lon, which has the \n# Read in your lat lon csv\nLat_lon = pd.read_csv('/home/425mussa/Desktop/lab11/cities_lat_lon.csv')\n\n#Loading the two pandas dataframes together\nresult=sample_df.merge(Lat_lon, on=['city'])\n\n## Creating random numbers to jitter our coordinates\nresult['RANDlat'] = [ random.uniform(-.1, .1) for k in result.index]\nresult['RANDlon'] = [ random.uniform(-.1, .1) for k in result.index]\n\n#Now just add the random value to each column\nresult['Lat']= result['Lat']+result['RANDlat']\nresult['Lon']= result['Lon']+result['RANDlon']\n\n# Now the points have been jittered\n# Establish the mean coordinates for where to start your map\nMean_coords=(42.959443, -85.742777)\n# Creates your map\nmap=folium.Map(location=Mean_coords, zoom_start=6)\n\n#getting the points to appear clustered at the different scales\nmarker_cluster= folium.plugins.MarkerCluster().add_to(map)\n\n#Adding the points and popup windows onto the map\n# for full details on this for loop look at the lab instructions\nfor i in range(0, len(result)):\n folium.Marker([result.iloc[i]['Lat'], result.iloc[i]['Lon']], \n popup=folium.Popup('Job Title: '+result.iloc[i]['job_title']+ '
' + \n 'Company: ' + result.iloc[i]['company_name'] + \n '
'+'Location: '+result.iloc[i]['location']+'
' +' Salary: ' + result.iloc[i]['salary']+ '
' +'Summary: '+result.iloc[i]['summary'], max_width=450), \n icon=folium.Icon(color='red')).add_to(marker_cluster)\n\n#saving the map as an html file\nmap.save(outfile='map.html')\n\n","repo_name":"EzequielMussambe/laboratory","sub_path":"python_code.py","file_name":"python_code.py","file_ext":"py","file_size_in_byte":7680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"37724619766","text":"## @file\n## @brief powered by `metaL`\n\n## @defgroup info metainfo\n## @{\nMODULE = 'metaL'\nTITLE = '[meta]programming [L]anguage'\nABOUT = 'homoiconic metaprogramming system\\n* powered by `metaL`'\nAUTHOR = 'Dmitry Ponyatov'\nEMAIL = 'dponyatov@gmail.com'\nYEAR = 2020\nLICENSE = 'MIT'\nGITHUB = 'https://github.com/ponyatov'\nLOGO = 'logo.png'\nMANIFEST = 'https://github.com/ponyatov/metaL/wiki/metaL-manifest'\n## @}\n\nimport os, sys, random, re\n\n## @defgroup persist Persistence\n## @brief inherit `Unison` *immutable global storage*: https://www.unisonweb.org\n\nimport xxhash\n\n## @defgroup object Object\n\n## @brief base object graph node\n## @ingroup object\nclass Object:\n\n ## construct object\n ## @param[in] V given scalar value\n def __init__(self, V):\n if isinstance(V, Object):\n V = V.val\n ## object name / scalar value (string, number,..)\n self.val = V\n ## slots = attributes = dict = env\n self.slot = {}\n ## nested AST = vector = stack = queue\n self.nest = []\n ## parent nodes registry\n self.par = []\n ## global storage id\n ## @ingroup persist\n self.gid = hash(self)#self.sync().gid\n\n ## @name storage/hot-update\n ## @{\n\n ## this method must be called on any object update\n ## (compute hash, update persistent memory,..)\n ##\n ## mostly used in operator methods in form of `return self.sync()`\n ## @ingroup persist\n ## @returns self\n def sync(self):\n # update global hash\n self.gid = hash(self)\n ## sync with storage\n #storage.put(self)\n return self\n\n ## fast object hashing for global storage id\n ## @ingroup persist\n def __hash__(self):\n hsh = xxhash.xxh32()\n hsh.update(self._type())\n hsh.update('%s' % self.val)\n for i in self.slot:\n hsh.update(i)\n hsh.update(self.slot[i].gid.to_bytes(8, 'little'))\n for j in self.nest:\n hsh.update(j.gid.to_bytes(8, 'little'))\n return hsh.intdigest()\n\n ## serialize to .json\n ## @ingroup persist\n def json(self):\n js = '{\"gid\":\"%x\",\"type\":\"%s\",\"val\":\"%s\",' % (\n self.gid, self._type(), self.val)\n slots = []\n for k in sorted(self.slot.keys()):\n slots.append('\"%s\":\"%.8x\"' % (k, self.slot[k].gid))\n js += '\"slot\":{%s},' % ','.join(slots)\n nested = []\n for i in self.nest:\n nested.append('\"%.8x\"' % i.gid)\n js += '\"nest\":[%s]' % ','.join(nested)\n return js + \"}\"\n\n ## @}\n\n ## @name dump\n ## @{\n\n ## `print` callback\n def __repr__(self): return self.dump()\n\n ## dump for tests (no hash/gid in headers)\n def test(self): return self.dump(test=True)\n\n ## dump in full text tree form\n ## @param[in] cycle already dumped objects (cycle prevention registry)\n ## @param[in] depth recursion depth\n ## @param[in] prefix optional prefix in `` header\n ## @param[in] test test dump option @ref test\n def dump(self, cycle=None, depth=0, prefix='', test=False):\n # header\n tree = self.pad(depth) + self.head(prefix, test)\n # cycles\n if not depth:\n cycle = []\n if self in cycle:\n return tree + ' _/'\n # slot{}s\n for i in sorted(self.slot.keys()):\n tree += self.slot[i].dump(cycle + [self],\n depth + 1, f'{i} = ', test)\n # nest[]ed\n for j, k in enumerate(self.nest):\n tree += k.dump(cycle + [self],\n depth + 1, f'{j}: ', test)\n # subtree\n return tree\n\n ## paddig for @ref dump\n def pad(self, depth, block=True, tab='\\t'):\n if block:\n ret = '\\n'\n ret += tab * depth\n else:\n ret = ''\n return ret\n\n ## short `` header only\n ## @param[in] prefix optional prefix in `` header\n ## @param[in] test test dump option @ref test\n def head(self, prefix='', test=False):\n hdr = '%s<%s:%s>' % (prefix, self._type(), self._val())\n if not test:\n hdr += ' #%.8x @%x' % (self.gid, id(self))\n return hdr\n\n ## object class name (lowercased as marker of instance)\n def _type(self): return self.__class__.__name__.lower()\n\n ## `.val` output for dumps (limited length, escaped control chars)\n def _val(self): return '%s' % self.val\n\n ## @}\n\n ## @name plot\n ## @{\n\n ## plot object graph via GraphViz/`dot`\n ## @returns `digraph{}` string for `dot`\n ## @param[in] cycle block (plottted nodes accumulator list\n ## @param[in] depth recursion depth\n ## @param[in] parent node\n ## @param[in] label on edge\n ## @param[in] color of edge\n def plot(self, cycle=None, depth=0, parent=None, label='', color='black'):\n # recursion root\n if not depth:\n dig = 'digraph \"%s\" {\\nrankdir=LR;\\n' % self.head(test=True)\n cycle = []\n else:\n dig = '\\t' * depth\n # node\n me = 'zid%s' % id(self)\n dig += '%s [label=\"%s\"]\\n' % (me, self.head(test=True))\n # edge\n if parent:\n dig += '\\t' * depth + \\\n '%s -> %s [label=\"%s\",color=\"%s\"]\\n' % (\n parent, me, label, color)\n # cycles block\n if self in cycle:\n return dig\n else:\n cycle += [self]\n # slots\n for i in sorted(self.slot.keys()):\n dig += self.slot[i].plot(cycle, depth + 1,\n parent=me, label=i, color='blue')\n # recursion root\n if not depth:\n dig += '}\\n'\n with open('/tmp/dot.dot', 'w') as f:\n f.write(dig)\n return dig\n else:\n return dig\n\n ## @}\n\n ## @name operator\n ## @{\n\n ## `A.keys()`\n def keys(self):\n return self.slot.keys()\n\n ## `A[key] ~> A.slot[key:str] | A.nest[key:int] `\n def __getitem__(self, key):\n if isinstance(key, int):\n return self.nest[key]\n if isinstance(key, str):\n return self.slot[key]\n # try:\n # return self.slot[key]\n # except KeyError:\n # return Undef(key) // self\n raise TypeError(key)\n\n ## `A.B`\n def dot(self, that, ctx):\n assert isinstance(that, Object)\n return self[that.val]\n\n ## `A[key] = B`\n def __setitem__(self, key, that):\n if isinstance(that, str):\n that = String(that)\n if isinstance(that, int):\n that = Integer(that)\n if isinstance(key, str):\n self.slot[key] = that\n elif isinstance(key, int):\n self.nest[key] = that\n else:\n raise TypeError(key)\n return self.sync()\n\n ## `A << B ~> A[B.type] = B`\n def __lshift__(self, that):\n if isinstance(that, str):\n that = String(that)\n return self.__setitem__(that._type(), that)\n\n ## `A >> B ~> A[B.val] = B`\n def __rshift__(self, that):\n return self.__setitem__(that.val, that)\n\n ## `A // B -> A.push(B)`\n ## @param[in] that `B`\n ## @param[in] sync push object with sync\n ## (hash/storage update, use `False` for massive & IO pushes)\n def __floordiv__(self, that):\n if isinstance(that, str): # wrap Python string\n that = String(that)\n that.pre__floordiv__(self)\n self.nest.append(that)\n that.post__floordiv__(self)\n return self\n\n ## pre-callback for `__floordiv__`\n def pre__floordiv__(self, parent): pass\n\n ## post-callback for `__floordiv__`\n def post__floordiv__(self, parent):\n self.par.append(parent)\n\n ## @}\n\n ## @name stack ops\n ## @{\n\n ## clean `.nest[]`\n def dropall(self):\n self.nest = []\n return self\n\n ## push to `.nest[]`\n ## @param[in] sync push with sync\n ## @param[in] that `B` operand to be pushed\n def push(self, that):\n return self // that\n\n ## insert `that` into parent node after the current\n def after(self, that):\n assert len(self.par) == 1\n for parent in self.par:\n index = parent.index(self)\n parent.insert(index + 1, that)\n that.post__floordiv__(parent)\n return self\n\n ## insert `A[index]=B`\n ## @param[in] index integer indsex in `.nest[]`\n ## @param[in] that `B` operand to be inserted\n def insert(self, index, that):\n self.nest.insert(index, that)\n return self\n\n def drop(self, count=1):\n for i in range(count):\n self.nest.pop()\n return self\n\n ## find index of subgraph\n def index(self, that):\n return self.nest.index(that)\n\n ## @}\n\n ## @name evaluation\n ## @{\n\n ## evaluate in context\n ## @param[in] ctx context\n\n def eval(self, ctx): raise Error((self))\n\n ## apply as function\n ## @param[in] that operand (function argument/s)\n ## @param[in] ctx context\n def apply(self, that, ctx): raise Error((self))\n\n ## @}\n\n ## @name code generation\n ## @{\n\n ## comment start (for sigle-line and block comments)\n def comment(self):\n return self.par[0].comment()\n ## comment end (for block comments)\n\n def commend(self):\n return self.par[0].commend()\n\n ## default f\"format\"ting for all nodes\n def __format__(self, spec=None):\n ret = f'{self.val}'\n if 't' in spec:\n ret = ret.title()\n if 'u' in spec:\n ret = ret.upper()\n if 'l' in spec:\n ret = ret.lower()\n return ret\n\n ## to generic text file: use `.json` in place of `Error`\n ## @ingroup gen\n\n def file(self, depth=0, tab=None):\n assert tab\n return self.pad(depth, self.par[0].block, tab) + self.json()\n\n ## @}\n\n\n## @ingroup object\n## nil value\nclass Nil(Object):\n def __init__(self):\n super().__init__('')\n\n## @defgroup error Error\n## @ingroup object\n\n## @ingroup error\nclass Error(Object, BaseException):\n pass\n\n# ## @ingroup error\n# class Undef(Object):\n# pass\n\n## @defgroup prim Primitive\n## @ingroup object\n\n## @ingroup prim\nclass Primitive(Object):\n ## primitives evaluates to itself\n def eval(self, ctx): return self\n\n## @ingroup Nil\nclass Nil(Primitive):\n def __init__(self): Primitive.__init__(self, '')\n\n## @ingroup prim\nclass Name(Primitive):\n\n ## names evaluate via context lookup\n def eval(self, ctx): return ctx[self.val]\n\n ## assignment\n def eq(self, that, ctx):\n ctx[self.val] = that\n return that\n\n## @ingroup prim\nclass String(Primitive):\n ## @param[in] V string value\n ## @param[in] block source code flag: tabbed blocks or inlined code\n def __init__(self, V, block=True, tab=1):\n super().__init__(V)\n self.block = block\n self.tab = tab\n self.rem = None\n\n def _val(self):\n s = ''\n v = '' if self.val == None else self.val\n for c in v:\n if c == '\\n':\n s += r'\\n'\n elif c == '\\r':\n s += r'\\r'\n elif c == '\\t':\n s += r'\\t'\n else:\n s += c\n return s\n\n def file(self, depth=0, tab=None):\n assert tab\n # assert len(self.par) == 1\n ret = self.pad(depth, self.par[0].block, tab) + f'{self.val}'\n if self.val is None:\n ret = ''\n ret += f' {self.rem}' if self.rem else ''\n for i in self.nest:\n ret += i.file(depth + 1, tab)\n return ret\n\n def py(self): return self.val\n\n def cc_arg(self): return '\"%s\"' % self._val()\n\n def post__floordiv__(self, parent):\n super().post__floordiv__(parent)\n\n\n## @ingroup prim\n## floating point\nclass Number(Primitive):\n def __init__(self, V):\n Primitive.__init__(self, float(V))\n\n def file(self, depth=0, tab=None):\n assert tab\n return '%s' % self.val\n\n ## @name operator\n ## @{\n\n ## `+A`\n def plus(self, ctx):\n return self.__class__(+self.val)\n\n ## `-A`\n def minus(self, ctx):\n return self.__class__(-self.val)\n\n ## `A + B`\n def add(self, that, ctx):\n assert type(self) == type(that)\n return self.__class__(self.val + that.val)\n\n ## `A - B`\n def sub(self, that, ctx):\n assert type(self) == type(that)\n return self.__class__(self.val - that.val)\n\n ## `A * B`\n def mul(self, that, ctx):\n assert type(self) == type(that)\n return self.__class__(self.val * that.val)\n\n ## `A / B`\n def div(self, that, ctx):\n assert type(self) == type(that)\n return self.__class__(self.val / that.val)\n\n ## `A ^ B`\n def pow(self, that, ctx):\n assert type(self) == type(that)\n return self.__class__(self.val ** that.val)\n\n ## @}\n\n## @ingroup prim\nclass Integer(Number):\n def __init__(self, V):\n Primitive.__init__(self, int(V))\n\n## @ingroup prim\n## hexadecimal machine number\nclass Hex(Integer):\n def __init__(self, V):\n Primitive.__init__(self, int(V[2:], 0x10))\n\n def _val(self):\n return hex(self.val)\n\n## @ingroup prim\n## bit string\nclass Bin(Integer):\n def __init__(self, V):\n Primitive.__init__(self, int(V[2:], 0x02))\n\n def _val(self):\n return bin(self.val)\n\n## @defgroup cont Container\n## @ingroup object\n\n## @ingroup cont\n## generic data container\nclass Container(Object):\n pass\n\n## @ingroup cont\n## var size array (Python list)\nclass Vector(Container):\n def __init__(self, V='', nest=None):\n super().__init__(V)\n if nest:\n self.nest = nest\n\n def eval(self, ctx):\n res = self.__class__(self.val)\n for i in self.nest:\n res // i.eval(ctx)\n return res\n\n def cc_arg(self):\n ret = ','.join([j.cc_arg() for j in self.nest])\n return '/* %s */ %s' % (self.head(), ret)\n\n## @ingroup cont\nclass Tuple(Vector):\n def __init__(self, nest=[]):\n super().__init__('')\n for i in nest:\n self // i\n\n def file(self, depth=0, tab=None):\n return f'{self}'\n\n def __format__(self, spec):\n assert not spec\n return ', '.join(f'{i.__format__(spec)}' for i in self.nest)\n\n## @ingroup cont\n## FIFO stack\nclass Stack(Container):\n pass\n\n## @ingroup cont\n## associative array\nclass Dict(Container):\n pass\n\n## @defgroup active Active\n## @ingroup object\n\n## @ingroup active\n## executable data elements\nclass Active(Object):\n pass\n\n## @ingroup active\n## function\nclass Fn(Active):\n\n def __init__(self, V, args=[], returns=Nil()):\n super().__init__(V)\n self['args'] = self.args = Args(args)\n self['ret'] = self.ret = returns\n self.block = True\n\n def eval(self, ctx): return self\n\n def apply(self, that, ctx):\n self['arg'] = that\n self['ret'] = Nil()\n print('self', self)\n print('that', that)\n return self['ret']\n\n def at(self, that, ctx): return self.apply(that, ctx)\n\n def file(self, depth=0, tab=None):\n pfx = ''\n assert tab\n res = self.pad(depth, self.par[0].block, tab)\n res += 'def %s%s(%s):' % (pfx, self.val, self.args.file(tab=tab))\n if not self.nest:\n res += ' pass'\n for i in self.nest:\n res += i.file(depth + 1, tab)\n return res\n\n## @ingroup active\n## operator\nclass Op(Active):\n def eval(self, ctx):\n if self.val == '`':\n return self.nest[0]\n # greedy computation for all subtrees\n greedy = list(map(lambda i: i.eval(ctx), self.nest))\n # unary\n if len(greedy) == 1:\n if self.val == '+':\n return greedy[0].plus(ctx)\n if self.val == '-':\n return greedy[0].minus(ctx)\n # binary\n if len(greedy) == 2:\n if self.val == '+':\n return greedy[0].add(greedy[1], ctx)\n if self.val == '-':\n return greedy[0].sub(greedy[1], ctx)\n if self.val == '*':\n return greedy[0].mul(greedy[1], ctx)\n if self.val == '/':\n return greedy[0].div(greedy[1], ctx)\n if self.val == '^':\n return greedy[0].pow(greedy[1], ctx)\n if self.val == '=':\n return greedy[0].eq(greedy[1], ctx)\n if self.val == '.':\n return greedy[0].dot(greedy[1], ctx)\n if self.val == ':':\n return greedy[0].colon(greedy[1], ctx)\n if self.val == '@':\n return greedy[0].at(greedy[1], ctx)\n # unknown\n raise Error((self))\n\n## @ingroup active\n## Virtual Machine (environment + stack + message queue)\nclass VM(Active):\n pass\n\n\n## @ingroup active\n## global system VM\nvm = VM(MODULE)\nvm << vm\n\n\n## @defgroup meta Meta\n## @ingroup object\n\n## @ingroup meta\nclass Meta(Object):\n pass\n\n## @ingroup meta\n## source code\nclass S(Meta, String):\n def __init__(self, start=None, end=None, block=True, **kwargs):\n if 'doc' in kwargs:\n String.__init__(self, kwargs['doc'], block, tab=0)\n else:\n String.__init__(self, start, block)\n self.rem = kwargs['rem'] if 'rem' in kwargs else None\n self.end = end\n\n def file(self, depth=0, tab=None):\n assert tab\n ret = super().file(depth, tab)\n ret += self.file_end(depth, tab)\n return ret\n\n def file_end(self, depth, tab):\n assert tab\n blocking = self.block if hasattr(self, 'block') else self.par[0].block\n if self.end is None:\n return ''\n elif self.end == '':\n return '\\n'\n else:\n return self.pad(depth, blocking, tab) + self.end\n\nclass CR(S):\n def __init__(self):\n super().__init__('', block=False)\n\n def file(self, depth=0, tab=None):\n assert tab\n return '\\n'\n\n## @ingroup meta\n## commented code block\nclass D(S):\n def __init__(self, V='', end=None):\n super().__init__(end=end, doc=V)\n\n def file(self, depth=0, tab=None):\n assert tab\n return super().file(depth - 1, tab)\n\nclass H(S):\n\n def __init__(self, V, *vargs, **kwargs):\n super().__init__(f'{V}', end=None if 0 in vargs else f'')\n for i in kwargs:\n self[i] = f'{kwargs[i]}'\n\n def file(self, depth=0, tab=None):\n assert tab\n assert len(self.par) == 1\n ret = self.pad(depth, self.par[0].block, tab) + f'<{self.val}'\n for i in sorted(self.slot.keys()):\n j = 'class' if i == 'clazz' else i\n j = re.sub(r'_', r'-', j)\n ret += f' {j}=\"{self.slot[i]}\"'\n ret += '>'\n for j in self.nest:\n ret += j.file(depth + 1, tab)\n ret += self.file_end(depth, tab)\n return ret\n\n## @ingroup meta\nclass Return(S):\n def __init__(self, V):\n super().__init__('return %s' % V)\n\n## @ingroup meta\nclass Arg(Meta, Name):\n def __int__(self): return self.val\n\n def file(self, depth=0, tab=' '):\n assert tab\n return f'{self}'\n\n## @ingroup meta\nclass Args(Meta, Tuple):\n def __init__(self, nest=[]):\n Tuple.__init__(self, nest=nest)\n self.block = False\n\n## @ingroup meta\nclass Class(Meta):\n def __init__(self, C, sup=None):\n if type(C) == type(Class):\n super().__init__(C.__name__)\n self.C = C\n else:\n super().__init__(C)\n if sup:\n self['sup'] = self.sup = Args(sup)\n self.block = True\n\n def colon(self, that, ctx):\n return self.C(that)\n\n def file(self, depth=0, tab=None):\n assert tab\n ret = self.pad(depth, self.par[0].block, tab) + f'class {self}'\n if 'sup' in self.keys():\n ret += f'({self.sup})'\n ret += ':'\n if self.nest:\n for j in self.nest:\n ret += j.file(depth + 1, tab)\n else:\n ret += ' pass'\n return ret\n\n## @ingroup meta\nclass Method(Meta, Fn):\n pass\n\n## @ingroup meta\nclass pyInterface(Meta):\n def __init__(self, V, ext=[]):\n super().__init__(V)\n self.block = True\n for i in ext:\n self // i\n\n def file(self, depth=0, tab=None):\n assert tab\n ret = '\\n' + \\\n self.pad(depth, self.par[0].block, tab) + f'## @name {self}'\n if 'url' in self.keys():\n ret += self.pad(depth, self.par[0].block,\n tab=tab) + f\"## {self['url']}\"\n ret += self.pad(depth, self.par[0].block, tab) + '## @{'\n ret += '\\n'\n # z = ''\n for i in self.nest:\n ret += i.file(depth + 0)\n #\n ret += '\\n'\n ret += self.pad(depth, self.par[0].block, tab) + '## @}'\n return ret\n\n## @ingroup meta\nclass Module(Meta):\n def file(self, depth=0, tab=None):\n assert tab\n return self.head(test=True)\n\n\nvm['module'] = Class(Module)\n\n## @ingroup meta\n## text files with any code are devided by sections (can be nested as subsections)\nclass Section(Meta):\n def __init__(self, V, comment=True):\n super().__init__(V)\n ## every section known its parent: file or other outer section\n assert not self.par\n ## sections always blocked in files\n self.block = True\n if not comment:\n self.comment = lambda: False\n\n # ## block mutiple parents for all `Section`s\n # def pre__floordiv__(self, parent):\n # assert not self.par\n # super().pre__floordiv__(parent)\n\n def file(self, depth=0, tab=None):\n assert tab\n # assert len(self.par) == 1\n if not self.nest:\n return ''\n #\n comment = self.comment()\n head = self.head(test=True)\n commend = self.commend()\n #\n ret = self.pad(depth, self.par[0].block, tab) if comment else ''\n if comment:\n ret += f'{comment} \\\\ {head}{commend}'\n for i in self.nest:\n ret += i.file(depth, tab)\n if comment:\n ret += self.pad(depth, self.par[0].block, tab)\n ret += f'{comment} / {head}{commend}'\n return ret\n\n\n## @defgroup io IO\n## @ingroup object\n## @brief base file output\n\n## @ingroup io\nclass IO(Object):\n pass\n\n## @ingroup io\nclass Dir(IO):\n\n def sync(self):\n if not self.par:\n try:\n os.mkdir(self.val)\n except FileExistsError:\n pass\n return super().sync()\n\n def pre__floordiv__(self, parent):\n assert isinstance(parent, Dir)\n super().pre__floordiv__(parent)\n\n def post__floordiv__(self, parent):\n assert isinstance(parent, Dir)\n super().post__floordiv__(parent)\n try:\n os.mkdir(self.fullpath())\n except FileExistsError:\n pass\n\n def fullpath(self):\n assert len(self.par) <= 1\n if self.par:\n assert isinstance(self.par[0], Dir)\n return self.par[0].fullpath() + '/' + self.val\n else:\n return self.val\n\n # ## append file\n # def __floordiv__(self, that):\n # super().__floordiv__(that)\n # # if isinstance(that, File):\n # # that.fh = open('%s/%s%s' % (self.val, that.val, that.ext), 'w')\n # # return IO.__floordiv__(self, that)\n # # if isinstance(that, Dir):\n # # that.val = '%s/%s' % (self.val, that.val)\n # # return IO.__floordiv__(self, that)\n # # raise Error((self))\n\n\n## @ingroup io\nclass File(IO):\n ## @param[in] V file name without extension\n ## @param[in] ext file extension (default none)\n ## @param[in] comment syntax comment (depends on a file type)\n def __init__(self, V, ext='', comment='#', tab='\\t'):\n ## file handler not assigned on File object creation\n self.fh = None\n ##\n self.comment = lambda: comment\n commends = {'', '/*': '*/'}\n commend = ' ' + commends[comment] if comment in commends else ''\n self.commend = lambda: commend\n ##\n super().__init__(V)\n self['ext'] = self.ext = ext\n self.tab = tab\n ##\n if comment:\n powered = f\"powered by metaL: {MANIFEST}\"\n if len(comment) == len('#'):\n self // f\"{comment} {powered}{commend}\"\n # self // f\"{comment*2} @file{commend}\"\n elif len(comment) >= len('//'):\n self // f\"{comment} {powered}{commend}\"\n # self // f\"{comment*2} @file{commend}\"\n else:\n raise Error((self.comment))\n ## every file has `top` section\n self.top = Section('top')\n self['top'] = self.top\n self // self.top\n ## every file has `mid`ddle section\n self.mid = Section('mid')\n self['mid'] = self.mid\n self // self.mid\n ## every file has `bot`tom section\n self.bot = Section('bot')\n self['bot'] = self.bot\n self // self.bot\n ## all files holds tab-blocked sections/strings\n self.block = True\n\n def pre__floordiv__(self, parent):\n assert isinstance(parent, Dir)\n super().pre__floordiv__(parent)\n\n def post__floordiv__(self, parent):\n assert isinstance(parent, Dir)\n super().post__floordiv__(parent)\n self.fh = open(self.fullpath(), 'w')\n\n def fullpath(self):\n assert len(self.par) == 1\n assert isinstance(self.par[0], Dir)\n return self.par[0].fullpath() + '/' + self.val + self.ext\n\n def __format__(self, spec):\n assert not spec\n return f'{self.val}{self.ext}'\n\n def file(self, depth=0, tab=None):\n assert tab\n ret = ''\n for j in self.nest:\n ret += j.file(depth, tab)\n if ret:\n assert ret[0] == '\\n'\n ret = ret[1:] + '\\n'\n return ret\n\n def sync(self):\n if self.fh:\n self.fh.seek(0)\n self.fh.write(self.file(tab=self.tab))\n self.fh.truncate()\n self.fh.flush()\n return super().sync()\n\n # ## push object/line\n # ## @param[in] that `B` operand: string of section will be pushed into file\n # ## @param[in] sync `=False` default w/o flush to disk (via `sync()``)\n # def __floordiv__(self, that, sync=False):\n # return super().__floordiv__(that, sync)\n\n## @defgroup net net\n## @brief Networking\n## @ingroup io\n\n## @ingroup net\n## networking object\nclass Net(IO):\n pass\n\n## @ingroup net\n## TCP/IP address\nclass Ip(Net):\n def __format__(self, spec):\n assert not spec\n return f'{self.val}'\n\n## @ingroup net\n## TCP/IP port\nclass Port(Net):\n pass\n\n## @ingroup net\nclass Email(Net):\n def file(self, depth=0, tab=None):\n assert tab\n return '<%s>' % self.val\n\n def __format__(self, spec):\n assert not spec\n return f'<{self.val}>'\n\n## @ingroup net\nclass Url(Net):\n def file(self, depth=0, tab=None):\n assert tab\n return self.pad(depth, self.par[0].block, tab) + self.val\n\n def __format__(self, spec):\n assert not spec\n return f'{self.val}'\n\n## @ingroup net\nclass GitHub(Url):\n def __init__(self, V, module=None, branch='master'):\n super().__init__(V)\n self['module'] = self.module = module\n self['branch'] = self.branch = branch\n\n def __format__(self, spec):\n assert not spec\n return f'{self.val}/metaL/tree/{self.branch}/{self.module}'\n\n## @ingroup net\nclass User(Net):\n pass\n\n## @ingroup net\n## password\nclass Pswd(Net):\n def __init__(self, V):\n Net.__init__(self, V, minsize=6)\n self.minsize = minsize\n\n def _val(self): return '*' * self.minsize\n\n## @defgroup doc Documenting\n\n## @ingroup doc\nclass Doc(Object):\n def file(self, depth=0, tab=None):\n assert tab\n return f'{self}'\n\n## @ingroup doc\nclass Color(Doc):\n pass\n\n## @ingroup doc\nclass Title(Doc):\n ## @ingroup py\n def py(self): return '## @brief %s' % self.val\n\n## @ingroup doc\nclass Author(Doc):\n pass\n\n\nfrom license import License, MIT\n\n## @defgroup gui GUI\n## @brief Generic GUI\n\n## @ingroup gui\nclass GUI(Object):\n pass\n\n## @ingroup gui\nclass Window(GUI):\n def htdump(self):\n ret = self.dump()\n ret = re.sub(r'\\<', r'<', ret)\n ret = re.sub(r'\\>', r'>', ret)\n return ret\n\n def html(\n self): return f'
{self.htdump()}
'\n\n\n## @ingroup info\n## @{\nvm['MODULE'] = vm.MODULE = Module(MODULE)\nvm['TITLE'] = vm.TITLE = Title(TITLE)\nvm['ABOUT'] = vm.ABOUT = String(ABOUT)\nvm['EMAIL'] = vm.EMAIL = Email(EMAIL)\nvm['AUTHOR'] = vm.AUTHOR = Author(AUTHOR) << EMAIL\nvm['YEAR'] = vm.YEAR = Integer(YEAR)\nvm['LICENSE'] = vm.LICENSE = MIT\nvm['GITHUB'] = vm.GITHUB = GitHub(GITHUB, MODULE)\nvm['LOGO'] = vm.LOGO = File(LOGO, comment=None)\n## @}\n\n\n## @defgroup gen CodeGen\n## @brief Code generators\n\n## @defgroup prj Project\n## @ingroup gen\n## @brief generic software project components\n\n## @ingroup prj\nclass README(File):\n def __init__(self, module):\n File.__init__(self, 'README.md', comment=None)\n self.module = module\n self // ('# `%s`' % module.val)\n self // (f'## {module[\"TITLE\"]}')\n # self // ''\n about = module['ABOUT'].val\n while about and about[-1] in '\\r\\n':\n about = about[:-1]\n about += '\\n* powered by `metaL`'\n self // ('%s' % about)\n self // ''\n self // ('(c) %s <<%s>> %s %s' % (\n module['AUTHOR'].val, module['EMAIL'].val,\n module['YEAR'].val, module['LICENSE'].val))\n self.github = Section('github')\n self // self.github\n self.github // '' // f\"github: {module['GITHUB']}\" // ''\n # install\n self.install = Section('install')\n self // self.install\n self.install // '## Install' //\\\n self.module.readme.install\n # tutorial\n self.tutorial = Section('tutorial')\n self // self.tutorial\n self.tutorial // '## Tutorial' //\\\n self.module.readme.tutorial\n # self.module.init_readme_tutorial()\n\n\n## @ingroup prj\nclass Makefile(File):\n def __init__(self, V='Makefile'):\n super().__init__(V, comment='#', tab='\\t')\n\n## @ingroup prj\n## system setting\nclass Setting(Meta):\n def __init__(self, key, val):\n super().__init__(key)\n self.key = key\n self // val\n self.block = True\n\n def __floordiv__(self, that):\n self.nest = []\n Meta.__floordiv__(self, that)\n\n## @ingroup prj\n## VSCode multicommand\nclass multiCommand(S):\n def __init__(self, key, cmd):\n super().__init__('{\"command\": \"multiCommand.%s\", ' % key, '},')\n self.cmd = String(cmd)\n self // (S('\"sequence\":[', ']') //\n '\"workbench.action.files.saveAll\",' //\n (S('{\"command\": \"workbench.action.terminal.sendSequence\",', '}') //\n (S('\"args\": {', '}', block=False) //\n (S('\"text\": \"\\\\u000D', '\\\\u000D\"', block=False) //\n self.cmd\n ))))\n\n## @defgroup dirmod dirModule\n## @ingroup prj\n## module with its own directory (root module = project)\n\n## @ingroup dirmod\n## module with its own directory (root module = project)\n##\n## https://www.notion.so/metalang/The-base-of-all-projects-dirModule-f6f20d6dd12b42738dd2a8aee7cc8a42\nclass dirModule(Module):\n ## @param[in] V default name is the current file name\n def __init__(self, V=None):\n # current file name\n if not V:\n V = __import__('sys').argv[0]\n V = V.split('/')[-1]\n V = V.split('.')[0]\n super().__init__(V)\n # fill metainformation from VM (metaL author/info)\n self['TITLE'] = self.TITLE = Title(self)\n self['ABOUT'] = self.ABOUT = String('\\n')\n self['AUTHOR'] = self.AUTHOR = vm['AUTHOR']\n self['EMAIL'] = self.EMAIL = vm['EMAIL']\n self['YEAR'] = self.YEAR = vm['YEAR']\n self['LICENSE'] = self.LICENSE = vm['LICENSE']\n self['GITHUB'] = self.GITHUB = GitHub(GITHUB, self)\n # diroot: directory with same name as the module\n self['dir'] = self.diroot = Dir(V).sync()\n #\n self.init_first()\n # apt.txt\n self.init_apt()\n # gitignore\n self.init_giti()\n # tmp\n self.init_dirs()\n # Makefile\n self.init_mk()\n # README\n self.init_readme()\n\n def init_first(self): pass\n\n def init_dirs(self):\n self['tmp'] = self.tmp = Dir('tmp')\n self.diroot // self.tmp\n self.tmp.giti = File('.gitignore')\n self.tmp // self.tmp.giti\n self.tmp.giti // '*.zip'\n self.tmp.giti.sync()\n\n ## create defaut Makefile\n def init_mk(self):\n self.diroot['mk'] = self.mk = Makefile()\n self['mk'] = self.mk\n self.diroot // self.mk\n # vars\n self.mk['vars'] = self.mk.vars = Section('vars')\n self.mk.top // self.mk.vars\n # module\n self.mk['module'] = self.mk.module = Section('module')\n self.mk.module // f'{\"MODULE\":<8} = $(notdir $(CURDIR))'\n self.mk.vars // self.mk.module\n self.mk.vars // f'{\"OS\":<7} ?= $(shell uname -s)'\n # version\n self.mk['version'] = self.mk.version = Section('version')\n self.mk.top // self.mk.version\n self.mk.version // f'{\"NOW\":<8} = $(shell date +%d%m%y)'\n self.mk.version // f'{\"REL\":<8} = $(shell git rev-parse --short=4 HEAD)'\n # dirs\n self.mk['dirs'] = self.mk.dirs = Section('dirs')\n self.mk.top // self.mk.dirs\n self.mk.dirs //\\\n f'{\"CWD\":<8} = $(CURDIR)' //\\\n f'{\"BIN\":<8} = $(CWD)/bin' //\\\n f'{\"LIB\":<8} = $(CWD)/lib' //\\\n f'{\"TMP\":<8} = $(CWD)/tmp' //\\\n f'{\"SRC\":<8} = $(CWD)/src' //\\\n f'{\"GZ\":<8} = $(HOME)/gz'\n # tools\n self.mk['tools'] = self.mk.tools = Section('tools')\n self.mk.top // self.mk.tools\n self.mk.xpath = Section('xpath', 0) //\\\n f'{\"XPATH\":<8} = PATH=$(BIN):$(PATH)'\n self.mk.tools //\\\n f'{\"WGET\":<8} = wget -c --no-check-certificate' //\\\n f'{\"CORES\":<8} = $(shell grep proc /proc/cpuinfo|wc -l)' //\\\n self.mk.xpath //\\\n f'{\"XMAKE\":<8} = $(XPATH) $(MAKE) -j$(CORES)'\n # lib\n self.mk.lib = self['lib'] = Section('lib')\n self.mk.mid // self.mk.lib\n # src\n self.mk.src = self['src'] = Section('src')\n self.mk.mid // self.mk.src\n # obj\n self.mk.obj = self['obj'] = Section('obj')\n self.mk.mid // self.mk.obj\n # all\n self.mk.all = self['all'] = Section('all')\n self.mk.all.targets = S('all:', block=False)\n self.mk.mid // ((D('.PHONY: all') //\n self.mk.all.targets //\n (S() //\n self.mk.all //\n '$(MAKE) test'\n )))\n # test\n self.mk.test = self['test'] = Section('test', comment=None) // '$^'\n self.mk.test.targets = S('test:', block=False)\n self.mk.mid // ((D('.PHONY: test') //\n self.mk.test.targets //\n (S() //\n self.mk.test)))\n # repl\n self.mk.repl = self['repl'] = Section('repl')\n self.mk.repl.targets = S('repl:', block=False)\n self.mk.mid // ((D('.PHONY: repl') //\n self.mk.repl.targets //\n (S() //\n '$(MAKE) test' //\n self.mk.repl //\n '$(MAKE) $@')))\n # rules\n self.mk.rules = self['rules'] = Section('rules')\n self.mk.mid // self.mk.rules\n # doc\n self.mk.doc = Section('doc')\n self.mk.doc.doc = S('.PHONY: doc\\ndoc:', '', 0)\n self.mk.mid // (self.mk.doc // self.mk.doc.doc)\n # install\n install = Section('install')\n self.mk.bot // install\n self.mk.install = S('install:') // '$(MAKE) $(OS)_install'\n install // '.PHONY: install' // self.mk.install\n self.mk.install // '$(MAKE) doc'\n # update\n update = Section('update')\n self.mk.bot // update\n self.mk.update = S('update:') // '$(MAKE) $(OS)_update'\n update // '.PHONY: update' // self.mk.update\n self.mk['linux'] = self.mk.linux = Section('linux/install')\n self.mk.bot // self.mk.linux\n self.mk.linux // '.PHONY: Linux_install Linux_update'\n self.mk.linux // (S('Linux_install Linux_update:') //\n '-sudo apt update' //\n '-sudo apt install -u `cat apt.txt`')\n # merge master/shadow\n self.mk['merge'] = self.mk.merge = Section('merge')\n self.mk.bot // self.mk.merge\n self.mk.merge // f'MERGE = {self.mk} {self.apt} {self.giti} .vscode'\n self.mk.merge // 'MERGE += doc src tmp README.md'\n self.mk.bot // S('.PHONY: master shadow release zip', '')\n # master\n self.mk.bot // (S('master:', '') //\n 'git checkout $@' //\n 'git pull -v' //\n 'git checkout shadow -- $(MERGE)')\n # shadow\n self.mk.bot // (S('shadow:', '') //\n 'git checkout $@' //\n 'git pull -v')\n # release\n self.mk.bot // (S('release:', '') //\n 'git tag $(NOW)-$(REL)' //\n 'git push -v && git push -v --tags' //\n '$(MAKE) shadow')\n # zip\n self.mk.bot // (S('zip:') //\n 'git archive --format zip \\\\' //\n '--output ~/tmp/$(MODULE)_src_$(NOW)_$(REL).zip \\\\' //\n 'HEAD')\n #\n self.mk.sync()\n\n ## create `apt.txt` with packages must be installed on Debian GNU/Linux\n def init_apt(self):\n self['apt'] = self.apt = File('apt.txt', comment='')\n self.diroot // self.apt\n self.apt // 'git make wget'\n self.apt.sync()\n\n ## `.gitignore` file masks will not included into repository\n def init_giti(self):\n self['gitignore'] = self.giti = File('.gitignore')\n self.diroot // self.giti\n self.giti.top // '*~' // '*.swp' // '*.log'\n return self.giti.sync()\n\n def init_readme(self):\n self.readme = Section('readme')\n self.readme.preinstall = Section(\n 'preinstall') // self.readme_preinstall()\n self.readme.postinstall = Section(\n 'postinstall') // self.readme_postinstall()\n self.readme.install = Section('install') //\\\n self.readme.preinstall //\\\n (S(\"\\n```\", \"```\\n\") //\n f\"~$ git clone --depth 1 -o gh https://github.com/ponyatov/metaL ~/metaL\" //\n f\"~$ cd ~/metaL/{self}\" //\n f\"~/metaL/{self}$ make install\") //\\\n self.readme.postinstall\n self.readme.tutorial = Section('tutorial') // self.readme_tutorial()\n\n def readme_preinstall(self): return ''\n def readme_postinstall(self): return ''\n def readme_tutorial(self): return ''\n\n## @ingroup prj\n## extended project template includes some IDE configs and build script extensions\n##\n## https://www.notion.so/metalang/extending-very-minimal-dirModule-to-more-general-anyModule-5286e00adefe4366925aeba6f7293a1d\nclass anyModule(dirModule):\n def __init__(self, V=None):\n super().__init__(V)\n self.init_lic()\n self.init_vscode()\n self.init_doc()\n self.init_config()\n\n def init_config(self):\n self.config = Dict('config')\n #\n import uuid\n secret_key = xxhash.xxh64(str((uuid.getnode(), self))).hexdigest()\n self.config['secret_key'] = self.config.secret_key = secret_key\n #\n self.config['host'] = self.config.host = Ip('127.0.0.1')\n #\n import crc16\n\n def port_scale(port):\n return int(1024 + ((0xBFFF - 1024) / (0xFFFF)) * port)\n self.config['port'] = self.config.port = port_scale(\n crc16.crc16xmodem(self.val.encode('utf8')))\n assert self.config.port in range(1024, 0xBFFF)\n\n def init_doc(self):\n self['doc'] = self.doc = Dir('doc')\n self.diroot // self.doc\n self.doc.giti = File('.gitignore')\n self.doc // self.doc.giti\n self.doc.giti // '*.pdf'\n self.doc.giti.sync()\n\n def init_lic(self):\n self.lic = File('LICENSE', comment=None)\n self.diroot // self.lic\n L = vm['LICENSE']\n self.lic //\\\n f'{L}' //\\\n L[0].val.format(\n YEAR=f'{self.YEAR}',\n AUTHOR=f'{self.AUTHOR}',\n EMAIL=f'{self.EMAIL}'\n )\n self.lic.sync()\n\n def init_vscode(self):\n self.diroot['vscode'] = self.vscode = Dir('.vscode')\n self.diroot // self.vscode\n self.init_vscode_settings()\n self.init_vscode_launch()\n self.init_vscode_tasks()\n self.init_vscode_ext()\n\n def init_mk(self):\n super().init_mk()\n # rules\n self.mk['rules'] = self.mk.rules = Section('rules')\n self.mk.mid // self.mk.rules\n self.mk.sync()\n\n def init_bin(self):\n self.bin = Dir('bin')\n self.diroot // self.bin\n self.bin.giti = File('.gitignore')\n self.bin // self.bin.giti\n self.bin.giti.sync()\n\n def init_dirs(self):\n super().init_dirs()\n self.init_bin()\n #\n self['src'] = self.src = Dir('src')\n self.diroot // self.src\n self.src.giti = File('.gitignore')\n self.src // self.src.giti\n self.src.giti.sync()\n\n # def init_giti(self):\n # # self.giti.bot // ('/%s' % self.val)\n # # self.giti.bot // ('/%s.exe\\n/%s' % (self.val, self.val))\n # # self.giti.bot // '*.o' // '*.objdump'\n # # return self.giti.sync()\n\n def init_vscode_settings(self):\n settings = File('settings.json', comment='//')\n self.vscode['settings'] = self.vscode.settings = settings\n self.vscode // self.vscode.settings\n settings.top // '{'\n settings.bot // '\\t\"editor.tabSize\": 4,'\n settings.bot // '}'\n #\n self.f9 = multiCommand('f9', 'make all')\n self.f11 = multiCommand('f11', 'make repl')\n self.f12 = multiCommand('f12', 'exit()')\n settings.mid //\\\n (Section('multiCommand') //\n (S('\"multiCommand.commands\": [', '],') //\n self.f9 // self.f11 // self.f12\n )\n )\n #\n watcher = Section('watcher')\n self.vscode['watcher'] = self.vscode.watcher = watcher\n settings.mid // (S('\"files.watcherExclude\": {', '},') // watcher)\n #\n exclude = Section('exclude')\n self.vscode['exclude'] = self.vscode.exclude = exclude\n settings.mid // (S('\"files.exclude\": {', '},') // exclude)\n #\n assoc = Section('assoc')\n self.vscode['assoc'] = self.vscode.assoc = assoc\n settings.mid // (S('\"files.associations\": {', '},') // assoc)\n assoc //\\\n '\"*{rc,sh}\": \"shellscript\",' //\\\n '\"ws.?\":\"json\",'\n #\n settings.sync()\n\n def vs_task(self, target, group='make'):\n return (S('{', '},') //\n f'\"label\": \"{group}: {target}\",' //\n '\"type\": \"shell\",' //\n f'\"command\": \"make {target}\",' //\n '\"problemMatcher\": [],'\n )\n\n def vs_make(self, target, group='make'):\n return self.vs_task(target, group)\n\n def vs_git(self, target, group='git'):\n return self.vs_task(target, group)\n\n def init_vscode_tasks(self):\n self.vscode['tasks'] = self.tasks = File('tasks.json', comment='//')\n self.vscode // self.tasks\n self.tasks.it = S('\"tasks\": [', ']')\n self.tasks // (S('{', '}') // '\"version\": \"2.0.0\",' // self.tasks.it)\n self.tasks.it //\\\n self.vs_make('install') //\\\n self.vs_make('update') //\\\n self.vs_git('master') //\\\n self.vs_git('shadow')\n self.tasks.sync()\n\n def init_vscode_ext(self):\n self.vscode['ext'] = self.vscode.ext = File(\n 'extensions.json', comment='//')\n self.vscode // self.vscode.ext\n self.vscode.ext.top // '{'\n self.vscode.ext.ext = Section('ext') // '\"stkb.rewrap\",'\n self.vscode.ext.mid //\\\n (S('\"recommendations\": [', ']') // self.vscode.ext.ext)\n self.vscode.ext.bot // '}'\n self.vscode.ext.sync()\n\n def init_vscode_launch(self):\n launch = File('launch.json', comment='//')\n self.vscode['launch'] = self.vscode.launch = launch\n self.vscode // launch\n launch.top // '// https://code.visualstudio.com/docs/python/debugging'\n launch.top // '{'\n launch.bot // '}'\n #\n launch['it'] = launch.it = Section('')\n launch.mid // (S('\"configurations\": [', ']') // launch.it)\n launch.sync()\n\n def mksrc(self, file):\n assert isinstance(file, File)\n self.mk.src // f'SRC += {file}'\n\n def file(self, depth=0, tab=None):\n assert tab\n return f'{self.__class__.__name__}:{self._val()}'\n\n## @defgroup cc ANSI C'99\n## @ingroup gen\n## @brief ANSI C'99 code generation targeted for @ref tcc\n\n## @ingroup cc\nclass CC(Object):\n pass\n\n## @ingroup cc\n## generic ANSI C module (POSIX)\nclass cModule(anyModule):\n\n def __init__(self, V=None):\n super().__init__(V)\n self.init_c()\n self.init_h()\n\n def init_c(self):\n self.c = cFile(self)\n self.src // self.c\n self.c.top // cInclude(f'{self}')\n self.c.sync()\n\n def init_h(self):\n self.h = hFile(self)\n self.h.top // stdlib // stdio // stdass\n self.src // self.h\n self.h.sync()\n\n def mixin_apt(self):\n self.apt // 'build-essential tcc'\n self.apt.sync()\n\n def init_apt(self):\n super().init_apt()\n self.mixin_apt()\n\n def init_giti(self):\n super().init_giti()\n self.giti.mid // '*.exe' // '*.o' // f'/bin/{self:m}'\n return self.giti.sync()\n\n def __mixin__(self):\n cModule.mixin_mk(self)\n cModule.mixin_apt(self)\n\n def mixin_mk(self):\n #\n self.mk.tools // f'{\"TCC\":<8} = tcc'\n self.mk.tools // f'{\"CC\":<8} = $(TCC)'\n self.mk.tools // f'{\"CXX\":<8} = g++'\n self.mk.tools // f'{\"AS\":<8} = $(CC)'\n self.mk.tools // f'{\"LD\":<8} = $(CC)'\n self.mk.tools // f'{\"OBJDUMP\":<8} = objdump'\n self.mk.tools // f'{\"SIZE\":<8} = size'\n #\n self.mk.sync()\n\n def init_mk(self):\n super().init_mk()\n self.mixin_mk()\n #\n self.mk['flags'] = self.mk.flags = Section('flags')\n self.mk.top // self.mk.flags\n self.mk.flags // f'{\"OPT\":<8} = -O0 -g2'\n self.mk.flags // f'{\"CFLAGS\":<8} = $(OPT) -I$(SRC) -I$(TMP)'\n #\n self.mk.obj // f'{\"OBJ\":<7} += $(TMP)/{self}.o'\n #\n # self.mk.all.targets // ' $(BIN)/$(MODULE)'\n self.mk.test.targets // ' $(BIN)/$(MODULE)'\n #\n self.mk.rules //\\\n (S('$(BIN)/$(MODULE): $(OBJ)') //\n '$(CC) $(CFLAGS) -o $@ $^'\n )\n for i in ['SRC', 'TMP']:\n cc = '$(CC) $(CFLAGS) -o $@ -c $<'\n mh = '$(SRC)/$(MODULE).h'\n self.mk.rules //\\\n (S(f'$(TMP)/%.o: $({i})/%.c {mh} $({i})/%.h') // cc) //\\\n (S(f'$(TMP)/%.o: $({i})/%.c {mh}') // cc)\n #\n self.mk.sync()\n\n def mixin_ragel(self):\n (self.apt // 'ragel').sync()\n (self.giti // '/tmp/ragel.c').sync()\n #\n self.mk.tools // f'{\"RAGEL\":<8} = ragel'\n self.mk.obj // f'{\"OBJ\":<7} += $(TMP)/ragel.o'\n self.mk.rules //\\\n (S('$(SRC)/ragel.c: $(SRC)/$(MODULE).ragel') //\n '$(RAGEL) -G2 -o $@ $<')\n self.mk.mid //\\\n '.PHONY: ragel\\nragel: $(SRC)/ragel.c'\n self.mk.sync()\n #\n self.h.mid //\\\n 'extern void parse(unsigned char *p , unsigned char *pe);' //\\\n 'extern void token(char *name, unsigned char *ts, unsigned char *te);'\n self.h.sync()\n #\n self.ragel = cFile(f'{self:m}', ext='.ragel', comment='//')\n self.src // self.ragel\n self.ragel.top //\\\n f'#include <{self:m}.h>'\n self.ragel.rex = Section('rex', comment=None) //\\\n r\"eol = '\\r'?'\\n';\" //\\\n r'ws = [ \\t];'\n self.ragel.scan = Section('scanner', comment=None)\n self.ragel.mid //\\\n (S('%%{', '}%%') //\n f'machine {self:m};' //\n 'alphtype unsigned char;' //\n self.ragel.rex //\n (S(f'{self:m} := |*', '*|;') //\n self.ragel.scan //\n 'eol => {token(\"eol\",ts,ts);};'\n )\n )\n self.ragel.sync()\n\n def mixin_skelex(self):\n self.mk.skelex = Section('skelex')\n self.mk.rules // self.mk.skelex\n self.mk.tools //\\\n f'{\"LEX\":<8} = flex' //\\\n f'{\"YACC\":<8} = bison'\n self.mk.obj //\\\n 'OBJ += $(TMP)/lexer.o' //\\\n 'OBJ += $(TMP)/parser.o'\n self.mk.skelex //\\\n (S('$(TMP)/lexer.c: $(SRC)/lexer.lex') //\n '$(LEX) -o $@ $<') //\\\n (S('$(TMP)/parser.c: $(SRC)/parser.yacc') //\n '$(YACC) -o $@ $<') //\\\n '$(TMP)/parser.h: $(TMP)/parser.c' //\\\n '$(SRC)/$(MODULE).h: $(TMP)/parser.h'\n self.mk.sync()\n #\n lexinc = (S('%{', '%}') // (S() // cInclude(f'{self}')))\n self.lex = lexFile('lexer')\n self.src // self.lex\n self.lex.comments = Section('comments')\n self.lex //\\\n lexinc //\\\n '%option yylineno noyywrap' //\\\n '%%' //\\\n self.lex.comments //\\\n f'{\".\":<8} {{yyerror(\"lexer\");}}' //\\\n '%%'\n self.lex.sync()\n #\n self.yacc = yaccFile('parser')\n self.src // self.yacc\n self.yacc.union = Section('union')\n YYERR = '\"\\\\n\\\\n%i: %s [%s]\\\\n\\\\n\",yylineno,msg,yytext'\n self.yacc //\\\n lexinc //\\\n '%defines' //\\\n (S('%union {', '}') // self.yacc.union) //\\\n '%%' //\\\n 'REPL:' //\\\n '%%' //\\\n (S('void yyerror(char *msg) {', '}') //\n f'fprintf(stderr,{YYERR});' //\n 'fflush(stderr);' //\n 'exit(-1);'\n )\n self.yacc.sync()\n #\n self.h // (Section('parser') //\n 'extern int yylex();' //\n 'extern char* yytext;' //\n 'extern FILE* yyin;' //\n 'extern int yyparse();' //\n 'extern void yyerror(char*);' //\n 'extern int yylineno;' //\n cInclude(f'parser'))\n self.h.sync()\n #\n (self.tmp.giti // 'lexer.*' // 'parser.*').sync()\n\n def init_vscode_ext(self):\n super().init_vscode_ext()\n self.vscode.ext.ext //\\\n '\"ms-vscode.cpptools\",'\n # // '\"tintinweb.vscode-decompiler\",'\n self.vscode.ext.sync()\n\n def init_vscode_settings(self):\n super().init_vscode_settings()\n #\n self.vscode.cpp = jsonFile('c_cpp_properties')\n self.vscode // self.vscode.cpp\n include = \\\n '\"includePath\": [\"${workspaceFolder}/src/**\", \"${workspaceFolder}/tmp/**\"],'\n linux = \\\n (S('{', '}') //\n '\"name\": \"Linux\",' //\n '\"compilerPath\": \"/usr/bin/tcc\",' //\n include //\n '\"cStandard\": \"c99\"'\n )\n self.vscode.cpp //\\\n '// https://code.visualstudio.com/docs/cpp/c-cpp-properties-schema-reference' //\\\n (S('{', '}') //\n (S('\"configurations\": [', '],') //\n linux\n ) //\n '\"version\": 4'\n )\n self.vscode.cpp.sync()\n #\n self.vscode.watcher //\\\n '\"**/*.o\":true,' //\\\n f'\"bin/{self:m}\":true,'\n self.vscode.assoc //\\\n '\"*.[c|h]\":\"c\",'\n self.vscode.settings.sync()\n\n## @ingroup cc\n## cross-compiler module / embedded C/C++\nclass ccModule(cModule):\n\n def __mixin__(self):\n cModule.mixin(self)\n\n def init_mk(self):\n super().init_mk()\n #\n self.mk.all // 'all: $(MODULE)'\n self.mk.all // \"\\t./$^\"\n # self.mk.mid // '$(MODULE): $(C) $(H)'\n # self.mk.mid // '\\t$(CC) $(CFLAGS) -o $@ $(C)'\n #\n self.mk.sync()\n\n def init_h(self):\n self['h'] = self.h = hFile(self.val)\n self.diroot // self.h\n self.h.top // \"ccInclude('assert.h')\"\n self.h.sync()\n self.mk.src // ('H += %s' % self.h.file())\n\n def init_c(self):\n self['c'] = self.c = cFile(self.val)\n self.diroot // self.c\n # self.c.top // ccInclude(self.h)\n self.c.sync()\n self.mk.src // ('C += %s' % self.c.file())\n\n\n## @ingroup cc\nclass cFile(File):\n def __init__(self, V, ext='.c', comment='//'):\n super().__init__(V, ext=ext, comment=comment, tab=' ' * 4)\n\n## @ingroup cc\nclass lexFile(File):\n def __init__(self, V, ext='.lex'):\n super().__init__(V, ext=ext, comment=None, tab=' ' * 4)\n## @ingroup cc\nclass yaccFile(File):\n def __init__(self, V, ext='.yacc', comment='/*'):\n super().__init__(V, ext=ext, comment=comment, tab=' ' * 4)\n\n## @ingroup cc\nclass cInclude(CC, Module):\n def __init__(self, V):\n if isinstance(V, File):\n V = V.file()\n super().__init__(V)\n\n def file(self, depth=0, tab=None):\n assert tab\n return f'\\n#include <{self}.h>'\n\n\n## @ingroup cc\nstdint = cInclude('stdint')\n## @ingroup cc\nstdlib = cInclude('stdlib')\n## @ingroup cc\nstdio = cInclude('stdio')\n## @ingroup cc\nstdass = cInclude('assert')\n\n## @ingroup cc\nclass hFile(CC, File):\n def __init__(self, V, ext='.h', comment='//'):\n super().__init__(V, ext=ext, comment=comment)\n self.top //\\\n f'#ifndef _{V:u}_H'\n self.bot //\\\n f'#endif {comment} _{V:u}_H'\n\n## @ingroup cc\n## C type\nclass cType(CC):\n def file(self, depth=0, tab=None):\n assert tab\n return '%s %s' % (self.cc_type(), self.cc_val())\n\n def __format__(self, spec):\n if spec in ['']:\n return f'{self:t} {self:v}'\n if spec in ['v']:\n return f'{self.val}'\n if spec in ['t']:\n return f'{self._type()[1:]}'\n raise TypeError(spec)\n\n def file(self, depth=0, tab=None):\n return f'{self:t} {self:v}'\n\nclass cInt(cType):\n def __init__(self, V=0):\n super().__init__(V)\n\n\ncint = cInt()\n\n## @ingroup cc\nclass cVoid(cType):\n def __init__(self, V=''):\n super().__init__(V)\n\n def cc_arg(self): return ''\n\n\ncvoid = cVoid()\n\n## @ingroup cc\nclass cArgs(Tuple):\n pass\n\n## @ingroup cc\n## function\nclass cFn(CC, Fn):\n\n def file(self, depth=0, tab=None):\n assert tab\n ret = self.pad(depth, self.par[0].block, tab) + \\\n f'{self[\"ret\"]:t} {self}({self[\"args\"]}) {{'\n for j in self.nest:\n ret += j.file(depth + 1, tab)\n ret += self.pad(depth + 1,\n self.par[0].block, tab) + f'return {self[\"ret\"]:v};'\n ret += self.pad(depth, self.par[0].block, tab) + f'}}'\n return ret\n\n # # def cc_call(self):\n # # return '%s(%s)' % (self.val, self['args'].cc_arg())\n\n # # def cc_arg(self):\n # # return self._val()\n\n\n## @ingroup cc\nargc = cInt('argc')\n## @ingroup cc\nargv = Arg('char *argv[]')\n## @ingroup cc\nmain = cFn('main', [argc, argv], returns=cint)\n\n\n## @defgroup py Python\n## @ingroup gen\n\n## @ingroup py\nclass PY(Object):\n pass\n\n## @ingroup py\nclass pyImport(PY):\n def file(self, depth=0, tab=None):\n assert tab\n assert len(self.par) == 1\n return self.pad(depth, self.par[0].block, tab) + 'import %s' % self.val\n\n## @ingroup py\nclass pyFn(PY, Fn):\n ## self-copy\n def cp(self):\n assert not self.nest\n ret = self.__class__(self.val)\n ret.args.dropall()\n for i in self.args:\n ret.args // i\n return ret\n\n## @ingroup py\nclass pyFile(PY, File):\n def __init__(self, V, ext='.py', comment='#', tab=' ' * 4):\n super().__init__(V, ext, comment, tab)\n\n # def __format__(self, spec): return f'{self.val}.{self.ext}\n\n## @ingroup py\nclass pyClass(Class):\n pass\n\n## @ingroup py\nclass pyMethod(pyFn):\n def __init__(self, V, args=[]):\n super().__init__(V)\n self.block = True\n self['args'] = self.args = Args(nest=[arg for arg in ['self'] + args])\n\n## @ingroup py\nclass pytestFile(pyFile):\n def __init__(self, py):\n super().__init__('test_%s' % py.val)\n self.top // pyImport('pytest')\n self['none'] = self.none = pyTest('none')\n self.top // self.none\n # self.sync()\n\n## @ingroup py\nclass pyTest(pyFn):\n def __init__(self, V):\n super().__init__(f'test_{V}')\n\n## @ingroup py\nclass pyModule(anyModule):\n def __init__(self, V=None):\n self.reqs = File('requirements.pip', comment=None)\n super().__init__(V)\n self.init_reqs()\n self.init_py()\n\n def init_apt(self):\n super().init_apt()\n self.apt // 'python3 python3-venv python3-pip'\n self.apt.sync()\n\n def init_reqs(self):\n self.diroot['reqs'] = self.reqs\n self.diroot // self.reqs\n self.reqs // 'xxhash' // 'ply'\n self.reqs.sync()\n\n def init_giti(self):\n super().init_giti()\n self.giti.mid // '*.pyc' // '/bin/' // '/include/'\n self.giti.mid // '/lib/' // '/lib64' // '/share/' // '/pyvenv.cfg'\n self.giti.mid // '/__pycache__/' // '/.pytest_cache/' // 'config.py'\n return self.giti.sync()\n\n def init_mk(self):\n super().init_mk()\n #\n self.mk.tools // '' //\\\n f'{\"PIP\":<8} = $(BIN)/pip3' //\\\n f'{\"PY\":<8} = $(BIN)/python3' //\\\n f'{\"PYT\":<8} = $(BIN)/pytest' //\\\n f'{\"PEP\":<8} = $(BIN)/autopep8 --ignore=E26,E302,E401,E402'\n #\n self.mk.all.targets // ' $(PY) $(MODULE).py'\n self.mk.all // '$^'\n self.mk.repl.targets // ' $(PY) $(MODULE).py'\n self.mk.repl // '$(PY) -i $(MODULE).py'\n self.mk.test.targets // ' $(PYT) test_$(MODULE).py'\n self.mk.test // '$^'\n # (S('.PHONY: test\\ntest: $(PYT) test_$(MODULE).py', '') // '$^')\n #\n self.mk.install //\\\n f'$(MAKE) $(PIP)' //\\\n f'$(PIP) install -r {self.reqs}'\n self.mk.update //\\\n f'$(PIP) install -U pip' //\\\n f'$(PIP) install -U -r {self.reqs}'\n self.mk.py = Section('py/install')\n self.mk.update.after(self.mk.py)\n self.mk.py //\\\n '$(PIP) $(PY):' //\\\n '\\tpython3 -m venv .' //\\\n '\\t$(PIP) install -U pip pylint autopep8' //\\\n '$(PYT):' //\\\n '\\t$(PIP) install -U pytest'\n self.mk.src // 'SRC += $(MODULE).py'\n self.mk.merge // f'MERGE += {self.reqs} $(MODULE).py test_$(MODULE).py'\n self.mk.sync()\n\n def init_vscode_settings(self):\n super().init_vscode_settings()\n settings = self.vscode.settings\n #\n settings.top // '\\t\"python.pythonPath\": \"./bin/python3\",'\n settings.top // '\\t\"python.formatting.provider\": \"autopep8\",'\n settings.top // '\\t\"python.formatting.autopep8Path\": \"./bin/autopep8\",'\n settings.top // '\\t\"python.formatting.autopep8Args\": [\"--ignore=E26,E302,E401,E402\"],'\n #\n # self.f11.cmd.val = 'make repl'\n # self.f12.cmd.val = 'exit()'\n #\n files = Section('python') //\\\n '\"**/bin/**\": true, \"**/include/**\":true,' //\\\n '\"**/lib*/**\":true, \"**/share/**\" :true,' //\\\n '\"**/*.pyc\": true, \"**/pyvenv.cfg\":true,' //\\\n '\"**/__pycache__/\": true, \"**/.pytest_cache/\": true,'\n self.vscode.watcher // files\n self.vscode.exclude // files\n #\n self.vscode.assoc //\\\n '\"**/.py\": \"python\",' //\\\n '\"**/requirements{/**,*}.{txt,in,pip}\": \"pip-requirements\",'\n #\n settings.sync()\n\n def init_vscode_ext(self):\n super().init_vscode_ext()\n self.vscode.ext.ext // '\"ms-python.python\",'\n self.vscode.ext.sync()\n\n def init_vscode_launch(self):\n super().init_vscode_launch()\n launch = self.vscode.launch\n # program\n launch.program = String(self)\n launch.django = String('false')\n launch.args = S('\"args\": [', '],', 0)\n launch.opts = S('//\"debugOptions\": [', '],', 0)\n #\n launch.it // (S('{', '}') //\n ('\"name\": \"Python: %s\",' % self.val) //\n '\"type\": \"python\",' //\n '\"request\": \"launch\",' //\n (S('\"program\": \"', '\",', 0) // launch.program) //\n (launch.args) //\n '\"console\": \"integratedTerminal\",' //\n (S('\"django\": ', ',', 0) // launch.django)\n )\n # # console\n # self.launch.mid //\n # self.launch.mid // '\\t\\t}'\n launch.sync()\n\n def init_py(self):\n self['py'] = self.py = pyFile(self)\n self.vscode.launch.program.val = f'{self.py}'\n self.vscode.launch.sync()\n self['dir'] // self.py\n self.py.top // pyImport('config')\n return self.py.sync()\n\n def init_config(self):\n super().init_config()\n self.config.py = pyFile('config')\n self['dir'] // self.config.py\n self.config.py //\\\n f'{\"SECRET_KEY\":<11} = \"{self.config.secret_key}\"' //\\\n f'{\"HOST\":<11} = \"{self.config.host}\"' //\\\n f'{\"PORT\":<11} = {self.config.port}' //\\\n \"assert PORT in range(1024,0xBFFF)\"\n return self.config.py.sync()\n\n\n## @ingroup py\nclass metalpyModule(pyModule):\n\n def init_py(self):\n try:\n os.symlink('../metaL.py', '%s/metaL.py' % self.diroot.val)\n except FileExistsError:\n pass\n self.py = pyFile(self)\n self.mksrc(self.py)\n # self.py.top // ('## @file %s' % self.file())\n self.diroot['py'] = self.py\n self.diroot // self.py\n self.py['metal'] = self.py.metal = Section('metaL')\n self.py.top // self.py.metal\n self.py['metaimports'] = self.py.metaimports = Section('metaL/imports')\n self.py.metal // self.py.metaimports\n self.py.metaimports // 'from metaL import *'\n self.py.metal // ('MODULE = %s()' % self.__class__.__name__)\n self.py.title = self.py['title'] = Section('title')\n self.py.metal // self.py.title\n self.py.title // \"TITLE = MODULE['TITLE'] = Title(MODULE)\"\n self.py.about = self.py['about'] = Section('about')\n self.py.metal // self.py.about\n # self.py.github = self.py['github'] = Section('github', comment=None)\n # self.meta // self.py.github\n self.py.readme = self.py['readme'] = Section('readme')\n self.py.metal // self.py.readme\n self.py.readme // 'README = README(MODULE)'\n self.py.readme // 'MODULE.diroot // README'\n self.py.readme // 'README.sync()'\n # meta // ('MODULE = pyModule(\\'%s\\')' % self.val)\n # meta // ''\n # meta // 'TITLE = Title(\\'\\')\\nMODULE << TITLE'\n # meta // ''\n # meta // '## `~/metaL/$MODULE` target directory for code generation'\n # meta // 'diroot = MODULE[\\'dir\\']'\n # meta // ''\n # meta // '## README\\nreadme = README(MODULE)\\ndiroot // readme\\nreadme.sync()'\n # meta // ''\n # meta // '## module source code\\npy = diroot[\\'py\\']'\n # meta // \"py['head'] // ('## @brief %s' % MODULE['title'].val) // ''\"\n # meta // 'py.sync()'\n # self.py['tail'] // Section('init')\n self.py.sync()\n # config\n config = pyFile('config')\n self.mksrc(config)\n self.diroot['config'] = config\n self.diroot // config\n # config['head'] // '## @brief site-local private config'\n # config['head'] // ''\n # config['head'] // '## @defgroup config config'\n # config['head'] // '## @brief site-local private config'\n # config['head'] // '## @{'\n # config['tail'] // '\\n## @}'\n config.sync()\n\n def py(self):\n ret = '## @brief %s' % self.head(test=True)\n return ret\n\n## @ingroup py\nclass replModule(pyModule):\n\n def init_py(self):\n super().init_py()\n self['test'] = self.test = pytestFile(self.py)\n self.diroot // self.test\n self.test.sync()\n\n def init_mk(self):\n super().init_mk()\n self.mk.all //\\\n (S(f'.PHONY: repl\\nrepl: $(PY) $(MODULE).py') //\n '$(MAKE) test' //\n '$(PY) -i $(MODULE).py' //\n '$(MAKE) $@')\n self.mk.sync()\n\n## @ingroup py\nclass bountyModule(pyModule):\n def __init__(self, V=None):\n pyModule.__init__(self, V)\n self.github = self['GITHUB'] = Url(\n f'https://bitbucket.org/ponyatov/{self}')\n self.github['branch'] = '/src/master/'\n self['ABOUT'] = self.ABOUT = S(\n '') // Url(f'https://bountify.co/{self}')\n\n\n## @defgroup samples\n## @brief samples on using `metaL` for programming and modeling\n\n## @defgroup web web\n## @brief Web Development\n## @{\n\nfrom html import *\n\nclass watFile(File):\n def __init__(self, V, ext='.wat', comment=';;'):\n super().__init__(V, ext, comment)\n\nclass webModule(pyModule):\n\n def __init__(self, V=None):\n super().__init__(V)\n self['back'] = self.back = Color('#222')\n self['fore'] = self.fore = Color('#DDD')\n self.init_static()\n self.init_templates()\n self.init_leaflet()\n # self.init_wasm()\n\n def init_wasm(self):\n (self.apt // 'wabt').sync()\n (self.static.giti // '*.wasm').sync()\n (self.src.giti // '*.wat.?').sync()\n self.vscode.ext.ext // '\"dtsvet.vscode-wasm\",'\n self.vscode.ext.sync()\n self.mk.wat = wat = Section('wat')\n self.mk.src // wat\n self.mk.wasm = wasm = Section('wasm')\n self.mk.all // wasm\n wasm // '.PHONY: wasm\\nwasm: $(WAT)'\n self.mk.install // '#$(MAKE) wasm'\n self.mk.update // '#$(MAKE) wasm'\n self.mk.rules // (S('static/%.wasm: src/%.wat') //\n 'wat2wasm -v $< -o $@' //\n 'wasm2wat -v $@ -o $<.s'\n )\n #\n self.src['hello'] = self.src.hello = hello = watFile('hello')\n self.src // self.src.hello\n wat // f'SRC += src/{hello}' // f'WAT += static/hello.wasm'\n hello.top //\\\n ';; https://developer.mozilla.org/en-US/docs/WebAssembly/Understanding_the_text_format' //\\\n ';; https://www.freecodecamp.org/news/get-started-with-webassembly-using-only-14-lines-of-javascript-b37b6aaca1e4/' //\\\n '(module'\n hello.bot // ')'\n #\n fn_hello = S('(func', ')')\n # fn_hello // '(param i32)'\n hello.mid // fn_hello\n #\n self.src.hello.sync()\n\n def init_static(self):\n self['static'] = self.static = static = Dir('static')\n self.diroot // static\n static.sync()\n static.giti = giti = File('.gitignore', comment=None)\n static // giti\n giti // 'jquery.js' // 'bootstrap.*'\n giti // 'leaflet.*' // 'images/marker-*.png' // 'images/layers*.png'\n giti.sync()\n self.init_static_manifest()\n # return static\n\n ## https://leafletjs.com/examples/quick-start/\n def init_leaflet(self):\n\n css_crc = \"sha512-xodZBNTC5n17Xt2atTPuE1HxjVMSvLVW9ocqUKLsCC5CXdbqCmblAshOMAS6/keqq/sMZMZ19scR4PsZChSR7A==\"\n js_crc = \"sha512-XQoYMqMTK8LvdxXYG3nZ448hOEQiglfqkJs1NOQV44cWnUrBc8PkAOcXy20w0vlaXaVUearIOBhiXZ5V3ynxwA==\"\n self['leaflet'] = self.leaflet = leaf = Dict('leaflet')\n leaf['css'] = leaf.css = H('link', 0,\n rel=\"stylesheet\", href=self.static_url(\"leaflet.css\"),\n integrity=css_crc)#, crossorigin=\"\")\n leaf['js'] = leaf.js = H('script', 1, src=self.static_url(\"leaflet.js\"),\n integrity=js_crc)#, crossorigin=\"\")\n leaf['id'] = leaf.id = f'leaf'\n leaf['div'] = leaf.div = H('div', clazz='leaflet',\n id=leaf.id) // f\"{leaf.id}\"\n leaf.sync()\n self.tmp.giti // 'leaflet.*' // 'images/marker-*' // 'images/layers*.png'\n self.tmp.giti.sync()\n self.static.css //\\\n (CSS('.leaflet *', 0) //\n 'background: transparent !important;')\n self.static.css //\\\n (CSS('.olMap *', 0) // 'background: transparent !important;') //\\\n (CSS('.olControlAttribution', 0) // 'visibility: hidden;') //\\\n (CSS('.olControlScale', 0) // 'visibility: hidden;') //\\\n (CSS('.olControlMousePosition', 0) // 'color:black !important;')\n self.static.css.sync()\n\n # ## intercept `A[key]=B` operations\n # def __setitem__(self, key, that):\n # super().__setitem__(key, that)\n # if isinstance(that, Title):\n # self.init_static()\n\n def init_static_manifest(self):\n self.static.manifest = File('manifest', ext='', comment='')\n self.static['manifest'] = self.static.manifest\n self.static // self.static.manifest\n self.static.manifest // (S('{', '}') //\n f'\"short_name\": \"{self.head(test=True)[1:-1]}\",' //\n f'\"name\": \"{self[\"TITLE\"]}\",' //\n f'\"theme_color\": \"{self.back}\",' //\n f'\"background_color\": \"{self.back}\",' //\n '\"display\": \"standalone\",' //\n (S('\"icons\": [', ']') //\n (S('{', '}') //\n '\"src\": \"/static/logo.png\",' //\n '\"type\": \"image/png\",' //\n '\"sizes\": \"256x256\"'\n )\n )\n )\n self.static.manifest.sync()\n\n def init_templates(self):\n self['templates'] = self.templates = Dir('templates')\n self.diroot // self.templates\n self.templates.sync()\n self.init_templates_css()\n self.init_templates_all()\n self.init_templates_index()\n\n def static_url(self, filename):\n return f'{filename}'\n\n def init_templates_css(self):\n self.static['css'] = self.static.css = self.css = cssFile('css')\n self.static // self.static.css\n self.static.css.print = (S('@media print {', '}') //\n (CSS(\"@page\", 0) //\n 'margin:5mm; ' // 'margin-left:30mm;') //\n (CSS(\"body\", 0) //\n \"padding:0;\") //\n (CSS(\"a[href]:after\", 0) //\n \"display: none !important;\")\n )\n self.static.css //\\\n (CSS('*', 0) // f'background:{self.back} !important; color:{self.fore};') //\\\n (CSS('pre', 0) // f'color:{self.fore};') //\\\n (CSS(\"body\", 0) // \"padding:4mm;\") //\\\n (CSS('.center', 0) // 'text-align: center;') //\\\n (CSS('.required', 0) // 'color:orange !important;') //\\\n (CSS('a:hover', 0) // 'color:lightblue;') //\\\n (CSS('label', 0) // 'color: white !important;') //\\\n S('select,option,button,') //\\\n (CSS('input,textarea') //\n 'background-color: lightyellow !important; ' //\n 'color: black !important;'\n ) //\\\n (Section('print', 0) // self.static.css.print)\n # (CSS('.login', 0) // f'background:{self.back};') //\\\n self.static.css.sync()\n\n def templates_load_static(self):\n return '{% load static %}'\n\n def templates_all_head(self):\n return (Section('templates_all_head') //\n '' //\n '' //\n '' //\n f'{{% block title %}}<{self.head(test=True)[1:-1]}>{{% endblock %}}' //\n f'' //\n f'' //\n f'' //\n f'')\n\n def if_authenticated(self, code):\n return code\n\n def init_templates_all(self):\n self.templates['all'] = self.templates.all = htFile('all')\n self.templates // self.templates.all\n self.templates.all.top //\\\n self.templates_load_static() //\\\n ''\n #\n self.templates.all.jinja = jinja = Section('jinja')\n html = H('html', lang='ru')\n html.end = ''\n self.templates.all.top // jinja // html\n # \n head = H('head')\n html // head\n head // self.templates_all_head() // '{% block head %}{% endblock %}'\n #